Stable isotope abundances convey valuable information about plant physiological processes and underlying environmental controls. Central gaps in our mechanistic understanding of hydrogen isotope abundances impede their widespread application within the plant and biogeosciences.Pinus nigra extracted from an annually resolved tree‐ring series (1961–1995).To address these gaps, we analysed intramolecular deuterium abundances in glucose of 1 and H2 introduced by closely related metabolic processes. Regression analysis indicates that these signals (and thus metabolism) respond to drought and atmospheric CO2 concentration beyond a response change point. They explain ≈ 60% of the whole‐molecule deuterium variability. Altered metabolism is associated with below‐average yet not exceptionally low growth.We found fractionation signals  at glucose HWe propose the signals are introduced at the leaf level by changes in sucrose‐to‐starch carbon partitioning and anaplerotic carbon flux into the Calvin–Benson cycle. In conclusion, metabolism can be the main driver of hydrogen isotope variation in plant glucose. Plant archives, such as tree rings, preserve this information over millennia enabling elucidation of physiological and environmental dynamics that occur beyond the short time frames covered by manipulation or monitoring experiments (Loader 1H) and deuterium (2H or D) exhibit the largest relative mass difference. As a result, hydrogen isotope effects are normally considerably larger than isotope effects of other elements  have a remarkable potential to further our knowledge about plant physiological and environmental processes. However, current plant δD models fail to predict the entire body of available empirical data (see later). Therefore, hydrogen isotopes, in contrast to carbon isotopes, have not yet evolved into standard tools within the plant and biogeosciences.Among all stable isotopes, the hydrogen isotopes protium (δD models of plant organic matter exhibit three main components: (1) the δD composition of plant water sources; (2) leaf water D enrichment; and (3) metabolic fractionations. (1) Plants take up soil water through roots. In most plants, this uptake and water transport through the xylem into leaves occur without detectable δD shifts  between modelled and measured δD data. These successful tests for robustness of leaf water δD models strongly suggest that inconsistencies between modelled and measured δD values of plant organic matter as reported by Waterhouse et al. . Roden & Ehleringer  in leaf α‐cellulose due to unknown fractionating metabolic processes. This shows the predictive abilities of plant δD models will improve by accounting for variability in metabolic fractionations. To this end, however, underlying mechanisms need to be elucidated.All hydrogen positions of leaf‐level metabolites inherit D fractionations present in leaf water, the hydrogen source of metabolism. By contrast, metabolic reactions cause D fractionations at specific intramolecular hydrogen positions that are directly or indirectly involved in the reaction mechanism . The site exhibits shallow, very dry soil and an open canopy with apparently low competition among trees , two 5‐mm stem cores were taken at breast height. Individual tree rings were dated by standard dendrochronological methods  were recorded using an AVANCE III 850 with a cryogenic probe optimised for D detection and 19F lock . Relative intramolecular D abundances were determined by signal deconvolution using Lorentzian line shape fits in TopSpin 4 . 3,6‐Anhydro‐1,2‐O‐isopropylidene‐α‐D‐glucofuranose has six methyl group hydrogens, which were introduced during derivatisation and were derived from a common batch of acetone. Their D abundance (arithmetic mean) was used as reference to compare glucose D abundances of tree‐ring samples from different years as follows:i and DME denoting relative D abundance at glucose H‐C positions . Air vapour pressure deficits, VPD, were calculated according to published procedures  and Ralph Keeling . Both the selected grid point and climate station are no more than a horizontal distance of 15 km from the sampling site with a negligible vertical offset. CO2 is well mixed in the atmosphere. Thus, all data should represent the site conditions well.Monthly resolved data of precipitation, δDi, to the whole‐molecule D time series, δDg, as follows: δDg equal to the covariance average between δDi and δDg asWe calculated the variance contribution of each intramolecular D time series, δDi to δDg are given asThus, variance contributions of See Supporting Information Notes Pinus nigra tree‐ring series . Additionally, we derived the molecular average time series, δDg (n = 31).To better understand metabolic fractionations, we measured intramolecular D abundances at all seven H‐C positions of glucose extracted across an annually resolved ies Fig. . Our timδDi time series (n = 31) as values that were 1.5 times the interquartile range below the first or above the third quartile. We removed one data point each from δD4 and δD5 (n = 30), three data points from δD6R (n = 28) and five data points from δD6S (n = 26) resulting in six missing data points in δDg (n = 25). The average standard errors of δDi and δDg measurements are 5.4‰ and 3.4‰, respectively.Outliers can potentially impair statistical analyses because they may reflect either experimental errors or extreme states of the system. Since we were interested in the normal functioning of plant metabolism and metabolic fractionation, we removed outliers to ensure robust statistical results. We identified outliers in Pinus nigra tree‐ring glucose differ among H‐C positions . This shows that metabolic fractionations, being position‐specific, have strong effects. Interestingly, in some years, intramolecular δD differences approach 450‰  groups time series according to covariability. Time series carrying common signals, that is information about a common process or strongly related processes, form clusters. Performing HCA on our D6R Fig. . AdditioδD3 to δD6R exhibit similar degrees of variability , δD2 varies considerably  and δD1 is also relatively variable . Increased D variability at two out of seven H‐C positions indicates highly variable metabolic fractionations at these positions, H1 and H2.While δDi patterns and found two groups  and a high‐value group . Average and median increases in δD1 and δD2 are statistically significant . Since differences in δDi patterns reflect differences in metabolism, this finding confirms that glucose H1 and H2 carry exceptionally variable metabolic fractionation signals. Underlying metabolic processes are closely related ; that is, δD1 and δD2 increase at a c. 1 : 1 ratio. For high values, we found a slope of c. 0.16 with a 95% confidence interval of c. 0.07 to c. 0.25 (n = 9); that is, δD1 and δD2 increase at a c. 1 : 6 ratio. Thus, while δD1 and δD2 variability in the low‐value group is consistent with expectations related to nonmetabolic fractionation processes, high δD1 and δD2 values reflect additional effects by metabolic fractionation processes.Nonmetabolic fractionation processes (e.g. leaf water D enrichment) have equal effects on all 1 and H2 asi denotes relative D abundance at glucose H‐C positions  and minimises signals of processes affecting all H‐C positions similarly (heterotrophic hydrogen exchange). Weaker metabolic fractionation signals present at glucose H3 to H6 , showing that the observed frequency distribution of εmet (Notes εmet data of 1961–1980 and 1983 to 1995 follow different probability distributions (1981 and 1982 are missing). Thus, the εmet time series exhibits a change point beyond which the fractionating metabolic processes are activated or upregulated.Increases in iod Fig. . We founet Notes  does notεmet with different climate parameters. Since εmet does not follow a normal distribution  occurs in response to dry conditions.Upregulation of the fractionating metabolic processes occurs beyond a change point. Before the change point, environmental variability exerts no significant control . For the year and spring, we found significant positive correlations between εmet and TMP   follows a normal distribution (Notes r. We found significant negative correlations between εmet and PRE (Table εmet) occurred in response to short‐term depletions in soil moisture when long‐term drought was beyond the critical level. In this respect, it is noteworthy that Pinus nigra reportedly has a well‐developed lateral root system in upper soil layers to access soil water but can additionally tap into deep water sources if available . Thus, the result indicates that high atmospheric CO2 concentrations counteracted upregulations of the fractionating metabolic processes (high εmet) when long‐term drought was beyond the critical level.In 1994 and 1995, long‐term drought was exceptionally strong Fig. . Howeverime Fig. . From 19ime Fig. . In thison Notes , we calcRE Table . Under ga Table . This coεmet groups as functions of PRE and Ca as follows:To further corroborate the existence of a response change point (Step 1), and the requirement of long‐term drought (Steps 2 and 3) for the manifestation of effects by secondary climate factors (Step 6), we modelled two PRE and annual Ca , and summer PRE and annual Ca . The most adequate model we found includes summed spring and summer PRE and annual Ca . Both PRE and Ca contributed significantly to all models (P < 0.05). For εmet data of the first group, 1961 to 1982, the most adequate model has no explanatory power . Modelling εmet by the most adequate model works well for the second group, 1983–1995, yet produces large offsets between all measured and modelled values of the first group  equals 1, else I(x > e) equals 0. The fitted model explains 94% of the variability in εmet and is highly significant . All explanatory variables and the change point contribute significantly . When years affected by metabolic fractionation are considered exclusively . Thus, altered metabolism is associated with below‐average growth. However, equally narrow tree rings were formed in > 50% of the years in which the fractionating metabolic processes were downregulated . Thus, upregulations of the fractionating metabolic processes are not associated with exceptional growth declines.Metabolic changes as reflected by changes in metabolic fractionation may impact on growth. To test this, we investigated the relationship between 1 and H2 . By contrast, other biomarkers develop in a purely linear manner with indistinct transitions between states hindering attempts to ascertain plant functioning. While the mechanisms behind other biomarkers in plant archives often remain elusive, εmet can be (1) linked to specific physiological processes and (2) incorporated into fractionation models (see later). Upon process elucidation, εmet may help to find plants with the make‐up to support crucial ecosystem services such as food and resource security.Here, we found highly variable metabolic fractionation signals at glucose Hmet Eqn  and prop2 concentrations and drought exert control over εmet in Pinus nigra studied here  may enable analyses of metabolic effects of short‐term drought and Ca during long‐term drought events (by interpretation of the respective model terms), and reconstructions of short‐term drought events during long‐term drought events .As indicated by modelling, atmospheric COe Tables . Values ism Fig. . Given cPinus nigra can access water from upper soil layers and from deep water sources  and Ca  detect δDg data sets affected by metabolic fractionation; (2) separate δDg data points affected by metabolic fractionation from other data points; and (3) retrieve information from δDg about metabolic fractionation  , which occurs upon the crossing of a response change point   , which may result in decreasing carbon assimilation. As carbon assimilation decreases below a change point, the stromal PGI reaction shifts towards equilibrium and G6P concentrations increase  confirming their prediction. However, changes in sucrose‐to‐starch carbon partitioning would have the same effect. Furthermore, two recent studies report isotope and gas exchange evidence consistent with anaplerotic flux in Helianthus annuus at low Ci , which corresponds to a whole‐molecule increase of c. 70‰ (2 × 250‰/7). Thus, εmet increases in the gymnosperm Pinus nigra are considerably larger than the c. 20‰ angiosperm average effect reported by Cormier et al.  about plant acclimation and adaptation (e.g. to CO2 fertilisation), biosphere–atmosphere CO2 exchange and palaeoclimate trends. This long‐term perspective is inaccessible to manipulation and monitoring experiments yet crucial for understanding plant and Earth system functioning. The better we understand plant isotope fractionation, the more information can be retrieved from archives . The present paper and recent studies on metabolic fractionation  should be directed to the Click here for additional data file.


Novosphingobium aromaticivorans has this ability, and its use as a bacterial chassis for lignin valorization could be improved by the ability to predict product yields based on thermodynamic and metabolic inputs. In this work, we built a genome-scale metabolic model of N. aromaticivorans, iNovo479, to guide the engineering of strains for aromatic conversion into products. iNovo479 predicted product yields from single or multiple aromatics, and the impact of combinations of aromatic and non-aromatic substrates on product yields. We show that enzyme reactions from other organisms can be added to iNovo479 to predict the feasibility and profitability of producing additional products by engineered strains. Thus, we conclude that iNovo479 can help guide the design of bacteria to convert lignin aromatics into valuable chemicals.Lignin is an abundant renewable source of aromatics and precursors for the production of other organic chemicals. However, lignin is a heterogeneous polymer, so the mixture of aromatics released during its depolymerization can make its conversion to chemicals challenging. Microbes are a potential solution to this challenge, as some can catabolize multiple aromatic substrates into one product.  Microbial production of chemicals from abundant renewable resources presents a potential cost-effective and sustainable alternative to current chemical production methods. One example of this alternative would be converting lignin, an under-utilized and abundant natural resource, to valuable products. Lignin is a polymer composed of aromatic subunits and accounts for a third of terrestrial carbon . HoweverNovosphingobium aromaticivorans as a microbial chassis for producing valuable chemicals from lignin aromatics. Some traits that make N. aromaticivorans advantageous as a lignin-valorizing microbial chassis include co-metabolism of multiple aromatic and non-aromatic compounds, catabolism of aromatics from all three major monomeric units in lignin (guaiacyl (G), syringyl (S), and hydroxyl (H) units), metabolism of aromatic monomers and oligomers, and converging catabolic pathways that funnel mixtures of aromatic compounds into one or a small number of products, such as 2-pyrone-4-6-dicarboxylic acid (PDC) [N. aromaticivorans is genetically tractable and can be handled using Biosafety Level-1 conditions. The ability of N. aromaticivorans to serve as a chassis for lignin valorization would be increased by predicting pathways and conditions needed to generate sufficient yields of valuable products.We are studying the α-proteobacterium id (PDC) ,8. In adN. aromaticivorans that contains the major pathways for carbon metabolism, including characterized pathways for catabolism of aromatic compounds. We used iNovo479 to combine genome annotation and published experimental data on engineered strains to make predictions on aromatic metabolism, thermodynamic aspects of using different enzymes for aromatic compound catabolism, and the potential products that could be made from deconstructed lignin by engineered strains of N. aromaticivorans. The lessons learned from assembling iNovo479 as well as the predictions made by this GEM should be of interest to those interested in lignin valorization, aromatic metabolism, and optimizing metabolic processes in other bacterial chassis.In this study, we built iNovo479, a genome-scale metabolic model (GEM) for N. aromaticivorans DSM 12444 genome (hereafter referred to as N. aromaticivorans). The length of the N. aromaticivorans genome is 4.2 megabases. Genes were linked to metabolic reactions based on annotation data available in the Integrated Microbial Genomes database in 2019 [iNovo479 was built using predicted KEGG annotations for the Rhodobacter sphaeroides [N. aromaticivorans and R. sphaeroides, as well as laboratory-determined biomass composition data on N. aromaticivorans  had an impact on biomass yield, as did the length and composition of any side chain on the aromatic ring. For example, vanillic acid and ferulic acid have the same ring structure but different side chains, resulting in varying biomass yields in iNovo479 simulations and experimental results. We propose that the observed increase in biomass yields reflects the use of aromatic side chains on substrates such as G-diketone or ferulic acid when compared to vanillic acid see .O-4 ether bond in GGE is cleaved, guaiacol and hydroxypropiovanillone (HPV) are produced [O-demethylation to catechol and then aromatic ring cleavage of catechol with either an intradiol or an extradiol dioxygenase and production of non-aromatic intermediates that are further transformed to TCA intermediates. These pathways are consistent with predictions of aromatic metabolism in N. aromaticivorans DSM 12444 based on gene annotations in the KEGG database [Sphingomonas [Sphingobium [The observation that the experimental biomass yield when GGE is the sole carbon was about twice the experimentally observed yield prompted us to use iNovo479 to explore yield predictions depending on which monoaromatic derived from GGE is not used for microbial growth. When the β-produced . In iNovproduced , whereasdatabase  and expengomonas ,15 and SN. aromaticivorans, the first aromatic O-demethylation step is known to be catalyzed by one of the native tetrahydrofolate (THF)-dependent enzymes, DesA or LigM [O-demethylation step can be catalyzed by DesCD with the release of methanol and without any known cofactor [p-hydroxybenzoic (p-HBA), vanillic, and syringic acids, respectively; O-demethylation may contribute to biomass yield.The chemical difference between H-, G-, and S-family aromatics is in the number of methoxy groups on the aromatic ring . In  or LigM . These e or LigM . In the cofactor  or LigM cofactor . Given tO-demethylation is converted to formate instead of proceeding through folate metabolism (p-hydroxybenzoic acid (0 methoxy groups) or glucose, which are not predicted to require O-demethylation  to simulate microbial growth. In this test, iNovo479 predicted that all three monoaromatics would be metabolized, and that vanillic acid would be consumed first, then syringic acid, and then p-hydroxybenzoic acid  if all the aromatic monomers in the deconstructed plant material are funneled towards the production of an aromatic-derived product such as PDC [p-HBA) and glucose in varying ratios. This analysis predicted that a three-to-two molar excess of aromatic to glucose was optimal for the highest rate of PDC production  and products for which a large fraction of the substrate could be directed to product formation , citrate, and urea, all with yields greater than 0.4 mol-Cproduct/mol-Csubstrate). In addition, the range of predicted production rates was large, from 0.0003 g/L/hr for ethanolamine production to 0.0048 g/L/h (16-fold higher) for urea.In this set of dFBA simulations, we used 5 mmol/L vanillic acid as the only carbon source to evaluate whether iNovo479 would predict production of the chosen products directly from an aromatic substrate. These simulations were performed allowing for a range of substrate conversion to product , and for each product, we selected the yield that would sustain the highest product production rate . This anSince the technoeconomic analysis of potential bioproducts  reportedphenol/mol-Csubstrate and 0.80 mol-Curea/mol-Csubstrate, respectively). In contrast, chemicals such as ccMA, glutarate, citrate, acetaldehyde, and carotenoids such as zeaxanthin were predicted in these simulations to be advantageous products to generate from aromatics by engineered N. aromaticivorans strains due to the combination of moderate or high value and relatively high product yields  varied, with syringic acid producing the most biomass and p-HBA producing the least  compared to either growth with p-HBA (no ring methoxy group) or when using the original THF-dependent demethylase. This effect was more pronounced with the VanAB demethylase than with the hypothetical demethylase for vanillic acid only, suggesting that the additional NADH cost associated with VanAB had a greater impact for vanillic acid than for syringic acid.We then repeated the biomass yield simulations with alternative demethylation strategies, as the THF-dependent demethylation enzymes represent a significant energy source for microbial biomass production from aromatic substrates that contain 1 or 2 methyl groups . Our simp-HBA. We found that vanillic acid was consumed first, then syringic acid, then p-HBA  that can be produced during oxidative lignin deconstruction, is known ,20 or pris known  to use awth rate . In the wth rate . HoweverN. aromaticivorans strains engineered to generate new products in this chassis microbe. Using this method, we found that iNovo479 predicted glutarate, zeaxanthin, ccMA, and citrate as candidates for production by N. aromaticivorans from plant aromatics given their predicted yields, the current market price per kilogram, and the high industrial demand for these compounds . However, using modified versions of iNovo479 to simulate the production of intermediates in microbial biomass synthesis, such as acetate, creates a competition for carbon between cell yield and product synthesis similar to that observed with some of the nitrogen-containing products we evaluated. Finally, iNovo479 predicts that synthesis of products such as 1-hexadecanol from aromatics via chain elongation is energy-intensive and likely to not be cost competitive since this pathway consumes large amounts of acetyl-CoA and NADPH.The use of iNovo479 to predict compounds that could be derived from biomass aromatics by engineered in silico predictions would likely rely on reactor operating conditions that allow higher biomass accumulation than observed in the simulations. This analysis illustrates the ability of a GEM such as iNovo479 to generate testable hypotheses for engineering a microbial chassis such as N. aromaticivorans to produce valuable chemicals from an abundant renewable resource such as deconstructed plant materials.While a GEM such as iNovo479 or its derivatives can model microbial metabolism, it is only one factor to consider when modeling lignin valorization at an industrial scale. For example, a complete technoeconomic analysis of lignin valorization must account for product need, price per kilogram, feedstock availability, cost of materials, equipment, and product purification when designing a microbial lignin valorization strategy for industrial use. Increasing production rates and product yields beyond the N. aromaticivorans strain DSM 12444 plus reaction and compound IDs in the Integrated Microbial Genomes Database [iNovo479 was built manually using KEGG annotation data for Database ,23. Gap Database . The oveN. aromaticivorans’ aromatic metabolism. This constraint is located in the catabolism of S-type aromatics, specifically the conversion of CHMOD to gallic acid. This pathway is modeled to operate at 15% of the flux through conversion of CHMOD to PDC in the wild-type version of the model based on experimental data [We also included a constraint in the model to simulate the known activity of side pathways in tal data . This cotal data .N. aromaticivorans, but these compounds are hypothesized to enter the cell via TonB siderophore transporters in related sphingomonad bacteria [+ consumed by the proton motive force per substrate molecule imported, based on the described mechanism of TonB-dependent transport which uses proton motive force to cross the outer membrane and an ATP-dependent ABC transporter to cross the inner membrane [Pseudomonas putida [Transport reactions and the predicted energy costs (ATP hydrolysis and/or proton motive force contributions) associated with nutrient import were included in iNovo479, even where genes encoding those transporters have not been identified. For example, transporters for aromatic compounds have not been identified in bacteria ,27. The membrane . While ps putida , we chosN. aromaticivorans DSM 12444 is an obligate aerobe [R. sphaeroides, as the base of the iNovo479 biomass reaction and modified it to account for known differences in cell composition and metabolic activities between R. sphaeroides and N. aromaticivorans (Dataset S1).Genome annotation and laboratory studies predict that the α-proteobacterium e aerobe . Conseque aerobe , a GEM oThe stoichiometry of the iNovo479 microbial biomass reaction was based on laboratory measurements of cellular protein, DNA, RNA, and phospholipid content. We measured the microbial biomass composition from duplicate 25 mL aerobic cultures grown on 2 mmol/L glucose and 2 mm/L vanillic acid as sole carbon sources in Standard Mineral Broth   without R. sphaeroides were used in iNovo479 with appropriate modifications based on the lipid content and lack of lipopolysaccharide in N. aromaticivorans. Sphingoglycolipid-1, the most abundant sphingolipid in N. aromaticivorans [Nucleotide amounts included in the biomass reaction were calculated based on the ratios of all four bases present in the genome  in combicivorans , was addcivorans . The rescivorans   is equivalent to 8 × 10civorans , while 1Escherichia coli [Within the pathways modeled, iNovo479 includes reactions that require non-carbon nutrients such as ammonia, sulfur, iron, and phosphate. These compounds were provided as input fluxes in the amounts found in SMB. In addition, the iNovo479 GEM was encoded to provide unlimited influxes and outfluxes of oxygen, hydrogen ions, and water. Outfluxes of end product metabolites such as carbon dioxide and dead-end metabolites (compounds produced by the model that cannot be used for biomass and cannot be catabolized) were added to the model based on those found in hia coli  and on tsK is the half-saturation constant (mmol/L), maxV is the maximum velocity (mmol/min), iK is the inhibition constant (mmol/L), and S is the current substrate concentration (mmol/L):To simulate growth under batch culturing conditions where substrate concentrations are not constant, we calculated maximum allowed uptake rates for organic and other substrates ,37 usingsK and maxV for glucose, vanillic acid, syringic acid, and p-hydroxybenzoic acid were approximated (N. aromaticivorans grown in the presence of these organic substrates (iK) was also included in iNovo479, as delayed growth is often observed when cells are initially grown in the presence of aromatics. Unless otherwise specified in the literature, the inhibition constant was assumed to be equal to the half-saturation constant.oximated  by fittibstrates . The parThe output of this equation was used as the maximum allowed uptake rate for substrate boundary reactions in the model, meaning that the model was permitted to choose an optimal flux between zero and the maximum allowed uptake rate for each. Typically, only one substrate boundary reaction was operating at its maximum allowed flux in any given iteration, indicating that uptake of this substrate was limiting biomass production.N. aromaticivorans under batch culture conditions. In this approach, for each cycle, steady-state simulations were performed with iNovo479, and after each run, steady-state fluxes of substrates and products and biomass production rates were recorded and used to modify the starting conditions for the next cycle. dFBA was run using a custom Python script and COBRApy [We used dFBA , an iter COBRApy .N. aromaticivorans [N. aromaticivorans and has been used to support the production of PDC from aromatics by engineered strains [Using the results of experiments with PDC-producing mutants of civorans ,6, we cr strains .Modeling the accumulation of product compounds not already included in the initial iNovo479 version required the addition of enzymatic reactions to the GEM . Additiogithub.com/GLBRC/iNovo/ (accessed on 13 April 2022). All code used in this research is also available at this link, along with instructions for its use. We hope that iNovo479 will be a resource for the research community.The GEM iNovo479 and its derivatives are publicly available for use at N. aromaticivorans and demonstrated its potential to make testable predictions about aromatic metabolism and the genetic modifications needed to improve microbial lignin valorization. We demonstrated the thermodynamic favorability of demethylating aromatic methoxy groups by specific enzyme types and of capturing the carbon and energy of the organic side chains that naturally exist on ferulic acid and other lignin aromatics. By modeling production of one valuable compound, PDC, iNovo479 predicts that reduced levels of non-aromatic carbon will allow increased rates of synthesis of this chemical. Finally, we combined iNovo479 predictions with a published technoeconomic analysis to suggest a set of priority candidates for microbial lignin valorization by N. aromaticivorans and make predictions on product characteristics that are likely to enhance acceptance by the industry. The current instance of iNovo479 includes reactions for the metabolism of aromatic compounds for which there is some enzymatic or genetic evidence in this or related bacteria. We have not attempted to use it to predict the utilization of aromatic compounds which have either not been tested as growth substrates or for which experimental evidence is lacking for their metabolism. We predict that the lessons learned from assembly and use of iNovo479 will aid useful future studies of biological lignin valorization and efforts to generate other valuable chemicals from N. aromaticivorans and other engineered microbes.In this research, we built a GEM for


Pyruvate is a hub of various endogenous metabolic pathways, including glycolysis, TCA cycle, amino acid, and fatty acid biosynthesis. It has also been used as a precursor for pyruvate-derived compounds such as acetoin, 2,3-butanediol , butanol, butyrate, and L-alanine biosynthesis. Pyruvate and derivatives are widely utilized in food, pharmaceuticals, pesticides, feed additives, and bioenergy industries. However, compounds such as pyruvate, acetoin, and butanol are often chemically synthesized from fossil feedstocks, resulting in declining fossil fuels and increasing environmental pollution. Metabolic engineering is a powerful tool for producing eco-friendly chemicals from renewable biomass resources through microbial fermentation. Here, we review and systematically summarize recent advances in the biosynthesis pathways, regulatory mechanisms, and metabolic engineering strategies for pyruvate and derivatives. Furthermore, the establishment of sustainable industrial synthesis platforms based on alternative substrates and new tools to produce these compounds is elaborated. Finally, we discuss the potential difficulties in the current metabolic engineering of pyruvate and derivatives and promising strategies for constructing efficient producers. Many chemicals and fuels are currently produced from fossil fuels, which will cause a worldwide decrease in fossil materials and many environmental problems. The emergence of metabolic engineering in the early 1990s provided ideas to solve the problems . To thisPyruvate is the hub of various endogenous metabolic pathways, which not only plays a crucial role in bioenergetic metabolism but also serves as a precursor for synthesizing a wide range of compounds. Compounds whose starting substance is sugar and whose target substance is pyruvate or are biosynthesized via pyruvate as an intermediate metabolite are called pyruvate-derived compounds such as acetoin, 2,3-BD, butanol, butyrate, and L-alanine. These derived chemicals are widely used in various fields. Acetoin and 2,3-BD have high potential applications as plasticizers, softeners, drug fumigants, and foods . ButanolPyruvate is currently produced by chemical conversion or microbial fermentation to meet the fast-growing market demand. In terms of chemical conversion, pyruvate is mainly synthesized via dehydration and decarboxylation of tartaric acid . In this2) and acetyl-CoA that combines with oxaloacetate to produce citrate to enter the TCA cycle, a process that requires pyruvate dehydrogenase complex (PDHc), whereas this conversion is carried out by pyruvate formate-lyase (PFL) under anaerobic conditions. Excess pyruvate is catalyzed by α-acetolactate synthase (ALS) to α-acetolactate into the 2,3-BD pathway and catalyzed by pyruvate oxidase (POX) to acetate. Under anaerobic conditions, pyruvate is catalyzed to lactate by lactate dehydrogenase (LDH) in most bacteria, e.g., Escherichia coli, while pyruvate is catalyzed to acetaldehyde via pyruvate decarboxylase in yeasts. Pyruvate produces alanine via alanine transaminase (ALT) to enter the amino acid metabolism for cellular biosynthesis, which occurs under both aerobic and anaerobic conditions. In addition, pyruvate is catalyzed by pyruvate carboxylase (PYC) to oxaloacetate to balance intracellular acetyl-CoA, present in a few microorganisms, such as Corynebacterium glutamicum and S. cerevisiae. Oxaloacetate produces various organic acids through the TCA cycle, such as α-ketoglutarate, succinate, and malate.Pyruvate is a central metabolite that has been reported in various microorganisms. The biosynthetic pathway of pyruvate is revealed in + + 2 Pi + 2 ADP → 2 pyruvate + 2 NADH + 2 ATP, indicating that the maximum theoretical yield of pyruvate from glucose is 0.966 g/g [+ and ADP influence pyruvate formation. The NADH/NAD+ and ATP/ADP participate in enzymatic reactions as substrates or products and affect the metabolic network of the cell through the cyclic regeneration of cofactors. The glycolytic pathway synthesizes pyruvate to produce 2 mol ATP and 2 mol NADH. In addition, pyruvate enters the TCA cycle via acetyl-CoA to produce 1 mol ATP and 3 mol NADH under aerobic conditions. NADH enters the lactate and ethanol pathways for NAD+ regeneration via NADH-dependent dehydrogenase or produces ATP via oxidative phosphorylation  growing with an acetate supplement led to a high pyruvate yield from glucose [E. coli ΔaceF Δppc generated 35 g/L pyruvate from glucose and acetate in 35 h at a yield of 0.78 g/g glucose [E. coli ΔpflB ΔpoxB ΔackA ΔldhA ΔadhE ΔfrdBC ΔsucA ΔatpFH produced 52 g/L pyruvate in 43 h under anoxic conditions with glucose [Since increasing glycolytic flux has limited pyruvate production, attention has been focused on other targets to direct more carbon flux to pyruvate. Therefore, it is necessary to prevent further pyruvate metabolism to increase its production. Undoubtedly, PDHc was selected as an early target to delete, because entering the TCA cycle via PDHc, which is composed of three subunits AceE, AceF, and Lpd, is the main catabolic pathway of pyruvate . However glucose . The bes glucose . Metabol glucose , (2) dis glucose . The majaceE gene, an essential gene encoding PDHc in E. coli, and combining it with the deletion of other genes produced 26 g/L pyruvate from glucose in 72 h [aceE or pdhR reduced aceE expression, resulting in a pyruvate yield of 0.36 g/g during glucose fermentation [aceE gene to regulate PDHc expression in E. coli, which resulted in the production of 26.1 g/L pyruvate in 73 h [aceE gene to reduce PDHc activity, thereby reducing carbon flow from pyruvate to acetyl-CoA, but not eliminating it [Knockouts usually represent a complete shutdown of the pathway and are less beneficial to the engineered bacteria than the regulated enzyme activity. Gene silencing is a promising metabolic engineering approach that has been proposed to replace gene deletion, which seems particularly suitable for targeting PDHc, because maintaining some activity of PDHc can support aerobic growth of the strain when glucose is the only carbon source. For instance, silencing the  in 72 h . The appentation . Gene si in 73 h . An addiating it . The str+. The conversion of glucose to pyruvate produces 2 mol NADH per mol glucose, and NADH is an inhibitor of the dihydrolipoamide dehydrogenase component of PDHc [+ increases the glycolysis flux [Cofactor engineering is another method for increasing pyruvate production by changing the rate of NADH/NAD of PDHc . High lesis flux .+ regeneration system is a direct way to influence the NADH/NAD+ ratio. Intracellular NAD+ is easily regenerated in situ by expressing an NADH oxidase (NOX). The overexpression of noxE encoding exogenous NOX improved glucose consumption and glycolysis strength [noxE gene of L. lactis into E. coli MP-XB010 and produced 93 g/L pyruvate by regulating NOX expression through a temperature-controlled promoter [noxE gene was expressed at the beginning to promote the growth rate and biomass accumulation, followed by down-regulation to increase pyruvate synthesis rather than entering the TCA cycle [Introducing an NADH or NADstrength . Howeverpromoter . The noxCA cycle . NeverthpdhR from E. coli, a pyruvate-responsive bifunctional gene circuit was established in Bacillus subtilis, which could spontaneously respond to pyruvate concentration and dynamically regulate carbon fluxes in the B. subtilis [Genetic optimization of metabolic networks to tailor natural microbial pathways is a conventional industrial microbiology strategy. However, the carbon flux balance between product synthesis and microbial cell growth is a persistent problem in previous industrial microbiology. The balance between production and growth requires precise manipulation of carbon fluxes, which can be achieved by regulating the expression of critical enzymes . The vitsubtilis . E. coli strain that cut off the pyruvate consumption pathway [E. coli strain to shift metabolic flux from the endogenous pathway to the target synthetic pathway [E. coli and S. cerevisiae with applications for improved production of various compounds [In addition to using the metabolite-responsive transcription factor to regulate carbon flux balance dynamically, two-stage fermentation combined with static pathway engineering was used to balance cell growth and production. Pyruvate production was improved by a two-stage fermentation process using an  pathway . The cel pathway . The res pathway . Alternaompounds ,72. A si+ ratio balance [There are two major pathways in the biosynthesis of acetoin and 2,3-BD . One pat balance . Further balance .S. cerevisiae YHI030, was constructed to promote the 2,3-BD pathway effectively, and the strain produced 81 g/L 2,3-BD [B. subtilis resulted in the highest increase of 17.1% for acetoin and 2,3-BD, and a high titer of 102.6 g/L for 2,3-BD was obtained by a three-stage oxygenation control strategy [acoABCD in Klebsiella pneumoniae blocked the re-utilization of acetoin and 2,3-BD [acoABCD mutant strain, K. pneumoniae Δbud ΔacoABCD, produced 62.3 g/L acetoin after 57 h of fed-batch fermentation [Many researchers have focused on enhancing acetoin and 2,3-BD production by designing the 2,3-BD pathway, such as overexpressing ALS or/and ALDC responsible for acetoin biosynthesis  or preveL 2,3-BD . Similarstrategy . When end 2,3-BD . The acoentation . + [+ by overexpression of endogenous or exogenous NOX received extensive attention. By introducing the nox gene from Lactobacillus brevis into Serratia marcescens H32, the NADH/NAD+ ratio in the cells was decreased 2.9-fold, and the acetoin production was increased by 33% [nox encoding the water-forming NOX was selected from L. lactis and overexpressed in C. glabrata, NADH levels decreased by 62.6%, while the acetoin titer increased to 7.33 g/L [K. pneumoniae obtained similar results by introducing exogenous NOX [E. coli without introducing an exogenous gene, and this strain produced 3 g/L of 2,3-BD [Cofactors are closely related to the 2,3-BD pathway. The production of acetoin and 2,3-BD could be improved by regulating the ratio of NADH/NAD+ ,77. Affed by 33% . When th7.33 g/L . In addinous NOX . Accordif 2,3-BD .yodc encoding a new water-forming NOX in B. subtilis redistributed metabolic flux from its NADH-dependent by-product pool to the acetoin pathway [ldhA encoding LDH, budC and dhaD encoding two BDH, and a gcd encoding glucose dehydrogenase in Enterobacter aerogenes reduced the conversion of acetoin to by-product and improved the acetoin production to 71.7 g/L [pflB encoding pyruvate formate lyase and gene ldhA in K. pneumoniae reduced the formation of ethanol and acetate by 88.8% and 99% but caused a 50% reduction in 2,3-BD productivity [ldhA and pflB reduced the production of 2,3-BD [pflB prevents formate synthesis from pyruvate and leads to a significant reduction in the expression of genes involved in the formate hydrogen lyase [+ regeneration is ineffective, the growth rate of the bacteria is reduced [E. coli strain was constructed by the promoter fine-tuning to balance cell growth and production for efficient production of 2,3-BD and no other by-products were formed from glucose [The by-products such as acetate, ethanol, and lactate formed during the accumulation of acetoin or 2,3-BD in microorganisms reduce the availability of precursors and cofactors. Because the by-products are NADH-dependent products involved in the 2,3-BD pathway, it implies the possibility of reallocating carbon fluxes to target products by manipulating NADH levels. Expression of  pathway . The pro pathway . Other s71.7 g/L . Deletiouctivity . Similarf 2,3-BD . The reaen lyase . Concurr reduced . In addi glucose .As revealed in thl-hbd-crt-bcd was overexpressed in Clostridium saccharoperbutylacetonicum, resulting in a 13% increase in butanol production and a final titer of 17.4 g/L [E. coli significantly increased butanol production [aceA in E. coli allowed more acetyl-CoA to be used for butanol synthesis [Clostridium acetobutylicum increased from 57% to 70.8% after disrupting the adc gene-encoded AAD to eliminate acetone production [Butanol is generated in the solvent-producing phases. Enhancing the synthesis pathway and reducing by-products are fundamental to increasing butanol yield. On the one hand, overexpression of crucial enzymes of the synthesis pathway could increase butanol yield . The cas17.4 g/L . On the oduction . CRISPRioduction . The invynthesis . Additiooduction .E. coli were related to membrane-associated functions, including fatty acid synthesis and iron transport [E. coli under butanol stress showed that the gene expression and physiological responses of E. coli in response to butanol stress were similar to those in response to osmotic, oxidative, heat shock, and interference with respiration [E. coli improved butanol tolerance of the strain [However, increasing the concentration of butanol leads to severe product inhibition , becauseransport . Moreovepiration . Overexpe strain .Clostridium are commonly used for the metabolic engineering of butyrate. Earlier researchers reduced by-products by directly disrupting genes involved in the acetate synthesis pathway. The mutants of Clostridium tyrobutyricum with PTA or ACK deletion were constructed to allow more carbon flux into the butyrate pathway for increasing butyrate production, and mutant tolerance to butyrate was improved [C. tyrobutyricum enhanced the flux of acetyl-CoA to butyrate and decreased acetate production, and the mutants exhibited higher specific growth rates and butyrate tolerance [Clostridium species. Therefore, some well-characterized microorganisms, such as E. coli, were engineered to produce butyrate. The metabolic engineering strategy used in E. coli consists of eliminating the acetate synthesis pathway and the major NADH-dependent reactions in metabolism and reconstructing a heterologous pathway targeting butyrate [E. coli thioesterase, unlike the Clostridial butyrate pathway. The natural thioesterase in E. coli contributes to the removal of coenzyme A from butyryl-CoA to produce butyrate directly [Butyrate is generated through the acid-producing phase. Acetate, though, is produced as a by-product in this process, which reduces the butyrate yield and increases the cost of product recovery and purification. Additionally, end-product inhibition is one of the critical factors limiting the fermentation production of butyrate. Thus, it is also crucial to improve the strain’s tolerance to butyrate. The strains of improved . Howeverimproved . Becauseolerance . Howeverbutyrate ,48. In sdirectly .Bacillus sphaericus, Lysinibacillus sphaericus, Arthrobacter oxydans, Clostridium sp. P2) can accumulate L-alanine in response to L-alanine dehydrogenase (ALD), which is converted to D-alanine by alanine racemase (ALR) [L-alanine is mainly synthesized for cellular biosynthesis and is converted using ALT in most microorganisms A. Howevese (ALR) ,88,89,90alaD from Bacillus sphaeroides in Zymomonas mobilis CP4thi [E. coli could be used to regulate ALD, which was able to obtain high expression in anaerobic fermentation [Geobacillus stearothermophilus in E. coli [Low levels of L-alanine were produced in two-stage fermentation by overexpression of the ALD gene s CP4thi . Howeverentation . The obtentation . Unfortu E. coli . By chan E. coli .aceF and ldhA genes were deleted in the E. coli mutant strain, 32 g/L of L-alanine was produced by a two-stage batch fermentation process [E. coli mutants. The obtained strain produced 88 g/L L-alanine with a yield close to the theoretical maximum [C. glutamicum. The genes for organic acid synthesis and ALR were deleted in C. glutamicum, resulting in carbon flux from organic acid to L-alanine and the production of 98 g/L L-alanine [Another challenge in the fermentative production of L-alanine is the formation of by-products. Strains were created by deleting genes involved in competing for metabolic pathways to meet the challenge. After the  process . Further maximum . A simil-alanine .2. Now, the utilization of alternative substrates was improved based on metabolic engineering by using microorganisms as a platform for sustainable industrial production  is an electrochemical process that relies on the interaction between electrodes and microorganisms to drive microbial metabolism for product synthesis ,109. As 20.2 mMC . In a subutyrate . A specilectrode . AlthougClostridium carboxidivorans for butyrate and butanol production [Conventional metabolic engineering is usually restricted to a limited number of genetic pathways. In contrast, genome-wide scale engineering promises a more comprehensive and rigorous design of microorganisms, which will undoubtedly involve more complex biological designs and larger datasets ,114. Adooduction . To furtoduction . HoweverE. coli, C. glutamicum, C. glabrata, K. pneumoniae, and S. cerevisiae are commonly used, producing strains, among which C. glutamicum is a food-grade microorganism with high safety and more suitable for industrial production. These microorganisms usually generate specific products by establishing synthetic metabolic pathways. Traditionally, microorganisms establish synthetic metabolic pathways mainly by gene overexpression or knockout to maximize production. However, improper gene expression or certain metabolic defects may cause problems such as cell growth inhibition, metabolic flux imbalance, and capacity reduction due to the inability to provide precise regulation. Therefore, metabolic engineering should shift from stepwise static regulation to dynamic regulation development, which helps maintain metabolic balance and cell growth. For example, switches in elements such as promoters can be induced upon exogenous signaling stimuli to regulate the expression of downstream genes, or dynamic regulation can be achieved by metabolite response elements such as promoters and transcription factors to regulate the expression levels of key enzymes of the pyruvate synthesis pathway, enabling cells to induce pyruvate spontaneously [This review aimed to present the progress of research on pyruvate and derived compounds based on metabolic engineering. Detailed pyruvate and derived compounds production and engineering strategies are described in aneously ,122,123.cis-repressors has been successfully applied in metabolic engineering and synthetic biology to control specific proteins in a modifiable manner and alter metabolic fluxes to produce valuable chemicals [cis-repressors have the advantage of being portable and do not require an inducer due to the built-in set threshold for protein synthesis [In addition, RNA-based engineering of hemicals ,125. Theynthesis ,127. Theynthesis , and wilynthesis . Cell-frynthesis .2 to acetoin, 2,3-BD, and butanol by some carbon-fixing microorganisms [In addition, low-cost chemical methods allow the synthesis of high-purity pyruvate and derivatives , making rganisms ,133. Altrganisms . Therefo


Fresh fruit and vegetables are invaluable for human health; however, their quality often deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. We currently lack any objective indices which indicate the freshness of fruit or vegetables resulting in limited capacity to improve product quality eventually leading to food loss and waste. In this conducted study, we hypothesized that certain proteins and compounds, such as glucosinolates, could be used as one potential indicator to monitor the freshness of broccoli following harvest. To support our study, glucosinolate contents in broccoli based on HPLC measurement and transcript expression of glucosinolate biosynthetic genes in response to postharvest stresses were evaluated. We found that the glucosinolate biosynthetic pathway coincided with the progression of senescence in postharvest broccoli during storage. Additionally, we applied machine learning-based hyperspectral image (HSI) analysis, unmixing, and subpixel target detection approaches to evaluate glucosinolate level to detect postharvest senescence in broccoli. This study provides an accessible approach to precisely estimate freshness in broccoli through machine learning-based hyperspectral image analysis. Such a tool would further allow significant advancement in postharvest logistics and bolster the availability of high-quality, nutritious fresh produce. Brassica oleracea L. var. italica) is a nutritious vegetable that is also enriched in chemical compounds like glucosinolates that can lower cancer risks [Broccoli  color imaging analyses were used to monitor pigment changes in broccoli during postharvest storage . HoweverThe rapid advancement of optical sensors such as multispectral imaging technologies has significantly impacted agriculture . HSI proNumerous techniques have been proposed to correlate hyperspectral data with fruit and vegetable quality and safety evaluations, including feature extraction via principal component analysis (PCA) , 13, linTo meet the need for detection of senescence progression through imaging sensors such as HSI, we leveraged HSI, applied spectral unmixing, and target detection algorithms to predict senescence at an early stage of symptoms. Compared with the previous works in dimensionality reduction and multivariate classification, our main contributions are as follows: (i) the application of spectral unmixing for physically meaningful and interpretable feature extraction; (ii) we estimated a spectral signature across all wavelengths for broccoli tissue with different glucosinolates concentration; and (iii) we generated improved maps which visualize the glucosinolates changes in broccoli. Here, we propose the accumulation of glucosinolates that correlated with onset and progression of senescence in broccoli following harvest. We first conducted biochemical measurements and gene expression analysis to illustrate that glucosinolates can serve as a freshness indicator before noticeable color changes in broccoli. Then, we demonstrated the feasibility of the HSI to determine the glucosinolate concentration of broccoli during postharvest storage. We concluded that a combination of glucosinolate analysis and HSI can evaluate the freshness signatures of postharvest broccoli.The crowns of freshly grown broccoli  were manually harvested from local farms in Hastings, Florida. All broccoli florets were selected with similar size and small beads in this study. The florets were subjected to different storage conditions: 4 °C for cold treatment and 25 °C for room temperature. Four biological replicates of broccoli florets were sampled for tissue collection. Tissue samples were collected from the broccoli florets every other day over a twelve-day period. The samples were wrapped in aluminum foil, immediately frozen in liquid nitrogen, and then either stored at -80 °C for later quantitative PCR or freeze-dried for HPLC. The similar broccoli samples were chosen for the spectral imaging analysis.g for 10 min. The supernatant was filtered through a 0.22 μm pore size hydrophilic PTFE syringe filter . The glucosinolate contents were estimated based on the peak area at 220 nm [HPLC analysis was performed to quantify the glucosinolates. Total glucosinolates were measured according to previously reported methods, with some modifications . For the quantitative real-time RT-PCR, the primers were designed using Primer-Quest from Integrated DNA Technologies (IDT). The primers for the genes of glucosinolate biosynthetic pathway are listed in μl for each gene, and the reactions were run in triplicate with thermocycler conditions as follows: 95 °C for 10 min, 45 cycles of 95 °C for 30 sec, and 60 °C for 30 sec. The relative gene expression was calculated by ΔΔCT method according to the qPCR machine manufacturer . The Actin2 from broccoli was used as an internal control. The experiment was repeated twice for expression studies.Floret tissue samples from days 1, 3, and 5 were chosen for expression analysis of genes related to the glucosinolate biosynthetic pathway. Total RNA was isolated from broccoli floret using the TRIZOL  method with DNase treatment . First-strand cDNA synthesis from 1 Broccoli hyperspectral images were collected with a HinaLea Model 4200 hyperspectral camera . The acquired raw images were calibrated to relative values with the white and dark reference:Considering that the HSI can detect changes in chemical compositions under various abiotic stress conditions, we reasoned that the initiation and progression of glucosinolates accumulation during postharvest senescence can be monitored through HSI analysis. To achieve this goal, we performed time course imaging experiments on broccoli florets under room temperature and refrigerated storage conditions.An overview of the analysis is shown in l2 normalization to reduce the magnitude variation caused by the point light source and focus more on the spectra shape [l2 norm [l2 normalization treats each spectrum as a vector and normalizes it to unit sphere in the vector space. The normalized result can be illustrated by comparing Figures For this study, preprocessing of the measured spectra for noise and illumination variation was performed prior to analysis. Since the illuminator was a single light source and did not evenly cover the entire imaging surface, the center of the imaging plane was brighter than the outer edge from Figures ra shape , 25. Spe[l2 norm . The l2 After preprocessing the spectra, the regions of broccoli florets were segmented from the remainder of the hyperspectral cube. This segmentation was accomplished in two steps: (1) segmenting the broccoli sample from the black background and (2) segmenting the floret from the stalk. Spectral readings were analyzed from different regions of the image for step 1 . There wSince the glucosinolate concentration was measured on broccoli florets, we hypothesized that focusing on the spectra of the broccoli head was generated stronger correlation than analyzing the spectra of the entire broccoli (including the stem). In order to segment the head from the stalk, we applied the GraphCut algorithm  of the iThe collected and preprocessed hyperspectral images consisted of 200 spectral bands of size 968 × 608 pixels. Each image usually covered around 40% to 80% of the broccoli sample and contained approximately 0.3 million of pixels per sample. Since the large data size will slow down the computing, the spectra were down-sampled to 5,000 per segmented broccoli sample via k-means clustering. Specifically, spectra of all the broccoli pixels were clustered into 5,000 groups, and each group spectra were represented by their average. Down-sampling was applied to the two segmentation scenarios being considered: (1) entire broccoli and (2) broccoli florets, respectively.i is the spectra of pixel i, εi is the noise vector, M is the number of endmembers, ek is the kth  endmember, and aik is the corresponding abundance value. The objective of unmixing is to estimate a set of endmembers and abundances that can indicate the freshness level of the broccoli being imaged. Particularly, the estimated endmembers are supposed to represent the range of “freshness” levels in the samples. Then, the associated abundances for the endmembers corresponding to “fresh” can be viewed as a freshness indicator. The endmembers and abundances were estimated using the Sparsity Promoting Iterated Constrained Endmember (SPICE) algorithm.The hyperspectral image is a high-dimensional image cube that described each pixel as the radiance or the reflectance at a range of wavelengths across the electromagnetic spectrum. The spectrum of a pixel is usually determined by the material of the object surface. These measured spectra are a mixture of a set of constituent spectrum, also known as endmembers. Spectral unmixing is the task defined as decomposing a mixed spectra into a collection of endmembers and their corresponding proportions, also known as abundances . A well-The SPICE algorithm benefited from simultaneous estimating the shape and number of endmembers as well as their abundances . The MatM , since this value produced the most replications, over 450 , a range of Γ values, starting from 10 to 150 in steps of 10, were explored. We conducted 10 repetitions of 3-fold cross-validation for 15 Γ values. With the estimated endmembers, the abundance feature can be derived from the training folds to fit the MLR model. Note that the large variation of errors is caused by the outliers in the training and validation folds. There was a greater prediction error on validation folds with a smaller Γ Figures , which iM Figure . In addii of an individual broccoli pixel i can be considered an instance, while all pixels of the entire broccoli sample (a group of instances) can be considered as a bag. A bag of one broccoli sample was labeled as positive if it contains high glucosinolates concentration, which meant that there existed at least one instance corresponding with the freshness indicator; otherwise, the bag was labeled as a negative. We set a threshold of glucosinolates level as 50 to distinguish less fresh (≥50) and fresh sample (<50). The threshold was decided by the significant increasing of glucosinolate level from day 1 to day 3.In addition to spectral unmixing, we explored a target detection method as an alternative feature extraction approach. In particular, the Multiple Instance Adaptive Cosine Estimator (MI-ACE) , 16 was i within one bag was assigned a confidence value ai, indicating the confidence of highly correlated with the discriminative target signature. Compared with unmixing approach, the MI-ACE in discriminative target signature was easier to interpret, implying that the freshness indicator can be distinguished from the negative instances of broccoli with low glucosinolates level. In this experiment, the target signatures are estimated in a similar setting of 3-fold cross-validation as SPICE.MI-ACE estimates a discriminative target signature t for the freshness indicator. Then, instance sN denotes the number of pixels) were used to predict measured glucosinolate concentration values. Specifically, a multivariable linear regression (MLR) model  was fitX with dimension (Ntraining × Bbands), where Ntraining denotes the number of training samples and Bbands denotes the number of wavelength (Bbands = 200 in our case). The glucosinolate measurements were considered a response vector Y with dimension (Ntraining × 1). The partial least square is a latent variable approach to find the relations between predictors and response. The prediction of Y can be written as follows [T is the wavelength scores matrix and β is the regression coefficient. The regression was conducted using Matlab 2019b [As a comparison approach, a PLSR model was investigated and compared with the above SPICE and MI-ACE approach. The average spectra of training samples were considered predictors matrix  follows :(4)Y^~Tab 2019b .When stored at room temperature, broccoli started to display visible yellowing after five days of harvest. There is no obvious color change found within a five-day storage. To assess the possibility that glucosinolate levels can be detected at the early stage of senescence and used as a indicator for senescence, we performed HPLC analysis to measure the total glucosinolate concentration during a twelve-day period in broccoli at either room (25°C) or cold (4°C) temperature. We found that there was a near-linear increase in glucosinolate concentration over the 12-day period when the broccoli was stored at room temperature . HoweverMAM1 and MAM3, were increased (4.3-fold from day 1 to day 3 and 11-fold) from day 3 to day 5 at room temperature. In cold conditions, MAM1 levels were undetectable on day 3 but increased on day 5 by 5.3-fold (MAM1 levels were increased in higher proportions during room temperature storage. Similar patterns were observed for the other genes encoded enzymes such as epithiospecifier modifier 1 (ESM1), α-ketoglutarate-dependent dioxygenase (AOP2), epithiospecifier protein (ESP25), CYP79, and ST5, as their transcripts increased significantly from day 0 to day 5. ESM1 and CYP79 modified and catalyzed the conversion of amino acid to aldoxime. However, under cold storage, the increase of transcript levels in flavinmonosygenases (FMOGSOX2) from day 0 to day 5 was not significant. The FMOGSOX2, an enzyme, catalyzed the conversion of methylthioalkyl glucosinolates to methylsulfinylalkyl glucosinolates. This observation provided evidence that changes in genes expression for the glucosinolate biosynthetic pathway were associated with the freshness of broccoli. Therefore, our results suggested that the production of glucosinolate was triggered by transcriptional regulation of glucosinolate biosynthesis during the postharvest storage. This data further validated that there is a correlation between postharvest senescence and glucosinolate production in broccoli when stored at room temperature, suggesting that the glucosinolate can be used to detect early senescence even without any visible color changes during storage.To investigate whether the glucosinolate accumulation was due to glucosinolate biosynthesis and how the glucosinolate biosynthetic pathway was affected during postharvest senescence, we carried out quantitative gene expression analysis on the key genes in the glucosinolate biosynthetic pathway. The first reaction of glucosinolate biosynthesis was catalyzed by two enzymes methylthioalkylmalate synthases (MAM1 and MAM3). MAM1 and MAM3 initiated the formation of glucosinolate chain products. The transcripts of both enzymes that produce intermediates, like 5.3-fold . This imTo validate if HSI imaging can detect the glucosinolate contents in broccoli, we performed imaging analysis on broccoli at two storage conditions after harvest and designed two approaches to test out hypothesis. In First, the estimated endmembers via SPICE are shown in Abundance maps and histograms shown in Figures The visualization of the MI-ACE result is shown in Figures Figures Y denotes the measured glucosinolates concentration and To correlate the hyperspectral image with the glucosinolate levels in postharvest broccoli, we investigated both the abundance feature generated via SPICE, and the confidence value generated via MI-ACE. The glucosinolate concentration and derived abundance feature as well as confidence value were applied to fit the (MLR) model. In addition, we compared the feature extraction + MLR model approaches with the classical PLSR model. 6 fitted models with variables based on different approaches were applied to the testing sample to generate the residuals shown in P values are shown in P values were less than the significant level of 0.1%, indicating that the derived models fit significantly better than a degenerate model with no predictor variables.In addition, the F-test of overall significance was conducted to evaluate the multivariable linear regression models and the PLSR model. F statistics and the corresponding The above models were first trained by cross-validation and then evaluated on the testing set to verify its effectiveness on validation and testing set. The entire datasets were split into training, validation, and testing folds. To be more specific, 48 samples under 12 conditions  were randomly divided into 4 groups, one for testing and the other 3 for training and validation. Each fold contained 12 samples; 1 replication randomly selected from each condition. The training and validation dataset were shuffled in every repetition.R-squared value from training and validation folds. The primary observation from The training process was conducted for 10 repetitions over 3-fold. In each repetition, we trained the model by 3-fold cross-validation and tested the trained model on the testing fold, by calculating the mean and standard deviation of the testing and training prediction error . The resThe smaller value of the prediction error for the testing fold compared with training and validation folds in Senescence is an important and fleeting state, and its onset and progression after harvest cannot be easily detected. The current lack of objective indices for defining tissue senescence in fruit and vegetables limited our capacity to control product quality and leads to food waste. However, senescence is a physiological process initiated at or near harvest that can be tracked by monitoring well-described changes in gene expression, physiological process, and metabolomic signatures. In this work, we propose that glucosinolates are associated with discrete stages of senescence, for potential use as diagnostic indicators of freshness. Using HPLC and quantitative real-time PCR analysis, we evaluated glucosinolate concentration and the expression of key genes in glucosinolate biosynthetic pathway in postharvest broccoli. We found that there is a linear correlation between the glucosinolate production and postharvest senescence in broccoli. This lineage of glucosinolate could result from the metabolic accumulation of glucosinolates during progression of senescence in stored broccoli. Our data determined that transcriptional level of glucosinolate biosynthesis increased rapidly when stored at room temperature. Therefore, we demonstrated that glucosinolate content can be used to detect the early senescence potentially serving as a “freshness indicator” in broccoli and to define a freshness signature when stored at room temperature.We further developed the imaging analysis on the initiation and progression of senescence in broccoli. We performed two featured extraction approaches in estimating the endmembers (a.k.a. signatures) of broccoli tissue over storage period. The estimated abundance value via SPICE and confidence value via MI-ACE approach not only visualized the broccoli in different postharvest stage, but also indicated the changes in glucosinolate concentration values. The prediction error was explained by the fact that the measurements of the hyperspectral data and the glucosinolate concentrations were conducted across different sample scales. Namely, the abundance values are derived from imaging across the entire surface of one side of a broccoli sample, whereas the glucosinolate value was measured using only one small component of the broccoli tissue. The RMSE values showed that unmixing the broccoli florets only has slightly less error than using the entire broccoli sample. Although the RMSE values of MI-ACE did not show much improvement compared with unmixing methods, the estimated discriminative target signature was easier to interpret, implying the specific wavelength where the broccoli spectra were affected by increasing glucosinolates concentrates. In addition, we compared the prediction result with the PLSR approach and showed that our feature extraction + MLR model were more stable and less overfitting.In summary, hyperspectral imaging held promising strength in demonstrating state-of-the-art performance in crop sciences through the modulation of imaging with spectroscopy. As shown in this effort, HSI had the potential to provide quantitative parameters in detecting the content of glucosinolates that associated with postharvest senescence. The accumulation of glucosinolates marks the beginning of senescence before it is visible in postharvest broccoli. Our results can be directly extended to the other fresh crucifers such as cabbage, kale, and cauliflowers as well as the other fresh produce. The outcomes of the results will provide insights into early detection of deterioriation of fruit and vegetables throughout the food production pipeline, therefore understand how food waste can be reduced.


Mathematical modeling of metabolic networks is a powerful approach to investigate the underlying principles of metabolism and growth. Such approaches include, among others, differential-equation-based modeling of metabolic systems, constraint-based modeling and metabolic network expansion of metabolic networks. Most of these methods are well established and are implemented in numerous software packages, but these are scattered between different programming languages, packages and syntaxes. This complicates establishing straight forward pipelines integrating model construction and simulation. We present a Python package moped that serves as an integrative hub for reproducible construction, modification, curation and analysis of metabolic models. moped supports draft reconstruction of models directly from genome/proteome sequences and pathway/genome databases utilizing GPR annotations, providing a completely reproducible model construction and curation process within executable Python scripts. Alternatively, existing models published in SBML format can be easily imported. Models are represented as Python objects, for which a wide spectrum of easy-to-use modification and analysis methods exist. The model structure can be manually altered by adding, removing or modifying reactions, and gap-filling reactions can be found and inspected. This greatly supports the development of draft models, as well as the curation and testing of models. Moreover, moped provides several analysis methods, in particular including the calculation of biosynthetic capacities using metabolic network expansion. The integration with other Python-based tools is facilitated through various model export options. For example, a model can be directly converted into a CobraPy object for constraint-based analyses. moped is a fully documented and expandable Python package. We demonstrate the capability to serve as a hub for integrating reproducible model construction and curation, database import, metabolic network expansion and export for constraint-based analyses. Theoretical analysis of metabolic pathways has a longstanding tradition. The early approaches to study glycolysis, for example, have considerably increased our understanding of fundamental regulatory principles in metabolism . In receSeveral theoretical techniques to study metabolism have been established. The most classic technique is the analysis of metabolic networks by representing them as systems of ordinary differential equations (ODEs). This representation heavily depends on detailed knowledge of stoichiometries, parameters of enzyme kinetics and regulatory mechanisms of reactions . This apTopological techniques are extremely useful in the process of curating models, in particular to identify and add missing reactions . This prMany of the techniques described above have been implemented as Python packages. However, most of these software packages are not directly compatible with each other.In this work, we present moped, a compact but useful Python package that serves as a hub, offering tools for analysis, development and extension or modification of metabolic models. The integration of BLAST and pathway/genome databases such as MetaCyc and BioCyc into moped allows reconstructing metabolic network models directly from genome sequences  and ensuTo facilitate curation of metabolic models, moped provides an interface to Meneco, a topological gap-filling tool based on answer set programming . All modmoped uses SBML files or PGDB flat files as input for constructing a metabolic network model. PGDBs are organism-specific pathway/genome databases containing annotated reactions and compounds of the metabolism of the organism . These dBecause of the wide range of import methods , one particular strength of moped is the integration of several analysis tools. An overview of mopeds functionalities is shown in A useful and valuable feature of moped is the fully implemented network expansion algorithm to perform metabolic network expansions on moped objects. Metabolic network expansion can be used to investigate structural properties of metabolic networks, such as biosynthetic capacities and their robustness against structural perturbations . The cor rev ’. This model modification can be reverted if no longer needed.Network expansion depends on a precise definition of reaction reversibilities and involved cofactors. Network expansion uses the stoichiometry of reactions to identify producible compounds. However, stoichiometric coefficients of reactions are annotated for one particular direction. To include the opposite direction (for reversible reactions) into the metabolic network expansion, moped provides a method for reversibility duplication. As illustrated in Many reactions depend on specific cofactors. Cofactors usually appear in pairs. One of the most prominent examples is the cofactor pair ATP and ADP. In the majority of reactions with ATP as substrate, ATP serves as a donor of a phosphate group, thus producing ADP. Only a few reactions actually modify the adenosine moiety . In network expansion, therefore, no reaction utilizing ATP or ADP as cofactor could proceed, unless these compounds are either included in the seed or can be produced from metabolites within the seed. If the purpose of network expansion is to realistically calculate a set of producible compounds, this behavior is not desired because it leads to a drastic underestimation of the scope. The most naive approach to directly include cofactors in the seed yields misleading results, because in such a case, all compounds that can be generated from digesting, e.g., ATP would be included in the scope.A pragmatic approach to solve this problem is the duplication of cofactors as proposed in . Here, r cof ’. The same modification is applied to the respective reaction identifiers. This model modification can be reverted if no longer needed.moped provides a convenient method for finding and duplicating all reactions using cofactor pairs. The cofactor pairs can either be automatically determined by moped for networks imported from BiGG or MetaCyc see  or they The implemented methods for cofactor and reversibility duplication are commonly used to obtain biologically meaningful results for metabolic network expansion. However, they are also highly useful for topological gap-filling using Meneco, during model reconstruction. This is further explained in the next section.Construction of metabolic networks highly depends on reliable databases. In order to enable user-friendly metabolic network reconstruction, moped includes methods for importing data from the MetaCyc and BioCyc databases, identifying homologous sets of genes with BLAST and gap-filling.MetaCyc is a universal, highly curated reference database of metabolic pathways and biochemical reactions from all domains of life. BioCyc is a database of organism-specific PGDBs containing metabolic network information based on predictions by the PathoLogic component of the Pathway Tools software ,23. The In order to use BioCyc and MetaCyc for metabolic network construction and analysis, moped offers a parser for PGDBs, allowing direct construction of moped objects from MetaCyc or BioCyc flat files. moped objects can directly be used for network analyses including network expansion and constraint-based modeling. Especially for the latter, it is extremely important that all reactions are mass- and charge-balanced to ensure that all solutions obey mass conversation. Therefore, only reactions which are mass- and charge-balanced are parsed in moped. While this process has the danger of omitting annotated genes, including reactions that are not mass- or charge-balanced would violate fundamental physical principles and lead to unrealistic model properties. This pipeline provided by the database import and parsing of moped makes it straightforward to construct prokaryotic network models. For eukaryotic metabolic networks, however, intensive and careful curation is required due to missing compartment information. More detailed information about the parsing of PGDBs using moped can be found in the documentation.There exist several pipelines to automatically extract a set of metabolic reactions from a genome or proteome sequence. One popular pipeline is the above mentioned PathoLogic software. moped integrates such a pipeline into the Python programming language, directly converting a genome/proteome sequence into a moped object that can be immediately used for modeling applications. This functionality is provided by an implemented wrapper for local BLAST to find enzyme reactions in genome sequence fasta files or proteome fasta files by similarity search against enzyme reference sequences from the MetaCyc database. This method constructs a new moped object representing a metabolic network of all reactions that are found in a genome sequence or proteome using enzyme monomer amino acid sequences and protein–reaction annotations from MetaCyc to ensure fulfilled gene–protein–reaction associations (GPRs) in all found reactions . This prThe next curation step after the initial automatic network construction is usually gap-filling. This describes a process in which reactions are added to the network in order to ensure that the reconstructed model reflects experimentally observed behavior, such as the production of experimentally measured compounds from the growth medium . There aThe topological network modifications, i.e., reversibility and cofactor duplication, harmonize ideally with the application of Meneco, resulting in networks with biologically realistic behavior. This again illustrates the integrative nature of the modelling hub moped. For an accurate manual curation, automatically determined gap-filling reactions should always be manually inspected before adding them to the network model.A major advantage and distinguished feature of moped is the complete reproducibility of the construction of draft models, which is much needed in systems biology, and the subsequent manual curation . In mopeE. coli, B. subtilis and Synechocystis sp. PCC 6803 have been parsed into moped for a comparative metabolic network expansion [To display the benefits of including the moped cofactor duplication, three established models of xpansion ,31,32. IE. coli core metabolism, which is freely available in SBML format from the BiGG database [We illustrate moped’s metabolic network expansion algorithm with a compact network of database . After ihttps://gitlab.com/marvin.vanaalst/moped-publication-2021/-/tags/final-publication, accessed on 13 December 2021.For each metabolite in the network, we calculate the scope size, i.e., how many new compounds are producible if only this metabolite, water and a set of mock cofactors are available. The results of that analysis are displayed in Escherichia coli str. K-12 substr. MG1655, Synechocystis sp. PCC 6803 and Bacillus subtilis strain 168 [E. coli biomass reaction from iJO1366 [We demonstrate how moped provides a complete and easy-to-use pipeline to construct genome scale models from genome and proteome sequences and how these models can be directly applied for constraint-based analyses. For this, we download the freely available proteome sequences of rain 168 ,35,36. W iJO1366 , includiIn order to test the quality of our draft models, we compared them with established models for the respective organisms  ,35,36. FEscherichia coli str. K-12 substr. MG1655 with a similarly constructed draft model using CarveMe, and the iML1515 model. For this analysis, the models automatically constructed moped and CarveMe were analysed without further modification. We calculated maximal growth rates, respective ATP production rates and exchange fluxes for compounds in the medium. Furthermore, we calculated optimal production rates for amino acids and nucleic acids. These model functionalities have been compared to the predictions of iML1515. The functionality and predictive power of draft models constructed by moped has been compared for Here, we present moped, a Python package representing a hub connecting the construction, modification and curation of genome scale metabolic networks with various analysis methods, which support studies of metabolic networks. moped supports the de novo construction of metabolic networks by importing databases, providing homology searches, including GPR associations and integrating an established gap-filling routine without the need to change software environments. Existing models from external sources can be imported using the standardized format SBML. Metabolic network models are represented as moped objects, which can be modified by easy-to-use and intuitive methods. moped models can be exported into various formats, thus integrating a diverse set of established analysis tools. Metabolic network expansion and constraint-based optimization can be easily performed for any model represented as a moped object.Examination of moped draft model reconstructions using MEMOTE demonstrated that the resulting models are generally of a high quality. The strength of draft model reconstructions with moped is the direct integration into the Python programming language: Every decision in the automatic and manual reconstruction process is documented in executable Python scripts. Therefore, the whole reconstruction process becomes fully transparent and is easily reproducible by any interested user.https://gitlab.com/marvin.vanaalst/moped-publication-2021 (accessed on 13 December 2021).The modular architecture of the open source package moped is particularly designed for allowing further extensions to enhance its functionality, such as the integration of additional software tools. We provide an extensive documentation for moped, as well as troubleshooting guides, unit-tests for all provided methods and example notebooks illustrating the usage of moped at


The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs. Dwelling in the human gut is a complex microbial community made up of various cell types spanning a wide range of taxa . This diThe essential role of microbiota entails the digestion of food and drugs in the intestine before they enter the bloodstream and reach the target tissue –16, whervia specific genes  in the form of notorious diseases like liver fibrosis and cirrhosis . Once thic genes , can be ic genes    –32. TMAOs (CVDs) , plateles (CVDs) , 35, blos (CVDs) , the riss (CVDs) , 38, as s (CVDs)   . Some sts (CVDs)  [for mors (CVDs) ]. The mis (CVDs) . The syns (CVDs) .Despite the significance of CVDs and their emerging etiological link to the microbiome , many quOmics methods provide a more in-depth metabolic signature of diseases, yet they are inadequate to draw concrete mechanistic conclusions. Comprehensive systematic views, which involve systems-oriented techniques and computational modeling, complement experimentation to unveil the microbiome causality of CVDs. Herein, we aim to review the state-of-the-art computational approaches proposed to determine microbiome-CVDs interplay. Given the importance of data-driven omics approaches, we briefly review the recent advances in this topic, followed by an elaborate discussion of common approaches that can build on these technologies to model microbiome-based pathologies.Advances in omics technologies have offered great insights into the biochemical processes interacting with the microbiome and CVDs by proving useful information on the regulatory role of different components, underpinning the equilibrium between human organs and the gut microbiota. Studies, for instance, have indicated that a variety of biological processes are regulated by microRNAs (miRNAs). miRNAs serve a key role in the host immunological response to counteract infections caused by bacterial pathogens , 48. CorRecent bioinformatics methodologies have boosted the capabilities of integrating data and identifying biomarkers and drug targets, presenting a new picture of treating gastrointestinal disorders including dietary interventions and their consequences on CVDs. The improvement of high-throughput technologies and culture-independent genomic methods over the past few years made it particularly possible to characterize the microbial ecology in great detail , 53. TheMicrobiome composition and function have been determined with high-throughput omics technologies, such as metagenomics , metaproTo more specifically profile the bacterial composition, novel approaches like shotgun metagenomics sequencing have been employed. Metagenomics has revealed constructive information on the impacts of the microbiome on the health and disease status of hosts . MetatraGenome-scale metabolic models , provide bases that can be used by stoichiometric methods. Constraint-based models (CBMs) have been shown as pragmatic tools to investigate genome-scale metabolic networks. CBMs in metabolic networks regularly come in a steady-state form with relevant constraints imposed on multiple reactions. This results in a bounded convex cone, which includes optimal solutions. Each point within this feasible solution space represents a single flux distribution, which contains fluxes of every reaction throughout the network. Flux balance analysis (FBA) is a common linear programming optimization problem that assumes metabolite accumulation is zero during the growth phase:cj determine the weights of the reaction j. Also, vj and Sij are the reaction j's flux and the stoichiometric coefficient of metabolite i in reaction j. Also, vj is constrained to where ion, see ]. In mosBesides the automated and semi-automated approaches, machine learning techniques have evolved to improve GEM prediction by determining principal features from large-scale datasets. BoostGAPFILL, using a standard matrix factorization, obtains the metabolite adjacency matrix to predict possible candidate reactions from a reaction network . The resCandida albicans with some improvement  models and human metabolic reaction (HMR), recognized as the two most comprehensive generic GEMs for humans . Once coLPL and FADS2 on CVDs  , have beThe sole application of one type of data, e.g., transcriptomic data, might not lead to explicit results. The integration of multi-omics data is necessary to better understand how phenotypic behavior of metabolic pathways changes in different environmental and even genetic conditions. The variation can be captured by machine learning tools like the support vector method and used as references to regulate metabolic model constraints for a narrower feasible solution space see .Considering disturbances existing within high-throughput data, machine learning approaches have boosted the quality of the integration process. To distinguish hidden features of metabolomic data, FIngerID  and BioTHuman gut microbiota exhibits a great deal of spatial and temporal heterogeneity. Microbial populations in the intestine significantly vary in both the cross-section and length of the gut. Spatiotemporal features play a fundamental role in a comprehensive understanding of the human-microbiome interaction since the microbiome fluctuates based on transient environmental factors. Since a bacterial imbalance at one position in the gut could precipitate a local inflammatory response , developPseudomonas aeruginosa  drew attention for simulating the behavior of consortiums where time was considered as the independent variable. Initial dynamic models applied a quasi-steady-state  assumptiruginosa , Bauer eruginosa  presenteruginosa  and d-Opruginosa , μbialSiruginosa , which aThe main challenge in the investigation of the gut-heart axis is to identify the interactions between the individual gut species and CVD progression, important pieces of information that can introduce potential new paths for drug discovery. Since it is unfeasible to investigate all microbiome dynamics experimentally, multiscale models are promising approaches. To this end, the selection of appropriate multiscale models is crucial see . To predBox 4The microbiome is a complex network interacting between different temporal and spatial scales. Multiscale models are used when answering a scientific question needs information from different resolutions. There are gaps between different measurable scales and multiscale models are intended to bridge them. Clostridium, Desulfovibrio, and Bifidobacterium as well as their metabolic functions. In terms of availability, MESA and AgentPy are recent open-source Python frameworks for implementing and analyzing ABMs and can be integrated with a wide range of Python libraries  prove to be particularly useful. This framework allows researchers to encode the intricacies of a multi-bacterial system into a series of relatively simple rules that can be tuned more precisely to determine the underlying functions . In ABMsibraries , 137.via logic-based models (The application of ABMs to cardiac fibrosis or platelet aggregation has been previously studied , 139. Cain-vitro and in-vivo platforms. GEMs integrated with other computational models at different scales can be used to investigate the role of individual species at the community level and their metabolic interactions with cardiometabolic diseases. Interventions hampered by experimental limitations can be identified to define clinical scenarios and elucidate underlying disease mechanisms.Using computational approaches highlighting biochemical processes involved in CVDs, different types of genotypic and phenotypic information are encoded into mathematical models. The tool can be used to mitigate CVDs at different stages, simulate cellular growth under different environmental conditions, and give hypotheses to be investigated by Although the widespread use of metabolic modeling is promising, several shortcomings limit their applications in that they are limited to biochemical interactions, while the roles of signaling and gene regulatory components are disregarded. It is also crucial to find new methodologies, based on personalized prediction, to alleviate uncertainties that emerge as the result of mapping GPR associations. Future efforts should be emphasized on establishing universal GEM reconstruction protocols with the aim of minimizing uncertainties arising in metabolic modeling of microbiome-CVDs crosstalk.In the healthcare industry, it is essential to find ways to provide personalized medicine, which maximizes the efficiency of treatments and reduces side effects while keeping costs down . The ultTo enable personalized prevention, diagnosis, and treatment of diseases such as CVDs new systems-oriented approaches have been used to study the structural characteristics of the microbiome to elucidate the causal mechanisms. Given the importance of the gut microbiome in human health, researchers have performed different types of studies to further reveal the behavior and structure of the gut microbial communities as well as their unknown interactions with the host. Advancements in high-throughput data equipment make the availability of different omics easier. However, even with access to this wealth of information, predicting the behavior of microbiome-host interplay is often still burdensome. Accordingly, mathematical tools and computational approaches have been deployed to better grasp these heterologous data.The emergence and integration of different meta-omics data, e.g., integration of time-series data, has made it more sensible how microbial communities interact with the human host and respond to disturbance. On the other hand, GEMs, particularly when integrated with omics, provide us with a great understanding of underlying mechanisms associated with CVDs. Several regulatory methods to define more accurate constraints and bi-level optimization algorithms to model the growth and interactions of the gut microbiota have been proposed; some of these can also be applied to study microbiome-host interactions. Combining the essential assumptions and theories from different types of models yields even deeper insight into communities. Mounting evidence from microbiome studies confirms the possible role of metabolites produced by bacterial communities in treating cardiometabolic diseases. In order to prevent CVDs and prevent early intervention, microbiome and host-diet interplay can promote personalized nutrition.via a multidisciplinary perspective featuring the collaboration between researchers with clinical and engineering backgrounds to render effective personalized treatment.The future focus should be on systems-oriented studies of microbial metabolites to modulate host physiology more effectively. Systems biology-type studies should be conducted All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.