Metabolite–protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite–protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite–protein interactions using supervised machine learning.Escherichia coli and Saccharomyces cerevisiae, we found that the implementation of SARTRE with random forest classifiers accurately predicts metabolite–protein interactions, supported by an average area under the receiver operating curve of 0.86 and 0.85, respectively. Ranking of features based on their importance for classification demonstrated the key role of shadow prices in predicting metabolite–protein interactions. The quality of predictions is further supported by the excellent agreement of the organism-specific classifiers on unseen interactions shared between the two model organisms. Further, predictions from SARTRE are highly competitive against those obtained from a recent deep-learning approach relying on a variety of protein and metabolite features. Together, these findings show that features extracted from constraint-based analyses of metabolic networks pave the way for understanding the functional roles of the interactions between proteins and small molecules.By using gold standards for metabolite–protein interactomes and well-curated genome-scale metabolic models of https://github.com/fayazsoleymani/SARTRE. Small molecules exert their regulatory role by binding to proteins . The regin vitro and in vivo approaches  machine/deep learning based on structural and ontology-based features and (ii) learning of MPIs by considering the effects of metabolites on fluxes of metabolic reactions. With respect to the first category, advances in deep learning have resulted in plethora of approaches to predict. presence/absence of interaction, binding affinity, and interaction sites [for a review, see Against this background, here, we explore the extent to which features extracted from analysis of metabolic networks can be used in prediction of MPIs via supervised machine-learning approaches. In such a way, we aim to capitalize on the available gold standards of MPIs for model organisms, while making use of the advances in constraint-based metabolic modeling , as the shadow price-based metabolite–protein interaction, and test its performance with different species-specific gold standards and genome-scale metabolic networks of Escherichia coli and Saccharomyces cerevisiae. We focused on the two model species due to the curated metabolic network models and gold standards of MPIs that can be used in development of models using supervised learning approaches. In addition, we investigate to what extent the usage of the shadow price-based features contributes to the prediction of MPIs.Unlike flux imbalance analysis, here, we use the concept of shadow prices in the context of flux variability analysis (FVA) to devise features of metabolite–reaction pairs that we then combine with information from gene–protein–reaction (GPR) rules as input to supervised learning of MPIs. We term the resulting framework SARTRE, for Stoichiometric genome-scale metabolic models (GEMs) have been developed for many organisms . Centralction Rj . We defiStoichiometric information, along with the steady-state assumption, leads to linear equations to reaction fluxes in a metabolic network. FBA is a constraint-based method to determine the flux distribution of reactions by solving a linear optimization problem that maximizes the flux through the biomass reaction, i.e. the specific growth rate.The primal LP formulation of FBA can be written as follows . (iii) We optimize models by considering biomass reactions as objective functions [see problem in In this study, we execute our method on two organisms and corresponding metabolic models: iJO1366, a genome-scale reconstruction of the metabolic network of In this subsection, we propose a framework named SARTRE to predict MPIs. In brief, we utilize the metabolic model to extract features for both metabolites and proteins. Then, we employ these features to train a random forest (RF) classifier to predict the interaction of unseen metabolite–protein pairs.We compute shadow prices of each metabolite as a set of features based on solving the dual problem [problem in In this part, we process the mentioned databases to make dataset Mapping the names of proteins and metabolites in the GS datasets to the corresponding names of the metabolic models to get the intersection of metabolites and proteins between the metabolic model and GS datasets.Calculating Evaluating two validation conditions in Selecting each metabolite Removing redundant protein features  protein–reaction associations, based on the GPR rules and (ii) metabolite–reaction flux effects, based on shadow prices. To extract the first type of features, we built a t of FVA . FVA detFor instance, in the toy network on E.coli, iJO1366 , we performed 10 random undersamplings. These were then used for training RF classifier with 100 trees on datasets resulting from the undersampling in 5-fold cross-validation. Last, to compare the performance of classifiers with standard features, we used metabolite topological fingerprints of size 128 obtained from RDKit  instead P-value <1e-5), namely accuracy, area under the receiver operating curve (AUC), and F1-measure . To conclude, extracted features of metabolites and proteins are valuable for predicting MPIs. Moreover, the RF classifier learns strong connections between data and labels, and dependency of features.Moreover, label permutation and feature permutation are executed on three ibutions , which aS.cerevisiae. To this end, we utilized the curated metabolic model Yeast-GEM , and considered 282 proteins whose orthologues are present in both models. We then used the gold standards of the two organisms from STITCH with medium confidence score . In total, we extracted all 1128 labels (i.e. interacting or non-interacting) of the metabolite–protein pairs with medium confidence score from the gold standards, of which 955 pairs had the same label in the two gold standards. We kept these 955 pairs to create a test set, and excluded the remaining 173 pairs from the test set to allow comparability of the predictions based on the features from the two different models. We then trained two separate models with 7086 and 7224 metabolite–protein pairs in E.coli and S.cerevisiae, respectively, and predicted MPIs on the test set. We found that the accuracies of predictions are 0.754 and 0.755 for E.coli and S.cerevisiae, similar to what was observed in the previous case studied. To compare the predictions of two separate models, we calculated the cosine similarity of two prediction vectors. Cosine similarity, which measures the similarity between two vectors by using the cosine of the angle between them, is defined for two vectors For the first analysis, we compared the predictions for the same set of metabolite–protein pairs on RF models trained with features obtained from the metabolic models of We found that the cosine similarity between predictions of two models is 0.980, demonstrating that the shared MPIs can be learned from two different organisms with features extracted from their respective metabolic models.E.coli with medium confidence score and the metabolites in each of these subsystems. We used the resulting metabolite–protein pairs as a test set, and to generate a training set we performed random undersampling on the remaining pairs from the gold standard  are based on features, obtained from a protein–protein interaction network, for 9631 proteins as well as different representations and fingerprints for 23 metabolites. The comparison is based on the four gold standards (F1 (E.coli with a cut-off of 150 and 700)  . The macSince shadow prices may change with media composition, here, we assess the sensitivity of SARTRE as media composition changes. To this end, we examined SARTRE with two different changes of media composition, namely by changing the carbon sources and the limitation of critical nutrients.First, we applied SARTRE with different carbon sources of the IJO1366 model and employ STITCH with the medium confidence score of 400. More specifically, our primary results were based on the glucose carbon source, while here we examine other carbon sources namely, acetate, fructose, glycerol, mannose, and succinate. Based on the media compositions, different metabolites would be retained due to the pre-processing approach and as a result, we have different sizes of metabolite features and number of metabolite–protein pairs per class. As shown in Furthermore, we examined the MPI predictions in different media compositions to identify the robustness of MPIs to different compositions, by distinguishing predicted MPIs across all compositions and pairs that are specific to only one of them. To this end, each MPI is given a score between 0 and 10, which reflects the number of times that this pair is predicted as positive in 10-folds of each undersampled dataset. Eventually, for the comparison between different compositions, we extract interacting pairs that are always predicted as positive and therefore receive a score of 10. By implementing this approach, we found that 2819 positive pairs were predicted in all examined media compositions. However, some MPIs are predicted only in one condition and are specific to that media composition , which can be achieved by constraining its rate of the corresponding uptake reaction.To better specify the effect of the restriction on the uptake rates to the value of the biomass flux, we define limiting and non-limiting uptake rates. If the uptake rate of the source results in a specific growth rate (i.e. flux through the biomass reaction) smaller than 0.9 of the optimum, predicted by the model, we consider the uptake value as limiting to growth; otherwise, the uptake value is considered non-limiting.Next, we examined the performance of SARTRE for each of the mentioned growth-supporting reactions with three limiting uptake rates and four non-limiting ones, according to the definitions above.−1 h−1, and, based on the definition above, we take the threshold of 0.8842 mmol gDW−1 h−1 to differentiate uptake rates that are limiting and non-limiting to growth. We consider the default iJO1366 as our baseline GEM, in which there is only one growth-supporting reaction  with non-zero uptake rate for these three key nutrients. In the baseline, the optimum flux through the biomass reaction equals 0.9824 mmol gDWE.coli with a low confidence score, and 41.79% of STITCH-yeast with the highest confidence score. To ensure the consistency of SARTRE, we excluded such trivial MPIs from the gold-standard datasets and re-evaluated the performance of SARTRE. In the minimum coverage case, metrics changed from 0.76, 0.83, and 0.77 to 0.76, 0.83, and 0.77 for accuracy, AUC, and F1-measure, respectively. In the maximum coverage case, the metrics changed from 0.75, 0.84, and 0.76 to 0.75, 0.83, and 0.76 for accuracy, AUC, and F1-measure, respectively. The same approach is applied to the other gold-standard datasets and their results are available in In this section, for the four gold standards used in our study, we investigate pairs that can be also obtained directly from the GEMs. First, to obtain the metabolite–protein relations from the GEMs, for each reaction, we assume that there is an interaction between each substrate/product and genes encoding enzymes in the GPR rule. These sets of interactions are then compared to the ones from the gold standards see . CoveragDespite recent research efforts, understanding the functional role of MPIs in modulating different cellular processes remains challenging. Machine and deep-learning approaches have provided advances in prediction of MPIs based on structural and ontology-based features. Our study adds to these advances by predicting MPIs in the context of metabolic networks and effects they have on metabolic fluxes. To this end, we expanded the usage of shadow prices in an innovative way to predict MPIs in metabolic networks using machine-learning approaches. We demonstrated that SARTRE results in an improvement of prediction performance in comparison to the usage of metabolic fingerprints and shows that shadow prices are the features that contribute most to the predictions. In addition, our comparative analyses showed that SARTRE is competitive against a recent study that used deep learning with a variety of features to predict MPIs. In the future, SARTRE can be extended to consider ensemble of weak classifiers shown to improve prediction performance in many applications. Due to the usage of constraint-based modeling formulation for the extracted features, SARTRE paves the way for improving the understanding of MPIs by further developments in this modeling framework and its application across other species for which metabolic network models of high quality have already been assembled and analyzed.vbad098_Supplementary_DataClick here for additional data file.


Microbial cell factories (MCFs) are typical and widely used platforms in biomanufacturing for designing and constructing synthesis pathways of target compounds in microorganisms. In MCFs, transporter engineering is especially significant for improving the biomanufacturing efficiency and capacity through enhancing substrate absorption, promoting intracellular mass transfer of intermediate metabolites, and improving transmembrane export of target products. This review discusses the current methods and strategies of mining and characterizing suitable transporters and presents the cases of transporter engineering in the production of various chemicals in MCFs. Biomanufacturing uses renewable biomass to produce bioenergy, biomaterials, natural products, and bulk chemicals, which has important significance for carbon emission reduction and sustainable development , 2. MicrTo solve this problem, transporter engineering was provided as an alternative strategy that can enhance substrate absorption, promote the intracellular mass transfer of intermediate metabolites, and improve the transmembrane export of target products. Generally, several strategies have been used for mass transfer intensification in MCFs. Subcellular compartmentalization strategy have been adopted to strengthen metabolic mass transfer by introducing a series of reactions into one compartment/organelle, which improved local concentrations in space for increasing product concentration –20. MembHerein, we summarized the current methods and strategies for mining and characterizing suitable transporters and introduced cases for improving the manufacturing efficiency of MCFs through transporter engineering. The understanding of cellular transport process and the application of transporter engineering would provide novel insights into the construction of MCFs for the biomanufacturing process.At present, the reported information about transporters is still inadequate, which causes tremendous challenge for engineering molecular transport in MCFs. Therefore, it has become exceptionally crucial for mining and characterizing more specific transporters for the target compounds Table .http://www.tcdb.org) is a common and freely accessible reference database [Dendrobium officinale via sequence alignment from TCDB [Bioinformatic tools are becoming popular due to their ability to analyze huge amounts of biological data . AntiSMAdatabase . Generalrom TCDB , and fourom TCDB   porin family and all known carboxylic acid transporters using CRISPR-Cas9 in Penicillium chrysogenum in D-glucose and L-arabinose restricted culture, respectively, the fungal transporter PcAraT specifically transporting L-arabinose instead of xylose and glucose was identified [Atropa belladonna were analyzed through binary classifier supervised learning models . The supervised classifier models based on tissue description showed greater efficacy in predicting transporters than traditional regression- and clustering-based methods. As a result, two identified transporters, AbPUP1 and AbLP1, were found to increase the production of target alkaloids in the engineered yeast  based on the biosensor of the target organic acid compounds cis, cis-muconic (CCM) and protocatechuic acids (PCA) [Xenopus expression assays  . As a reLow substrate uptake rates would hamper the productivity of MCFs, especially in engineered strains that use unnatural substrates Table . TherefoS. cerevisiae, which significantly improved the substrate uptake rate and simultaneously reduced the production of byproduct xylitol  are not natural substrates of baker’s yeast. The utilization of pentose can substantially improve bioresource utilization. An artificial complex consisting of endogenous sugar transporter Gal2 and heterologous xylose isomerase (XI) was constructed in ~371 mM) , 42. Therabinose . In anotxpressed . D-galacel waste .cis, cis-muconic acid production strain, the crucial intermediate 3-dehydroshikimic acid (DHS) can diffuse to the outside of the cell along the concentration gradient, resulting in the draining of precursor. By expressing a membrane-bound transporter ShiA to import DHS into the cytosol, the production of cis, cis-muconic acid was significantly improved [β-oxidation pathway in S. cerevisiae, which limits the production of cytosolic acyl-CoA. Therefore, the fatty acyl-CoA peroxisomal transporter Pxa1 was knocked out to prevent oxidation, which improved the production of acyl-CoA-derived triacylglycerols [Strengthening the reuptake of intermediate metabolites or the mass transfer between cells and extracellular media or subcellular compartments could significantly improve the flux of metabolic pathways, which is essential for the manufacturing efficiency and capacity of the MCFs. In the improved   transporter gene cluster PP_1271 [Schizosaccharomyces pombe was found to export L-malic acid effectively [S. cerevisiae [C. glutamicum combining with the strengthening of L-serine synthetic pathway, which further enhanced its industrial application [Export of target products in MCFs has many benefits, including releasing intracellular space, eliminating product inhibition, and reducing potential product toxicity. Propionic acid is a valuable C3 platform chemical, but it is toxic to microorganisms. Propionic acid tolerance and production in  PP_1271 . Howeverectively . Researcrevisiae . In anotlication .The export of many fuel chemicals was conducive to their bioproduction, such as fatty acids , fatty aE. coli [Burkholderia, thereby promoting the forward biosynthesis of the heterologous polyketide compound epothilone [Polyketides are a large class of natural products with great therapeutic value. Resistance-nodulation-cell division (RND) family efflux pumps play major roles in the resistance of gram-negative bacteria to a wide range of compounds, such as polyketides . An RND E. coli . It is nE. coli . In the E. coli . The ratothilone .E. coli [β-carotene was enhanced by 4.04-fold through adopting an inducible GAL promoter to overexpress the endogenous plasma membrane ABC transporter Snq2p in S. cerevisiae [E. coli by introducing the multidrug and toxic compound efflux family transporter AtDTX1 from Arabidopsis thaliana [Terpenoids are the largest family of secondary metabolites of plants, and they are widely distributed in archaea, bacteria, and eukaryotes . The RNDE. coli . The coorevisiae  (Figure thaliana .Transporter engineering has been documented to improve substrate absorption, promote the intracellular mass transfer of intermediate metabolites, and reinforce the transmembrane export of target products, which play a decisive role in the metabolism and mass transfer of the MCFs. However, reported information about transporters is insufficient, severely limiting the application of transporter engineering in MCFs. Therefore, it is extremely necessary to vigorously develop efficient methods and strategies for mining and characterizing transporters. The current methods for identifying specific transporters still have limitations, such as relatively low throughput, low efficiency, and labor intensive. It is necessary to develop high throughput, low-cost, and efficient methods for automated identification and characterization of transporters.Transporters generally have a broad substrate spectrum, which increases the transport flux of target compounds through the synergistic effect of multiple transporters. The heterologous production of various classes of compounds could combine different types of pumps. However, selecting the proper type of pump is also vital for a specific heterologous product. In nature, the expression of some efflux pumps is often tightly controlled by the relevant regulatory proteins. Thus, tuning the expression of pump regulators may be an effective option for transporter engineering as well. As the transport mechanism becomes clear, the semirational or rational design based on the protein structure could further expand their substrate spectrum and improve the transport affinity and transport rate of target compounds. In addition, combining transporter engineering with other regulation strategies may further boost production and efflux of target compounds based on their synergism.


Hence there are two distinct ways in which an enzyme destined for use in plants can require improvement: In catalytic properties such as substrate and product specificity, kcat, and KM; and in general compatibility with the milieu of cells that express the enzyme. Continuous directed evolution systems can deliver both types of improvement and are so far the most broadly effective way to deliver the second type. Accordingly, in this review we provide a short account of continuous evolution methods, emphasizing the yeast OrthoRep system because of its suitability for plant applications. We then cover the down-to-earth and increasingly urgent issues of which enzymes and enzyme properties can — or cannot — be improved in theory, and which in practice are the best to target for crop improvement, i.e. those that are realistically improvable and important enough to warrant deploying continuous directed evolution. We take horticultural crops as examples because of the opportunities they present and to sharpen the focus.Synthetic biology creates new metabolic processes and improves existing ones using engineered or natural enzymes. These enzymes are often sourced from cells that differ from those in the target plant organ with respect to, e.g. redox potential, effector levels, or proteostasis machinery. Non-native enzymes may thus need to be adapted to work well in their new plant context (‘plantized’) even if their specificity and kinetics  First, the ‘what’ facet of enzyme improvement: Building a synthetic metabolic pathway can require enzymes (‘parts’) with activities that do not exist in nature, at least as anything more than minor side-activities of other enzymes [kcat and KM values [Enzymes can need improving in two conceptually different ways: In  enzymes . Further enzymes  because M values . Hence, M values , herbiciM values , and vulM values .how well’ facet: If an enzyme is to operate optimally, or even at all, in a plant cell it must be compatible with that cell's physicochemical and biochemical environment, e.g. the temperature, oxygen tension, redox potential, osmolarity, small-molecule effector concentrations, and protein degradation system [Escherichia coli, Chlamydomonas mitochondria, and pumpkin peroxisomes [2 fixation cycle comprising 17 enzymes from six bacteria, an archaeon, humans, and Arabidopsis [Turning to the ‘n system . These poxisomes ; folate oxisomes ; the CETbidopsis ; engineebidopsis ; and thebidopsis . Such inbidopsis ,16. Morecontinuous directed evolution [Saccharomyces cerevisiae) OrthoRep system due to its special utility in adapting enzymes for plants.Enzymes can in principle be improved by rational design, by directed evolution, or by a combination of both  ,18. Rativolution  — can imvolution ,17. Methvolution . This rein vitro, e.g. by error-prone PCR, introduces the resulting library into the host (platform) microbe, screens or selects the clones for improved variants, then repeats this cycle until the desired performance is reached or progress ceases  [5-fold higher than the natural rate. The large effective library size and small labor demand enable long evolutionary walks to explore multi-peaked fitness landscapes by simultaneously evolving multiple independent populations [Classical directed evolution generates sequence diversity in a target gene s ceases . Classicrm cells ; hypermusystems) ,21–23. Iulations . By usinulations ,14,24,25ulations . MutaT7 ulations ; the oriulations  but nextulations .E. coli MutaT7 systems [vs. hours) and population sizes (thousands vs. billions). Plantizing in yeast is therefore a far more realistic option, even if conditions in yeast only approximate those in plant cells. For core characteristics such as specificity and kinetics, the approximation should be quite close enough to allow selection for improvements of the desired type and scale that will be retained when the enzyme is transferred to plants.OrthoRep can be run indefinitely and makes no off-target mutations , which i systems . As a pl systems , yeast o−5 substitutions per base (s.p.b.) in the target gene. A second cytoplasmic plasmid, p2, encodes the RNA polymerase that transcribes genes on p1 and p2. Expression of the target gene can be tuned via the strength of its promoter or via the length of its p1-encoded poly(A) tail, enabling adjustment of the selection window [one passage of a 3 ml culture, each base in the target is mutated 3.8 × 104 times [In OrthoRep , the tarn window . OrthoRe04 times . Various04 times ,22. A siTwo potential pitfalls for any continuous directed evolution system are especially important during the long campaigns that OrthoRep makes possible. One pitfall is subclinical contamination; a yeast population can be infected with bacteria yet look and smell normal and show improved growth. Countermeasures include regular checks by plating evolving populations on rich bacterial media and PCR-screening for 16S ribosomal RNA using generic primers. The other pitfall is cheater mutations, which improve growth but occur outside the targeted sequence. Such mutations can be in the nuclear genome or in the target gene promoter on the p1 plasmid . PromoteTo benefit agriculture, directed evolution must be interfaced with plant breeding programs. Three ways to do this are as follows, ordered from the least to the most desirable. First and worst comes transgenesis. Although long-established technology, inserting transgenes and regenerating plants is still not routine, or even possible, for many crops  includinNumerous recent reviews and perspectives have covered how synthetic biology in general and directed evolution in particular could help agriculture, e.g. ,38–48. WA directed evolution campaign can basically improve one enzyme-encoding gene at a time, or a small number of genes such as those for a multi-subunit enzyme . Campaigkcat and KM values — which dictate how much enzyme is needed to convert substrate to product at the desired rate [Thus directed evolution's scope is limited in practice to enzymes encoded by just one or two genes, to improvements that lie within a few mutations of the starting point, and — for OrthoRep — to activities upon which yeast cells can be forced to depend. Although seemingly small, this scope in fact encompasses a large range of metabolically and agriculturally important characteristics, as we sketched in the opening section and cover here and in red rate ,55. Thenred rate , and senred rate  and to ered rate . Last cored rate , oxygen red rate , or the red rate , and overed rate .A higher-order limitation on the scope of directed enzyme evolution is the target enzyme's natural or designed position in the metabolic network. Primary metabolic enzymes have been subjected to longer and stronger selection for specificity and high flux capacity than secondary metabolic enzymes, making primary metabolic enzymes harder to improve, especially with respect to action on their physiological substrates ,48. FurtBreeding fruit and vegetable crops nearly always requires selecting simultaneously for multiple traits . The traIt would be poor intellectual property management to identify specific target enzyme traits at this point, but TM tomato , available 1994–1997; the nonbrowning Arctic® apple (enzyme: polyphenol oxidase), which debuted in 2017; and the lycopene-enriched PinkglowTM pineapple , released in 2020 [Horticultural breeding priorities and directed evolution capabilities overlap strongly in quality traits. As the arrows in  in 2020 . These p in 2020 ,72–74 is in 2020 , natural in 2020 , and pre in 2020 .Production traits that hinge on resistance to an exogenous compound are another area where horticultural crop breeding and directed evolution overlap . When thTM tomato mentioned above, where the cell wall enzyme polygalacturonase was the target. A recent success was the use of FIND-IT to identify single nucleotide polymorphisms that reduce mixed-linkage glucan in elite barley lines  [Cell wall-related traits cross over between production and quality; these, too, are amenable to directed enzyme evolution. Provided that they can be coupled to growth or probes for fluorescence-activated cell sorting, carbohydrate-active enzymes could be improved in yeast for subsequent use in plants . For exa, CSLF6) . While m, CSLF6) .vs. holism) and biological interactions studied (molecular vs. genotype × management) through project length (days to months vs. years to decades) to performance criteria (publications vs. cultivars and publications). Moreover, breeding programs are essentially a set of interconnected hub-and-spoke operations in which the breeder is the hub, supported by a set of spokes among which the biochemist is just one. Other spokes include pathologists, food scientists, soil scientists, industry stakeholders, and consumers. Deploying directed evolution effectively in a breeding program therefore demands mutual acculturation. This takes time but it eventually leads to actionable conversations in which aims are defined so that experiments start, grant proposals get written, and products are delivered to consumers. Horticulture has a long, productive tradition of such intradisciplinary dialog, as does agricultural biotechnology [Directed enzyme evolution is the domain of biochemists. Crop improvement is the domain of breeders and agronomists. While biochemists, breeders, and agronomists talk the common language of genetics, beyond that they differ in many ways ranging from worldview (reductionism chnology ; for horchnology  and gibbchnology  are exceContinuous directed evolution in the yeast OrthoRep system can be used both to improve various aspects of plant enzyme function and to adapt nonplant enzymes to work well in plants.Many recent articles express justified optimism about prospects for deploying directed evolution in crop improvement, but only identify target traits in vague terms such as ‘yield’ or ‘resilience’.It is now urgently necessary for biochemists and plant breeders to jointly identify target enzyme traits that consumers or producers want and that directed evolution could substantially improve, and to work together to start integrating these traits into breeding programs.


Pedobacter lusitanus NL19 (NL19) and Pedobacter himalayensis MTCC 6384 (MTCC6384) and respective influence on the production of biotechnologically relevant compounds. Solid-phase microextraction combined with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry with time-of-flight analyser (GC × GC-ToFMS) was applied to comprehensively study the metabolites produced by NL19 and MTCC6384 both in tryptic soy broth 100% (TSB100) and tryptic soy broth with 25% casein peptone (PC25). A total of 320 metabolites were putatively identified, which belong to different chemical families: alcohols, aldehydes, esters, ethers, hydrocarbons, ketones, nitrogen compounds, sulphur compounds, monoterpenes, and sesquiterpenes. Metabolites that were statistically different from the control (sterile medium) were selected allowing for the construction of the metabolic profile of both strains. A set of 80 metabolites was tentatively associated to the metabolic pathways such as the metabolism of fatty acids, branched-chain aminoacids, phenylalanine, methionine, aromatic compounds, and monoterpene and sesquiterpene biosynthesis. This study allowed to better understand how slight changes of the culture media and thus the composition of nutrients impair the metabolic profile of bacteria, which may be further explored for metabolomics pipeline construction or biotechnological applications.Microbial metabolomics allows understanding and to comprehensively analyse metabolites, and their related cellular and metabolic processes, that are produced and released to the extracellular environment under specific conditions. In that regard, the main objective of this research is to understand the impact of culture media changes in the metabolic profile of The online version contains supplementary material available at 10.1007/s00216-022-04505-6. Biochemical processes that take place within a bacterial cell give rise to a complex mixture of metabolites that reflect the conditions under which microorganisms grow. As such, parameters such as culture medium composition, pH, temperature, among others, represent the first line of response of a microorganism to conditions it encounters. Logically, all these parameters influence the microbial metabolome, and its study will be fundamental for the identification of metabolic and cellular pathways of specific compounds. Under each abovementioned specific conditions, a set of metabolites are released to the extracellular environment, the exometabolome, which mirrors the catabolic, metabolic, and anabolic reactions that occur in a given organism , 2. FurtPedobacter lusitanus NL19 (NL19) from the Pedobacter genus. In selected growth conditions , NL19 produces compounds with antimicrobial activity against Gram-positive and Gram-negative bacteria and yeast. Nonetheless, in culture media with high concentrations of casein peptone (TSB100), antimicrobial activity was completely abolished. Further, this strain presents a relevant biotechnological profile, as it encodes in its genome other secondary metabolites with foreseen application in the most diverse sectors, including human health [Pedobacter himalayensis MTCC6384 (MTCC6384), which our group also concluded to be a producer of antimicrobial compounds with different nature and spectra of activity of those produced by NL19 [Our group has been working on secondary metabolites (SM) of a new bacterial species,  by NL19 . In addiptone TSB0, antimiThe main objective of this research was to understand the impact of culture media composition on the alteration of volatile exometabolome, by comparing differences detected in the metabolic profile of each species, when cultivated in two different culture media (TSB100 and PC25) which are known to affect the production of bioactive compounds, like pedopeptins . This st600nm = 0.05, in 100-mL shake flasks. Control samples consisted of the same volume of cell-free growth medium. Three replicates of each test condition and control were prepared. Shake flasks were incubated in an orbital shaker for 48 h, at 26 °C and 180 rpm. The following conditions were analysed: NL19 in TSB100 (PL100), NL19 in PC25 (PL25), MTCC6384 in TSB100 (PH100), MTCC6384 in PC25 (PH25%), control TSB100 (TSB100), and control PC25 (PC25).NL19 and MTCC6384 were grown on tryptic soy agar (TSA) plates for 48 h at 26 °C . The fol600nm was measured. CFU/mL estimation was used to normalize areas of the detected metabolites. CFU/mL in each OD600nm was determined, under the same conditions, and using the single plate-serial dilution spotting (SP-SDS) method [After incubation, the number of colony-forming units per millilitre (CFU/mL) was determined as follows: 0.1 mL was withdrawn from each culture and OD) method .After incubation, cultures were collected into 50-mL falcon tubes. To quench the samples, all tubes were centrifuged at 13,000 rpm for 15 min. Supernatants were quickly separated from the pellet into new falcons and were filtered with the help of a vacuum filtration system equipped with a GN-6 Metricel® Grid 47-mm filter mixed cellulose esters with 0.45-µm pore size, to remove remaining cells and other compounds that may compromise the analysis. After filtration, 18 mL of each filtrate was transferred to 60-mL glass vials containing 4 g of NaCl and a stirring bar of 20 × 5 mm. The vial was closed with a silicone/polytetrafluoroethylene septum and aluminium cap and stored at − 80 °C until analysis .−1. The following temperature programmes were used: the primary oven temperature was ranged from 40 °C (1 min) to 145 °C at 5 °C min−1, and then to 200 °C (1 min) at 7 °C min−1. The secondary oven temperature programme was 5 °C offset above the primary oven. Both the MS transfer line and MS source temperatures were 250 °C. The modulation period was 5 s, keeping the modulator at 20 °C offset above the primary oven, with hot and cold pulses by periods of 0.80 and 1.70 s, respectively. The ToF analyser was operated at a spectrum storage rate of 100 spectra s−1, with a mass spectrometer running in the EI mode at 70 eV and detector voltage of − 1523 V, using an m/z range of 35–300. Automated data processing software ChromaTOF® (LECO) was used to process total ion chromatograms, at signal-to-noise threshold of 100. For identification purposes, the mass spectrum and retention times of the analytes were compared with standards, when available. The mass spectrum of each peak was compared to those existing in mass spectral libraries, including an in-house library of standards and two commercial databases  V.2.0–Mainlib and Replib). Moreover, a manual analysis of mass spectra was performed using additional information such as linear retention index (RI) value, experimentally obtained through van Den Dool and Kratz RI equation [8-C20n-alkanes series was used (solvent n-hexane was used as C6 standard), comparing these values with reported ones in existing literature for chromatographic columns similar to first dimension column. The RI parameter calculated also supports the identification, since the calculated retention index  differed 0 to 5.8% when compared to literature data (RILit) for the 1D column or equivalents, corresponding to a median of 0.4% and a standard deviation of 0.8%. The majority (> 90%) of compounds show a variation of ≤ 2%. Also, the majority (> 90%) of identified compounds showed similarity matches > 800/1000. The deconvoluted total ion current GC × GC area data were used as an approach to estimate the relative content of each metabolite of the strains under study.The headspace analysis was performed by HS-SPME/GC × GC-ToFMS methodology, as described by Costa et al., 2016 . The SPMPeak areas were extracted from chromatograms and used to form a data set of 18 observations  and 320 variables (see Electronic Supplementary Information — Table S1). Bubble plots were built which included all identified compounds and raw peak areas to take a quick snapshot of the headspace composition of each sample. Further analysis of the number of compounds and respective chemical families was also carried out. MetaboAnalyst 5.0 , Edmonton, AB, Canada) software was used to produce a hierarchical clustering analysis with a heatmap that allowed to compare the abundance of the 320 variables in each condition, with and without normalization by probabilistic quotient normalization (PQN) and CFU/mL. Autoscaling was applied to each variable. A dendrogram was also made to analyse relational distance quantitatively. In addition, normalized data (CFU per condition) is also provided as a bar plot.p-value of < 0.05 were considered significant, and thus were selected through the application of an ANOVA or Kruskal–Wallis test with post hoc analysis with Tukey test or Dunn test with Holm correction . In total, 123 variables were selected, after previous statistical analysis, and these are marked in bold in Online Resources 1 and 2. Variation in the relative abundance of each compound is represented by an arrow, where arrows pointing up or down represent high or low abundance, respectively, when compared to control (see Electronic Supplementary Information — Table S2). Selected metabolites were normalized with the respective CFU estimation for each condition, which allowed a comparison between each bacterium and both growth media. An additional heatmap was constructed using data from the 123 variables, by applying CFU normalization and autoscaling.The significance of the analytes was compared to assess which of the 320 variables were statistically significant. All test conditions were compared with the respective control . Analytes that presented a p-values of the 24 analytes, that were considered significant (p < 0.05), are marked in bold. Arrows indicate the condition with a higher abundance of a given analyte. Statistically different values for each strain were used to produce bubble plots containing normalized peak area, which allowed the comparison of abundances of each analyte between the two-growth media.To assess the effect of the medium in each bacterium growth (PL100-PL25 and PH100-PH25), an ANOVA was performed with multicomparison Tukey test or Kruskal–Wallis with Dunn test using the Holm correction (R software™ v.4.1.2), with a significance level of 0.05. To understand the role of the 123 selected metabolites, a network of metabolic pathways was designed using information available in the databases  and also in the literature. This exercise allowed the association of each metabolite to a putative metabolic pathway, as well as the possible contribution of each identified pathway. Each metabolite was associated with the heatmap for each condition, which allowed us to quickly understand variations between conditions. Statistically irrelevant metabolites, when compared to control conditions, were marked with an asterisk (*).1tR) and second dimension (2tR) retention times, compound name, formula, CAS number, calculated RI , and RI presented in the literature (RILit). These metabolites were distributed over 11 chemical families: 51 alcohols (15.9%), 24 aldehydes (7.5%), 50 esters (15.6%), 11 ethers (3.4%), 44 hydrocarbons (13.8%), 57 ketones (17.8%), 17 N-compounds (5.3%), 10 S-compounds (3.1%), 44 monoterpenes (13.8%), 9 sesquiterpenes (2.8%), and 3 norisoprenes (0.9%). The respective GC × GC total ion chromatograms can be consulted in Electronic Supplementary Information — Fig. Electronic Supplementary Information — Table S1 lists the 320 compounds putatively identified in the volatile exometabolomes of PL100, PH100, PL25, and PH25, which also includes detailed information of these metabolites, namely, ID number, first dimension  based on statistical difference between, at least, one of the conditions tested compared to the respective control.p > 0.05). The formation of two clusters is due to the high abundance of compounds in MTCC6384 and low in NL19. PL100 and PL25 conditions have clades with lower height in relation to PH100 and PH25, indicating a higher similarity between conditions of the NL19 strain. Regarding the chromatic scale, a high content of compounds is observed in PH100 and PH25. This difference contributes to the formation of two clusters, one containing NL19 conditions (PL100 and PL25) and the other containing MTCC6384 conditions (PH100 and PH25). Condition PL25 shows a greater abundance of metabolites belonging to any of the following chemical families: alcohols, aldehydes, esters, ethers, hydrocarbons, ketones, N-compounds, S-compounds, monoterpenes, and sesquiterpenes, whereas in PL100, a lower abundance is observed. Regarding MTCC6384 conditions, a greater variability is observed. In PH100, a higher abundance of alcohols, aldehydes, esters, N-compounds, S-compounds, and monoterpenes is observed, while in PH25 a greater abundance of ethers, hydrocarbons, ketones, and sesquiterpenes is observed. From the 123 selected metabolites, 73 were more abundant and 47 were less abundant in at least one condition when compared to control (sterile TSB100 or PC25 media). Still, pentanal (55), 2-ethoxy-2-methylpropane (126), and nonane (137) are more or less abundant depending on the medium (see Electronic Supplementary Information — Table S2). Most metabolites that appear in statistically higher abundance (p < 0.05), regardless of condition, belong to alcohols, hydrocarbons, ketones, N-compounds, monoterpenes, and sesquiterpenes. Conversely, the vast majority of statistically less abundant (p < 0.05) metabolites were aldehydes, esters, and S-compounds. The most abundant alcohol in all conditions was 2-ethylhexan-1-ol (28). The least abundant alcohol was α,α,4-trimethylcyclhexanemethanol (49) in PL100 and PH25, 2-methylpentan-1-ol (12) in PH100, and 4-methylpentan-2-ol (9) in PL25. Among the aldehydes, 4-methylbenzaldehyde (71) and deca-2,4-dienal (65) were in high and low abundance, in PH100, PL25, and PH25, respectively, while octanal (60) and pentanal (55) appear in higher and lower abundance, respectively, in PL100. 3-Hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate (101) was the most abundant ester, while methyl methacrylate (77) appears in vestigial (vt) amounts in all four conditions. In addition, isoamyl acetate (81) and 2-methylbutyl acetate (82) were also detected in vestigial abundances in PL100. As for ethers, 2-ethoxy-2-methylpropane (126) was the most abundant in all conditions, while 2-butoxyethanol (127) and 1,1-dimethylethoxybenzene (133) were less abundant in PL100, PH100, PL25, and PL25. In TSB100, 6-methyloct-1-ene (141) is the most abundant hydrocarbon, whereas in PC25 a higher abundance of naphthalene (169) is observed. In all conditions, but PL100, compound nonane (137) remains vestigial. Also, in PL100, the less abundant and vestigial hydrocarbon is 1-ethyl-2-methylbenzene (163). The ketones 5-methylhexan-3-one (191) and 4-methylheptan-2-one (196) are highly abundant in PL100 and PL25, and in PH100 and PH25 conditions, respectively. Still, the less abundant ketones were nonan-3-one (205) in PH100, 1-phenylbutan-1-one (222) in PL100 and PH25, and cyclohexanone (227) in PL25. As for the nitrogen-containing compounds, 4-cyanocyclohexene (242) is highly abundant in all conditions. Yet, 1-isoamylpyrrole (245) is less abundant in PH100, PL25, and PH25 and benzylidenepropylamine (249) in PL100. Among sulphur-containing compounds, methyl thioacetate (255) is highly abundant in PL100, and 2-acetylthiazole (262) in PH100, PL25, and PH25. Conversely, the least abundant sulphur-containing compounds were 3-(methylthio)propanal (259) in PL100, PH100, and PH25, and 2-tertiobutylthiophene (264) in PH100. Levomenthol (isomer) (286) is the most abundant monoterpene in both conditions. α-Pinene (265) appears in trace amounts in PL100, while the least abundant were carveol (294) in PH25 and carvotanacetone (300) in PH100 and PL25. Regarding sesquiterpenes, in all tested conditions, caryophyllene oxide (314) is the most abundant metabolite and δ-cadinene (312) is the least abundant.To understand the impact of the medium on the growth of each species, a hierarchical clustering heatmap was made Fig. , to compp < 0.05), the majority of them between PH100 and PH25. Despite that, around 83% of the metabolites have a statistical difference (p < 0.05) in only one strain (MTCC6384), and 4 metabolites are common to all conditions. Bubble plots  are observed in hydrocarbons, esters, and monoterpenes, while for PH100-PH25, the most statistically different families (p < 0.05) are esters and hydrocarbons.A set of 123 metabolites was statistically analysed to compare conditions PL100-PL25 and PH100-PH25. Table n MTCC638, and 4 mFor NL19, almost all metabolites are relatively abundant in PL25, except for 137 which is more abundant in PL100. However, regarding the abundance and when considering differences observed in relation to control, the interpretation of the results must be more careful. For example, 2-methyldodecan-2-ol (37) is more abundant in PL25 than in PL100. However, the abundance of this metabolite is lower in both conditions when compared to the control. For MTCC6384, the metabolites seem to be more abundant in PH25, except for 3-hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate (101), benzyl butyrate (119), and carveol (294) which are highly abundant in PH100.p < 0.05). Significantly different methyl ketones are observed in PL100 and PH25. Significant aldehydes are observed in PH100 and PL25. Four 1-alkanols are significantly different in PL100, PH100, and PL25. 3-Methylheptyl acetate (90), a putative acetate ester from methyl ketones, is significantly different in PL25. Ethyl ketones and 3-alkanols have higher statistically different metabolites (p < 0.05) in PH25 and PL100, respectively. Alkanes and 5-methylheptan-1-ol (31) are statistically different (p < 0.05) in PL100, PH100, and PL25. Esters isoamyl acetate (81), 2-methylbutyl acetate (82), isononyl acetate (91), and isopropyl laurate (107) may have different origins, with high amount of statistically significant metabolites (p < 0.05) in PH100 and PL25. However, alcohols 2-methylpropan-1-ol (2), 4-methylpentan-2-ol (9), and 2-methylpentan-1-ol (12), which arise from branched-chain aminoacids, have significantly different metabolites only in PH100; 2-methylpropan-1-ol (2) and 4-methylpentan-2-ol (9) appear in greater abundance in this condition. Several metabolites, such as methyl thiolacetate (255), 3-(methylthio)propanal (259), and S-methyl 3-methylbutanethioate (260), arise from the methionine pathway and significant difference is observed in PL100. Acrylate esters octyl acrylate (94) and 2-ethylhexyl acrylate (95) result from acrylate with 1-alkanols. Most significantly, different metabolites are observed in PH100. Metabolites from the metabolism of phenylalanine are also observed such as benzenemethanol (43), benzeneethanol (45), benzeneacetaldehyde (69), methyl benzoate (113), 1-phenyl acetate (116), 2-phenyl acetate (117), and 1-phenylbutan-1-one (222). Most significant differences are observed in PL100. However, significant differences are also noted in PH100 for benzenemethanol (43), benzeneethanol (45), 1-phenylethyl acetate (116), and 2-phenylethyl acetate (117). Aromatic metabolites 2-methylbenzaldehyde (70), 4-methylbenzaldehyde (71), 1,3-dimethylbenzene (160), naphthalene (169), biphenyl (173), and cyclohexanone (227) appear as statistically significant (p < 0.05) in PL100 and PH100. A large number of terpenoids have been identified resulting from the terpenoid skeleton biosynthesis pathway, probably through the non-mevalonate pathway. High metabolic activity is detected in PL100 and PH100 conditions to produce these metabolites. Metabolites 3-methylbut-3-en-1-ol (7) and 3-methylbut-2-en-1-ol (11) result from the precursors isopentenyl-PP and dimethylallyl-PP and are statistically significant (p < 0.05) in PL100 and PC25, respectively.Figure Pedobacter, a genus that has gained relevance in its application in various sectors, including in human health [To the best of our knowledge, this is the first study reporting the volatile exometabolome of members of n health . The voln health . The conn health . A compan health , 16, 17.n health . The absn health . In the n health . This sin health . Althougn health .p < 0.05) amounts when compared to the respective controls. Figure Only 123 compounds were present in statistically significant p < 0.0 amounts As mentioned above, aliphatic aminoacids are associated with fatty acid pathways. Fatty acid metabolism generates great diversity of mVOCs with chains of different sizes and structural diversity. They start from acetyl-CoA and suffer several elongation cycles with malonyl-CoA until the desired length of the fatty acid is reached with an even number of carbons . The samMetabolites resulting from phenylalanine are originated through the shikimate pathway. The metabolite benzeneethanol (45) is largely produced among bacteria and results from reduction of benzeneacetaldehyde (69) . The conS-compounds are common microbial volatiles and are mainly formed by sulphur aminoacid degradation, such as methionine. Transamination of methionine and dimethiolation of the intermediate α-keto-methylthiobutyric acid (KMBA) metabolite results in 2-oxobutanoate and methanethiol, the latter used for the formation of methyl thioesters such as methyl thiolacetate (255) and S-methyl 3-methylbutanethioate (260). 3-(Methylthio)propanal (259) arises from KMBA .Monoterpenes are the most diverse group of microbial volatile compounds and are synthesized by two possible pathways. In the case of NL19 and MTCC6384, this synthesis seems to occur via a non-mevalonate pathway, originating sesquiterpenoids through farnesyl-PP and monoterpenoids through geranyl-PP. Metabolites 3-methylbut-3-en-1-ol (7) and 3-methylbut-2-en-1-ol (11) result from the primary precursors of the terpene backbone and are indicators of high production of terpenoids . Howeverp < 0.05) are highly abundant in PL25 and PH25. In NL19, several metabolites (2-methylbutyl acetate (82), but-1-en-1-yl-4-methylpentanoate (96), heptadecane (150), 4-phenylcyclohexene (172), and 1,1′-bis-benzene (176)) are highly abundant in PL25 condition. Nonane (137), 4-methylpentan-2-one (187), levomenthol (286), and linalyl acetate (302) are increasingly abundant in PL100. 2-Methyldodecan-2-ol (37) and 2-tertiobutylthiophene (264) are less abundant in PL100 and PL25. Furthermore, in the case of MTCC6384, an increase in metabolic activity results in a decreased abundance of several metabolites such as 2-methylpentan-1-ol (12), 2-methyldodecan-2-ol (37), 2,4-Di-tert-butylphenol (47), octyl acrylate , 1,3-dimethylbenzene (160), 1-ethyl-2,3-dimethylbenzene (168), and 4-methylhept-3-en-2-one (198) when compared to control. In general, metabolites that appear increased versus control condition occur in PH25 (4-phenylcyclohexene (172), 1,1′-bis-benzene (176), and tridecan-3-one (216)) except metabolites pentanal (55) and carveol (294) whose abundance is high in PH100. Furthermore, pentanal (55) appears statistically abundant (p < 0.05) in PH100 and, at the same time, statistically less abundant (p < 0.05) in PH25. 3-Hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate (101) and benzyl butyrate (119) appear in statistically high abundance (p < 0.05) in PH100, probably because there is a significant decrease in PH25. Other metabolites such as 4-methylpent-3-en-2-one (190) and 2-tertiobutylthiophene (264) decrease only in PH25, and both PH25 and PH100, respectively, but maintain a high abundance in PH25. However, there is an increased metabolic activity observed in PH100 that leads to a decrease in the abundance of the metabolites.Several metabolites were identified whose abundance is different between media (PL100-PL25 and PH100-PH25) (Table p < 0.05) relative to PH100, and it is also related to the fatty acid metabolism pathway. This mixed behaviour does not allow conclusions to be drawn about the behaviour of MTC6384 in PH100 and PH25. However, due to the decreased abundance of many metabolites in PH100, it can be suggested that there is a high metabolic activity in this condition, in comparison to PH25, leading to the mobilization of metabolites involved in fatty acid metabolism, probably to replenish cellular energy. For NL19, metabolites with putative pathways are not sufficient to explain the reason for such different behaviour between PL100 and PL25. However, a slightly increased metabolic activity is observed in PL25 and the possible metabolic pathways identified, despite being few, suggest that there is an increase in the mobilization of metabolites probably involved in important metabolic functions such as the citric acid cycle and others, due to starvation.Of the smaller group of 24 metabolites Fig. . AccordiOverall, the results presented here suggest that a decrease in the casein peptone concentration to 25% appears to have a strong impact on microbial growth and in metabolic profiles of NL19 and MTCC6384. Apparently, the reduction of this nitrogen source leads, respectively, to an increase and decrease in the metabolic activity of cells growing in PL25 and PH25. This metabolic alteration translates into an increase or decrease of metabolites involved in the citric acid cycle, which may be involved in cellular energy production processes and other processes important for the maintenance of normal cellular functions.Pedobacter, contributing to a better understanding of how the composition of the culture medium impacts the metabolic profile and, therefore, the volatile exometabolome of two closely related species, NL19 and MTCC6384. Analysis of the volatile exometabolome revealed that the decrease in nitrogen source concentration had a strong impact on energy-producing metabolism. Clearly, NL19 is most affected by this effect, while MTCC6384 adapts best to this change. A detailed analysis of 123 metabolites  to the respective control media) showed that the metabolic activity of the cells is markedly increased in PL25. This is explained by the activation of accessory pathways responsible for energy production, probably due to the stress caused by starvation. In PH100, a slight decrease in cell biomass is observed, and as such, increased activity may be a result of excess nitrogen in growth media, which in turn may activate several different pathways involved in aminoacid metabolism. The fact that we identified many metabolites that did not match in pathways databases and the literature made the study very difficult. Thus, even more efforts are needed to improve and contribute to feeding these databases, which will certainly facilitate access to information in the literature. With this study, it was possible to understand the impact of the media on the growth and metabolism of microorganisms. Slight changes in the culture medium can lead to alterations in the primary metabolism which in turn can alter the secondary metabolism, leading to the formation of biotechnologically relevant metabolites. Finally, it is important to point out that the implemented methodology based on HS-SPME/GC × GC-ToFMS was shown to be suitable for the profiling of exometabolome from bacteria of the genus Pedobacter, which represent the crucial step in the construction of a metabolomics workflow. We are just at the tip of the iceberg, and further and more comprehensive studies are needed. Still, the information gathered allows the creation of an omics pipeline that can certainly be useful for other studies and approaches such as genomics, transcriptomics, and proteomics.To the best of our knowledge, this is the first study conducted on bacteria of the genus Supplementary file1 (DOCX 1087 KB)Supplementary file2 (XLSX 141 KB)Below is the link to the electronic supplementary material.


Skeletal muscle is closely linked to energy metabolism, but it is inevitably deprived of energy. Cellular differentiation is an essential and energy-demanding process in skeletal muscle development. Much attention has been paid to identifying beneficial factors that promote skeletal muscle satellite cell differentiation and further understanding the underlying regulatory mechanisms. As a critical metabolic substrate or regulator, α-ketoglutarate (AKG) has been recognized as a potential nutritional supplement or therapeutic target for skeletal muscle. We have previously found beneficial effects of AKG supplementation on the proliferation of C2C12 myoblasts cultured under both normal and energy-deficient conditions and have further elucidated the underlying metabolic mechanisms. However, it remains unclear what role AKG plays in myotube formation in different energy states. In the present study, we investigated the effects of AKG supplementation on the differentiation of C2C12 myoblasts cultured in normal medium (Nor myotubes) and low glucose medium (Low myotubes) and performed NMR-based metabonomic profiling to address AKG-induced metabolic changes in both Nor and Low myotubes. Significantly, AKG supplementation promoted myotube formation and induced metabolic remodeling in myotubes under normal medium and low glucose medium, including improved energy metabolism and enhanced antioxidant capacity. Specifically, AKG mainly altered amino acid metabolism and antioxidant metabolism and upregulated glycine levels and antioxidase expression. Our results are typical for the mechanistic understanding of the effects of AKG supplementation on myotube formation in the two energy states. This study may be beneficial for further exploring the applications of AKG supplementation in sports, exercise, and therapy. Skeletal muscle generates strength and power to sustain physical activity, which is closely related to its energy state. Skeletal muscle has been previously shown to contribute to basal energy metabolism and to store many important metabolic substrates, such as amino acids and carbohydrates . In skelAs an important metabolic substrate or metabolic regulator, α-ketoglutarate (AKG) has been validated by extensive research as a potential nutritional supplement or therapeutic target . He et a1H nuclear magnetic resonance (NMR) has been identified as a robust technique for metabonomic applications due to its prominent advantages  and followed by centrifugation at 12,000× g for 15 min at 4 °C. The supernatants were transferred into 5 mm NMR tubes for NMR detection.Aqueous metabolites were extracted from C2C12 myotubes for NMR analyses according to the previously described protocol . Briefly1H spectra were acquired using the pulse sequence NOESYGPPR1D [RD-G1- 90°-t-90°-τm-G2-90°-ACQ] with the following acquisition parameters: relaxation delay (RD), 4 s; short delay (t), 4 μs; mixing time (τm), 10 ms; spectral width, 20 ppm; acquisition time, 1.93 s; and 128 transients. The methyl groups of the TSP molecule were set at 0 ppm for chemical shift calibration. The two-dimensional (2D) 1H–13C heteronuclear single quantum coherence (HSQC) spectrum was recorded with a spectral width of 10 ppm in the 1H dimension and 110 ppm in the 13C dimension, a data matrix of 1024 × 256 points, and a relaxation delay of 1.5 s. The 2D total correlation spectroscopy (TOCSY) spectrum was recorded with a spectral width of 10 ppm in both 1H dimensions, a data matrix of 2048 × 256 points, and a relaxation delay of 1.5 s.All NMR measurements were performed at 298 K on a Bruker Avance III 600 MHz NMR spectrometer equipped with a BBFO cryoprobe . One-dimensional (1D) 1H-NMR spectra were adjusted by phase correction, baseline correction, and resonance alignment using MestReNova 9.0 software . The 1D 1H spectral regions of 9.5–0.6 ppm were segmented into bins with a width of 0.01 ppm for further multivariate statistical analysis. The water region of 5.2–4.7 ppm was excluded to eliminate the distortion from the residual water resonance in all 1D 1H spectra. The peak integrals of the segments were normalized by the sum of the peak integrals to compensate for possible variations in sample concentrations. The sum of the peak integrals was set to 100 for each spectrum. The normalized integrals were used to represent the relative concentrations of the assigned metabolites. Singlet or non-overlapping resonances in each NMR spectrum were selected to calculate metabolite integrals for pairwise comparison between groups.All 1D 1H-NMR spectral data of myotube extracts were performed using SIMCA-P software . Pareto scaling was applied to the normalized NMR spectral data to enhance the significance of low-abundance metabolites without increasing noise. PCA was used to illustrate metabolically grouping trends of myoblasts and show clustering for the NMR dataset. OPLS-DA was further used to confirm the grouping trends and improve metabolic separation between groups.For multivariate statistical analysis, the 1D http://www.hmdb.ca/ (accessed on 4 February 2021)). In addition, 2D 1H–13C HSQC and 1H–1H TOCSY spectra were recorded to aid in metabolite assignment.The robustness and stability of the OPLS-DA model were measured by cross-validation with a response permutation test (200 times). Parameters R2 and Q2 were calculated to indicate the interpretability and predictability of the model, respectively. The reliability of the model increased when the R2 and Q2 values approached 1. Metabolites were assigned using the Chenomx NMR Suite  and the Human Metabolome Database . Data are expressed as mean ± SD. Metabolites with a statistical significance of p < 0.05 were identified as differential metabolites.Relative concentrations of the assigned metabolites were calculated based on their integrals normalized to the sum of all integrals. Univariate data analysis was used to quantitatively compare the relative concentrations of metabolites between groups using a https://www.metaboanalyst.ca (accessed on 6 February 2021)). Significantly altered metabolic pathways were identified by a combination of metabolite set enrichment analysis (p < 0.05) and pathway topological analysis .Based on the relative concentrations of the assigned metabolites, metabolic pathway analysis was performed on the MetaboAnalyst 5.0 webserver  and the BCA protein assay  were used to calculate the ATP concentration per mg of protein.The medium was removed, and the cells were washed twice with ice-cold PBS buffer. After the addition of 1.2 mL of lysis buffer, the lysates were scraped and collected in an Eppendorf tube. The Eppendorf tubes were centrifuged for 5 min at 12,000× g, and then the supernatant was discarded. A quantity of 2.4 mL of lysis buffer was added, and the lysates were centrifuged for 10 min at 12,000× g. Cellular T-AOC was detected using the total antioxidant capacity kit . The protein concentration in each sample was analyzed by BCA protein assay  to calculate the concentration of T-AOC per mg of protein.Cells were centrifuged at 3000× g, and the supernatant was discarded. ROS levels were then detected using the H2DCFDA  probe . The myotubes were incubated with H2DCFDA (10 μM) for 30 min at 37 °C, and the fluorescence intensity of H2DCFDA was detected using the Multilabel Reader .Cells were digested with trypsin-EDTA  and transferred to Eppendorf tubes. The Eppendorf tubes were then centrifuged for 1 min at 12,000× Our results revealed the metabolic remodeling of C2C12 myotubes induced by AKG supplementation and confirmed the critical role of AKG during the differentiation process of skeletal muscle cells. In addition, we provided the potential molecular mechanisms underlying the beneficial effects of AKG supplementation in promoting myotube formation. Specifically, AKG promoted differentiation, stimulated amino acid metabolism and antioxidant metabolism, facilitated ROS clearance, and upregulated the expression of antioxidase, which robustly protected myotubes from energy deprivation. Prospectively, the knowledge gained from this study may be of benefit to the application of AKG as a therapeutic target or nutritional supplement.


The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics. Glioblastoma multiforme (GBM), a World Health Organization (WHO) grade IV central nervous system (CNS) tumor, is characterized by intra-tumoral heterogeneity and inter-individual variability, which are key determinants of disease progression and low survival rates  . The curMetabolomics has come forward as a prevailing analytical strategy that maps holistically the molecular status in a given biological sample informing about the interplay of the genome and environmental influences. Such individual profiles (metabotypes) serve as snapshots, which are extremely informative in nature as they are the net result of tumor and host biology in the presence of xenobiotics. Therefore, metabolomics datasets not only allow for hypothesis-driven data interpretation, but also the generation of hypotheses. Of note, metabotypes may complement other omics data layers (metabolomics-based multi-omics) and/or predict response to treatment via mathematical modeling (pharmacometabolomics).Metabolic reprogramming, a hallmark of cancer, is involved in tumor aggressiveness and treatment resistance allowing for tumor cells to adapt to their bioenergetic and biosynthetic demands . In GBM,Efforts to map the GBM metabolic landscape are multi-level . Key cell metabolism alterations in GBM relate to disordered lipid metabolism, dysfunctional oxidative phosphorylation, and increased Warburg effect ,15. ReceUp to now, only a limited number of studies have explored the metabolic landscape of GBM patients focusing on biofluids in line with the anticipation that the latter will pave the way towards non-invasive procedures in the clinic, despite the advantage of untargeted mass spectrometry-based metabolomics to detect as many metabolites as possible at once, identify unexpected metabolic alterations, and characterize novel metabolites in biological samples ,23,24,25Herein, we designed, employed, and optimized a strategy coupling a mixed-methods content analysis  to a metabotype approach for GBM patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. For this, GBM bioenergetic profiles were explored at the time of diagnosis and during follow-up . Next, the informative relationships through which metabolites are connected were interrogated. This holistic strategy presents a great opportunity to unveil patterns and provide new insights for GBM biology and drug repurposing.www.nlm.nih.gov/mesh, accessed on 23 January 2020) included “GBM OR glioblastoma AND plasma AND metabolomics”, “GBM OR glioblastoma AND untargeted metabolomics”, and “GBM OR glioblastoma AND metabotypes”. We questioned the interim output further for open data (yes/no), sample size , research approach, and publication impact/metrics. Studies that failed to meet inclusion criteria or studies on non-human samples were excluded. Two co-authors (V.B. and T.K.) co-analyzed the interim and final outputs, and then, the percentage of inter-rater agreement was calculated. To account for biases, Cohen’s kappa statistic and percentage agreement were also determined with multi-categorical ratings.We employed a mixed-methods content analysis, a gold standard approach for a content analysis that consists of deductive (quantitative) and inductive  phases, while contemporary definitions are considered. For data and text mining as well as data analysis, peer-reviewed literature, omics datasets, and clinical trial outcomes (as of 2022) were mined to interrogate GBM plasma metabotypes. We have also developed a novel framework to meet our analytical demands, exploring data (both context and content). Literature data from Scopus and PubMed/MEDLINE were queried. Scopus and PubMed/MEDLINE are the largest citation and abstract databases of peer-reviewed literature. To account for selection biases, private and publicly available texts have been assessed (based on the inclusion/exclusion criteria set). Keywords and MeSH terms , and Metabolights .Piperidine was found to be increased in low-risk GBM patients enabling risk stratification  and was Our findings agree with Sugimoto et al. who identified piperidine as an oral cancer-specific marker by mass spectrometry-based saliva metabolomics . Saliva Taking into account the crosstalk of epigenetic and metabolic signaling in GBM , we alsoAmong key pathway perturbations, half of the highly enriched and statistically significant pathways relate to amino acid metabolism, namely tryptophan and arginine/proline metabolism . Our finInterrogating the predictive ability of GBM plasma metabotypes alone  or upon Overall, such key pathway perturbations have been already identified as subjects of epigenetic modulators ,73. To oOur results point to the role of metabolic remodeling in GBM plasticity and disease severity via bioenergetic profiles that map patterns and shed light upon GBM biology and RT + TMZ response. GBM plasma metabotypes alone or upon miRNA data integration are of predictive value, as shown by both supervised and unsupervised analyses. An in-depth understanding of aberrant metabolism promises to provide the framework for personalized metabolic modulation, in particular when the GBM epigenome-metabolome interplay is considered. We envisage that this is how drug repurposing in translational precision medicine will be of benefit : right d


Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory.Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO In the quest to find more sustainable, suitable, and economically viable hosts for the production of omega-3 fatty acids, cyanobacteria, and microalgae have emerged as alternative organisms to native  and non-native sources  . By cont2  are based on the annotated genome sequence and describe metabolic pathways as stoichiometric coefficients and mass balances of participating metabolites . They ca2 . To date iSyf715 , iJB785   were updated, among others. In addition, a meticulous analysis of orphan reactions included in iJB792 was carried out in order to identify the genes responsible for such reactions. We manually added 20 new GPRs, which were mainly involved in photosynthesis, respiration, and photorespiration processes  associations of several reactions were updated while others were corrected in order to improve the model accuracy . For exarocesses . The resr growth . The modiMS837 when compared with previous models while analyzing potential flaws or shortcomings  terms to metabolites, reactions, and genes in the updated GEM which were not previously included in iJB792. The model scored 55% for the critical category of consistency, which represents accuracy in reaction stoichiometry, mass and charge balances, connectivity of metabolites, and reaction cycles. A major gap was found due the lack of annotation of outside references for some genes, metabolites, and reactions. This limitation only will have some impact when using automated tools or scripts; however, its accuracy and usability should not be affected. Taking together, the MEMOTE analysis demonstrated that iMS837 is a highly complete and detailed model that can be used as a reference for other GEM constructions.MEMOTE is a platform that has been developed to promote standardization of GEMs, as well as to assess quality control metrics in order to improve model reproducibility and applicability . Therefotcomings . The ove2.2.iMS837, we conducted an extensive gene essentiality analysis of the genes included in this GEM by comparing the predicted results in silico with available essentiality experimental data for S. elongatus PCC 7942  and all the reactions involved in integrating this omega-3 in the phospholipids of cellular membranes.The capability of a GEM to provide accurate predictions of experimentally supported data of a target organism’s functional states is a key feature in order to assess the accuracy and completeness of the final reconstruction. Once PCC 6803  that incPCC 6803 . A totalembranes . Among tIn silico, the production capabilities of a given strain can be shown using production envelope plots, which represent all possible production rates of a selected metabolite and their associated feasible growth rates   . We usedctively) . This flctively) . We analiMS837): (i) FabF-UP-2x, with double flux for FabF reaction than the control strain; and (ii) FabF-UP-4x, with quadruple flux for FabF reaction than the control strain  and ALA  that we denominated FabH-DOWN and FabH-UP, respectively  . This mein vivo FabF, FabH, FabD, FabZ, and FabG with the aim to improve ALA yields , the only reaction which flux should be increased is the one for the Acetyl-CoA synthetase . Therefore, we concluded that the single use of knockout strategy is not feasible to reroute carbon flux toward the production of ALA.The lack of growth coupled ALA designs under photoautotrophic conditions encouraging us to explore alternative nutritional regimens, such as photomixotrophy. It has been previously demonstrated that photomixotrophic conditions can enhance growth performance of erouting . To addrerouting , as detaerouting . Glucoseerouting . In facterouting . Subsequvia Acetyl-CoA is not connected with the production of other components of the biomass. Compounding the problem, the complete list of genes involved in fatty acid biosynthesis are essential under the three nutritional regimens analyzed, excluding the possibility to reroute carbon flux from fatty acid biosynthesis to ALA production via removing competitive pathways. Taking together, the reduced connectivity of Acetyl-CoA and the essentiality of genes surrounding fatty acid biosynthesis and ALA production explains, at great extent, the unfeasibility of designing growth coupled ALA overproducer S. elongatus PCC 7942 strains.To gain further insights on this hypothesis, we analyzed the metabolic flux of the reactions from central metabolism including Calvin–Benson–Bassham (CBB) cycle and TCA and those directly involved in ALA synthesis under photoautotrophic and photomixotrophic conditions –S5. The in vivo  reaction , as it hin vivo . Intereswth rate , we obsewth rate . This is3.S. elongatus PCC 792 with considerable improvements in model annotation and accuracy of essentiality prediction. The metabolic reaction and genome coverage of the reconstruction was expanded, the format and annotations were updated to be consistent with current best practices. Model improvements were quantified through various metrics such as accuracy of growth yield and ALA yield predictions as well as MEMOTE benchmarking. Overall, iMS837 has increased the coverage of the metabolic functionality of S. elongatus PCC 7942 and is one of the highest-quality cyanobacterial GEMs. While the updates included in iMS837 largely improved the model accuracy, there were still incorrect predictions about gene essentiality that were not able to be addressed. In S. elongatus PCC 7942, a significant proportion of genes has still an unknown function  that is comparable to the performance of the well-curated E. coli models, iJO1366     and iML1, 93.4%) .iMS837 only captures 30.2% of the protein coding genes of S. elongatus PCC 7942, this metabolic reconstruction was able to make correct computational predictions related to experimental data . The correct functional annotation of the genes encoding metabolic enzymes involved in the fatty acid biosynthetic pathway helped with prediction involving enzymes of this pathway .The 92 model  as a sta92 model . Updates package . A detai5.2.−1) predicted under the specified media conditions.Flux Balanced Analysis (FBA) uses linear programming to maximize an objective function while assuming no metabolite accumulation during cellular growth. We used FBA to evaluate the biomass production (growth prediction) once the biomass reaction was fixed as the objective function  . The res5.3.iMS837 was modified to introduce the required reactions to produce ALA. A detailed list of the reactions and metabolites added is included in iMS837_ALA  was set as the objective function. The analysis of gene essentiality was performed using the “single_gene_deletion” function of COBRApy . If the To evaluate the performance of our GEM to correctly predict gene essentiality, we employed a variety of statistical index-based methods, including: accuracy, sensitivity, specificity, precision, F1-score, and Cohen’s Kappa coefficient . All theThe accuracy measures the degree of correctness of a model with respect to both positive and negative classes. The sensitivity estimates the proportion of essential genes that have been correctly identified, whereas the specificity measures the proportion of true negatives that have been correctly predicted. The precision calculates the probability that the essential genes are correctly predicted. The F1-score represents the harmonic mean between precision and sensitivity, combining these two parameters into a single measure . Lastly,5.6.The distribution of feasible fluxes in the condition-specific models was calculated by Markov chain Monte Carlo sampling  implemen5.7.The identification of gene amplification targets was based on the strategy of flux scanning based on enforced objective flux (FSEOF) . We firs5.8.in vivo by knocking out one or more of the genes encoding the enzymes catalyzing the reaction.OptKnock  and GDLSIn addition, the recently developed gcFront algorithm was also used to identify knockouts that growth-couple synthesis . gcFront5.9.iMS837_ALA. The experimental data obtained under photomixotrophic conditions with acetate and glucose in vivo  by the Spanish Ministry of Science and Innovation (MICINN) grants RobExplode PID2019-108458RB-I00 (AEI/10.13039/501100011033) and TED2021-130689B-C33 to JN, and PID2020-117923GB-I00 to FdlC. Funding was likewise provided by CSIC’s Interdisciplinary Platform for Sustainable Plastics toward a Circular Economy+ (PTI-SusPlast+). MS-M was the recipient of a Ph.D. fellowship (BES-2012-057387) from Spanish Ministry of Economy and Competitiveness (MINECO).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.