To reduce the potentially irreversible environmental impacts caused by fossil fuels, the use of renewable energy sources must be increased on a global scale. One promising source of biomass and bioenergy is sugarcane. The study of this crop’s development in different planting seasons can aid in successfully cultivating it in global climate change scenarios. The sugarcane variety SP80-3280 was field grown under two planting seasons with different climatic conditions. A systems biology approach was taken to study the changes on physiological, morphological, agrotechnological, transcriptomics, and metabolomics levels in the leaf +1, and immature, intermediate and mature internodes. Most of the variation found within the transcriptomics and metabolomics profiles is attributed to the differences among the distinct tissues. However, the integration of both transcriptomics and metabolomics data highlighted three main metabolic categories as the principal sources of variation across tissues: amino acid metabolism, biosynthesis of secondary metabolites, and xenobiotics biodegradation and metabolism. Differences in ripening and metabolite levels mainly in leaves and mature internodes may reflect the impact of contrasting environmental conditions on sugarcane development. In general, the same metabolites are found in mature internodes from both “one-year” and “one-and-a-half-year sugarcane”, however, some metabolites  and natural antisense transcript expression are only detected in the leaves of “one-year” sugarcane. The continuous use and misuse of fossil fuels have led to the risk of their depletion, as well as severe and potentially irreversible environmental threats ,2. CritiIn Brazil, sugarcane plants are mainly planted in two different seasons (January to March or October/November) in South-Eastern Brazil and the cropping season and best suited varieties vary for each . When plDuring plant development, plants respond to developmental and environmental signals in their internal and external environments by relying on the activity of a variety of cellular components, on gene expression, and on metabolite  regulation, that trigger the appropriate metabolic responses needed for survival ,10 and aTaking a systems biology approach when integrating metabolomics data with gene expression information and conducting in-depth data mining, it is possible to find connections between specific traits, the genes that code for them, and the metabolites and/or metabolic pathways that play a role in the regulation of important agronomic and economically valuable processes . In addiThe commercial sugarcane variety SP80-3280 was planted in two different climatic conditions at the Agricultural Science Center of the Federal University of São Carlos (UFSCar) in Araras City, São Paulo State, Brazil. For this purpose, two experimental fields were prepared with eutroferric oxisol soil fertilized with 400 kg ha-1 of N:P:K mixed in a ratio of 5:25:25. Each field experiment consisted of eight plots (10 m × 3 m) in which the sugarcane billets were planted in four rows of 10 m with 1.35 m spaces between the furrows in each plot. The first field experiment (F1) was initiated in April 2012 and extended up until May 2013, representing the conditions under which “one-and-a-half-year” sugarcane crops are cultivated. The second field experiment (F2) with the same technical specifications was initiated in October 2012 and extended up until November 2013, representing the conditions under which “one-year” sugarcane crops are cultivated. No irrigation was applied in this study and during these periods the plants experienced variations in the climatic conditions with regards to precipitation and temperature. Tissue samples were collected from the two middle rows of the plots from both fields at 4, 8, 11 and 13 months after planting for metabolomics and transcriptomics studies. Samples were collected from the uppermost visible dewlap leaves, referring to them as the Leaf +1 (L1); the immature internodes near the apical meristem, referring to them as I1; the internodes 5 which are considered intermediate internodes, referring to them as I5; and the internodes 9 which are considered mature internodes, counted from the first immature internode (I1) and referring to them as I9, as described in Papini Terzi et al., 2009 .AGDD) analysis, calculated by the following equation:iTa is the average temperature (°C) on the ith day and Tb is the base temperature (°C) for sugarcane development. The Tb considered here was suggested in [AGDD at each sampling point was calculated by temperature recorded until the day before sampling (n − 1).The effect of temperature variations on sugarcane development through the two conducted essays was investigated by the accumulated growing degree days , stalk height (m), stalk number (SN), and number of internodes . Using thttp://sucest-fun.org/wsapp/, accessed on 23 October 2015). Normalization was performed using non-linear LOWESS normalization [Total RNA extraction was done using TRIzol Invitrogen™ and grinded frozen sugarcane tissues according to the manufacturer’s instructions. Total RNA was treated with DNase I  followed by purification with the RNeasy mini kit . RNA integrity and concentration were evaluated using a NanoDrop 1000 spectrophotometer  and 200 ng of total RNA was used for labeled cRNA synthesis. For oligoarray analysis, the custom 4 × 44 k Agilent™ sugarcane oligonucleotide array was used, and sample preparation and hybridizations were performed as per Lembke et al. (2012) and Agilent™ protocols provided with the following kits: Quick Amp Two-Colour Labelling Kits , Gene Expression Hybridization Kit, and Gene Expression Wash Buffer Kit. The RNeasy Mini Kit (Qiagen™) was used again for purification. For each sampling point, four biological replicates (two from each plot) and dye swaps were used . For thelization  to correThe significantly expressed transcripts, both sense and antisense, from all replicates from all tissues, sampling points, and fields, were selected via the SUCEST-FUN database to be used for MDS analyses using the DGEList and plotMDS functions implemented in the “edgeR” R package .Differentially expressed genes (DEGs) were estimated by functions implemented in the “limma” R package . BackgroHeatmaps were constructed with the 500 most variable genes within the datasets. Specific gene clusters were detected, and gene set enrichment analyses (GSEA) were conducted using the “topGO” R package with the respective gene sets in each cluster to determine the top gene ontology (GO) terms related to each cluster .2O:MeOH solution mixed in the ratio 3:1 and kept at 4 °C. The H2O:MeOH phase from each sample was collected and placed in 2 mL microtubes in which they were stored at −80 °C prior to the HPLC-MS analysis. The pellet of each tube was dried down using speedvac for 1 h at 60 °C and weighed again for normalization purposes.The samples were prepared by using eight biological replicates of each tissue  and sampling points , that is four replicates from one plot and four from another plot. Pools of two samples each were made to reduce within-sample variation, rendering four biological replicates. The frozen samples were homogenized and aliquots of approximately 50 mg tissue powder were used for global metabolite extraction using the extraction method proposed by Salem et al. (2016)  with met2O with a pH of 2.65 (MPA), and MPA:MeOH:acetonitrile mixed in the ratio 5:25:70 (MPB). On the other hand, MPA and MPB used for compound separation and detection in the negative ionization mode were 20 mM NH4Ac in water with a pH of 7.35 (MPA) and MPA:MeOH mixed in the ratio 5:95 (MPB). A volume of 200 μL of each sample was transferred to a glass insert inside a glass vial prior to the analysis. A volume of 10 μL was injected into the HPLC-MS system and the flow rate was set at 500 μL/min. Gradient runs were conducted as in In order to separate, ionize, and detect the polar and semi-polar metabolites in each sample, high-performance liquid chromatography  coupled to the high-resolution time-of-flight mass spectrometer  was implemented. A pentafluorophenyl propyl ligand column  was coupled to an ESI-microTOF mass spectrometer, operating in the positive and negative ionization modes. Mobile phase A (MPA) and Mobile phase B (MPB) used for compound separation and detection in the positive ionization mode were 0.1% formic acid (FA) in HThe raw HPLC-MS data were extracted and converted to the mzXML file format after which they were separated based on tissue type to prevent possible matrix effects since the four tissues are anatomically and metabolically different. The tools and packages used here were all implemented using the R-software. The XCMS (various forms (X) of chromatography mass spectrometry) package was used for pre-processing of the HPLC-MS data  .The CAMERA package (Collection of Algorithms for Metabolite Profile Annotation) was used for ion, adduct and fragment annotation . The outRaw metabolomics data require pre-processing, in this case via the use of XCMS, CAMERA and ProbMetab, in order to generate “clean” data, which were the inputs for the data pre-treatment steps. Data was normalized by dry weight (DW), log10 transformed and no scaling technique was needed to be applied to yield optimal results for the untargeted metabolomics data based on Shapiro–Wilk’s normality tests and PCAs.The tissues were then separated to identify metabolic differences observed in each tissue between the plants that were planted in different seasons of the year (field comparisons). The same discriminant analyses were thus conducted namely PCA and PLS-DA, as described.After the raw HPLC-MS data was generated and processed, the KEGG metabolites predicted by ProbMetab and their normalized, transformed, and scaled intensities in each sample were subjected to pathway activity profiling (PAPi) . This R In order to determine which and where variation is shared between the two data modalities represented in this study, namely metabolomics and transcriptomics data, MOFA  was implhttp://sucest-fun.org/wsapp Accessed on 14 August 2019). The transcript sequences were used as an input for blast2GO [The sequences of the significantly expressed transcripts from the activated metabolic pathways identified by PAPi were obtained from the SUCEST-FUN database . Analysis of variance (ANOVA) and Fisher’s least significant difference (LSD) tests were conducted. When the interaction between the two factors (F1 and F2) was not significant, the data were analyzed as completely randomized design under one single factor. The analysis was done using ExpDes R Package .The significantly expressed transcripts from the oligoarray analysis were identified as described in Lembke et al., 2012 . BrieflyFor the untargeted metabolomics, the ProbMetab R package was used for most likely compound estimation . It usesA summary of the experimental design and a guide to the results and datasets is presented in The plantings of SP80-3280 were carried out in two seasons of the year and with different climatic conditions which are common for sugarcane cultivation, and are classified as “one-and-a-half-year” sugarcane (F1) and “one-year” sugarcane (F2), respectively A.The sugarcane plants from F1 experienced an initial growth period under low average temperature and precipitation, whereas the plants from F2 experienced an initial growth period under high average temperature and precipitation A. These Instantaneous photosynthetic rate, transpiration rate, stomatal conductance and water use efficiency were similar in the two fields . The plaComparing the plants from the two different fields to each other at C2 (8 months), it was evident that the sugarcane plants from F2 were significantly taller, had more internodes, and presented a narrower culm diameter which persisted throughout development B–D whichSucrose contents increased from leaf tissues to immature internodes all the way down to the mature internodes. The biggest difference between sucrose content in I5 and I9 was at C4. The I9 from the plants from F2 presented higher sucrose content than the I9 from F1 at all sampling points .Out of the 21,902 different probes  represented on the CaneRegNet oligoarray, 15,552  were significantly expressed when all tissues, sampling points, and field conditions were considered . MultidiIn addition, the third dimension contributed to a clear distinction between I9 and the remainder of the tissues B. HeatmaIn total, 108 samples consisting of L1, I1, I5, and I9 tissues were used for HPLC-MS analyses and the acquisition of untargeted metabolomics data. After feature detection, nonlinear retention time alignment, and relative quantification across all samples, hundreds of metabolic features were detected in both positive and negative ionization modes. The data were normalized by dry weight (DW), log10 transformed, and no scaling technique was needed to be applied to yield optimal results for the untargeted metabolomics data based on Shapiro–Wilk’s normality tests and PCAs . In the The highest number of metabolites (103 XCMS features matching to 90 unique KEGG metabolites) was detected in L1, the source, decreasing down the stem towards I9, the sink (74 XCMS features matching to 54 unique KEGG metabolites). In L1 most of the metabolites suggested by ProbMetab were tissue-specific (42 unique KEGG metabolites) and merely 11 metabolites were shared among all tissues . Tissue-When the processed metabolomics datasets from all tissues , sampling points  and fields (F1 and F2) were concatenated for the generation of PCA and PLS-DA models, clear separations were observed between the four tissues  proving Following the heatmap analysis, five main clusters (and two minor ones) were observed, separating the different tissues . ClusterThe main purpose of this study was to evaluate the transcriptomics and metabolomics of sugarcane during development until ripening in two different seasons. As we have demonstrated, the main source of variation in both fields are the different sugarcane tissues therefore, we combined both metabolomics and transcriptomics using the multi-omics factor analysis (MOFA)  for the Based on the combination between metabolomics and transcriptomics, it was possible to point out the main metabolic pathways containing both the identified metabolites and the transcripts of the genes coding for the enzymes directly upstream or downstream of these specific metabolites that are responsible for the principal sources of variation.http://www.genome.jp/kegg/, accessed on 17 December 2019), that were activated and altered throughout development were amino acid metabolism (36%), biosynthesis of secondary metabolites (29%), xenobiotics biodegradation and metabolism (21%), carbohydrate metabolism (7%), and metabolism of cofactors and vitamins (7%). Most of the GO terms generated from the transcriptomics data from the MOFA model for the L1 tissue, pointed towards amino acid metabolism and thus complementing the findings reported from the metabolomics data, as shown in For the L1 tissue, it was found that the main metabolic pathways, based on the KEGG BRITE classification  of altered pathways when considering all of the altered pathways. At the top of the list for altered pathways with regards to the metabolomics data, zeatin biosynthesis is listed . When anLastly, for I9, amino acid metabolism and carbohydrate metabolism each represented 20% of the altered pathways. Biosynthesis of secondary metabolism and xenobiotics biodegradation and metabolism represented 10% and 30%, respectively. Most of the gene expression data for this tissue pointed towards alterations in aromatic amino acid metabolism, specifically tryptophan metabolism as pinpointed by the metabolomics results . The glySince the analysis of all tissues in both fields together highlighted the differences among tissues and the aim was to study the differences in the development of sugarcane in two different planting seasons, we evaluated the metabolite profiles in each tissue separately. When each tissue was separately analyzed and PCA and PLS-DA models were generated, they mainly reflected the differences between the two different conditions. The most striking differences were observed in L1 and I9 as was observed in the transcriptomic data .For the L1 tissue, separations were observed with regards to the different planting seasons (F1 and F2) when analyzing the first components A,B.The metabolites with VIP scores from the PLS-DA models above 1.0 , were used for heatmap analysis. An overall view of the heatmap showed that most of the identified metabolites were higher in L1 from F2 as compared to F1 C. These For the I1 tissue, the separations between the two fields were not as prominent as in L1 when the PCA and PLS-DA models were inspected . Nine meFor the intermediate internode (I5), even less of a separation could be seen between the fields (F1 and F2) . Nine meLastly, in correspondence with L1, for the mature internode (I9) greater separations between the different conditions (F1 and F2) were observed . Regardip < 0.05) as compared to when thick culm tissue had been formed at C2, and the vegetative growth and elongation phases were being carried out at C3 and C4 from F1  alterations were observed (Some of the main metabolites responsible for the separations between F1 and F2 are part of three related metabolic pathways in both L1 and I9: phenylpropanoid biosynthesis (map00940), phenylalanine, tyrosine and tryptophan biosynthesis (map00400), and flavonoid biosynthesis (map00941). The calculated activity scores estimates how these three pathways behave throughout the sugarcane development in both fields in leaves and mature internodes. The higher the score, the lower the activity  and thus from F1 A. The ph from F1 A, thus iobserved B. On theobserved B.The metabolic intermediates in these pathways and the SAS (EST contigs) directly related to the metabolites (via the specific enzymes directly upstream or downstream of these metabolites on the KEGG database) were identified  and the The two significantly expressed PAL sense (SS) transcripts (SCEQRT1024E12.g and SCCCLR1048D07.g) were expressed in F1 and F2 in both leaves and internodes. In the mature internode both PAL transcripts showed higher expression while in leaf +1 only the SCEQRT1024E12.g transcript was included in the cluster with higher expression . NAT froSugarcane development is mainly affected by four abiotic factors namely water availability , temperaThicker culms for the “one-and-a-half-year” sugarcane were also observed, supporting the findings by Cardozo (2015)  which stIt is known that sugarcane has the ability to tolerate early phase water deficits without significantly affecting future yields , as was The majority of the variation within the transcriptomics and metabolome datasets could be explained by the differences between the four tissues. From a biotechnological perspective, these types of genes with tissue-specific expression can possibly be considered as strong candidates for providing promoters that can be used for a more controlled expression of transgenes in crops .Genes involved in secondary metabolic processes and organic acid metabolism were highly expressed in the leaves, in agreement with the results from metabolomics data analysis in which most of the metabolites responsible for the separation of the different anatomical tissues formed part of secondary metabolism and organic acid metabolism as well. Genes responsible for specialized carbohydrate metabolism such as cell wall organization and construction, and sugar metabolism were highly expressed in all internodes. It is well known that cell wall construction and lignification increase down the sugarcane culm .In plants, the leaf tissue is one of the main elements responsible for perceiving and responding to changes in the external environments. Plant secondary metabolism was evolved to allow plants to perceive and adapt to these changing environments , thus juThe highest number of metabolites were present in the leaves and decreased down the stem  which agCompounds involved in plant growth that could, in the future, be used to improve sugarcane were identified. Spermidine, a polyamine (PA), was found in higher levels in the immature and intermediate internodes as compared to the leaves and mature internodes  and highSalvia hispanica) led to increased shoot elongation and biomass accumulation and presented positive correlations with proline contents [Our results indicated higher levels of p-coumaric acid in the same tissues as spermidine and it has been shown that PAs can bind with phenolic compounds such as hydroxy cinnamic acid, coumaric acid, caffeic acid, or ferulic acid to form CC-PAs ,64, thuscontents , as was Metabolites involved in tyrosine metabolism and glucosinolate biosynthesis were found to be higher in the leaves (L1) and mature internodes (I9) as compared to the intermediate internodes (I1 and I5) studied here . TyrosinThe flavonoid quercitrin was detected mainly in sugarcane leaves from F2 . QuercitThe metabolites above mentioned and the lack of studies in sugarcane metabolomics, highlights the new possibilities for the exploration of the sugarcane metabolome and its use in the production of economically valuable products.https://sucest-fun.org/wsapp/, accessed on 30 April 3021).In most “multi-omic” or “double-omic” studies, the statistical analyses of metabolomics and transcriptomics data was done separately without using a specific tool for their direct integration ,82,83,84For the different sugarcane tissues of SP80-3280 analyzed here, the main metabolic systems that were altered throughout development and in response to changes in the external environments were amino acid metabolism, mostly Phe, Tyr, Trp metabolism; and secondary metabolism, mostly phenylpropanoid metabolism and the metabolism of its upstream precursors and/or downstream products. Previous studies on the effects of abiotic stress on plants such sugarcane and Arabidopsis have reported alterations in amino acid metabolism ,86. In sPhenylpropanoids, such as flavonoids, are key players in the synthesis of cell wall precursors and function in plant development . PhenylpIn order to identify the exact metabolites responsible for the separations between the “one-and-a-half-year” sugarcane from F1 and “one-year” sugarcane from F2 in each tissue, the PCA and PLS-DA models were inspected separately, and the main metabolites responsible for the separations in each tissue  were highlighted.Looking at the leaf tissue, the majority of the identified metabolites were in the “one-year” sugarcane plants from F2, which presented earlier maturation at 11 months old as Brix%, Pol % juice, Pol % cane and purity% were higher than 18%, 15.3%, 13% and 85%, respectively ,39. AfteOut of the 32 metabolites identified in the leaves for being responsible for the separation between the two fields, the majority of them were phenylpropanoids and intermediates of the phenylpropanoid biosynthesis pathway C. It is Afzelin, together with quercitrin above mentioned, are two flavonoids reported to be present in many plant leaves ,94,95 anIn addition to the phenylpropanoids that were abundant in the leaves of the “one-year” sugarcane, the monolignols cis-beta-D-Glucosyl-2-hydroxycinnamate, caffeic acid and caffeoylquinic acid, were also abundant C. MonoliIn the immature and intermediate culm tissue, the main metabolites highlighting the relatively small differences between the two fields (F1 and F2), that is between “one-year” and “one-and-a-half-year” sugarcane, were found to be mostly involved in primary metabolism . HoweverThe cyanogenic glucoside, dhurrin, synthesized from tyrosine, was initially identified in sorghum (Sorghum bicolor) and it is reported that this compound is abundant in intermediate plants and absent in mature plants . A studyS. officinarum, S. spontaneum and S. robustum) and a commercial hybrid (RB867515). To deepen our study on the high number of identified NATs (49 in L1 and I9 from the three highlighted pathways), RNAseq approaches must be used to characterize the size and sequences of the NATs that where here identified as the Agilent oligoarray only informs us that a sequence of at least the size of the probe (60 mer) is being expressed [The oligoarray used in this study for transcriptomics studies was already used for antisense transcription detection ,105,106.xpressed .As cited earlier, some of the main metabolites responsible for discriminating between F1 and F2 are part of three metabolic pathways in both leaf 1 and mature internode: Phenylpropanoid biosynthesis, Phe, Tyr, Trp biosynthesis and Flavonoid biosynthesis. The developmental profile of sugarcane is established within the initial months after planting, and long-term adjustments to plant growth and culm thickening take place. The thicker culm phenomenon of the “one-and-a-half-year” sugarcane plants was the first indication that secondary metabolism is affected when there is a shortage of water within the initial months after planting. Studies on maize , wheat  and riceThe main goal of this project was to integrate transcriptomics and metabolomics data to study sugarcane development in two different seasons. Morphological and technological data evidenced that precipitation and temperature have significant influences on sugarcane development, and that the developmental profiles are established within the first four months of planting.For the “one-and-a-half-year” sugarcane from the first field experiment, the observed retarded vegetative growth could be attributed to the lower precipitation and lower temperatures since it has been proved that internode development and culm elongation are the most sensitive morphological alterations in sugarcane affected by water deficits. It was also concluded that SP80-3280 can support early phase water limitation by continuing photosynthate production and carbon assimilation, however the carbon flow is directed towards culm thickening instead of culm elongation or sugar accumulation. This is known as a long-term adjustment to the changes in the external environment. By planting SP80-3280 under low precipitation and temperatures, more biomass could be produced in the form of fiber over a longer period of time, thus highlighting optimal conditions for the cultivation of SP80-3280 when fiber accumulation is desired. In contrast, when higher sucrose accumulation is desired, rapid early-stage culm development and elongation are necessary, followed by a period of moderate water deficit in order to shift from the vegetative growth to the ripening phase.With the untargeted metabolomics pipeline, we identified 90, 84, 79, and 54 metabolites in the leaf +1, immature internodes, intermediate internodes and mature internodes of SP80-3280, which were assigned to 67, 70, 69, and 64 activated KEGG metabolic pathways. The high number and the chemical diversity of the metabolites detected here contributed to knowledge gaps regarding the relatively unexplored sugarcane.Economically valuable compounds in specific sugarcane tissues were identified, specifically secondary metabolism compounds such as phenolic compounds and glucosinolates, which were higher in the leaves and the mature culm tissue. Compounds with economic value for having biopesticide, allelopathic, antioxidant, pharmaceutical, flavor enhancing and antiviral properties were also identified. In the sugar and ethanol production industries, the leaves and remaining bagasse after the sucrose extraction processes are not currently being used to their full potential, thus the metabolites highlighted here and their economic importance might aid in the decision to explore the opportunities of the improved utilization of these tissues. One possibility would be to increase production in the specific tissue by producing sugarcane under different conditions, by controlling the outcome of the developmental profiles as was seen in this study, or by engineering pathways.Overlaps between the metabolomics and transcriptomics data were observed when they were analyzed separately via data dimensionality-reduction techniques, as well as when they were directly integrated in an unsupervised manner using the MOFA tool. The combination of metabolomics and transcriptomics data highlighted the metabolites and genes from amino acid metabolism, secondary metabolism, and the metabolism of its upstream precursors and/or downstream products and helps to determine and to validate if a biosynthesis pathway is present in an organism.For the different tissues of SP80-3280 analyzed, the differences in metabolite profiles between F1 and F2 were found in the leaf and mature internodes with metabolites and genes from the Phe, Tyr, Trp biosynthesis pathway, the phenylpropanoid biosynthesis pathway and the flavonoid pathway. Leaves from “one-year” sugarcane present phenylpropanoids and antisense transcript expression of many genes not detected in “one-and-a-half-year” sugarcane. Here we present a summary figure of the main results .Since the main phenotypic differences of the plants from the two different fields from our study were related to plant height and culm diameter, the reported findings regarding the pathway activities conclude that the phenylpropanoid biosynthetic pathway and its derivative pathway, flavonoid biosynthesis, are crucial to plant development and adaptation to changes in the plant’s internal and/or external environments. It is suspected that the Phe, Tyr, Trp biosynthesis pathway is a key pathway which will provide energy and intermediates for the proper functioning of the above-mentioned pathways.


Candida parapsilosis is an emerging human pathogen whose incidence is rising worldwide, while an increasing number of clinical isolates display resistance to first-line antifungals, demanding alternative therapeutics. Genome-Scale Metabolic Models (GSMMs) have emerged as a powerful in silico tool for understanding pathogenesis due to their systems view of metabolism, but also to their drug target predictive capacity. This study presents the construction of the first validated GSMM for C. parapsilosis—iDC1003—comprising 1003 genes, 1804 reactions, and 1278 metabolites across four compartments and an intercompartment. In silico growth parameters, as well as predicted utilisation of several metabolites as sole carbon or nitrogen sources, were experimentally validated. Finally, iDC1003 was exploited as a platform for predicting 147 essential enzymes in mimicked host conditions, in which 56 are also predicted to be essential in C. albicans and C. glabrata. These promising drug targets include, besides those already used as targets for clinical antifungals, several others that seem to be entirely new and worthy of further scrutiny. The obtained results strengthen the notion that GSMMs are promising platforms for drug target discovery and guide the design of novel antifungal therapies. Candida spp. accounting for the majority of cases [Candida albicans is the most commonly isolated species from candidiasis patients, the 1990s saw a shift in incidence within the genus towards non-Candida albicans Candida species (NCAC) [Candida parapsilosis has seen one of the most significant increases, often surging as the second most common etiological agent of Candida spp. infections, subverting historical trends in species incidence and even outranking C. albicans in some European countries [C. parapsilosis’ already complex pathogenicity, are both the rise in resistance to first-line antifungals and the intrinsically lower susceptibility to alternative therapies, such as azoles [In a world of climate and social change, human susceptibility to microbial disease is increased. In particular, fungal infections have seen a significant rise in incidence worldwide since the 1980s, with s (NCAC) . From thountries . Non-geos azoles  and echiGenome-Scale Metabolic Models (GSMMs) have emerged as a systems biology approach to tackle this issue . GSMMs cC. parapsilosis, iDC1003. This model is provided in the well-established SBML format and can easily be read in most metabolic engineering platforms, such as OptFlux [Candida spp. was obtained from the validated model, and their targetability as putative novel antifungal drug targets is discussed.This work presents the first validated in silico genome-scale metabolic reconstruction of the human pathogen  OptFlux  and COBR OptFlux . A set oCandida parapsilosis with the taxonomic ID 5480. Model reconstruction was performed using merlin 4.0.5 [The herein described metabolic model reports on the yeast in 4.0.5 , and furin 4.0.5  using thCandida parapsilosis CDC317 was obtained from NCBI’s Assembly database, accession number ASM18276v2 [merlin based on taxonomy and frequency of similar sequences through remote Basic Local Alignment Search Tool (BLAST) [The initial draft model construction comprised enzyme and subsequent reaction annotation. The genome sequence of the reference strain M18276v2 , which i (BLAST)  similari (BLAST) . Hit sel (BLAST) , and phy (BLAST) . Protein (BLAST)  databasemerlin, which implements information from remote tools such as eQuilibrator [The initial reversibility curation was automatically performed by librator , as desclibrator . Furtherlibrator  and exislibrator , ChEBI [librator , Brenda librator , and exiCompartmentalisation was implemented using WoLF PSORT , a protemerlin’s integrated tool TranSyT [2O, CO2, and NH3, often carried by facilitated diffusion, were added to the model with no gene association.A compartmentalised model calls for the implementation of transport reactions to connect intercompartment pathways. Transport reactions were generated using genomic information, together with the public database TCDB , using m TranSyT . TranspoThe biomass equation encompasses the cells’ major components and their relative numerical contributions—DNA, RNA, carbohydrates, lipids, and proteins—and acted as the objective function in the presented essentiality analysis. The content of each component was determined based on the literature. All the calculations were performed as described previously .The reconstructed model also includes ATP requirements for both biomass production and cell maintenance—growth-associated maintenance (GAM) and non-growth-associated maintenance (NGAM), respectively. A GAM value of 25.65 mmol ATP/gDCW was considered for the biomass equation, calculated based on the ATP requirements for the biosynthesis of cell polymers, as shown in Mishra et al. , then adCandida tropicalis [Non-growth-associated ATP maintenance, the amount of ATP required by the cell repair and similar processes, was implemented in this model as an autonomous equation, thus forcing a basal ATP consumption—flux bounds inferred from opicalis . The bioS. cerevisiae iMM904 metabolic model for the phosphorus-to-oxygen ratio was applied. Three generic reactions contributing to this ratio were automatically generated by merlin and were updated to replicate the same ratio as in the iMM904 model:The theoretical ratio used in the Reaction R00081:Reaction T02161:Reaction T00485:The final balance reaction:The model simulations were performed using the flux balance analysis (FBA)  formulatC. parapsilosis type strain CDC317 was batch cultured at 30 ºC in orbital agitation (250 rpm) in yeast extract–peptone–dextrose (YPD) for inoculating cultivation and in synthetic minimal media (SMM) for growth parameter determination. Media compositions were as follows: YPD: 20 g/L glucose , 20 g/L peptone , and 10 g/L yeast extract ; SMM: 20g/L glucose , 2.7 g/L ammonium sulphate , 0.05 g/L magnesium sulphate (Riddle-de-Haen), 2 g/L potassium dihydrogen phosphate , 0.5 g/L calcium chloride , and 100 µg/L biotin (Sigma). 600nm) of 0.3. Aerobic batch cultivations were incubated in SMM at 30 ºC with orbital agitation (250 rpm) for 10 h.Precultures (100 mL) for aerobic batch experiments were grown in SMM in 500mL flasks at 30 ºC in orbital agitation (250 rpm). Cells were grown until the exponential phase and used to inoculate fresh media at an initial optical density at 600nm  and 536 are transport reactions—and 1278 metabolites across four compartments  and within an intercompartment, the plasma membrane. The model can be found in an SBML format in The herein described Manual curation assessed a total of 847 reactions, from which 83 were mass balanced, 162 were corrected regarding reversibility or directionality, and 625 were added or removed from the model or had their annotation corrected or completed, as detailed in The biomass equation  encompasC. glabrata [S. cerevisiae [C. albicans [C. parapsilosis. Although iDC1003 uses standard identifiers for reactions (KEGG ID), it is not possible to assess how the reactions differ among the four models, as the remaining models do not use the same identifiers . Thus, a comparison across the existing models was carried out based on the proteins associated with an EC number. After intersecting the EC numbers present in each of the three models, 85% of the proteins with an associated EC number in the C. parapsilosis model were found to also be present in at least one of the other three models , resulting in the release of ferrous iron.- 1.16.3.1: ferroxidase, involved in iron homeostasis, oxidises Fe(II) to Fe(III), which allows the subsequent incorporation of the latter into proteins such as apotransferrin and lactoferrin.C. parapsilosis strains, other than the reference CDC317 strain, were also considered in the analysis to increase the number of carbon and nitrogen sources accounted for. A total of 47 sole carbon sources and 17 sole nitrogen sources were evaluated. The C. parapsilosis model correctly predicted growth in 94% of the carbon sources tested and in 100% of the nitrogen sources (C. parapsilosis can use for growth. Interestingly, as far as it could be assessed, there is no experimental evidence of any enzymes behind these pathways existing in yeasts. It would be interesting to look deeper into these organisms’ eventually unique 2-Keto-D-gluconic acid, L-arabinose, and ribitol assimilation pathways.Simulations were performed in SMM and compared to phenotypic growth data from existing literature to assess iDC1003’s reliability in predicting biomass production from different sole carbon or nitrogen sources. Data related to  sources . The modC. parapsilosis. C. parapsilosis CDC 317 cells were cultivated in SMM medium, and growth was followed by regular measurements of the OD600nm and the cell dry weight. Samples were harvested to assess glucose concentration as a function of time during the exponential growth phase. In these conditions, a glucose consumption rate of 2.098 +/− 0.404 mmol·gDCW−1·h−1 was determined, while no ethanol, glycerol, or acetate production could be detected.Specific growth rate, glucose consumption rate, and metabolite production rates were experimentally determined to validate the model quantitatively due to the lack of literature data for −1 dry weight·h−1 was performed to evaluate the model’s ability to predict the specific growth rate. The remaining nutrient fluxes were left unconstrained, as the system in these conditions is glucose-limited. Considering the experimentally determined glucose consumption rate of 2.098 mmol·gDCW−1·h−1, the model predicted a specific growth rate of 0.172 h−1. Compared to the experimentally determined rate of 0.159 +/− 0.027 h−1, the predicted growth rate is within the uncertainty interval of the experimentally determined parameter. Thus, there is no significant difference between both, which suggests iDC1003 can predict C. parapsilosis growth parameters (C. parapsilosis is a Crabtree-negative yeast. Altogether, iDC1003 was proved to predict the main metabolic features of C. parapsilosis quantitatively.A simulation of the system’s behaviour in environmental conditions of SMM medium with a fixed glucose uptake flux of 2.098 mmol·grameters  quantitaIdentification of essential enzymes of a given pathogen should, in principle, lead to the identification of good drug targets since, if one enzyme is essential for its growth or survival, a compound capable of inhibiting it could potentially be used as a drug with pharmacological activity against that pathogen. The drug target will be ideal if it is essential, or at least essential under conditions that mimic the human host environment, while having no human homolog. Based on these principles, iDC1003 was used to identify potential new drug targets by determining enzyme essentiality. For that, a list of essential reactions was obtained through simulation of the system’s behaviour in RPMI medium, which mimics the environmental conditions of human serum. A total of 147 enzymes were predicted as essential in iDC1003, excluding drains, transport reactions and those without an associated or incomplete EC number. The complete list of predicted essential enzymes can be found in C. parapsilosis model with those resulting from the two existing models for pathogenic Candida species, C. albicans [C. glabrata [Candida species. After the intersection, 56 essential enzymes common to the three models were found (Finally, we decided to intersect the potential drug targets obtained through the albicans  and C. gglabrata , to focure found .Streptococcus pyogenes, Fol1, a target of sulfonamides, and Fas1, a target of ethionamide, used in the treatment against Mycobacterium tuberculosis. These confirmatory results illustrate the potential of the used approach in the quest for new drug targets. More interesting, however, is the identification of numerous potential targets, as identified herein, that are not currently targeted by any drug used in clinical practice. There are some new targets with tremendous potential, as these do not have an orthologous enzyme in humans. Although not an excluding factor, the absence of a human ortholog is a preferable attribute, as this translates into lower chances of host drug toxicity and may allow for greater freedom of drug design.Consistently, well-established antifungal drug targets were identified, including the well-known targets of azoles and echinocandins, Erg11 and Fks1, respectively. Additionally, some of the identified predicted drug targets are homologs of enzymes used as drug targets against other pathogenic organisms, including Imh3, which is targeted by inosinic acid in Among the identified potential new drug targets, Abz1/2, Erg4, and Ura1 emerge as enzymes without any human homolog or drug association.FOL1, ABZ1, and ABZ2 encode key enzymes in the folate biosynthesis pathway (FOL1 has been shown to be successfully inhibited by antifolates, such as sulfonamides, in a series of microorganisms [Candida infections is not particularly effective considering current antifolate compounds [C. albicans, only sulfanilamide is used clinically, and it is restricted to topical use [Candida Fol1. On the other hand, no inhibitors of the para-aminobenzoate synthetase encoded by ABZ1 or of the 4-amino-4-deoxychorismate lyase encoded by ABZ2 are currently known, making these two enzymes fully novel putative drug targets worth further exploitation.Fungi rely on folate de novo biosynthesis given their inability to uptake folate from the environment . FOL1, A pathway . Furtherrganisms ,51. Howeompounds . In factical use . Given tPlasmodium falciparum infections. In turn, Erg4, also with no human ortholog, is involved in ergosterol biosynthesis, a pathway currently targeted by azole drugs. The Ura1 and Erg4 proteins may also be interesting drug targets. Ura1 has no human ortholog and, although this protein is not the target of any known drug, Aro9, from the same pathway, is a known target of atovaquone used to treat C. parapsilosis is presented in this study. The model was manually curated and validated experimentally and proved to be able to predict the main metabolic features of C. parapsilosis quantitatively. Furthermore, iDC1003 is robust in predicting the use of several carbon and nitrogen sources. The model shares 85% of the proteins with an associated EC number in other published yeast models. However, 15% of them are exclusive to this model and may represent some unique metabolic features of C. parapsilosis. Using iDC1003, several enzymes were predicted to be essential in RPMI medium, including some already known targets of antifungal agents and other antimicrobial agents used in clinical practice, illustrating the potential of the used approach in the quest for new drug targets. Several of the identified potential drug targets are not currently targeted by any drug used in clinical practice, and deserve further study. Among the identified potential new drug targets, Abz1/2, Erg4, and Ura1 stand out as enzymes without any human homolog or drug association. However, all other targets are worthy of scrutiny as, in fact, many of the drugs currently used in clinical practice have human orthologs. In these cases, the strategy may involve taking advantage of the structural differences between the proteins in the two organisms to design efficient compounds.The first validated global metabolic model for the human pathogen Despite the clear usefulness of these types of models, it is important to highlight that these reconstructions have limitations. Firstly, the basis of GSMMs is the genome’s functional annotation. Depending on the stringency of the criteria imposed on hit selection, such a procedure may lead either to compromising rates of false positive or negative annotations. Furthermore, such models do not encompass regulatory processes due to the high complexity of such an integration. Note how enzymatic activity can be regulated at different levels—from gene expression to post-translational modifications—and may result in given pathways being preferential in certain environmental conditions. The exclusion of such mechanisms, particularly regarding essentiality prediction, may result in predicted essential ECs that would otherwise not be metabolically relevant in the conditions of interest. Lastly, these simulations do not consider supra-metabolic factors, such as stress factors. Even so, and considering all these limitations, GSMMs allow for increasingly guided and reliable drug target discovery—as illustrated in this paper.


The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation. Carbon metabolism is highly conserved across species and, even, kingdoms. Comprehension of its complexity has remained elusive3. Despite similarities in carbon assimilation pathways in plants, there is an abundance of diverse metabolic products varying in quantity. In addition to the sophisticated and highly redundant relationships between metabolic reactions and metabolites, the process dynamically changes depending on prevailing conditions. Isotope labeling is typically used to gain insights into the metabolic conversion of carbon4; nonetheless, its application in plants is challenging due to expensive instrumentation, the short half-life of labeled precursors, and heterogeneity of plant cells, i.e. multicellular organisms with diverse cellular compartments6, among others.Carbon assimilation is an essential metabolic process underlying the biosynthesis of basic building blocks for organismal growth and development. In plants, the process is related to the conversion of atmospheric carbon dioxide captured during photosynthesis to carbon-derived compounds composed of cellular biomass7 and enhance our understanding of complex metabolic pathways in plants. They enable interpolation of flux conversion for metabolic intermediates between the measurable biomolecules, connecting metabolic phenotypes to the physiological growth of plants. GSMMs are basically constructed from the entire metabolic reactions annotated for any studied genome8 and simulated according to the thermodynamic mass balance and condition-specific constraints, so-called constraint-based modeling (CBM)9. The first model of the minimal sets of metabolic reactions required for plant growth under heterotrophic conditions was developed for Arabidopsis10. Genome-wide metabolic models exploring primary and secondary cellular metabolisms in plants, e.g. AraGEM11, as well as models enabling the study of tissue-level  metabolism12 have also been developed. Moreover, CBM has deepened our understanding of metabolism, yield improvement and stress response in major crop plants, for example, changes in metabolic behavior in response to drought and flooding in rice13, lipid biosynthesis and accumulation in developing embryos of rapeseed14, and metabolism underlying photosynthesis, photorespiration, and respiration in maize15. During the past year, CBM was used to study carbon assimilation in cassava storage roots (rMeCBM)16 through a simple flux balance analysis (FBA). The rMeCBM model demonstrated cultivar-dependent differences in carbon utilization for storage root development, particularly between KU50 (Kasetsart 50), a high-yield cultivar, and HN (Hanatee), a low-yield cultivar. In addition, rMeCBM described the growth phenotype of cassava storage roots and robustly predicted carbon assimilation in both cultivars, but direct validation of the interpolated intracellular fluxes was experimentally infeasible. Performing a C-labeling experiment with tuber plants such as cassava remains a challenge.Genome-scale metabolic models (GSMMs) have been introduced to complement experimental studies19. Hence, omics data is incorporated into the model to improve the prediction quality, thereby making the model context-specific21. According to the central dogma of gene to protein function, metabolic reactions cannot achieve their function unless the enzymatic protein-coding genes are expressed. Transcriptome data, which provide genome-wide gene expression information, are then exploited to trace the active reactions associated with metabolic processes under specific conditions. Covert et al. proposed the first transcriptome-integrated CBM, investigated metabolic fluxes in central carbon metabolism of Escherichia coli (E. coli), and demonstrated the effect of transcriptional regulation on metabolism22. Various computational methods have been developed to constrain inactive metabolic reactions under context-specific conditions during the last decades. Among them, GIMME (Gene Inactivity Moderated by Metabolism and Expression) pre-defines inactive reactions based on a low-level of enzymatic gene expression under a set threshold and then minimizes fluxes through these reactions. The method incorporates gene expression to constrain the CBM model as demonstrated for E. coli23 and in Arabidopsis leaves under drought stress24. To avoid the bias of threshold setting, E-Flux was proposed to directly constrain predicted fluxes to the measured level of gene expression. This method was originally developed to study changes in metabolic flux capacity in Mycobacterium tuberculosis25, and Arabidopsis rosettes26. Later, Lee et al. proposed a method to maximize the correlation between the magnitude of reaction fluxes and expression levels of corresponding enzymatic genes27. The HPCOF  approach uses the Huber penalty function to resolve local optima problems in the previous approaches. The method was successfully applied to study metabolic fluxes in both E. coli and yeast metabolism28. Although gene expression data do not directly relate to fluxes because of post-transcriptional and translational modifications, they can confine the viable flux space to reflect the biological behavior better than the generic CBM model26. To date, very few studies have incorporated transcriptome data into CBM models of plants30, especially plant storage organs.Given their simplicity and minimal data requirement, FBA models often weakly predict metabolic fluxes at elaborate branched-chain reactions and in a cyclic pathway, for example, pentose-phosphate pathway and non-cyclic TCA, where fluxes change dynamically with prevailing environments31, using three different conceptual methods, denoted as rMeCBMx-GIMME, rMeCBMx-EFlux, and rMeCBMx-HPCOF. The performance of the models was analyzed through the consistency of the predicted fluxes and expression of corresponding genes in various datasets of cassava storage roots. Results showed the prediction by rMeCBMx-HPCOF corresponded best to the metabolic gene expression observed in cassava storage roots, especially in the high-yield cultivar. Compared to the original rMeCBM model, rMeCBMx-HPCOF showed a significant improvement in carbon flux prediction in the complicated branched pathways: (1) carbon substrates supplied via oxidative pentose phosphate pathway, (2) TCA cycle and glycolysis in respiration pathway, and (3) carbon precursors for alanine biosynthesis. These reactions were consistently weakly predicted by the basic FBA model. This study showed that integration of gene expression data could enhance the prediction power of the rMeCBM model, yielding more biologically relevant information on carbon assimilation in developing storage roots of cassava.Here, transcriptome-integrated CBMs (rMeCBMx) were developed to improve metabolic flux prediction of carbon assimilation in cassava storage roots (rMeCBM). The models were constructed based on transcriptome data of developing storage roots32. The transcriptome data included expression of 33,033 genes in developing storage roots of four cassava cultivars 34. The rMeCBMx models were constrained using gene expression data from Wilson et al.31, which contained the highest number of replications among available transcriptome datasets.Eight RNA-seq datasets of developing storage roots of cassava were collected from the literature32; Wilson et al.31; scenario 1), (2) in developing storage roots of high-yield cultivars , and (3) in developing storage roots of the KU50 cassava cultivar . A meta-transcriptome analysis of datasets in each scenario was performed, and only concurrently expressed genes across datasets were collected to represent genes expressed in the condition. Genes were considered similarly expressed between datasets under similar conditions (same scenario) if the coefficient of variation (CV) of expression across datasets was less than 25 percent.The predictive performance of the models was assessed by comparing the metabolic fluxes with the expressed enzymatic genes in three representative scenarios: (1) in developing storage roots of cassava , respiration pathway (RES), pentose-phosphate pathway (PPP), cell wall biosynthesis pathway (CEL), amino acid biosynthesis pathway (AMI), fatty acid biosynthesis pathway (FAT), and nucleotide biosynthesis pathway (NUC), essentially representing carbon assimilation toward the biosynthesis of storage root biomass. It contains 393 metabolites and 468 reactions, namely 330 biochemical reactions associated with 762 enzymatic genes in cassava genome version 6.1, 116 transport and exchange reactions, and 22 auxiliary and non-gene reactions. In this study, the model of carbon assimilation in developing storage roots of cassava was simulated according to growth and physiological data of the KU50 cassava cultivar as presented in the original publication (specific sucrose uptake rate to roots = 0.0548 mmolSucgDW−1storage roots day−1 and storage root growth rate = 0.0090 day−1)16. To refine the prediction of metabolic conversion in storage roots, the rMeCBM model was further constrained with high-resolution gene expression data of developing storage roots of cassava31, denoted as rMeCBMx 37. All models were simulated in MATLAB  environment.The genome-scale metabolic model of cassava storage roots (rMeCBM) by Chiewchankaset et al.23 minimized predicted fluxes through inactive reactions in the metabolism of developing storage roots (Eq. th (middle-rank), and 75th (high-rank) percentiles of all enzymatic gene expression in the rMeCBM model.As guided by gene expression levels, GIMMEoots Eq. . The inai participating in reaction j; j; j; Subject to25. The rMeCBMx-EFlux model employed information on the gene expression level (x) to set the boundary of flux for each reaction (j), enabling the optimization of biomass production to simulate the growth of cassava storage roots  and 75th (high-rank) percentiles of enzymatic gene expression in the rMeCBM model. The active reactions, predicted non-zero fluxes, were compared with their responsible gene expression, and the consistency was evaluated using a confusion matrix with the following indices: accuracy (ACC), sensitivity (SEN), specificity (SPC), negative predictive value (NPV), and positive predictive value (PPV).The concurrence of the flux predictions and transcriptome data was assessed on both qualitative and quantitative bases. The predicted fluxes were compared with gene expression in developing storage roots of cassava, developing storage roots of high-yield cassava cultivars (KU50 and Arg7), and developing storage roots of the KU50 cassava cultivar. The reactions with zero- and non-zero fluxes were firstly contrasted based on the absence and presence of enzymatic gene expression, respectively. The activity of reactions linked to multiple enzymatic genes was considered depending on the highly expressed genes in the set relative to the thresholds: 25SSD; Eq. SSD as follows:jth reaction, respectively.In addition, the sum of square difference SSD; Eq.  was empl16. The model was employed to simulate the growth of storage roots of cultivar CMC9, to ensure specificity of simulation to the modeled conditions. Besides, the sensitivity of the model was analyzed to support its performance. Simulation of the biomass growth rate was performed under a varying range of SGAM (stoichiometric coefficients of energy for root growth), for each of which the deviation from the best fit model was determined (Eq. .)16.The rMeCBMx-HPCOF model was validated similarly to rMeCBMined Eq. .16.5\doc32; Supplementary Table 37, Li et al.38, Owiti et al.39, Vanderschuren et al.40, and Naconsie et al.41; Supplementary Table 42 and Obata et al.43; Supplementary Table th percentile rank were used. For the proteome data, existing proteins in developing storage roots were measured from five cassava cultivars through 2D SDS-PAGE and high-resolution liquid chromatography mass spectroscopy (LC-MS). The proteins were BLASTp searched against cassava genome v.644 using identity percentage ≥ 60, coverage percentage ≥ 80 and e-value ≤ 10−10. For the metabolome data, metabolic compounds in storage roots were retrieved from 20 studied conditions, including fourteen cassava cultivars planted in different systems. The analyses were based on gas chromatography-mass spectrometry (GC-MS) and LC-MS.The predictions of the rMeCBMx were validated with multi-omics data of cassava storage roots, consisting of seven transcriptome data  and high-yield (KU50) cassava cultivars to determine the metabolic basis for their distinct root biomass. Simulation results obtained for KU50 were comparable to those for CMC-9, a high-yield cultivar from an independent study used for the validation, confirming its reliability, along with the sensitivity analysis of the predictionsth percentile of overall gene expression in the model. The rMeCBM model was examined with the genome-wide gene expression data of KU50 storage roots at 150 DAP32  model predicted 251 non-zero flux (active) and 217 zero-flux reactions, including two reactions with inferior gene expression recovered from predefined inactive reactions during optimization . These models showed improved flux prediction relative to rMeCBM, owing to the metabolic gene expression information. They predicted the use of pyruvate-glutamate transaminase  to synthesize alanine instead of serine-pyruvate transaminase . The rMeCBMx-GIMME-P25 and rMeCBMx-GIMME-P75 showed similar predictions but not identical. The carbon precursors imported from the cytosol for utilization in the plastid were different. rMeCBMx-GIMME-P75 preferred beta-D-Fructose 1,6-bisphosphate (d-glucose-6-phosphate (d-Glc-6P) for the respiration pathway and biosynthesis of other biomass components. Hill and Smith45 reported that d-Glc-6P is imported as the substrate for starch synthesis in the plastids of developing pea embryos45. Additionally, the models were different in the use of the bypass reaction R01830 in non-oxidative PPP for the conversion of d-erythose-4-phosphate (d-E4P) and d-xylulose-5-phosphate (d-X5P) to d-glyceraldehyde-3-phosphate (d-G3P) and beta-d-fructose-6-phostphate (d-Fru-6P) by transketolase (EC 2.2.1.1). rMeCBMx-GIMME-P25 predicted R01830 in the plastid, whereas rMeCBMx-GIMME-P75 predicted it in the cytosol. The activity of PPP in the cytosol and plastid is complicated and varies in several plants. Cellular fractionation studies with leaves and roots of maize, pea, and spinach have provided evidence that the non-oxidative reactions are confined to the plastids46. Debnam and Emes47 found non-oxidative enzymes in cytosolic and the plastidic compartments in tobacco roots and leaves47. Moreover, rMeCBMx-GIMME-P75 predicted higher fluxes of non-cyclic TCA than rMeCBMx-GIMME-P25. The results demonstrated the GIMME integrative model could improve flux prediction of rMeCBM; however, the prediction was strongly dependent on the set threshold. The flux variability analysis (FVA) showed the variation in fluxes predicted at different threshold settings by the GIMME models was mostly from substitutable reactions, defined according to Hay and Schwender14, indicating viable alternative solutions for the prediction , hereafter referred to as rMeCBMx. The storage root transcriptome of 3-month-old plantsely Fig. . The rMe25. The rMeCBMx-EFlux model predicted 252 non-zero flux reactions and 216 zero-flux reactions . Lastly, the predictions were compared with 589 genes commonly expressed in developing storage roots of KU50 cultivar (scenario 3) across three datasets32  and high (75th percentile) thresholds. The functional modules were slightly different at both cutoffs  from eight datasetsd-Glucose (d-Glc) to beta-d-Glucose 6-phosphate (d-Glc-6P) (denoted as R01600), synthesis of d-Ribose 5-phosphate (d-Rib-5-P) using 6-Phospho-d-gluconate (d-6PGL) as a substrate in the oxidative pentose phosphate pathway in the cytosol, and conversion of sugar phosphate to pyruvate in the cytosol. rMeCBMx-HPCOF showed high positive predictions (active reactions) with few negative predictions (inactive reactions). The predictive performance of the models was comparable at high gene expression , sensitivity (SEN), specificity (SPC), negative predictive value (NPV), and positive predictive value (PPV). The rMeCBMx-HPCOF model outperformed the other models and accurately predicted flux through active reactions (~ 80%) in developing storage roots with high sensitivity ~ 85%). Unlike others, rMeCBMx-HPCOF was able to capture particular functions in developing storage roots of the KU50 cultivar, such as the conversion of beta-%. UnlikeSGAM was determined. rMeCBMx-HPCOF perfectly simulated the specific growth rate of the KU50 cultivar, 0.0090 day−1, with SGAM equal to 19.7 mmolATP gDW−1 52. To acquire better ATP predictions, the model needs to take into account full costs of ATP usage, e.g. for cell maintenance and metabolite transportation19. This issue is worth revisiting when essential data such as energy-linked transporters for nutrient uptake and intercellular transportation become available for cassava.To show that rMeCBMx-HPCOF better simulates physiological growth of developing storage roots of cassava and provides a more insightful prediction of metabolic flux than the previous FBA model (rMeCBM), rMeCBMx-HPCOF was validated similarly to the original model, employing the KU50 and CMC-9 cultivars, and its sensitivity to the 53, and the predictive performance, to the basic rMeCBM model  carbon substrates supply via the pentose phosphate pathway, (II) TCA cycle and glycolysis in the respiration pathway, and (III) carbon precursors for alanine biosynthesis Fig. .Figure 654. The prediction of oxidative PPP by FBA differed with the 13C-MFA  experiment, in a study of fluxes in central metabolism in heterotrophic Arabidopsis cells under stress conditions18. The rMeCBMx-HPCOF model predicted the oxidative PPP (OPPP) pathway, the conversion of d-Glc-6P into d-Ru5P through the sequential actions of glucose-6-phosphate dehydrogenase (G6PDH) and 6-phosphogluconate dehydrogenase (6PGDH), with lactonase catalyzing the hydrolysis of its d-6PGL product, in both the cytosol and plastid, unlike rMeCBM, which only captured the oxidative PPP in the plastid. The results were corroborated by Krook et al.55, who reported the existence of oxidative pentose phosphate pathway-mediated sugar converting cycles in both the cytosol and plastid, based on the redistribution of labeled hexoses in carrot cells grown in batch culture on [13C]-labeled glucose or fructose55. In higher plants, the OPPP provides high amounts of NADPH required for reductive biosynthesis and protection against oxidative damage in the cytosol and plastids, providing key intermediates, C-5 sugar phosphates  for the shikimate pathway to synthesize amino acids, which is an essential process during storage root formation57. The cytosolic G6PDH gene is crucial for the supply of NADPH required for plant defense responses to pathogenic infection in tobacco58, salt stress in Arabidopsis roots59, and drought stress in soybean roots57. Deletion of the G6PDH gene results in the overproduction of reaction oxygen species (ROS), reductions in root elongation and germination rate of plants under stress, and a decrease in plant productivity59. Overexpression of the G6PDH gene induced drought tolerance in transgenic tobacco58 and increased the length of roots during early development in rice60. Regulation of the G6PDH enzyme in the chloroplast of photosynthetic tissues is governed by the ferredoxin/thioredoxin system under darkness, while high sugar levels in cytosol of non-photosynthetic tissue enhance the transcription of the G6PDH gene61. The activated G6PDH predicted in developing storage roots of cassava may reflect the metabolism of high sugar levels to support starch biosynthesis and accumulation in the root parenchyma61.The pentose phosphate pathway (PPP), a maintenance pathway, is crucial for the biosynthesis of amino acids and nucleotides and is a major source of NADPH, which is required against oxidative stress and for the synthesis of fatty acids. The PPP is composed of oxidative and non-oxidative phases located in both cytosol and plastid. The PPP is not a simple linear pathway since several carbon atoms are recycled back into glycolysis. Predicting the metabolic activity in PPP by a computational approach is challenging62. This process occurs in both cytosol and plastids of plant cells63. The rMeCBM model could not capture the glycolysis process in the cytosol as pyruvate was generated only in the plastid and then exported into the cytosol, whereas rMeCBMx-HPCOF could. This result is relevant to the transcriptome analysis of cassava storage roots from Yang et al.64, who proposed enolase, l-lactate dehydrogenase and aldehyde dehydrogenase for glycolysis/gluconeogenesis as rate-limiting enzymes that are essential for starch biosynthesis in storage roots. Voll et al.65 reported that antisense inhibition of enolase, which catalyze the conversion of 2-phosphoglycerate (2-PG) and phosphoenolpyruvate (PEP), strongly limits the levels of PEP and amino acids in transgenic tobacco leaves because PEP is a precursor for aromatic amino acids in the shikimate biosynthesis pathway65. PEP can be converted by cytosolic pyruvate kinase to pyruvate, which is an important substrate for the TCA cycle. Lack of enzymes in the cytosol might affect the synthesis of buildings blocks for amino acids and other biomolecules of storage roots. Additionally, the HPCOF simulation results showed the complete metabolic process in the mitochondrial TCA cycle that might have resulted from the presence of glycolysis in the cytosol. Jenner et al.66 reported that downregulation of the NAD+ dependent malic enzyme (NADME) in the TCA cycle did not affect the TCA cycle activity but significantly increased starch yield in potato tubers66, meaning that the regulation of AGPase might depend on the exchange of compounds from central carbon metabolism67.The TCA and glycolysis pathways are related to ATP production in cells. The main function of glycolysis is to oxidize hexoses to provide ATP, pyruvate, and precursors for anabolism. Glycolysis is a crucial process in plant because it is the predominant pathway that “fuels” plant respiration and also provides sugar phosphates and carbon substrates for the biosynthesis of numerous compounds such as amino acids, starch, and cellulosel-alanine (l-Ala) was synthesized using l-serine (l-Ser) as a substrate, but it was not consistent with the gene expression as the related genes of serine-pyruvate transaminase (EC 2.6.1.51) showed very low expression levels (~ 0.12 FPKM). On the other hand, rMeCBMx-HPCOF predicted l-Ala biosynthesis using l-glutamate (l-Glu) as a precursor (R00258). Several pathways of l-Ala synthesis have been proposed, the most likely being the formation of l-Ala from l-Glu and pyruvate by transamination through glutamate-pyruvate aminotransferase 68. The prediction was relevant to Good et al.69, who reported that alanine is a major product of anaerobic metabolism in barley root by pyruvate-glutamate transaminase69. GPT, also known as alanine aminotransferase, has been widely studied using genetic engineering to enhance nitrogen use efficiency (NUE) in crop plants71. l-Alanine acts as an intercellular nitrogen and carbon shuttle and plays a key role in carbon and nitrogen metabolism in plants73. Overexpression of GPT successfully increased yield in canola, rice72, wheat74, and sugarcane75.The rMeCBM model predicted that Moreover, we ensured flux prediction by using multi-omics data to support active flux reactions either by gene expression, protein expression, or availability of the substrate/product metabolites in developing storage roots of cassava. In total, 473 expressed genes from seven transcriptome datasets, 192 expressed proteins from five proteome datasets, and 53 available metabolites from 20 metabolome datasets were used to infer active reactions  were developed by incorporating gene expression data of cassava storage roots into the genome-scale metabolic model of cassava storage roots (rMeCBM) to reduce infeasible metabolic fluxes. Among three different conceptual algorithms, GIMME, E-Flux, and HPCOF, rMeCBMx-HPCOF showed the highest accuracy to predict carbon fluxes in the metabolism of storage roots and was, in particular, highly consistent with the transcriptome of high-yield cultivars. It improved flux prediction through (1) carbon substrates supply via pentose phosphate pathway, (2) TCA cycle and glycolysis, and (3) carbon precursors for alanine biosynthesis. The flux prediction also was evaluated by proteome and metabolome data of developing cassava storage roots. However, this study only takes advantage of expression data; the inclusion of high-quality physiology and other omics data might increase its predictive power.Supplementary Dataset.Supplementary Figures.Supplementary Tables.


Pseudomonas aeruginosa PA14, through metabolic coverage expansion, format update, extensive annotation addition, and literature-based curation to produce iPau21. We then validate iPau21 through MEMOTE, growth rate, carbon source utilization, and gene essentiality testing to demonstrate its improved quality and predictive capabilities. We then integrate the GENRE with transcriptomic data in order to generate context-specific models of P. aeruginosa metabolism. The contextualized models recapitulated known phenotypes of unaltered growth and a differential utilization of fumarate metabolism, while also revealing an increased utilization of propionate metabolism upon MUC5B exposure. This work serves to validate iPau21 and demonstrate its utility for providing biological insights.Mucins are present in mucosal membranes throughout the body and play a key role in the microbe clearance and infection prevention. Understanding the metabolic responses of pathogens to mucins will further enable the development of protective approaches against infections. We update the genome-scale metabolic network reconstruction (GENRE) of one such pathogen,  It serves as a key mechanism of protection against pathogens. The component responsible for the gel-like properties of the mucosal layer is the glycoprotein mucin3. The dysregulation of mucins underlies diseases like cystic fibrosis4 and chronic obstructive pulmonary disorders2. As mucins are involved in the clearance of microbes5, a dysregulation of mucins can result in pathogen overgrowth and severe infections6. While some bacterial species, including pathogenic strains of Pseudomonas3, are capable of residing within the mucosal layer, mucins typically impair the formation of biofilms and surface attachment7. Furthermore, mucins are reported to downregulate virulence genes involved in siderophore biosynthesis, quorum sensing, and toxin secretion1. By disturbing these key mechanisms of infection, mucins attenuate the virulence and infective potential of P. aeruginosa.The mucosal barrier is a hydrated mucus gel that lines wet epithelial cells throughout the body, including eyes, mouth, lungs, and the gastrointestinal and urogenital tractsP. aeruginosa to mucins can enable the development of protective approaches against infection8. Genome-scale metabolic network reconstructions (GENREs) and associated genome-scale metabolic models (GEMs) are well suited for this purpose as they can enable the prediction of cellular behavior under different biological conditions such as the absence or presence of different mucins in an environment9. A GENRE can also be used to contextualize high-throughput data, such as transcriptomics or proteomics data10. Gene expression data can, for example, be used to constrain specific predicted metabolic fluxes11 and thereby increase the predictive value of the model. Metabolically active pathways under different conditions can be identified by integrating high-throughput data with a metabolic network12.Elucidating the metabolic responses of P. aeruginosa is a critical bacterial species in the ‘Priority Pathogens List’ for research and development of new antibiotics published by the World Health Organization (WHO)13. However, the lack of novel antibiotics15 emphasizes the need for the development of innovative and protective therapeutic approaches. This pressing need for protective strategies coupled with new insights from recent research present an opportunity to further refine the GENRE of the highly virulent strain UCBPP-PA14 by Bartell et al.16. An updated GENRE can be used to better understand the metabolic underpinnings of P. aeruginosa infections and ultimately develop new therapeutic strategies from those insights.P. aeruginosa strain UCBPP-PA14 metabolism. We improve predictions of carbon source utilization and growth yields in order to better recapitulate the behavior of the pathogen. Metabolic network coverage is expanded through the addition of genes, reactions, and metabolites supported by literature evidence. The quality of the reconstruction was improved through an update of standardized formatting, improved annotation, and the addition of binning metabolites representing macromolecular categories to assist with analysis. The metabolic network model was validated by comparing phenotypic predictions to experimental datasets21 and the quality of the reconstruction was assessed with the MEMOTE benchmarking software22. This updated reconstruction was further contextualized with recently published transcriptomic data1 in order to demonstrate its utility in elucidating the metabolic shifts of P. aeruginosa after exposure to mucins. The validated reconstructions will serve as a key resource for the Pseudomonas and microbial metabolic modeling communities and the insights into mucin-driven metabolic shifts in P. aeruginosa may serve to inform the future development of therapeutic strategies.Here, we present iPau21, an updated GENRE of P. aeruginosa PA14 (iPau1129) was previously published16 and served as a starting point for an updated reconstruction (iPau21). The metabolic coverage of the reconstruction was expanded, the format and annotations were updated, and an ATP-generating loop was resolved in order to produce a refined model with improved accuracy and extensive annotation.A metabolic network reconstruction of 23 to Level 324, which enables additional functionality such as the utilization of several extension packages and the transfer of information content to dedicated new data structures. Annotations from various databases were added to metabolites, reactions, and genes where possible.We expanded iPau1129 by 40 genes, 24 metabolites, and 76 reactions Fig.  through 22, a recently developed GENRE test suite. The MEMOTE score of iPau21 improved in all subcategories when compared to iPau1129 resulting in an increase of the overall score from 30% to 89%  to better represent the categories of components that are required for growth.The biomass objective function (BOF) was updated to better reflect the macromolecular components found experimentally in 19. Doubling time predictions of iPau21 were 25%, 19%, and 22% more accurate than those of iPau1129 in simulated LB, SCFM, and glucose minimal media, respectively. Compared to the original model, iPau21 doubling times are higher, which reflects the resolution of the ATP-generating loop that previously allowed the model to costlessly convert ADP to ATP. The iPau21 doubling time prediction on glucose minimal media of 40.2 min showed agreement with experimental data, falling within the range of experimentally determined values19. Model doubling time predictions on LB and SCFM were faster than observed experimentally, which is consistent with metabolic network models that are structured to predict the optimal growth of an organism.Validation of iPau21 was performed by comparing in silico predictions of biomass flux, carbon source utilization, and gene essentiality to experimental data. Biomass flux and subsequent doubling time predictions in simulated lysogeny broth (LB), synthetic cystic fibrosis media (SCFM), and glucose minimal media were compared to experimental values found in literature  of 0.78, while iPau1129 demonstrated an accuracy of 80% and MCC of 0.62  rules were found to be valid and the prediction error could be due to unaccounted for regulatory control, we opted to leave the pathways intact. Overall, we were able to increase carbon source utilization prediction accuracy by 9% in comparison to the previously published model.Carbon source utilization predictions were compared to previously collected experimental results across 91 carbon sources.62 Fig. . This in21. The number of genes accounted for by iPau21 was expanded to 1169 and the gene essentiality prediction accuracy was maintained at 91%, which is equivalent to iPau1129  medium supplemented with either MUC5AC, MUC5B, or mucin-glycans were collected from literature34. MUC5AC and MUC5B are mucin types found both individually and together at different sites of the human body that P. aeruginosa is known to infect8. The mucin-glycans used in the published experiments were isolated from the backbone of MUC5AC. The experiments were performed with strain PAO1, which has a highly similar genome to strain PA1435. The main difference between the strains is the presence of additional gene clusters in PA14 (most linked to virulence) that we would not expect to have a large effect on overall metabolism. PAO1 genes in the transcriptomic dataset were mapped to PA14 orthologs and then the data was integrated with the iPau21 using the RIPTiDe algorithm36. RIPTiDe uses transcriptomic evidence to create context-specific metabolic models representative of a parsimonious metabolism consistent with the transcriptional investments of an organism. This analysis resulted in four contextualized models that more accurately represent the metabolism of P. aeruginosa when grown without mucin exposure (ABTGC) and when exposed to MUC5AC, MUC5B, and glycans.Transcriptomic profiles of 1. The flux distributions underlying the BOF values were compared across models using non-metric multidimensional scaling (NMDS) in order to compare the metabolic mechanisms of growth utilized by the condition-specific metabolisms , recapitulating the phenotype that was observed experimentallysms Fig. . The fluP. aeruginosa metabolism in response to different environmental mucins.The differences between networks were further investigated through the metabolites that were produced and consumed by models in silico. This analysis offers a snapshot of the substrates used and byproducts of particular metabolic states, which can be informative of the metabolism underlying that state. All models were found to consume the same metabolites with some small differences in specific flux values however, there were key differences in the metabolites that models produced . Since the NMDS analysis revealed that the ABTGC and MUC5B models had the largest difference in functional metabolism, these two models were further investigated to find key attributes that underlie these large differences. Random forest analysis was conducted on the flux samples from consensus reactions of the ABTGC and MUC5B models to find which reactions were most differentially utilized between the two cases  media, it is a known byproduct of mucin fermenters and has anti-lipogenic and anti-inflammatory properties in humans40. This analysis revealed that the exposure to MUC5B elicited the largest shift in metabolism compared to MUC5AC and Glycans. Further, an increased utilization of fumarate and propionate metabolism during simulated growth was responsible for this shift.Further analysis was conducted on the contextualized models to better understand the shifts in metabolism that resulted in the observed dissimilarities in the NMDS analysis. Reactions not shared across all models (non-consensus reactions) were identified and compared to investigate how network structure varies across models Fig. . This anP. aeruginosa PA14 metabolism with considerable improvements in model annotation and accuracy of growth rate and carbon source utilization predictions. The metabolic reaction coverage of the reconstruction was expanded, the format and annotations were updated to be consistent with current best practices, and an ATP-generating loop was resolved. Model improvements were quantified through various metrics such as accuracy of growth yield and carbon source utilization predictions as well as MEMOTE benchmarking22.We generated an updated network reconstruction of P. aeruginosa is exposed to mucins present in the human body. This analysis recapitulated an unaltered growth rate and differential fumarate metabolism that has been reported in literature and also revealed an increased utilization of propionate metabolism in the presence of mucins. Propionate is a short chain fatty acid with beneficial effects to human health such as anti-lipogenic, anti-inflammatory, and anti-carcinogenic action41. While propionate is not present in the ABTGC medium, it is known to be produced by bacteria such as Akkermansia muciniphila when they come into contact with and catabolize mucins40. This shift of P. aeruginosa metabolism towards propionate metabolism may indicate a cross-feeding mechanism where MUC5B mucins signal to Pseudomonas to prepare to metabolize the propionate produced by other microbes as they break down the mucins. Once validated, this insight could be used to develop therapeutic strategies for P. aeruginosa infections of body sites containing MUC5B such as the lung, oral cavity, and middle ear1. Antibiotics could be designed to target proteins for propionate metabolism in order to combat drug-resistant strains that cannot be treated with traditional antibiotics.The updated network reconstruction was contextualized using transcriptomic data in order to investigate the shifts in metabolism that occur when dsdA31. There are other opportunities for further curation that would result in additional improvements to the MEMOTE score, which can be further interrogated by uploading the iPau21 reconstruction to the MEMOTE website (memote.io).While the updates made to the model broadly improved the model accuracy, there were incorrect predictions about carbon source utilization, gene essentiality, and growth rate that were not able to be addressed. Some incorrect predictions are due to a lack of literature evidence, such as incorrect carbon source utilization predictions that are due to the absence of metabolic pathways in the model. Other incorrect predictions are due to factors that are outside of the scope of the model, such as the incorrect prediction of growth on D-serine that is caused by the transcriptional regulation of P. aeruginosa strain PAO1. Therefore, the genes in the transcriptomic dataset were mapped to their PA14 orthologs before being integrated with the network reconstruction. While the genomes of PAO1 and PA14 are highly similar, the PA14 (6.5 Mb) genome is slightly larger than PAO1 (6.3 Mb) and contains gene clusters that are not present in PAO135. The genes absent in PAO1 therefore would not be accounted for in the transcriptomic dataset. However, since most of these genes are linked to virulence, they should not have large effects on whole metabolism as simulated here. Therefore, we expect that this application of the model would allow the identification of broad shifts in metabolism due to exposure to mucins irrespective of the specific strain simulated.The transcriptomic data that was used to contextualize the model was collected through experiments with P. aeruginosa metabolic network reconstruction were made to reconcile key disagreements between in silico predictions and in vitro results, ultimately producing a higher quality metabolic network reconstruction. Through the update process, we identified key predictions that remain incorrect and offer targets for further curation. The application of the model to investigate metabolic shifts that occur upon exposure to mucins recapitulated phenotypes observed in literature and offered mechanistic insights that would be difficult to delineate experimentally. This application of the reconstruction serves as an example of how the reconstruction and associated models can provide insights into context-specific metabolism. Ultimately, this reconstruction can serve as a resource for investigating the metabolism of P. aeruginosa in a variety of settings and conditions.The improvements in the 42. The updated GENRE was named iPau21 according to the community standard naming convention43.GENREs are network reconstructions that represent the metabolic capabilities of an organism and can be analyzed for various applications. An organism’s genes are connected to the proteins they code for and the reactions that those proteins catalyze. These associations are stored as gene-protein-reaction (GPR) relationships with the reactants and products of each reaction cataloged in a stoichiometric matrix. Metabolites in the reconstruction are assigned to compartments that mirror biologically discrete spaces such as the cytosol and the extracellular space. Exchange and transport reactions allow metabolites to flow between the compartments in the reconstruction. A GENRE is turned into a GEM  by adding reaction bounds that capture the flux constraints and the reversibility of reactions. The flux bounds dictate the amount and direction of flux that a reaction can carry. Objective functions (OFs) that represent metabolic goals are added to the model to simulate biological processes. GEMs can be analyzed using flux balance analysis (FBA)-based methods to investigate and gain insights into the metabolic state of a network44 can be used to annotate metabolites and reactions of a metabolic model. To do so, identifiers of the BiGG database45 (BiGG-IDs) are required as metabolite or reaction identifiers, respectively. Since the identifiers of the model were obtained from the ModelSEED database30, BiGG-IDs needed to be determined. For each metabolite, the BiGG-IDs were assessed manually. Since this is a very time-consuming procedure, the BiGG-IDs for the reactions were resolved in a semi-automated way: The cross-references of the ModelSEED database to other databases, such as BiGG or KEGG28, were used to automatically obtain the BiGG-IDs for the respective ModelSEED reaction identifier. If more than one BiGG-ID was returned, the correct identifier was determined by manual inspection of the respective reaction. The BiGG-IDs of the metabolites and reactions were added as biological qualifier (‘BQB_IS’) annotations to the model using libSBML Version 5.17.046. The annotations were added in accordance with the MIRIAM guidelines47. After adding the BiGG-IDs to the model, ModelPolisher was used for further annotations of the model’s reactions and metabolites for references to other databases, such as KEGG, MetaNetX48, or MetaCyc44.Initially, the PA14 reconstruction did not contain extensive annotations for metabolites, reactions, or genes. ModelPolisherFor the reactions, the obtained KEGG annotations were used to further add all pathways that are associated with the respective reaction to the model. The pathways were obtained using the KEGG-ID and KEGG API to request all associated pathways. The pathways were then added to the respective reactions using the biological qualifier “BQB_OCCURS_IN” in libSBML.49 Protein IDs and Uniprot50 IDs, which were subsequently added as respective annotations to the model. Additionally, the ID mapper from PATRIC51 was used to request RefSeq and NCBI49 gene identifiers, as well as identifiers of the ASAP database.The identifiers of the model genes are from the KEGG database. With the help of libSBML, the KEGG gene annotation was added to the model. For further gene annotations, the KEGG API was used to request NCBI52 terms can give semantic information or be used for annotation purposes. In our network reconstruction, all genes were labeled as genes with the SBO-term “SBO:0000243”. All metabolites without a valid SBO-term were labeled as simple chemicals with the SBO-term “SBO:0000247”. Transport reactions were divided into (1) active transport if ATP is required for the respective transport reaction (SBO:0000657), (2) passive transport if no external energy is required (SBO:0000658), (3) symporter-mediated transport if two or more molecules are transported into the same relative direction across a membrane (SBO:0000659), or (4) antiporter-mediated transport if two or more molecules are transported in relative opposite directions across a membrane (SBO:0000660). All metabolic reactions were labeled as biochemical reactions with the SBO-term “SBO:0000176”.Systems Biology Ontology (SBO)53. The current reconstruction was updated to the latest SBML edition (Level 3)54. With the help of libSBML, both the fbc-plugin55 and the groups-plugin56 were enabled. Initially, the chemical formulas and charges of the metabolites were stored in the notes field. With the fbc-plugin, the charges were added as features of the metabolites to the reconstruction. The fbc-plugin also enables the addition of gene products to the reconstruction. In the initial reconstruction, the subsystems of the reactions were saved in the notes field. With libSBML and the groups-plugin, the subsystems were extracted from the notes field and added as groups to the reconstruction. For each subsystem, a list of reactions associated with that pathway according to the notes was created and added to the subsystem as members.The initial PA14 reconstruction was represented in SBML Level 2 Version 129: (1) If the reaction status in ModelSEED was balanced (“OK”), but differed from the reaction equation in the reconstruction, the reaction was adapted according to ModelSEED and again checked for imbalances. (2) If the reaction in ModelSEED also had an imbalanced reaction status, other databases like MetaCyc57, BiGG45, or KEGG28 were explored and the reactions were adapted according to the respective reactions in the external databases. Where required, chemical formulas, charges, and coefficients were corrected, or chemical compounds were added or subtracted from the reactions according to the respective database reaction. All changed reactions are listed in Supplementary Data A list of all mass- and charge-imbalanced reactions was extracted from the reconstruction. From this list, all exchange, sink, demand and biomass reactions were excluded. Each remaining reaction was manually checked by looking up the reaction-ID in ModelSEED29:  If the r22. Every change and improvement of the model was continuously documented and quality-assessed using MEMOTE Version 0.9.11. Full MEMOTE reports are provided for iPau1129, iPau21, and iML1515  was calculated in order to assess the quality of predictions58. Biomass flux and subsequent doubling time predictions in lysogeny broth (LB), synthetic cystic fibrosis media (SCFM), and glucose minimal media were compared to experimental values found in literature  was unable to accurately recapitulate experimental data. This assessment included 18 incorrect carbon source predictionsP. aeruginosa were identified through a literature survey. Metabolites in the biomass objective function (BOF) were organized into these macromolecular categories in order to better represent the components required for growth. During organization, no additional metabolites were added and the ratios of metabolites in the BOF were kept the same.Macromolecular categories represented in the dry weight of 60. A metabolite representing biomass was also added to the products of the BOF to represent the accumulation of biomass.The BOF was also updated to include lipopolysaccharide (cpd17065) to reflect its presence in Gram-negative bacteriaA list of all extracellular metabolites in the reconstruction was compiled and compared to a list of all exchange reactions in the reconstruction. Exchange reactions were added for 33 extracellular metabolites that previously did not have one.2, and H+). The model was able to generate flux for only the ATP energy dissipation objective function, which indicated that an energy generating cycle existed. The cycle was resolved through the addition of a periplasm compartment to contain hydrogen involved in the electron transport chain and correcting the reversibility of four participating reactions.Exchange reactions were closed and the objective function was set to energy dissipation reactions for electron carriers  using the Vegan package in R62. Only consensus reactions across all four contextualized models were included in the flux sample dataset and a constant was added to each flux value in the dataset to make all data points positive to facilitate comparison. Median fluxes for every reaction in each model are provided in Supplementary Data NMDS analysis was conducted on flux samples from each contextualized model (n = 500 samples per model) from the consensus reactions of the ABTGC and MUC5B models using the randomForest package in R63. Reactions that were differentially present in contextualized models were identified and connected to their corresponding metabolic pathways manually.Random forest analysis was conducted on flux sampling data (64. The NMDS distance was calculated as the distance between the median NMDS coordinates of network pairs. Spearman’s correlation was used to calculate a p-value for the relationship between network structure and network utilization across all pairs of networks.The Jaccard distance of network structures was calculated by comparing the reactions contained in pairs of networksSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Information