Hermetia illucens) is popular for its applications in animal feed, waste management and antimicrobial peptide source. The major advantages of BSF larva include their robust immune system and high nutritional content that can be further developed into more potential agricultural and medical applications. Several strategies are now being developed to exploit their fullest capabilities and one of these is the immunity modulation using bacterial challenges. The mechanism underlying metabolic responses of BSF to different bacteria has, however, remained unclear. In the current study, entometabolomics was employed to investigate the metabolic phenoconversion in response to either Escherichia coli, Staphylococcus aureus, or combined challenges in BSF larva. We have, thus far, characterised 37 metabolites in BSF larva challenged with different bacteria with the major biochemical groups consisting of amino acids, organic acids, and sugars. The distinct defense mechanism-specific metabolic phenotypes were clearly observed. The combined challenge contributed to the most significant metabolic phenoconversion in BSF larva with the dominant metabolic phenotypes induced by S. aureus. Our study suggested that the accumulation of energy-related metabolites provided by amino acid catabolism is the principal metabolic pathway regulating the defense mechanism. Therefore, combined challenge is strongly recommended for raising BSF immunity as it remarkably triggered amino acid metabolisms including arginine and proline metabolism and alanine, aspartate and glutamate metabolism along with purine metabolism and pyruvate metabolism that potentially result in the production of various nutritional and functional metabolites.Black soldier fly (BSF,  Hermetia illucens (Diptera: Stratiomyidae), is one the most essential insects for their values in animal feed, bioconversion, and biological active molecules 6. BSF has been reared by many companies on an industrial scale with the number up to a thousand kilograms per week or month, especially those in Netherlands and United Kingdom with an average funding from investors of more than 100 million USD8. The larval stage of BSF provides not only a high protein content (from 37 to 63% of their dry matter), but also other macro- and micronutrients such as fat, chitin, vitamins, and minerals9. Compared to common protein-rich feeds , BSF contains high levels of some essential amino acids such as lysine, methionine, and threonine and demonstrates a better amino acid profile10. With this valuable content of nutrition, BSF is believed to bring several significant benefits for human consumption thus discussing on food safety concerns, functional properties, and biomolecule fractionation protocol is gaining more attention12. Moreover, another remarkable potential of BSF is antimicrobial property which is a promising approach to cope with the multidrug-resistant bacteria crisis. A transcriptome study of BSF has identified several antimicrobial peptide (AMP) sequences with putative anticancer, antiviral, and antifungal functions13. Hence, this has strengthened the multipurpose uses of BSF to be exploited for agriculture, nutrition, as well as medicine.Insect has been widely used nowadays in the world as a sustainable food source with high nutrient values. The black soldier fly (BSF), 7. Different bacterial strains were applied to trigger biological processes in BSF larvae in studying nutritional immunology, purification, and characterisation of novel bioactive compounds such as AMPs and fatty acids19. The results, notably, showed the enhanced antimicrobial abilities against various strains of Gram-negative18 and Gram-positive bacteria17, fungi16, and proposed potentials of antiviral and anticancer properties20. However, none of the studies have, so far, discussed the metabolic phenoconversion of BSF to different kinds of bacterial challenges.Given that the robust immune system of BSF plays an important role in improvement of feeding and bioactive compound synthesis, bacterially-challenged immunity is one of the main approaches used nowadays21. Metabolomic analysis in studies of Drosophila, a classical model organism, was successfully applied in not only physiology aspects of invertebrates but also in disease model and drug treatments24. Thus, in the current study, entometabolomics has been applied to characterise the alteration of biochemical components in BSF larva and to investigate the metabolic phenotypes in response to different bacterial challenges that may result in various biomolecule pools for further agricultural and medical uses.Entometabolomics, the metabolic profiling application in insect study, is the powerful analytical measurement of cellular interaction and intercorrelation of insect metabolites1H NMR CPMG spectra, a total of 37 metabolites were identified in BSF larval extracts challenged with either single or combined bacteria and unchallenged control  spectroscopy-based metabolomics. Spectral data was then acquired and pre-processed. With the median rol Fig. . The maj2: PC1 = 35.3% and PC2 = 23.0%; Q2 = 0.61). Moreover, single S. aureus and combined challenge groups were differentiated from the single E. coli challenge group and unchallenged control along the first principal component (PC1) , single S. aureus  or combined  bacterial injection compared with unchallenged control. Metabolic differences between BSF larva challenged with single E. coli and single S. aureus were observed along the second principal component (PC2) . Interestingly, no metabolic differences were observed when comparing single E. coli challenge and combined challenge groups , while clearer class separation was detected between single S. aureus and combined challenge groups .To investigate the metabolic overview, similarities, and differences of all classes, principal component analysis (PCA) was performed. The PCA cross-validated score plot shows a tight clustering of quality control (QC) samples indicating no analytical variation and high analytical precision  , tricarboxylic acid (TCA) cycle intermediates , amino acid  polyamine  and organic compounds  accompanied with decreased levels of glutamate and betaine compared with unchallenged control  was performed to provide insights into specific metabolic reprogramming of BSF larva induced by different bacterial challenges. The O-PLS-DA cross-validated score plots from all pairwise comparison models show the distinct class discrimination (R01) Fig. A,D. Hencely Fig. G. BSF larol Fig. G and S1Bine Fig. G and S1Cnge Fig. G and S1Eose Fig. G and S1FE. coli challenge included only arginine and proline metabolism, single S. aureus challenge impacted the two additional pathways that are amino acid-related  and energy-related metabolism (pyruvate metabolism). Moreover, as shown in Fig. p < 0.05, FDR < 0.25) in those models involving the combined challenge.In order to measure the involvement and directions of post-bacterial challenge effects on metabolic adaptation of BSF larva, pathway analysis of significantly contributed metabolites was performed using MetaboAnalyst based on KEGG pathway of insect database. The results showed a total of four metabolic pathways that had remarkable impacts including alanine, aspartate and glutamate metabolism, arginine and proline metabolism, purine metabolism, and pyruvate metabolism Fig. . Apparen7. Hence, several approaches have been applied to fulfil the knowledge and to maximise the use of BSF, especially its robust immune system. However, the global metabolic profile and cellular metabolism of BSF in response to bacterial infection has remained unclear. By using 1H NMR spectroscopy-based metabolomics approach, our study has indicated a total of 37 metabolites from BSF larva that were bacterially-challenged and unchallenged. The major biochemical classes found in BSF included organic acids , fundamental amino acids , and sugars . Moreover, gut-microbiota metabolites , conditionally-essential nutrients26 , biogenic amines  and other amino acid derivatives , plant metabolite , nucleoside , and nucleotide  were observed. Compared to previous metabolomic studies of other insects, BSF shared 11 metabolites similar to Drosophila melanogaster22 (Diptera), 13 metabolites to Manduca sexta27 (Lepidoptera), and 14 metabolites to Schistocerca gregaria28 (Orthoptera). The similarities, mostly from amino acids, organic acids, and sugars, have consolidated the baseline biochemical profile of BSF and other insects in general.BSF is one of the most essential insects for their outstanding potential in animal feed, bioconversion, and antimicrobial agentPachnoda ephippiata larva (Coleoptera) suggesting the gut microbial fermentation in insects29. Various studies in BSF demonstrated that the gut microbial community is expressively involved in growth, nutrition, as well as its utilities in waste-management and animal feed34. Hence, the detection of gut microbiota metabolites in this study will help further our understanding of BSF-gut microbiota metabolic interactions. N-formylglycine is known as peptide derived from enzymic formylation of melittin—main component of honeybee  venom and antimicrobial peptide36. The bioactivities of this metabolite were further emphasised in the research of biosynthetic pathway from marine Streptomyces bacteria, reporting that the metabolic product, streptophenazines, with the attachment of N-formylglycine provides the strong antibiotic activity37. Thus, N-formylglycine could be employed in BSF to cope with bacterial infection through either AMP modification or gut microbiota metabolism. Another identified metabolite involved in defense mechanism against pathogens was 1,7-dimethylxanthine or paraxanthine, one of the well-known products of the metabolism of caffeine—a plant purine alkaloid that is employed to defend against herbivores and pathogens38. Paraxanthine is involved in caffeine degradation in D. melanogaster39, suggesting that it may serve a similar function in BSF following bacterial challenges, especially single S. aureus and combined E. coli and S. aureus challenges.Although the appearance of some distinct metabolites in BSF provided an insight in their biochemical characteristics, most of the underlying mechanisms in response to different bacterial challenges, however, inquired further study. Ethanol and formate, the compounds produced by anaerobic metabolism of microorganisms, have been previously found in the gut of 40. In addition, it has been defined as a metabolite of anthocyanin-related metabolism in human gut microbes43. Nevertheless, the biosynthesis and degradation of anthocyanin in insects have not been fully investigated thus far. This suggests that syringic acid found in the current study was potentially derived from the food components, brewer’s grain and palm kernel meal48 used in the current study. Indoxyl sulfate (IS), the metabolite known as uremic toxin in human, was also identified in BSF larval extracts. The production of IS begins with dietary tryptophan that can be metabolised by gastrointestinal bacteria to produce indole which is eventually absorbed through gut epithelial barrier and converted into IS in the liver by sulfotransferase Family 1A Member 1 (SULT1A1)51. Though, to the best of our knowledge, the production or related information of IS in insect has yet to be reported. The possibility could be that the intercorrelations between microbial communities and digestive enzymes in BSF gut facilitate the production of IS. In accordance with this concept, a previous study performing qualitative and quantitative enzymatic assays demonstrated the rich and robust gastric enzymes compared with housefly52 along with its gut microbiota function that aid several metabolic reactions in BSF7. Due to some certain similarities of enzymatic content and gut microbiota function to human, BSF may serve as a model to study specific metabolisms.What is more, our findings depicted the unexpected metabolites of which their detections in insect still lack adequate information. Syringic acid is a common phenolic compound found in vegetables and fruitsS. aureus challenge versus control, combined challenge versus control, combine challenge versus single E. coli challenge, combined challenge versus single S. aureus challenge. Consistently, the correlation coefficients obtained from these O-PLS-DA models have indicated the differential metabolites that were significantly changed in their concentrations in either group of comparison. Our data suggests that combined challenge mostly triggered the metabolic phenoconversion of larva compared to individual bacterial strain challenge, that was evident by altered concentrations of 21 differential metabolites in larva. While single S. aureus challenge resulted in the metabolic changes, such a significant metabolic effect was not observed following E. coli challenge. This indicates that even though the responses caused by single E. coli challenge were not significant, its contribution to the integration with S. aureus provided more intense metabolic impacts in BSF larva. Also, the analyses between bacterially-challenged groups described the similar patterns, supporting the given hypotheses that the domination of S. aureus to the combined challenge were more powerful than E. coli alone. Our findings are consistent with the previous study suggesting that the insects employed the specific signalling pathways to encounter Gram-positive or Gram-negative bacterial infections53. Moreover, our results shed light further into the metabolic effects of BSF on the combined bacterial challenge. The clear clustering and separation between groups with quite a large degree of inter-variations in metabolite concentrations were observed in the comparison of single E. coli challenge versus combined challenge, whereas fewer metabolic alterations occurred between single S. aureus challenge versus combined challenge clearly suggesting that the metabolic effects of combined challenge mainly resulted from S. aureus. Over the years, studies involving the investigation of functional biomolecules and AMPs from BSF were inconsistent in the use of either single S. aureus, single E. coli or both as the immunomodulator, resulting in varying metabolic products55. Therefore, there has been, so far, no such scientific evidence providing the baseline understanding and appropriate guideline on the use of bacterial immunomodulators and their expected functional biomolecules that suit the specific utilisation, for example, nutritional animal feeds, prebiotics and probiotics. Here, we have reported, for the first time, the distinct BSF metabolic fingerprints that discriminate bacterial challenges as demonstrated by single E. coli, single S. aureus or combined challenge.Multivariate analyses, including PCA and O-PLS-DA, were performed to visualise the similarities and differences of entometabolome datasets between non-challenged and bacterial-challenged BSF larval extracts. The results exhibited the valid class discrimination in four pairwise comparison models comprising of single 56. It employs a variety of sugars and non-carbohydrate precursors to initiate the carbohydrate synthesis57. Trehalose, the highly representative haemolymph sugar of insect synthesized by fat body, is also known as the main source of carbohydrate, whereas maltose is the gut-digested carbohydrate source57. Together with glucose, they marked the significant carbohydrate adaptation of BSF larva under microbial infection. The larva is likely to either overuse or reduce the blood sugar and exerts more of dietary carbohydrate.Taken together, we have summarised the proposed altered metabolic responses to different bacterial challenges in BSF larva Fig. . The mai56. These processes have been previously reported as the most active reaction occurring in insect fat body58. Particularly, amino acids are defined as one of the primary energy sources in Diptera, Orthoptera, and Coleoptera, besides lipids and carbohydrates resources58. Putrescine, another product from arginine and proline pathway, is a polyamine related to juvenile hormone specifically involved in the regulation of insect neuroblast59. Hence, the utilisation of amino acids by fat body to facilitate energy and protein derivative syntheses is proposed to represent BSF-defense metabolic phenotype.Amino acids including arginine, proline, alanine, aspartate, and glutamate are all involved in mediating the intermediates of the citric acid cycle to generate energy through transamination and oxidative deamination61. In addition, thymidine metabolism is the minor pathway of DNA synthesis promoting the cell differentiation throughout the time of insect metamorphosis62. Another pathway playing a crucial role in the development of insect is choline metabolism63. Choline is a vital nutritional metabolite for normal growth of insect. Its metabolism accommodates the production of larval haemolymph component  and the regulation of nervous system metabolites 63. This clearly indicates that BSF larva has reprogrammed its biological processes prior to the bacterial defense mechanism to take place.In regards with nucleotide metabolism, purine metabolism plays important roles in producing uric acid for the removal of excessive ammonia and the other processes such as pigmentation, nitrogen reservation, nucleic acid synthesis, and antioxidantThe current methodology employing bacterial challenge in BSF lacks the molecular baseline information whether single or combined challenge is truly required for immunomodulation. Our study has defined the biochemical components of BSF larva with a total of 37 identified metabolites using entometabolomics. Moreover, the combined challenge exhibited the most effective immunomodulator that was evident by a large degree of metabolic phenoconversion related to energy and amino acid metabolisms. Collectively, our findings suggest the nutritional and functional benefits of combined bacterial challenge for any study involving an exploration of alternative bioactive compounds and/or AMPs from BSF larva for developing the novel agricultural and medical applications. It is noteworthy that bacterially-exposed BSF can provide additional value from nutritional and functional metabolites that suggest the alternative breeding protocol and gain greater attention from the breeders.H. illucens) were supplied by Industrial Insects Pilot Production Plant . This study has been approved by the Animal Ethics Committee of Khon Kaen University (IACUC-KKU-13/64). The five-day-old larvae were reared in the tray filled with palm kernel meal and brewer’s grain in the ratio of 5:1 and 70% moisturised with water until they reached the age of 10 days under 12 h/12 h light-dark cycle at 30–32 °C. A total of 80 larvae were disinfected in 75% alcohol for 30 min then washed with sterile water three times. The immunisation process were conducted as described previously by Alvarez et al (2019)18. To challenge BSF immune system, 20 larvae were individually injected using insulin needle with either E. coli, S. aureus or combined bacterial strains  while control larvae were injected with 10 µl phosphate-buffered saline. Each challenge group included five replicates in which each replicate contained four larvae   as a chemical shift reference (δ1H = 0 ppm) and optionally 0.2% NaN3 was added to dissolve the sample. Mixture was sonicated using ultrasonicator  for 10 min and filtered through 0.20 μm filter  before centrifugation at 12,000 g at 4 °C for 5 min. An equal amount of 30 μl was aliquoted from all samples and pooled for the QC. Then, a total of 550 μl of supernatant was transferred into NMR tube for metabolic profiling. Proton NMR spectra were acquired using a 400 MHz NMR spectrometer  with CryoProbe Prodigy and Carr−Purcell−Meiboom−Gill (CPMG) pulse sequence [RD−90˚−(τ−180˚−τ)n−acquisition] was applied to analyse the samples at 310 K in 64 scans.Each replicate was weighed out and ground thoroughly in methanol:chloroform:water . The volume of the extraction solvent was adjusted according to weight of the sample (1 mL of solvent / 100 mg of sample). The upper aqueous phase was collected after centrifugation at 1000 g at 4 °C for 15 min and further evaporated using a speed vacuum concentrator  at 40 °C until dry. The crude extracts were stored at − 80 °C prior to analysis. A total of 600 μl buffer containing 100 mM sodium phosphate, pH 7.4 in Dhttps://doi.org/10.5281/zenodo.3077413) to conduct probabilistic quotient normalisation (PQN). Statistical total correlation spectroscopy (STOCY)64 was used to verify the appearances of correlated resonances on 1-dimensional NMR spectra which were searched against public databases including human metabolome database (HMDB)68 and ChenomxNMR Suite version 9.0 . To further confirm the metabolite assignment, two-dimensional (2D) NMR experiments including correlation spectroscopy (COSY), total correlation spectroscopy (TOCSY), heteronuclear single quantum coherence (HSQC) and heteronuclear multiple bond correlation (HMBC) were performed. Data can be accessed at Open Science Framework (https://osf.io/w9tx7/).Chemical shift referencing, baseline correction and phasing of NMR spectra were performed in TopSpin . NMR spectral data were processed using MATLAB R2021a version 9.10  software equipped with IMPaCTS toolbox (https://doi.org/10.5281/zenodo.3077413). Pre-processed spectral data were imported into MATLAB environment for conducting principal component analysis (PCA) and orthogonal signal correction-projection to latent structures discriminant analysis (O-PLS-DA) with a pareto scaling method. All O-PLS-DA models in this study were constructed based on one predictive component and one orthogonal component. The O-PLS-DA scores and coefficient plots were also obtained. The correlation coefficient values |r| were represented through red-blue colour spectrum, where red colour indicates higher correlation while blue colour indicates lower correlation of the variables. Benjamini-Hochberg false discovery rate correction was also applied for differential biomarker discovery. The goodness of fit and predictability of the models were determined by R2 and Q2 values, respectively. The validation of all models involved in this study was assessed using cross-validation and permutation p-value (p < 0.05). Maximum intensity of selected discriminatory metabolites from valid O-PLS-DA models were calculated for quantification of absolute concentration in regards with maximum intensity and concentration of TSP. The univariate analysis was also performed in GraphPad Prism version 9.2.0 for MacOS  using Kruskal Wallis, Dunn post-hoc test with Benjamini-Hochberg correction.Multivariate statistical analysis in this study was performed in MATLAB 2021a version 9.10 environment equipped with IMPaCTS toolbox (69 based on KEGG pathway library of insect72 (model organism—Drosophila melanogaster) to investigate the impact of different bacterial challenges on BSF metabolism. The cut-off values of 0.1 for pathway impact score, p < 0.05 and false discovery rates < 0.25 were applied to cover the confidence of practicable and to filter insignificant pathways73.Absolute concentrations of selected metabolites were further used for pathway impact analysis using MetaboAnalystSupplementary Information.


In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout’s (feature’s) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An  Escherichia coli an attractive cell factory. However, some challenges, such as low growth rate and yield, the use of a rich medium, the generation of by-products, and various anaerobic requirements, need to be overcome for bio-based succinic acid production, considering cost-effective issues, as compared with the petroleum-based approach.Shifting from petrochemical sources to renewable, abundant, and inexpensive feedstocks to obtain valuable chemicals has become a promising goal for the chemical industry . The bioThe main goal of using microbial cell factories is to design cheap and high-yield biotechnology-based conversion processes. A significant problem to be solved is how to enhance cell growth while using its capabilities to obtain a high-yield target chemical product. A classical approach for that is adaptive laboratory evolution (ALE), which is based on the selection of microorganisms with superior production capability after random mutagenesis screening. Another approach to strain improvement is metabolic engineering, which uses genetic manipulation to optimize the production of desired compounds. Metabolic engineering selects targets that increase productivity based on the rationality of trial-and-error development cycles and an understanding of the routes playing a significant role in the synthesis. Strain design with this method has been extensively applied to use and/or produce interesting compounds , includiWhen DNA was discovered in the last century, a new approach called metabolic network modeling for the study of cellular metabolism was developed . It alloOptKnock applies a flux balance analysis (FBA) approach for simulating genome-scale metabolic models (GEMs). It assumes that each organism’s metabolic network has been tuned through evolution for some objective function, be it a maximal growth rate or energy efficiency . While this assumption may be valid for wild-type (WT) organisms that have evolved over many hundreds or thousands of generations, it may be less appropriate for engineered mutants (KO) because they have been engineered in a controlled environment and unexposed to the same evolutionary forces. Hypothesizing that mutant organisms are unable to immediately adapt their metabolic network to achieve the WT objective function, computational tools such as minimization of metabolic adjustment (MOMA) were developed . This apSeveral approaches to optimize cell factories have been developed, but conventional and computational approaches have not always been successful due to unexpected changes in the cell where an intracellular complex interconnected network of genes, proteins, and reactions exists. Systems metabolic engineering has emerged as an approach that integrates metabolic engineering and combined metabolic and “omics” network models. This approach could be beneficial for genome-scale modeling because it reduces the solution space and generates accurate predictions . Mainly E. coli ATCC 8739 was used through integration of transcriptomics data to metabolic models and classification tree analysis using the random forest to classify gene targets predicted by OptKnock. Our strategy took advantage of transcriptomics data obtained from an evolved E. coli in glycerol and an optimized culture media. These data were subsequently integrated into a metabolic network model to predict targets using OptKnock. Predicted combinations were then evaluated using FBA, flux variability analysis, and MOMA to determine the effects of gene reaction knockout in the cell. Finally, predicted target reactions were evaluated using random forest to determine the importance of each target using succinic acid production, growth rate, and Euclidian distance between the WT strain and each mutant as response variables.In this study, systems metabolic engineering for overproduction of succinic acid from glycerol in E. coli ATCC 8739 was used in this study. It was obtained commercially from the American Type Culture Collection (ATCC). A glycerol-based medium containing the following components (per liter) was used as the reference culture condition: 5 g of yeast extract, 2.5 g of NaCl, 5 ml of trace metal solution , 5 ml of MgSO4 (1 M), and 30 g of glycerol. A 50 ml culture was carried out in a 250 ml baffled-conical Erlenmeyer flask and cultivated aerobically at 37°C and 200 rpm.E. coli on high glycerol concentrations  and an optimized culture condition. For the first condition, four E. coli cultures were continuously subcultured each for 72 h in Luria–Bertani (LB) medium . After every three subcultured rounds (216 h), the concentration of tryptone was decreased from 1 until reaching 0 g/L. Then, 10 subcultured rounds each for 72 h were carried out. During the complete experiment, a 50 ml culture was carried out in a 250 ml non-baffled-conical Erlenmeyer flask and cultivated aerobically at 37°C and 200 rpm. For each subcultured round, an OD ∼0.33 600 nm was considered as inoculum starting point. At the end of each tryptone decreasing, 1 ml of culture was kept at –80°C and used for further evaluation of growth and glycerol uptake. For the optimized culture condition, the glycerol-based medium was supplemented with 1 g of NH4Cl, 6 g of Na2HPO4, and 3 g of K2HPO4 at the same conditions as the reference culture.Two conditions were evaluated in this study: an adapted RNA-Seq was carried out in triplicate for all conditions. For the adapted strain, the culture conditions for RNA-Seq were the same as those for the optimized culture medium condition. To harvest cells for total RNA purification, the culture sample was first treated with RNAprotect Bacteria Reagent (Cat No./ID: 76506), and enzymatic lysis and proteinase K digestion of the bacteria were carried out following the manufacturer’s protocol. Then, the Qiagen RNeasy Mini kit (Cat No./ID: 74104), following the manufacturer’s protocol, was used to obtain the total RNA for further analysis. Each sample was treated with DNase following the protocol in order to remove the DNA. The samples were sent to commercial RNA-Seq services for further sample processing and sequencing .GSE140847.Clean, raw data was obtained by removing the reads containing adapters using Trimmomatic. The sequence RefSeq: NC_CP010468 was employed for mapping. RNA reads were mapped using the software bowtie2, and featureCounts was employed to read counts. SARTools   was usedE. coli models were used: EColiCore2 (ECC2) (data under peer review) and iTA1338 for E. coli ATCC 8739  above a certain value. Expressed genes were considered according to their expression level with log2 fold change (FC) ≥ |1|. Next, according to the GPR rules and the defined gene expression states, a specific activity state for each reaction was identified. Finally, a specific context model was obtained from the transcriptomic data. Metabolic engineering targets were obtained using OptKnock. However, MOMA was used to understand the probability of those mutants predicted to be adapted and to reach the optimal state (predicted succinic and growth flux) considering the Euclidean distance. It because OptKnock predicts an optimal state, but after genetic manipulation cell are not in this state. The maximum uptake rate of glycerol was set to 13.3 mmol/g DW h–1. The OptKnock, GIMME, and MOMA methods were conducted using COBRA Toolbox v.3.0 (The gene inactivity moderated by metabolism and expression (GIMME)  method wox v.3.0  in MATLAIncNodePurity, which measures the decrease in tree node purity that results from all splits of a given variable over all trees , a growth curve was carried out, showing that a maximum of 7 g/L consumption of glycerol could be achieved naturally by E. coli. Nevertheless, after the ALE experiments, an increase of 3 g/L in the glycerol consumption was observed for the strain growing in a supplementation of 30 g/L of glycerol. Despite this data showing an increase of around 30% in glycerol consumption, it is far below that obtained in the optimized culture, which reaches a consumption of 30 g/L of glycerol (data under peer review).Luria–Bertani is one of the most common cultures used industrially for the growth of A cell is considered a complex system where a large number of processes are carried out. These processes then involve an interaction between genes, transcripts, proteins, metabolites, and reactions, among others . MetabolP < 0.05. Relevant genes with log2 FC > | 1| for glycerol metabolism or under the same regulon were taken into account. DEGs were determined using the DESeq2 statistical package after filtering out low count reads with an average value of <100. Significant DEGs were defined as those whose abundance had at least a log2 fold change [(log2 FC) > | 2|] between the reference condition (glycerol-based medium) and a chosen experimental condition (optimized culture medium and evolved strain) at a false discovery rate (FDR)-corrected phoB and phoR, which are involved in phosphorous uptake and metabolism since, under excess phosphorous, PhoR inactivates phoB , chemicals, stress and stimulus responses, and heterocyclic compound systems. Between downregulated genes, we found tes phoB . Figure tes phoB . The 117ed genes .GO analysis revealed that shared upregulated DEGs  are invoE. coli is mediated by glp operons. In consequence, transcriptomic analysis shows shared upregulation of glpBCFKQTX genes. The changes in bacterial gene expression in response to glycerol utilization are summarized in E. coli for further transformation into glycerol-3-phosphate (Gly-3-P) by GlpK under aerobic conditions. Comparing glpK expression with the values obtained for other genes in the glp regulon showed that glpK was one of the most highly expressed genes. However, a difference of ∼1 log2 FC between the evolved strain and the optimized culture condition was exhibited in the glpFKX operon (glpX was detected (2.76 log2 FC), which is part of the glpFKX operon and works as an alternative fructose-1,6-bisphosphatase involved in gluconeogenesis by catalyzing the hydrolysis of fructose-1,6-bisphosphate to fructose 6-phosphate , but not for the evolved strain (−0.53 log2 FC). FumA has been reported to be predominantly expressed under aerobic conditions , which is used in anaerobic growth, was observed over the optimized culture condition.The electron-transport chains of nditions . Table 1nditions . Under a are defined as a complete set of reactions involved in cell metabolism, given by genome annotation, regardless of whether the annotated metabolic genes are expressed in a given environment. This assumption could be correct in genome-scale models because core models represent the central metabolism, but the full potential of GEMs remains unexploited mainly . To avoiE. coli ATCC 8739 was carried out based on the iEcolC 1368 (E. coli ATCC 8739 growing in an optimized culture medium (iTA818) (E. coli ATCC 8739 strains evolved to grow on glycerol (iTA821)   and (2) (iTA821) . Manual E. coli strains that overproduce succinic acid from glycerol, OptKnock was used  was observed in mutants predicted in ECC2 when 9 or 10 reactions were deleted. However, this implies a substantial reduction in the growth rate to ∼4% compared with the WT strain. Thus, selecting these mutants is unrealistic for the industrial production of succinic acid. The same behavior in the reduction of the growth rate was observed for those mutants that required more than six deletions in mutants predicted in iTA818 and iTA821. In contrast, a considerable reduction in the growth rate (28% of the WT growth rate) as well as an increasing succinic acid production rate (around 30% more than those with 9–10 knockouts) for those mutants with six knockouts was observed. In addition, it was observed that there is no direct correlation, in the same magnitude for all the mutants, between the Euclidean distance and the numbers of knockouts in each mutant. However, Since metabolic manipulation of cells results in a stressful process, the negative impact of deletions on the maximum growth rate can be observed. To determine the effects of reaction knockouts over the cell, FBA was carried out and Euclidean distance was calculated for each mutant predicted. in silico mutants that overproduce succinic acid.OptKnock results are a large list of knockout combinations where maximum product synthesis occurs at a maximum growth rate reachable . HoweverIncNodePurity. This model exhibited a mean square error (MSE) value of 0.293 when using the reaction flux of EX_succ_e flux obtained by FBA as a variable response. For growth rate (biomass reaction) as a response variable, the MSE value was 0.0002. Finally, when the Euclidian distance for each mutant was used as the response variable, the MSE value was 9,175.158, indicating that the Euclidian distance is not a good response variable to predict cell behavior when using the random forest model. Moreover, this result allows the use of machine learning models to predict the largest number of mutants than those obtained by OptKnock in terms of growth rate and succinic acid production since OptKnock is more time-consuming.In order to filter and select potential mutants to be tested in the lab, a random forest model to predict the importance of each reaction knockout was developed based on the OptKnock predictions. Each possible combination of reactions using binary values that increase the succinic acid production was associated with the flux of the extracellular succinic acid and biomass reaction obtained by FBA and the Euclidian distance obtained by MOMA. The dataset was divided into two groups: 70% for training and 30% for the test. Following feature selection and cross-validation, a robust model that associated any combination of 58 reaction variables to a predicted growth rate and succinic acid production ratio was obtained. A measure of the importance of the contribution of each feature to the random forest model is shown in The pyruvate dehydrogenase complex is a critical connection point between glycolysis and the TCA cycle, both of which function during aerobic respiration through catalyzing the conversion of pyruvate to acetyl coenzyme A (acetyl-CoA) . PDH deaE. coli has been described in the literature  and upregulation of ompF (log2 FC 2.43) in the optimized culture suggest that it could play an essential role in E. coli ATCC 8739 glycerol metabolism at high uptake rates avoiding toxicity.After glycerol incorporation in the cell mediated by GlpF, glycerol can be metabolized through two pathways. The first is mediated by the glycerol kinase GlpK through phosphorylation of glycerol to Gly-3-P, followed by GlpD activity under aerobic conditions, leading to dihydroxyacetone phosphate (DHAP) . The altlization . Howeverlization . The expglpQ , which catalyzes the hydrolysis of glycerol-phosphodiesters to alcohol plus Gly-3-P together with ompF, could explain the partially higher transcript abundance of glpT since the externally generated (or supplied) Gly-3-P activates GlpT  . PDH dean source . Howeverin silico models  can interact inside the cell for industrial purposes. In the same way, This study proposes that computational tools can accelerate the optimization of cell factories by identifying metabolic engineering targets (genes/reactions) and not just by predicting mutants that may be biologically unviable. Therefore, systems metabolic engineering reduces time in rational strain design and guides in the selection of metabolic engineering targets based on cell behavior under experimental conditions. Simultaneously, departing from traditional computational tools, new methods such as machine learning could be proposed as an interesting alternative for the reduction of computational demand. However, these techniques are dependent on the level of completeness and accuracy of the metabolic model considered, which could be improved by using omics data.GSE140847. Further inquiries can be directed to the corresponding author/s.The original contributions presented in the study are included in the article/AT: conceptualization, methodology, software, validation, investigation, visualization, formal analysis, and writing—original draft preparation. WR, DM, and CO: investigation. RC: methodology, software, and writing—review and editing. JG: supervision and writing—review and editing. AG: conceptualization, supervision, and writing—review and editing. All authors contributed to the article and approved the submitted version.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 and Saccharomyces cerevisiae with metabolic engineering of the mevalonate (MVA) and methylerythritol phosphate (MEP) pathways coupled with the expression of heterologous terpene synthases. Yet conventional microbial chassis pose several major obstacles to successful commercialization including the affordability of sugar substrates at scale, precursor flux limitations, and intermediate feedback-inhibition. Now, recent studies have challenged typical isoprenoid paradigms by expanding the boundaries of terpene biosynthesis and using non-model organisms including those capable of metabolizing atypical C1 substrates. Conversely, investigations of non-model organisms have historically informed optimization in conventional microbes by tuning heterologous gene expression. Here, we review advances in isoprenoid biosynthesis with specific focus on the synergy between model and non-model organisms that may elevate the commercial viability of isoprenoid platforms by addressing the dichotomy between high titer production and inexpensive substrates.Isoprenoid compounds are biologically ubiquitous, and their characteristic modularity has afforded products ranging from pharmaceuticals to biofuels. Isoprenoid production has been largely successful in  Isoprenoids are ubiquitous across all domains of life and span a wide and varied range of natural products. Isoprenoids are characterized by condensation of the five carbon precursor molecules isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), which are typically generated through either the mevalonate (MVA) or methylerythritol phosphate (MEP) pathways. The ease with which specialized synthases and cytochromes can conjugate or decorate these precursors has led to a uniquely diverse class of chemicals. Estimates of natural isoprenoid compounds in the last several decades have steadily increased from 20,000  to over Isoprenoids serve a number of critical roles both as primary and secondary metabolites. Primary metabolites are essential to cell survival and propagation. They include carotenoids that serve as auxiliary molecules for photoprotection and antioxidants  as well as sterols that help maintain membrane structure. Other isoprenoids function as components of dolichols, quinones, and essential proteins that aid in glycosylation and electron transport . SecondaEscherichia coli and Saccharomyces cerevisiae, which have produced many of the aforementioned isoprenoid compounds. E. coli and S. cerevisiae maintain certain metabolic advantages including a fast growth phenotype, historical breadth of knowledge, ease of transformation and hence heterologous protein expression, substrate specificity, and published successes of bioproduction . Recent estimates assert that sugar switching from glucose to pretreated cellulosic biomass could yield a 53% decrease in cost  with furPseudomonas putida and Rhodosporidium toruloides that can readily degrade aromatic compounds  and phototrophic microbes  capable of fixing CO2. We also explore advances in engineering of oleaginous yeast naturally capable of efficient lipid toleration and accumulation (R. toruloides and Y. lipolytica) and finally soil bacteria with special focus on their propensity for survival on and degradation of aromatic substrates (B. subtilis and P. putida). Collectively, these advances move isoprenoid biosynthesis toward economic and environmental feasibility.Addressing the disparity between the prevalence of nature’s clever solutions to environmental challenges and the genetic tractability of those organisms remains a principal obstacle in isoprenoid bioproduction. In many instances, it poses the question of whether to heterologously express pathways in common metabolic chassis or to optimize pathways via either the MVA or MEP pathway, respectively  and pyruvate ectively . These pectively . The recectively .The MVA pathway is native to eukaryotes, some ancient and often predatory gram-positive bacteria , as wellOver the last decade, the origin of the archaeal MVA pathway – either progenating from horizontal gene transfer or a cenancestor – has been hotly debated. However, recent analysis of monophylogenetic candidate phyla radiation and DPANN  have provided conclusive evidence to support an extant ancestral MVA in all domains of life . Most noS. cerevisiae/E. coli production titers by capitalizing upon enzyme promiscuity or efficiency. Collectively, heterologous expression and fine-tuning of the MVA pathway to minimize flux bottlenecks has included the expression of genes across different domains. Overexpression of HMGS and HMGR from Staphylococcus aureus , the elucidation of archaeal MVA pathways and their associated enzymes has proven instrumental in optimizing s aureus  as well M. mazei , for exa pathway .The MEP pathway is native to most Gram-negative bacteria and cyanobacteria as well as to algae and higher plants, but in the latter eukaryotes it is compartmentalized in the plastid. Despite being theoretically more efficient than the MVA pathway, the MEP pathway is more tightly regulated and challenging to engineer. Studies have elucidated rate limiting enzymatic steps in the MEP pathway, namely IDI and DXS, for β-carotene production . HoweverE. coli, though even careful balancing in other organisms like the cyanobacterium Synechococcus elongatus has proven challenging  into IPP and DMAPP, respectively. Direct feeding of alcohols for production of isoprenoid precursors could thereby decouple isoprenoid production from central carbon metabolism. Of particular interest were IPKs from nnaschii . In one  pathway . Expresss to 97% . Growth-soprenol . IPK-medalcohols .E. coli AtoB, Staphylococcus aureus HMGS) prior to diverging with the expression of the hydratase LiuC and Myxococcus xanthus decarboxylase AibAB (M. xanthus AibC and Clostridium acetobutylicum AdhE2 (Clostridium beijerinckii acyl-CoA reductase (cbjALD), and E. coli YahK to generate prenol at the highest titer reported for biological production (E. coli hydroxyethylthiazole kinase (ThiM) and finally converted to DMAPP by the M. thermoautotrophicus IPK  in which the terpenoid skeleton is dephosphorylated and cyclized. The third module involves further decoration by cytochrome P450s (CYPs), acetyltransferases, methyltransferases, dehydrogenases, and in some cases, glycosylations. This overall framework is consistently repeated in nature with some variations  and present an important opportunity for generating highly decorated natural products. To date, over 300,000 CYPs have been discovered, with less than 1% actually characterized . There iArtemisia annua) and the Pacific yew tree (Taxus brevifolia) and were heterologously expressed in E. coli and S. cerevisiae, respectively. Indeed, the diversity and complexity of plant TSs, presented elsewhere -dependent methyltransferases that methylate the fundamental IPP/DMAPP building blocks and thereby break the natural C5 dogma. In a pioneering study, a GPP methyltransferase (2meGPP) from the cyanobacterium ynthases . Each syhylation . A produhylation . The wore and TS . A finale and TS . SpecifiIt is probable that the control of specific methylation sites decreases with compound size such that targeting specific triterpenoids would remain an obstacle. In the case of C11 targets, site-directed mutagenesis of the monoterpene synthases did enable higher selectivity , which iPartial isoprenoids or meroterpenoids are a class of compounds containing an isoprenoid chain paired with another structure and may have beneficial bioactive properties. Broadly, meroterpenoids include cytokinins, quinones, steroids, and porphyrins like heme A and chlorophyll a. The optimization of heterologous meroterpenoids poses a unique engineering challenge as the isoprenoid compound must be cogenerated with another structure, then converted to the terpenoid by a specified synthase.S. cerevisiae. Cannabinoids are of commercial interest but, like many natural products, suffer from low in planta yields. In a recent publication, the production of olivetolic acid from acetyl-CoA was engineered using a six gene pathway (Cannabis sativa prenyltransferase (csPT4) and further heterologous synthases led to in vivo production of cannabigerolic acid, Δ9-tetrahydrocannabinolic acid, cannabidiolic acid, Δ9-tetrahydrocannabivarinic acid, and cannabidivarinic acid  that facilitate growth in unique environments. Full reduction of these long length C20/C40 membrane isoprenoid chains is accomplished by downstream geranylgeranyl reductases (GGR). While they provide an evolutionary advantage for survival in extreme conditions, membrane isoprenoids may also be utilized to generate unsaturated chemicals of interest . Archaea2via heterogeneous catalysts have enabled the conversion of emissions into valuable C2+ substrates , a Type II methylotroph, has been studied for over 60years such that many genetic tools are available  that includes a series of anaperlotic activated CoA esters useful for pathway remodeling  has arisen as a promising candidate for isoprenoid production on methanol. P. pastoris maintains several unique characteristics including a tightly regulated and highly expressed alcohol oxidase AOX1, which catalyzes oxidation of methanol to formaldehyde. AOX1 is strongly induced by methanol but repressed by glucose and glycerol  Cycle enzymes along with native genes in the xylulose monophosphate (XuMP) cycle and deletion of certain fatty acid enzymes, ultimately yielding a mutant exhibiting autotrophic growth on CO2 -ambglycerol , and 208glycerol . Interesh on CO2 .2 conversion. E. coli or S. cerevisiae and methylotrophic cultures require significant supplementation with a rich medium that may somewhat reduce the benefits of C1 production.Methylotrophic production has garnered special interest for reducing bioproduction costs either through valorization of commercial waste streams or COP. pastoris demonstrated de novo production using heterologously expressed CBB cycle enzymes, overexpressed native genes in the XuMP cycle, and deletions of certain fatty acid enzymes. The mutant was ultimately capable of autotrophic growth on CO2  biomass. The isoprenoids generated are characterized by race and consist of either Botryococcenes (C30–C37), methylated squalenes (C31–C34), or odd-number n-alkadienes or trienes  like trienes, tetraenes, and pentaenes intrinsic to some diatoms with potential for pharmaceutical or biofuel usage , possiblRhodobacter sphaeroides is a well-established isoprenoid producer, with industrial production of sesquiterpenes valencene and nootkanone demonstrated by BASF (R. sphaeroides accumulates polyhydroxybutyrate (PHB), a biopolymer with industrial bioplastic applications in of itself, under nitrogen limited conditions. Elimination of the PHB biosynthetic pathway  and expression of the heterologous MVA pathway contributed to increased flux through the isoprenoid pathway under nitrogen limited conditions  and technoeconomic analysis (TEA) will both be critical in quantifying the relative process level sustainability and monetary impacts, validating whether modified microbes are competitive with conventional production on glucose or from petroleum, and prioritizing future optimization opportunities based on projected impact gains. Growth and production on C1 substrates are inherently more sustainable than on pure sugar substrates, however the sustainability of the entire process from cradle-to-gate will be contingent on nontrivial improvement of production titer, rate, and yield. While LCAs and TEAs are common in COublished . NonetheYarrowia lipolytica and Rhodosporidium toruloides, capable of high titer isoprenoid production from woody biomass and waste cooking oil (WCO). Lastly, we describe two prototypical isoprenoid production platforms: P. putida, which is a prime candidate for conversion of pretreated lignocellulosic biomass, and B. subtilis, a candidate bacteria renown for high titer protein production.Certain microbes are capable of valorizing more complex waste streams due to unique evolutionary predispositions. Here, we describe two strains of oleaginous yeasts, Yarrowia lipolytica can naturally assimilate many atypical carbon sources including glycerol, organic acids, succinate, citrate, and even WCO. Likewise, Y. lipolytica is of keen interest due to its natural accumulation of β-carotene, farnesene, and linalool. Multi-copy pathway integration has proven especially successful in targeted isoprenoid overproduction (Agastache rugosa (Korean mint) and limonene synthase from Solanum lycopersicum , even approaching efficiencies observed on glucose (Y. lipolytica has shown up to 90% DCW lipid accumulation (Y. lipolytica strains on WCO. Growth on WCO increased lipolytic activity (Y. lipolytica strain expressed D-limonene synthase (Citrus limon) and L-limonene synthase (Mentha spicata) to yield 2.4mg/L of each enantiomer on WCO  of P. putida , a designation that reduces regulatory boundaries to commercialization. Collectively, these factors suggest that B. subtilis could be an excellent candidate for isoprenoid production. Unfortunately, production studies remain relatively limited in part due to a poorly defined metabolic toolkit, which has historically been hampered by a limited subset of selection/counterselection markers that have made genetic manipulation challenging. enzymes . B. subt E. coli . Unlike E. coli from the early 2000s, recent production studies demonstrated that incorporation of amorphadiene synthase (ADS) with overexpression of DXS and IDI led to the accumulation of 20mg/L amorphadiene , supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC0205CH11231 with Lawrence Berkeley National Laboratory.This work was supported by the DOE Joint BioEnergy Institute (The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants  enable it to thrive under various harsh environmental conditions. Constraint-based metabolic models constructed from genome sequences enable quantitative insight into the central and specialized metabolism within a target organism. These genome-scale models have been utilized to understand, optimize, and design new strategies for improved bioprocesses. Reduced modeling approaches have been used to elucidate Nitrosomonas europaea metabolism at a pathway level. However, genome-scale knowledge about the simultaneous oxidation of ammonia and pollutant metabolism of N. europaea remains limited. Here, we describe the reconstruction, manual curation, and validation of the genome-scale metabolic model for N. europaea, iGC535. This reconstruction is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. Calculated flux distributions under different trophic conditions show that several key pathways are affected by the type of carbon source available, including central carbon metabolism and energy production.The ammonia-oxidizing bacterium  Nitrosomonas europaea catalyzes the first step of the nitrification process (ammonia to nitrite). It has been recognized as one of the most important members of microbial communities of wastewater treatment processes. Genome-scale models are powerful tools in process optimization since they can predict the organism’s behavior under different growth conditions. The final genome-scale model of N. europaea ATCC19718, iGC535, can predict growth and oxygen uptake rates with 90.52% accuracy under chemolithotrophic and chemolitoorganotrophic conditions. Moreover, iGC535 can predict the simultaneous oxidation of ammonia and wastewater pollutants, such as benzene, toluene, phenol and, chlorobenzene. iGC535 represents the most comprehensive knowledge-base for a nitrifying organism available to date. The genome-scale model reconstructed in this work brings us closer to understanding N. europaea’s role in a community with other nitrifying bacteria. Wh. Wh3- asod, 1983  showed to growth . OverallNe is able to achieve maximum methane uptake rates between 0.34–1.96 mmol/gDW/h [Under chemolithotrophic conditions, methane and ammonium oxidation occurs simultaneously. Methane uptake is catalyzed by AMO, which requires oxygen to produce methanol that is eventually secreted to the medium. Experimentally, ol/gDW/h –34 when ol/gDW/h . Predictol/gDW/h . Simulatol/gDW/h .Ne can grow in the presence of toluene, using ammonium and HCO3- as the energy and carbon sources [Ne to oxidize benzene to phenol by AMO activity under ammonium assimilation conditions. The phenol generated by this process is transported to the external medium. Radniecki et al. [iGC535 predicted an optimal oxygen uptake rate of 33.58 mmol/gDW/h when the benzene uptake rate was 1.258 mmol/gDW/h and a nitrite production rate of 25.15 mmol/gDW/h were set as constraints . Calculations regarding high fructose uptake rate are explained in section 2.6. The same consumption rate was established for pyruvate to ensure a high concentration of this metabolite.3- are present in the culture media, the CBB cycle enzymes are activated. Nevertheless, the CBB cycle is incomplete because of the lack of the enzyme NADPH-dependent glyceraldehyde 3-phosphate dehydrogenase (GAPD) [Ne metabolism [Ne has only one form of PGK enzyme, which is used by the glycolytic pathway and the CCB cycle [When HCOe (GAPD) . This entabolism  Fig 2B)3- are prtabolism –38. But,CB cycle ,29. PGK Ne GAPD uses NAD/NADH as coenzymes rather than NADP/NADPH. The preference of GAPD towards NAD/NADH can be potentially associated with the differential activity of NAD and NADH in the respiratory chain , a nucleotide precursor. Interestingly, NADH16pp also generates NAD+ but not NADH as during chemolithotrophic growth. A considerable amount of NAD+ is needed by the pyruvate dehydrogenase. This reaction connects the pyruvate with the TCA cycle.However, when pyruvate serves as the sole carbon source, GAPD produces NAD+ . NAD+ prNe shares enzymes between the CBB cycle and the rPPP pathway, such as transketolase 1 (TKT1), ribulose 5-phosphate 3-epimerase (RPE), ribose-5-phosphate isomerase (RPI), transketolase 2 (TKT2), and transaldolase (TALA)  regenerates NADPH (anabolic) that is used in the biosynthesis of the lipid. The reductive pathway (rPPP) produces glycolytic intermediaries (catabolic). e (TALA) . Under ce (TALA) . The pre3- in the medium. The simulation predicted that oPPP is the greatest significant generator of NADPH. The Ru5P formed is used by rPPP to produce G3P, which is utilized by glycolysis. Nonetheless, when pyruvate is in the medium, some CBB cycle enzymes are activated to produce Ru5P  enzyme is deactivated when HCO3- is present. In other organisms such as aerobic heterotrophs, AKGDH catalyzes succinyl-CoA formation from 2-oxoglutarate and produces CO2 and NADH. In Ne, the NADH is supplied by NADH16pp. The 2-oxoglutarate is generated from the phosphoenolpyruvate and pyruvate formed by CBB/Glycolysis/rPPP superpathway. These two compounds are metabolized to oxaloacetate and acetyl-CoA, respectively, which are further condensed to citrate by the citrate synthase and the rest of the TCA cycle.During chemolithotrophy growth, model simulations predicted that not all TCA cycle enzymes are activated . AlthougUnlike the chemolithotrophic growth, under chemolithoorganotrophic conditions, the simulations predicted the full activation of the TCA cycle . By thisNe lacks malate synthase and isocitrate lyase. These two enzymes belong to the glyoxylate pathway. Plants and some bacteria use them to avoid the two decarboxylation steps catalyzed by AKGDH and isocitrate dehydrogenase [Ne can avoid one decarboxylation step by AKGDH deactivation under chemolithotrophic conditions. Moreover, during chemolithoorganotrophic growth, Ne does not spare any of the decarboxylation steps. Thus, HCO3- is excreted by the organism.Interestingly, rogenase . Ne can The succinate dehydrogenase (SUCDi) activity is unclear under chemolithotrophic conditions . The simNe obtains energy by ammonium assimilation. 4+ is oxidated to NO2- by the activity of AMO and HAO. The metabolic flux continues through the electron transport chain to the terminal oxidase (C552oxi). The ATP synthase uses the proton gradient generated between the periplasm and the cytosol to produce ATP. Out of the total flux through HAO, 50% returns to AMO, 30.8% passes to the terminal oxidase, and 19.25% goes towards NADH16pp.-4 mmol/gDW/h, almost zero . The most important enzyme in pollutant transformation is AMO, which catalyzes the oxidation of the contaminants and methane in the presence of ubiquinol. The oxygen required for oxidation depends on the methane or pollutant uptake rate. For example, if Ne uptakes ammonium at 1.266 mmol/gDW/h, the experimentally observed oxygen uptake rate is 2.03±0.05 mmol/gDW/h, as reported by Hyman and Wood, 1983 [iGC535 predicted an oxygen uptake flux of 1.67 mmol/gDW/h (-4 1/h (with methane). The predictions showed that during methane oxidation, more ubiquinol is needed. Ubiquinol is necessary for the oxidation of ammonium to hydroxylamine. Hydroxylamine participates in the electron transport through the oxidative phosphorylation chain and energy production. Therefore, by decreasing energy production, cell growth is reduced. Some studies have reported inhibition of ammonium oxidation by methane and aromatic compounds, triggering an energy drain [od, 1983 . For theol/gDW/h . Howevergy drain ,14,41,42Ne does not contain enzymes capable of mineralizing them [All the products formed by pollutant oxidation are excreted to the external environment since ing them ,30,42,43ing them ,15,30,41Flux distributions were evaluated under different growth conditions . Fig 3A 3- shows higher fluxes, which is in sync with the nitrification and carbon fixation processes occurring at greater rates under chemolithotroph conditions. Group 2 is the biggest one and contains the largest amount of reactions involved in lipid, amino acid, and vitamin metabolism. Simulations with greater growth rates show higher fluxes. 68.7% of group 3  decreases , only iGC535 and iFC578 can simulate growth and nitrite production under chemolithotrophic conditions. But only iGC535 incorporates the phosphorylate oxidation chain while being fully mass and charge balanced, enabling the simulation of pollutant transformation pathways and growth under both chemolithoorganotrophic and chemolithotrophic conditions. To our knowledge, iGC535 is the first genome-scale model of Ne able to predict flux distributions simultaneous oxidation of pollutant and ammonium.The model accuracy  comparediGC535 were validated using experimental data (>90% accuracy). Higher prediction accuracies were calculated for chemolithoorganotrophic growth with fructose as the sole carbon source, for oxygen uptake rates under chemolithotrophic conditions, and during methane and pollutant oxidation. The model also provides advanced quantitative insights at the metabolic level about chlorobenzene and phenol oxidation. The manual curation performed to iGC535 resulted in a metabolic network of Ne with high certainty about reaction addition and cofactors usage. For example, using the glycolytic isoenzyme NADH-dependent GAPD instead of NADPH-dependent GAPD. Chain et al. [Ne saves a significant amount of energy by reducing 3-phosphoglycerate through the NADH-dependent GAPD. Our model simulations showed that Ne carries flux through this reaction (-2.9597 mmol/gDW/h), thus optimizing energy utilization.Predicted growth phenotypes and fluxes across the network of n et al.  suggesteNe [Ne is growing anaerobically (nitrite as the electron acceptor) while using organic carbon as the carbon source [+ (2.05 mmol/gDW/h) when fructose is present at high concentrations. This flux is reversed when HCO3- is the carbon source (-10.19 mmol/gDW/h). Adessi and De Philippis, 2013 [+ is due to the excess of reduced ubiquinol. Model simulations showed that Ne maintains healthy levels of ubiquinol by activating the reaction NADH16pp.Currently, the complete TCA cycle has been characterized in Ne . Experimn source ,45. Moren source . Model sis, 2013  suggesteNe. However, SUCDi was active at a low flux (8.0939x10-5 mmol/gDW/h). Experimental observations revealed low activity or no significant expression amount of SUCDi under chemolithotrophy growth. Even more, some attempts to measure SUCDi activity by Deutch, 2013 [iGC535 will provide insights into the experimental design to better understand SUCDi activity in Ne.Flux distributions also showed that under chemolithotrophic growth, all TCA cycle enzymes were active except for AKGDH. This prediction agrees with the study done by Beyer et al. and Hooper et al. ,48, who ch, 2013  were unsiGC535 provides high resolution at metabolic and electron transfer levels. For example, model simulations showed that 50% of the electron flux from HAO returns to AMO under chemolithotrophic conditions, 30.8% passes to the terminal oxidase, and 19.25% goes towards NADH16pp. Wood 1986 [2 is fixed. Other organisms, such as Pseudomonas oxalaticus OX1, have also shown a progressive increase in RBPC activity and CO2 fixation when acetate concentration (organic carbon source) decreases in formate-limited culture [The model predicted the increase in RBPC activity when the fructose concentration drops. Thus, CO culture .iGC535 is a reliable systems biology tool that will be the base to understand and generate new hypothesis about Ne metabolism under a great variety of growth conditions.Overall, N. europaea ATCC19718 was obtained from The NCBI Reference Sequence database  [-30, and query coverage ≥ 50%). The initial draft was reconstructed following semi-automatic reconstruction methods [iML1515 [iHN637 as the second template, and iPC815 [Ne and the first template. New reactions were added to the initial draft model using the remaining templates. The generated draft model also contained genes from the models used as references, which were later removed during the model refinement.The proteome sequence of : 2,507) . Protein methods . iML1515[iML1515  was usedd iPC815  as the ld iPC815 . We geneiHN637 (template) were imported to iGC535 because of the physiological similarity between both C. ijungdahlii and Ne . Then, the stoichiometric coefficients of amino acids in the BOF were updated using the abundance of the amino acid in the proteome of Ne, following the standard protocol for generating genome-scale metabolic reconstructions [Ne proteomic data under chemolithotrophic conditions [N. europaea mineral requirements [Ne biomass was 45.9, 12.8, 29.5%, respectively. This BOF generated was used as the objective function in all simulations executed in this paper. The model was also constrained by the non-growth-associated ATP maintenance (NGAM) (0.45 mmolATP/gDW), which was imported from the iHN637 model too. The NGAM is represented in the model as the reaction ATPM.The biomass objective function (BOF) includes key metabolites part of biomass composition. The stoichiometric coefficient of each metabolite in this modeling reaction enforces an overall growth rate at a certain energy rate, here 46.66 mmol/gDW/h. The biomass objective function (BOF) and the growth-associate ATP maintenance value (46.66 mmolATP/gDW) of ructions . The breirements . The finModel refinement included two main stages: first, manual curation of the gene-protein-reaction associations of the model, including new metabolic reactions from The BiGG Database, and second, gap-filling that identified what metabolites were disconnected in the metabolic network and which reactions were missing by pathway.N. europaea for each reaction using the Enzyme Commission (EC) number as reference to identify the protein functionality. As the second step, these reactions without N. europaea proteins associations were checked through BLASTp. We determined sequence similarity among the proteins of these reactions comparing N. europaea proteome against the proteins assigned in the GPR associations for other organisms in the same reactions of the BiGG database. We identified proteins based on BLASTp criteria of > = 40% identity, e-value < = 1e-30, and query coverage > = 50%. The last step of manual curation was performed in order to validate the quality of the GPR associations [In the first step of manual curation, the reactions with exogenous proteins in the GPR associations were reviewed using different bioinformatic databases . Exogenous proteins were replaced with homologous proteins of ciations . The proAfterward, remaining reactions with exogenous genes in the GPR associations of the model were identified and analyzed through Flux Balance Analysis (FBA) from the COBRA Toolbox . The remNe missing gene or at least one erroneous annotated gene.A total of 687 false-positive GPR’s in the initial draft model were corrected. It was considered a false positive GPR if the reaction had at least one The gap-filling process was performed in two steps: first, gap-filling of the metabolic pathways already present in the manually curated draft model, and second, the addition of new metabolic pathways to the model from different bioinformatics databases .3-, fructose, pyruvate). The reactions added in the gap-filling process with no GPR associations were annotated as orphan reactions. Ultimately, reaction fluxes were validated using FBA to verify the predicted internal fluxes.Gap analysis was executed to identify which metabolites were disconnected in the metabolic network and which reactions were missing by pathway. Disconnected reactions were manually added using different bioinformatic databases . From these analyses, gap-filling was employed to manually connect pathways through the data retrieved . Subsequently, the second round of gap-filling was performed to connect the metabolites from the medium conditions retrieved using literature information  through N. europaea central metabolism pathways and gene associations were added to the refined model using semi-automatic algorithms. The names of new reactions and metabolites were assigned according to BiGG and SimPheny databases [N. europaea [During the second step of gap-filling, new atabases ,55. Reaceuropaea ,18. The QC/QA included balance check-ups to ensure mass and charge balance of all reactions added to the model. Ultimately, the final model was tested for free ATP, NADH, and NADPH production without inputs.3- was set as the principal carbon source. The M-model metabolic flux distributions were calculated under ten different growth conditions . The predicted fluxes by reaction (or by column) were normalized by Z-score using the Statistics and Machine Learning Toolbox of MATLAB. For more Z-score normalization details, see The culture medium composition under chemolithotrophic conditions retrieved from the literature  was usedNe metabolic capability to use fructose (from 0 mmol/gDW/h to 1 mmol/gDW/h) under various ammonium assimilation rates (from 0.5 mmol/gDW/h to 6.5 mmol/gDW/h). Predicted growth rates were compared with the experimental data.Ultimately, a robustness analysis was performed to determine the iGC535 was successfully validated under a broad variety of conditions. It is a powerful tool to unravel the metabolism of Ne, and it will be a great tool to understand nitrification in the context of microbial communities to optimize the wastewater treatment and nitrogen cycle behavior. iGC535 represents the most comprehensive knowledge-base for a nitrifying organism available to date.In conclusion, our model i, each v represent fluxes. The analysis was designed to exclude the formation of the loops [Flux Variability Analysis  was usedhe loops  by applyhe loops  to a COBS1 Text(DOCX)Click here for additional data file.S1 Fig(A) Flux prediction change of CBB cycle, glycolysis, rPPP reactions when fructose uptake is 0.014 (low concentration) or 0.746 (high concentration) mmol/gDW/h (ammonium uptake rate setting on 0.5 mmol/gDW/h). (B) Flux prediction change of exchange reactions when fructose uptake is 0.014 or 0.746 mmol/gDW/h (ammonium uptake rate setting on 0.5 mmol/gDW/h). (C) Flux prediction change of nucleotide metabolism reactions when fructose uptake is 0.014 or 0.746 mmol/gDW/h (ammonium uptake rate setting on 0.5 mmol/gDW/h. (D) Flux prediction change of oPPP reactions when fructose uptake is 0.014 or 0.746 mmol/gDW/h Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R The liver is the largest internal solid organ (by mass) and has various essential functions for body homeostasis, including digestion, balancing glucose and storing glycogen, regulating blood amino acids, carrying away wastes, detoxifying chemicals, and metabolizing drugs. The liver has a mysterious ability to regenerate. It is the only organ that can regenerate itself to 100% of original weight in mammals . It is kIt is important to obtain accurate liver weight for major hepatic resection and living donor liver transplantation. Simply, the total liver volume can be predicted based on body surface area and body weight . However13C]Methionine breath test and the production of 13CO2 are considered valuable indicators for evaluating liver regeneration .Methanol (chromatography grade), n-Heptane (chromatography grade), methoxyamine, pyridine, and N-methyl-N-trimethylsilyl-trifluoroacetamide+1% trimethylchlorosilane (MSTFA+1%TMCS) were purchased from Merck KGaA . Stable-isotope-labeled -myristic acid; after oximation and derivatization, 0.5 μL of the sample were injected into a SHIMADZU QP2010Ultra/SE GC/MS system  with an RTx-5MS fused silica capillary column . The raw data acquired were processed by GCMSSolution (version 4.11). The metabolites were identified using NIST 14 , Wiley 9 , and an in-house mass spectra library database (The metabolites in serum were profiled by a GC/MS-based metabolomics method as previously reported . Brieflydatabase .2). All of the machine learning methods were performed and tuned using the “caret” package in the R project (version 3.6.3). Variable importance in the projection (VIP) analysis was used to evaluate metabolites’ contribution to the model.PCA was performed for dimension reduction using SIMCA-P 13.0 software . Nine machine learning methods including LASSO, PLS, PCR, KNN, SVM, RF, xgbDART, NNET, and BRNN were used for regression between the liver index and metabolites. The code used was shown in www.metaboanalyst.ca). Hypergeometric test for over-representation analysis and relative-betweenness centrality for pathway topology analysis was selected, and Mus musculus (KEGG) library was chosen.Metabolomics pathway analysis of the metabolites with VIP >1 was carried out using MetaboAnalyst . The metabolite with the most significant VIP value, the metabolites ranked top 4, 8, 12, 20, 40, 59 and the whole dataset without one metabolite whose VIP is 0  was furt2 in each model, Kruskal–Wallis Test followed by Wilcox test was used; for statistical analysis of metabolites among groups, One-way ANOVA followed by Fisher’s LSD multiple comparison test and corrected by the Benjamini-Hochberg method to control the False Discovery Rate (FDR) was conducted by R project (version 3.6.3). The correlation coefficients were calculated by the “corrplot” package in the R project. p < 0.05 was considered statistically different.For statistical analysis of MAE, RMSE, and RTo investigate liver regeneration progress and the associated metabolic change after partial hepatectomy, 2/3 PHx in C57BL/6J mice was performed and samples were collected at five time points . The liver index was calculated using liver weight and body weight. The remaining liver exhibited an elevated growth rate in the first 3 days and returned to nearly 90% of the original weight after 7 days . During Typical serum GC/MS chromatograms from each time point after PHx are shown in 2 were calculated to evaluate the model performance. As shown in 2. xgbDART method is rather time-consuming and showed no obvious superiority over the RF method; thus, we selected the RF method for further analysis. To choose the most important metabolites contributing to the RF model, we performed VIP analysis and the metabolites which ranked top 20 were selected. Ornithine, phenylalanine, 2-aminobutanoic acid, 2-hydroxybutyric acid, and lysine had the highest VIP values . The chosen metabolites were mapped to KEGG metabolic pathways for over-representation and pathway analyses. The pathway was considered to be significantly related which had a p value of less than 0.05. Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation, and beta-Alanine metabolism, etc. were the most influenced pathways, To reveal the key pathways changed during liver regeneration, the selected most important metabolites in serum were further analyzed by the online tool MetaboAnalyst  to G1 transition, then S phase, and ultimately mitosis. The progress of liver regeneration includes initiation, progression, and termination, and each of these phases was tightly regulated by numerous signaling pathways . To expl2 of the methods used showed RF and xgbDART are the most accurate methods. xgbDART is much more time-consuming than RF, whereas it offers no significant advantage. Thus we select RF as the method used for further optimization and analysis. From the VIP analysis of RF, we choose different amounts of metabolites to validate the model performance further and evaluate its prediction ability. A metabolite set containing ornithine, phenylalanine, lysine, and 2-hydroxybutyric acid was selected as the potential metabolite set for predicting the liver index after 2/3 PHx.Machine learning has variable applications in healthcare. The main functions of machine learning algorithms include classification, regression, and dimensional reduction. Here we aimed to establish a relationship between metabolites in the serum and liver index at a different time of liver regeneration by regression and select the potential biomarkers of liver regeneration. We compared nine machine learning algorithms for regression, LASSO, PLS, PCR, KNN, SVM, RF, xgbDART, NNET, and BRNN. LASSO is a regression model originally formulated from the linear regression model and performed both for variable selection and regression. PLS and PCR are methods where multivariate data is projected into a smaller coordinate space  before regression. SVM method constructs hyperplanes that can be used for classification and regression. RF and xgbDART are both tree-based models which construct a multitude of decision trees. NNET and BRNN are considered deep learning methods and they simulate biological neural networks that constitute animal brains. These methods are more complex non-linear machine learning methods applicable for analyzing high-dimensional metabolomics data. The comparison of MAE, RMSE, and Rin vivo and is necessary for the accurate timing of liver regeneration (Networks, including cytokine, growth factor, and metabolic, are the essential circuitry required for liver regeneration . Metabolneration . Urea cyneration , and theneration . HoweverThere remain some shortages in this study. Firstly, four time points were selected to represent the initiation, progression, and termination phase of liver regeneration. More time points constituting a complete curve should be evaluated to establish the mathematical model and accurately predict liver weight. Secondly, due to the limitation of GC/MS, many metabolites had not been measured; further use of LC-QTOF/MS is essential to cover more metabolites. Thirdly, a mechanism study to reveal the change of metabolic pathways should be performed. Last but not least, although our model showed good performance in mice, there remains a gap between animals and humans; thus, the transformation from mouse to human should be considered for benefit in the clinic.In conclusion, by using a high-throughput GC/MS-based metabolomics technology and machine learning algorithms, we establish mathematical models of liver index and metabolites to predict liver regeneration after 2/3 PHx and compared their performance. We finally choose a time-saving RF method and a set of 4 metabolites containing ornithine, phenylalanine, lysine, and 2-hydroxybutyric acid as a metabolic clock for the accurate prediction of liver index during liver regeneration. Glucose metabolism and amino acid metabolism pathways, including Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine, and isoleucine degradation and beta-Alanine metabolism were the most influenced pathways. In the future, we are planning to utilize LC-QTOF/MS based metabolomics to cover more metabolites, and liver regeneration under different circumstances in animals and humans will be performed to validate our model and transform the model into clinic.