Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) poses a mortal threat to human health. The elucidation of the relationship between peripheral immune cells and the development of inflammation is essential for revealing the pathogenic mechanism of COVID-19 and developing related antiviral drugs. The immune cell metabolism-targeting therapies exhibit a desirable anti-inflammatory effect in some treatment cases. In this study, based on differentially expressed gene (DEG) analysis, a genome-scale metabolic model (GSMM) was reconstructed by integrating transcriptome data to characterize the adaptive metabolic changes in peripheral blood mononuclear cells (PBMCs) in severe COVID-19 patients. Differential flux analysis revealed that metabolic changes such as enhanced aerobic glycolysis, impaired oxidative phosphorylation, fluctuating biogenesis of lipids, vitamins (folate and retinol), and nucleotides played important roles in the inflammation adaptation of PBMCs. Moreover, the main metabolic enzymes such as the solute carrier (SLC) family 2 member 3 (SLC2A3) and fatty acid synthase (FASN), responsible for the reactions with large differential fluxes, were identified as potential therapeutic targets. Our results revealed the inflammation regulation potentials of partial metabolic reactions with differential fluxes and their metabolites. This study provides a reference for developing potential PBMC metabolism-targeting therapy strategies against COVID-19. Severe respiratory failure (SRF) frequently occurs in severe cases due to alveolar structure damage in lungs by SARS-CoV-2 [Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) poses one of the most momentous threats to human health and safety. As of April 2022, the epidemic of coronavirus disease 2019 (COVID-19) has caused 496 million cases and 6 million deaths worldwide (RS-CoV-2 . In addiRS-CoV-2 .2), thus triggering macrophages’ inflammatory response [The accumulation of pro-inflammatory factors is one of the characteristics of COVID-19 ,7,8. Mecresponse . Excessiresponse .Importantly, most subpopulations of peripheral blood mononuclear cells (PBMCs) including monocytes, T cells, and B cells, tend to adaptively respond to cytokine levels in the peripheral circulation or local lesions, and these immune metabolic responses are closely related to cell functions ,13,14. CM. tuberculosis [Considering this, COVID-19 treatment schemes are expected to be designed from the perspective of immunometabolism. Based on the metabolic profiles of immune cells in response to inflammatory signals, the correlation between the metabolism of inflammatory cells and pro-inflammatory processes has been widely investigated. A number of in vitro studies have indicated that the specific inhibition of metabolic gene expression can change or reverse the phenotype of inflammatory cells and improve anti-inflammatory prognosis by regulating the flux of local metabolic responses. Through the activation of pro-inflammatory cells or the maintenance of pro-inflammatory phenotype, some key proteins indirectly involved in the metabolism can inhibit the development of inflammation by regulating the distribution of metabolic flux. For example, autophagy-related gene 5 (ATG5) has been reported to be a negative regulator of glycolysis and inflammatory cytokine production in dendritic cells infected with respiratory syncytial virus . The crorculosis . GeneralWith the advancement of omics technology, omics analysis has been widely used to characterize the physiology and pathology of specific tissues. However, omics-based analysis alone can no longer meet the requirements of revealing the subtle change in materials and energy under specific conditions. Metabolic networks can comprehensively reflect information on global metabolite circulation and transformation. At present, some algorithms integrate multi-type omics data into genome-scale metabolic models (GSMMs), which makes the reconstruction of context-specific metabolic network more reasonable and convenient. Combined with flux balance analysis (FBA) , the recfindCPcli was developed for target prediction based on the GSMM of human airway epithelial cells infected with SARS-CoV-2 [Metabolic networks can act as platforms to facilitate the studies of viral disease pathogenesis and the screening of related-drug targets, especially for COVID-19. The bulk RNA-Seq/single cell RNA-Seq data from cells infected with SARS-CoV-2 were integrated into human GSMM through the metabolic transformation algorithm rMTA to predict anti-SARS-CoV-2 targets in airway epithelial cells . In addiRS-CoV-2 . The metRS-CoV-2 . The aboIn this study, to characterize the metabolism of PBMCs in severe COVID-19 patients, we reconstructed GSMMs by integrating the published single-cell RNA-Seq (scRNA-Seq) data of PBMCs from healthy populations and severe patients using the pyTARG algorithm, and verified the rationality of transcriptome data through traditional gene expression analysis. Our reconstructed GSMM pair was pruned to approach authentic metabolic states. Our results of network flux sampling indicated that the genes responsible for some metabolic reactions with significant flux differences might be potential immunotherapy targets.In order to characterize the global transcriptome of PBMC during SARS-CoV-2 infection, the published PBMC scRNA-Seq data were applied to differentially expressed gene (DEG) analysis. The data were obtained from peripheral blood samples from 7 severe patients with similar symptoms and 6 healthy humans. Compared with those from healthy humans, 95% DEGs from severe patients were significantly up-regulated . These iConsidering this, active functional modules during severe infection were identified via gene set enrichment analysis (GSEA), and GSEA was performed in pre-set gene background with GSEA software. The gene ontology (GO) enrichment analysis indicated that the up-regulated genes were significantly enriched in the GO terms related to immune response  includinTaken together, the PBMC transcriptome datasets characterized by severe inflammation in COVID-19 patients cannot precisely shape the metabolic profile of PBMCs. Thus, the reconstruction of metabolic networks was implemented.Metabolic network reflects the metabolic disturbance of cells by predicting the reaction rate (or flux) of metabolites. Due to the ambiguous expression differences in most metabolism-related genes, traditional DEG analysis can not reveal the actual metabolic changes of PBMCs. To investigate the metabolic peculiarity of PBMCs during severe infection with COVID-19, a human GSMM covering 5588 metabolites and 8079 reactions for drug development ,38 was iAfter the above HMRs were modified, the context-based GSMM pair representing severe patients and healthy humans was generated. The flux value of each metabolic reaction was predicted by flux sampling, and the flux difference between severe patients and healthy humans was applied to define the regulation state of each reaction. The results showed that extensive elevation of glycolytic fluxes  and the serious interception of tricarboxylic acid cycle  and electron transport chain (in HMR_6916 and HMR_6918) were basically consistent with aerobic glycolysis of active macrophages and T cells during inflammation ,13. NotaTo determine metabolic modules that were significantly affected during severe COVID-19 infection, reactions with differential fluxes were investigated via hypergeometric test. The enrichment results showed that the transport reaction module, similar to the hubs of material exchange, exhibited the greatest differential fluxes, indicating that this module was most pronouncedly affected. The major reactions with up-regulated fluxes were distributed in “nucleotide metabolism”, “glycolysis”, and “pyrimidine metabolism”, and “purine metabolism” modules A. The reIn order to visualize flux differences, metabolic networks were divided into multiple blocks . The glyBased on metabolic remodeling, we investigated four metabolic module sets with the main flux differences, revealed the relationship between these metabolic changes and immune activation, explored the application prospect of metabolites or targets in these modules against COVID-19. The detailed information on inhibitors, metabolic enzymes, and flux changes in metabolic pathways was summarized in 4+ T cells  during inflammation. The phenotypic polarization M0-to-M1 induced by inflammation leads to the metabolism of macrophages from OXPHOS to aerobic glycolysis, and the latter transforms about 90% of glucose into lactic acid rather than pyruvate [8+ T cells utilized TCA cycle to produce energy [The metabolic reconstruction results showed that glucose metabolism-related pathways were widely disturbed. Twelve reactions with obviously up-regulated flux were related to glucose transmembrane transport , glycolysis , or lactate production . However, the sharp reduction in OXPHOS flux  implied that the net production of downstream ATP might have been exhausted. Aerobic glycolysis („Warburg effect”) is a metabolic adaptive transformation with a large consumption of glucose but rapid energy generation, which can occur under aerobic conditions . The abopyruvate ,54. The pyruvate . Unexpecpyruvate . At the The enhancement of glycolytic pathway in activated PBMCs was the characteristics of inflammatory response, and enzymes in glycolytic pathway had the potential to be developed into immunometabolic therapy targets ,56,57,582 decrease, acidic metabolite accumulation, and the viral diarrhea-induced massive loss of bicarbonate [Lactic acidosis, a kind of metabolic acidosis, is a common complication in severe COVID-19 patients, especially gravida, the elderly, or patients suffering from primary diseases. Lactic acidosis mainly results from PaOarbonate . Our fluarbonate , eculizuarbonate ,67 and rarbonate  can restarbonate . Furtherarbonate . The twoOur reconstructed networks presented the fluctuation in lipid metabolism. The reaction HMR_1917 with extremely high differential flux (5.25) possibly indicated the increased utilization of extracellular cholesterol by PBMCs, although the de novo synthesis of cholesterol was not remarkable. Since cholesterol is involved in the formation of lipid rafts on cell plasma membrane and the synthesis of a variety of biomolecules, the flux surge may contribute to the activation and immune response function of immune cells (T and B cells) ,72. TherIn addition, the fluctuation in the network was attributed to some fatty acid and sphingolipid metabolism-related reactions. For example, the metabolic pathways of fatty acid synthase  were up-regulated. FASN can ensure LPS-induced macrophage activation, although fatty acid synthesis-related metabolic pathway flux exhibited no significant differences in this study. Orlistat, a weight-loss drug restraining triglyceride hydrolysis to inhibit FASN, can be used as a potential drug due to its immunomodulatory effect on COVID-19 patients. Lactosylceramide (LacCer), a kind of glycosphingolipids widely distributed in a variety of cells, mainly activates the inflammatory response by stimulating cytoplasmic phospholipase A2 (cPLA2) . Our fluThe activation of the pentose phosphate pathway (PPP) and the intracellular accumulation of reactive oxygen species (ROS) belong to the consequence of PBMC activation ,36,75,76The pentose phosphate of the metabolisms of nucleotide, purine, and pyrimidine are derived from noxPPP. Our results showed that only part of noxPPPs were up-regulated , and the 36 metabolic reactions  with significantly up-regulated fluxes, accounting for ~19% of the total up-regulated reactions, which might be due to noxPPP metabolic adaptation. Although the active purine metabolism has been reported in the metabolomic study of PBMCs in COVID-19 severe patients , future Our data showed that the transmembrane transport of folate (FA) , the transformation of folate into dihydrofolate (DHF) , and the transmembrane shuttle of 5-methyltetrahydrofolate (5-MTHF)  were significantly increased in PBMCs during severe COVID-19 infection. Folate has been reported to inhibit a wide range of downstream inflammatory responses by limiting monocyte uptake of immunoreactive cyclic dinucleotide , and foltrans or 9-cis forms [Our metabolic network indicated an increase in the biosynthesis of retinal  and the intracellular accumulation of retinoate . Retinol, retinal, and retinoate (retinoic acid) are all active forms of vitamin A (VA). Retinoate plays an important role in regulating differentiation and functions of macrophages in is forms . The funis forms . The retis forms ,79. AM58is forms . Therefo2 into HCO3- and the increased efflux of HCO3-  may contribute to alleviate excessive acid accumulation. Carbonic anhydrase 2 (CA2), one of the five cytoplasmic carbonic anhydrases, is abundant in M0, M1, and M2 BMDMs [In addition to the above-mentioned four metabolic module sets, more metabolic reactions with remarkable flux changes should be further examined. Two amino acid transport-related proteins SLC7A5  and SLC3A2  have been reported to inhibit inflammation triggered by COVID-19 by activating mTORC1 pathway [M2 BMDMs . CA2 inhM2 BMDMs . Our datM2 BMDMs , thus coM2 BMDMs . In termFurthermore, investigating the expression trend of metabolic enzymes responsible for differential flux can contribute to determining whether these active members possess the potential to become biomarkers of PBMCs for anti-inflammatory treatment. There were 10 up-regulated enzymes responsible for the increased flux of related metabolic reactions, including pyruvate kinase M1/2 (PKM), cytidine/uridine monophosphate kinase 2 (CMPK2), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), SLC38A5, SLC16A1, SLC2A3, glutathione-disulfide reductase (GSR), myeloperoxidase (MPO), cytochrome P450 family 1 subfamily B member 1 (CYP1B1), and galactosidase beta 1 (GLB1). As a known metabolic marker in serum, GSR shows a significant negative correlation with increased IL-10 in COVID-19 patients . In addihttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150728) [The data used in this study were from the public National Center for Biological Information, GSE150728 (NCBI) database. The peripheral blood mononuclear cell (PBMC) blood samples were collected from 7 severe COVID-19 patients and 6 healthy humans for single-cell RNA-Seq (scRNA-Seq)  . The donE150728) . The RNAE150728) ,89,90. TE150728) , and thep < 0.05, FDR q < 0.25) and the corresponding normalized enrichment scores (NES) were shown in In order to evaluate the expression differences of immune- and metabolic-related DEGs between severe COVID-19 patients and healthy humans, the normalized transcriptome data (transcript per million (TPM)) from the RNA-Seq data were compiled and submitted to the software GSEA_4.1.0 to perform GO enrichment analysis . Gene OnAn updated version of the metabolic model HMR used in this study (MODEL1707250000) was obtained in SBML format from BioModels database ,38. BefoAs objective functions of the PBMC metabolic model cannot be determined, the flux balance analysis (FBA) based on objective function cannot be performed. Thus flux sampling , rather than FBA, was performed to obtain the metabolic flux distributions of infected and uninfected groups. Artificial centering hit-and-run (ACHR) was selected as the sampler for space sampling in the metabolic model, and the academic version (22.1.0) of IBM ILOG CPLEX was used as the linear programming solver in metabolic modeling. The flux differences in reactions were determined by the following two methods: (1) If the flux directions of a reaction were the same under two conditions (infected vs. uninfected), or if a reaction exhibited no flux (flux=0) under a certain condition, the flux difference was calculated as the difference (infected group—uninfected group); (2) If the two flux directions were the opposite, the flux difference was calculated as the sum of the absolute values of the two groups (|infected group| + |uninfected group|). All sampling results were presented in In this study, the combination of transcriptomic data and metabolic networks was applied to explain the metabolic perturbations and obtain metabolic targets with differential fluxes in PBMCs of severe COVID-19 patients, so as to expand therapeutic strategies targeting immunometabolism. However, there are some limitations in this study. Firstly, the metabolic reactions with significant differential fluxes were only presented in some subsystems, including purine/pyrimidine metabolism, glycolysis, TCA, transport, etc., and the snapshots of systematic flux difference in most subsystems were not well captured. Secondly, the population composition was complex. The samples of the experimental group were collected from a limited number of male patients with severe diseases who were distributed in a wide agespan and had similar infection symptoms (admission level of 6/7 patients: ICU) . However3− production, were observed. However, the relationship between these metabolic changes and inflammatory development during COVID-19 infection needs to be further explored. Our findings provide a reference for the development of novel drug targets against COVID-19.This study integrated the transcriptome data of PBMCs into metabolic network, which contributed to revealing the metabolic characteristics of PBMCs in severe COVID-19 patients. After pruning the inappropriate reactions and integrating transcriptome data, the metabolic model pair was generated to comprehensively reflect monocytes, T cells, and B cells. The active disturbances of some metabolic subsystems responsible for glycolysis, PPP, and biosynthesis of lipid and nucleotide were found to be related to the polarization and activation of PBMCs. Some novel metabolic enzymes, such as SLC2A3, SLC16A1, FASN, and biogenesis-related LacCer, were identified as potential anti-inflammatory targets, and some metabolites, such as folate, retinoate, and melatonin were found to have anti-inflammatory prospects. In addition, some unexpected metabolic changes, including HCO


The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites. The pentose phosphate pathway (PPP) is crucial in many cellular metabolic functions. The pathway is important for amino acid and nucleotide biosynthesis, metabolic processes regulation, oxidative stress and redox homeostasis prevention, and stress response activation ,2,3. PPPPPP metabolism has been well studied over the years using various methods of analysis to gain an in-depth understanding of its function and regulation. Nonetheless, detailed understanding of the mechanistic information of PPP remains the interest of much research as most studies are unable to record the absolute quantification analysis of its intermediate metabolites. The absolute concentration levels of metabolites of PPP immediately reflect the actual metabolic conditions of a cellular system. A number of quantification methods have been used to measure the levels of targeted metabolites, but each comes with some limitations. The major challenges are due to the rapid turnover rates of the intermediates, with low abundances and poor separation between the sugar phosphates during analysis . The devIsotope tracer or labeling is not an entirely new approach used in metabolite identification and quantification as well as in flux analysis . MetabolThe metabolic reactions in PPP are divided into oxidative and non-oxidative phases, where the activities majorly occur in the latter phase . The oxiAcinetobacter baumannii and Pseudomonas aeruginosa treated with combinations of antibiotics demonstrated significantly reduced levels of D-ribose 5-phosphate, D-erythrose 4-phosphate, and D-sedoheptulose 7-phosphate over 1 h of treatment -glucose to determine PPP metabolism and estimate the relative metabolite fluxes through enzymatic reactions of transaldolase and transketolase in human hepatoma cells. -glucose isotopomer was also employed to re-examine the central metabolic pathways of Clostridium acetobutylicum [13C]-glucose was the most suitable substrate in interpreting oxidative PPP metabolite fluxes in mammalian cells of HEK-293 cells compared to [1-13C]-glucose  glucose detected the upregulation of fluxes of oxidative PPP to drive transketolase like reaction and reduce oxidative stress during the stationary phase . In. In12C6-um in MS . Experim12C6-aniline and IS tagged with 13C6-aniline peaks of the chromatograms. The results by Yang et al. (2008) indicated that the method successfully determined most of the intermediates involved in central carbon and energy metabolism of S. cerevisiae in a single run of reversed-phase LC-MS with high accuracy (RSD < 10%). In a separate study, using the same approach, structurally paired isomers of glucose-6-phosphate and fructose-6-phosphate were successfully separated in a single run of LC-MS [The absolute levels of metabolites are obtained by comparing the ratio between the intensity of the corresponding sample tagged with of LC-MS . The tecof LC-MS ,42. The of LC-MS ,42. VilkAbsolute quantification of metabolite concentration levels provides a detailed understanding towards the characteristics of metabolites as well as pathway changes under different conditions. As the method of absolute quantification has become a major challenge especially in bacterial metabolomics studies, isotope labeling coupled with MS has significantly improved the analysis. The use of a stable isotope tracer has facilitated the identification and absolute quantification of biomolecules and enhanced our understanding of metabolic fluxes. Importantly, the approach of the isotope tracer highlights the opportunity to elucidate novel cellular pathways of biological systems, such as antibiotic mechanisms of action, host–pathogen interaction, and biomarker discovery. Compared to the conventional in vivo method, an in vitro synthesis of isotope labeled-metabolites via GSIST introduced by Yang et al. (2008) allows for absolute quantification of metabolites in a more simple and robust method. The approach could serve as an alternative, especially in targeted metabolomics studies, with increased accuracy of metabolite quantification.


Staphylococcus epidermidis RP62A enables the researcher to understand better their metabolic networks, which allows the design of effective strategies to treat infections caused by this species or others. S. epidermidis is the second most commoncause of infection in patients with joint implants, so treating its proliferation seems vital for public health. There are different approaches to the analysis of metabolic networks. Flux balance analysis (FBA) is one of the most widespread streams of research. It allows the study of large metabolic networks, the study their structural properties, the optimization of metabolic flux, and the search for intervention strategies to modify the state of the metabolic network. This work presents the validation of the Staphylococcus epidermidis RP62A metabolic network model elaborated by Díaz Calvo et al. Then, we elaborate further on the network analysis’s essential reactions. The full set of essential reactions (including a previously unobserved one) was computed, and we classified them into equivalence classes. Some proposals to intervene in the network and design knock-outs by studying minimal cut sets of small length are also introduced. In particular, minimal cut sets related to the medium (including exchange reactions associated with medium metabolites) have been computed. In this sense, the unique external MCS (composed of cysteine and sulfate ion) has been found, and all hybrid MCS  of length two have also been computed. The paper also points out the possible importance of these new intervention strategies.Increasingly, systems biology is gaining relevance in basic and applied research. The combination of computational biology with wet laboratory methods produces synergy that results in an exponential increase in knowledge of biological systems. The study of microorganisms such as  During the last two decades, the study of biological systems from a holistic point of view, together with the application of improvements in laboratory techniques and computational resources, has allowed a significant development of knowledge of living organisms at the molecular level. Cellular metabolism can be studied as a set of reactions that occur in the cell and allow its development and activity. These reactions are coordinated to maintain the balance of the cellular metabolism, also known as cellular homeostasis. With the assistance of high-performance computing, the metabolic network can be examined better to understand the overall activity of a whole cell. The metabolic network enables the generation of large amounts of omic data that allow identifying and quantifying biological molecules to discover the connections between genotype and phenotype. Then, researchers can make predictions of hypothetical states of the metabolic network by generating metabolic fluxes at different levels of the biological system.Due to a large amounts of omic data which are continuously generated, the most successful approaches are those known as data-driven ones. These are based on combining omics data and computational methods to generate parts of or complete metabolisms called genome-scale metabolic models (GSMMs) . GSMMs aAfter the generation of GSMMs, the network has to be analyzed. To this end, the modeling approaches can be divided into two frameworks: constraint-based modeling (CBM) and kinetic modeling. Constraint-based modeling offers a representation of the metabolic network based on structural and stoichiometric aspects of the network with the aim of simulations of different states of metabolic flux and thus predicting phenotypes. On the other hand, the kinetic model aims to study the variations in metabolite concentrations over time through parameters related to the kinetics of chemical reactions and the concentration of enzymes in the metabolic network. Due to the nature of both frameworks, the first is used to study large-scale metabolic networks in a steady state; the second is restricted to networks of reduced size or parts of more extensive networks. Both frameworks are compatible and can be used to obtain a complete view of the model and draw more well-founded conclusions .Focusing on CBM, one of the main tools to develop the analysis is flux balance analysis (FBA), a computational method to predict flux distributions while optimizing (maximizing or minimizing) a given cellular function or a combination of them . Inside Staphylococcus epidermidis RP62A, a pathogen, proposed by Teresa Díaz Calvo et al. [Staphylococcus epidermidis as a pathogen. Therefore, we thought we could check and validate the proposed model and extract from the network model some intervention strategies to produce knock-outs of the pathogen.In this work, an analysis of the GSMM model of the S. epidermidis is a human opportunistic pathogen known to cause infections in medical implants (joints and heart). It is a facultative anaerobic bacterium with a Gram-positive coccus phenotype that produces a biofilm that, in the absence of disease, is located in collagen-rich regions such as the skin and mucous membranes. It belongs to the Staphylococcus genus, a vast group with well-known species such as Staphylococcus aureus, known for causing respiratory infections, among others. Concretely, prosthetic joint infections (PJI) are a recurring problem for arthroplasty and knee replacements. This type of infection occurs during the operation due to contamination of the material or the implant. PJI occurs when bacteria manage to adhere to the surface of the prosthetic implant and colonize it through the production of a biofilm. Most PJIs are of monomicrobial origin; the Staphylococcus genus was the first etiological agent discovered in PIJs . Arthroplasty allows millions of patients relief from their pain and to recover or improve the mobility and functionality of the operated area. However, it is common for the diagnosis of PJI to be made when it is already chronic. At this point, surgical replacement of the implant is the only treatment with guarantees of success, although relapse is not unlikely [S. epidermidis) to find solutions to prevent the growth of this bacterium . Finding feasible and safe solutions would improve patients’ quality of life and reduce the economic impact on the health system.unlikely . The ecoThere are several ways to intervene in the metabolic network. The first way of doing so is by knocking out one  internal reactions. In this approach, the minimal interventions are provided by the essential reactions . When canceling one of these reactions, the cell must die . A second way is removing (or limiting) some of the available nutrients. This limitation can be simulated by knocking out exchange reactions associated with external metabolites . Finally, a third approach (a mix of the previously mentioned ones) is based on knocking out both internal and exchange reactions (hybrid cut sets).Replicating the results obtained from Díaz Calvo et al. , validatStaphylococcus epidermidis RP62A metabolic network.Providing a better understanding of the S. epidermidis.Proposing new minimal interventions to the metabolic network to feasibly eliminate The main goals we try to cover in our work are the following:A metabolic model is defined by metabolites, the reactions that interconvert them (described by a fixed stoichiometry), and the fluxes that quantify the extent to which each reaction intervenes. The different components that make it up should be identified to establish a metabolic model.m and n the numbers of metabolites and reactions in the network. Each reaction would convert specific amounts of some metabolites into amounts of other metabolites. This information is provided by the stoichiometric coefficients of the reactions that can be summarized in a matrix Let R and M be the sets of reactions and metabolites of the system, and denote by Each possible state of the network can be represented by a vector Equation (1)dxdt=The state of equilibrium in which the concentrations of internal metabolites of the metabolic network do not change over time  is called the steady-state constraint (2)S·v=0S.Observe that, in this formulation, only internal metabolites must be included as rows in the stoichiometric matrix Considering the thermodynamic directionality, the reactions of a metabolic network can be classified into irreversible and reversible. A reaction The restrictions on the sign of the flux of the irreversible reactions are called the thermodynamic constraint :(3)ri≥0C.A mode (or state) of the network is a flux vector satisfying Equations  and 3)..3). Two v. That is,Given a mode r is said to be essential if r is active in any non-trivial state of the metabolic network. Having the essential reactions implies knowing which reactions are necessary for the organism’s life. On the other hand, a reaction is said to be blocked if it is always inactive.A reaction T is a set of reactions whose inactivation induces the inactivation of all the reactions in T. Formally, if T if S is a MCS if T .Thus, an essential reaction is just an MCS of length 1 for the whole set of reactions.Another important concept is that of the dependencies of reactions. Given two unblocked reactions Flux balance analysis. FBA performs the network analysis of a model. In this framework, adding a function transforms our steady-state and thermodynamic constraints into a linear programming (LP) optimization problem. Concretely, if The obtained solution is a network mode. Using different additional constraints and functions, different scenarios in the metabolic network can be emulated (see ).Staphylococcus epidermidis RP62A was implemented by Díaz Calvo et al. [https://sbml.org/), a free and open data format for computational systems biology used by thousands of people worldwide. The authors used the ScrumPy metabolic modeling package, a widely used framework for metabolic network analysis.The original model of S. epidermidis RP62A consists of 893 reactions, plus 97 transport reactions, 864 internal metabolites, and 74 external metabolites. The curated model has mass and conservation energy balanced. Biomass generation is represented by pseudo-transporters (exchange reactions) to simulate the export of biomass precursors. A simplification of the model is shown in The GSMM of Staphylococcus epidermidis RP62A. On the left is the modified HHW culture medium  [Simplification of the model for d White)  which reBiomass composition. The model simulates biomass production (mmol gDCW−1) through 48 precursors that are declared as external metabolites by using transport pseudo-reactions. Biomass consists of biofilm-forming precursors and planktonic cell components, such as amino acid residues, deoxynucleotides, nucleotide triphosphate, cell wall/membrane components, and soluble metabolites. The fluxes of the reactions associated with these precursors were measured experimentally in continuous culture at a steady state. A complete explanation of the biomass composition can be found in the supplementary material of [erial of .Denote by Culture medium. A set of nutrients represents the medium made up of ions, amino acids, and secondary metabolites that are incorporated into the metabolic network through transport reactions and is measured in gDCW−1 units [−1 units . This is−1 units  which ha−1 units  by addin−1 units .Denoting by ATP maintenance cost and Specific Growth Rate. Measures of growth (−1), and non-growth-associated ATP maintenance cost (8 mmol gDCW−1h−1), respectively, were taken by experimental procedures.In this model, an ATPase reaction, −1h−1. The specific growth rate  package  working All the Python computer programs have been run in the Gacop’s Cluster formed by several x86-64 computing nodes connected with an internal Gigabit Ethernet Network running Centos GNU/Linux 8.2. Cluster support has been provided by the Research Group of the High-Performance Computer Architecture (GACOP) of the University of Murcia (Spain). After finishing the importation of the model by COBRApy, an analysis of the structural properties was performed.S. epidermidis model was successfully imported in COBRApy after solving a few issues  that behave as external ones in the sense of having no reaction producing or consuming them. These metabolites can be considered "blocked", and their presence in any reaction implies that these reactions are blocked. The average percentage of blocked metabolites in other models has been computed by checking curated models similar in size taken from the BIGG database . The avehttps://github.com/biogacop/Sepidermidis_Analysis.Regarding the directionality of reactions, there are 405 reversible reactions and 585 irreversible ones. An unfolding of the reversible reactions of the model (substituting each reversible reaction with two irreversible ones that represent the two possible directions) was done to avoid the appearance of absolute values while calculating modes of minimal total flux. This unfolding step turned our optimization problem posed in Equation (Mixed-integer linear programming (MILP). Many solutions are obtained depending on the parameter that controls the optimization procedure.https://github.com/biogacop/Sepidermidis_Analysis.Formulating a new optimization problem by creating a lumped biomass pseudo-reaction that gathers all its precursors in a simplified way. The modified model can also be downloaded from We have also explored two additional ways to solve the original optimization problem . This value can be considered quite reasonable compared to models of similar sizes from the BIGG database , which iAfter fixing the minimal total flux as an additional constraint, we also examined the possible oscillations of the concentration values of the culture medium’s component under the minimal total flux condition. These variations were as expected: they ranged between 0 and a value corresponding to the maximum values for the corresponding exchange reactions. An exception was found for a few amino acids  and niacin (vitamin B3). In these cases there were significant differences between the minimum and maximum values that were incorporated into the metabolic network and those obtained in our analysis. Isoleucine, leucine, tyrosine, and phenylalanine were halved, lysine was doubled, methionine was increased by one order of magnitude, and niacin was increased by two orders of magnitude . We found 130 reactions (not counting the transport reactions) present in all the 108 obtained solutions and 553 different reactions that appeared in support of at least one solution.Moreover, we used two other techniques to solve the optimization problem: MILP and a new optimization problem by creating a lumped biomass pseudo-reaction that gathers all its precursors. The solutions obtained in both methods are very similar and entirely consistent with those obtained with the previous LP method.In their paper, Díaz Calvo and colleagues also calculated the impact of removing individual amino acids. The impact was obtained by Euclidean distance between the flux vectors obtained by solving the original FBA problem and the one obtained after removing each amino acid. We decided not to replicate this part of the research because, as previously stated, the solution associated with the optimal flux value is not unique. Thus, it makes no sense to estimate the impact of the elimination of an amino acid by just comparing two of those solutions.Regarding the concentrations of the components of the culture medium, the flux of the reactions that incorporate them into the metabolic network has been analyzed. We looked for the optimal total flux conditions subject to the minimization objective function. We observed that the maximum values consumed for some components ) in minimal flux condition are lesser that the maximum amounts available in the culture medium . This value is nearly identical to the one obtained in the original paper . The new essential reaction we found is the All possible implications among essential reactions have been calculated and used to obtain equivalences. It is worth noting that there appear trivial equivalences  into 35 equivalence classes. Seventeen essential reactions have no equivalent ones, and the other 111 are located in 18 equivalence classes with more than one element.T and T. Thus, we can think of the set of essential reactions (cut sets of length 1 for the biomass precursors) as chains formed by cut sets of those precursors, cut sets for these (primary) cut sets, and so on. This distinction allows us to differentiate between reactions whose deletion directly blocks a biomass precursor (direct implication) and those whose deletion blocks a reaction that directly implies a biomass precursor (indirect implication). This classification leads to a better explanation of the essential reaction behavior.After obtaining these equivalences, we also studied their implications to distinguish between direct and indirect implications. Let us start by observing that if Five evident implications: the ATPase reaction, the NAD cofactor activation reaction, the hydrolysis of pyrophosphate (which has many secondary implications), RXN66-532 , and the phosphorylation of diacylglycerol, which participates in the glycolytic pathway.Seven other implications: We have itemized this part to avoid long sequencesNICONUCADENYLYLTRAN-RXN .RXN-12002 .SHIKIMATE-5-DEHYDROGENASE-RXN (shikimate dehydrogenase (NADP+), catalyzes the reversible NADPH linked reduction of 3-dehydroshikimate to shikimate and involved in the biosynthesis of aromatic amino acids).HYDROXYMETHYLGLUTARYL-COA-SYNTHASE-RXN .IPPISOM-RXN .Palmitate_synth .Two subsets of essential reactions are significant here: the set of direct implications and final ones . Regarding the direct implications, we found 20 of them . Due to the importance of these two reactions, a deeper study of their impacts on the biomass precursors has been carried out. The results are included in sulfate (which was not in the medium) and RIB5PISOM-RXN.As there are no more MCSs that consist only of exchange reactions, the next natural step was to calculate cut sets in which culture medium components intervene along with some internal reactions (hybrid cut set). In this sense , 48 hybhttps://github.com/biogacop/Sepidermidis_Analysis. These results are also included in Continuing with the idea of finding ways to intervene in the network and taking into account that cysteine and sulfate are essential for the functioning of the network, hybrid cut sets were calculated in which at least one of the two elements was present in the cut set. In this sense, an additional MCS of length three containing cysteine has also been found. The complete list of computed MCSs can be downloaded from ids (identifiers) and names of both metabolites and reactions.It has been verified that the model created by Díaz Calvo et al. is interesting and has quite good quality for being so recent. The model is in its first iteration, and it would be very good to improve aspects such as the standardization of the Even though the solution to the optimization problem posed is not unique, it was able to obtain solutions that are very similar to that of Díaz Calvo et al. However, it was not possible to check if we obtained precisely the same solution as in the reference due to the lack of the total flux value. Even if the comparison between solutions cannot be completely accurate, we consider that the model has been validated by reproducing these results.The minimization problem has also been studied from a more technical point of view. The same metabolic network flux minimization problem has been solved using MILP. We observed that the calculated solutions are similar to those obtained using LP methods. Once again, getting multiple solutions showed that the network has different states for the same conditions. Regarding the inclusion of a lumped biomass pseudo-reaction, the results obtained fall within the range of support sizes and the optimal obtained total flux.S. epidermidis incorporates into its metabolism under steady-state conditions and continuous culture. Taking in lower concentrations of certain nutrients can be due to the conditions in which the state of the metabolic network is simulated. In short, the biological system is subjected to optimal minimum flux conditions . Therefore, it is logical to think that the body in basal conditions does not have the same energy and nutritional needs as when it is in ideal conditions. Even so, it is striking that these components of the medium are not fully incorporated, and yet the rest of the components are.We considered that the maximum limit of nutrients available in the medium that is the concentration of nutrients that id and name naming system. Although, indeed, the notation of the model’s reactions, metabolites, and genes is usually automatized, when curating the model, a naming system must be taken into account to maintain the consistency of the model. Otherwise, it can lead the observer misinterpreting the model, making it difficult to interact with the metabolic network, leading to calculation mistakes.Homogenize the dead-end: missing annotation, missing/absent exchange reactions, or simply that the reaction cannot carry flux at steady-state. In any case, this model is expected to reduce this ratio in future updates. Thus, it is important to declare the external and dead-end metabolites of the model explicitly. If there are differences between external metabolites that represent the limit of the metabolic network represented by the model and metabolites related to the representation of biomass and by-products, they should be mentioned.There are several possible reasons for a metabolite to be a Detail on which databases (version) and organisms (assembly accession) the notation has been based on to build the model.Declare if there are pseudo-reactions or pseudo-metabolites and what functions they fulfill in the model.If a solution to an FBA optimization problem is given, explicitly declare the total flux obtained. The solution support should be included in the Finally, after our experience replicating the network model, we considered proposing a few points that should be taken into account when building a model (best practices):In conclusion, we have observed that the solution indicated in the original paper fits perfectly in the ranges of values we found. Therefore, we claim that the original model has been validated by our in silico experiments, and therefore, the model is correct.Staphylococcus epidermidis’s behavior.After the metabolic network model, some studies were performed to gain insights and properties of r is inactive. However, if r is inactive, it is not possible for all the biomass precursors to simultaneously achieve their corresponding values in the biomass composition. Let us remark that this reaction is catalyzed by the enzyme diacylglycerol kinase (ATP), a transferase that catalyzes the ATP-dependent phosphorylation of phospholipids for the cell membrane. Among the different phospholipids that this reaction can synthesize is diacylglycerol (DAG), one of the biomass precursors present in the S. epidermidis RP62A model. Therefore, it is a reaction that participates indirectly in synthesizing this precursor.Regarding the essential reactions, we found Additionally, we have studied some intervention methods in the metabolic network to find ways to design possible knock-outs for the network. In this sense, we have examined the set of essential reactions, including the previously unobserved one.S. epidermidis RP62A metabolic network is capable of synthesizing the amino acid cysteine, but requires sulfate. However, a sulfate MCS alone would not be viable, since the culture medium also provides exogenous cysteine to the microorganism so that it can be incorporated into the metabolic network through transporters. Consequently, even if the entry of sulfate into the metabolic network or even enzymes of the cysteine synthesis pathway were interrupted, the metabolic network would continue to carry the flux through another subset of reactions; in other words, the microorganism would not die. This MCS corroborates that there is no auxotrophy for cysteine. S. epidermidis RP62A model.We have also explored other ways of achieving the intervention in the metabolic network for the biomass precursors. Focusing only on reactions associated with the importation of nutrients, we found a new MCS of length two (composed of cysteine and sulfate ion). Due to the importance of these two reactions, a deeper study of their impact on the biomass precursors was carried out. This MCS is interesting, as the S. epidermidis in infections suffered by patients. Drugs such as antibiotics (experimented related to tetracycline water-soluble formulations) [This MCS provides minimal intervention through the deprivation of cysteine and sulfate from the medium or by blocking the transporters that facilitate the entry of these components into the interior of the microorganism. This finding could lead to clinical research on treatments that try to reduce the proliferation of lations) , and newlations) , have beContinuing with the study of the MCS of the network, we checked which reactions sulfate is essential for, and the same for cysteine. As expected, the reactions that specifically require cysteine are those that participate in pathways related to cysteine biosynthesis. The only one not associated with sulfate was the reaction involving ribose-5-phosphate isomerase, belonging to the pentose phosphate pathway.The reactions of biomass precursors blocked in the absence of cysteine and sulfate were also explored. The results were as expected, since the blocked reactions were related to cysteine, methionine, and compounds necessary for synthesizing both amino acids and cofactors acetyl-CoA and CoA. The utilization of cysteine to synthesize methionine and vice versa is a process that occurs continuously through the transsulfuration pathway. Both are sulfur-containing amino acids and are of vital importance for synthesizing a wide variety of enzymes . Another aspect of interest is the reactions in which cysteine and sulfate are involved.An interesting aspect for future works would be to carry out the gene and functional annotation of the genes that code for the enzymes that carry out the reactions of interest.During the last decade, there has been a revolution in systems biology and the study of increasingly sophisticated and realistic computational models. However, there is still a long way to go. Various models are emerging at different levels of study of biological systems, but these must be subjected to a rigorous curation process to reduce design flaws. However, despite efforts to standardize the format in which models should be written, there is much divergence when it comes to naming reactions and metabolites. In addition, there is also a lot of ambiguity in the terminology used to define key concepts when working with computational biological models. Hence, we suggest following the best practices provided in the discussion.Staphylococcus epidermidis was performed. Although it is not the only model that exists for S. epidermidis, it was one of the first. What makes this model interesting is that it was created and curated while taking into account empirical observations from experiments performed in the wet laboratory. Now, it has also been externally validated in silico by our research group. This provides great support to the model and offers guarantees of its quality. We have also been able to reproduce the first part of the results obtained by the creators of the model.In this work, an exhaustive analysis of a model for After the validation process, the set of essential reactions was computed. We also detected a previously unobserved essential reaction that is associated with the distribution of biomass precursors as described in the original model. This new essential reaction, DIACYLGLYKIN-RXN, is a reaction that participates indirectly in synthesizing diacylglycerol (DAG), one of the biomass precursors.The strategy proposed in the original work was only based on the intervention of essential reactions. Although this strategy is a good option, we propose different minimal intervention strategies in which mixed interventions are used that involve the energy sources (nutrients from the culture medium) of the microorganism and non-essential reactions belonging to the metabolic network. In this sense, the unique external MCS (composed of cysteine and a sulfate ion) has been found. Finally, all hybrid intervention strategies  of length two were also computed.Further experimental investigations are now needed in clinically relevant conditions to study the possible applications of these intervention strategies.


Komagataella phaffii (aka Pichia pastoris) is a yeast able to grow in methanol as the sole carbon and energy source. This substrate is converted into formaldehyde, a toxic intermediary that can either be assimilated to biomass or dissimilated to CO2 through the enzymes formaldehyde dehydrogenase (FLD) and formate dehydrogenase, also producing energy in the form of NADH. The dissimilative pathway has been described as an energy producing and a detoxifying route, but conclusive evidence has not been provided for this. In order to elucidate this theory, we generated mutants lacking the FLD activity (Δfld1) and used flux analysis to evaluate the metabolic impact of this disrupted pathway. Unexpectedly, we found that the specific growth rate of the Δfld1 strain was only slightly lower (92%) than the control. In contrast, the sensitivity to formaldehyde pulses (up to 8mM) was significantly higher in the Δfld1 mutant strain and was associated with a higher maintenance energy. In addition, the intracellular flux estimation revealed a high metabolic flexibility of K. phaffii in response to the disrupted pathway. Our results suggest that the role of the dissimilative pathway is mainly to protect the cells from the harmful effect of formaldehyde, as they were able to compensate for the energy provided from this pathway when disrupted. Komagataella phaffii (formerly known as Pichia pastoris) is a methylotrophic yeast extensively used for recombinant protein production  and sequenced for verification purposes. The resulting vector RIP293 was used to introduce at I-SceI restriction site 1.3 kb Zeocin resistance gene (ZeoR), rescued from vector RIE280. The resulting vector RIP293_ZeoR was digested by BglII and the 2.7 kb purified P-ZeoR-T was used to transform the K. phaffii RIY232 strain as described by Lin-Cereghino et al. [FLD1) as compared to FLD1_PF. The resulting strain was named RIY428 (Δfld1 strain). All molecular biology techniques were as described in Theron et al. [The strains used in this study were t RIY428 . An FLD o et al. . The corn et al. .Unless stated otherwise, cultures were performed in triplicate using 500 mL shake flasks  with 100 mL of the defined medium Yeast Nitrogen Base (YNB) without amino acids, supplemented with ammonium sulphate (5 g/L) and either methanol or glycerol (3 g/L) as the only carbon source. Flasks were inoculated with each strain to an initial biomass concentration of 0.3 g/L using washed cells previously grown in 3 g/L of the same carbon source (either methanol or glycerol) employed in the respective experiment. Samples (1 mL) were taken regularly to follow biomass production and methanol consumption. Formaldehyde pulses were made 4 h after inoculation and performed by adding the required volume for final concentration of 2, 5 or 8 mM.µ) was estimated from the positive slope in the linear region (R2 > 0.98) of the semi log plot of biomass concentration (X) vs. time [dk).Specific growth rate (vs. time . CorrespXSY) was estimated from the slope in the linear zone (R2 > 0.98) of the methanol concentration (S) vs. X, according to Equation (1):The yield of substrate-into-biomass (Sq) was estimated from the Equation (2) in the range where both the specific growth rate of biomass (µ) and XSY were constant :The specific consumption rate of substrate (µ was quantified by estimating the inhibition constant (iK). For this, we have used the Monod equation and assumed a growth substrate inhibition kinetics [Form) was included:SK is the substrate saturation constant of the Monod equation, and i,SK is the inhibition constant of the substrate. Reference values of these constants can be found in Canales et al. [µ were observed when methanol concentration ranged between 1.0 and 4.0 g/L in shake flask cultures [S was experimentally set within the range where it was higher than the substrate constant (S >> SK), but without a relevant inhibition effect . This was experimentally verified in the period where a constant µ was observed; meanwhile, methanol concentration varied within the range of 3.0 to 2.0 g/L due to cell consumption . Under these experimental conditions, Equation (3) can be simplified to:The formaldehyde effect on kinetics . An addis et al. . In a prcultures . Thus, SiK and maxμ from Equation (4), according to the following equation:µ values. Optimisation was performed using the SOLVER tool in Microsoft Excel.The minimisation of mean square error (MSE) between the predicted values and experimental data was used for estimating Sq and µ was evaluated by estimating their linear correlation according to the following equation:The relationship between Sm [XSY′ and Sm′ are apparent parameters obtained under energy-sufficient growth that consider a growth-rate dependent maintenance energy coefficient as described by Pirt [Equation (6) is analogous to that described for estimating the maintenance energy coefficient mS , but her by Pirt .K. phaffii, as reported previously [1.89N0.137O0.785, as it was obtained from the K. phaffii elemental composition [Metabolic fluxes for each strain were calculated by means of a stoichiometric model for eviously . The orieviously ,20,21 anposition ,23. The position ,25,26, mposition . The ATPposition ,20,28. A2 as mobile phase, using 200, 80, and 200 °C as the temperature for the injector, column, and detector, respectively.The biomass was determined by optical density to be 600 nm and it was converted to dry cell weight (DCW) using a calibration curve as previously described . Methanot-test was used to compare the means using the software GraphPad InStat version 3.05 . The threshold for statistical significance was p < 0.05.Unless otherwise stated, kinetic parameters and yields were calculated from measurements obtained in three independent experiments. Where suitable, values are expressed as mean ± standard error of the mean (SEM) or as a percentage of the mean. An unpaired K. phaffii in methanol metabolism and formaldehyde detoxification, the FLD1 gene encoding formaldehyde dehydrogenase was disrupted in strain GS115. The specific growth rate (µ) was estimated for Δfld1 and control strains in either methanol or glycerol as the only carbon source (fld1 mutant of C. boidinni [fld1 strain of K. phaffii was able to grow in methanol (maxµ = 0.114 ± 0.003 h−1) at a similar (but significantly lower) specific growth rate of the non-disrupted control strain (maxµ = 0.125 ± 0.001 h−1). In contrast, using glycerol as a carbon source lead to the same maxµ (0.3 h−1) with Δfld1 and control strains  that with the Δfld1 strain displayed a value that was approximately half the control strain (7.57 mM). This indicates that the inhibition effect in the disrupted strain occurs at a lower formaldehyde concentration than the control. Yano et al. [fld1 strain grown in glucose as a carbon source to be hypersensitive to formaldehyde 2mM. Other formaldehyde consuming enzymes such as DAS are normally repressed in the presence of glucose, probably explaining the reason for the toxic effect observed even after 48 h of culture [C. boidinii [P. methanolica [K. phaffii. Moreover, recent genome-scale metabolic models of K. phaffii have not included this intermediary in their reaction networks containing more than a thousand metabolites [K. phaffii. Further research is required to verify this hypothesis.A lower effect of formaldehyde pulses on ion of µ C. The quo et al.  reportedboidinii  and P. mhanolica  has beenabolites ,35, suggP. methanolica, where concentrations up to 5 mM had a major impact on cell growth [AOX and FLD genes proved to be a key factor in cell prevention of formaldehyde toxicity. On the other hand, glycerol cultures showed a higher sensitivity to formaldehyde than methanol cultures (P. methanolica and has been explained by the presence of the DAS enzyme that is induced by methanol (but not by glycerol) and metabolised the formaldehyde, reducing its toxic effect [Formaldehyde toxicity has also been evaluated in other methylotrophic yeasts such as l growth . The dyncultures . This bec effect . Formaldc effect , the effSq) in batch cultures was estimated to be within the range where balanced growth was verified, i.e., when both µ and XSY were constant. This was observed for at least 4 h before and after the formaldehyde pulse  between these two variables, with different slopes and y-axis intercepts for each strain. This trend matches the previously described behaviour associated with the growth-dependent maintenance energy described by Pirt [XSY′) and apparent maintenance coefficient (Sm′) as shown in Equation (6). Here, μ is a function of formaldehyde concentration ) instead of nutrient concentration. The value of Sm′ for Δfld1 strain was 2.5-fold higher than control strain (7.4 compared to 3.0 mmol/ gDCW·h), revealing a higher demand of maintenance energy in the Δfld1 strain in the presence of formaldehyde. This finding suggests that the lack of dissimilative pathway changes the ability of cells to manage the toxic effects of formaldehyde in terms of maintenance energy. This was explored more deeply by means of a metabolic flux estimation.For a better insight into the methanol utilisation after the formaldehyde pulses, we have explored the relationship between qS and µ . Remarka by Pirt  which wa by Pirt . For simK. phaffii performed through a metabolic flux analysis, using a stoichiometric model previously described [FLD1 gene and formaldehyde pulses were contrasted. Fluxes through both the dissimilative and assimilative pathways in the control strain decreased as a consequence of formaldehyde pulses compared to the reference condition , NAD (P) H and FADHeostasis ,20,28. Rfld1 does not produce but rather consumes net energy, since no NADH is produced, while at the same time ATP is required for phosphorylation of dihydroxyacetone by the enzyme dihydroxyacetone kinase in the assimilative pathway [K. phaffii GS115 showed that the increased amount of enzymes of the Mut pathway caused by methanol induction occurred together with a decrease of fumarase and aconitase in the TCAc [Ogataea methanolica indicates that TCAc was up or downregulated as a response to intracellular level of acetyl-CoA associated with the methanol concentration [K. phaffii indicates that cells exhibit a metabolic flexibility to generate reducing power. In the present case, cells lacking the dissimilative pathway were able to fulfil the energy requirements through pathway flux rearrangement at glycolytic and TCAc levels. Besides this, formaldehyde pulses had a major impact on flux distribution in the Δfld1 strain, which is mainly caused by its toxicity.Based on these results, the estimation of net energy contribution of the Mut pathway, glycolysis and TCAc, and PPP was made B. Result pathway . In addithe TCAc  describentration , suggestFLD1 gene is not essential for cell viability and the growth of K. phaffii when methanol is the only carbon and energy source, in contrast with a previous report on Δfld1 strains obtained by random mutagenesis. Our results revealed a high metabolic flexibility of K. phaffii in response to the disrupted dissimilative pathway, as the Δfld1 strain was able to produce energy to fulfil their requirements and compensate for the energy that is normally produced in this pathway. Despite this metabolic flexibility, the toxic effect of formaldehyde was significantly increased in the Δfld1 strain, supporting the idea that the dissimilative pathway is a formaldehyde detoxifying route.In this work, we have demonstrated that the


Shewanella psychrophila WP2, under suboptimal (4°C), optimal (15°C), and supraoptimal (20°C) growth temperatures. The models were calibrated with experimentally determined growth rates of WP2. Gibbs free energy change of reactions (ΔrG′), metabolic fluxes, and metabolite concentrations were predicted using random simulations to characterize temperature-dependent changes in the metabolism. The modeling revealed the highest metabolic efficiency at the optimal temperature, and it suggested distinct patterns of ATP production and consumption that could lead to lower metabolic efficiency under suboptimal or supraoptimal temperatures. The modeling also predicted rearrangement of fluxes through multiple metabolic pathways, including the glycolysis pathway, Entner-Doudoroff pathway, tricarboxylic acid (TCA) cycle, and electron transport system, and these predictions were corroborated through comparisons to WP2 transcriptomes. Furthermore, predictions of metabolite concentrations revealed the potential conservation of reducing equivalents and ATP in the suboptimal temperature, consistent with experimental observations from other psychrophiles. Taken together, the WP2 models provided mechanistic insights into the metabolism of a psychrophile in response to different temperatures.Microbial acclimation to different temperature conditions can involve broad changes in cell composition and metabolic efficiency. A systems-level view of these metabolic responses in nonmesophilic organisms, however, is currently missing. In this study, thermodynamically constrained genome-scale models were applied to simulate the metabolic responses of a deep-sea psychrophilic bacterium, IMPORTANCE Metabolic flexibility is a central component of any organism’s ability to survive and adapt to changes in environmental conditions. This study represents the first application of thermodynamically constrained genome-scale models in simulating the metabolic responses of a deep-sea psychrophilic bacterium to various temperatures. The models predicted differences in metabolic efficiency that were attributed to changes in metabolic pathway utilization and metabolite concentration during growth under optimal and nonoptimal temperatures. Experimental growth measurements were used for model calibration, and temperature-dependent transcriptomic changes corroborated the model-predicted rearrangement of metabolic fluxes. Overall, this study highlights the utility of modeling approaches in studying the temperature-driven metabolic responses of an extremophilic organism. Temperature has a major impact on microbial physiology, affecting growth through physicochemical changes such as altered enzyme stability –3, react–5–Compared to the extensive understanding of temperature-related physiological responses in mesophiles, a comprehensive view of these processes is still being developed for psychrophiles, organisms having maximum growth temperatures around 20°C, and optimal growth temperatures between 5°C and 15°C. Unlike the mesophiles, some psychrophiles constitutively express proteins commonly involved in cold shock responses , 22. A v23–Shewanella psychrophila WP2 (here referred to as WP2), was isolated from benthic sediments in the western Pacific Ocean, where the ambient environmental temperature was approximately 4°C , Shewanella loihica PV-4 and Shewanella piezotolerans WP3 , into the model representation  . The resulting WP2 GEM had an overall consistency score of 99% based on the MEMOTE test suite , etc.] (Table 1 in rG′) for the representation of models under optimal (15°C), supraoptimal (20°C), and suboptimal (4°C) temperatures .A genome-scale metabolic reconstruction was developed following the iterative annotation of the WP2 genome (GenBank assembly accession no. st suite  and cont10.1128/msystems.00588-22.3DATA SET S1Shewanella species. Reactions associated with each gene are provided for each of the previously modeled strains. (Table 3) Composition and stoichiometry of each biomass component and the overall biomass reaction in the WP2 GEM. (Table 4) Gap reactions included in WP2 GEM. The reason for including the gap reaction and source are listed for each reaction. (Table 5) Standard Gibbs free energy predictions for reactions in the WP2 GEM based on the group-contribution method. Values are provided as kJ/mol. (Table 6) Metabolite structural information. Table contains metabolite names, formulas, and charges from the model. SMILES strings were provided along with an indication of the source of the structure. A mapping to the TECRdbDB used by the group-contribution package was also included. (Table 7) List of all reactions that were excluded from the thermodynamic constraints. (Table 8) Net number of protons and net charge transported from outside the cell to inside the cell for each transport reaction in the WP2 GEM. (Table 9) Nondefault concentration bounds used in the WP2 TMFA simulations. Concentrations were based on the LMO-812 media composition for all compounds except cpd_cbasp and cpd_dhor-S, which were modified based on previous literature . (Table 10) Exchange constraints used for the temperature-dependent model simulations based on the LMO-812 media. Download Data Set S1, XLSX file, 1.3 MB.Metabolic model information and constraints used for model simulations. (Table 1) Comparison of model-predicted and experimentally measured growth and no-growth phenotypes. G, growth was observed  or predicted (model); NG; no growth was observed  or predicted (model). (Table 2) Ortholog mapping between WP2 and four previously modeled Copyright © 2022 Dufault-Thompson et al.2022Dufault-Thompson 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 Each temperature-dependent model was calibrated by using experimentally measured growth rates to constrain an ATP hydrolysis reaction (ATPM) that accounts for the non-growth-associated ATP utilization by a cell. Briefly, this ATP maintenance flux was determined by fitting the model predictions to experimentally measured growth rates , and theN-acetyl-d-glucosamine (GlcNac) as a sole carbon source in aerobic conditions, replicating the conditions used in the WP2 growth experiments . For each temperature-dependent model, 1,000 random simulations were performed, with each simulation constrained by fixing the biomass flux to a random value selected within the range of experimentally derived growth rates . Predictions of reaction fluxes, Gibbs free energy change of reactions, and metabolite concentrations were generated from each random simulation and were used to characterize the metabolic changes between different temperatures.The temperature-dependent models were simulated with The metabolic efficiency was estimated based on each random simulation using three global efficiency parameters, CUE, ATP production per carbon substrate (ATP-produced/GlcNac), and ATP consumption per gram dry weight (gDW) of biomass . Of the three temperature-dependent models, the 15°C model showed the highest metabolic efficiency, with a high CUE (2- to 3-fold higher than what was predicted in 4°C or 20°C models), a high ATP production, and a low ATP consumption in biomass synthesis . While trG′ and reaction fluxes predicted from the 1,000 random simulations revealed a broad, temperature-dependent remodeling of metabolic pathway utilization in WP2. Of the 940 metabolic reactions, significant changes in metabolic flux were identified for 326 (35%) reactions, and significant changes in ΔrG′ were observed in 259 (28%) reactions  reaction . PTAr waThe three temperature-dependent models also varied in their utilization of the tricarboxylic acid (TCA) cycle. The 4°C model used the isocitrate lyase (ICL), fumarase (FUM), and malic enzyme (ME2) reactions, conserving carbon for the production of pyruvate (PYR), which, in turn, could be used for the synthesis of the precursor metabolites, PEP and AcCoA . The FUMOutside the central carbon metabolism, differences were also observed in the modeled utilization of the electron transport system. The ATP synthase reaction (ATPS4r) was used in the proton-pumping direction in the 15°C model and was optional in the same direction in the 20°C model. However, ATPS4r was not used in the 4°C model. The 20°C model obligately used the proton-translocating NADH/quinone oxidoreductase reactions  to carry out electron transport from NADH to the quinone pool. This was connected to the fumarate reductase reaction (FRD10 and FRD11), apparently contributing to the cycling of electron carriers in the 20°C model. In contrast, the 4°C model could utilize the alternative, non-proton-translocating NADH dehydrogenase reaction (NADH4), which was not used in either the 15°C or the 20°C model . Overall10.1128/msystems.00588-22.1FIG S12 fold change in expression and had an adjusted P value of less than 0.05 based on the differential expression analysis . Download FIG S1, TIF file, 28.2 MB.Temperature-dependent metabolic fluxes and corresponding gene expressions in the key steps of WP2 central metabolism. Metabolic fluxes predicted from the 4°C (blue), 15°C (yellow), or 20°C (red) models are shown as boxplots based on values obtained from 1,000 random simulations. Significant differences between fluxes were identified using the Kruskal-Wallis test  and are marked by brackets with an asterisk. Gene expression data are shown separately for the early exponential phase (early), late exponential phase (late), and stationary phase (stat). Counts obtained from median ratio normalization (MRN) were presented in a log scale for the comparison of gene expression levels across different temperatures. The expression values are marked as significantly different if any of the genes associated with the reactions showed greater than 1 or less than −1 logCopyright © 2022 Dufault-Thompson et al.2022Dufault-Thompson 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 +] and [NADPH]/[NADP+] ratios, which may indicate the conservation of reducing equivalents and was consistent with the reduced metabolic fluxes in the electron transport system, such as the NADH/quinone oxidoreductase . Comparisons of the transcriptomes revealed a very weak negative correlation between the expression of genes at 4°C compared to the other two temperatures, similar to what was observed on the model-predicted metabolic fluxes and Δ′ values . Analyse′ values  but also′ values .10.1128/msystems.00588-22.2FIG S2rG′, metabolic flux, and gene expression under different temperature conditions. The ΔrG′ (top row) and metabolic flux (middle row) values were obtained from simulations of the temperature-dependent models, scaled for each reaction across the three temperature conditions . The gene expression (bottom row) was based on MRN values, scaled for each gene across the three temperature conditions . The scatterplots demonstrate pairwise comparisons of the scaled values between each pair as follows: 4°C versus 15°C (left), 4°C versus 20°C (center), and 15°C versus 20°C (right). The linear fitting was shown as a blue line in each plot, with gray shading showing the 95% confidence interval. The corresponding linear equations and r2 values were shown with black labels. Download FIG S2, TIF file, 21.5 MB.Comparisons of ΔCopyright © 2022 Dufault-Thompson et al.2022Dufault-Thompson et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.00588-22.5DATA SET S3Data Set S3, XLSX file, 1.7 MB.Summary of differential gene expression analyses. (Table 1) Differential expression of genes between temperatures during early exponential growth phase. (Table 2) Differential expression of genes between temperatures for late exponential growth phase. (Table 3) Differential expression of genes between temperatures for stationary growth phase. Download Copyright © 2022 Dufault-Thompson et al.2022Dufault-Thompson 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 The pyruvate formate lyase (PFL) gene was significantly upregulated under 15°C in the late exponential phase. This was consistent with the high metabolic flux predicted for the PFL reaction and the prediction of a more active glycolytic pathway, an upstream pathway to PFL, in the 15°C model. The expression of the PFL gene at 20°C was higher than 4°C during the early exponential phase but lower than 4°C during the late exponential or stationary phases. This was in line with the high variability of PFL metabolic fluxes in the 20°C model.Other examples of differentially expressed genes were observed in the central carbon metabolism. Genes encoding the FBA, glucose-6-phosphate dehydrogenase (G6PDHy), and phosphogluconate dehydratase (PGDHY) reactions were upregulated under 4°C . This waAdditional examples involved the differential expression of genes in the electron transport systems. Specifically, multiple genes encoding subunits of the ATP synthase and the proton-translocating NADH/quinone oxidoreductase were upregulated under 20°C compared to 4°C, and the non-proton-translocating NADH dehydrogenase was upregulated at 4°C compared to 20°C. These were consistent with the model predictions, as the 20°C model predicted higher fluxes through the NADH11, NADH13, and NADH16 reactions, and the 4°C model predicted the potential usage of NADH4 and lack of utilization of ATPS4r .Genome-scale metabolic modeling has been applied to a variety of different biological problems, but the incorporation of complex environmental factors like pH, pressure, or temperature into simulations of growth remains challenging. In this study, thermodynamically constrained models were developed of WP2, a deep-sea psychrophilic bacterium, setting an example for modeling metabolic responses to different temperatures.The temperature-dependent, thermodynamically constrained models were calibrated based on experimentally measured growth of WP2 under optimal (15°C), suboptimal (4°C), and supraoptimal (20°C) temperatures, suggesting the lowest ATP maintenance cost in the 15°C model and a 2- and 7-fold-higher ATP maintenance cost in the 4°C and 20°C models, respectively . Higher To provide a quantitative estimation of the metabolic efficiency, three global parameters, including the CUE, the ATP production per carbon substrate, and the ATP consumption per gDW of biomass synthesis, were calculated for each temperature-dependent model based on metabolic fluxes from 1,000 rounds of random simulations . These global parameters revealed the highest metabolic efficiency in the 15°C model, characterized by high CUE, high ATP production per carbon substrate, and low ATP consumption compared to the other two temperatures. The predicted CUE under 15°C had a median value of 0.26 . This waThe two models of nonoptimal temperatures (4°C and 20°C) predicted similar CUE but distinct measures of ATP production and ATP consumption . The 4°CThe suboptimal temperature modeled in this study (4°C) represents the ambient temperature of WP2 in the deep sea . Our mod52–The temperature-dependent changes in metabolic efficiency may be linked to altered metabolic pathway usage predicted under the different temperatures. The higher ATP production in 15°C and 20°C models could be attributed to the utilization of the payoff phase of glycolysis , as the 10.1128/msystems.00588-22.4DATA SET S2Data Set S2, XLSX file, 0.4 MB.Summary of TMFA simulation results. (Table 1) Summarized values for reaction fluxes from random TMFA simulations. The median, minimum, maximum, 25th percentile, and 75th percentile values were included for each reaction based on the 1,000 random TMFA simulations. Results of a Kruskal-Wallis test, estimate of the effect size, and results of pairwise Wilcoxon tests are shown for each reaction. Values of ND are reported when the 3 distributions being compared have no difference. (Table 2) Summarized values for reaction Gibbs free energy values from random TMFA simulations. The median, minimum, maximum, 25th percentile, and 75th percentile values were included for each reaction based on the 1,000 random TMFA simulations. Results of a Kruskal-Wallis test, estimate of the effect size, and results of pairwise Wilcoxon tests are shown for each reaction. Values of ND are reported when the 3 distributions being compared have no difference. (Table 3) Summarized values for metabolite concentrations  from random TMFA simulations. The median, minimum, maximum, 25th percentile, and 75th percentile values were included for each reaction based on the 1,000 random TMFA simulations. Results of a Kruskal-Wallis test, estimate of the effect size, and results of pairwise Wilcoxon tests are shown for each metabolite. Values of ND are reported when the 3 distributions being compared have no difference. Download Copyright © 2022 Dufault-Thompson et al.2022Dufault-Thompson 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 Colwellia psychrerythraea  . It has rythraea .2-producing steps in the TCA cycle  of 0.002 was measured for each inoculation. Turbidity was monitored throughout the growth phases of the cultures, and the OD600 values were converted to gram dry weight values for each culture using a previously established relationship between OD and dry weight biomass in Shewanella  were performed in 150 mL of fresh LMO-812 media  with 1 mewanella . Growth ewanella .g for 2 min, and the cell pellet was used for the application of RNA extraction and sequencing using services provided by the Sangon Biotech in Shanghai, China. Total RNA was extracted from the cell pellets and cleaned using the Ribo-off rRNA depletion kit. cDNA libraries were prepared using the VAHTS stranded mRNA-seq V2 library prep kit for Illumina. Paired-end sequencing was performed on the HiSeq X Ten system generating 2 × 150-bp reads.Samples for transcriptome sequencing were taken from the early exponential, late exponential, and stationary phases for each replicate in the 4°C, 15°C, and 20°C cultures . Two bio2 fold change between conditions was greater than 1 or less than −1 and if the adjusted P value was less than or equal to 0.05. The gene expression data , S. piezotolerans WP3 (GCA_000014885), S. oneidensis MR-1 (GCA_000146165), Shewanella sp. strain MR4 (GCA_000014685), Shewanella sp. strain W3-18-1 (GCA_000015185), and S. denitrificans OS217 (GCA_000013765). Orthologous genes between the genomes were identified using a bidirectional best hit BLASTp approach as detailed in a previous study  was calculated for reactions in the WP2 model using the group-contribution python package  identifiers in the Thermodynamics of Enzyme-Catalyzed Reactions Database (TECRDB)  and exported using the SMILES export function. Artificial metabolites representing macromolecule biomass components like DNA, RNA, and the overall biomass compound were not assigned SMILES strings. The ΔrG′° of the biomass objective function, macromolecular biosynthesis reactions, and several reactions in the cell wall biosynthesis pathway were not predicted due to the complexity of the metabolites involved in these reactions.Standard Gibbs free energy change of reaction (Δ package  (Table 5 package . Multipl(TECRDB) , which p(TECRDB) , which cdatabase  in the frG′° estimations as described in a prior section -dihydroorotate, were expanded with a range from 1e-6 to 0.05 mol/L following prior conventions . The resulting predictions of reaction fluxes, ΔrG′, and metabolite concentrations were summarized by calculating the minimum, maximum, median, and 25th and 75th percentiles over the 1,000 random simulations for each temperature-dependent model . The effect size was calculated using the epsilonSquared function in the R package rcompanion (version 2.4.15). Comparisons of the 4°C, 15°C, and 20°C models with effect size ≥0.36, following previously established thresholds . The ATP consumed per gDW biomass was calculated through dividing the sum of all stoichiometrically scaled ATP-consuming fluxes by the biomass flux.rG′, metabolic flux, and gene expression values across different temperature conditions (rG′ and metabolic flux were scaled for each reaction across three temperature conditions. This was done by dividing each metric by the root mean square of the same metric across three temperature conditions. The expression values were normalized by MRN and scaled for each individual gene by the root mean square values across three temperatures.The metabolite ratios  were calnditions  were vishttps://github.com/zhanglab/psamm. The WP2 GEM in both YAML and SBML formats, all inputs used for the modeling, and all analysis scripts are available on GitHub at the following address: https://github.com/zhanglab/GEM-iWP2. Transcriptome raw read data were deposited in the NCBI Sequence Read Archive database under BioProject accession no. PRJNA739408.Modeling approaches implemented in this study are accessible through the open-source PSAMM software, release v1.1 and are freely available in a git repository