Brachypodium sylvaticum to investigate the molecular mechanisms that contribute to cold and freezing adaption. The study was performed on two different B. sylvaticum accessions, Ain1 and Osl1, typical to warm and cold climates, respectively. Both accessions were grown under controlled conditions with subsequent cold acclimation followed by freezing stress. For each treatment a set of morphological parameters, transcription, metabolite, and lipid profiles were measured. State-of-the-art algorithms were employed to analyze cross-component relationships. Phenotypic analysis revealed higher adaption of Osl1 to freezing stress. Our analysis highlighted the differential regulation of the TCA cycle and the GABA shunt between Ain1 and Osl1. Osl1 adapted to freezing stress by repressing the GABA shunt activity, avoiding the detrimental reduction in fatty acid biosynthesis and the concomitant detrimental effects on membrane integrity.Perennial grasses will account for approximately 16 billion gallons of renewable fuels by the year 2022, contributing significantly to carbon and nitrogen sequestration. However, perennial grasses productivity can be limited by severe freezing conditions in some geographical areas, although these risks could decrease with the advance of climate warming, the possibility of unpredictable early cold events cannot be discarded. We conducted a study on the model perennial grass  Predictions showed that perennial grasses will account for approximately 16 billion gallons of renewable fuels by the year 20223. Perennial grasses have shown to support carbon (C) and nitrogen (N) sequestration in the soil6, contributing to mitigate climate change and environmental degradation9. Brachypodium sylvaticum is a perennial grass with a simple genome, self-fertility, a short life cycle, and low growth requirements. Its recently released genome, expression gene atlas10 and well established genetic transformation have made it an efficient model to study the genetic, molecular, and physiological components of perennial grasses12.The increase in energy demand coupled with the environmental impact of fossil fuels has led to the expansion of programs using perennial species as an alternative source of energy. In this context, perennial grasses, traditionally used as forage and turf, have become the preferred candidates for biomass cropping systems attributed to their ability to grow under low input conditions and high interception of sunlight14. Low temperatures combined with high light intensity provoke the inhibition of the photosystem II and ROS production leading to oxidative stress15. In addition, ice formation in the apoplast induces both, cell membrane integrity stress and osmotic stress16. The plant’s response to low temperatures involves extensive reprograming of gene expression via transcription factors17, resulting in concerted shifts of the metabolic network. Moreover, plants respond to cold stress by increasing the degree of unsaturation of the membrane lipids and by remodeling lipids, which in turn may help the plant to avoid membrane integrity damage and achieve chilling tolerance18. The molecular and biochemical pathways regulating winter-hardiness in perennial grasses are poorly understood and deserve further attention.Unfavorable winter conditions, including freezing, limit the geographical range of many important crops and reduce their productivity due to growth repression24. The biological interpretation of the data, however, is always study specific. Many of these tools make use of graph theory and network analysis, in one way or another, as the integration of heterogenic data is accomplished with ease and graph theory is readily equipped with a myriad of analytical and interpretational tools27. Correlation-based network analysis (CNA) is based on mathematically defined (dis)similarity measures that correlate different components to each other and the resulting correlation coefficients reflect the magnitude of the co-linear relationship of the components. In complex biological data, with thousands of different components assessed at various data points, the correlation is synonymous with coordinated behavior. With the exploration of the structural properties of graphs, CNA has been successfully used to postulate biological hypotheses which have been verified a posteriori28. Recently, a novel approach that combines CNA with machine learning (ML) techniques, was used to predict the presence of metabolic pathways in tomato correlation networks (CN)29.With the advent of high-throughput platforms in the biological sciences, data driven analysis has become a key element for the integration of biological data of different backgrounds. A plethora of tools have been developed which tackle the complex problem of data integration from different perspectives, e.g.Brachypodium sylvaticum accessions (Osl1 and Ain1) to cold and freezing stress. Accession Osl1 is endemic to Northern Europe, while Ain1 can be predominantly found in Northern Africa  were identified when comparing the different treatments of Ain1 and Osl1 samples . The observation of metabolite specificity to accession and treatments supports the notion of the intertwined metabolic network, where metabolites are involved in many different metabolic pathways regulating various cellular aspects37.Posthoc Tukey test showed that sample means of metabolites Fig.  and lipi38.For the 53 significantly changing lipids repetitive patterns were detected for the sulfoquinovosyldiacylglycerols (SQDG) and the triacylglycerides (TAG) Fig. . For the31 and to the entire compounds dataset. For both PCAs, principal component (PC) 1 accounted for more than 90% of the variation observed . Principal component analysis (PCA) was applied to the 500 most variable genes of the normalized gene expression datasetved Fig. . The PCAved Fig.  reflectived Fig. , on the 39. In the CN, the nodes represent compounds and the edges between them the significant correlation coefficients (r). Thresholds for the Ain1 and Osl1 CNs were set to r ≥ 0.8 and a q-value ≤ 0.05 to remove spurious correlations. The Ain1 CN was composed of 334 nodes, corresponding to the compounds in the dataset, and 4,538 edges, of which 3,995 corresponded to positive correlations and 543 to negative correlations, resulting in a positive edges to negative edges (pe/ne) ratio of 7.36. The Osl1 CN contained 334 nodes and 6,317 edges, of which 6,230 corresponded to positive correlations and 87 to negative correlations, resulting in a pe/ne ratio of 71.6. Due to the greater number of edges in the Osl1 network, it exerted greater values for the edge density  and the average weighted degree of the network . Clustering of metabolites in a CN into communities may identify functional groups of nodes of similar chemical properties or groups of metabolites that are tightly connected due to shared regulatory processes40. Nodes in the network were clustered together using the walktrap community detecting algorithm41, revealing four lipid communities and one community containing metabolites in the Ain1 network  was constructed for each Ain1 and Osl1 compounds datasetsork Fig.  and two ork Fig. . Of the 42. However, while the sugars associated with the Ain1 network have shown to be a stabilizing agent for the plasma membrane during cold stress43, raffinose (Osl1 network) appeared to be restrained to the chloroplast inner membrane44. Nevertheless, raffinose has been demonstrated to act as a cryoprotectant in cold acclimated Arabidopsis ecotypes45. In the same study, a positive correlation between raffinose and glutamine was demonstrated. Asparagine, on the other hand, decreased during cold adaptation in the perennial ryegrass46. The occurrence of different long chain fatty acids in the Ain1 and Osl1 difference network may hint at the discerned regulation of lipid biosynthesis47.Among the lipid communities a small number of non-lipids were incorporated, mainly sugars, amino acids, fatty acids, and unknowns. To identify commonalities and differences between the networks, difference Fig. , interseAmong the putative functional group of lipids in the Ain1 network, one positive significant connection between GABA and TAG 50:6 (correlation coefficient 0.92) and three negative significant correlations to TAG’s 54:7, 54:8, 54:9 (correlation coefficient < −0.8) were present. Furthermore, maltose maintained two significant negative connections to TAGs 54:7 and 54:8 (correlation coefficient < −0.8). In the Osl1 network, the GABA and maltose significant correlations did not appear. However, galactinol displayed a significant positive correlation to TAG 60:5, while hexacosanoate displayed significant positive correlations to all TAG’s (correlation coefficient > 0.85).29. This approach explores the fact that metabolic pathways shape the topology of a CN. By mapping known metabolic pathways into the CN and learning their topological conformation, a ML model is generated capable of predicting the presence of unknown pathways. To do so, for each CN one ML model was generated  employing the extreme gradient boost algorithm48. Performance evaluation of the ML models showed an AUC of the receiving operating characteristic curve of 0.986 for the Ain1 CN model and 0.992 for the Osl1 CN model . Correlations were estimated at absolute values so that negative correlating genes were included in the resulting set of genes. As a result of the previous analyses pointing to GABA and organic acids in relation to lipid regulation in Ain1, we investigated pathways associated with GABA metabolism (4-aminobutanoate degradation I and IV) and the TCA-cycle. For the metabolites associated with the 4-aminobutanoate degradation I pathway, 74 genes were identified  to a large number of different substrates and are essential for lipid biosynthesis59. Here, the two acetyltransferases , Brasy3G151900 (transcription factor HY5), Brasy4G087400 (BHLH transcription factor), Brasy6G010100 (MYB transcription factor) were, in general, more abundant in Ain1 and increased during freezing conditions, while Brasy4G312600, Brasy4G395200 (MYB family transcription factors) and Brasy5G38300 (RF2a transcription factor) were more abundant in Osl1 . PC1 of the gene expression PCA showed a clear separation between accessions and separation of treatments on PC2 Fig. . The PCAks Figs. . The hig65. Our statistical, multivariate, and correlation-based network analyses emphasized the role of GABA, glutamate, putrescine, TCA cycle intermediates, and SQDGs and TAGs lipids. While the link between GABA and the TCA cycle, and its involvement in abiotic stress, is well known67, we were particularly interested in their roles with respect to lipid biosynthesis and their potential impact on the membrane integrity of the two B. sylvaticum accessions. We employed state-of-the-art algorithms that place big biological data into biological context by combining CNA with machine-learning techniques. The idea behind this approach is that the computer learns from existing data. Based on recurring patterns that known metabolic pathways imprint onto correlation networks, the computer is able to predict the existence or ‘activity’ of unknown metabolic pathways. The approach has proven so powerful that it was able to predict the melibiose degradation pathway in tomato, although melibiose was not part of the original dataset29. Rooted on the two correlation-based networks, we generated two ML models . For the cold experiments, the seeds were stratified in a cold room at 10 h/1 h light/dark cycles at 4 °C for 2 weeks before sown into soil. Germination and growth were achieved in a chamber room 16 h/8 h light-dark cycles at 26 °C/18 °C, respectively, for 3 weeks. For cold acclimation, the plants were moved into a cold room at 10 h/14 h light/dark cycles at 4 °C, for 3 weeks.Seeds from The freezing experiments were conducted in a temperature and light controlled growth chamber. The initial 24 h were composed of an 8 h light period at 4 °C, followed by a dark period of 2 h at 4 °C, 30 min at 2 °C, 30 min at 0 °C, 4 h at −2C, 8 h at −5.5 °C, 30 min at −2 °C, and 30 min at 1 °C. For the subsequent 24 h, plants were kept at dark at 4 °C. After the freezing treatment, all plants were moved to growth chambers where they were kept at 16 h/8 h light-dark cycles at 26 °C/18 °C, respectively.Leaf samples of seedlings were collected i) before the cold acclimation (CK) treatment was initiated, ii) before the freezing challenge was initiated, and iii) after 8 h of freezing at −5.5 °C. All samples were subjected to RNA and metabolite extraction for subsequent gene expression and metabolomics analysis, respectively (see below). Ion leakage measurements and immunoblot analysis were performed immediately after freezing challenge using control plants as a reference control. The plant damage phenotyping and sampling for chlorophyll and MDA contents were performed after 2 weeks of recovery in growth chamber rooms using control plants as a reference control.74.Shoot samples for electrolyte leakage were harvested immediately after freezing challenge. Electrolyte leakage was measured as described previously2, and ground in three volumes of 2 × Laemmli sample buffer. Total proteins were separated by SDS-PAGE, transferred to a polyvinylidene difluoride membrane (Bio-Rad), and probed as described previously75. Antibodies raised against PsbA/D1 (AS01016), and Lhcb2 (AS01003) and PsbO 1 (AS05092), were obtained from Agrisera . Horseradish peroxidase-conjugated secondary antibodies were purchased from Santa Cruz Biotechnology.Plant leaf tissues were weighed, frozen in liquid N2 and weighed. Chlorophyll was extracted in 80% acetone, and the absorbance was measured at 663 nm and 645 nm . Total chlorophyll content was calculated as described elsewhere76.Shoots were grinded in liquid N2 and weighed. Tissue was homogenized with 5 ml of 50 mM NaH2PO4-Na2HPO4 buffer pH 7.5 and centrifuged at 20,000 g for 25 min. For MDA measurements, 4 ml of 20% trichloroacetic acid containing 0.5% thiobarbituric acid were added to a 1 ml aliquot of the supernatant. The mixture was heated at 95 °C for 30 min, quickly cooled in ice, and then centrifuged at 10,000 g for 10 min. The absorbance of the supernatant was measured at 532 nm and 600 nm77.Shoots were grinded in liquid N78. One hundred-fifty μl vacuum-dried polar phases samples were derivatized and subjected to GC‒MS analysis as described previously79. The GC‒MS data were obtained using an Agilent 7683 series auto-sample , coupled to an Agilent 6890 gas-chromatograph‒Leco Pegasus two time-of-flight mass spectrometer (Leco: http://www.leco.com/). Identical chromatogram acquisition parameters were applied to those previously used80. Chromatograms were exported from the LECO CHROMATOF software (version 3.34) to the R software. Ion extraction, peak detection, retention time alignment and library searching were obtained using the TargetSearch package from Bioconductor81. The resulting data matrix was used for further analysis.Metabolites were extracted using the MTBE method as described elsewhere77. Five hundred μl vacuum-dried organic phase samples were processed using ultra-performance liquid chromatography, on a C8 reverse phase column coupled with Fourier transform mass spectrometry (UPLC-FT-MS)  in positive and negative ionization modes. Processing of chromatograms, peak detection and integration were performed using REFINER MSH 10 . Processing of mass spectrometry data included the removal of the fragmentation information, isotopic peaks and chemical noise. Selected features were annotated using an in-house lipid database80.Lipids were extracted using the MTBE method as described previouslyhttp://www.bioconductor.org/) package pcaMethods82. Analysis of variance (ANOVA) was run for each metabolite and lipid. To detect significant differences, p-values (threshold ≤ 0.05) were Bonferroni corrected to account for multiple hypothesis testing. Subsequently, a posthoc Tukey test was performed.All metabolite and lipid data was natural-log transformed. Missing values (4.4%) were imputed using data imputation provided by Bioconductor 31 DESeq. 2 was also used to determine differentially expressed genes between pairs at a q-value of ≤0.05.RNA was extracted with Trizol (Invitrogen) and PureLink RNA Mini Kit (Ambion) and then treated with TURBO DNase (Ambion). The RNA quality control was accessed using 2100 Bioanalyzer and RNA 6000 Nano Kit (Agilent), Qubit RNA BR Assay Kit (Thermol Fisher Scientific) and Nanodrop (Thermol Fisher Scientific). RNA-sequencing libraries were constructed using TruSeq Stranded mRNA Library Preparation Kit (Illumina). PolyA + RNA was isolated from 1 μg total RNA and then fragmented. The cDNA was synthesized, adenylated at 3′ ends and ligated with adaptors. The libraries were amplified with 8 cycles of polymerase chain reaction (PCR). Sequencing was done on a Illumina Hiseq platform of 2 × 150 bp. Filtered reads from each library were aligned to the reference genome using HISAT version 0.1.4-beta39. In brief, all metabolites were pairwise-correlated using Pearson correlation. Threshold tests for correlation coefficients (r) and p-values were applied to detect significant correlations. The threshold for r-values was set to ≥0.8 for the Ain1 and Osl1 network, respectively. A false discovery rate84 was applied to adjust for multiple hypothesis testing at a q-value of ≤0.05. Spurious correlations were removed, while significant correlations were transformed into network form.Principal component analyses were performed on the metabolite combined with the lipid dataset as well as on the gene expression datasets to determine clusters of associated samples. For the identified clusters, metabolite correlation networks (CN) were constructed as described before29. The approach employs machine-learning techniques to train a model based on known metabolic pathways and then predicts the presence of pathways. The applied method uses pathways based on the PlantCyc repository (https://plantcyc.org/)36 However, a collection of metabolic pathways associated with B. sylvaticum has yet to be established. Therefore, for the current study, a consensus set of pathways found in all 101 plant species listed in PlantCyc version 13 was generated, detecting 38 consensus pathways (positive instances). The consensus pathways were mapped to the CN’s upon which for each pathway a set of network features was computed = positive training set29. As a negative training set 19 random pathways (negative instances) from MetaCyc collection (https://metacyc.org/)85 that could not be found within the PlantCyc collection but could be mapped to the CN’s were selected. In addition, 19 random subsets of metabolites were generated accounting for the other half of the negative training set. Also here, for each instance of the negative training set the aforementioned network features were computed. To avoid over-fitting, the most important features were selected for each training-set. Next, the gradient boosting (xgboost) algorithm48 was employed with a 10-fold cross validation to generate ML models for the Ain1 and Osl1 networks. Predictions for all test-set metabolic pathways were generated for the final models. The quality of ML models were assessed using performance measures, i.e. the area under the curve (AUC) of the receiving operating characteristic curve, which was generated by plotting the true positive rate against the false positive rate at different prediction thresholds. An AUC close to 1 indicated a perfect classifier, while an AUC of 0.5 indicated a poor classifier that could also be achieved by chance. Another mean to demonstrate ML performances is the usage of confusion matrices, where the predicted vs. the actual classification of positive and negative instances is captured in table form. The classifications of the predicted instances correspond to the rows, while the actual classifications correspond to the columns. Positive instances are denoted as ‘1’ and negative instances are denoted as ‘2’. Derived from the confusion matrix is the accuracy measure, which is the proportion of correctly classified positive and negative instances. Finally, sensitivity analyses were performed based on the final models, where subsets (80%) of the training-instances were randomly chosen to recreate models with identical settings. Sensitivity analyses were performed to test for the robustness of the ML model. After each model generation, the test-set instances were subjected to prediction. This analysis was performed with 100 repetitions, after which the average prediction values and their corresponding variance values were computed. Test-set pathways that demonstrated prediction values ≥ 0.5 in the original model as well as in the average values of the sensitivity analysis were considered validly predicted.Identification of metabolic pathways within CN’s was performed essentially as described in30. The genetic algorithm was adapted so that essentially:C is a subset of genes in the module, ExprC, M is a subset of metabolites corresponding to the metabolic pathway, PrM, prcomp is the 1st principal component of a matrix, and cor is the Pearson correlation.To identify genes associated with metabolic pathways, a genetic algorithm to optimize the correlation between a trait of interest and a subset of genes within a gene co-expression network trait to gene was used as described previouslySupplementary figure 1.Supplementary figure 2.Supplementary figure 3a.Supplementary figure 3b.Supplementary figure 3c.Supplementary figure 4.Supplementary figure 5.Supplementary figure 6.Supplementary figure 7.Supplementary data S1.Supplementary data S2.


Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel  production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also present novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.Solving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as biocatalysts. The anaerobic thermophile  C. thermocellum is capable of efficient degradation of lignocellulosic biomass to produce biofuels and biomaterial precursors, making this organism an ideal candidate for consolidated bioprocessing (CBP), where production of lignocellulosic enzymes, saccharification, and fermentation take place in a single step  and hydG  was successfully applied to increase ethanol yield by electron rerouting H is likely the most rate limiting step. In a subsequent study, Lo et al.  in the ΔhydGΔech strain that is expected to enhance NADH supply, but did not achieve improved ethanol yield.For the past decade, significant efforts have been dedicated to characterize and manipulate the central metabolism of o et al.  over-expC. thermocellum. By converting phosphoenolpyruvate (pep) to oxaloacetate (oaa) and then to pyruvate (pyr), this shunt can interchange one mole of NADPH with one mol of NADH generated from glycolysis. Interestingly, the authors noted that replacement of the malate shunt by alternative pathways not linked to NADPH increased ethanol production and carbon recovery but reduced amino acid formation, confirming the role of the malate shunt as an NADPH source in C. thermocellum.In an attempt to redirect carbon and electron flows for enhanced ethanol production, Deng et al.  manipulaC. thermocellum and has been investigated for its role in ethanol production. Sulfate, a component of C. thermocellum media, serves as an electron acceptor, which is capable of oxidizing sulfate to sulfite and then sulfide. Thompson et al.  or homocysteine and then methionine by SHSL2 (succinyl-homoserine succinate lyase) and METS (methionine synthase), but the connection between the cessation of cysteine uptake and sulfate metabolism remains unclear.Sulfur metabolism also plays a key role in redox metabolism of n et al.  demonstrs et al.  reportedC. thermocellum metabolic pathways remain challenging to understand and engineer with conventional methods, and hence require a quantitative systems biology approach to decipher. To this end, several genome-scale metabolic models (GSMs) of C. thermocellum have been developed. The first GSM, named iSR432, was constructed for the strain ATCC27405 and applied to identify gene deletion strategies for high ethanol yield  in C. thermocellum. Furthermore, we used the model, in combination with the previously developed ModCell tool , and (ii) blocked reactions involving molecular oxygen  that are not possible in strict anaerobes like C. thermocellum. Furthermore, tRNA cycling reactions were unblocked by including tRNA in the biomass reaction  the blocked selenate pathway which lacks experimental evidence   are parameters that capture the consumption of ATP toward cell division and homeostasis, respectively. These are known to be condition-specific; however, genome-scale models do not include a mechanistic description that allows to determine these ATP consumption rates as part of the simulation. Instead, GAM is incorporated into the biomass pseudo-reaction and NGAM has its own pseudo-reaction that hydrolyzes ATP at a rate tuned by the constraint parameters.E. coli model  annotation indicates if an object in the model refers to a metabolite, reaction, or gene, while the respective annotation scores of these elements correspond to properties  and identifiers linking them to relevant databases .To perform the analysis, we formulated an omics integration protocol anchored at the quantification of fold change (FC) between case and control samples . In thisTo start the FC-based omics integration, we obtained measured FCs by mapping the measured proteomics data to 510 out of the 856 reactions in the model through the gene-protein-reaction (GPR) associations . Then, wC. thermocellum  an increased protein level of FRNDPR2r  that converts one mol of reduced ferredoxin (fdxr_42) and one mole of NADH into two moles of NADPH , thermodynamics and protein cost constraints, or a combination of both. While the contribution of thermodynamic constraints is beyond the scope of this study, a recent analysis  anof NADPH , and  cell that can be rapidly combined with exchangeable pathway modules in a plug-and-play fashion to obtain modular production strains exhibiting target phenotypes with minimal strain optimization cycles , that corresponds to the minimum product synthesis rate at the maximum growth rate. The ModCell mathematical formulation, computational algorithm, and implementation were described in details previously , PFL (formate), LDH_L (lactate). Interestingly, ACKr or PTA (acetate) does not appear in this list, likely because acetate production can serve as a regulatory valve for redox metabolism, especially in a modular cell that must be compatible with products of diverse degrees of reduction.To understand the characteristics of each group, we can inspect the deletions of each design , Table 3More interestingly, we also found important branch-point deletion reactions  through reaction j in the simulation condition k. Constraint (1) enforces mass balance for all metabolites in the network, where Sij represents the stoichiometric coefficient of metabolite i in reaction j. Constraint (2) enforces lower and upper bounds ljk and ujk, respectively, for each reaction j in the network.Here k, Sij remains fixed given the structure of the network for all ujk and ljk are modified to represent specific metabolic constraints. For example, to apply measured reaction fluxes such as in the case of GAM and NGAM calculation or the omics integration protocol (section 4.6), ljk and ujk are specified using the experimentally measured average (μjk) and standard deviation (σjk), which for normally distributed samples with 3 replicates produces an interval with a confidence level above 90% (3-4). Similarly, to represent a certain gene deletion mutant k, the bounds are set to be ujk = ljk = 0 for the associated reaction j.In different simulation conditions, k can be explored in different ways; . This feature can be adjusted through ujk and ljk for exchange reactions. In our simulations, only essential metabolites required for in silico growth may be consumed and only commonly observed metabolites may be produced, unless otherwise noted. The second feature involves biomass objective function. iCBI655 contains 3 possible biomass reactions: (i) BIOMASS_CELLOBIOSE used for growth in cellobiose with cellulosan constituting 2% of cell dry weight (CDW)  or may exit the intracellular environment . This procedure enables the most general scenario which produces the smallest number of blocked reactions in each model. Additional details can be found in The The omics integration protocol developed in this study consists of three steps: (i) simulation of fold changes, (ii) mapping of measured gene fold changes to reactions, and (iii) comparison of measured and simulated fold changes.To simulate metabolic fluxes, lower and upper bounds (2) are constrained according to experimental data as described in section 4.2. Then, for the pFBA method, a quadratic optimization problem (6) is solved, leading to a unique flux distribution For the FVA method, a sequence of linear programming problems is solved where each flux is minimized (7) and maximized (8):k to remove thermodynamically infeasible cycles from all feasible flux distributions.Note that for computation we applied the loop-less FVA method , we define the FVA center, a scalar variable that generally indicates a change in this range (9).FVA produces a flux range k. Furthermore, the FVA center could potentially fail to capture hypothetical permutations of fluxes. Despite these considerations, the FVA center remains a useful heuristic to analyze simulated fold changes.The FVA center is a heuristic analysis with the main purpose of determining whether a reaction exhibits an upward shift (center increase) or a downward shift (center decrease) between two conditions k. It should be emphasized that the FVA center, x is an arbitrary scalar variable.Finally, to determine the fold change for either pFBA or FVA simulated fluxes, the conventional procedure for fold change calculation in omics data is emulated. First, values are floored to avoid very large (or infinite) fold changes in cases with very small magnitude change. This is accomplished through a flooring piece-wise function (10), where ϵ = 0.0001 is the minimum value and vkuptake,  and fold change is calculated in log2 space (11).Then, the fluxes are normalized to the substrate uptake rate FCl, is calculated in log2 space for each gene FCl ≠ 0 in the GPR of reaction j:Fold change between case and control samples, j is said to have a consistent fold change if the measured fold change has the same sign of at least one of the simulated fold changes, more formally:A reaction where https://github.com/trinhlab/ctherm-gem) and in Model development was performed using Python and Jupyter notebooks with open-source Python libraries including cobrapy  operating in data-dependent acquisition   were performed per sample run with each followed by 120 min organic gradient to separate peptides.C. thermocellum DSM1313 proteome database concatenated with common contaminants and reversed sequences to control false-discovery rates using MyriMatch v.2.1.  were searched against the MSV000084488 (MassIVE) and PXD015973 (ProteomeXchange). Data files are available upon publication (ftp://massive.ucsd.edu/MSV000084488/).All raw and database-searched LC-MS/MS data pertaining to this study have been deposited into the MassIVE proteomic data repository and have been assigned the following accession numbers: https://github.com/TrinhLab/ModCell2.The ModCell formulation, computational algorithm, and implementation followed the previous reports  and PXD015973 (ProteomeXchange). Data files are available upon publication (ftp://massive.ucsd.edu/MSV000084488/).All raw and database-searched LC-MS/MS data pertaining to this study have been deposited into the MassIVE proteomic data repository and have been assigned the following accession numbers: CT managed the project. SG and CT conceived the study, designed experiments, and analyzed the data. SG, RT, RG, and SD performed the experiments. SG prepared the draft with co-authors' inputs. All read, wrote, and approved the final draft.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.


These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at  Integration of transcriptomics data with genome-scale metabolic models is appealing but challenging due to the number of parametric decisions required to be made to by the user. This is further exacerbated by models failing to capture functionalities which are important for cellular maintenance. In this study, we propose a thresholding method for functionally qualifying a metabolic reaction to be active. We used our method to extract models of NCI-60 cancer cell lines, human tissues, and C. elegans cell types. We show that our thresholding method improves the coverage of functions required for cellular maintenance. We also validated and compared models built with our approach against those with existing approaches using CRISPR-Cas9 essentiality screens. Overall, our study provides novel insights into how cells may deal with context-specific and ubiquitous functions. Integration of omics data with genome-scale metabolic models (GEMs) has facilitated insights into diverse questions, spanning from the elucidation of disease mechanisms ,2 to theThe integration of omics data and GEMs is complicated by the complexity of cellular metabolism and enzyme regulation. Metabolic phenotypes are driven by not only gene expression alone but also other orthogonal processes, including enzyme assembly, post-translational modifications, localization, and substrate concentration. In other words, gene expression data provide considerable, but partial, insight into metabolic activity. To address this, data integration efforts often infer a ‘core’ set of active reactions from gene expression data. This ‘core reaction set’ is then used to produce a context-specific model via various model-extraction algorithms –20, whicDespite this importance, thresholds have often been poorly defined, and it is not fully understood how these thresholds should be set. Most often a single threshold value is used to evaluate all genes, disregarding complex and pathway-specific regulations over metabolism. Furthermore, such thresholds are often left to be defined by the user with little guidance or standardization, leading to varying and arbitrary model parameterization. Lastly, such single, catch-all thresholds often fail to identify lowly expressed but biologically important genes, including ‘housekeeping’ genes which are constitutively expressed for tissue maintenance functions . A limitC. elegans cell type models. Thus, StanDep provides a novel approach to obtain more complete context-specific models of metabolism from transcriptomics data.Here we propose a novel thresholding approach called StanDep, which clusters gene expression data based on their expression pattern across conditions , purple.Established data integration methods have struggled to consistently capture housekeeping features , likely An important parameter in evaluating hierarchical clustering is the number of clusters (N). Here, the number of clusters were selected in two steps. First, we calculated the Jaccard similarity between core reaction list between any pairs of N and N+1, and then, we chose N after which Jaccard similarity between core reaction lists is over 90% . As we iCurrently, the common approach to identifying core reactions is to define a single ‘global’ threshold on gene expression . All genth: genes expressed above the top 25th percentile are considered active, (2) localT2: local thresholds are derived from cross-tissue mean expression; genes expressed above the top 25th percentile and below the bottom 25th percentile are interpreted unconditionally active and inactive, respectively, and (3) localT1: threshold settings are similar to localT2, but genes are never considered unconditionally inactive, even if they are below the bottom 25th percentile.To compare these thresholding methods to StanDep, we applied the methods to a comprehensive transcriptomic profile of human cancer cell lines . From thCore reactions resulting from these thresholding methods were compared to 929 housekeeping reactions associated with metabolic housekeeping genes  in ReconWe then analyzed the clustering captured housekeeping reactions, and how such captured reactions differed between StanDep and the other methods. Housekeeping reactions were enriched in 6 clusters; out of which, clusters 1, 2, and 11 had the largest number of housekeeping reactions , Fig 2A.Using StanDep, we built hundreds of models of the NCI-60 cell lines by varying 4 model uptake/secretion constraint types  and 6 moStanDep-based core reaction lists were larger than those from other thresholding methods . We wondWe have shown, so far, that StanDep-derived core reaction lists  containeBiotin metabolism is part of the metabolism of cofactors and vitamins. We found that the housekeeping reactions belonging to biotin metabolism were differentially present in the StanDep-derived models compared to global or localT2 regardless of extraction method . There aIn mammalian cells, glycosylation performs essential functions such as protein folding, targeting, stabilization, and adhesion . FurtherGiven this successful application of StanDep in capturing housekeeping genes and reactions in cancer cell lines, we also applied it to transcriptomics data from Human Protein Atlas (HPA) data . We founIn addition, we analyzed the content of the extracted models and evaluated their capacity to predict gene essentiality, as experimentally measured by CRISPR-Cas9-mediated loss-of-function screens. ,25,27. ILastly, we compared the list of essential genes in the two sets of fastCORE models, and then analyzed accurate predictions of matches and mismatches among the two thresholding methods. Among accurate matching predictions, we found that 113 genes were accurately predicted in at least one cell line by both models. Among these, 22.12% were ubiquitously essential while 26.54% were cell line-specific black, . Among 5C. elegans cell types using previously published animal-level RNAi screens Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Context-specific metabolic networks that integrate omics data with metabolic networks have been widely used to make important biological discoveries from microbes to human. One of the most important decisions in making context-specific models is defining a threshold above which a reaction is considered active. Despite the importance, the decision is often made somewhat arbitrarily and without much guidelines or consensus in the modeling field. The authors presented StanDep, a rigorous method that uses clustering to mitigate the arbitrariness in making this decision, potentially improving the robustness and consistency of context-specific models in the future. The authors did extensive benchmarking to demonstrate the performance of StanDep. StanDep is a new valuable tool for the metabolic network modeling community. However, this novel method and the manuscript itself might benefit from a few modifications suggested below.Major:StanDep included larger fraction of housekeeping reactions. But does it also included potentially false-positive reactions that should not be in the model?Besides increasing the coverage of house-keeping genes/reactions, does StanDep improve models’ ability to simulate tissue-specific metabolic functions ?In the StanDep-extracted models accurately capture essential genes, the authors seem to suggest that 58 genes that were accurately predicted by StanDep-derived models only are better supported by CRISPR loss of function screens. In addition to the high-level statistic, can specific examples be provided that the lethal genes uniquely captured by StanDep are biologically meaningful?Figure 2B-D are not clearly explained. What does the numbering on x-axis mean, how are they ordered, how is the p-value on y-axis calculated? The figure legend mentioned that “(C) Reactions which are captured by only StanDep mainly belong to low but tightly expression clusters.” However, there is no expression level information in Figure 2C.Minor:MEM was first used on page 5, line 138 in the main text without fully spelling it out as model extraction method. Although it is spelled out in Figure 1 caption, it still helps reading if it is also described in main text as well.Self-consistent is not quantitatively defined in page 5, line 139-142. Does higher self-consistency mean that the core reactions defined by StanDep require less non-core reactions to support?In figure legends, HK is used as an acronym for housekeeping. To some people, HK is often an acronym for hexokinase, a well-known, classical metabolic reaction, causing potential confusion.Reviewer #2: The paper of Joshi et al. deals with an issue arising during the generation of context-specific metabolic networks based on transcriptomics data. There are already several types of algorithms (reviewed and categorized in reference [45]) which can be used for context-specific metabolic networks, and majority of them require setting a threshold value for declaring a reaction active. This threshold is either set to hold for all genes or is gene-specific. The authors propose a heuristic, StanDep, that takes a middle road, whereby the threshold value is determined per groups of genes. The extracted models from different scenarios (i.e. conditions and organisms) are partially tested against lists of housekeeping genes, CRISPR data, and RNAi phenotypic data. Therefore, the main contribution is the way to set the threshold, from which point the study depends on existing constraint-based approaches for context-specific network extractions.I find that this a small contribution which also suffers from additional issues, which I detail below:1. The author claim that the thresholding method involves: (1) cluster distribution of individual gene expression, considering multimeric and isozyme relationships (line 86). Is there support that the clusters contain protein comlplexes and / or isozymes. To the best of my knowledge, co-expression / transcript similarity can partly explain protein complexes, but only if complex stoichiometry is factored in; in addition, this rarely holds for isozymes. The authors should provide evidence for this claim.2. The first step in the threshold selection requires hierarchical clustering based on Euclidean distances between gene expression profiles. While it is obvious that magnitude of gene expression is taken into consideration by following this approach, the reasons for not using other approaches , are not provided. The results in the Supplementary Figures must be critically discussed in the main text—for instance, why is the comparison done for 26 clusters? Do all similarity measures yield the same number of clusters when following the procedure described in the Methods?3. The approach for selecting the number of clusters does not correspond to the steps described in the Methods. Lines 313 and 314 state that “the number of clusters is determined such that all pathway[s] is enriched in at least one cluster”. This is a—major—concern for three reasons: (1) it is difficult to imagine how this condition can be ensured, and if so, that this results in a single number of clusters, (2) the logic for selection of thresholds is then circular, since every pathway will get respective thresholds, thus partly ensuring its inclusion of the context specific model. The bottom line is that the selection of thresholds—is not—based only on the transciptomics data, as claimed, but also includes the model structure (i.e. pathway information), and (3) Jaccard distance of core reactions, mentioned on lines 91-94, is not used for selection of thresholds, but for comparison of the resulting sets of core reactions. The authors must resolve the circular argument, since the results obtained are no surprise and represent artefact of the threshold selection approach, and account for the other noted discrepancies.4. The approach for replacing the biomass reactions must be detailed. The reason is that the authors already comment that forcing flux through the biomass reaction “rescues” some core reactions from central carbon metabolism.5. Figure S1 cannot be discussed without having access to the size of the models, which are not presented in the present manuscript. Going over the procedure for threshold generation, it seems that the larger number of HK genes may be due to the fact that the extracted models are themselves larger? Another reason why I believe this to be the case is that the comparisons in Fig. 3 A and B are based on fractions, and not the number of reactions!6. Line 151 – 152 “Overall, we found that StanDep contained more housekeeping reactions than localT2 models .” –does not—reflect what is shown in the figure panels! These figure compare fractions, which is not the same as numbers. In addition, a larger fraction of HK reactions was found only for mCADRE, MBA, and GIMME (and only MBA and GIMME with respect to HK genes).7. The higher predictability / accuracy of predictions for essential genes, based on inspection of Fig. S17, is not justified. Further, I do not see what the further grouping of predictions (paragraph on lines 192-200) brings to the performance of the proposed heuristic.8. The authors talk about “enzyme expression” and “enzyme-specific variability”, which is technically wrong. These can be misinterpreted to mean “abundance of enzyme”, which is not available in transcriptomics data (but only in quantitative proteomics data). This can be corrected by writing “expression of enzyme-coding genes” and “variability of enzyme-coding genes”.Minor9. LocalT1, LocalT2, and global should be described for completeness in the main methods, rather than in the supplementary materials.10. The authors should comment about the reasons for excluding threshold-independent approaches for context-specific network generation.Reviewer #3: In this manuscript, the authors developed a heuristic method to support the existing model reduction algorithms to prune the genome-scale models to build context-specific metabolic models. The new method takes into account expression pattern across different contexts and determines thresholds based on the hierarchical clustering compared to an arbitrary threshold used in the current methods. The developed method can be useful to improve the quality of context-specific models built from human genome-scale metabolic models; however, such evaluation was only tested purely based on inclusion of housekeeping reactions and gene essentiality. Furthermore, the results are not presented consistently with too many details leaving to the Supplementary material.Major concerns:-There are other model reduction methods in the literature that do not use any arbitrary cut off values , so authors need to establish first why to use thresholds after all?-It is evident from their analysis that the previous methods were not capable of capturing many reactions belonging to housekeeping genes indicating the poor quality of the generated networks. Even with StanDep, mean fraction of housekeeping reactions included in core reaction lists was 0.80, indicating many more housekeeping reactions are still missing in the final models. Therefore, authors need to check the quality of the generated context-specific models based on some functional evaluations together with gene essentiality.-The authors claim development of hundreds of models for the NCI-60 cancer cell lines that increased inclusion of housekeeping genes. It would be helpful for the reader if you exemplify application of one such model and show how StanDep makes a difference in predicting the outcomes compared to a model developed with arbitrary thresholds.-The supplementary section contains too many figures, not referred consistently in the main text. Some of those supplementary figures depicting important findings should be in the main text for easy reference to the reader.- The authors should condense the Supplementary material by reducing the unnecessary figures that are not adding value to the main text.-As mentioned in the Discussion, the method requires availability of large sets of gene expression data to define thresholds. Therefore, the authors should mention what is the minimum amount of data required to use the method. They should also mention other limitations of their proposed method, if any.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: No: Large-scale data are used from the original publications.Reviewer #3: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Anders WallqvistAssociate EditorPLOS Computational BiologyDaniel BeardDeputy EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The authors have adequately addressed my previous concerns. One further suggestion is that all supplemental figures should be mentioned somewhere , so that readers have better context when navigating them.Reviewer #2: Review of the revised version of “StanDep: capturing transcriptomic variability improves context-specific metabolic models”I would like to thank the authors for attempting to consider all points I raised on the first version of the manuscript. Nevertheless, I find that the revised version still contains some pressing issues which need further explanations. Meanwhile, I found additional issues with the figures in the main text, which I detail in my comments bellow:1. The authors state that they resolved AND GPR rules, but did not consider OR GPR rules. Integration of transcriptomics data with both rules is well established, as the authors state in their response, so I do not see the reason for neglecting the consideration of OR rules. This must be addressed as the decision seems arbitrary.2. The presentation of the approach to arrive at the thresholds based on the different clusters is greatly improved. However, what struck my curiosity is that if the respective thresholds \\Theta_c is 100%, then the mean of the data set is used. In how many cases in the analysis did this occur? Again, this sounds like an arbitrary choice!3. Another point to arbitrariness is the way histograms are created – the number of bins used will certainly affect the derived distance measures. The authors should detail some well-established rules from data science used to address this point.4. I still do not agree with the justification regarding the number of clusters used. This, too, is an arbitrary choice, since the > 90% similarity is an arbitrary choice. .5. The authors should detail the replacing of the biomass reaction, since the methods section should suffice to understand what was done in the present study.6. The performance of the approach, other than the inclusion of more housekeeping genes , does not truly provide further conclusive arguments that they improve context-specific models. The authors write that with respect to essentiality they do not perform worse than the other approaches. Hence, more tests are needed to justify the title and this major claim of the manuscript.7. The authors did not cast their net sufficiently wide to find out that there are other threshold-free approaches for integration of transcriptomics data, other than the one cited. For instance, Robaina-Estevez et al. published and used RegrEx in several situations; comparison with this approach is also desired.8. The number of supplementary items is staggering. I did attempt to consider all of the tables and figures, but I chose to comment only on the figures of the main text (see next point). The authors may consider removing items which are not truly necessary.9. In figure 1, it is desirable to somehow specify that distributions are clustered, rather than gene expression profiles . Figure 2 A, x-axis label makes no sense; Figure 2B, x-axis label has issues; Figure 3 C – check the % in the pie charts, some do not correspond to the visuals. Figure 3D is very difficult to follow: what are total predictions? Accurate predictions with respect to what? accuracy in % (figure panels should be self-explanatory and understandable based on the caption provided.)10. I find that one example in Fig. 3C is too little a justification for the success of the presented approach.Reviewer #3: No further comments**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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Showcasing the complex l-histidine production with engineered Corynebacterium glutamicuml-histidine-on-glucose yields could be improved to 8.6 ± 0.1 mol% by PIK analysis, starting from a base strain. Amplification of purA, purB, purH, and formyl recycling was identified as key targets only analyzing the signal transduction kinetics mirrored in the PIK values. Today’s possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream  Since the advent of synthetic biology, a steadily increasing number of tools for successful strain engineering have been developed. Recent advances in genome editing , creatio13C-labeled metabolomics for this purpose.The well-known design-build-test-learn (DBTL) cycle requires a knowledge-driven design strategy to start subsequent rounds of strain engineering. Enabling tools such as metabolite analysis  and bioiThe standard approach for testing is to detect a target molecule, in most cases, the actual product, through techniques like gas or liquid chromatography  with UV absorbance or mass spectrometry (MS). A different approach, especially suited for high-throughput applications, is the use of biosensors via protein or transcript-based sensing of a target molecule . However13C-labeled key metabolites of cellular metabolism like sugar phosphates, organics acids, or amino acids can be quantified without derivatization [To extract more information out of engineered strains, the analysis of intracellular and extracellular metabolites, called metabolomics, has proven to be promising since the metabolome most closely reflects the actual cellular physiological state compared to the genome or proteome as underlying regulatory mechanisms ,11,12. Atization  and the tization . 13C metabolic flux analysis (MFA) by analyzing 13C-isotopologues of metabolic intermediates or endproducts [Depending on the experimental setup of the cultivation experiments, model-based approaches enable an assessment of the actual metabolic state, deriving valuable criteria, and systemic parameters. In vivo metabolic fluxes can be quantified through products ,19, provproducts . Theoretproducts ,22. Alteproducts  could beproducts ,25. Both13C-based perturbations of anabolic biosynthesis pathways can be analyzed excluding the well-known drawback of metabolic leakage during cellular quenching and further streamlining the identification of strain engineering targets [l-histidine producing Corynebacterium glutamicum strains . l-histidine is an especially challenging amino acid product due to the entwined biosynthesis with pathways such as purine biosynthesis and C1 metabolism  may simplify and reduce modeling efforts, still producing similar results as model-based MCA approaches . Even sm targets . To showtabolism .l-histidine and purine biosynthesis share the same precursor in phosphoribosyl pyrophosphate (PRPP). Additionally, ATP is covalently incorporated in the first step of the l-histidine biosynthesis and is regenerated through 5-aminoimidazole-4-carboxamide (AICAR) in the purine biosynthesis [13C-tracer experiments with integrated shaking flask sampling (ISFS) and subsequent HILIC-based QTOF-HRMS analytics, we show that pool influx kinetics (PIK) can be an alternative to PEC and further streamline the test and learn segments of the DBTL cycle for strain engineering  with d-glucose as the sole carbon source, 20 mM d-ribose was added under exponential growth conditions. LC-QTOF-HRMS analysis of extracellular titers enabled the determination of biomass-specific uptake rates by linear regression of the concentration and biomass dry weight (BTM) divided by the growth rate (µ) [The choice of an appropriate stimulus substrate is of high importance for the success of any tracer experiment. Ideally, the substrate is readily taken up by the production host and part of the analyzed reaction network or in close proximation to achieve sufficient perturbation. Due to the instability of relevant metabolites under cultivation conditions  or the d-ribose . d-glucorate (µ) . Figure d-glucose was taken up with 3.8 ± 0.1 mmol gCDW−1 L−1 h−1 (WT), 1.3 ± 0.4 mmol gCDW−1 L−1 h−1 (HIS1), 3.4 ± 0.3 mmol gCDW−1 L−1 h−1 (HIS2), and 1.3 ± 0.3 mmol gCDW−1 L−1 h−1 (HIS3). d-ribose uptake rates were significantly lower with 1.0 ± 0.1 mmol gCDW−1 L−1 h−1 (WT), 0.7 ± 0.2 mmol gCDW−1 L−1 h−1 (HIS1), 1.6 ± 0.2 mmol gCDW−1 L−1 h−1 (HIS2), and 0.7 ± 0.3 mmol gCDW−1 L−1 h−1 (HIS3). Interestingly, the ratio of the d-ribose to d-glucose uptake rates changed for the mutants from 0.26 in the wild-type to 0.54 (HIS1), 0.47 (HIS2), and 0.54 (HIS3) in the producer strains. Based on these d-ribose uptake rates, the amount of 13C5-d-ribose tracer was calculated to be taken up in 20 min for subsequent labeling experiments. l-histidine producing C. glutamicum mutants. Dynamic metabolic labeling experiments are heavily dependent on fast and straight forward sampling and quenching strategies to generate representative snapshots of cellular metabolism enabling valid pool quantification and subsequent modeling and data calculations . Hence, 13C5-d-ribose tracer, samples could be taken every seven seconds and were continuously increased up to 60 min (−1) methanol at −20 °C. Exponential pre-stimulus growth rates (µmax) of C. glutamicum HIS1 and HIS2 were 0.29 ± 0.02 h−1, and µmax for HIS3 was slightly lower with 0.25 ± 0.01 h−1. Interestingly, the HIS2 strain clearly had the highest optical density at the end of the experiment with OD600 = 14.2 ± 0.5 compared to 11.3 ± 0.8 (HIS1) and 10.7 ± 0.1 (HIS3)  .l-histidine biosynthesis is deeply intertwined with the central carbon metabolism, purine biosynthesis, and one-carbon metabolism  coupled to hydrophilic interaction chromatography (HILIC) enables untargeted analysis of a mining . Even coa mining .l-histidine and purine biosynthesis could be automatically extracted. The ratios of m + 5 isotopologues during the sampling period of the tracer experiments for the l-histidine producing C. glutamicum mutants  are depicted in Using this approach, labeling patterns of twelve relevant metabolites in the d-ribose inside of the cell. Since ribose 5-phosphate, ribulose 5-phosphate, and xylulose 5-phosphate cannot be distinguished by retention time and accurate mass , these metabolites are shown as the pentose 5-phosphate pool (P5P) [d-ribose uptake rates for the three strains. The sampling intervals were sufficient to depict the initial labeling of the P5P pool for HIS1, but not for HIS2 and HIS3. However, the reaction cascade showed clear propagation of the 13C-labeling signal  . The luml-histidine biosynthesis, the purine biosynthesis plays an essential role in the biotechnological production of l-histidine with C. glutamicum [l-histidine biosynthesis, SAICAR also did not reveal labeling in all three mutants due to pool levels below the detection limit. As seen before, multiple subsequent dynamics were observed in the metabolites of the purine biosynthesis like FGAR and SAICAR.As a recycling pathway for AICAR, which is produced in the utamicum . As PRPPn = 3) in a fast fashion using automated data processing. Taken together, the ISFS approach, combined with HILIC based QTOF-HRMS analytics, enabled the analysis of the complete isotopologue space of twelve metabolites , 7-fold (AdSucc), and 20-fold (AMP) decrease in PIK values compared to SAICAR. The corresponding enzymes for these reactions are PurA, PurB, and PurH , which c by PurH . Based oCorynebacterium jeikeium. Through the introduction of GCV, the supply of 5,10-methylenetetrahydrofolate (mTHF), and finally, fTHF was improved by enhanced recycling of the glycine pool. As a consequence, a higher l-histidine yield compared to C. glutamicum HIS2, and no secretion of glycine into the medium was observed [−1) than HIS2 (0.0028 ± 0.0005% (m + 5) s−1), even though the availability of fTHF should have been increased. Noteworthy, fTHF is also needed by PurN, producing the purine biosynthesis intermediate FGAR  PurH amplification, (ii) debottlenecking l-histidine biosynthesis, and (iii) improving l-histidine export. These should be topics of subsequent DBTL cycles.However, future strain engineering efforts could also focus on the linear reaction network of the Systematic testing of engineered producer strains for qualifying genetic manipulations and for deriving novel hypothesis for the next DBTL cycle is an equally crucial and demanding task that is often laborious and time-consuming. A thorough metabolic analysis may require multiple weeks , whereas13C-tracer perturbation [13C-labeling patterns (perturbations) with subsequent automated data extraction streamlines the test and learn segments of the DBTL cycles significantly. In this work, we presented a straightforward data-driven approach, defining pool influx kinetics (PIK) for identifying reactions that exert high flux control in linear pathways. PIK analyses follow the idea of pool efflux capacities (PEC) by ranking the net reaction rates of metabolite pools after non-stationary urbation ,27. Highurbation  measurin13C5-d-ribose as an informative and cost-efficient full-labeled stimulus substrate closely related to PRPP as a common precursor for l-histidine and purine biosynthesis. The rapid and concomitant uptake of d-ribose and d-glucose by a ribose-specific ABC transporter showed no catabolite repression [l-histidine. Interestingly, the d-ribose to d-glucose uptake ratios stayed virtually the same with around 0.5 for all producer strains, pointing toward similar central carbon flux distributions, irrespective of engineered targets in the biosynthesis routes. However, the latter impaired maximal growth rates in l-histidine producers [However, the reduction of perturbation strengths in complex and strictly regulated metabolic networks  demands sophisticated stimulus strategies ,34. A prpression , but enhroducers , apparen13C-based metabolic analysis has the advantage that the time of the actual experiment can be reduced significantly compared to stationary analysis [13C-isotopologues instead of absolute concentrations of metabolites, the drastic unspecific leakage of metabolites, which was previously described for C. glutamicum [l-histidine production hosts in biological triplicates. Accordingly, the approach should be readily applicable in multiple labs without requiring sophisticated technical bioreactor equipment and dedicated skills to run fermentation tests properly.Instationary analysis ,35,36,37analysis . By deveutamicum , did notTime and manual operations were also saved by analyzing the metabolite extracts with HILIC-QTOF-HRMS. Compared to other approaches, no sample derivatization  or analy13C5 tracer amount was a priori designed for a perturbation period of 20 min, reflecting allosteric regulation of the reaction network at a metabolic level [d-ribose uptake rates. Consequently, any kind of labeling analysis requiring a constant uptake rate during a long-term window of labeling is likely to fail. In contrast, the idea of pool influx kinetics (PIK) only requires the comparison of up- and downstream labeling dynamics, even analyzing short time windows. Here, initial labeling dynamics of the first 500 s well fulfilled the constraint and provided the basis for DBTL cycling due to the short time scale. However, some metabolites showed labeling dynamics at the end of the labeling period, which could be due to an interfering regulation on the transcriptional level [The ic level . As showal level .−1) and also in HIS2 (1.158 ± 0.033% (m + 5) s−1). Apparently, both strains have very low activity of the pentose phosphate pathway. Identifying proper metabolic engineering targets is a key issue of the learn segment in the DBTL cycle. Here, we proposed the iterative use of PIK values to identify targets for the next round of cell design. As described before , the stapurA and purB genes into C. glutamicum HIS1, AxP levels improved, and PIK values of the metabolites downstream of SAICAR also significantly rose. Even though the metabolic engineering strategy successfully improved the precursor supply of AICAR, HIS2 did not achieve increased l-histidine yields (0.054 ± 0.002 mol l-histidine per mol d-glucose). These results shifted the focus to so far not addressed reaction networks as C1 metabolism. In particular, the availability of the cofactor fTHF was indirectly enhanced by inserting a plasmid harboring the GCV system into HIS2, which represented the second optimization round. The yield could be increased to 0.086 ± 0.001 mol l-histidine per mol d-glucose, reflecting the diminished by-product formation of glycine as previously observed for other l-histidine producing mutants of C. glutamicum and Brevibacterium flavum [The DBTL cycle started with HIS1, identifying the targets PurA, PurB, and PurH by qualifying the drastic PIK reduction from SAICAR to AICAR as a limited precursor supply due to the inability of the cell to recycle ATP. By introducing an overexpression plasmid of the native m flavum ,44.l-histidine. However, in-depth quantitative analysis of the various THF species of the C1 metabolism is not possible due to low pool sizes caused by interconversion, polyglutamylation, and degradation [Interestingly, PIK values of FGAR and IMP, both downstream of formyl integration from fTHF, did not improve as expected. Consequently, one may conclude that fTHF supply still deserves further amplification to boost the carbon flux into radation . purH, which was already on the task list of the first round of DBTL cycling, still remains as a highly promising metabolic engineering target. The importance of PurH debottlenecking as even increased by analyzing the PIK values of AICAR: Given that labeling signals of ribose are exclusively transferred into IGP and that ATP only revealed very poor labeling intensity (data not shown), AICAR produced by the l-histidine biosynthesis was also poorly labeled. In contrast, AICAR originating from purine biosynthesis should receive fast labeling transfers from SAICAR. Strikingly, downstream reactions of AICAR leading to IMP only disclosed very low PIK values of 0.0013 ± 0.0003% (m + 5) s−1 (HISF1) and 0.0032 ± 0.004% (m + 5) s−1 (HISF2), which correspond to decreasing PIK values of 9-fold (HISF1) and 47-fold (HIS2) with respect to AICAR. The substantial slow-down of labeling signal transduction may be interpreted by (i) low downstream rates catalyzed by PurH and (ii) dominating carbon fueling from the non-labeled AICAR originating from l-histidine biosynthesis. The latter requires further engineering work to equilibrate carbon fluxes in the purine and l-histidine biosynthesis. The necessity of amplifying fluxes through PurH has already been outlined. Additional consideration deserves the finding that accumulation of AICAR was identified to inhibit HisG [E. coli. Consequently, PurH engineering remains the prime task for further strain optimization.Nevertheless, the amplification of bit HisG  competitl-histidine pathway. The integrated approach, comprising cultivation, sampling, and HILIC-QTOF-HRMS based instationary 13C tracer analysis, offers fast DBTL cycling to identify promising metabolic engineering targets in linear pathway segments. However, further correlation analysis with classical 13C MFA approaches needs to be done to determine the suitability of PIK as a metric for flux indication. Additional improvements in analytical equipment and the expansion of the analyzed metabolites to a more holistic system-wide approach paired with even faster sampling and simplified cultivation will elevate testing and learning for future industrial strain engineering.In summary, the PIK concept is not exclusive for the analysis of the C. glutamicum WT, HIS1, HIS2, and HIS3 were carried out in biological triplicates , as described in [600) at the start of the cultivation of the main culture was 2.0. The maximal growth rate µmax was calculated by the linear regression of ln(OD600) plotted against the cultivation time in h during the exponential growth phase of the respective strain.All relevant strains for this study were introduced and published previously  and are ribed in  with the13C5-d-ribose (99 atom%) were bought from Sigma–Aldrich . MS-grade water was purchased from VWR . MS-grade acetonitrile and HPLC-grade methanol were purchased from Carl Roth . Standard stock solutions were prepared in LC-MS water and stored at −70 °C. Chemical reagents (>99% p.a) and 13C tracer experiments were carried out with fully labeled 13C5-d-ribose as a stimulus substrate. After six hours of shaking flask cultivation, the 13C5-d-ribose stimulus was added via a sterile syringe. Sampling was immediately performed, while shaking, by using a peristaltic pump  and in adapted time intervals. For metabolic inactivation, 1 mL of suspension culture was directly quenched in 1.5 mL 60% (v∙v−1) methanol precooled at −60 °C in 5 mL Eppendorf tubes and centrifuged for 30 s at 4000× g and −11 °C . Additionally, 0.5 mL of analogous suspension culture was directly centrifuged for 20s at 20,000× g and room temperature  for supernatant analysis. Quenched biomasses and supernatants were shock frozen by liquid nitrogen (−196 °C) and temporarily stored at −70 °C. l-norvaline solution  were added to the stored cell pellets to obtain an extraction concentration of 20 gCDW∙L−1 (CDW = OD600 × 0.21 g∙L−1). The resulting cell suspension was immediately incubated for 30 s at 100 °C in a water bath, vortexed for 20 s, and again incubated for 3 min at 100 °C. Subsequently, the lysed cell suspension was centrifuged for 10 min at 20,000× g and 4 °C . The separated metabolite extracts were stored at −70 °C until further analysis.For the extraction of metabolites, defined amounts (80–120 μL) of 300 μM −1, and 5 μL injection volume. Samples were diluted 1:2000 with water and prepared in 60% (v·v−1) acetonitrile and 10 mM ammonium acetate (pH 9.2). Mobile phase compositions of the quaternary pump system were as follows, A: 100% acetonitrile, B: 10% acetonitrile/90% water, C: 100% methanol, D: 400 mM ammonium acetate pH 9.2 (adjusted with 25% (v v−1) ammonium hydroxide). Gradient elution was carried out according to Quantification of monosaccharides was performed on an Agilent 1260 bio-inert HPLC coupled to an Agilent 6540 Accurate-Mass LC-MS/MS Q-TOF system with ESI Jet Stream Technology . The developed method utilized a Waters XBridge BEH Amide column  coupled to a Waters XBridge BEH Amide VanGuard Cartridge   at 35 °C, 0.2 mL min −1 with a gas temperature of 260 °C, nebulizer with 40 lb per square inch gauge, sheath gas flow rate of 12 L min−1 with a gas temperature of 340 °C, capillary voltage of 4000 V, and fragmentor voltage of 100 V. During analysis, online mass calibration was ensured by continuous introduction of reference masses (m/z 119.03632 and m/z 980.016375). System control and acquisition were performed using MassHunter Data Acquisition . Peak extraction and integration were done in MassHunter Qualitative Analysis . All samples were analyzed in negative ESI mode (tuned in extended dynamic range MS mode), and the parameters were as follows: drying gas flow rate of 8 L minl-histidinol phosphate (Hisol-P), l-histidinol (Hisol) l-histidine (His), imidazole-glycerol phosphate (IGP), adenylosuccinate (AdSucc), 1-(5′-phosphoribosyl)-5-amino-4-imidazolecarboxamide (AICAR), phosphoribosyl-N-formylglycineamide (FGAR), inosine monophosphate (IMP), phosphoribosyl-aminoimidazolesuccinocarboxamide (SAICAR), adenosine monophosphate (AMP), and the pentose 5-phosphate pool (R5P) was carried out as previously described [The LC-QTOF analysis of escribed . Automatl-norvaline was monitored to investigate the possible intensity drifts on a global MS level.Throughout the analysis, the internal standard 13C-labeled tracer.Pool influx kinetics (PIK) is a straightforward, purely data-driven approach for the analysis of linear pathway segments and is based on the concept of pool efflux capacities (PEC) ,27. In cm + n)/mtotal over time t. In this work, this was done based on at least four data points at the beginning of the first linear increase (R2 > 0.9) of the m + 5 isotopologue of each metabolite.Equation (2) shows that the PIK value can be calculated as linear regression of the isotopologue percentage of choice (


This suggests that RAFs identify attributes of biochemical origins conserved in metabolic networks. RAFs are consistent with an autotrophic origin of metabolism and furthermore indicate that autocatalytic chemical networks preceded proteins and RNA in evolution. RAFs uncover intermediate stages in the emergence of metabolic networks, narrowing the gaps between early Earth chemistry and life.Modern cells embody metabolic networks containing thousands of elements and form autocatalytic sets of molecules that produce copies of themselves. How the first self-sustaining metabolic networks arose at life's origin is a major open question. Autocatalytic sets smaller than metabolic networks were proposed as transitory intermediates at the origin of life, but evidence for their role in prebiotic evolution is lacking. Here, we identify reflexively autocatalytic food-generated networks (RAFs)—self-sustaining networks that collectively catalyse all their reactions—embedded within microbial metabolism. RAFs in the metabolism of ancient anaerobic autotrophs that live from H The origin of the first cells from the elements on the early Earth roughly 4 billion years ago –4 must hOf special interest for metabolic evolution are a class of mathematical objects called reflexively autocatalytic food-generated networks—RAFs—in which each reaction is catalysed by a molecule from within the network and all molecules can be produced from a set of food molecules by the network itself . RAFs arMoorella thermoacetica and the methanogenic archaeon Methanococcus maripaludis, lineages thought to be primitive as they live on the simplest source of carbon and energy known, the H2-CO2 redox couple a shows a2-independent network were identified for different food sets, that is, molecules provided by the environment . An inorganic food set containing H2O, H2, H+, CO2, CO, 2S, NH3, N2, all metals, FeS clusters and other metal clusters, a generalist acceptor, donor, and metal produced a minute maxRAF with eight reactions linking ammonia, carbon, and sulfide transformations. The addition of formate, methanol, acetate, and pyruvate, which are central metabolites with experimental evidence for synthesis from CO2 and metals [2 food set expanded the maxRAF from 16 to 914 reactions (b). The addition of all cofactors germane to the anaerobic network generates a maxRAF with 1335 reactions spanning 25% of the starting anaerobic network. Sequential addition of the eight compounds that were most frequent in that maxRAF, to the metal–CO2 food set, expands the maxRAF from 16 to 1066 reactions, whereas sequential addition of the five compounds with the greatest impact  on anaerobic maxRAF size followed by the three most frequent in the largest maxRAF yields a final maxRAF of 1248 reactions (b). These results indicate that RAFs can grow in size through sequential incorporation of organic cofactors (b). RAFs can thus provide structure, contingency, increasing complexity, and direction to interactions among molecule food sets, given a sustained geochemical source of carbon, energy, and electrons.The maxRAFs in the global prokaryotic Od metals  to the meactions b. The adeactions b. These ofactors b. RAFs c(c)If autocatalytic sets were instrumental at the origin of metabolism , lineage2-dependent CO2 reduction, we can identify their conserved common features. From comparative physiology [2-independent network were obtained by parsing the genomes of the acetogen Moorella thermoacetica (Ace) and the methanogen Methanococcus maripaludis (Met). These were completed with reactions from corresponding manually curated genome-scale metabolic models [Several lines of evidence indicate that methanogens and acetogens reflect the ancestral state of microbial physiology in the bacteria and archaea, respectively ,47–52,54ysiology  and fromysiology , their sc models ,61, resu2-independent prokaryotic network.Carbon fixation and biosynthetic pathways are represented and amino acid biosynthesis is highly enriched in all maxRAFs, recovering autotrophic components of early autocatalytic metabolism. Note that, none of the food sets described so far in these analyses contained ‘peptide'. Addition of the generic ‘peptide' catalyst to the food set increases the maxRAF sizes obtained with the global anaerobic network, Met, and Ace by 93%, 47%, and 25%, respectively . This indicates that adding protein catalysis expands cofactor-supported autocatalytic sets, but it does so to a much lesser degree in the metabolism of Met and Ace than it does in the global O(d)2O, ATP, protons, phosphate, CO2, NAD+, pyruvate, ammonia, diphosphate, coenzyme A, and AMP; highly connected non-food metabolites (more than eight edges) include ADP, NADH, and other pyridine dinucleotides, glyceraldehyde-3-phosphate, and acetyl-CoA . The network is able to produce six amino acids—asparagine, aspartate, alanine, glycine, cysteine, and threonine—plus the two nucleosides UTP and CTP. Cytochromes and quinones do not figure into the network.The intersection of the Ace and Met maxRAFs should be more ancient than each of them individually. Three-quarters of the  Met maxRAF overlap with the (larger) Ace maxRAF in a connected network harbouring 172 reactions and 175 metabolites  with a half-moon core structure with node degree varying from 49 to 4 . Food molecules cluster in the most connected area, suggesting that autocatalytic metabolism is initiated by a handful of central substrate molecules with degree higher than 10.(e)a), whereas thiamine diphosphate (a carrier of C2 units in metabolism) is the only organic cofactor that is significantly enriched in catalysing the primordial network when compared with the global network, even though several others are present and essential for the network to grow (b). The primordial network is also enriched in reactions for amino acid biosynthesis, carbon metabolism, and 2-oxocarboxylic acid metabolism when compared with the global network (b). Comparing reactions in the primordial network to those catalysed by genes that can be traced to LUCA by independent phylogenetic criteria [c). The maxRAF obtained within the primordial network contains 120 reactions and is enriched in amino acid and carbon metabolism but produces cysteine as the sole amino acid, which is noteworthy because cysteine is the hub of sulfur metabolism and also is the sole ligand for incorporating Fe–S and Fe–Ni–S clusters in proteins .In search of the distinct contributions for autocatalysis, we tested for enrichment in individual catalysts, functions, and ancient genes encoding for reactions in the primordial network. There is a significant enrichment for metal and metal–sulfur cluster catalysis a, wherea to grow b. The pr network b. Comparcriteria  uncovers(f)+ is strongly embedded in the RAF and its removal reduces the size of the maxRAF by approximately 50% . The role of NADs, which we use to collectively designate NAD(P)+ and NAD(P)H, in the food set of the largest maxRAFs is striking. It exhibits the strongest effect we observed for any cofactor. NAD donates and accepts hydride for redox-dependent reaction catalysis. The strict dependence of the largest maxRAFs upon NAD reflects the circumstance that microbial metabolism, without exception, always involves redox reactions [Crucial catalysts can be identified by removing them from the food set. NADeactions . Though eactions , they peeactions ,66. Fe–SSurprisingly, when we remove ATP from the food set of organic cofactors, this has no impact on the size of the maxRAF (the number of reactions in the maxRAF remains unchanged), both for the individual networks  and LUCA's network . Why does ATP removal from the food set with organic cofactors have no effect on RAFs? ATP is an essential intermediate in the maxRAF, but it is not required to kick-start it when other organics are present. This reflects the increasingly evident role of alternative energy currencies in primordial metabolism , thioest2 , produces a miniscule 33-reaction maxRAF . The maxRAF contains 47 metabolites, 27 of which are food molecules. This indicates that primordial autocatalytic networks embedded in microbial metabolism generated amino acids and bases using small-molecule catalysis.RAFs provided with a food set containing catalysts can generate amino acids and bases , but the3.Autocatalytic networks are objects of molecular self-organization –12. TheiRAFs have not been applied in the study of the evolution of chemical networks that led to the metabolism of modern cells, themselves large natural autocatalytic networks. By embracing the simple and robust premise that reactions catalysed by simple molecules and inorganic compounds preceded metabolic reactions catalysed by enzymes ,26,30, wMethanothermus fervidus [M. fervidus lives from H2 and CO2 as carbon and energy sources (the food set) and requires only inorganic, geochemical nutrients, no other cells for survival [2 and CO2 were present in abundance on the early Earth and may have given rise to the first metabolic pathways that brought forth the first archaeal and bacteria cells [2 and CO2, which is found in the acetogen Thermoanaerobacter kivui, encoding 2378 proteins [Our analyses started with the enzymatic and spontaneous reactions charted in modern metabolism and used RAFs as a filter to uncover elements with self-organizational properties, to address the nature of processes in the earliest phases of evolution, before the origin of eukaryotes and before the appearance of oxygen. We found evidence for a role of autocatalytic networks at the onset of metabolism. The largest RAF that we identified in the whole prokaryotic anaerobic biochemical space has 1335 reactions and points to early autotrophy. This RAF is larger than the genome size of the smallest free-living archaeon, fervidus . With a survival . H2 and ia cells ,47. Our proteins .Methanothermus fervidus and T. kivui harbour primitive forms of methanogenesis and acetogenesis in that they both lack cytochromes and quinones, suggesting that they represent energy metabolic relics from the earliest phases of biochemical evolution on the primordial Earth, before anaerobic respiratory chains had evolved [2-CO2-dependent archaea and bacteria, the methanogen Methanococcus maripaludis and the acetogen Moorella thermoacetica. Remarkably, a food set containing only small abiogenic molecules and a handful of organic cofactors generates sizeable RAFs in each of the networks, with 209 and 394 reactions, respectively. The inclusion of organic molecules as catalysts in our food set is in line with a premise common to all scientific theories for the origin of life, namely that the environment provided starting material from which metabolism and life evolved. The small sizes of maxRAFs compared to full metabolic networks in the genome-scale metabolic models are contingent upon two aspects. First, all reactions in the maxRAFs presented here require small-molecule catalysis. This excludes important edges in the network catalysed by peptides only, that allow for significant expansion, when the generic catalyst ‘peptide' is added to the food set . Moreover, in genome-scale models, a method known as gap-filling allows genome-scale metabolic models to be connected to the necessary degree for the production of all biomass components [ evolved . To invemponents . To avoimponents ,61.M. maripaludis and M. thermoacetica uncovers commonalities—a core, conserved network with 172 reactions that is enriched in metal catalysis and carbon-metal bonds [RAFs uncover elements of metabolic evolution that predate the divergence of archaea and bacteria from the LUCA. The intersection of the RAFs of al bonds  and to tal bonds . This coal bonds . Our modal bonds  and receal bonds . More imal bonds ,74.2) only produces a minute RAF with 33 reactions. The result that autocatalytic networks detectable in microbial metabolism as RAFs generate amino acids and bases using small-molecule catalysts is in accord with the recent report of amino acid synthesis catalysed by native metals [2- and CO2-dependent metabolism, they are polymerised to make the substance of cells.An important insight uncovered by RAFs is the observation that although a food set with organic cofactors sparks a large autocatalytic metabolic network that generates amino acids and bases, the opposite does not occur: adding amino acids and bases to the simplest food set  chemical reactions preceded reactions catalysed by genetic material, and that those exergonic reactions continuously gave rise to substrate-product relationships ,79. From4.Detailed methods including annotation procedures and algorithms can be found in the electronic supplementary material.


It is used in the treatment of depression, insomnia, migraine, and other diseases. Due to a lack of effective biosynthesis methods, 5-HTP is mainly obtained by natural extraction, which has been unable to meet the needs of the market. Through the directed evolution of enzymes and the introduction of substrate supply pathways, 5-HTP biosynthesis and yield increase have been realized. This review provides examples that illustrate the production mode of 5-HTP and the latest progress in microbial synthesis. It is derived from tryptophan (trp), and the hydrogen atoms at the 5′-position on the benzene ring of trp are replaced by hydroxyl groups. 5-HTP appears as a fine white powder that is insoluble in water, but is soluble in alcohol. In mammals, 5-HTP is the precursor of the neurotransmitter serotonin and the amine hormone melatonin. It has been successfully used in the treatment of depression, insomnia, migraines, and other diseases due to its regulatory effects on sleep, pain, appetite, and other physiological functions  Birdsal. 5-HTP iGriffonia Simplicifolia, but this method of extraction is unable to meet the market demand due to high cost and a lack of raw materials. Chemical synthesis does not depend on natural products. However, it is not currently possible to synthesize 5-HTP economically and effectively due to the tedious steps involved and harsh conditions required. With the development of biotechnology in bioinformatics, genetics, metabolic engineering, biochemistry, protein engineering, and so on, new strategies are available for the use of microorganisms to synthesize 5-HTP. The production of 5-HTP by the microbial method has the advantages of short production cycle, continuous production and mild reaction conditions. Among the organisms used for this method, Escherichia coli is a model strain of prokaryotes with clear genetic information and well-established fermentation conditions, making it particularly well-suited for use as a host cell for the study of 5-HTP biosynthesis.At present, 5-HTP is mainly extracted from natural products, specifically from the seeds of the African plant As shown in Dieting leads to a sharp decrease in serotonin levels in the serum and brain, and a decrease in serotonin leads to gluttonous gluttony. 5-HTP can prevent dieting induced decrease in serotonin in patients with obesity, thus reducing appetite and assisting with weight loss  using L-trp as a substrate (4) and Fe2+ are required as cofactors in its catalytic process , and tyrosine Hydroxylase (TH) are pterin-dependent aromatic AA hydroxylases (AAAHs)   was increased by mutation breeding  from different sources, 4 g/L BH4 was obtained under fed-batch fermentation enzymatic modification to improve the hydroxylation activity of TPH, (b)reduction of the copy number of the trp synthesis gene, and (c) regulation of the promoter strength of genes involved in BH4 synthesis and regeneration, the yield of 5-HTP in the modified recombinant strain HTPL01-LMT was 1.29 g/L, 3.1-fold increase  introducing the trp synthesis pathway. To date, the yield of shake flask culture has reached 1.61 g /L.This review assesses the synthesis and research progress of 5-HTP. Compared with natural product extraction and chemical synthesis, biosynthesis has the advantages of a short cycle, continuous production and mild reaction conditions; thus, it has garnered considerable research attention. In the biosynthesis process, three aspects of optimization have been implemented to increase the output of 5-HTP; (a) improving the hydroxylation activity of the TPH enzyme by directed evolution, (b) introducing BHAlthough 5-HTP production has increased 10-fold, remains insufficient for large-scale commercial production. The metabolic network is a complex system, and the efficiency of the target metabolic pathway is often affected by other metabolic pathways. Therefore, strain evolution and breeding for global metabolism may effectively improve the yield of the target product. Besides, high-density cell culture is another strategy for increasing the yield. Combined with the optimization of fermentation conditions and the improvement of cell culture density, the yield of 5-HTP could be further improved.X-XL wrote the manuscript. BZ and L-ZA revised the Metabolic engineering strategy for 5-HTP synthesis section. All the authors contributed to the literature collection and data analysis, 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.