Bdellovibrio bacteriovorus by constructing a fully compartmented, mass and charge-balanced genome-scale metabolic model of this predatory bacterium (iCH457). Considering the differences between life cycle phases driving the growth of this predator, growth-phase condition-specific models have been generated allowing the systematic study of its metabolic capabilities. Using these computational tools, we have been able to analyze, from a system level, the dynamic metabolism of the predatory bacteria as the life cycle progresses. We provide computational evidences supporting potential axenic growth of B. bacteriovorus’s in a rich medium based on its encoded metabolic capabilities. Our systems-level analysis confirms the presence of “energy-saving” mechanisms in this predator as well as an abrupt metabolic shift between the attack and intraperiplasmic growth phases. Our results strongly suggest that predatory bacteria’s metabolic networks have low robustness, likely hampering their ability to tackle drastic environmental fluctuations, thus being confined to stable and predictable habitats. Overall, we present here a valuable computational testbed based on predatory bacteria activity for rational design of novel and controlled biocatalysts in biotechnological/clinical applications.In this study we analyze the growth-phase dependent metabolic states of  Bdellovibrio and like organism (BALOs) have received recently a great attention due to the high potential applications such as biocontrol agent in medicine, agriculture, aquaculture and water treatment. Despite the increasing interest in this predatory bacterium, its complex lifestyle and growth conditions hamper the full exploitation of their biotechnological properties. In order to overcome these important shortcomings, we provide here the first genome-scale model of a predatory bacterium constructed so far. By using the model as a computational testbed, we provide solid evidences of the metabolic autonomy of this interesting bacterium in terms of growth, as well as its dynamic metabolism powering the predatory biphasic life style. We found a low metabolic robustness thus suggesting that Bdellovibrio is more niche-specific than previously thought and that the environmental conditions governing predation may be relatively uniform. Overall, we provide here a valuable computational tool largely facilitating rational design of novel and controlled predator-based biocatalysts in biotechnological/clinical applications.Bacterial predation is an interspecific relationship widely extended in nature. Among other predators,  Bdellovibrio and like organisms (BALOs) which are small, highly motile, and aerobic gram-negative predatory bacteria that prey on a wide variety of other gram-negative bacteria. Originally discovered in soil  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. 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Please don't hesitate to contact us if you have any questions or comments.Sincerely,Vassily HatzimanikatisAssociate EditorPLOS Computational BiologyDaniel BeardDeputy EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This manuscript by Herencias et al reports the construction of a metabolic model for the predatory bacterium Bdellovibrio bacteriovorus.Findings highlight a higher number of amino acid auxotrophies than previously known, while nucleotides and phospholipids can be synthesised de novo by Bdellovibrio.Experimental validation of some of the model’s predictions (i.e. growth rates in various media) was done using host-independent (HI) mutant of Bdellovibrio, based on the assumption that the mutation leading to host-independency does not affect metabolism. Since the model was based on wild-type genomic and metabolic data, and because it described fairly well the metabolic activity of HI cells, it questions the obligate predatory lifestyle of Bdellovibrio from a metabolic point of view, which is interesting and intriguing.The authors also constructed metabolic models specific for AP and GP stages by adding in silico constraints to their initial global model .Major comments:1) Figure 4: what is the statistical significance of the growth rate differences/similarities?2) Lines 479-481: from the results section, I had understood that the investment of most resources to ATP generation was an assumption/input  for the generation of the iCHAP model (see for ex. lines 366-368), not a conclusion/output from that model. Same remark for next line concerning GP. Please clarify.3) Figure 5: the authors could discuss what are the intracellular microorganism’s exclusive essential reactions4) related to the assumption that HI mutants’ metabolism reflects that of the wild-type Bdellovibrio:- Lines 84-87: can the authors exclude the possibility that the hit mutation has a major impact on metabolism via an indirect regulatory effect?Minor comments:- Title: biphasic instead of biphasic- Line 44: interest in this predatory bacterium, its complex lifestyle (…)- Line 77: septates instead of septs- Lines 78-79: whether a large array of hydrolytic enzymes is used to breach the prey OM is not known.- Line 81-82: it is unclear how HI mimics the dimorphic pattern of HD cells since there is no attack phase stage. Moreover, what is meant by differentiation here is unclear.- Line 94: septates- Lines 108-111: this sentence is unclear, please rephrase.- Line 219: please clarify what such an energy-saving mechanism implies- Line 224: please clarify how the finding that Bdellovibrio has the ability to synthesise fatty acids and nucleosides de novo, and the reported uptake from the prey, demonstrates a more efficient incorporation of components from the prey.- Line 249: bacteriovorus- Line 268: please briefly define what FBA means (for readers who haven’t read the methods section yet).- Line 305: after constrained, the growth rate- Line 555: gene/protein- Figure 1: Model draft instead of mode draft?- Figure 1: species-specific instead of specie-specific- S1 text, page 2: major instead of mayor- S1 text, Table S2: abbreviations; also consider changing “thesis” to “study”.- Supplementary table .xls file: read me page: present instead of presentsReviewer #2: This article describes the metabolic reconstruction of the predatory bacterium Bdellovibrio bacteriovorus. The authors test this reconstruction by predicting the bacterium’s growth rate under different growth conditions. The unique nature of this organism makes this reconstruction a useful resource for the scientific community.While the reconstruction is useful, there isn’t anything novel in the study. There is no novel modeling approach or significant biological insights. Upon reading the abstract and title, I had presumed this study involves a novel predator-prey interaction metabolic model, which would have been very interesting. Instead this study builds a metabolic model using well-established methods and simulates various conditions in silico. The literature validation data provided isn’t extensive either and any new insights reported in the abstract are speculative.I think the reconstruction process could be greatly improved over its current state. A lot of information about model curation and annotation have not been provided. Finally, a major goal of the reconstruction process is to identify gaps in knowledge and directions to further curate the model. These need to be emphasized in the manuscript.There is also circular logic in their discussion and results. For their comparison of growth phase (GP) vs attack phase (AP), the authors report that they found no flux in biosynthetic pathway in the attack phase. This is just the consequence of their objective which was maximizing ATP.Further, they report in the discussion section that they discovered “Through detailed analysis of these specific-condition models it has been possible to conclude that during AP, B.bacteriovorus invests most of its resources in ATP generation… In contrast, GP was characterized by the biosynthesis of biomass building blocks.”But while performing FBA, they assumed that the AP phase maximizes ATP and the GP maximizes biomass! So it doesn’t make sense to conclude this result based on their assumption. If they had found this using just the transcriptomics data alone then that would have been interesting.How was reaction directionality determined? was it using literature evidence or other evidence such as basic thermodynamic estimates? This is not discussed in the manuscriptDoes the model include growth and non-growth-associated ATP maintenance parameters? if so how were they estimated?How were Genes-Protein-Reaction annotations done? Was protein complex information taken into account during the reconstruction process to assign GPRs?Regarding compartments in the model, was some sequence analysis done to determine subcellular location of different enzymes (i.e cytosol or periplasm)?How many dead end reactions are there in the model? These could be highlighted/discussed for future model curation and to identify less studied parts of the network.the confidence scores for reactions are not provided in the spreadsheet supplement. The confidence scores are valuable for model curation. The transcriptomic and genomic evidence mentioned earlier can be used to assign these confidence scores .How were the reactions mass- and charge balanced?What other quality controls were performed during the reconstruction process? Did they check for ATP, nadh, nadph production leaks (i.e they should not be produced when there are no inputs)? thiele and palsson, 2010 have a good protocol for testing reconstructions. This can be used as a guideline for reconstructing metabolic networks.The authors mention that there are gaps in the model. How many gaps were there and how many were filled manually and using automated tools?The authors report that this reconstruction has high number of transporters and cell wall metabolism enzymes. For example they state “In fact, this category was found to be one of the most representative in terms of number of reactions (161)”. Are these statistics higher than those observed in ecoli or other bacteria? the actual number (161) alone doesnt really convey if transporters are over-represented. This needs to be described in comparison with other organisms.The authors predicted growth and biomass yield in 5 conditions. They report 100% accuracy in predicting growth in 3 conditions and 70% accuracy in 2 conditions. I think the term accuracy is confusing and misleading in this case. Instead they should say ‘the model predicted 70% of the actual value’.Please include a pvalue for the rank correlation reported as it is a function of the sample size.table s2 in the supplementary text: What’s the literature evidence for these reannotations? this should be included in the tableNo references or evidence code is provided in s1 table for the curation of these reactionsOther commentsThere’s a typo in the title. Biphasic is misspelled.The abstract uses unnecessary adverbs like ‘unprecedented’, ‘solid’.Figure 2 would be more informative if it had statistics rather than just words and cartoons describing the processNotes in s1 table in the spreadsheet are in Spanish**********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: No:Reviewer #2: 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  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. 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A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Vassily HatzimanikatisAssociate EditorPLOS Computational BiologyDaniel BeardDeputy EditorPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: The manuscript has been greatly improved by the addition of technical details, explanations, and validation/QC data, allowing a better understanding of the findings from a physiological point of view. The authors provided satisfying answers to most of my concerns and questions.I just have a few comments and suggestions:- Line 356/358: "HI strains exhibit a similar lifecycle when growing in a rich medium to the WT strain…" It is unknown if the “lifecycle” is similar: there must be major differences (despite the likely metabolic similarity) between intra-periplasmic and free-living lifestyles; so far the cellular processes at play during HI growth, including elongation, cell division, etc, have not been investigated in sufficient molecular and cellular details to claim similar lifestyles (incl. ref 64 from 1969 and ref 10 from 1977). However I agree with the authors’ response that metabolically speaking, HI strains are supposed to possess similar capabilities. I would try to be more specific in that sentence and avoid firmly stating that HI lifecycle is similar to the WT HD one.- Legend of Fig 5 is missing- Line 203: define RPKM- typos/etcLine 31: based onLine 87: multiple fissionLine 132: GeneLine 133: correspondsLine 183: representsLine 199: replace “us”Line 207: takenLine 286-289: rephraseLine 294: do you mean “when a specific gene”, instead of “although a specific gene”?Line 295: Examples of this includeLine 296: it is evidentLine 318: Evolution hasReviewer #2: The authors have addressed all my concerns**********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: Yes**********what does this mean?). 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Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides. High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.An oleaginous yeast  Rhodosporidium toruloides is a non-model basidiomycete fungus known for its ability to produce carotenoids and accumulate lipids. The high flux in lipid and carotenoid biosynthetic pathways makes R. toruloides a promising host organism for producing biofuels and value-added bioproducts from carbon sources derived from lignocellulosic biomass  while S. cerevisiae accumulates xylitol , 1 μM myo-inositol , 1 μM thiamine HCl , 1 μM p-aminobenzoic acid , and 1 μM calcium pantothenate  plus Biolog dye mix E  and 1 μM menadione sodium bisulfite . For nitrogen, phosphorous, and sulfur sources 100 mM glucose was added to the inoculation fluid. Hundred microliters of the cell suspension was added to each well in plates PM1, PM2, PM3, and PM4. Plates were sealed with clear sealing film  and incubated for 120 h at 30°C in the dark. Respiration in each condition was detected by measuring reduction of the dye by comparing absorbance at 590 nm to absorbance at 750 nm.Phenotype microarray plates and standard components for yeast phenotypic analysis were obtained from Biolog Inc. . Wild type 2PO4 , 24 mM K2HPO4 , and 100 nM FeSO4  with 1% w/v carbon source. Cultures were grown to OD 600 = 5–10 (20–50 h depending on carbon source) at 30°C, 200 RPM in baffled flasks . Ten milliliters mL samples were pelleted and frozen for DNA extraction. DNA extraction, barcode amplification, and sequencing was performed as described in https://github.com/stcoradetti/RBseq). Raw barcode sequencing data are available at the NCBI Sequence Read Archive (PRJNA595384). Fitness scores are available at the fungal fitness browser.1Fitness analysis was performed as described in a previous study . BrieflyTwenty milligrams of lyophilized cell mass was suspended in 750 μl 3N methanolic HCl  and 50 μl chloroform . A total of 100 μl of 10 mg/mL tridecanoic acid methyl ester  in methanol  was added as an internal standard and all the resuspended cell mass was transferred to bead-bug screw-top tubes with glass beads. Tubes were shaken vigorously in a Retsch Tissue Lyser at 30 cycles/second for 5 min to break up cell aggregates and disrupt cell walls, then incubated in an 80°C water bath for 2 h with occasional vortexing. fatty acid methyl-esters (FAMEs) were extracted with 500 μl n-hexane , and diluted 1:10 in hexane. Methyl esters of palmitic, palmitoleic, heptadecanoic, stearic, oleic, linoleic, alpha linoleic, arachidic, behenic, and lignoceric acid were separated in a DB-wax column  on a Thermo Scientific Focus gas chromatograph (AS 3000 II) with a flame ionization detector. Standard curves of ratios of peak areas from standards of those FAMEs (Sigma) to tridecanoic acid methyl ester  were established. FAME concentrations were determined by comparing ratios of peak areas of FAMEs to the internal standard in the samples.R. toruloides IFO0880 was grown in synthetic defined (SD) medium supplemented with different carbon sources . The SD medium was made with Difco yeast nitrogen base without amino acids  and complete supplemental mixture . Cells were pre-cultured in LB media, and pelleted and washed once with sterile ddH2O before inoculation. Initial pH was adjusted to 7.4 with NaOH, and cells were inoculated to 30 mL of medium with a starting optical density at 600 nm of 0.1. Cultures were grown at 30°C and shaken at 200 rpm. Samples were taken in triplicates at 24 and 48 h in SD glucose, at 24, 66, and 90 h in SD glucose + D-xylose, at 66 and 90 h in SD D-xylose, at 40 and 66 h in SD L-arabinose, and at 40 and 90 h in SD p-coumarate. RNA extraction was performed on Promega’s Maxwell RSC machine using Plant RNA extraction kit . For proteomics and metabolomics, cells were pelleted and washed twice with 100 mM NH4HCO3 at pH 7.8, and spent media was filtered through a 0.45 μm filter.Wild type RNA samples were sequenced and processed at Joint Genome Institute . Raw read counts were used to perform differential gene expression analysis using DESeq2 .Metabolites were extracted from the cell pellets using MPLEx method . Brieflyhttps://osf.io/tnqwx/.The stored metabolite extracts were completely dried under speed-vacuum to remove moisture and chemically derivatized as previously reported . Brieflyin vacuo to remove moisture and reconstituted in 50 μl methanol, 10 μl of which was injected onto a reversed phase Waters CSH column (3.0 mm × 150 mm × 1.7 μm particle size) connected to a Waters Acquity UPLC H class system interfaced with a Velos-ETD Orbitrap mass spectrometer. Lipid molecular species were separated over a 34 min gradient  at a flow rate of 250 μl/min. Samples were analyzed in both positive and negative ionization using higher-energy collision dissociation and collision-induced dissociation to obtain high coverage of the lipidome. Confident lipid identifications were made using in-house developed identification software LIQUID  as outlined before . Brieflye LIQUID  where the LIQUID . Aligned4HCO3 containing 8 M urea and the protein concentration was measured by BCA assay. Disulfide bonds were reduced by adding dithiothreitol to a final concentration of 5 mM and incubating at 60°C for 30 min. Samples were alkylated with a final concentration of 40 mM iodoacetamide for 1 h at 37°C. The reaction was then diluted 10-fold with 100 mM NH4HCO3 followed by the addition of CaCl2 to 1 mM final concentration. Digestion was carried out for 3 h at 37°C with 1:50 (weight:weight) trypsin-to-protein ratio. Salts and reagents were removed by solid-phase extraction using C18 cartridges according to the manufacturer instructions and the resulting peptides were dried in a vacuum centrifuge. The peptides were then resuspended in milliQ water and 500 ng of material was loaded onto in-house packed reversed-phase capillary columns (70-cm × 75 μm i.d.) with 3-μm Jupiter C18. The separation was carried out using a nanoAcquity HPLC system (Waters Corporation) at room temperature. The mobile phase A is 0.1% formic acid in water while mobile phase B is 0.1% formic acid in acetonitrile. The elution was carried out at 300 nL/min with the following gradient: 0–2 min 1% B; 2–20 min 8% B; 20–75 min 12% B; 75–97 min 30% B; 97–100 min 45% B; 100–105 min 95% B; 105–110 min 95% B; 110–140 min 1% B. MS analysis was carried out using a Q Exactive HF (Thermo Fisher Scientific) in data dependent mode. Mass spectrometer settings were as following: full MS ; MS/MS .The protein disks were dissolved in 100 mM NH2 was used to append the relevant MASIC stats for each peptide hit result in MS-GF+. The MS-GF+ data were then filtered based on 1% false discovery rate (FDR) and less than 5-ppm mass accuracy to generate a list of qualified peptide hit results. The abundances of peptides were determined as the highest peak area identified for the peptide within a sample. RRollup algorithm in InfernoRDN software  from at least two species . It is gR. toruloides metabolic network consisted of 1596 proteins, 3804 reactions, and 3589 metabolites. Among the 1596 proteins, 1137 proteins had orthologous R. toruloides proteins, but 459 were not yet mapped to R. toruloides proteins. Manual investigation of functional annotation and BLAST search were used to determine whether the unmapped proteins were present or absent in R. toruloides. For the reactions associated with a protein absent in R. toruloides, the absent protein was removed from the gene association if there were isozymes and at least one mapped isozyme was present. A reaction was removed from the metabolic network if the absent protein was a subunit of an enzyme complex and no other isozyme was present. One of the reasons why the number of reactions and metabolites in the initial reconstruction was very large was that protein localization was not yet considered, and same reactions in multiple compartments were present including compartments that are present in other organisms but absent in R. toruloides. These reactions were examined and either removed or assigned to appropriate compartments based on the localization prediction and the presence of a signal peptide sequence. It was also found that, although reconstructed from published metabolic models, the initial reconstruction contained many erroneous reactions and metabolites including duplicate reactions or metabolites with different names as well as mass or charge imbalanced reactions.The initial draft reconstruction of R. toruloides is known to have coenzyme Q9 (ubiquinone-9) with nine isoprenyl units . Lactate dehydrogenase and related enzymes involving cytochrome c had many duplicated reactions and incorrect compartment assignments. Cytochrome c oxidase, peroxidase, reductase, NADH:ubiquinone reductase and other mitochondrial electron transport chain reactions were curated. yl units , and ourR. toruloides possesses both mitochondrial and peroxisomal beta-oxidation enzymes. Sphingolipid metabolism was extended to include sphingolipid desaturase, methyltransferase, and fungal ceramide biosynthesis and the reactions were placed in endoplasmic reticulum. Localization of reactions involved in phospholipid biosynthesis and remodeling, and triacylglycerol were also updated.There were many incorrect reactions involved in fatty acid biosynthesis and beta-oxidation, especially for unsaturated fatty acids. Impossible lumped reactions were removed considering the cis and trans configuration of fatty acids, and irrelevant fatty acid reactions (bacteria or plant specific unsaturated fatty acids) were removed. Long-chain fatty acid biosynthesis and fatty acid desaturase reactions were moved from cytosol to endoplasmic reticulum, and short-chain peroxisomal fatty acid beta-oxidation reactions were moved to mitochondria since Rhodosporidium toruloides naturally produces and accumulates carotenoids that are derived from mevalonate pathway products. Metabolic reactions and genes in mevalonate pathway and sterol biosynthesis were inspected, and it was found that an ERG27 ortholog is missing in R. toruloides. A previous phylogenomics study of sterol synthesis found that a 3-ketosteroid reductase exists in vertebrates and fungi (HSD17B7 in vertebrates and ERG27 in S. cerevisiae) but is missing in land plants and other eukaryotic phyla  that are homologous to human aldo-keto reductases 1C. One of the aldo-keto reductases (14213) had a predicted signal peptide by SignalP and predicted localization in endoplasmic reticulum by WoLF PSORT, and was assigned to reactions catalyzed by the 3-ketosteroid reductase. Carotenoid biosynthesis in endoplasmic reticulum and accumulation in lipid droplets were added based on previous studies  were either removed from the model or updated with corresponding genes if found. Reactions were checked for mass and charge balance, and chemical formula and charge information was updated for all metabolites. When needed, chemical equations were modified based on metabolic databases or new evidence in literature. The curated metabolic reconstruction consisted of 1106 genes, 1934 reactions, and 2010 metabolites (1246 unique metabolites) in nine compartments.The reactions and genes in the central metabolic pathways were manually checked for their co-factor usage and localization. Reactions in the compartments that are present in other organisms but irrelevant in S. cerevisiae metabolic model and exchange reactions for external metabolites were added to the metabolic network reconstruction to develop a draft metabolic model that can be used to make growth and flux predictions. In addition, transport reactions for water, carbon dioxide, and oxygen in different compartments were added. We first examined whether each biomass precursor in the biomass reaction can be synthesized in an aerobic glucose minimal medium. A flux balance analysis (FBA) problem maximizing the biomass production was solved, and the shadow prices of metabolites were examined to identify biomass components that could not be synthesized. The cytosolic components in S. cerevisiae biomass reaction whose biosynthesis reactions were moved to endoplasmic reticulum were replaced with the respective metabolites in endoplasmic reticulum, and transport reactions for fatty acids, phospholipids, and sterols were added to allow lipid production. In addition, mitochondrial transport reactions for several amino acids and their precursors were added and incorrect reactions in lysine biosynthesis were manually curated to allow synthesis of all components in S. cerevisiae biomass reaction. Several reactions generating a free proton gradient across mitochondrial membrane via a loop were identified by mixed-integer programming and removed to prevent unrealistic ATP production.A biomass reaction from the We used multi-omics and other experimental measurement to update the biomass reaction. The DNA composition was updated using the genome sequence, RNA composition was updated using transcriptomics data, amino acid composition was updated using proteomics data, and lipid composition was updated using fatty acid methyl ester analysis. For lipid composition, we measured fatty acid profiles in multiple media conditions to cover from low lipid to high lipid production states . We obsep-coumarate. The initial metabolic model was able to predict growth on glucose, but not on D-xylose, L-arabinose, and p-coumarate. We examined the existing reactions in the model to identify the missing links within known catabolic pathways. In order to predict growth on D-xylose, the xylose reductase reaction was needed. Two potential xylose reductase encoding genes were found in R. toruloides with sequences that are similar to larA and xyrA in Aspergillus niger. For growth on L-arabinose, the L-arabinose transporter, L-arabinitol 4-dehydrogenase, and L-xylulose reductase reactions were needed but BLAST of known genes for these reactions resulted in multiple hits with moderate scores. For p-coumarate utilization, four reactions in the known p-coumarate degradation pathway in bacteria were present in the model. We identified genes that could potentially catalyze the missing reactions in the known p-coumarate degradation pathway using functional annotation and BLAST searches. However, additional experimental data was still needed to identify which of these candidates are actually responsible for the missing or added metabolic functions in the D-xylose, L-arabinose, and p-coumarate utilization pathways. We therefore performed multi-omics experiments for R. toruloides grown in these carbon sources to elucidate the genes and reactions necessary for their utilization.We tested the model’s capability to predict growth on carbon sources that can be often found in lignocellulosic biomass hydrolyzate – glucose, D-xylose, L-arabinose, and R. toruloides. Cells were grown with glucose, glucose + D-xylose, D-xylose, L-arabinose, or p-coumarate as carbon source, and samples were taken during exponential growth phase and stationary phase. An additional sample was taken between exponential and stationary phase for cells grown with glucose + D-xylose to study the co-utilization pattern. Gene expression profiles in cells grown on D-xylose, L-arabinose, or p-coumarate was compared to glucose in order to identify genes specifically upregulated or downregulated by each carbon source. RB-TDNA sequencing was also performed in glucose, D-xylose, L-arabinose, p-coumarate and other related metabolites. RB-TDNA sequencing uses sequence barcoded random insertions throughout the genome to identify genes required for growth in a given condition. A mixed population of hundreds of thousands of different mutant strains, each bearing an insertion at a different genomic location, is cultured in the condition of interest. The relative abundances of all barcoded strains in the population are simultaneously measured from a single sample by high throughput sequencing. For the more than 6000 genes with three or more independently tracked insertions within their coding sequence, those abundances are aggregated into a single “fitness score” for mutations in each gene in the tested condition. Genes with an essential function in a given condition  will have negative fitness scores in that condition. Transcriptomics, proteomics, and fitness scores were used to assign genes to reactions when annotations were ambiguous or multiple isozymes with substrate promiscuity were present. Metabolomics data was used to identify intermediates in utilization pathways to provide additional support for the proposed pathways.We performed transcriptomics, proteomics, and metabolomics analysis to investigate genes involved in carbon utilization pathways in R. toruloides uses an alternative pathway involving D-arabinitol and D-ribulose forming ribulose-5-phosphate instead of the known fungal xylose pathway forming D-xylulose-5-phosphate  with relatively high identity (over 50%). Consistent with this annotation, Protein ID 9990 had significant fitness defects in many pentose sugars and alcohols including D-xylose, xylitol, D-xylulose, D-arabinitol, L-arabinose, L-lyxose, and L-arabinitol, but not in D-ribulose. Protein ID 9837 was identified as an ortholog of RHTO_07702 by OrthoMCL, and a BLAST search found matches to D-arabinitol dehydrogenase (NADP+), D-arabinitol 2-dehydrogenases, and D-arabinitol 4-dehydrogenase (converting D-xylulose to D-arabinitol) with lower identity (less than 40%). BLAST analysis of Aspergillus niger D-arabinitol 4-dehydrogenase (An04g09410) against the R. toruloides genome found several hits including protein ID 9837 suggesting its role as D-arabinitol 4-dehydrogenase. However, protein ID 9837 had a weaker fitness defect suggesting that other enzymes participate in the conversion of D-xylulose to D-arabinitol. Among other BLAST hits, protein ID 8905 was upregulated in D-xylose and L-arabinose and had some fitness defect during growth on pentose sugars and alcohols. Therefore, the weak fitness defects for either protein ID 9837 and protein ID 8905 are consistent with genetic redundancy at this step in the xylose utilization pathway. The proposed alternative pathway is supported by our observation that the D-xylulose kinase (protein ID 16850) had very low RNA abundance and no detectable peptides in every condition we tested, and that mutants for protein ID 16850 had no significant fitness defect in any condition tested. Another supporting observation is that the D-ribulose kinase (protein ID 14368) had significant fitness defects in all pentose sugar and alcohol media conditions tested. This pathway is also consistent with recent observations that R. toruloides grown on D-xylose transiently accumulates D-arabinitol in the culture media -3-hydroxy-propanoyl-CoA and subsequently produces 4-hydroxybenzoyl-CoA and acetyl-CoA. We found that, in R. toruloides grown in p-coumarate media, enzymes that are similar to peroxisomal fatty acid beta-oxidation enzymes ACSL (long-chain acyl-CoA synthetase), FOX2 , and POT1 (3-ketoacyl-CoA thiolase) were upregulated. Our fitness data from RB-TDNAseq on p-coumarate and previously published RB-TDNAseq data on oleic and ricinoleic acid  as well as cytosolic aspartate aminotransferase AAT2 had pronounced fitness defects in p-coumarate and ferulate , and there are many examples that highlight the importance of HGT in ascomycete yeasts  in nine compartments. The lipid body was separated from the biomass equation allowing the independent simulation of lipid accumulation or mobilization in oleaginous yeasts. The separation of the lipid body from biomass eliminated the need for more than one biomass equation depending on the growth condition or lipid content. Multi-omics analysis and metabolic network reconstruction identified unique reactions and enzymes as well as their localization for unique pentose and aromatic compound utilization pathways in R. toruloides. The pentose and aromatic compound utilization pathways proposed in this study have not been suggested in previously published multi-omics studies or genome-scale metabolic models of R. toruloides . The mass spectrometry proteomics data are deposited to the ProteomeXchange Consortium via the PRIDE (10.6019/PXD02237. The metabolomics data are deposited to the Open Science Framework (OSF) and available at https://osf.io/tnqwx/. Processed multi-omics dataset is available as a https://github.com/AgileBioFoundry/Rt_IFO0880).The RNA sequencing data (SRP143805–SRP143836 and SRP143838) and RB-TDNA sequencing data (PRJNA595384) are available at the NCBI Sequence Read Archive. Fitness scores are available at the fungal fitness browser (he PRIDE  partner JK, SC, JS, JG, and JM conceived and designed the experiments. JK, SC, JZ, JY, NM, Y-MK, YG, and KB-J performed the experiments and analyzed the data. JK, SC, Y-MK, and YG wrote the initial manuscript. All authors read, revised, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Escherichia coli has become the preferred host cell for microbial biochemicals production. In this review, the recent advances in biological lycopene production using engineered E. coli strains are summarized: First, modification of the endogenous MEP pathway and introduction of the heterogeneous MVA pathway for lycopene production are outlined. Second, the common challenges and strategies for lycopene biosynthesis are also presented, such as the optimization of other metabolic pathways, modulation of regulatory networks, and optimization of auxiliary carbon sources and the fermentation process. Finally, the future prospects for the improvement of lycopene biosynthesis are also discussed.Lycopene, a potent antioxidant, has been widely used in the fields of pharmaceuticals, nutraceuticals, and cosmetics. However, the production of lycopene extracted from natural sources is far from meeting the demand. Consequently, synthetic biology and metabolic engineering have been employed to develop microbial cell factories for lycopene production. Due to the advantages of rapid growth, complete genetic background, and a reliable genetic operation technique,  Lycopene, a member of the carotenoid family , is wideC-methyl-d-erythritol- 4-phosphate (MEP) pathway [Lycopene, a linear carotenoid with a C40 backbone, is composed of seven isopentenyl diphosphates (IPP) and one dimethylallyl diphosphate (DMAPP), both being its biosynthetic precursors ,13,14,15 pathway  or the m pathway . The MEP pathway ,19, begi pathway ,20. In c pathway . Escherichia coli is widely used for the biosynthesis of secondary metabolites and high-value chemicals by metabolic engineering because of its rapid growth and powerful tools to enable genetic manipulation [E. coli facilitates the production of terpenoids including lycopene. However, this pathway showed low metabolite flux in metabolic engineering [E. coli for lycopene production.Engineered pulation . Moreoveineering , leadingE. coli using metabolic engineering strategies. Current investigations on the modification of the two metabolic pathways, MEP and MVA, for lycopene production in E. coli, were reviewed. The optimization of other metabolic pathways, modulation of regulatory networks, optimization of auxiliary carbon sources, and the fermentation process were also described. Furthermore, the common challenges, strategies, and prospects for lycopene biosynthesis in metabolically engineered E. coli were discussed in this review.In this review, we summarized the recent advances in lycopene production by the engineered C-methyl-d-erythritol (CDP-ME) cytidylyltransferase (IspD), CDP-ME kinase (IspE), 2C-methyl-d-erythritol-2,4-cyclo-diphosphate (MEC) synthase (IspF), 4-hydroxy-3-methyl-2-(E)-butenyl-4-diphosphate (HMBPP) synthase (IspG), and HMBPP reductase (IspH), successively catalyzes the conversion of MEP into IPP, along with the respective intermediates of CDP-ME, 4-diphosphocytidyl-2C-methyl-d-erythritol-2-phosphate (CDP-MEP), MEC, and HMBPP. Subsequently, isopentenyl-diphosphate isomerase (IDI) catalyzes the isomerization of IPP to DMAPP [The MEP and MVA pathways are the major pathways producing the precursors of lycopene, IPP, and DMAPP. As shown in to DMAPP ,25. crtEBI from various sources can exhibit differential activities when introduced into the host strains. For instance, when different carotenoid genes crtEBI were introduced into E. coli, higher lycopene yield and cell growth were reached using the genes derived from Pantoea agglomerans compared with those of Pantoea ananatis. Furthermore, it was identified that crtE was responsible for the difference between the engineered E. coli strains harboring the crtEBI genes of P. agglomerans and P. ananatis, respectively [The MVA pathway initiates with acetyl-CoA, which is converted into MVA through three reactions, catalyzed by acetoacetyl-CoA thiolase (ACCT), 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) synthase (HMGS), and HMG-CoA reductase (HMGR), respectively. Subsequently, MVA is converted to mevalonate-5-phosphate (MVAP) catalyzed by mevalonate kinase (MK). The transformation of MVAP into IPP is reported through different pathways. The one in eukaryotes involves two reactions successively catalyzed by MVAP kinase (PMK) and MVAPP decarboxylase (MDD), while the other pathway in archaea harbors two reactions catalyzed by MVAP decarboxylase (MPD) and isopentenyl phosphate kinase (IPK), respectively . After tectively .E. coli for lycopene production. Based on this, further work on the modification of the two major pathways was conducted. Lycopene production via various metabolic engineering optimization strategies in E. coli are summarized and listed in To summarize, the sole utilization of the endogenous MEP pathway or co-expression of the MEP and heterogeneous MVA pathways, and the subsequent expression of three key enzymes, CrtE, CrtB, and CrtI ,28,29,30E. coli, the utilization and modification of the endogenous MEP pathway were extensively investigated. Generally, the overexpression of the key enzymes of the MEP pathway is important in lycopene production, especially the major rate-limiting enzymes  [dxs and dxr were overexpressed solely or jointly on different expression vectors using three different promoters and E. coli host strains, the highest lycopene yield (22 mg/L) was reached with the arabinose-inducible promoter on a medium-copy plasmid pBAD24 in the E. coli XL1-Blue strain [E. coli strain exhibited lycopene production of 1.3 mg/g dry cell weight (DCW) [dxs, idi, and the crt gene operon, leading to an increase in lycopene yield by 32% [dxs, idi, and crt operon genes was modulated using the RBS library, a significant improvement in lycopene yield was observed [In the early development stages of lycopene production using engineered and IDI) . When the strain . Severalht (DCW) . Similarht (DCW) . Moreoveht (DCW) . Besidesd by 32% . Similard by 32% . Lv et ad by 32% . In addiobserved .E. coli need to be explored in the future.Most research has been focused on improving activities of the desired enzymes via conventional strategies, such as overexpression, direct evolution, and RBS optimization for lycopene production using the MEP pathway. However, fewer studies have been performed to investigate the regulatory mechanisms of the metabolic pathway. Therefore, further understanding of the regulatory mechanism of the MEP pathway and the combination of different strategies for lycopene production in E. coli [E. coli. The MVA pathway is composed of the upper and lower pathway, separate from MVA [E. coli, lycopene production increased by over 2-fold compared with the control strain [E. coli DH5α, combined with the overexpression of a type 2 IDI from Bacillus licheniformis significantly elevating lycopene production [E. coli and establish a highly efficient platform which was employed for lycopene production. The fed-batch fermentation process was scaled up to 100 L, reaching 1.23 g/L of lycopene concentration with a maximum productivity of 74.5 mg/L/h [E. coli strain. They reported that overnight induction of the MVA pathway was toxic to cells, which could recover if the lycopene pathway was not heterologously expressed simultaneously. Further, they validated that the intermediate homocysteine could contribute to the growth inhibition and the antagonistic effect between the mevalonate and lycopene pathways, resulting in the homocysteine-induced toxicity in lycopene production. This work indicated that metabolomics would be beneficial to reveal the mechanisms of the metabolite toxicity, and subsequently help to improve the metabolic engineering for the biosynthesis of carotenoid [Although the MEP pathway is natively present in E. coli , the utiE. coli . In contE. coli , and itsfrom MVA . When thl strain . Similarl strain . These roduction . However5 mg/L/h . In addirotenoid .E. coli caused dramatic improvement in lycopene production and could serve as a platform for the production of carotenoid compounds. This was attributed to the better elucidation of the MVA pathway than the MEP pathway. Moreover, the endogenous MEP pathway could be influenced by native regulation. In contrast, the introduction of the exogenous MVA pathway could play its role without regulation. However, because of the common intermediates (IPP and DMAPP), the two pathways are not totally independent, which provides potential optimization strategies for balancing two different pathways for carotenoid compounds production, including lycopene.The introduction of the MVA pathway into In metabolic engineering, inhibition or even knockout of the competitive pathway of intermediate products, and elimination of potential bottlenecks in the upstream pathways not only reduces the generation of by-products but also increases the yield of the target products. Meanwhile, the enhancement of some metabolic pathways is also adopted to modulate the metabolite flux in the synthesis of the target products by supplying more precursors or intermediates. Thus, a rational design strategy of metabolic pathways is of great potential to increase the yield of the target products.As per the description of the MEP pathway, the initial precursors, G3P and pyruvate, are condensed into DXP in equal amounts. The unbalance between the two precursors can reduce the synthetic efficiency, making it necessary to maintain the balance by modulating the metabolic pathways based on a rational design strategy. As is known, the conversion from phosphoenolpyruvate to pyruvate , an essezwf gene encoding glucose-6-phosphate dehydrogenase (G6PD)  , which cE. coli for lycopene production employed an artificial phenotypic screening system to identify the genes affecting lycopene formation. As a result, multiple genes, including two unknown genes elb1 and elb2, were identified that might be involved in the early reactions in lycopene synthesis. In more detail, the regulator encoded by elb2 could regulate the biosynthesis of ubiquinone, including early steps of isoprenoid biosynthesis [gdhA/aceE/fdhF  exhibited a nearly 40% increase in lycopene yield  ΔyliE (di-GMP phosphodiesterase) mutant was obtained that significantly improved lycopene production. The Hnr protein could function as RpoS degradation, which is important for carotenoid production in E. coli. Moreover, the absence of the hypothetical protein YliE could positively influence lycopene production only in the Δhnr background [Identification of the genes directly or indirectly related to the production of the target products in metabolic engineering is of utmost importance. The elimination or overexpression of these genes can affect metabolic progress, either by redistributing the metabolic precursors or rewiring regulatory networks. Previous research on metabolically engineering ynthesis . Alper ene yield . By exploduction . Furtheroduction . Based ockground . The adockground , global ckground , engineeckground , and a fckground . The impBesides the modulation of the metabolite flux, a specific strategy such as membrane engineering plays a role in improving the lycopene yield. As shown in In addition, the precursors of lycopene, IPP and DMAPP, were biosynthesized via the MEP or MVA pathway, during which they need NADPH and ATP . ConsequAs mentioned above, most studies on lycopene production were focused on the overexpression of the key rate-limiting enzymes, and the elimination or inactivation of the competitive branch pathway. The regulation of metabolic pathways could be used to reprogram the metabolic genes to improve the yield of the target products by eliminating metabolic imbalance. glnG product) included in the two-component system Ntr could activate transcription from the glnAp2 promoter by binding to its cognate binding sites on the DNA. Further, NRI itself is capable of sensing the level of acetyl phosphate (ACP), an indicator of glucose flux. Therefore, an artificially engineered glnAp2 promoter containing NRI binding sites and the core glnAp2 promoter was constructed to serve as a control valve that controls gene expression according to the ACP level. Then, this control valve was adopted to regulate the expression of IDI and Pps, which have been identified to control the flux to the final product, and the balance between pyruvate and G3P. The introduction of this regulatory circuit resulted in the full utilization of the excess carbon flux for the enhancement of lycopene production and bypassing the toxic product, acetate [pfkA, fbaA, and ispG involved in the lycopene biosynthetic pathway were up-regulated. Thus, it helped reveal the possible mechanism for the improvement in lycopene production caused by the engineered CRP. As explored, in mcrp26, residue D8 (Asp) had been mutated into V , which belongs to the N-proximal cAMP-binding domain, altering the cAMP-binding capacity. This influenced the CAP-dependent promoters, which raised the differential expression of the above genes related to lycopene production [csgBA for curli surface fiber formation) also enhanced lycopene production by regulating the expression of lycopene synthesis enzymes and energy metabolism operons [E. coli host strains through systems analysis including genetic complementation, quantitative sequential windowed acquisition of all theoretical fragment ions (SWATH) proteomics, and biochemical analysis. It revealed that RpoS could help accumulate lycopene by decreasing oxidative stress in the growth stationary phase, which reduced the degradation of lycopene to its colorless oxidation—and cleavage products [E. coli have been summarized in E. coli for the production of lycopene and other biochemicals.The phosphorylated response regulator NRI , supplemented with glycine addition, exhibited an increased lycopene production rate by 76% [Optimization of the fermentation process is a traditional and direct strategy for improving lycopene production. Traditional fermentation that includes shake-flask and fed-batch fermentations is usually applied . Besidesstrategy . In anotstrategy ,63, a testrategy . Optimize by 76% .E. coil, some novel engineering strategies have been explored. For instance, the enhancement of mRNA stability by varying the mRNA secondary structures was adopted to modulate the metabolic flux to improve lycopene production [E. coli chromosome [E. coli for lycopene production [Besides the conventional engineering approaches adopted to enhance lycopene production in oduction . Moreoveromosome . A multioduction . Coussemoduction .E. coli. However, there is still much room for improvement for lycopene yield in engineered E. coli. The following perspectives could be focused on in future research. First, the combination of the endogenous MEP pathway and the heterogeneous MVA pathway should be further investigated. The mechanisms of the cross-talk between the two pathways should also be uncovered. Second, the regulatory mechanism of the MEP pathway has not been well understood, giving rise to the potential of exploring regulatory engineering to enhance the biological production of lycopene. Third, the compartmentalization of the lycopene biosynthetic pathway should be paid more attention to, which is beneficial to eliminate the cytotoxicity of IPP and DMAPP as well as enhance the compartmental synthesis efficiency. Accumulation of the target products results in gradual cellular pressure of tolerating the target products, thus requiring the exploration of new strategies to resolve this issue, such as adaptive evolution, membrane engineering and efficient extraction methods. Finally, more concerns are given to the modification of upstream metabolic pathways to increase lycopene production, while the optimization of the downstream fermentation process is ignored. Therefore, more attempts should be made to the process engineering of the fermentation, including optimization of the fermentation mode and process parameters, in situ product recovery (ISPR) processes, and so on. Actually, there are numerous research directions in engineered E. coli, showing a promising prospect for lycopene production and other biochemicals.In summary, all these efforts on exploring various engineering strategies have facilitated an increase in lycopene production by metabolically engineering


Catharanthus roseus.Mechanical damage is an unavoidable threat to the growth and survival of plants. Although a wound to senescing (lower) leaves improves plant vitality, a wound to younger (upper) leaves often causes damage to or death of the whole plant. Source-sink models are often used to explain how plants respond to biotic or abiotic stresses. In this study, a source-sink model was used to explain the difference in the metabolic mechanism of mechanical damage to young and senescing leaves of Catharanthus roseus after mechanical damage to the upper/lower leaves (WUL/WLL). Compared with that of the control group, the energy supplies of the WUL and WLL groups were increased and delivered to the secondary metabolic pathway through the TCA cycle. The two treatment groups adopted different secondary metabolic response strategies. The WLL group increased the input to the defense response after damage by increasing the accumulation of phenolics. A source-sink model was applied to the defensive responses to local (damaged leaves) and systemic (whole plant) damage. In the WUL group, the number of sinks increased due to damage to young leaves, and the tolerance response was emphasized.In our study, GC-MS and LC-QTOF-MS metabolomics techniques were used to explore the differences in source-sink allocation and metabolic regulation in different organs of Catharanthus roseus uses different trade-offs between tolerance (repair) and defense to respond to mechanical damage. Repairing damage and chemical defenses are thought to be more energetically expensive than growth development, confirming the trade-offs and allocation of resources seen in this source-sink model.The accumulation of primary and secondary metabolites was significantly different between the two mechanical damage treatments. The online version contains supplementary material available at 10.1186/s12870-021-02934-6. Plants are subjected to various biotic and abiotic stresses in the natural environment during their growth and development . WoundinSource-sink models have explained the relationship between plant demand changes and energy allocation, which involves complex metabolic and signaling networks –13. PlanCatharanthus roseus (C. roseus), a member of the Apocynaceae, is an ornamental plant in botanical gardens [C. roseus contains vinblastine, vincristine, and other alkaloids, which can effectively inhibit tumors, making it the most widely used natural antitumor ingredient extracted from plants. The sulfate composition of the plant has been widely used in practical medicine [ gardens . This pl gardens  and is a gardens . Terpeno gardens . These m gardens , 28. Mormedicine .C. roseus after mechanical damage to young (upper) and senescing (lower) leaves. GC-MS and LC-MS were used to detect and identify metabolites and provide insight into the metabolic pathway of mechanical damage regulation [C. roseus after leaf mechanical damage were related to the trade-off and source-sink relationship. For this purpose, we analyzed the metabolites and associated gene changes in different organs. The metabolomics strategy was used to analyze the changes in metabolic pathways. Our research can increase the understanding of the defense mechanisms of plants, thus providing a basis for research on different metabolic responses to mechanical damage.In the present study, we researched the defensive strategy in gulation –33. We hP-values (P < 0.05)  Table S. Each me05 Table , consist05 Table .Fig. 1TC. roseus. Sugars in the WUL and WLL were 5.25 and 13.74% higher than those in the CK   Fig. , Fig. 4.05) Fig. . a. Cinn05) Fig. . b. Caff05) Fig. . c. Syri05) Fig. . d. In W05) Fig. . e. 3–4-In most organs, the response of luteolin, naringenin, naringin, and galangal in WLL was more robust Fig. . a. MyriP < 0.05, Fig. The PCA model results showed that the TIAs were different in the treatment groups Fig. . e. The WLL consumed more energy in the TCA cycle than CK. More resources flowed into secondary metabolism through the TCA cycle. Compared with WUL, genes that were more highly expressed in WLL were anthranilate synthase (AS), chorismate mutase (CM), and isochorismate synthase (ICS) Fig. . AlthougIn terms of primary metabolism, mechanical damage promoted carbohydrate and amino acid metabolism and inhibited TCA cycle metabolism Fig. S. The metMetabolism is a general term for all aspects of the physiological, biological, and functional regulation of compound synthesis . PrimaryAmino acids are essential metabolites in plant primary metabolism and play an important role in plant physiological processes: acting as osmolytes, modulating stomatal opening, and serving as precursors for the synthesis of defense-related metabolites and signaling metabolites . An enorPlant growth stage, environmental stress, nutrition, and plant genetics influence the production of secondary metabolites . PCs areTIAs and PCs were the major secondary metabolites in plants. PCs are synthesized via the phenylpropanoid pathway . Hence, C. roseus, which readjusted the resource allocation through the source-sink model after mechanical damage, effectively changing source and sink activities  leaves are the key tissue parts of plant growth and development, mechanical damage to young leaves will produce more negative effects on the plant and consume more resources and energy allocation. As the redundant tissue part of plants aging, damage to senescing leaves often brings more sufficient resource allocation and beneficial stimulation to plants. The results showed that WUL had more negative effects on ies Fig. , Fig. S3C. roseus must be effectively balanced and allocated under limited resources. The potential benefits of this division of labor are to minimize the metabolic cost caused by injury and ensure the implementation of the most effective defense strategy.The factors leading to the sink strength increase in mechanical damage stress include the repair process (tolerance) and secondary metabolism regulation (resistance). Different treatments lead to different responses and coping strategies. After plants suffer from damage, they first consume carbon-based primary metabolites and then choose tolerance or resistance through trade-offs . Our stuC. roseus. Metabolites were transferred from source to sink. Changes in metabolites changed the balance of the strength between the source (undamaged organs) and sink (damaged organs). Therefore, the WLL group had more abundant resource allocation and defense metabolite accumulation. They will play a critical defensive role in the damaged area. The damaged plants use their resources to improve the maximum damage adaptability and put in the proportion of resources. This leads to a trade-off between physiological and metabolic investment and ultimately chooses tolerant or resistant to respond to stress. However, there will be a configuration effect of the plant trade-off allocation. An increase in one kind of metabolite will lead to a decrease in other metabolites. In our study, the PC response was the main response in the treatment group. There was a timing difference for the TIA responses. Therefore, the WLL group, with more induced resources, might have increased allocation to self-repair.The results showed that mechanical damage changed primary and secondary metabolite accumulation in C. roseus under mechanical damage stress resulted from metabolic pathway alterations, which varied depending on the growth stage of the damaged leaves. The results indicate that mechanical damage will cause primary metabolic resources and the redistribution of secondary metabolites in C. roseus. A source-sink model was proposed to explain this mechanism of resource reallocation. The results provided useful information for studying plant responses to mechanical damage in different ways and exploring new plant models of responses to adverse ecological conditions and stress. Although we have connected the flow of resources for metabolic in series under mechanical damage, the mechanisms of repair damage by metabolic responses are not fully explained. We will focus on cellular damage in future studies, and attempt to connect the leaf damage/tolerance/repair with metabolic/molecular responses. Thus, the system response mode from the induction of apparent damage to internal metabolic regulation to repair external damage will be revealed.In conclusion, the changes in the primary and secondary metabolites in C. roseus seeds were purchased from Guangdong Shicheng Farm Co., Ltd. . The purchased seed identity was verified by Prof. Zhonghua Tang . Seed specimens were deposited at the Key Laboratory of Plant Ecology, Northeast Forestry University. The seeds were grown in a growth chamber with temperature conditions at a 27–28 °C/23–25 °C day/night temperature cycle, 75% relative humidity, and a 12-h light/12-h dark photoperiod cycle. After germination, the seedlings were irrigated with 1/2 strength Hoagland solution (pH 5.9–6.0). After growing 7–8 leaves (3 months later), each mature plant was separated into five different physiological areas or sections: roots, stems, lower leaves, middle leaves, and upper leaves.The C. roseus. Six biological replicates per treatment group were included for statistical analyses. The relationship between metabolite response and time was examined to find the optimum processing time. The samples were collected at 0 h, 0.5 h, 1 h, 3 h, and 5 h. The results showed that the most intense responses among the treatment groups were seen at 1 h  was without mechanical damage treatment. The upper (young) leaves and lower (senescing) leaves were treated with mechanical damage by sterilized scissors, WUL and WLL, respectively. As shown in Fig. S h Fig. S; therefoSixty milligrams of plant tissue were weighed and mixed with 360 μL of cold methanol and 40 μL of internal standards. The mixture was then homogenized using a tissue layer system and sonicated for 30 min. Afterward, 200 μL of chloroform and 400 μL of water were added. After an adequate response, the sample was centrifuged at 10,000 g for 10 min at 4 °C. Another 200 μL of chloroform and 400 μL of water were added. Samples were allowed to dry and then methoxyaminated and silylated .m/z, acquisition started after a 5 min delay, and the acquisition speed was 20 spectra/s.After derivatization by the steps described above, samples were analyzed on the GC-MS system. A nonpolar DB-5 capillary column was used for separation. The carrier gas was high purity helium with a flow rate of 1.0 mL/min. The temperature program was 50–125 °C for 8 min, raised at 125–170 °C for 15 min, raised at 170–210 °C for 4 min, raised at 210–270 °C for 10 min, raised at 270–305 °C for 5 min, and maintained at 305 °C for 5 min. The inlet temperature was 260 °C, the EI source temperature was 230 °C, and the EI source voltage was − 70 V. Mass spectra were collected by scanning from 50 to 600 Fresh plant tissue was first freeze-dried in a refrigerator at − 180 °C and then pulverized. One gram of the pulverized sample was weighed and dissolved in 20 ml of methanol and subsequently low-frequency ultrasonicated for 40 min. The simple solution was centrifuged for 10 min at 8000 rpm. TIAS detection was based on a Waters ACQUITY UPLC system  and a Qtrap 5500 ion trap mass spectrometer  equipped with an ACQUITY UPLC BEH C18 column. The column temperature was 30 °C. The flow rate was 0.25 ml/min. The UPLC mobile phase consisted of water (0.1% formic acid) (A) and acetonitrile (B). MS was an electrospray ion source with positive and multiple reaction monitoring scanning modes. The MS conditions were as follows: ion spray voltage, 5500 V; atomization temperature, 500 °C; atomizing air pressure, 25 psi; and air curtain pressure, 20 psi.S) was performed using a Waters ACQUITY UPLC system  coupled to a quadrupole time-of-flight (QTOF) mass spectrometer . Chromatographic columns: ACQUITY UPLC-BEH C18 column . The mobile phase system consisted of 0.05% formic acid-water (A) and 0.05% formic acid-acetonitrile (B). Multiple reaction monitoring transitions in positive mode were performed at 120–1200 m/z; the internal standard was Leu-enkephalin [Targeted analysis of phenolic metabolites . Ribosomal protein subunit 9 (Rsp9) was used as an internal control to evaluate all C. roseus plants [The conditions of quantitative real-time PCR (qRT-PCR) analysis were based on our previous research . All plas plants , 58.Tabwww.bioconductor.org) [http://www.umetrics.com/simca). An unsupervised principal component analysis (PCA) analysis was applied to visualize the global metabolic profiles among groups. Next, supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify differential metabolites between treatment groups. By OPLS-DA analysis, the differential metabolites responsible for discriminating between the two treatment groups were identified with variable importance plot values of greater than 1.0 and P values of less than 0.05. Differential metabolites were annotated using the KEGG database (http://www.kegg.jp/kegg/pathway.html) and MBRole 2.0 (http://csbg.cnb.csic.es/mbrole2/) [https://www.R-project.org/).The GC-MS raw data files were converted into CDF format (NetCDF) netCDF (*.cdf) format with Agilent GC/MS 5975 data analysis software  and were subsequently processed by XCMS (brole2/) . The scoAdditional file 1: Figure S1. The Pattern diagram of Catharanthus roseus. Figure S2. Effects of different treatment time on primary metabolites: Treatment time: 0 h, 0.5 h, 1 h, 3 h, and 5 h; Q value was combined with the treatment groups of CK, WUL, and WLL. Figure S3. The changes in the network of primary metabolism. The grid was CK, WUL and WLL group from left to right, respectively; the content of primary metabolites from low to high indicated by the color of green to red, respectively; CK: Control group, WUL: damaged upper leaf group, WLL: damaged lower leaf group. Table S1. Differential metabolites among treatment groups (GC-MS). Table S2. Differential metabolites (sugars and fatty acids) among treatment groups (GC-MS).


Biotechnology involves the use of living organisms to create high-value products. Bacteria and yeast, in particular, are widely applied for such processes, but they may not naturally produce certain products, such as amino acids, organic acids, and alcohols in large amounts, if at all. Hence, the field of metabolic engineering has emerged for “tweaking” the biosynthetic pathways of these cells to encourage the high production of desired products. However, the complexity of the many metabolic pathways in natural cells makes it difficult to ensure that only the molecular components and pathways related to the desired product are enhanced. Very often, competing metabolic pathways and toxic intermediates will lower the production efficiency. Biological scaffolds have thus emerged as one strategy for anchoring the correct enzymes and substrates in place, and in the right orientation, to improve the production process in the cells. This review introduces the different categories of molecular scaffolds  that have been developed, and compares their pros and cons and enhancement of production yields. It is emphasized that the design of these scaffolds is still a trial-and-error process, and further studies are needed to shed light on their underlying mechanisms so that better scaffolds can be developed.Escherichia coli and Saccharomyces cerevisiae, with varying degrees of success. Herein, we review the recent developments and applications of protein-based and nucleic acid-based scaffold systems and discuss current challenges and future directions in the use of such approaches.Microbes have been the preferred hosts for producing high-value chemicals from cheap raw materials. However, metabolic flux imbalance, the presence of competing pathways, and toxic intermediates often lead to low production efficiency. The spatial organization of the substrates, intermediates, and enzymes is critical to ensuring efficient metabolic activity by microorganisms. One of the most common approaches for bringing the key components of biosynthetic pathways together is through molecular scaffolds, which involves the clustering of pathway enzymes on engineered molecules via different interacting mechanisms. In particular, synthetic scaffold systems have been applied to improve the efficiency of various heterologous and synthetic pathways in  Metabolic engineering involves the use of rational approaches for altering the metabolic pathways of an organism ,2, with 2 fixation, while metabolosomes sequester enzymes involved in and intermediates resulted during the oxidation of alternative carbon sources  . T. T44]. TThe highest enhancement of metabolic pathway efficiency, achieved using a protein scaffold system, was the 77-fold increase in mevalonate yield reported by Dueber et al. . Those aCompared with proteins, nucleic acids are more predictable in structure and easier to manipulate . FurtherRNA scaffold systems for the binding of target enzymes are based mainly on aptamers, which are short synthetic single-stranded oligonucleotides that bind specifically to various molecular targets b 55]. T. T55]. TTo ensure metabolic efficacy, it is critical that the orientation of the enzyme active site is controlled in order to channel intermediates directly toward the next enzyme in the cascade . The oriE. coli UDP-glucose dehydrogenase (UGD), 80% of expressed Anti-Ras single-chain variable fragment (anti Ras-ScFv), and 50% of expressed S. cerevisiae alcohol dehydrogenase 1 (Adh1p) were soluble ,2912,29]inations . Using tsubtilis ,30,47.Optimal utilization of the DNA scaffold system was achieved through fine tuning of the spacing and stoichiometry of the enzymes on the scaffold. Because the double-helical structure of DNA turns 360° every ~10 bp, the addition of a 1-bp spacer between enzymes on the scaffold adds a 36° turn in the axial view . ConsequOptimization of the enzyme stoichiometry on a scaffold involves control of the ratio between the enzyme-binding sites on a single scaffold unit and the number of scaffold unit repeats n for a three-enzyme scaffold, where a, b, and c represent the number of each enzyme within a single scaffold unit, and n is the number of times the scaffold unit is repeated on the plasmid) ,47. TherThe TALE-based DNA scaffold system was developed as an alternative to the ZFP-based system . In thisIn general, DNA scaffold systems are more stable and easier to design than protein and RNA scaffold systems ,29. SimpWith the advent of metabolic engineering and synthetic biology, it has become possible to introduce heterologous and/or synthetic enzymes into host organisms to “force” them to produce heterologous chemicals that they would otherwise not naturally produce in high quantities, if at all. However, enzymes with different reaction kinetics require optimization for balancing the metabolic flow and for reducing any negative impact on the host organism. Attempts to address these issues using traditional approaches, such as the rational design of expression systems and control of the enzyme expression level, have been met with limited success. By contrast, the complementary approach of controlling the spatial organization of pathway enzymes via synthetic scaffolds has proven more effective in solving the problems related to inefficient cascade reactions, low biosynthetic yields, and reduced host viability.Although protein and RNA scaffold systems are able to improve the efficiency of metabolic pathways by up to 77- and 48-fold, respectively, such outputs require highly complex scaffold structures, which are prone to misfolding and degradation. By contrast, DNA scaffold systems offer higher structural stability, but their limited structural flexibility results in generally lower enhancements of metabolic pathways.Current efforts toward increasing the efficiency of metabolic pathways via synthetic scaffold systems have mostly involved laborious trial-and-error processes with multiple factors to consider, such as the number of scaffolds to use and the optimal design of the arrangement, orientation, and spacing of the enzymes. Further investigation is needed on the exact mechanisms underlying the beneficial effects of scaffold systems on metabolic pathways. Specifically, in-depth studies on the structural biology of substrate channeling would provide valuable information for aiding the future design of simple, accurate, and tunable synthetic scaffold systems.


Lactococcus lactis subsp. cremoris, Lactococcus lactis subsp. lactis, Streptococcus thermophilus and Leuconostoc mesenteroides, commonly used in cheese starter cultures, were grown in pure and four different co‐cultures. We used a dynamic metabolic modelling approach based on the integration of the genome‐scale metabolic networks of the involved organisms to simulate the co‐cultures. The strain‐specific kinetic parameters of dynamic models were estimated using the pure culture experiments and they were subsequently applied to co‐culture models. Biomass, carbon source, lactic acid and most of the amino acid concentration profiles simulated by the co‐culture models fit closely to the experimental results and the co‐culture models explained the mechanisms behind the dynamic microbial abundance. We then applied the co‐culture models to estimate further information on the co‐cultures that could not be obtained by the experimental method used. This includes estimation of the profile of various metabolites in the co‐culture medium such as flavour compounds produced and the individual organism level metabolic exchange flux profiles, which revealed the potential metabolic interactions between organisms in the co‐cultures.In this study, we have investigated the cheese starter culture as a microbial community through a question: can the metabolic behaviour of a co‐culture be explained by the characterized individual organism that constituted the co‐culture? To address this question, the dairy‐origin lactic acid bacteria  Co‐cultures of lactic acid bacteria were investigated using dynamic genome‐scale metabolic modelling techniques. Özcan and co‐workers estimated the metabolic capacity, microbial abundance, potential of flavour compounds production and metabolic interactions of the co‐cultures using the features of individual organisms that constituted the co‐cultures.  For both pure and co‐cultures, the bioreactor was inoculated with 2% (vol/vol) inoculum culture grown till late exponential phase. Initial biomass compositions of the co‐cultures, based on optical density (OD) measurements, were 1:1 (OD:OD) and 1:1:1 (OD:OD:OD) for two and three‐species co‐cultures respectively. For each different batch experiment (pure and co‐cultures), two independent culture replicates were run.LAB used in this study were 2.2g for 10 min, and cell‐free supernatant was used for glucose, organic acids and amino acids analyses. Biomass samples of co‐cultures were immediately stored at −20°C until the analysis of relative microbial abundance analysis. Glucose concentration was determined by reducing sugar analysis using DNS method . Culture samples were centrifuged at 10,000 Miller, . Organic Miller, . CO2 pro2.3Quantitative‐PCR (qPCR) method was employed for quantifying relative microbial abundance ratios of different bacterial strains during co‐culturing. Total cell dry weight concentrations of the co‐cultures were multiplied by the relative microbial abundance ratios to estimate the individual biomass concentrations. DNA extraction was done using peqGOLD Bacterial DNA Kit , according to manufacturer's protocol, from 3 ml of culture. The primers used for the qPCR procedure Table  are spec2.4Leu. mesenteroides ATCC 19254  (ii) the new reaction set was compared with that of the original model and the reactions only available in the new draft model was added to the original model to get the revised GSMM of S. thermophilus LMG 18311. The revised GSMM of S. thermophilus LMG 18311 containing 829 reactions between 886 metabolites governed by 429 genes is available in SBML format in Supporting Information File‐3. Non‐growth associated ATP maintenance rates used in the GSMMs were obtained from original model studies  constraints, which results in an infeasible solution. In such cases, due to modelling purposes, we gradually decreased the mATP value by 0.1 mmol/gDW/h until a feasible solution was obtained. Such a decrease in mATP was also recently demonstrated in a study, where L. lactis and Leu. mesenteroides were grown in a retentostat reactor as a co‐culture, and mATP values of both species decreased at low growth rates compared to high growth rates  of 2.5L. lactis subsp. cremoris, L. lactis subsp. lactis, S. thermophilus and Leu. mesenteroides, while dynamic co‐culture metabolic modelling approach  approach  as the optimization solver. The ordinary differential equation (ODE) sets in dynamic models were solved by ode45, which is a MATLAB function based on the Runge‐Kutta numerical method. Finally, experimentally obtained initial biomass and metabolite concentrations were used as initial conditions for the solution of ODE sets.Metabolic flux distribution in the dynamic models was calculated by two sequential optimizations. The first one is a linear programming (LP) problem . To mimic the cheese fermentation, pH was not controlled for all batches, and the microbial growth was inhibited by undissociated lactic acid which is increasingly formed by low pH. Therefore, glucose and amino acids were not consumed completely in all batches. On the other hand, during the stationary phase at such low pH levels, glucose consumption and lactic acid production slightly continued. The yield of lactic acid produced per glucose consumed was almost constant during all batches .The pure cultures of L. lactis, S. thermophilus and Leu. mesenteroides strains were then simulated by dFBA. The strain specific parameters of the substrate uptake kinetics used in the dynamic models were estimated by dynamic parameter estimation (Supporting Information File‐2). The lower bound of the substrate uptake rates (which are defined in GSMMs as negative fluxes) was fixed to the values obtained through substrate uptake kinetics, while the upper bound of the substrate uptake rates was free to let the model consume less, or even produce the corresponding substrate. In addition to the glucose and amino acids uptake rate constraints, the experimental glucose to lactic acid yield was also used as a constraint by fixing the ratio between glucose consumption and lactic acid production rates in GSMMs of L. lactis and S. thermophilus, which assured in‐silico homolactic fermentation as observed experimentally. Glucose to lactic acid yield as a model constraint was implemented using the addRatioReaction function of COBRA Toolbox , while Leu. mesenteroides, which is known as a citrate consumer  that describe the co‐culture specific pH profiles.We simulated mesophilic and thermophilic co‐cultures by a previously described dynamic co‐culture metabolic modelling approach  is around 1% in the mesophilic cheese starter cultures comprised of L. lactis and Leu. mesenteroides strains.Mesophilic co‐culture models fitted closely to the experimental data. The mesophilic co‐culture models and experiments showed the domination of y Figure  stating Leu. mesenteroides in the co‐cultures could be explained by the rapid acidification of the medium by L. lactis strains. In other words, lactic acid pool mostly produced by L. lactis strains decreased the uptake rates of Leu. mesenteroides according to the substrate uptake kinetics, which made Leu. mesenteroides disadvantageous in the competition for sugar and amino acid source in co‐cultures. Another reason for the suppression of Leu. mesenteroides could be the ATP yield of Leu. mesenteroides per mole of glucose consumed. Leu. mesenteroides is an obligate heterolactic lactic acid bacterium, and ATP yield of the obligate heterolactic fermentation is lower than homolactic fermentation as observed in L. lactis . In addition to a possible interaction between S. thermophilus and L. lactis, which the co‐culture models might miss, this unexpected result could be explained by the difference in the fermentation temperature of L. lactis and S. thermophilic in pure and thermophilic co‐cultures, which were 30°C, 37°C and 33°C respectively (see methods). In terms of the growth performance based on the experimental growth rates with respect to pH values , L. lactis showed  higher and S. thermophilus showed a lower growth performance in thermophilic cocultures compared to their pure cultures at above the pH values causing the inhibitory effect . This result is consistent with the reported effect of different temperatures on growth rate for several LAB including L. lactis and S. thermophilus at optimal pH values of related species . Although individual biomass abundance ratio of L. lactis subsp. cremoris decreased in the early phase of the batch, its abundance increased afterwards, and this experimental result was also predicted by the model .Similar to the mesophilic co‐cultures, d Figure . Unlike L. lactis subsp. cremoris being the most and L. lactis subsp. lactis being the least abundant species, the model could not predict the individual biomass profiles of L. lactis strains precisely  in a co‐culture has cross‐feeding potential between the organisms.L. lactis and S. thermophilus . In the three‐species thermophilic co‐culture, the model estimated that aspartate was produced and methionine was consumed by L. lactis subsp. lactis and leucine was slightly produced by S. thermophilus at stationary phase, which was a different metabolic behaviour than the other co‐culture members .To this end, we used the co‐culture models to estimate the exchange flux profiles of various metabolites of the individual organisms in the co‐cultures. Metabolic exchange flux profiles estimated by the co‐culture models showed that, in the two‐species mesophilic co‐culture, asparagine, methionine, proline and tryptophan were produced by one organism, while the other consumed them . In the three‐species mesophilic co‐culture, asparagine, aspartate, methionine, phenylalanine, proline, tryptophan and inosine were the metabolites that one organism produced while the others consumed or vice versa Figure . In the 4This study showed that monoculture level properties could be used to understand the co‐culture level properties for the LAB consortia. We estimated the strain‐specific kinetic parameters using the monocultures experiments and used these parameters in the co‐culture models. The dynamic co‐culture models then accurately predicted the experimental results such as dynamic biomass compositions and the concentration profiles of glucose and lactic acid. Conventional kinetics expressions based on an enzyme‐substrate relationship such as the Michaelis–Menten kinetics could not explain our system (results not shown), as the substrate itself was not a rate limiting compound in our system where none of the substrates were totally consumed due to pH inhibition. Hence, we defined the substrate uptake kinetics of the dynamic models with an empirical equation as a function of undissociated lactic acid concentration, and it was a suitable kinetic expression for the fermentations without pH control. Due to the mathematical nature of the substrate uptake kinetics (see methods), the models start fermentation with maximum substrate uptake rate values in the early stage of the batches, where undissociated lactic acid concentrations were below the rate limiting level. This could have been a disadvantage for fermentations with long lag phases, but in our case the lag phase period of the batches were short enough to be ignored, and fits were good.S. thermophilus consumes peptide and amino acid that are released by proteolytic Lactobacillus bulgaricus, in turn S. thermophilus supplies some growth stimulating factors to L. bulgaricus such as formic acid and folic acid . This result showed that the metabolic interaction in the co‐cultures did not create an advantage in terms of the yields, which is contrary to the ones observed in other dairy cultures such as yoghurt and kefir. This could be due to the lack of common evolutionary history of the strains used and the chemically defined medium used, which contains all compounds required for cell growth. However, the estimation of potential metabolic interactions is still important for such experimental set‐ups to design better starter cultures. It is noteworthy to mention that the potential metabolic interactions estimated by the co‐culture models are study‐specific results, and they should not be generalized for all cheese starter cultures since amino acid, vitamin and carbon source auxotrophies of LAB to be used in the dairy starter cultures vary across different LAB (Teusink & Molenaar, Emrah Özcan, Tunahan Çakır, Ebru Toksoy Öner and Bas Teusink conceived and designed the study. Emrah Özcan performed all computational and experimental studies except qPCR and amino acid analysis. Merve Seven performed qPCR analysis. Burcu Şirin performed amino acid analysis. Emrah Özcan, Emrah Nikerel, Tunahan Çakır, Ebru Toksoy Öner and Bas Teusink discussed the results and wrote the manuscript.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.