Fluoride tolerance is an important economic trait in sericulture, especially in some industrial development regions. Analyses of physiological changes involving structural damage to the insect body and molecular analyses of some related genes have focused on this area; however, the changes that occur at the metabolic level of silkworms after eating fluoride-contaminated mulberry leaves remain unclear. Here, metabonomic analysis was conducted using gas chromatography-mass spectrometry (GC-MS) to analyze the changes in midgut tissue after NaF stress using silkworm strains 733xin (susceptible stain) and T6 (strain resistant to fluoride), which were previously reported by our laboratory. Differential metabolomics analysis showed that both T6 and 733xin strains displayed complex responses after exposure to 200 mg/kg NaF. The purine metabolism and arginine and proline metabolic pathways of fluoride-tolerant strains reached significant levels, among which 3′-adenylic acid and hypoxanthine were significantly upregulated, whereas guanine, allantoic acid, xanthine, N-acetyl-L-glutamic acid, and pyruvate were significantly downregulated. These metabolic pathways may be related to the fluoride tolerance mechanism of NaF poisoning and tolerant strains. Fluoride is one of the important factors causing environmental pollution. Fluoride-contaminated mulberry leaves are absorbed and accumulated in the midgut of the silkworm after being eaten . The resThe fluoride ion is a protoplasmic toxicant that produces toxicity through the cell wall of mulberry leaves and binds with the protoplasm ,4. AfterBmcyp306a1 in the P450 family and another Bmbcl-2 gene. Real-time fluorescent quantitative PCR analysis showed that the expression of Bmcyp306a1 in different tissues of fluoride-tolerant and sensitive individuals was significantly different, which might be related to the mechanism of fluoride tolerance. Chen et al. [Bombyx mori N cell line (BmN) via two-dimensional electrophoresis. These include two upregulated proteins: mitochondrial aldehyde dehydrogenase (ALDH2) and prohibitin protein WPH, and two upregulated proteins: calreticulin precursor and DNA supercoiling factor. Li et al. [DefNIL) by using RNA-seq analysis [In response to the fluorosis of silkworm, researchers have adopted some control strategies, such as breeding fluoride-tolerant varieties and using drugs ,7, and mn et al.  identifiFluoride stress can activate the autoimmune response, including the synthesis of fluoride-tolerant metabolites, or signal molecules to regulate the metabolic process. Metabonomics has been used to further study the functions of some key metabolites by comparing the differences in metabolic patterns between two or more groups of samples. It has been widely used in functional gene research ,11, metaIn this study, metabonomic analysis was conducted using gas chromatography-mass spectrometry (GC-MS) to analyze the changes in midgut of fluoride-tolerant T6 strain and the fluoride-sensitive 733xin strain at the metabolomic level after NaF stress, to screen the differential metabolites, and to further explore the relevant metabolic pathways. Our present study will provide useful information for understanding the fluoride-tolerance mechanism in silkworm.The silkworm was a fluoride-tolerant strain T6 and a fluoride-sensitive strain 733xin preserved in our laboratory. The T6 silkworm strain has high tolerance to fluoride, its larvae can survive with few effects even when they are fed with mulberry leaves treated with 200 mg/Kg of NaF solution. While, the high degree of change in growth in 733xin strain when fed with mulberry leaves even treated with 50 mg/Kg. Fluoride poisoning is a cumulative process. The larvae can recover from early instars fluoride toxicity and convalescence from fluorosis by feeding of fresh fluoride free mulberry leaves in subsequent stages. In order to prevent the early larvae from being too susceptible to poisoning and death, and to facilitate the extraction of tissues, we carried out the present experiment starting from the fourth instar. Silkworms reared to the fourth instar  were divided into control and experimental groups. Commercially available anhydrous sodium fluoride (NaF) AR grade produced from Sangon Biotech Ltd., Shanghai, China was used as a toxicant in this research. The control group was fed with fresh mulberry leaves dried naturally after 5 min immersion in clear water, labeled T6_C and 733xin_C, respectively, whereas the experimental group was fed fresh mulberry leaves dried naturally after 5 min immersion in 200 mg/kg NaF solution, labeled T6_F and 733xin_F, respectively. The midgut tissues of different silkworm varieties were dissected and stored in liquid nitrogen. We performed eight replicates per sample.v/v) mixed solution was added, followed by eddy oscillation for 1 min. After, ultrasonic extraction in an ice water bath was performed for 5 min. After storing for 10 min at −20 °C and centrifuging for 10 min , 300 μL supernatant was placed in a glass derivative bottle. Quality control samples (QC) were prepared by mixing the extracts of all samples in equal volume. The volume of each QC was the same as that of the samples. The samples were dried using a freeze-concentration centrifugal dryer. The oxime reaction occurred at 37 °C for 90 min in an oscillating incubator after adding 80 μL methoxyamine hydrochloride pyridine solution (15 mg/mL), followed by swirling oscillation for 2 min. A quantity of 80 μL BSTFA (containing 1% TMCS) derivative reagent and 20 μL hexane was then added to samples. After 2 min of swirl oscillation, the samples were reacted at 70 °C for 60 min. Finally, the samples were collected and placed at room temperature for 30 min for GC-MS metabonomics analysis [Firstly, put the samples at room temperature after take out from the −80 °C refrigerator, and then added to water of the same volume as the samples. Then, 2 small steel beads were added in turn, placed in the refrigerator at −80 °C for 2 min and ground in a grinder . Then, the samples were moved from 100 μL to 1.5 mL tubes. A quantity of 20 μL internal standard  was added, followed by whirlpool oscillation for 10 s. Then, 300 μL of methanol-acetonitrile .The chromatographic conditions were as follows: DB-5MS capillary column ; carrier gas was high0purity helium (purity not less than 99.999%); flow rate was 1.0 mL/min; and temperature of the inlet was 260 °C. The injection volume was 1 μL not diverted and the solvent was delayed for 5 min. For programmed heating, the initial temperature of the column temperature box was 60 °C, and programmed temperature was 8 °C/min to 125 °C, 5 °C/min to 210 °C, 10 °C/min to 270 °C, and 20 °C/min to 305 °C for 5 min.m/z 50 to 500.The mass spectrometry conditions were as follows: electron bombardment ion source (EI), ion source temperature 230 °C, fourth-stage rod temperature 150 °C, and electron energy 70 eV. Scanning mode was full scan mode (SCAN), with quality scanning range of A quality control (QC) sample was inserted into every 16 analysis samples to examine the repeatability of the whole analysis process.The original GC-MS data (D format) was transformed into general format (CDF format) using ChemStation  analysis software, and then imported into Chroma TOF  software for pre-processing, including peak extraction, noise removal, and deconvolution. Qualitative analysis of metabolites used NIST and Fiehn databases. Finally, peak alignment was performed to derive the three-dimensional (3D) data matrix  in CSV format.The internal standard was used for data quality control. The internal standard peaks in the original data matrix and any known false positive peaks  were removed and missing values replaced by 0. In each sample, all peak signal intensities (peak area) were normalized; the relative intensity of each peak signal intensity was transformed into the relative intensity of the spectrum. After data were normalized and multiplied by 10,000, the data matrix was obtained by de-redundancy and peak merging.After log2 conversion , the values in the data matrix were imported into the SIMCA software package . Unsupervised principal components analysis (PCA) was used to observe the overall distribution of samples and the stability of the whole analysis process. Then, supervised  partial least squares discriminate analysis (OPLS-DA) was used to distinguish the overall differences of metabolic profiles among groups and to find the different metabolites among groups.p-value returned by Student’s t-test < 0.05. The fold change (FC) of differential metabolites was calculated, in which the change multiple was the ratio of the average content of the differential metabolites in the two groups. FC > 1 indicated that the content of metabolites was upregulated; FC < 1 indicated that the content of metabolites was downregulated.Multidimensional analysis and one-dimensional analysis were used to screen different metabolites between groups. The criteria for screening were a VIP value of the first principal component (PC1) of the OPLS-DA model > 1 and http://www.genome.jp/KEGG/pathway.html). Mapping differential metabolites to the KEGG database can be used to obtain the enrichment results of their metabolic pathways.The pathway enrichment analysis of differential metabolites helps with understanding the mechanism of metabolic pathway change in different samples. Common pathway analysis is based on the KEGG metabolic pathway  and obtain more reliable metabolite information that results in significant differences between the two groups of samples, orthogonal partial least squares discriminate analysis (OPLS-DA) was used to filter the unrelated signals with model classification, i.e., orthogonal, to obtain the OPLS-DA model . We founA total of 173 differential metabolites were screened and identified between T6_F and T6_C; 70 were upregulated and 103 were downregulated. The differential metabolites included guanine, 3′-adenylic acid, N-acetyl-L-glutamic acid, L-glutamic acid, pyruvic acid, nicotinamide, trehalose, and 3-indoleacetonitrile, indicating that these compounds might be involved in the tolerance reaction of T6 strains to NaF. A total of 136 differential metabolites were screened and identified between 733xin_F to 733xin_C; 75 were upregulated and 61 were downregulated. The differential metabolites included fumaric acid, phosphate, urea, indole-3-acetamide, 2-aminophenol, glycine, and raffinose, suggesting that these compounds might be related to the sensitivity of the 733xin strain to NaF. T6_C–733xin_C screened a total of 209 differential metabolites; 117 were upregulated and 92 were downregulated. We found 182 differential metabolites between T6_F and 733xin_F; 71 were upregulated and 111 downregulated. The greater the VIP value in the OPLS-DA model, the more significant the difference [http://csbg.cnb.csic.es/mbrole/) pathway analysis function, the KEGG ID of differential metabolites was used for pathway enrichment analysis, and screened differential metabolites were enriched into related pathways. Differential metabolites of 733xin were enriched to 46 metabolic pathways after NaF stress, and differential metabolites of T6 were enriched to 56 metabolic pathways after NaF stress. The metabolic pathway enrichment maps are drawn with the metabolic pathway name as the abscissa and the log  as the ordinate; the maps of the four control groups are shown in p-value is 0.01 and the blue line p-value is 0.05. When the top of the column is higher than the blue line or the red line, the signal pathway it represents significant. The common metabolites of T6 and 733xin under NaF stress include fumaric acid and phosphate. Among the significant metabolic pathways of T6, fumaric acid participates in arginine and proline metabolism; nicotinate and nicotinamide metabolism; oxidative phosphorylation; and alanine, aspartate, and glutamate metabolism. Fumaric acid is involved in oxidative phosphorylation, nicotinate, and nicotinamide metabolism; arginine and proline metabolism; and the TCA cycle in the 733xin metabolic pathway. Phosphate participates in oxidative phosphorylation and ABC transporters in the significant metabolic pathways of T6. It participates in a significant metabolic pathway of 733xin, namely, oxidative phosphorylation  significantly increased and nicotinamide was downregulated. The nicotinamide content, the differential metabolite of T6F–T6C, significantly increased, and the nicotinic acid, pyruvic acid, and fumaric acid contents were significantly downregulated. From the changes in differential metabolites, we found that fluoride can inhibit the conversion of nicotinic acid to nicotinamide in the 733xin fluoride-sensitive strain but not in the T6 fluoride-tolerant strain. We speculate that the mechanism of fluoride tolerance in the midgut of the silkworm is related.Metabolome analysis showed that both T6 and 733xin silkworm strains induced complex responses under 200 mg/kg NaF stress. The infection of NaF can lead to significant levels of metabolic pathways such as oxidative phosphorylation, purine metabolism, and glutathione metabolism in fluorine-sensitive strain. Among the significantly upregulated differential metabolites are fumaric acid, phosphate and guanine, while the significantly downregulated differential metabolites are L-cysteine, glycine, and xanthosine. It was speculated that fluoride-susceptible strains could respond to NaF infection by changing these metabolic pathways. Metabolic pathways such as purine metabolism, arginine and proline metabolism, ABC transporters, and oxidative phosphorylation of fluoride-tolerant strain were induced changed, and reached a significant level, among which 3′-adenylic acid and hypoxanthine were significantly upregulated, whereas guanine, allantoic acid, xanthine, N-acetyl-L-glutamic acid, pyruvate, fumaric acid, and succinic acid were significantly downregulated. We hypothesized that these metabolic pathways may be related to the fluoride tolerance mechanism of NaF poisoning in tolerant strains. The results can provide some reference for exploring the metabolic mechanism of fluoride regulating the midgut tissue of the silkworm.


Saccharomyces cerevisiae, Scheffersomyces stipitis, and Yarrowia lipolytica, display some paramount advantages in expressing heterologous enzymes and pathways from various sources and have been engineered extensively to produce natural products. In this review, we summarize the advances in the development of metabolically engineered yeasts to produce natural products from xylose, including aromatics, terpenoids, and flavonoids. The state-of-the-art metabolic engineering strategies and representative examples are reviewed. Future challenges and perspectives are also discussed on yeast engineering for commercial production of natural products using xylose as feedstocks.Xylose is the second most abundant sugar in lignocellulosic hydrolysates. Transformation of xylose into valuable chemicals, such as plant natural products, is a feasible and sustainable route to industrializing biorefinery of biomass materials. Yeast strains, including  Saccharomyces cerevisiae and Yarrowia lipolytica with the exception of Scheffersomyces stipitis , and acetyl-CoA biosynthesis is much higher on xylose than on glucose . CompareSc. stipitis and recombinant xylose-fermenting yeasts Sa. cerevisiae and Y. lipolytica. For each of these organisms, the description will be mainly centered on four aspects, which include general physiology related to metabolic properties, tools, and strategies available for genetic manipulation, metabolism of xylose, and the biosynthesis of typical natural products from xylose.In this review, we will summarize the recent progress on genetic modification of yeast strains for the biosynthesis of natural products using xylose as the carbon source, with a focus on three yeasts including the natural xylose-fermenting yeast Sc. stipitis and Candida shehatae, in which xylose is reduced to xylitol by xylose reductase (XR) and then oxidized to xylulose by xylitol dehydrogenase  to xylono-γ-lactone and converted to xylonate by xylono-γ-lactone lactonase XylC; . XylonatSa. cerevisiae and Y. lipolytica  was enhanced. Through further deletion of the iron regulon repressor FRA2 and serial adaptive evolution, the engineered strain was capable of metabolizing up to 57% of the carbon from assimilated xylose into biomass and carbon dioxide in the mixture of glucose and xylose. The growth using xylose as the sole carbon source has not been reported, possibly due to the weak activity of the introduced Weimberg pathway.Among these pathways, the XR-XDH route is the mostly explored in recombinant yeasts owing to its ease of expression and high metabolic flux, and this route has been successfully expressed in non-xylose-fermenting yeasts including polytica . The funrevisiae . In comptabolism . In thisScheffersomyces stipitis, previously known as Pichia stipitis, is a facultatively anaerobic yeast that exists mostly in haploid form H oxidoreductase reactions for balancing of cofactors and redox potential  dominates DNA repair of double strand breaks, leading to very low efficiency (<10%) of genome editing  fungus that proliferates through budding or fission. Saccharomyces cerevisiae has high tolerance to environmental stress, such as low pH, high osmotic pressure, and phage infection, making it advantageous in industrial fermentation. Moreover, the well-defined physiological information and sophisticated metabolic engineering tools of this yeast greatly facilitate its engineering and applications.Sa. cerevisiae contains native xylose metabolic pathway genes, such as aldose reductase-encoding GRE3, sorbitol dehydrogenase-encoding SOR1 and xylulose kinase (XKS) gene XKS1, the counterpart of XYL1, XYL2, and XYL3 from Sc. stipitis , activates ethanol-producing metabolism, and represses mitochondrial activities . Meantime, the gene encoding ethanol reoxidizing alcohol dehydrogenase (ADH2) is also highly induced upon xylose utilization  and phosphotransacetylase (PTA) was further introduced to directly convert xylulose-5-phosphate into acetyl-CoA. The resultant strain produced 1.6-fold more lycopene using the mixture of glucose and xylose than using glucose alone (Sa. cerevisiae showed 8-fold higher production on xylose than on glucose (Sa. cerevisiae was engineered to carry the pathway for p-coumaric acid production through the expression of tyrosine ammonia lyase (TAL) and overexpression of some tyrosine biosynthetic pathway genes (p-coumaric acid from xylose while the titer was only 5.35 mg/L on glucose. Moreover, a xylose-fermenting strain expressing the biosynthetic pathway of shinorine, a natural sunscreen material, produced a trace amount of shinorine in glucose, whereas the titer was dramatically increased by adding xylose in the medium (To direct the production of natural compounds from xylose, the relative pathways need to be constructed and introduced into xylose-utilizing n xylose . For insse alone . In anot glucose . A recomay genes . This ste medium . This inYarrowia lipolytica is an obligate aerobe that has a high flux of the TCA cycle and high translational efficiency of mitochondrial genes involved in aerobic respiration (Y. lipolytica is considered to be nonconventional (Sa. cerevisiae, with the large and abundant intergenic regions suitable for gene integration (Y. lipolytica are rich in introns (piration . This yepiration . It is apiration . Phylogeentional . Its genentional . The genegration . The gen introns , the pre introns . In addi introns .Y. lipolytica is generally challenging compared with Sa. cerevisiae. First, it is not easy to precisely integrate gene fragments into the genome of Y. lipolytica at specified loci because this organism prefers NHEJ (KU70 and KU80 by CRISPR-dCas9 greatly improves the efficiency of homologous recombination (>90%; Y. lipolytica given that this yeast has only been used in metabolic engineering for less than two decades. The emergence of Y. lipolytica-based CRISPR systems (Y. lipolytica engineering can be found in a very recent review (Genetic engineering of ers NHEJ , whereasers NHEJ . This isers NHEJ . Deletioers NHEJ , and repon >90%; . Second, systems , transpo systems  and arti systems  has great review .Y. lipolytica could not naturally utilize xylose as the only carbon source (Y. lipolytica without the need of complicated genetic engineering, despite the fact that xylose-based cell growth is, in many cases, slower compared with cell growth on glucose.It had long been thought that n source , and earn source . Howevern source , althougn source . Overexpn source , whereasn source , suggestn source . In addin source , and oven source . Interesn source  as long n source , which in source . These sYarrowia lipolytica has excellent capability of accumulating acetyl-CoA and malonyl-CoA, and is hence a theoretically preferred host for the production of fatty acids, terpenoids, flavonoids, and other compounds that use acetyl/malonyl-CoA as a precursor (Y. lipolytica (Sc. stipitis and endogenous XKS, followed by xylose adaptation and overexpression of xylose transporter YALI0B00396, enabled the strain to consume 20 g/L of xylose in 72 h, reaching an OD600 of 32 in shake flasks. Introduction of the biosynthetic pathway of protopanaxadiol together with fusion expression of pathway enzymes and overexpression of genes involved in precursor supply resulted in a titer of 300 mg/L using fed-batch fermentation. In this process, xylose was preferred to glucose as the carbon source, due to the fact that glucose supported fast cell growth and led to rapid generation and accumulation of acetyl-CoA, which was then channeled for the generation of metabolites other than precursors (recursor . In the recursor ; howeverpolytica . On one polytica ; on the polytica . Overexpecursors . In the ecursors ,b.Y. lipolytica has been engineered to produce triglycerides, which can form oil droplets inside cells. These droplets are capable of trapping and storing certain heterologously generated compounds, whose overproduction imposes a threat to cell growth. Thus, Y. lipolytica can be used as a good host to produce hydrophobic natural products, including terpenoids and aromatics, which are prone to formation of crystals in the cytosol that are harmful to the host. Concomitant generation of triglycerides allows hydrophobic natural compounds to be stored in oil droplets, and hence protects the host and guarantees continuous production of the target compounds.Recently, Sc. stipitis, Sa. cerevisiae and Y. lipolytica has been accomplished for the biosynthesis of natural products, mainly terpenoids, flavonoids, and aromatics. Some of these microbial production processes function better on xylose than on glucose, as xylose induces reprograming of central metabolism and enhances the supply of key precursors, such as acetyl-CoA.Biorefinery of lignocellulosic hydrolysates as feedstocks to produce advanced fuels and chemicals holds great promise to develop sustainable bioeconomy. As the second most abundant monosaccharide in lignocellulosic hydrolysates, xylose has been attracting much attention for its efficient utilization and conversion into valuable compounds beyond ethanol. Metabolic engineering of yeasts to convert xylose into natural products is a promising option to implement the biotransformation due to some special characteristics of certain yeast strains, such as moderate tolerance to toxic inhibitors in lignocellulosic hydrolysates, and moderate compatibility of heterologous pathway genes derived from both prokaryotes and eukaryotes. Recently, metabolic engineering of Sa. cerevisiae due to the availability of facile genetic engineering tools and methods. However, this yeast relies on heterologous pathways for xylose utilization, which are not always highly efficient due to cofactor imbalance. Sc. stipitis is a superior xylose-utilizer and, when grown on xylose, generates many intermediate metabolites that are critical for the formation of natural compounds. Nevertheless, the lack of physiological information and genetic engineering tools limits its applications mainly to ethanol production. It appears that Y. lipolytica has the merits of both Sa. cerevisiae and Sc. stipitis. This organism contains native xylose-utilizing genes, and can be turned into an efficient xylose-utilizing factory upon overexpression of these native genes. In addition, the gene arrangement in its genome as well as the established gene manipulation strategies allows for the construction of Y. lipolytica workhorse for the generation of natural compounds from xylose. However, the growth rate of this yeast is generally much lower on xylose than on glucose, making the whole bioconversion inefficient. Extensive engineering work is needed to overcome these difficulties.Currently, most of the studies are centered on Another challenge associated with the utilization of lignocellulosic hydrolysates is the presence of toxic compounds, such as furfural and phenolics, generated in the physiochemical pretreatment of biomass. These compounds greatly suppress the growth of fermentation microorganisms and inhibit the biosynthesis of the target products. Thus, development of highly tolerant yeast strains is of critical significance. Strain adaptation or engineering should be considered for the construction of highly efficient yeast cell factories capable of utilizing lignocellulosic hydrolysates for the generation of natural products.JZ, WQ, and XW conceived the project. JZ, MY, and XW wrote the manuscript. All the authors read and approved the manuscript.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.


In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering.Saccharomyces cerevisiae was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain.Production of ethanol by the widely used cell factory Momo, is open-source and uses PolySCIP (Available at http://polyscip.zib.de/). as underlying multi-objective solver. Momo is available at http://momo-sysbio.gforge.inria.frThe multi-objective programming framework we developed, called  Multi-objective (multi-criteria) optimization is a method used to tackle problems when several objective functions have to be optimized simultaneously. Particularly, multi-objective programming is a class of optimization problems that have more than one objective function over a set of constraints. Except under limited circumstances, it is not possible to find a single optimal solution which simultaneously optimizes all of the objective functions, i.e., in general, the resolution of such problems leads to a trade-off curve (known as “Pareto frontier”) such that at any point of the curve, a compromise is made in favor of some objective function.Multi-objective optimization has been extensively exploited in science and engineering. It has become a powerful tool for solving real-world problems and is also used in numerous bioinformatics and computational biology as shown in . SuccessOPTKNOCK is the more paradigmatic case [ROBUSTKNOCK [OPTPIPE method [Nevertheless, the most common algorithms used to guide strain design do not focus on a multi-objective approach but instead, in general, work within a bi-level framework , adoUSTKNOCK ), or expE method ), as recE method .OPTKNOCK, but it does not handle the optimization of multiple simultaneous compounds.There are also some other more generic approaches such as the one of Hädicke and Klamt  that is Escherichia coli to produce succinic acid. Another example of studies exploring multi-objective optimization by heuristic methods is the work of Costanza et al. [The use of multi-objective optimization for strain engineering is usually performed exploring heuristic/meta-heuristic approaches. Examples of these studies are the work of Maia et al. , in whica et al.  that useMOMO, given a metabolic network and a set of target reactions, the user can identify possible reaction deletions that could improve the flux of those targets, always in a multi-objective setting. To the best of our knowledge, this is the first time that such a tool has been developed combining these features.Although heuristic methods are usually computationally less expensive than exact ones, they do not guarantee that an optimal solution is found. In this work, we present an exact integer-linear multi-objective optimization methodology that can be explored for strain engineering, thereby expanding the current set of tools available for this objective. In our framework, called MOMO was used to model ethanol production undertaken by S. cerevisiae cells, and deletion of reactions that could optimize ethanol production were predicted and tested in vivo. Simultaneous maximization of growth and ethanol were used as objective functions. Despite many years of utilization in the industrial biotechnology sector, alcoholic fermentation undertaken by yeast cells gained a further boost in recent years with an increase in the interest of exploring ethanol as a biofuel [To validate the approach,  biofuel .Saccharomyces cerevisiae production of ethanol [Previous works have also aimed at exploring non-linear multi-objective optimization formulations to model  ethanol , 15. AltOPTKNOCK that adopts a bi-level model  is higher that the number of metabolites (denoted by m) in the majority of the real-world metabolic networks, there can be an infinite number of reaction fluxes satisfying the system of Eq. (vbiomass). This is obtained by solving a continuous linear programming problem. In order to formulate such a problem, assume that LBj and UBj denote the given lower bound and upper bound on the flux of reaction j, respectively. This optimization problem can therefore be written as:Since the number of reactions  –22 whichOPTKNOCK [OPTKNOCK was developed to identify multiple reaction deletions that may lead to the overproduction of a target chemical while cellular growth is maximized. This hierarchical optimization model comprises two decision or competing strategies, namely a cellular objective and a chemical production objective. It therefore is formulated as a Bi-Level Mixed Integer Program (BLMIP). Binary variables, denoted by yj,j∈J, are defined which enable to perform reaction removals inside the model by multiplying both LBj and UBj by (1−yj) in the lower bound and upper bound constraints as follows:We now describe the mathematical formulation of the OPTKNOCK  procedurK denotes the given number of knock-outs. Then the Bi-Level Mixed Binary (linear) Program (BLMBP), which is a hierarchy of two optimization problems, can be mathematically formulated as follows:Assume that vprod corresponds to the flux of a chemical of interest, the vectors v and y denote MBL represents a minimum level of biomass production. We may also need to fix a substrate uptake that in this model we suppose is specified through the vectors LB and UB.where OPTKNOCK, known as SIMPLEOPTKNOCK and implemented in the COBRA toolbox [There is also one variation of  toolbox  that doeJ instances of a variant of  is presented in the Appendix, along with the main references.The formulation is based on multi-objective programming, and could be used for more than two objectives. In the case-study of this paper, we however concentrate on only two. Those are growth on one hand, and target chemical overproduction on the other, knowing that in general the two compete inside a cell, i.e., producing more of a target chemical leads to less production of biomass. We use such formulation to propose reaction knock-outs that enable attaining a high value for the second objective while achieving an acceptable value of the first, that is, cell growth rate. Comparing with bi-level optimisation, conceptually speaking, in bi-objective optimization we give the same importance to both objectives and we can evaluate We briefly describe however here one key concept, namely of the Pareto-efficient frontier, whose precise definition is given in the Appendix. Informally speaking, the Pareto-efficient frontier represents a set of solution points that are optimal in the sense that we cannot improve one objective without worsening another one. One example of a Pareto-optimal frontier is given in Fig. J into  in such a way that JE indexes the essential reactions which are required for the cell to meet the biological objectives and JI indexes the inessential reactions that are not necessary for the cell. The Bi-Objective Mixed Binary (linear) Program (BOMBP) can therefore be mathematically formulated as follows:Considering the notations of “yj are only attributed to inessential reactions. We highlight that instead of formulating the problem of identifying reaction knock-out strategies with a bi-level model which gives exactly one optimal solution, we formulate this problem using a bi-objective model (as presented above) which provides a number of optimal solutions on the trade-off Pareto-optimal frontier. Hence, we are able to analyze each optimal solution of the bi-objective model more carefully in order to find the most suitable reaction deletion scenarios. Also using constraint  [We therefore now describe a classical enumeration technique that can be applied in conjunction with the models presented so far in order to enumerate all equivalent deletions, that is, all deletion sets that allow the same production rates of the desired product and of biomass. We also present a modification of this technique in order to enumerate not all equivalent deletions, but a subset of these deletions that allows a flux distribution with a minimal change of the fluxes in comparison to a reference flux vector. In our case, we use as reference the flux distribution obtained by the FBA model since it represents the one of the wild-type organism. This idea of minimizing the distance between a reference flux distribution and the mutant flux distribution is known as minimization of metabolic adjustment (MOMA) .Z∗ be set of points of the Pareto frontier obtained by solving the BOP. For each Let ρ(y) be a function that takes a vector of integers and returns the set of indexes L such that for all l∈L,yl=1. Consider the following constraint:Let Adding Eq. K is large. One alternative is to enumerate only a subset of the deletions. For that we can add an objective function to ENUMP so that only those deletion sets that are closer to a reference flux set are enumerated. One possible objective function would be to consider the strategy introduced in [Observe that the amount of sets with equivalent deletions may grow considerably if duced in , minimizWe can now define the new problem for the enumeration procedure as follows:f(v) can be reformulated as:where entclass1pt{minimaFor the enumeration, it suffices to add two constraints. The first is a constraint fixing the value of the objective function to the optimal one obtained by solving ENUMPM for the first time:f∗(v) is the optimal value of ENUMPM. Then at each step, one should also add Eq. where Using ENUMPM in the enumeration scheme presented here allows to obtain all deletion sets that enable the desired production rates, and, at the same time, have minimum difference in the flux distribution compared to the wild-type theoretical fluxes.MOMO. The latter is coded in C++ and uses SCIP [The ideas presented in the previous sections “” sectionses SCIP  and Polyses SCIP  as solveMOMO allows to solve instances of the FBA, BOP, ENUMP and ENUMPM models, using exact methods, and further allows for variations of those when there are more than two objective functions, which may include for instance maximizing and/or minimizing other bio-products. The software is open-source and available at http://momo-sysbio.gforge.inria.fr/.MOMO to predict knockout strategies for the overproduction of a desired product:Solve an instance of FBA in order to obtain a reference flux distribution.Identify the reactions that have a non-zero flux in the previous step and consider them as knock-out candidates.Solve an instance of the BOP or of one of the multi-objective models to obtain points in the Pareto curve.For each point, fix the values of the objective functions and enumerate all the knock-outs using ENUMP or ENUMPM.We present below a sequence of steps that can be used with MOMO with the objective of finding gene deletions that could optimize the production of ethanol while also maximizing biomass in yeast.To biologically evaluate the proposed approach, we applied Saccharomyces cerevisiae BY4741  and W303 . BY4741 was acquired from the Euroscarf collection while the W303 strain was kindly provided by Dr Dennis Winge (University of Utah). The mutant strains devoid of genes FUM1, NDI1, MDH1, ADE3, ALT1, PGI1, FUM1, SCS7ADO1, BAT2, LOT6, IDP1, LOT6, YND1, FRD1, IPT1 and SUR2 were derived from the BY4741 background , while strains devoid of SDH2 or of SDH6 and SDH7 were kindly provided by Dr Dennis Winge (University of Utah).The fermentations were performed using the wild-type strain oC in YPD growth medium , after which they were inoculated in the fermentation medium YPF which differs from YPD by having 300 g/L glucose as well as 240 mg/L leucine, 80 mg/L histidine, 80 mg/L methionine and 80 mg/L uracil which were added to the medium to complement the auxotrophies of the BY4741 strain. The different fermentations were assessed measuring cell growth (through the increase in culture OD600nm) and accompanying the alterations in the concentration of glucose, ethanol and glycerol in the supernatant. For this, 10 μL of each supernatant sample were separated in an Aminex HPX-87 H Ion Exchange Chromatography column, eluted at room temperature with 0.005 M H2SO4 at a flow-rate of 0.6 mL min-1 during 30 min, using a refractive-index detector. Under such experimental conditions, glucose had a retention time of 8.3 min and ethanol of 19.4 min. Reproducibility and linearity of the method were tested, and the concentrations were estimated based on appropriate calibration curves.For the very high gravity fermentations, a protocol similar to the one described in  was usedTo simulate the yeast-based production of ethanol, the Yeast 5.01 model  was usedUnder the established conditions and using biomass as the objective function, the predicted flux of ethanol was 8.8 mmol/g dry weight/h, while the predicted flux for glycerol production was of 2.2 mmol/g dry weight/h. When changing the objective function towards “maximization of ethanol production”, no flux for the biomass could be predicted, thus reflecting the competing nature that exists between ethanol production and growth . For this, a set of possible deletions was generated for each of the points of the Pareto frontier . It MOMO we considered a reduced set of predictions, among the whole set generated by our software. We eliminated many candidate reactions identified at the Pareto frontier points showing a very small amount of biomass (points F and G) to avoid solutions pointing to increased ethanol production merely by compromising cellular growth. This is a relevant aspect since some of the solutions proposed as means to improve ethanol production could actually be not possible by compromising cellular viability. We also aimed to include, in the set of strains to be tested, those genes that could not be intuitively be linked to ethanol production. We ended up with a set of 19 different strains to be tested on the capacity of yeast cells to produce ethanol under very high gravity conditions (300g/L glucose as starting sugar concentration). Among the 19 mutant strains tested, four were demonstrated to exhibit increased ethanol levels in comparison with those exhibited by the wild-type strain (14.1% (v/v) ±0.5 ethanol for the BY4741 strain): Δidp1, devoid of the mitochondrial isocitrate dehydrogenase (14.6% ±0.6); Δlot6, devoid of a cytosolic FMN-dependent NAD(P):quinone reductase (16.1% ±0.9); Δipt1, devoid of a inositolphosphotransferase (14.9% ±0.7) and the W303_ Δsdh2 mutant devoid of the succinate dehydrogenase subunit Sdh2   very difficult , this is problematic from the modelling point of view since in the standard models only one objective function is used. In the specific case of end-point metabolites, maximization of its production often results in no growth and vice-versa. Other examples where more than one objective has to be envisaged is the need to reduce the synthesis of by-products that could be coupled to the production of the compound of interest but whose total elimination could be difficult or, in some cases, even impossible. This is the case of glycerol, which is a known product of ethanol production undertaken by iewed in ).MOMO, a framework that is able to consider metabolic networks in multi-objective settings and that also pinpoints for a selected set of reactions that could be deleted in order to reach different combinations of the various objectives selected by the user. Several other tools capable of performing identification of interesting knock-outs have also been developed along time (as reviewed in [OPTKNOCK or OPTGENE are particularly known [MOMO in relation with other methods is that it is based on multi-objective otpimization.In this work we present iewed in ), out ofly known , 32. TheMOMO approach, in contrast with the bi-level model used by OPTKNOCK, is that in this tool we do not need to impose constraints on the biomass values since all the optimal solutions for different biomass values can be obtained. This is advantageous since it does not require to specify an a priori biomass growth rate, which could impact/bias the obtained solution and allows an a posteriori analysis of the optimal frontier and choice of the points to enumerate deletions.One interesting aspect of the MOMO all models are solved using exact methods. This have both the positive aspect that guarantees global optimality and the negative aspect that in practice, depending on the number of objectives to optimize and the choice of K, the problem may be too hard to be solved in a reasonable time, since in the worst case, the space of solutions may grow in the order of nK. Our experiences show that, using a modern laptop, solving instances with K>3 and/or more than 3 objective functions may be prohibitive in practice. To illustrate this, in Table K=1,2,3.In MOMO compared with other approaches is the integration of an enumeration framework that instead of giving one single deletion is able to enumerate candidate(s) that may provide an improved production of the target(s) compound(s). As described above, this approach is based on having a reference flux so we can identify the active reactions to be candidates for the knock-out strategies. This reference flux could come from empirical data or FBA simulations for example. In our case we considered the flux obtained from the FBA model optimizing for biomass. A potential problem may be that this model have many optimal solutions that are different enough to have an impact on the enumeration procedure. An alternative to reduce this impact is to replace the MOMA approach by the more computationally costly one known as ROOM [SIMPLEOPTKNOCK that also have enumeration features. The drawback with SIMPLEOPTKNOCK is the fact that it enumerates all possible deletions ranking them trough a score (wild-type prediction of target production). This results in an unpractical number of deletions to try empirically and often the score is unrelated with the real impact of the deletion. On the other hand, our approach enumerates a reduced number of deletions, instead of all possible ones, thus enabling a more suitable identification of candidates for subsequent experimental approaches.Other advantageous aspects of  as ROOM  that conMOMO for ethanol production, with particular emphasis on reactions whose link with ethanol synthesis was not intuitive and could not be easily anticipated.We have tested the effect of the reaction deletions predicted by IDP1, IPT1, LOT6 and SDH2) led to improvement in the production titers, comparing with the titers obtained by wild-type cells; these improvements ranging from 2% obtained for the Δidp1 strain to 13% obtained for the Δlot6 mutant. Although these improvements might seem small, it is well known that even small increases in the final tier of ethanol represent highly significant add-values to the economy of the bioprocess, highly restrained by the high costs of the distillation step required to obtain ethanol from the fermentation broth [IDP1 and SDH2 encode enzymes that participate in the Krebs cycle and therefore their inactivation may result in a lower flux of the cycle thereby increasing the amount of pyruvate that can then be channeled for increased production of ethanol. In the case of IPT1 and LOT6, which encode respectively a inositolphosphotransferase involved in synthesis of complex sphingolipids and a cytosolic FMN-dependent NAD(P) H:quinone reductase, the improvements that appear to result from their deletions in ethanol production are far less easy to understand. Under the conditions that we have used we could not detect a significant difference between growth of the Δipt1 and Δlot6 strains and of the wild-type strain BY4741 thereby indicating that the improved production exhibited by the mutants does not appear to result from their reduced fitness. Despite the molecular basis underlying the improved production phenotype may not be easily understood, the fact that MOMO has identified non-obvious target reactions is of particular high interest.Out of the 19 deletions tested, four  and discrete (deletions). We tested the proposed approach for ethanol and biomass maximization in yeast, identifying all the possible single deletions belonging to the Pareto front. MOMO is implemented and freely available at http://momo-sysbio.gforge.inria.fr. It can be easily applied to other cases where several metabolic functions can be optimized.In this paper, a new method, called MOMO significantly expands the metabolic engineering tools available for identifying the best mutant strains and can therefore provide a wider view of the solution space for improving the production of valuable compounds.Ongoing work includes the testing of other objectives to be minimized, such as toxicity, and of other specific linear combinations of reactions to be included in the model. Project name: MOMOProject home page: http://momo-sysbio.gforge.inria.fr/Operating system(s): LinuxProgramming language: C++Other requirements: SCIP optimization suite v. 3.2.1License: ZIB AcademicAny restrictions to use by non-academics: licence needed for SCIP optimization suiteAdditional file 1 Appendix S1. Additional theoretical material on multi-objective optimization.Additional file 2 Table S1: Flux distribution at each Pareto point. Table S2: All deletion candidates enumerated by Momo.


Spirulina under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of Spirulina to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein–protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The Spirulina, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of  Spirulina (Arthrospira platensis), a cyanobacterium, was performed to explore the changes at the protein level and the protein interaction cascade when the cells undergo many cellular modifications under thermal stress conditions. In previous proteome-wide studies, the identification of proteins regulated at both the translational and post-translational levels, that is, bi-level-regulated, could ideally represent the key signaling proteins that played a significant role in low- and high-temperature responses. The key two-component system (TCS) proteins SPLC1_S041070 (histidine kinase Hik28), SPLC1_S082010 and SPLC1_S230960 were identified as bi-level-regulated proteins and were linked to SPLC1_S270380 or glutamate synthase, an important enzyme in nitrogen assimilation that synthesizes glutamate from 2-oxoglutarate (2-OG), which is known as the signal compound that regulates the carbon/nitrogen (C/N) balance of cells. Therefore, the signaling mechanism can link to the metabolite 2-oxoglutarate, which controls the C/N balance and the key enzyme in the N assimilation of the cells  and  for the reversible and irreversible reaction, respectively. When either the proteome or GPR for a certain reaction was unavailable, then the upper bounds were not constrained . If the reaction was catalyzed by multiple genes in an enzyme complex, the Boolean expression formula corresponding to enzyme complexes was utilized for that reaction. The mathematical operation for the Boolean operators “AND” and “OR” relationships were calculated as follows:To incorporate the proteomic data into the metabolic flux analysis, we followed the process of the E-flux method, as described in Colijn et al. and Brandes et al. ,21. For X “AND” Y: the minimum value of the two values; Min ;X “OR” Y: the maximum value of the two values; Max .Z, the objective function, is the biomass production; C’ is a row vector of the coefficient that defines the weight of each flux in the objective function; S is the stoichiometric matrix; v is a flux vector of n reactions, i.e., v = ; and iL and iU are the lower and upper flux bounds, respectively, for each reaction i, which is an approximation based on the normalized expression of the gene catalyzed in that reaction.After constraining the metabolic model using the experimental proteomic data, the flux distribution of the model was predicted by solving the following linear optimization problem:v(iθ) is a vector of reaction flux prediction given the proteome set (θ).Since there were multiple feasible solutions within the constraints, in this case, the method found a unique metabolic flux distribution among the alternative optima by minimizing the total sum of absolute flux. Based on the assumption that the cell attempts to achieve its objective function while allocating the minimum amount of resources;A. platensis C1 under various in silico gene knockouts and overexpression was simulated. The gene knockout condition was simulated in the model by restricting the flux through the designated reactions by setting the upper and lower bounds of the corresponding reaction to zero. in silico overexpression was simulated by increasing or doubling the level of the upper and lower bounds of the corresponding reaction.According to the proteome-based genome-scale metabolic model (GSMM), the metabolic flux of 2 function. The log2-flux values were normalized with the standard deviation (SD) of each dataset. Second, the difference in the log2 of the simulated flux obtained from Conditions 1 and 2 was calculated as the fold change value. Third, the cut-off values for the significant change of the simulated metabolic fluxes compared between the two conditions were determined based on the proportion of the significant change reactions less than 30% of the total reaction number. Accordingly, in the case of temperature stresses and in silico knockout or overexpression, when the log2 values of simulated flux obtained from T35, T22 or T40 were used as the basal level, the cut-off fold change for the comparative analysis was 1.206.The present study is the first to present a comparative analysis of the metabolic fluxes obtained from proteome-based GSMM. Thus, the data normalization and the cut-off criteria for the significant change of the flux level have not been determined to date. In the case of flux values equaling zero, the original simulated values were used in the comparative analysis. Otherwise, the simulated fluxes obtained from the model were subjected to a logBased on the KEGG global metabolic pathway as a template, the simulated metabolic fluxes of each condition were mapped on the designated reactions by using the KEGG Pathway Mapper tool. In the result images, other reactions that were not involved with the fluxes were manually eliminated.The proteome-based constraint method is applied to the newly expanded GSMM as shown in To investigate the correlation of the temperature change and the simulated metabolic fluxes, scatter plots of the flux distribution were plotted between flux distributions at various temperature conditions. All the flux distribution profiles show a positive trend with a high correlation .The simulated flux distribution was analyzed by pathway mapping using KEGG pathways as the templates . The affMore than 90% of the flux level in some central metabolic pathways, e.g., glycolysis, the Calvin cycle and the pentose phosphate pathway, was decreased or unchanged when comparing the optimal- and high-temperature conditions. In contrast, an increasing flux level was observed in the central metabolic pathways after the low-temperature shift. However, the comparative analysis showed a decreasing level of fluxes in the TCA cycle, and as a result, 2-OG biosynthesis was possibly reduced under low-temperature stress. Moreover, the flux levels of the two glutamate (Glu)-synthesizing enzymes glutamate synthase and aspartate transaminase, one using 2-OG and the other using oxaloacetate as substrates, were upregulated at 22 °C.The fluxes of the reactions catalyzed by three enzymes, isocitrate dehydrogenase (ICDHy), ferredoxin-dependent glutamate synthase (GLUS3) and glutamine synthetase (GLNS), which were found in the group of client proteins of Hik28 in the PPI network , were su2 uptake and oxygen evolution in photosystem II were induced in the ICDHy-overexpressing model, whereas an increase in citrate exchange flux was observed in both mutant models. Moreover, the exchange of 2-OG was also significantly enhanced as a result of the in silico overexpression of ICDHy. However, the biomass of these two mutant models was not changed.For example, an overview of metabolic flux analysis after the in silico knockout and overexpression of the reaction catalyzed by ICDHy, which presumably caused the reduction and enhancement of 2-OG, respectively, showed a decreasing flux of approximately 5% of the total metabolic reactions for both ICDHy mutants. However, approximately 4% of the flux level in the ICDHy-knockout and 7% of the ICDHy-overexpressing models was increased, and among these reactions, the flux catalyzed by GLUS3 was increased in the ICDHy-knockout model and vice versa in the ICDHy-overexpressing model. It is worth noting that the fluxes involved in COIn the case of overexpression of the reaction catalyzed by GLUS3 and GLNS, the simulated biomass was increased only in the GLUS3-overexpressing model. Similarly, the simulated fluxes involved in chlorophyll biosynthesis, photosystem II and fatty acid metabolism were induced after in silico knockout and overexpression . It shouSpirulina metabolism under an optimal growth temperature and suboptimal stress temperatures. The expression levels of significantly DE proteins were used as constraints for the new Spirulina metabolic flux model to provide an in silico-simulated snapshot of the metabolic changes that occur under different temperature conditions. Whether proteomics data are hard to obtain, and quantitative proteomic data is not widely available, the proteomics experiments are more precise and more suitable for metabolic integration than transcriptomics approaches. As reaction activities will correlate more strongly with protein abundance than with mRNA abundance, flux bound should be more correlated with protein expression than with mRNA expression. The estimated results from the integration of the proteomic dataset with the metabolic model enable more reliable metabolic flux predictions. Moreover, the estimated flux from the integration of a statistically significant differential expressed proteomic dataset into the metabolic model could refine our understanding of biological changes in Spirulina under temperature stress.To the best of our knowledge, this study is the first to employ experimental fold change proteomic datasets to represent relative change in A. platensis C1 was observed under the optimal temperature compared to that under the stress conditions, and the biomass of the culture grown at 22 °C was higher than that at 40 °C , a key enzyme in carbon dioxide fixation, was the only downregulated reaction, whereas pyruvate kinases, PYK1, PYK5 and PYK6, and adenosylcobinamide kinase, ACBIKGTP, were the upregulated reactions found in common among the four models . NotablyMoreover, it was previously reported that the uncoupling of catabolism and anabolism could be an indicator of the delayed adaptation of bacterial cells to sudden unbalanced conditions ,28. In oAn analysis of the ratio of the affected reactions in C- and N-metabolism after pathway mapping, as shown in The proteomic data obtained under growth temperature stress conditions were integrated to develop the proteome-based model in the present study. The model was subjected to biological applications in terms of gene manipulation and flux alteration in in silico knockout and overexpression.A. platensis, as mentioned earlier (In cyanobacteria, there have been intensive studies on 2-OG, which is the intermediate in the TCA cycle at the interconnection of C- and N-metabolism, as the signal molecule for nitrogen stress . It is w earlier . TherefoThe in silico knockout and overexpression of the reactions of interest can be performed by using proteome-based GSMM to simulate the systematic-scale fluxes that may have occurred after genetic manipulation. According to our previous proteomic analysis, several differentially expressed proteins under low-temperature stress were the same as those under nitrogen depletion, e.g., proteins involved in N assimilation and fatty acid desaturation . MoreoveA. platensis C1 were integrated into our existing GSMM by using an optimization method. Taken together with the pathway mapping and analysis, the metabolic fluxes simulated from the proteome-based model led to prospective biological interpretation and enabled the prediction of genome-scale metabolic fluxes under the designated conditions, including those generated from in silico knockout and overexpression. Therefore, by using the model, the cultivated conditions for biomass production and production of the desired biochemical compounds, as well as the strategy for genetic manipulation for biochemical production, can be anticipated. However, the inability to directly integrate the cell regulatory mechanisms into the model remains a limitation regarding whether the proteome data obtained from stress response studies can be integrated into the GSMM. Moreover, the approach presented in this study is applicable to the in silico modification of a metabolic flux catalyzed by an irreversible enzyme.The GSMM is a computational analysis through FBA, which is used to predict and explain the performance and behavior of microorganisms. The mathematical approach is able to simulate metabolism providing information difficult to obtain in laboratory or field cultivations to maximize the production yield of microalgal biomass. In the present study, proteome analysis data obtained under growth temperature stress conditions of


Medicago truncatula (plant) nodulated by Sinorhizobium meliloti (bacterium). The reconstructed nodule tissue contains five spatially distinct developmental zones and encompasses the metabolism of both the plant and the bacterium. Flux balance analysis (FBA) suggests that the metabolic costs associated with symbiotic nitrogen fixation are primarily related to supporting nitrogenase activity, and increasing N2-fixation efficiency is associated with diminishing returns in terms of plant growth. Our analyses support that differentiating bacteroids have access to sugars as major carbon sources, ammonium is the main nitrogen export product of N2-fixing bacteria, and N2 fixation depends on proton transfer from the plant cytoplasm to the bacteria through acidification of the peribacteroid space. We expect that our model, called ‘Virtual Nodule Environment’ (ViNE), will contribute to a better understanding of the functioning of legume nodules, and may guide experimental studies and engineering of symbiotic nitrogen fixation.The mutualistic association between leguminous plants and endosymbiotic rhizobial bacteria is a paradigmatic example of a symbiosis driven by metabolic exchanges. Here, we report the reconstruction and modelling of a genome-scale metabolic network of  Medicago truncatula nodulated by the bacterium Sinorhizobium meliloti.The association between leguminous plants and rhizobial bacteria is a paradigmatic example of a symbiosis driven by metabolic exchanges. Here, diCenzo et al. report the reconstruction and modelling of a genome-scale metabolic network of the plant  The intimate association between organisms is often driven by metabolic exchanges: many insects obtain essential nutrients from obligate bacterial symbionts3, most plants can obtain phosphorus from arbuscular mycorrhiza in exchange for carbon4, and the gut microbiota is thought to contribute to animal nutrition6. Complex global patterns often emerge during these intimate biological associations7, especially when nutritional inter-dependencies are involved10. The communication between the two metabolic networks of the interacting organisms may give rise to unpredicted phenotypic traits and unexpected emergent properties. Metabolic relationships can span over a large taxonomic range and have profound biological relevance14. For example, the interactions between bacteria and multicellular organisms have been suggested to be key drivers of evolutionary transitions, leading to eukaryotic diversification and to the occupancy of novel niches16. The study of the association of two biological entities is mainly challenged by the size of the system and by the unpredictability of their metabolic interactions. Theoretical, systems-level models are required to unravel the intimate functioning of metabolic associations and to eventually exploit their potential in biotechnological applications.Macroorganisms are colonized by a staggering diversity of microorganisms, collectively referred to as a ‘holobiont’Frankia spp.), in which the plants provide a niche and carbon to the bacteria in exchange for fixed nitrogen17. SNF involves constant metabolic cross-talk between the plant and the bacteria18, and it is a paradigmatic example of bacterial cellular differentiation19 and sociomicrobiological interactions20. The rhizobia intra-cellularly colonize plant cells of a specialized organ known as a root (or stem) nodule. The intra-cellular rhizobia (referred to as bacteroids) are surrounded by a plant-derived membrane, and the term symbiosome is used in reference to the structure consisting of the bacteroid, the plant-derived membrane , and the intervening space . Nodules with an indeterminate structure, such as those formed by the plant Medicago truncatula, are divided into spatially distinct developmental zones21 with a distal apical meristem and a proximal nitrogen fixation zone.Symbiotic nitrogen fixation (SNF) is a paradigmatic example of the importance and the complexity of natural biological associations. SNF is a mutualistic relationship between a group of plant families, including the Fabaceae, and a polyphyletic group of alpha- and beta-proteobacteria known as rhizobia, or a taxa of Actinobacteria , GENREs can be used to identify emergent system-level properties, to predict active reactions, and to identify essential genes29. Compared to simple enrichment analyses that are typical in-omics studies, GENRE-based methods allow for the interpretation of data in a connected manner based on network topology and to infer the effects of changes in remote pathways on the overall cell physiology. When considering interacting entities, for example, this approach can predict the consequence of mutations in one organism on the metabolism of the other. However, multi-organism metabolic reconstructions are still in their infancy, and very few examples of combined models exist compared to single strain GENREs32.From a metabolic perspective, genome-scale metabolic reconstruction (GENREs) and constraint-based modeling has great potential to fulfill these roles. A GENRE also serves as a comprehensive knowledgebase of an organism’s metabolism, containing hundreds to thousands of metabolic and transport reactions, most of which are linked to the corresponding gene(s) whose gene product(s) catalyzes the reaction18, there has been limited use of metabolic modeling in the study of rhizobia and SNF. To date, GENREs of varying quality have been reported for only three rhizobia: Sinorhizobium meliloti35, Rhizobium etli38, and Bradyrhizobium diazoefficiens39. Currently, M. truncatula40 and Glycine max41 are the only legumes with published GENREs. With the exception of the G. max GENRE, these GENREs have been used in preliminary analyses of SNF, providing results generally consistent with expectations. However, all analyses to date suffer from two major limitations. Simulations with the rhizobium models ignore plant metabolism, while simulations with the M. truncatula GENRE  involved a very limited draft S. meliloti metabolic reconstruction (whose genome sequence was published in 200145). Furthermore, all simulations have focused on the final stage of SNF and have not considered the different steps of the preceding developmental progression where metabolism remains poorly understood18.Despite the importance of metabolism to SNFM. truncatula plant nodulated by S. meliloti, which we refer to as a Virtual Nodule Environment (ViNE). Our combined, multi-compartment reconstruction accounts for the metabolic activity of shoot and root tissues together with a nodule consisting of five developmental zones. We report initial characterizations of ViNE using FBA, including zone-specific metabolic properties, trade-offs between nitrogen-fixation and plant growth, and the usage of dicarboxylates as a carbon source by bacteroids. Going forward, we expect ViNE will provide a powerful platform for hypothesis generation aimed at understanding and quantitatively evaluating SNF, as well as guiding attempts at engineering SNF for increased symbiotic efficiency.Here, we report a holistic in silico representation of the integrated metabolism of the holobiont consisting of a S. meliloti Rm1021 was prepared as described in Methods and Supplementary Note 34 was combined with the comprehensive accessory metabolism of the genome-scale metabolic network iGD157533. Most of the reactions were manually curated through comparison against the literature, referenced where possible, and mass and charge balanced. The updated model consists of 1348 genes  was used to simulate growth using glucose or succinate as the sole source of carbon, with or without the inclusion of an NGAM reaction. Inclusion of an NGAM reaction resulted in a specific growth rate reduction of ~0.043 h−1 and 0.030 h−1 for growth with glucose and succinate, respectively. This result confirmed the absence of energy leaks in iGD1348 that would allow for spontaneous energy production.Several tests were performed to validate the quality of the newly prepared S. meliloti to catabolize 163 carbon sources to support growth was predicted with the iGD1348 and iGD1575 models  substrates experimentally shown to support growth of S. meliloti. Nicely, simulations with the iGD1348 model correctly predicted growth with 76 of these 85 (89%) substrates, including all 67 that supported growth of the iGD1575 model. This result confirmed that iGD1348 incorporates the majority of the accessory metabolism of S. meliloti, and that it is a better representation of total cellular S. meliloti metabolism than the previous genome-scale model.Using FBA, the ability of 34 using Tn-Core48. The accuracy of the resulting core metabolic models was determined through comparison with iGD726, a manually prepared core metabolic model of S. meliloti34. As summarized in Fig. S. meliloti than does the iGD1575 reconstruction. Overall, these tests confirmed that iGD1348 is a high-quality representation of S. meliloti metabolism, and that it is an improvement over previous iterations of S. meliloti GENREs.Context-specific core metabolic models, containing a minimal set of reactions for biomass production in a given environment, were extracted from the iGD1348 and iGD1575 metabolic models through the integration of Tn-seq dataM. truncatula and S. meliloti metabolism was a prerequisite to generating an in silico genome-scale metabolic network of an entire nodulated legume (referred to as ViNE). For S. meliloti, we used the updated model described in the previous section. In the case of M. truncatula, we used a recently published reconstruction that was updated to match the most recent version of the M. truncatula genome annotation  and is consistent with ViNE being a high-quality metabolic reconstruction. The detailed reports are provided in Supplementary Data Integrating the S. meliloti genes and 1,327 unique M. truncatula genes. The nodule was subdivided into five zones to match the spatially, and transcriptionally, distinct developmental zones that are simultaneously present in indeterminate legume nodules such as those formed by M. truncatula50. Several simulations were performed to evaluate the reliability of the model. Using FBA, the maximal rate of plant (shoot + root) growth of the nodulated system was predicted to be ~0.044 g day−1 (g plant dry weight)−1, while the N2-fixation rate was predicted to be ~3 µmol h−1 (g plant dry weight)−1. Both values are reasonable predictions; Medicago sativa plants have an experimentally determined growth rate of ~0.1 g day−1 (g plant dry weight)−1 51 and N2-fixation rates around 7 µmol h−1 (g plant dry weight)−1 based on acetylene reduction assays52. Moreover, the predicted carbon cost of supporting N2-fixing nodules (nodule CO2 export per N2 fixed) was ~4.2 g C g−1 -N, falling within the normal range of 3–5 g C g−1 -N based on experimental measurements53.In total, ViNE includes 746 unique 2-fixation efficiency (defined as the rate of N2-fixation per gram nodule) could vary while the rate of nodulation  was constant at 2%, and (ii) the rate of nodulation could vary while the N2-fixation efficiency was constant at 150 µmol h−1 (g nodule dry weight)−1. As expected, increasing the availability of exogenous ammonium increased the rate of plant growth, with the effect more pronounced when the rate of nodulation was allowed to decrease with increasing ammonium since the plant no longer had to invest in nodule maintenance  the Nnce Fig. .Fig. 3EfS. meliloti mutants. For example, S. meliloti genes such as nifH (nitrogenase), dctA (succinate transport), ilvI (branched-chain amino acid biosynthesis), aatA (aspartate transaminase), pgk (phosphoglycerate kinase), and nrdJ (ribonucleotide reductase) were correctly predicted to be essential, while pyc (pyruvate carboxylase), glnA (glutamine synthetase), pckA (phosphoenolpyruvate carboxykinase), and leuB (leucine biosynthesis) were correctly predicted to be non-essential61. Similarly, the removal of plant-encoded nodule sucrose synthase, phosphoenolpyruvate carboxylase, and homocitrate synthase reactions abolished nitrogen fixation, as expected64. However, it is important to note that the predictions were not perfect. For example, deleting argG (arginine biosynthesis) or carA (carbamoyl phosphate synthase) did not result in the expected phenotypes, while the incorrect malic enzyme (tme instead of dme) was predicted to be essential67. Taken together, these analyses provide support for the general reliability of ViNE as a representation of nodule metabolism.We then simulated the effects of individual bacteria gene deletions on plant biomass production  are the primary carbon source for N2-fixing bacteroids74, the source of carbon for differentiating bacteroids has not been established. The FBA results suggested that differentiating bacteroids primarily use sugars, likely sucrose, as a carbon source. This is consistent with micrographic evidence suggesting that bacterial mutants unable to use C4-dicarboxylates can undergo at least partial differentiation73. Currently, it is commonly accepted that nitrogen is primarily exported from bacteroids as ammonia76; however, some studies have suggested that L-alanine could be a major nitrogen export product78. Our FBA simulations were consistent with ammonia being the primary export product in the S. meliloti – M. truncatula symbiosis. However, prior to constraining the nodule reaction space, reducing the availability of oxygen to the bacteroids resulted in a shift in the nitrogen export product from ammonia to L-alanine. Thus, the detection of L-alanine versus ammonia as an export product could be due, in part, to differences in experimental set-up that may influence oxygen availability to the bacteroid. Also, experimental data suggest that rhizobial biosynthesis of some amino acids is essential for the symbiosis while the biosynthesis of others is not, and that the phenotypes may be symbiosis-specific [reviewed by79]. Similarly, our FBA simulations suggested that rhizobial biosynthesis of approximately half of the amino acids was essential for the symbiosis.Next, nutrient exchange between the plant and bacterial partners was examined. While the prevailing evidence suggests C2-fixation  was estimated to be ~72% of the maximal growth rate of a nodule-free system supplied with non-limiting amounts of exogenous ammonium Fig. , Table 22-fixation efficiency (while keeping the rate of nodulation at 2%) and the rate of plant growth. When the N2-fixation efficiency was below the optimum, there was a linear relationship between N2-fixation and biomass production −1 increased or decreased the rate of plant growth by 14.7 or 3.4 mg day−1 (g plant dry weight)−1 when below or above the optimum, respectively. We next examined the consequences of varying the rate of nodulation  while maintaining a constant N2-fixation efficiency. The simulations demonstrated linear relationships between the rate of nodulation and plant growth both above and below the optimum  are the primary carbon source provided to nitrogen-fixing zone III bacteroids73; however, the reason for this remains unclear. We therefore attempted to uncover a metabolic explanation using ViNE. Surprisingly, preliminary FBA simulations with the ViNE precursor model  suggested that the N2-fixing bacteroids of nodule zone III are provided sucrose, not C4-dicarboxylates, as the primary carbon source. Unexpectedly, forcing the use of C4-dicarboxylates resulted in the model being unable to fix nitrogen or produce plant biomass. During those simulations, protons of the plant cytosol could be transferred to the peribacteroid space but were not allowed to be used by the N2-fixing bacteroids. However, this may not be realistic since the peribacteroid space of N2-fixing bacteroids is acidic due to import of protons from the plant cytosol83. If the analysis was repeated and the zone III bacteroids were provided access to the protons of the peribacteroid space, it became possible for C4-dicarboxylates to serve as the primary carbon source and support N2-fixation and plant growth. These results suggest that the plant-driven acidification of the peribacteroid space is essential for the metabolic functioning of the bacteroid.It is well-established that C4-dicarboxylates to support N2-fixation, the rate of plant biomass production nevertheless remained higher when the N2-fixing bacteroids were provided sucrose instead of C4-dicarboxylates. To further investigate this difference, ViNE was modified to contain reactions for the transport and metabolism of sucrose by N2-fixing bacteroids (see Supplementary Note 4-dicarboxylates) increased the plant growth rate by 6.4% relative to when bacteroids were supplied only C4-dicarboxylates. ViNE contains a limit on the rate of oxygen uptake by zone III nodule tissue as the concentration of free oxygen in the N2-fixation zone is known to be low84 to protect the highly oxygen-sensitive nitrogenase enzyme. The expected consequence of this is a constriction in the rate of flux through the electron transport chain, thus, restricting nodule and bacteroid metabolism by limiting ATP production and the removal of reductant. We wondered whether the use of sucrose versus C4-dicarboxylates may be modulated by the free oxygen concentration of the nodule. The concentration of free oxygen in the N2-fixation zone has been experimentally demonstrated to be <50 nM84. Notably, the Km values of the mitochondrial and bacterial terminal oxidases towards oxygen are 50−100 nM86 and 7 nM87, respectively. These enzyme kinetics suggest that the metabolism of the plant fraction, but not the bacteroid fraction, of the nodule is likely to be oxygen limited89, a conclusion that is supported by measurements of nodule adenylate pools90. Therefore, we ran a series of simulations in which the upper limit of the mitochondrial terminal oxidase reaction of zone III was varied, with no overall limit on the use of oxygen by the nodule. Gradually reducing the flux through the mitochondrial terminal oxidase was associated with a gradual replacement of sucrose with C4-dicarboxylates as the carbon source provided to N2-fixing bacteroids  was similar to experimentally determined uptake rates by S. meliloti bacteroids (1.1 to 1.3 mmol h−1 [g bacteroid dry weight]−1)91. Considered together, these simulations provide evidence that C4-dicarboxylates can support optimal plant growth under physiologically relevant conditions.Assuming the nodule (consisting primarily of zone III tissue) accounts for 2% of total plant biomass, and that bacteroid biomass accounts for 25% of nodule biomass, the maximal rate of predicted C93. However, to date there are few examples of constraint-based metabolic modeling being used to study metabolic interactions [e.g.31], with this approach most commonly used to study the human gut microbiome94. Here, we developed a broadly adaptable pipeline for modeling the metabolism of interacting organisms across physiologically distinct tissue (sub)sections. Using metabolic network reconstruction and constraint-based modeling, we studied the metabolism of a legume root nodule and SNF, a well-established model of inter-organismal metabolic exchange and cellular differentiation.Models of the integrated metabolism of various holobionts (consisting of a host and its symbiotic microorganisms) would be valuable tools to understand the emergent properties of these systems40, most of which focused solely on bacterial metabolism while treating the plant as a black box. The increased complexity of ViNE allows for: i) more accurate simulations of the nutrient exchange, ii) analysis of the metabolic differentiation associated with nodule development, iii) examination of unexpected emergent properties of the symbiosis resulting from inter-organism interactions, and iv) the possibility to perturb the network at the single reaction level. Initial simulations with ViNE supported that this model does a good job at capturing the metabolism of a legume nodule. Nevertheless, as with all models, ViNE predictions were imperfect; of the 38 genes both present in ViNE and listed as being involved in symbiosis according to Additional File 5 of Galardini et al.95 , deletion of 84% were correctly predicted to have a symbiotic phenotype in ViNE. However, as we often compared simulated phenotypes for M. truncatula with experimental data for M. sativa, and given that rhizobium mutant phenotypes are often plant specific (e.g.98), we cannot rule out that some of the inconsistencies are the result of plant-specific phenotypes. Going forward, we intend to continue to manually refine and update ViNE to maximize consistency with experimental observations.Our model (ViNE) accounts for plant shoot, root, and nodule tissues, with the nodule encompassing the metabolism of both the plant and bacterial partners and subdivided into five developmental zones. This is an advance over previous attempts at modeling SNF2-fixation efficiency increased, assuming that the rate of nodulation could also vary  as the Nary Fig. . This ob100. In particular, the evolution of N2-fixation efficiency may be influenced by the rhizobium community diversity, assuming that nodule infection increases the fitness of rhizobia101. In an environment dominated by a single rhizobium, kin selection may favor the evolution of a poorly efficient symbiosis as it would increase nodule number and thus the size of the niche for colonization by the rhizobia. On the other hand, in a highly diverse environment, evolution of strains capable of entering into a highly efficient symbiosis may be favored, as this would lead to fewer nodules and thus less plant resources being allocated to competing rhizobium strains, thereby limiting the spread of less mutualist (viz. cheater) strains102.At the same time, the pattern of diminishing returns is interesting from an evolutionary perspective2-fixation and during differentiation. The carbon source(s) of differentiating rhizobia remain poorly understood. Results with ViNE suggested that sucrose may be a major carbon source for the differentiating bacteroids. However, S. meliloti mutants unable to transport sucrose are not impaired in nodule formation103, suggesting that differentiating bacteroids have access to at least one other carbon source. Interestingly, a S. meliloti pyc mutant unable to grow with glycolytic carbon sources was not impaired in differentiation60. Similarly, S. meliloti pckA58 and tpi104 mutants unable to grow with gluconeogenic carbon sources remained capable of differentiating. Thus, it seems likely that differentiating bacteroids have access to a variety of glycolytic and gluconeogenic carbon substrate, with sugars possibly serving as the main carbon source in wild type nodules. If so, the restriction of carbon flow to N2-fixing bacteroids to just C4-dicarboxylates may be the result of active remodeling of the peribacteroid membrane during differentiation.Of particular interest to us were the metabolic exchanges between the plant and rhizobia, both during N4-dicarboxylates as a carbon source by N2-fixing bacteroids, ViNE also provided insights into the metabolic exchange in the N2-fixation zone. The peribacteroid space of N2-fixing bacteroids is known to be acidic due to the activity of H+-ATPases on the peribacteroid membrane83. This acidification contributes to the import of C4-dicarboxylates and the export of ammonium from/to the plant cytosol and the peribacteroid space105, and it may contribute to the lysis of non-functional symbiosomes106. Our FBA simulations suggest that the plant-derived protons of the peribacteroid space may also be actively used by the bacteroid to support its metabolism.In attempting to identify conditions favoring the use of C84, the site of O2-limitation has been debated. Based on the average concentration of free oxygen in the nodule, enzyme kinetics data are consistent with the mitochondria being O2-limited and the bacteroids being O2-sufficient87. Measurements of the adenylate pools of the plant and bacterial nodule fractions support this conclusion90. However, others have argued that nodule adenylate measurements suggest that bacteroids, not the plant, are O2-limited107. Similarly, it was suggested that mitochondria cluster near the periphery of the cell near air pockets, resulting in elevated local O2 concentrations109. The FBA results presented here predicted that C4-dicarboxylates are the optimal carbon source for N2-fixing bacteroids only when the plant mitochondria are O2-limited while the bacteroids are O2-sufficient  updated the biomass composition  mass and charge balanced all reactions, and (v) added an ATP hydrolysis reaction to account for non-growth associated maintenance (NGAM) costs. The final S. meliloti reconstruction, termed iGD1348, is available in Supplementary Data M. truncatula reconstruction, a recently published reconstruction40 was used in this work. Since that version was based on the M. truncatula genome annotation version Mt3.5v542, the gene names in the reconstruction were updated to match the most recent annotation , and a few ion transport reactions were added. See Supplementary Notes S. meliloti and M. truncatula GENREs, respectively.Before starting the reconstruction of the integrated metabolic model, we refined/updated the existing reconstructions for S. meliloti and M. truncatula reconstructions were integrated following the procedure described in detail in Supplementary Notes M. truncatula – S. meliloti zone-specific RNA-seq data of Roux and coworkers50, which was reanalyzed as described in Supplementary Note Afterward, the 110, libSBML version 5.13.0111, and scripts from the COBRA Toolbox commit 9b10fa1112, the TIGER Toolbox version 1.2.0-beta113, FASTCORE version 1.0114, and the Tn-Core Toolbox version 2.248. The iLOG CPLEX Studio 12.7.1 solver (ibm.com) was used for nearly all FBA simulations; the exception was for the preparation of iGD1348, during which the Gurobi version 7.0.1 solver (gurobi.com) was used. The switch to CPLEX was prompted by numerical issues that were solved by switching the solver. All custom scripts used in this study are available through a GitHub repository (github.com/diCenzo-GC/ViNE_Reconstruction).Model integration, model manipulations, and FBA simulations were performed in MATLAB R2016b (mathworks.com) using the SBMLToolbox version 4.1.04-dicarboxylates as the carbon source, a modified version of ViNE was prepared as described in Supplementary Note Each gene found in multiple tissues or nodule zones was distinguished by a unique gene name to facilitate tissue-specific gene deletion analyses. When performing global single or double gene deletion analyses, all versions of the gene were simultaneously deleted followed by the removal of all constrained reactions. In contrast, zone- or tissue-specific gene deletion analyses involved deleting just the gene version specific to the zone or tissue of interest. Flux variability analyses were performed with the requirement that flux through the objective function was at least 99% the optimal flux. The robustness analyses involved first identifying the approximate flux range for each reaction in which the plant growth rate was non-zero. Then, for each reaction, the flux rate of the reaction was set to various values within the previously identified flux range, and the objective value was maximized. For simulations in which the rate of nodulation could vary, nodule biomass was removed from the objective reaction and instead forced through a nodule biomass sink reaction at the appropriate rate; maintenance costs and oxygen availability were modified accordingly  the rate of oxygen uptake by zone III tissue was 8.9847 µmol h−1 (g plant dry weight)−1; (iii) the rate of light uptake by the leaves was 1000 µmol h−1 (g plant dry weight)−1; (iv) the rate of CO2 uptake by the leaves was 1000 µmol h−1 (g plant dry weight)−1; (v) maintenance costs (ATP hydrolysis) for shoot and root tissue were ~74 and 37 µmol h−1 (g plant dry weight)−1, respectively; (vi) maintenance costs (ATP hydrolysis) for zone I, IId, IIp, IZ, and III plant nodule tissue were ~111, 750, 750, 83, and 833 nmol h−1 (g plant dry weight)−1, respectively; (vii) maintenance costs (ATP hydrolysis) for zone IId, IIp, IZ, and III bacteroid tissue were ~5.7, 5.7, 0.6, and 6.3 µmol h−1 (g plant dry weight)−1, respectively; and (vii) the only carbon sources available to bacteroids in the zone III nodule tissue was succinate, malate, and fumarate at a maximal rate of 1000 µmol h−1 (g plant dry weight)−1 each. No limit on the carbon sources available to the bacteroids of other nodule zones was set.Except when stated otherwise, the following constraints were placed on ViNE during the simulations: (i) when NFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Reporting Summary