Mycoplasma pneumoniae and Mycoplasma fermentans) were examined using a novel approach involving nuclear magnetic resonance spectroscopy and principle component analysis. Characterisation of metabolic changes was facilitated through the generation of high-density metabolite data and diffusion-ordered spectroscopy that provided the size and structural information of the molecules under examination. This enabled the discrimination between biofilms and planktonic states for the metabolomic profiles of both organisms. This work identified clear biofilm/planktonic differences in metabolite composition for both clinical mycoplasmas and the outcomes serve to establish a baseline understanding of the changes in metabolism observed in these pathogens in their different growth states. This may offer insight into how these organisms are capable of exploiting and persisting in different niches and so facilitate their survival in the clinical setting.Mycoplasmas are fastidious microorganisms, typically characterised by their restricted metabolism and minimalist genome. Although there is reported evidence that some mycoplasmas can develop biofilms little is known about any differences in metabolism in these organisms between the growth states. A systematic metabolomics approach may help clarify differences associated between planktonic and biofilm associated mycoplasmas. In the current study, the metabolomics of two different mycoplasmas of clinical importance ( Mycoplasmas are typically divided into fermentative and non-fermentative groups according to their ability to produce acid from glucose and degrade arginine respectively3. When grown under anaerobic conditions, lactate is the end product of the glucose oxidation, whereas under aerobic conditions, partial oxidation occurs and acetate and carbon dioxide (CO2) are formed4. Glucose and arginine are the substrates that are utilised as energy sources for mycoplasmas; thus, the majority of strains may be characterised according to whether they produce acid from glucose metabolism, ammonia from arginine hydrolysis or carry out both or neither of these reactions5. Amino acids, fatty acids, metabolic precursors and even cholesterol are essential growth factors and play a vital role in mycoplasma survival6. Due to the inability of mycoplasmas to synthesise macromolecules, they are dependent upon the nutrients found in host cells during infection7. Mycoplasma survival can be enhanced by biofilm formation9 and as a result, biofilms tend to be resistant to environmental conditions and host immune responses, relative to their planktonic counterparts10.Mycoplasmas are facultatively anaerobic mollicutes that require an enriched growth media supplemented with nucleic acid precursors, fatty acids as well as amino acids to sustain their culture in laboratory conditions. These organisms are therefore considered to have a minimal genome12 suggested that bacteria  attached to surfaces have differing metabolic activities compared to their planktonic counterparts due to physiochemical conditions and physiological characteristics expressed as a result of their growth. This phenomenon has been shown in biofilms produced by Vibrio parahaemolyticus, where a variety of cellular process including energy metabolism and biofilm development are influenced by certain polysaccharides13. In order to better understand mycoplasma biofilms and their influence in disease a comprehensive characterisation of their differing metabolic states is necessary within this complex cellular community. Metabolomics can provide such a systematic approach helping to clarify the complex mechanistics of biofilm states when compared with their free-living planktonic counterparts15.The view proposed by17.In the current study, NMR spectroscopy in conjunction with principle component analysis (PCA) was used to examine the mycoplasma metabolome (both planktonic and biofilm) and characterise metabolic changes in mycoplasma species. However, as the cellular metabolome normally incorporates thousands of metabolites it was not possible to exhaustively analyse every individual component. In order to manage the analysis of these metabolites, chemomx software was utilised to identify the major features within NMR spectrum, such as the absence or presence of peaks, the change in the shape of peak intensity, or the change in chemical shifts that distinguish between the two classes1H NMR) is commonly used for global analysis of a metabolome23. These experiments were able to identify the network of resonances associated with specific metabolites through J-coupling in a model system25. The current study also used diffusion ordered spectroscopy (DOSY) NMR methodology to study the metabolome of planktonic and biofilm states for human mycoplasma preparations, providing size and structural information about the molecules therein26. In DOSY- NMR, the diffusion motions of the components in samples are encoded and decoded using the application of a set of pulsed field gradients (PFG)31. A particularly novel application of DOSY NMR is the capacity for spectral binning and principal component analysis (PCA) of the diffusion coefficients for different regions. This is the first time this technique has been employed in mycoplasmal metabolome characterisation and significant metabolic changes between biofilm and planktonic mycoplasmas have been determined.One-dimensional proton NMR , respiratory tract (2 isolates), urine, urethra and a cell line isolate. All strains were stored as freeze-dried cultures and were grown and sub-cultured in fresh Eaton’s broth medium.A selection of clinical strains from the Kingston University culture collection were utilised in this study including eight clinical strains of M. fermentans and M. pneumoniae were inoculated into Eaton’s broth medium (at a cell density of 106 cfu/ml) and incubated at 37 °C for 3–7 days to grow planktonic cells, whilst M. pneumoniae strains were incubated at 37 °C for approximately 30 days.Isolates of both M. fermentans and M. pneumoniae (at a cell density of 106 cfu/ml) were inoculated into 10 ml tissue culture flasks  containing Eaton’s broth medium, and then incubated as previously described. Following incubation, biofilms were acquired from the flask using a cell scraper  and aliquoted into sterile bijous for processing.Aliquots (2 ml) of 1H NMR experiments. 5 mm Bruker Single Use NMR tubes (serum and DOSY). All spectra were acquired on Topspin 3.1 . All NMR experiments were carried out at 25 °C. The Pulsecal routine was run prior to all experiments and the pulse angles adjusted accordingly for total correlated spectroscopy (TOCSY) and diffusion ordered spectroscopy (DOSY) experiments.A Bruker Avance III 600 MHz NMR spectrometer with 5 mm TXI Probe and temperature control unit was used for all 1D 1H spectra for serum samples were acquired using 65,536 complex data points over a sweep width of 20.57 ppm using a pre-saturation of the water signal at 4.7 ppm and one spoil gradient, Relaxation delay (d1) was set to 10 s with the RGA set to 256 for quantitation (See Supplementary Information 2O.Diffusion spectra were obtained over 64 K data points (SW 10.3112 ppm) using longitudinal eddy current delay bipolar pulsed field gradient with 2 spoil gradients and pre-saturation sequence (LEDBPGPPR2s). This was used to obtain the diffusion series with δ = 4.6 ms and 31.Diffusion data was processed using a sine bell shaped window function phase over all data points prior to Fourier transformation  using Topspin 3.0 . Diffusion data was processed using DOSY Toolbox, created by Mathias Nilsson, Manchester University. Individual peaks were fitted exponentially after a 2nd order polynomial baseline correction was employedca 0.1 × 10–10 m2 s−131. The Residual Sum of Squares for each of the diffusion curves is less than 5 × 10–3 in all cases.Errors in diffusion coefficient were calculated based on the Standard Deviation for each diffusion curve and are in line with the estimated error as reported for a similar mixture of 29, internal reference standards such as trimethylsilyl propionate (TSP) can often complex with proteins in the analysis and it is highly questionable that reliable direct quantitation of individual metabolites is possible in such a mixture. The relative amounts of individual metabolites were of greater interest in a qualitative sense.All 1D datasets were processed using Chenomx . As has recently been reportedMetabolites used in the PCA were restricted to those for which a concentration was detected by Chenomx in all samples. All data points were normalised (area) using Unscrambler 10, using a constant weighting and cross validation.30.NMR spectra of metabolite mixtures are by their very nature complex and it was hypothesised that further information about metabolite mobility could be obtained using Diffusion Ordered Spectroscopy (DOSY). In addition, 1D NMR data provides many overlapping signals and while the Chenomx software is good at resolving some of the well-known metabolites, there was also a desire to investigate the existence of certain unknown metabolites. Through the use of a range of known metabolites as internal mass/diffusion standards it is possible to plot a LogMr vs LogD graph to calculate the approximate mass of an unknown and this has been used to good effect in complex mixtures of this typeTrying to automatically calculate diffusion coefficients from a pseudo 2D plot is not easily achieved and neither Amix nor Dosytoolbox are capable of dealing with the raw data to provide this output. DOSY spectra for both mycoplasmas were obtained for biofilm and planktonic samples in serum.2 > 0.95)All DOSY spectra were calibrated with respect to both chemical shift and also diffusion, using internal standards of acetate, glucose, alanine and tyrosine. The range of molecular mass (Mr) afforded by this range of metabolites would enable the identification of an unknown Mr with good linearity  with 0.02 ppm increments and all signal intensities for each bin for each of the 64 gradient incremented spectra.The diffusion coefficients for each bin were calculated using an Excel curve-fitting algorithm.The above three steps were carried out for each spectrum.Using the 8 samples for each of the strains of mycoplasma we obtained a spreadsheet 8 column wide by 515 rows long with the matrix showing the diffusion coefficients for each bin for each sample. As water suppression was used the area of suppression was discounted from the spreadsheet.For each strain of mycoplasma, the spreadsheet from 5 was analysed using Multibase 2015 addon to Excel and any differences analysed using PCA.Therefore, the following process was adopted;30.Using Chenomx Profiler  1D processed datasets were analysed and the components assigned with the aid of TOCSY where appropriate. The pH was verified for each of the samples prior to processing so no internal pH standards were used and the line width and shifts were calibrated to formate as per the standard procedure for Chenomx data processing. Where assignment was ambiguous DOSY NMR was used to approximate the relative molecular mass (Mr) for individual components as previously shownThis research raised no ethical concerns for consideration as no human or animal subjects included in the experimental work.M fermentans were considered in isolation. Following the same processing parameters described previously in the methodology section, PCA analysis was carried out . The media components used in this study (Eaton’s media) in the absence of any Mycoplasma species, were also identified by 1D 1H NMR in order to provide baseline recognition of known metabolites, Chenomx software was used to analyse and identify the different metabolite components . Further work would be required to automate the correlation of the signals with a specific component. The introduction of operator assignment error when the bin size is as small as it is could easily lead to incorrect assignment of a signal to a specific metabolite.M. fermentans initially showed little grouping in the scores plot and this was confirmed by the statistical output, which showed a negative value for Q2 and poor statistical explanation of the data. The residual variation was too high for this model and therefore further interrogation would have yielded unreliable pattern data  system37. Glucose, and other fermentable sugars, are a crucial energy source, especially for the fermentative mollicutes. Glucose may also act as a precursor for the synthesis of other saccharides that stimulate the growth rate of mollicutes40.Mycoplasmas are divided into fermentative and non-fermentative organismsM. fermentans and M. pneumoniae43. Physiological characterisation of biofilms is therefore vital to develop effective anti-mycoplasma treatments25.Host cell attachment by mycoplasmas, as a biofilm or part thereof, and cytotoxic metabolite production may contribute towards significant host cell damage, commonly observed in infection with these pathogens. Biofilm formation also frequently results in increased resistance to antimicrobial agents and persistence of many organisms including 27 the work presented here is the first DOSY-NMR metabolomics-based study in human mycoplasmas.Although global metabolite analysis has been proven as a powerful tool for understanding bacterial physiology17. The purpose of the evaluation of biofilm and planktonic mycoplasmas using 1D 1H NMR was to establish the amplification of one or more metabolites/media components (markers) dependent on the mycoplasma being grown either as a biofilm or in planktonic state. The use of 1D NMR techniques for the analysis of serum and also cytosolic contents are well established31 and analysis of Acinetobacter baumannii strains using 1D NMR, 2D TOCSY and PCA analysis demonstrate the usefulness of these types of investigations in potentially elucidating details of the different metabolic pathways. The aim of the current research was to use these techniques to identify differences in metabolite composition for M. pneumoniae and M. fermentans in both biofilm and planktonic forming conditions. In addition, we aimed to evaluate the potential of Diffusion Ordered Spectroscopy (DOSY) to assist in discriminating between planktonic and biofilm mycoplasmas and what significance these points of discrimination may have, either to assist in the classification of a mycoplasma as either biofilm or planktonic derived, or to provide more information about the constituents of the biofluid and potentially the differences in metabolic pathways. Additionally, this approach could also help to understand the potential changes in mycoplasmal metabolism under different growth conditions. The current study examines the overall metabolism of the organisms in the stated growth condition (planktonic or biofilm) and not the rate of metabolism. The measurement of bacterial metabolism for communities in a biofilm cannot reliably be measured and currently the metabolomic differences between the growth states as a whole is reported rather than growth rate measurements48.NMR-based metabolomics is a relatively new technology in biological applications and correspondingly has seen limited exploration in the investigation of microbial biofilms54. Oxidative phosphorylation deficient mycoplasmas can metabolise pyruvate via two alternate pathways to generate ATP: oxidation by pyruvate dehydrogenase to acetyl-CoA, followed by the action of phosphate transacetylase and acetate kinase to yield acetate and ATP. Alternatively, pyruvate is reduced to lactate by lactate dehyrdogenase with concomitant oxidation of NADH to NAD to facilitate continued ATP generation via the EMP53. Energy metabolism in mycoplasmas is therefore principally dependent on fermentation via the EMP to lactate under anaerobic conditions or acetate and CO2 under aerobic conditions34.In reference to carbohydrate metabolism. many mycoplamas lack hexokinase, therefore glucose is transported into cells and 'captured' by the phosphoenolpyruvate: phospho-transferase (PEP:PTS) system for degradation to pyruvate in the Embden–Meyerhof–Parnas (EMP) glycolytic pathwayM. fermentans, acetate was found to be greater in the biofilm suggesting that there is a bias toward the pyruvate dehydrogenase route for pyruvate metabolism. For both mycoplasma strains, the data indicates biofilms potentially produce more energy than planktonic cells. Glucose-6-phosphate was more abundant in planktonic cultures of both M. fermentans and M. pneumoniae, compared to biofilm serum . The results showed that the proline level (in the form of 4-Hydroxy-L-Proline) in M. pneumoniae was greater in biofilm cell serum compared to its planktonic counterpart  pathway, similar to that found in M. hominis58. The ADH pathway catalyses the conversion of arginine to ornithine, ATP, CO2 and ammonia. The current work showed that arginine levels in Mycoplasma fermentans were greater in planktonic cells than biofilms.In M. fermentans compared to its planktonic growth state . Depending on the examination of the phylogenetic distribution of the enzymes participating in isoleucine/valine metabolic pathway, it was found that all the genes coding for the enzymes of isoleucine/valine biosynthesis were missing in M. pneumoniae65. Thus, the presence of these metabolites in the profile might be related to the high nutritional value and complexity of the surrounding growth medium. This study also identified the presence of creatine in the biofilm of both M. fermentans and M. pneumoniae were higher than their planktonic counterparts (Figs. 66. Although creatine biosynthesis in mycoplasmas is still elusive and not fully described, these findings could be connected to arginine metabolism, as described above.The metabolite profiles in the current study showed some interesting observations, such as the presence of valine and alloisoleucine in ts Figs.  and 6. TM. fermentans and M. pneumoniae (Figs. 25. In Mycoplasma pneumoniae, Uridine is involved in the Leloir pathway that comprises a step in galactose phosphorylation67. Furthermore, EPS production requires both free galactose and the Leloir pathway in order to form a biofilm67.In relation to nucleotide Metabolism, levels of Uridine were higher in the biofilm than planktonic state after for both ae Figs.  and 6 UrM. fermentans and M. pneumoniae were regulated by multiple enzymatic reactions in order to fulfil key metabolic activities. Therefore, protein abundance in the metabolic network could provide a qualitative picture of M. fermentans and M. pneumoniae metabolic pathway activity in both biofilm and planktonic cells under laboratory growth conditions. There are examples of how metabolomic differences have been observed in in planktonic and biofilm cells of Mycoplasma genitalium46, Staphylococcus aureus45, Gardnerella vaginalis47 and Rhizobium alamii48. Whilst these studies all discuss the difference in metabolomic profiles in the species discussed between planktonic and biofilm growth states, none look at the rate of metabolism. As there is a range of growth states throughout a biofilm from the apical to the basolateral surface measuring the rate of growth is widely viewed as not possible. As a result, studies often analyse the production of materials, compounds metabolic markers from the biofilm as a whole and not the rate of metabolism as this is not currently possible. Hence the data presented here is the first use of DOSY-NMR to examine the metabolism of these mycoplasmas in their planktonic and biofilm growth states, as a whole and not on a rate wise basis. The clarification of metabolic pathway activity in both biofilm and planktonic cells enables us to gain a better understanding of vital and non-essential metabolites involved in the formation and establishment of human mycoplasma biofilms.This work is the first study to quantify a set of key metabolites, including glycolysis compounds, amino acids, and nucleotides, in the growth of human mycoplasmas. The results obtained suggested that metabolic pathways in human mycoplasmas, such as Supplementary Information 1.


Mycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogen’s obligate intracellular lifestyle and the fact that it has never been grown in vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro, we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M. leprae grown in mouse foot pads, and performed Differential Producibility Analysis to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.Leprosy, caused by  Mycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network of M. leprae- GSMN-ML, a powerful in silico tool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes that M. leprae utilizes during intracellular growth. Mycobacterium tuberculosis and M. leprae, responsible for tuberculosis (TB) and leprosy respectively. Leprosy is one of the oldest plagues of mankind yet remains prevalent in developing countries. In 1981, the World Health Organization (WHO) recommended multidrug treatment (MDT) of disease with dapsone, rifampicin and clofazimine. Implementation of MDT achieved a 98% cure rate leading to reduction of prevalence by a remarkable 45% within three years. Yet, although MDT reduced global prevalence of leprosy, annual new case detection has remained fairly constant over the last decade ..MycobactM. tuberculosis. Both M. tuberculosis and M. leprae are intracellular pathogens that replicate primarily in macrophages but whereas the TB bacillus appears to primarily infect macrophages , the leprosy bacillus replicates in many cell types but particularly Schwann cells and endothelial cells . Th. ThM. tural ions , whereasplements . This la–14 days , 12]. T. TM. tubM. tuberculosis in 1998 . T. TM. tubd lysine . The biod lysine . Gene dethe body .M. tuberculosis and Mycobacterium bovis where they have been used to identify novel metabolic pathways and examine their growth and likely substrate utilization within their host cells , . I. IM. tubesponses .All animal studies were performed under the scientific protocol number A-105 reviewed and approved by the National Hansen’s Disease Programs (NHDP) Institutional Animal Care and Use Committee, and were conducted in strict accordance with all state and federal laws in adherence with PHS policy and as outlined in The Guide to Care and Use of Laboratory Animals, Eighth Edition.7M. leprae, strain Thai-53, in each hind footpad. At 4–5 months post inoculation mice were euthanized and footpad tissues removed. These tissues were either fixed in 70% ethanol for at least 48 h or minced and homogenized immediately for purification of viable M. leprae . S. S33]. Seviously , 16], . D. DM. lepd growth , 52]. D. DM. lepM. tuberculosis GSMN-TB at two different growth rates was validated with experimental data from M. tuberculosis grown in the chemostat at the two growth rates, highlighting the importance of the model to predict the metabolic state of M. tuberculosis, in experimentally challenging situation such as during infection [M. leprae in cultures. Our analysis did predict diaminopimelate, lysine, methionine, formate, tetrahydrofolate, pantothenate, cobalamin and protoporphyrinogen as nutrient supplements for growth and biomass production of GSMN-ML that could be further investigated.Genome scale metabolic reconstructions and flux balance analysis aims to find a state that maximizes growth of an organism in the given media. To validate these metabolic models, it is important to test experimentally if the bacteria grows optimally in the given media. Identification of discrepancies between model predictions and experimental data are informative in identifying errors in the model and gene annotation. The predictive capacities of our nfection . But, thM. leprae intracellularly [M. leprae’s metabolism for additional nutrients that are identified.GSMN-ML is applicable for interrogation and interpretation of RNA-seq data using DPA and for exploration, for testing scenarios, for scanning conditions, and for eliminating impossibilities in experimental design. A limitation of DPA analysis is that the predictions rely on the carbon substrate used for the analysis. In this study we chose glucose and glycerol based on our previous studies and evidences of these substrates available to llularly . As moreM. leprae relies primarily on the nutrient provision from the host, and that the pathogen probably does not maximize growth rate during infection, the use of an objective function based exclusively of maximization of growth rate is unlikely to correctly simulate the M. leprae's in vivo metabolic state. In the future, it will be useful to construct a host-GSMN-ML dual model and validate with experimental datasets to explore the M. leprae’s metabolism in an infected cell.A limitation of GSMN-ML model is that it will test the metabolic abilities of the bacteria alone. Considering that metabolism of M. leprae compared to M. tuberculosis. This is likely to be associated with a lower degree of metabolic robustness in the leprosy pathogen, compared to the TB pathogen; a vulnerability that might provide novel opportunities for new drugs. We believe that GSMN-ML will be a useful model that will be made freely available to researchers aiming to help researchers perform in silico DPA analysis of RNA-seq data for metabolic predictions and pre-experimental gene essentiality analysis and growth tests aiding experimental design.With new research being available in the future, GSMN-ML will be developed by continuous curation of the metabolic model, improved gene annotation and integration of biochemical and high throughput datasets. The predictive capacities of the GSMN-ML confirmed the expected higher degree of gene essentiality in M. leprae has never been grown in the laboratory, severely hampering research on this important pathogen. In this study, we describe the construction of an in silico genome scale metabolic model of M. leprae GSMN-ML that is able to simulate growth. The model was used to devise a base medium for efforts to grow the pathogen in the laboratory. We also used the model to interrogate gene expression data from M. leprae grown in mouse foot pads and identified the active metabolic pathways during intracellular growth of the pathogen. We also found evidence for major differences in the intracellular metabolism of M. leprae compared to the related pathogen, M. tuberculosis. The availability of the GSMN-ML model provides a new tool for the further exploration of the biology of this important pathogen that could lead to development of new approaches for control of leprosy.Despite intensive efforts, S1 FileM. tuberculosis and the problem file (p-file) including the list of medium supplements and the upper and lower limits of each medium component uptake used for nutrient utilization analysis. The network was constructed using SurreyFBA software.The network file includes metabolite lists, pathways and reactions for (XLSX)Click here for additional data file.S2 FileThis composition was used to test and incorporate pseudogenes and orphan reactions that facilitated the growth of GSMN-ML. The p-file includes the list of medium supplements and the upper and lower limits of each medium component uptake used for nutrient utilization analysis.(XLSX)Click here for additional data file.S3 FileM. leprae and the problem file (p-file) including the list of medium supplements and the upper and lower limits of each medium component uptake used for nutrient utilization analysis. The network was constructed using SurreyFBA software.The network file includes metabolite lists, pathways and reactions for (XLSX)Click here for additional data file.S4 FileThe p-files include the list of nutrients and their uptake rates upper and lower limits.(XLSX)Click here for additional data file.S5 FileM. leprae freshly harvested vs. 48h incubation in axenic medium.The file includes expression data and fold changes in expression of (XLSX)Click here for additional data file.S6 FileMetabolites are listed for GSMN-ML and RNA-seq analysis using glucose and glycerol as sole carbon sources. The p-files include the list of nutrients and their uptake rates- upper and lower limits.(XLSX)Click here for additional data file.S7 FileFile shows flux variability analysis and gene essentiality analysis for the model. Two media p-files was set for this test. One is to use glycerol as only carbon source and the other is to use glycerol as only carbon source and the other necessary molecules. Table 'FVA' shows results for all reactions with maximized biomass. Table 'media' shows media settings, those substrates whose lower-bound < 0 are allowed to be uptaken. Table 'metabolite_name' shows metabolite IDs and their names. Table 'KO_gene' shows gene essentiality analysis results on the media set in 'Media'. GRateKO: growth rate when the gene knocked out. Ratio: calculated as GRateKO/GRateWT(wildType). Genes are considered as essential If Ratio < 0.01 (marked red).(XLSX)Click here for additional data file.S8 FileFile shows flux variability analysis and gene essentiality analysis for the model. Two media p-files was set for this test. One is to use glycerol as only carbon source and the other is to use glycerol as only carbon source and the other necessary molecules. Table ‘FVA’ shows results for all reactions with maximized biomass. Table 'media' shows media settings, those substrates whose lower-bound < 0 are allowed to be uptaken. Table 'metabolite_name’ shows metabolite IDs and their names. Table 'KO_gene' shows gene essentiality analysis results on the media set in 'Media'. GRateKO: growth rate when the gene knocked out. Ratio: calculated as GRateKO/GRateWT(wildType). Genes are considered as essential If Ratio < 0.01 (marked red).(XLSX)Click here for additional data file.S1 FigM. leprae using palmitic acid as the only carbon source [Radiorespirometry (RR) was used to measure oxidation rate of n source . For the(TIF)Click here for additional data file.


Arabidopsis thaliana which respond to drought stress by omics-based in silico analyses. We proposed an analysis pipeline to understand metabolism under specific conditions based on a genome-scale metabolic model (GEM). Context-specific GEMs under drought and well-watered control conditions were reconstructed using transcriptome data and examined using metabolome data. The metabolic fluxes throughout the metabolic network were estimated by flux balance analysis using the context-specific GEMs. We used in silico methods to identify an important reaction contributing to biomass production and clarified metabolic reaction responses under drought stress by comparative analysis between drought and control conditions. This proposed pipeline can be applied in other studies to understand metabolic changes under specific conditions using Arabidopsis GEM or other available plant GEMs.Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the metabolic pathways of  In the era of global climate change and increasing food demand caused by population increases, it may become challenging to grow enough agricultural crops to produce sufficient amounts of foods for human consumption. Drought is among the major environmental problems in agriculture worldwide, as water deficits limit plant growth and reduce crop production ,2. TherePlants have evolved phenotypic plasticity under changing environments and have altered their metabolism to balance their growth and specific stress responses. Plants utilize various strategies to cope with drought stress of different degrees and durations. The complex regulatory circuits under drought stress lead to physiological or morphological changes across a range of temporal and spatial scales. One well-known regulatory circuit is the abscisic acid (ABA) biosynthesis and signaling pathway. When water deficit occurs, plant cells undergo osmotic changes and induce regulatory genes. Subsequently, ABA levels are transiently increased ,4. ABA tTo understand the whole metabolic system response, simulation using biochemical metabolic networks, known as a genome-scale metabolic model (GEM), is performed . Based oIn general, a GEM contains all possible reactions that organisms of interest can process. This means that some reactions may not occur in specific cell types or under specific conditions. Consequently, integration of omics data representing gene expression, protein expression, or metabolite accumulation levels enables recapitulation of the metabolic pathway in a specific cell type under specific conditions . ParticuArabidopsis thaliana in response to water deficit. We used publicly available transcriptome data to tailor the Arabidopsis GEM and reconstructed context-specific metabolic network models. The context-specific GEMs were used to estimate the flux distribution under drought stress and well-watered control conditions. The biomass production rate, which is considered as an important agricultural trait, was predicted and compared to the actual leaf biomass production rate. By comparative analysis of the flux distribution under drought and control conditions, we identified reactions and metabolites associated with drought adaptation. Finally, we discussed the limitations of this method and provided considerations for further applications.In this study, we established a pipeline for generating context-specific GEMs to understand the metabolic changes in plants under specific conditions. We performed an in silico investigation of metabolic adaptation of A. thaliana [A. thaliana rosette leaves under progressive drought stress [AraGEM, a genome-scale metabolic network model of thaliana , was uset stress  were rett stress ,18.The data set consisted of time-series data from days 1–13 under drought stress and control (well-watered) conditions. The average expression value of four replicates was used as the gene expression value. We selected the Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm  as a taiIn AraGEM, the reactions are grouped into metabolic subsystems according to metabolic pathways in which they are involved. Concerning the subsystem “flavonoid biosynthesis”, more reactions remained in drought GEMs than in control GEMs at the later stage of treatment c. This iTo examine whether the reconstructed context-specific GEMs represented actual metabolic states, we defined the occurrence percentage of metabolites. Among the metabolites in the global GEM, 66 metabolites were detected by metabolite profiling in Bechtold et al. . They weWe reconstructed the context-specific GEMs so that the time-dependent change in the biomass production rate, the objective function to be maximized b, was coWe further investigated which reactions are important for metabolic adaptation in terms of biomass production. First, we identified reactions that accelerated the biomass production rate in control GEMs on days 12 and 13 b. To thiEscherichia coli gdhA were reported to increase their biomass production by enhancing drought tolerance [Transgenic tobacco and maize lines overexpressing olerance ,22. ImprArabidopsis plants under progressive drought stress showed the greatest physiological changes at day 13 [We investigated the difference in the flux distribution between drought and control GEMs on each day by flux comparative analysis. We considered that, if the flux size of a reaction is larger under drought conditions than in controls, the reaction was more active under drought conditions. Next, we calculated the fold-change in the flux size (drought/control) a and cout day 13 . For inst day 13 .2, and amino acid transporters and reactions associated with the TCA cycle.Further, cluster analysis of these reactions was performed c and metNext, we employed the flux-sum, which is defined as half of the summation of all consumption flux (efflux) and generation flux (influx) related to a specific metabolite under a pseudo-steady state ,24. As tThis analysis clarified the metabolic changes from the perspective of metabolites, while cluster analysis of the reactions c revealeAs distinct differences were observed at day 13, the metabolic change on day 13 was summarized in a pathway map . SeveralArabidopsis, wheat (Triticum aestivum L.), and shoreline purslane mangrove (Sesuvium portulacastrum) [Arabidopsis enhanced the susceptibility to drought stress [Some of the abovementioned reactions were reported to be involved in the drought stress response, validating our analytical pipeline for identifying significant reactions related to the drought stress response . Reactiocastrum) ,26,27. Scastrum) ,29. Hydrt stress .We aimed to establish a systematic pipeline to gain insight into metabolic adaptation to environmental changes. The pipeline integrating genome-scale model and omics data allowed us to capture the reactions and metabolites that may enhance drought stress tolerance without prior knowledge. As an example, we clarified that the reaction R00243_c (glutamate dehydrogenase), which was lacking in drought GEMs, is an important reaction affecting the biomass production rate. Previous physiological and molecular genetic studies indicated the significance of glutamate dehydrogenase in drought tolerance ,22. HoweIn this study, we tailored context-specific GEMs by using the established tailoring algorithms. The tailoring algorithms were categorized into three families: GIMME-like, Integrative Metabolic Analysis Tool (iMAT)-like, and The Model Building Algorithm (MBA)-like families . Among tTo perform in silico analysis using this pipeline, two points should be carefully considered. The first point is setting the threshold of the expression value of transcriptome data. The threshold setting has a major effect on the content of the tailoring model . In thisThe pipeline proposed in this study can be applied in studies aimed at understanding metabolic adaptation to stresses other than drought. Plant GEMs have been reported for several species, including rice, maize, and soybean . The pipA. thaliana, was downloaded as an sbml file from [AraGEM, a genome-scale metabolic model of ile from . The dowile from ,34, wherMicroarray data were retrieved from Gene Expression Omnibus under accession number GSE65046  using paThe rate of fresh weight increase in rosette leaves was used to represent the actual biomass production rate. From the experimental data reported by Bechtold et al. , the freZ) was the biomass production rate  was regularized by subtraction with a strictly concave function equation [−6. The FBA was performed using COBRA toolbox version 3.0 in MATLAB (version 9.5-R2018b) [FBA was performed on context-specific GEMs to simulate the maximum possible flux of the feasible flux distributions . The objequation . The min-R2018b) .n reactions calculated from a setting threshold value and gene expression values mapped to each reaction, m metabolites and n reactions, a and b were lower and upper bounds, respectively. The maximal value of the objective value was set to 90%. The lower bounds, corresponding to the objective function (Z), were set to the maximal value, whereas other bounds were set as the original bounds.Reconstruction of context-specific GEMs was performed using Gene Inactivity Moderated by Metabolism and Expression (GIMME) . BrieflyThe expression threshold was set to be the same on all days and under all conditions. The expression thresholds ranged from 0.7 to 0.9  were tested . Then, wThe occurrence percentage of metabolites in context-specific GEMs was calculated for each GEM by comparison with the metabolites for which contents were reported in Reference . If a meEach single reaction in the day-13 control GEM was deleted individually using the singleRxnDeletion function from COBRA toolbox version 3.0 in MATLAB (version 9.5-R2018b) . Next, ti participating in reactions (j) and j).Flux-sum is defined as one-half of the summation of all incoming fluxes (influx) and outgoing fluxes (efflux) concerning individual metabolite (tion (5) :(5)vxi=1m is the number of context-specific GEMs and n is the number of reactions. The missing values in the matrix were filled with one-tenth of the minimum value of flux sizes. The fold-change in flux size under drought to control conditions was calculated in Equation (6) and transformed into logarithm to base 2.All reactions present in each context-specific GEM were listed to create a matrix of simulated fluxes, m is the number of context-specific GEMs and n is the number of metabolites. The missing values in the matrix were filled with one-tenth of the minimum value of flux-sum values. The fold-change in flux-sum under drought to control conditions was calculated in Equation (7) and transformed into logarithm of base 2.All metabolites present in each context-specific GEM were listed to create a matrix of flux-sum values, Cluster analysis was performed using agglomerative clustering using a correlation metric with average linkage. Reactions showing a correlation greater than 0.5 were grouped into clusters and are shown in different colors. Heatmap representation was performed using python library SciPy and library Seaborn ,39.In this study, context-specific GEMs were reconstructed using transcriptome data under drought stress. In silico single reaction deletion experiments revealed the reactions important for increasing the biomass production rate. Comparative analysis between drought and control conditions clarified the candidate reactions associated with the drought stress response, which can be utilized, after further biological validation, to improve the drought tolerance of crops. This strategy can be applied to other stresses to gain a better understanding of metabolic pathways affecting the stress response involved in growth and yield, which will help improve future agricultural strategies.


Hence, a compromise modeling solution has been developed between the non-generalizable accuracy of the first principles models (FPMs) and the oversimplification of the empirical models (EMs). The work presents a novel model of moderate complexity that is simplified enough to be generalizable and computationally affordable, while retaining physical meaning. The methodology is based on a general degradation mechanism that can be algorithmically generated from the carbon number of the target compound, as well as from the knowledge of two kinetic parameters, one for the faster initial rate and the other one for the subsequent degradation steps. The contaminant degradation mechanism has been combined with an appropriately simplified implementation of the well-known Fenton and photo-Fenton kinetics. This model describes the degradation not only of the target compound and of the oxidant, but also of total organic carbon (TOC), which is used to define the overall quality of the water. Experimental design techniques were used along with a non-conventional modeling methodology of programmable process structures (PPS). This novel modeling approach was applied and validated on the degradation of three model compounds. A successful prediction of the evolution of the contaminants H A notable experimental effort has been spent in the last 20 years in investigating the advanced oxidation processes (AOPs), being an effective alternative to conventional biological processes for the treatment of toxic and recalcitrant wastewaters, and for the removal of contaminants of emerging concern (CECs)  radicals, advanced electrochemical oxidation technologies (such as electro-Fenton and electro-photo-Fenton), nanocatalytic heterogeneous Fenton technology, and semiconductor photocatalysis (TiO2/UV), as well as the combination of processes involving at least one AOP.A recent review by Mazivila et al.  presentsIn particular, among the classical AOPs, the photo-Fenton process has attracted special attention of the scientific community with an actual number of scientific papers related to photo-Fenton process of about 2700, as recently highlighted by Cabrera Reina et al. . ScientiNevertheless, the transition from a pure experimental approach to a model-based approach is mandatory to allow process design, monitoring, control, and eventually process automation on which industrial applications are generally based on.The literature review reveals that the proposed models can be enclosed in two main categories: first principles models (FPMs) and empirical models (EMs).Some FPMs rely on the detailed kinetic modeling of Fenton and photo-Fenton processes and follow the scheme proposed by Kang et al.  based onThe FPMs are accurate models that aim at a full understanding of the chemical process. However, two main weak points arise, limiting their applicability in the real world. On one hand, the resulting computational effort might be unaffordable. On the other hand, the high complexity of Fenton and photo-Fenton systems, involving many reaction mechanisms that are still under investigation, makes it difficult to develop a comprehensive FPM that can include all the involved reaction mechanisms for more general cases of various and multiple targets.Regarding the empirical models, in literature, it is possible to find preliminary works proposing the use of response surface platform for regression models  is the first selected target compound, being the most widely used antipyretic and analgesic. As the second model compound, sulfaquinoxaline sodium salt  is selected because it is a potential food contaminant in animal products. Finally, formic acid  is studied as the third target compound since it is a by-product of the degradation of many hazardous organic compounds and belongs to the group of carboxylic acids that are hardly degradable even by chemical oxidation  technique was used to select the proper set of Fenton and photo-Fenton experiments to be performed in order to properly describe the system behavior for the different selected targets. The DOE was then combined with a non-conventional methodology named direct computer mapping (DCM)  (33% w/v) from Panreac Química SLU  and iron sulfate (FeSO4·7H2O) from Merck , adopted as the ferrous ion (Fe2+) source, were used as Fenton reagents. HPLC gradient-grade methanol (MeOH) purchased from J.T. Baker Inc.  and filtered milliQ-grade water were used as HPLC mobile phases. High-purity (> 99%) ascorbic acid from Riedel de Haën , 0.2% 1,10-phenanthroline from Scharlab SL , sodium acetate anhydrous, and 95–98% sulfuric acid, both from Panreac Química SLU , were used to perform measurements of iron species. Hydrogen chloride HCl 37% from J.T. Baker Inc.  was used to adjust the initial pH. Deionized water with a conductivity lower than 1.25 μs was provided by Adesco S.A.  and was used as water matrix in all experiments.Paracetamol and sulfaquinoxaline sodium salt of 98% and 95% purity, respectively, purchased from Sigma-Aldrich , and formic acid (98%) purchased from Honeywell Fluka  were used as target compounds. Reagent-grade hydrogen peroxide  concentrations were performed with a Shimadzu VCHS/CSN TOC analyzer  and samples were taken every 15.0 min (the measuring time of the equipment being 15.0 min). The samples were maintained refrigerated after extraction by submerging the flask in ice during the whole sampling time, with the aim of slowing down the degradation of the organic matter since the cooling of the samples to 2–5 °C is a preservation technique in case of measuring organic content  in order to stop further degradation of PCT, being a well-known HO• scavenger . In particular, paracetamol concentration ([PCT]) was determined using an Akady 5 μm C-18150 × 4.6 mm column, maintained at 25.0 °C as stationary phase, and a mixture of methanol:water (25:75) flowing at 0.4 mL min−1. In this case, the diode array detector was set at 250 nm. Under these conditions, retention time was 2.0 min. In this case, samples were taken at 0.0, 2.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 45.0, 60.0, 75.0, 90.0, and 120.0 min, and were previously treated with methanol (in proportion 50:50) in order to stop the oxidation reactions.For the determination of SQX concentration ([SQX]), the same Akady 5 μm C-18150 × 4.6 mm column, kept at 25.0 °C, was used as stationary phase, while methanol and 0.1% (v/v) formic acid  were used as the mobile phase flowing at 1.5 mL minIn the case of formic acid (FA), only the TOC concentration was followed during the treatment span.2O2 and iron species concentrations were performed with a Hitachi U-2001 UV-VIS spectrophotometer . Hydrogen peroxide concentration ([H2O2]) was determined following the spectrophotometric technique described by Nogueira et al.  were analyzed using the 1,10-phenantranoline method following ISO 6332:2+-phenantroline complex at 510 nm. To measure total iron concentration, ascorbic acid must be used so as to convert all the ferric ions (Fe3+) to ferrous ions (Fe2+). Then, through the difference, ferric ion concentration could be determined. In this case, samples were taken every 5.0 min until a reaction time of 30.0 min, and then every 15.0 min until the end of the assay.The iron species (FeVIRR) of 1.5 L and connected to a glass jacketed reservoir tank, both provided by Thermo Fisher Scientific, Barcelona, Spain.The pilot plant that was used to perform Fenton and photo-Fenton assays is a 15.0-L system composed of an annular photo-reactor with an irradiated volume (E = 3.36 × 10−4 Einstein min−1 (300 and 420 nm). The pilot plant is also equipped with sensors  connected to a programmable logic controller (PLC) that allows data acquisition and process control and to a SCADA system that allows data management.The annular photo-reactor is equipped with an Actinic BL TL-DK 36 W/10 1SL lamp (UVA-UVB) provided by Barcelona LED , and the incident photon flux, measured by Yamal-Turbay et al.  using poFinally, a flowmeter is available for the control of the recirculation flow rate.A schematic view of the pilot plant is shown in Fig. 2+]0, [Fe3+]0, [FeTOT]0). Finally, in the case of performing photo-Fenton experiments, the light was switched on 10.0 min before starting the experiment in order to allow the lamp to stabilize. The final step is the addition of hydrogen peroxide that makes the experiments start and that was added manually and all the once.Regarding the experimental protocol, the glass reservoir was first filled with 10.0 L of deionized water, and then after 10.0 min of recirculation, 4.9 L of deionized water in which the model contaminant was previously dissolved was added and recirculated for 15.0 min with the aim of ensuring a good homogenization of the matrix. After that, a sample was taken to measure the initial concentrations of TOC and the target compound. Then, after the pH was adjusted to 2.8 ± 0.2, the remaining 0.1 L of deionized water, in which iron sulfate was previously dissolved, was filled. After 10.0 min more of recirculation, another sample was taken with the aim of checking the initial concentration of the iron species ([Fe2+]0 was set to 10.0 mg L−1 (corresponding to 0.18 mmol L−1), or rather to the maximum legal value in wastewaters in Spain (DOGC 2O2]0) was set to 378.0 mg L−1, corresponding to 11.12 mmol L−1. The latter was chosen being the highest value of the oxidant concentration among the ones used to perform the experiments with the target compounds that will be detailed in the following sections.As the first instance, a set of blank assays without any target compound (BLANK 1–4 in Table ain DOGC . The ini8H9NO2) as the target compound was carried out. A subset of these experiments was used to fit the proposed model to the specific degradation mechanism. Another subset of the experiments in Table Then, a set of experiments using paracetamol  and photo-Fenton  experiments were performed fixing the initial concentration of PCT ([PCT]0 = 40.00 mg L−1) corresponding to 0.26 mmol L−1 and to an initial TOC concentration ([TOC]0 = 25.92 mg L−1), and changing the initial concentrations of ferrous ion [Fe2+]0 and hydrogen peroxide [H2O2]0.In particular, Fenton .In this case, a 2−1 as the maximum value of the factorial design (+ 1), and half such value, 0.09 mmol L−1, as the minimum value of the factorial design (− 1), and one and a half times less, 0.13 mmol L−1, as the central value of the factorial design (0).The initial ferrous ion concentration was varied between 0.18 mmol L−1) the stoichiometric dose as maximum and minimum values of the factorial design . The stoichiometric dose (5.56 mmol L−1) corresponds to the central value of the factorial design (0).The initial hydrogen peroxide ion concentration was varied between half and twice  of the experiments that were performed. Especially, the experiments from EXP1_PCT to EXP9_PCT are the experiments performed under dark conditions while those from EXP10_PCT to EXP_18_PCT are the ones performed under irradiated conditions.The resulting design of experiment in the case of PCT is presented in Table Moreover, a set of preliminary standard blank assays, but with the presence of the target compound, was also performed and it is presented in Table Next, a new target compound, the sulfaquinoxaline sodium salt , was used for validating the novel modeling approach that was proposed for PCT.In this case, the aim was proving that the proposed methodological approach allows obtaining a generalizable solution that can be used to describe the photo-Fenton degradation of any kind of target compound.Hence, in this case, to estimate the kinetic parameters of the relative degradation mechanism, a set of six experiments, three Fenton (dark conditions) and three photo-Fenton (irradiated conditions), was performed.−1 corresponding to 0.08 mmol L−1 and to an initial concentration of TOC ([TOC]0 = 13.72 mg L−1). The initial concentration of ferrous ion was also fixed and set to 0.18 mmol L−1. Conversely, the initial concentration of hydrogen peroxide was varied.The initial concentration of SQX was fixed to 25.00 mg L2 factorial design was applied. The initial concentration of hydrogen peroxide was varied between the stoichiometric value  to achieve total mineralization of 0.08 mmol L−1 of SQX  and twice  such value.Again, a 2T, λ, and VIRR) of the experiments that were performed, is reported. Especially, the experiments from EXP1_SQX to EXP3_SQX are the experiments performed under dark conditions while those from EXP4_SQX to EXP6_SQX are the ones performed under irradiated conditions.The resulting design of experiment in the case of SQX is shown in Table In addition, as for PCT, a set of five preliminary standard blank assays was performed in order to check the possible direct photolysis of SQX (BLANK ASSAY 14 in Table 2O2).Finally, the modeling approach was validated for the degradation of a third compound, formic acid  and two photo-Fenton (irradiated conditions), was performed.−1 corresponding to 0.87 mmol L−1 and to an initial concentration of TOC, ([TOC]0 = 10.43 mg L−1). The initial concentration of ferrous ion was also fixed and set to 0.09 mmol L−1. Conversely, the initial concentration of hydrogen peroxide was changed between the stoichiometric value (0.88 mmol L−1) to achieve total mineralization of 0.87 mmol L−1 of FA (according to the stoichiometric reaction shown in Eq. (−1) such value.The initial concentrations of FA were fixed to 40.00 mg L−1 and 4.41 mmol L−1, respectively, so to test the effect of higher concentrations of the Fenton reagents.Additionally, another photo-Fenton experiment was performed by increasing the initial concentrations of ferrous ion and hydrogen peroxide to 0.18 mmol LT, λ, and VIRR) of the experiments that were performed. Especially, the experiments from EXP1_FA to EXP2_FA are the experiments performed under dark conditions while those from EXP3_FA to EXP5_FA are the ones performed under irradiated conditions.The resulting design of experiment in the case of FA is shown in Table −1.All the experiments were performed for 120.0 min in batch mode with recirculation and ensuring perfect mixing conditions by setting the recirculation flow rate to 12.0 L min3+ to Fe2+ (Fenton-like reaction) at an appreciable rate. Temperature was also continuously monitored and it was checked that it was inside the range T = 28.0 ± 2.0 °C.In particular, pH was continuously monitored in order to ensure that it was inside the range (2.5 < pH < 3.0–4.0) that, according to Pignatello , Pignate(i)To develop a model that can be exploited for future industrialization of Fenton and photo-Fenton processes. In particular, a model that can be simplified enough to be computationally affordable but without losing its physical meaning. Or, in other words, a compromise modeling solution between the unaffordable accuracy of the first principle models (FPMs) and the oversimplification of the empirical models (EMs).(ii)To develop a rationally simplified but easily configurable and generally usable approach that can be applied for different wastewater systems containing single or multiple targets by also modeling a lumped parameter that can be used to define the overall quality of the water such as the TOC.The modeling effort had to face two main challenges:In the present work, starting from well-known FPMs, a set of hypotheses, rationally selected and based on empirical observations of the Fenton and photo-Fenton system behavior, have been proposed and are listed below:2O, H2O2, and Fe2+ and described by a set of kinetic reactions involving these compounds, as well as Fe3+, free radicals (at least HO• and + and OH−), was assumed as the core mechanism of the proposed model. The target invariant free radical production of Fenton process was described by the H2O2-driven Fe reaction cycle:The existence of a general target independent model of Fenton system, initiated by mixing H• in the lighted photo-Fenton system. The target invariant light initialized free radical production of the photo-Fenton process proposed by Cabrera Reina et al. :k0 of the photo-Fenton reaction was determined in line with data by Cabrera Reina et al.  is to be identified and validated.Kinetic parameter  and of their fragments (FR) may be interpreted as the series of consecutive breakage steps (B), comprising second-order binary reaction of the target and its fragments with free radicals. The number of these steps was assumed to be formally  determined by the number of carbon atoms (NC) in the molecule to be decomposed. It was assumed that in the decomposition steps the targeted compounds and their fragments interact basically with free radical HO• (main oxidant species) as also proposed by other authors, e.g., by Conte et al.  for the first breakage step of the targeted compound, andkfragment = kb  for all the following fragmentation steps.As the first preliminary hypothesis, it was also supposed a faster initial rate of the degradation mechanism based on the hypothesis that the by-products are more hardly degradable than the parent compound. Hence, it was assumed that the breakage steps can be characterized by two target-specific parameters, as follows:ktarget and kfragment have to be estimated and validated for the various targets, individually.The target-specific temperature-dependent parameters b) in the fragments originating from the parent compound (FRb). The number of fragments depends on the number of carbon atoms in the starting molecule, as follows:It was assumed that the concentration of the TOC can be calculated from the fictitious number of carbon atoms  also participates in this competition.In order to avoid infeasible stiffness that can be generated by the competitive reactions  with HOHence, the developed model presents two main steps: a core mechanism , as , as 8), The non-conventional methodology of PPS ;GraphML-based model data files ;MS Excel user interface with VBA for initial data and parameters, as well as for visualization of simulated results.Recent experimental implementation consists of:2O2 reactant is fed and mixed with the previously mixed solution of the target compound and Fe2+;Compartment [1] the tank reactor of 9.00 L, where the HCompartment [2] of 2.25 L, connecting compartments [1] and [3];Compartment [3] of 1.50 L, representing the irradiated volume of the annular reactor;Compartment [4] of 2.25 L, connecting compartments [3] and [4].In particular, in the present work, in order to follow the real hydro-dynamical conditions as well as to support possible future scaling up of the process, we compartmentalized the pilot plant into a circular set of four parts:2O2 that followed the fast but realistically finite time because it was observed that this short-term dosage better fits the experimented behavior of the system than starting from a calculated initial condition;Compartment [1] is associated with the feeding of HThe reaction in Eq.  was takeAll the other reactions were taken into account in the whole set of compartments ([1]–[4]).Accordingly, it must be noted that:Two-state prototypes: one for the general Fenton components (core mechanism) and another one for the target-related components ;2O2 feeding, and one for recirculation of the water ensured by a recycle pumping between the compartments.Six transition prototypes: one for the Fenton system (Eqs.  and 5)))5)), oneIn the actual model implementation, the following state and transition prototypes were defined:Figure All the abovementioned state elements and transition elements are summarized in Table Figure Once the model was formulated and the experimental designs were proposed for the selected model compounds, it was possible to proceed to the model training and validation step.First, the estimation of the kinetic parameters of the core mechanism was carried out by using the data of the blank assays, performed without target compounds Table .Then, the model was trained and validated by using PCT as the target compound  by using the respective experimental dataset  were used.Next, in the knowledge of the previously fixed general parameters, the identification and validation were limited to the 2 additional parameters, considering model hypotheses H4, H5, and H6, while 3 evaluation measures  and for each of the three selected targets. The comparison is evaluated by the RMSE, calculated by the following expression:ith variable, where:yik, value of the kth measurement of the ith variablei = 1, 2, …I, the ith element of the set of measured variablesroot mean square error of the 2O2 (I = 3).The measured data, involved in the RMSE calculations, were the normalized concentrations of the target compound, TOC, and Hk0, k1, k2, and kelimination of reactions in Eqs. , .From the experimental results in Fig. 3+ and oxidation of Fe2+, both reacting with H2O2  were assumed equal to those estimated in the previous step and presented in Table The kinetic parameters of the core mechanisms  obtained in the case of using [PCT]0 = 0.26 mmol L−1; [Fe2+]0 = 0.09 mmol L−1; [H2O2]0 = 5.56 mmol L−1 under dark and irradiated conditions, respectively, is reported.An illustrative case of the model training for the case of EXP4_PCT and EXP_13_PCT  obtained in the case of using [PCT]0 = 0.26 mmol L−1; [Fe2+]0 = 0.18 mmol L−1; [H2O2]0 = 5.56 mmol L−1 under dark and irradiated conditions, respectively, is reported.Finally, an illustrative case of the model validation for the cases of EXP6_PCT and EXP15_PCT  performed using PCT as the model compound. RMSE resulted in 30% for PCT, 7% for TOC, and 11% for HIn addition, a comparative study illustrating the differences between the proposed model and a FPM developed by Audino et al. , for twoAs can be observed, the proposed PPS model, compared with the more accurate FPM by Audino et al. , slightlIn this step, the experimental data derived by the assays performed using SQX as the model compound were used to validate the proposed methodology, previously fitted to the degradation of PCT.k0, k1, k2, and kelimination, were not estimated and were assumed equal to those summarized in Table −1] for the first degradation step of the target compound;−1] for the following degradation steps of the fragments.Also, in this case, the kinetic parameters of the core mechanism, or rather Hence, as can be observed, the estimation strategy of the kinetic parameters describing the degradation mechanism of SQX is based on the same methodology followed for PCT. However, having the SQX a different number of carbon atoms and consequently a different number of breakage steps, compared with PCT, it was necessary to proceed to the estimation of the specific kinetic parameters of its degradation mechanism.2O2 rather than the target and the specific SQX degradation mechanism. The estimated values of the relative kinetic parameters are summarized in Table As for PCT, also in this case, parameter estimation strategy was tuned in order to give priority to a best fitting of TOC and HIn this case, the rate of the first degradation step resulted to be similar to the rate of the subsequent degradation steps.The degradability of SQX resulted to be obviously less than the one observed for PCT because of the greater carbon number, as well as a direct consequence of its structure.2O2 are presented to discuss the SQX case because the main aim was to validate the proposed methodology as a general approach that can be applied for single or multiple targets in different wastewaters, whose quality can be described by a lumped parameter (as it is the TOC). Hence, Fig. 2O2 obtained in the case of EXP2_SQX and EXP5_SQX  performed using SQX as the model compound, resulting in 17% for SQX, 26% for TOC, and 19% for H2O2). However, it must be taken into account that a smaller number of experiments (6 against 18) was employed.The RMSE values slightly increased compared with the previous results obtained with PCT  than to train it (with PCT), since a smaller number of kinetic parameters needed to be estimated. This shows a clear advantage of the proposed simplified but generalizable methodology.In this step, the experimental data derived by the assays performed with FA were used to validate the proposed methodology with a third model compound.This model compound was selected in order to test the response of the model for the degradation of a very simple molecule.k0, k1, k2, and kelimination, were not estimated and were assumed equal to those summarized in Table As for PCT and SQX, also in this case, the kinetic parameters of the core mechanism, or rather Moreover, even though the starting molecule of FA is composed of only one carbon atom, for coherence with the methodological approach defined for the first two targets, it was decided to take into account the attack to the parent compound and the attack to the intermediates originating from it.−1] for the first degradation step of the target compound;−1] for the following degradation steps of the intermediates.Consequently, to validate the methodology using FA, the two kinetic parameters describing the first degradation step and the following ones needed to be estimated:In this case, it must be noticed that it was decided to only follow the TOC evolution due to the simplicity of the starting molecule of FA composed of only one carbon atom.The specific FA degradation mechanism and the estimated values of the related kinetic parameters are summarized in the following Table As can be observed, the first step (attack to the parent compound) is characterized by a kinetic parameter of one order magnitude higher than the one describing the subsequent attack to the intermediates originating from the first degradation step. In this case, the parameter estimation suggests that the second degradation step could probably be avoided. However, this hypothesis needs to be experimentally confirmed by the evolution of formic acid during the treatment span .2O2 and particularly the comparison between experimental and simulated data obtained in the case of EXP2_FA and EXP4_FA . Hence, only detection via HPLC of the target molecule and of the produced intermediates can help describe the system behavior during the first minutes of the Fenton and photo-Fenton treatments of formic acid.The model in this case overestimates the enhancement of the process performance due to the use of irradiation, describing the photo-Fenton process as the faster one. This result is probably due, as already observed, to the lack of experimental measurements during the first minutes of the reaction. Moreover, it must be noted that a very low number of experiments (five) was performed to validate the proposed methodology with this third compound.2O2 is completely consumed under both dark and irradiated conditions in about 45.0 and 60.0 min, respectively.Finally, also in this case, H2O2, and highlighted the acceptable prediction of the model.The root main square error (RMSE) average value (averaged as in the previous cases between the RMSEs obtained for each experiment (dark and irradiated conditions) performed using FA as model compound) resulted in 16% for TOC and 18% for H2O2 remained constant , so showing an improvement of the estimation capability of the proposed methodology.As can be observed, after the third validation, the RMSE value for TOC decreased while the one related to HTo conclude and summarize the results of the model fitting, Table 2O2 slightly increased.Despite the fewer number of experiments used to validate the proposed methodology with SQX rather than with PCT, it was possible to improve the prediction of the target compound. However, the RMSE associated with the predictions of TOC and HThe acceptable fit of the model was finally illustrated by plotting the predicted values versus the actual measured values for each of the selected targets  and the related TOC  it supports fast model discovery by the automatized generation and configuration of various models from the description of the process network and from two general meta-prototypes; (ii) it supports the easy modification of the locally implementable declarative programs, associated with the state and transition prototypes; and (iii) the transition-based model representation helps the availability controlled normalized description of competitive fast processes.The generalization capability of the methodology was successfully confirmed by the experimental validation of the developed model first for the degradation of paracetamol and next for the degradation of sulfaquinoxaline sodium salt and formic acid. Once the model is systematically created and validated to the first compound, the partial fitting of the model to further compounds was shown to require the adjustment of significantly fewer number of model parameters, which in turn needs less experimental data to produce the validation of the model at acceptable levels (RMSE).The proposed model proved to be a causally interpretable and generalized candidate solution for the simulation-based analysis and design of Fenton and photo-Fenton reaction mediated degradation of various wastewaters that contain one or multiple target compounds.2O2 is to be studied to improve the decontamination ability of the process.Further research should include applying and validating the proposed methodology for multiple target compounds or mixtures of organic contaminants. Also, the simulation supported design of controlled multi-step or fed-batch dosage of H


Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in  The microbial production of various useful compounds has been actively studied. With the recent development of synthetic biotechnology, the production of useful compounds that microorganisms do not naturally produce can be made possible by introducing biosynthetic pathways (synthetic metabolic pathways), designed by combining heterologous and modified (mutant) enzymes. In addition, active research on the use of metabolic models has contributed to an effective improvement in the yield of useful compounds ,2,3,4. IFBA is a method that assumes a steady state in which the concentration of a metabolite does not change with time. An FBA solution (the distribution of metabolic fluxes) that maximizes or minimizes the objective function is searched, using a stoichiometric matrix composed of stoichiometric coefficients of reactions that constitute the metabolic model . In the In addition, the theoretical maximum yield under given constraints (conditions) can be obtained with FBA, although this is usually different from the actual yield in a dynamic production process with growth. From this point of view, dynamic FBA (dFBA) has been developed, which is an extension of FBA applicable to dynamic production processes, such as batch and fed-batch cultures with growth . The dFBSaccharomyces cerevisiae and Scheffersomyces stipitis . On the other hand, the units of the data extracted from the experimental data and their approximated values are mM or g/L. Therefore, it is necessary to perform unit conversions so that the values can be used as constrains in dFBA. The equations of glucose and cell concentrations were polynomially approximated as a function of time t. These equations were differentiated with respect to t, and then divided by the equation of cell concentrations, to obtain the equations of specific glucose uptake and growth rate. The approximated time course of the obtained specific glucose uptake rate and specific growth rate is shown in In the dFBA of this study, the FBA is sequentially performed at an arbitrary time. Therefore, it is necessary to prepare a time course of the specific uptake rate of the substrate (glucose), and the specific growth rate of the bacterial cell (biomass) as constrains (lower and upper boundaries) in dFBA. The constraints to dFBA are the values of the flux at any time The approximated specific glucose uptake rate −1) =The approximated specifics growth rate E. coli used for FBA was iJO1366 [th FBA repetition. Thus, the time course of each flux was obtained. Among the obtained fluxes, glucose uptake, cell growth (biomass synthesis), and shikimic acid production (discharge) fluxes were numerically integrated, and time-course data of each concentration were obtained  model, FBA assuming a steady state is used for the intracellular simulation. Therefore, it is impossible to express the limitation of metabolic flux or the accumulation of intermediate metabolites, due to some factors such as imbalance of enzyme amounts and cofactor supply, feedback regulations.Therefore, in this study, the dFBA can more easily acquire the time courses of the metabolic fluxes in the metabolic model using time-course data approximation. Additionally, in compound production, by using the fluxes of specific substrate uptake and growth obtained by approximation as FBA constraints, it is possible to estimate the difference between the current strain and the theoretical maximum values, and to estimate the room of improvement.In a more detailed analysis, such as pathway optimization, dFBA does not reach simulation and analysis using a dynamic (kinetic) model. In the construction of a dynamic model, parameter estimation is a very costly operation, which is a barrier to the construction of a large-scale dynamic model. It has been suggested that combining flux time-course data with metabolome-based intracellular metabolite concentration time-course data facilitates parameter estimation ,35,36,37The workflow of the method is summarized in https://automeris.io/WebPlotDigitizer/) [Using the online version of WebPlotDigitizer 4.2 (itizer/) , the ploitizer/) .E. coli is 0.33 g DCW/OD600 [The cell concentration obtained from the literature is either DCW or OD values. In this study, the conversion factor of DCW per OD in CW/OD600 . If the t by the following polynomial equations (Equations (7) and (8)).The extracted time-course numerical data was divided into arbitrary sections as needed, and a polynomial approximation using the least squares method was performed. The approximated concentrations glucose and cells were expressed as a function of time −1) used for the dFBA constraints were calculated by dividing the derivatives of the approximation equations of the substrate and cell concentration with respect to the time t by the cell concentration (g/L) at that time.The specific rate of glucose uptake (mmol/g/h) and the specific rate of growth (ht (h).In the dFBA method, instead of formulating a differential equation and estimating the kinetic parameter values in the equations, the obtained values by approximation equations are used in the FBA at any time t. Depending on the approximation result, the specific glucose uptake rate and the specific growth rate may show negative values. In such cases, 0 was compulsorily set. In addition, as mentioned in The specific substrate uptake rate and the specific growth rate given as input constraints to the FBA part were calculated by differentiating the approximate equation of the substrate concentration and the bacterial cell concentration divided by the approximate expression of the bacterial cell concentration at time t (h) was sequentially performed to obtain time-course data of the flux. Using this method, the time-course data of metabolic fluxes were collected.As mentioned above, the FBA part is divided into two steps. In the first step, the objective function was to maximize the specific growth rate while relaxing constraints as needed. In the second step, the specific growth rate was fixed at the value obtained in the first step, and the production flux of the target compound was maximized as an objective function. The step size of the calculation was 0.01 (h), and FBA at any time The time course of extracellular metabolites and the cell concentration were calculated by numerically integrating the time courses of the fluxes obtained by dFBA using Simpson’s rule. In addition, the final concentration of the target compound obtained by numerical integration was regarded as the maximum production concentration of the target compound under experimental substrate consumption and growth constraints. The obtained maximum production concentration of the target compound was compared to the actual experimental data, and the production performance of the strain used in the experiment under defined constraints and its theoretical maximum value were estimated.https://github.com/opencobra/cobratoolbox.git) were used for the dFBA simulation, and Gurobi 8.1  was used for the FBA solver. The home-made MATLAB script (computer code) used for the calculation of time-course of metabolic fluxes are available on GitHub .The MATLAB© 2018b  and COBRA Toolbox (E. coli. Time-course data on shikimate production by E. coli was selected from previous publication [https://automeris.io/WebPlotDigitizer/) [600 value from the literature for the conversion. In this case, the unit of cell concentration was (g/L) for DCW in the time course data, which was used as is.The above dFBA was applied to the example of shikimic acid production from glucose in lication . The numitizer/) . The ext−1) by dividing the derivatives of the approximation equations for glucose and cell concentration with respect to time t by the corresponding cell concentration (g/L).The values for FBA constraints in dFBA were converted to specific glucose uptake rate (mmol/g DCW/h) and specific growth rate , shikimic acid, and cells were calculated by numerical integration using the Simpson’s rule, based on the output values sequentially obtained FBA. From these results, data at the time of maximum production of the target compound under the constraints of experimental substrate consumption and cell growth were estimated.By comparing with the actual experimental data, the theoretical yield using the current strain was estimated under the same constraints on substrate consumption and bacterial growth as in the experiment. The actual yield of the strains compared to the theoretical yield under the experimental constraints will inform whether the yield will be increased by optimizing the culture process, or by metabolic engineering in the future. These results will also be used for strain evaluation and planning of engineering strategies.