Saccharopolyspora pogona, exhibits strong insecticidal activity than spinosyn. However, the low synthesis capacity and unknown metabolic characteristics of butenyl-spinosyn in wild-type S. pogona limit its broad application and metabolic engineering. Here, we showed that S. pogona exhibited increased glucose consumption ability and growth rate compared with S. spinosa, but the production of butenyl-spinosyn was much lower than that of spinosyn. To further elucidate the metabolic mechanism of these different phenotypes, we performed a comparative proteomic and metabolomic study on S. pogona and S. spinosa to identify the change in the abundance levels of proteins and metabolites. We found that the abundance of most proteins and metabolites associated with glucose transport, fatty acid metabolism, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine and pyrimidine metabolism, and target product biosynthesis in S. pogona was higher than that in S. spinosa. However, the overall abundance of proteins involved in butenyl-spinosyn biosynthesis was much lower than that of the high-abundance protein chaperonin GroEL, such as the enzymes related to rhamnose synthesis. We speculated that these protein and metabolite abundance changes may be directly responsible for the above phenotypic changes in S. pogona and S. spinosa, especially affecting butenyl-spinosyn biosynthesis. Further studies revealed that the over-expression of the rhamnose synthetic genes and methionine adenosyltransferase gene could effectively improve the production of butenyl-spinosyn by 2.69- and 3.03-fold, respectively, confirming the reliability of this conjecture. This work presents the first comparative proteomics and metabolomics study of S. pogona and S. spinosa, providing new insights into the novel links of phenotypic change and metabolic difference between two strains. The result will be valuable in designing strategies to promote the biosynthesis of butenyl-spinosyn by metabolic engineering.Butenyl-spinosyn, a secondary metabolite produced by  Saccharopolyspora pogona (S. pogona) and Saccharopolyspora spinosa (S. spinosa), respectively. These two metabolites share similar chemical structure and belong to a new class of highly efficient and environment-friendly antibiotic insecticides that possess broad market potential  that was responsible for the two additional carbons in the C-21 tail of the spinosyn. This suggested that the spinosyn biosynthetic gene cluster might have been derived as an in-frame deletion from butenyl-spinosyn biosynthetic gene cluster. The biosynthetic pathways of butenyl-spinosyn and spinosyn are very similar  gene has been overexpressed in S. spinosa, improving spinosyns yield by 1.28-fold compared with that of the wild-type strain  and Kyoto Encyclopedia of Genes and Gnomes (KEGG) database analysis, we focused on the changes in the abundance of proteins and metabolites associated with primary metabolic pathways and the synthetic pathway of target products. We found at least 48 differential abundant proteins that may greatly affect strain growth, glucose and phosphate utilization, and butenyl-spinosyn biosynthesis. Using these datasets, we over-expressed the rhamnose synthetic genes and methionine adenosyltransferase gene in S. pogona and greatly increased the production of the target product. The results observed in this paper not only aided in elucidating how the metabolic differences affect phenotypic changes in S. pogona and S. spinosa but also provided key target genes for promoting butenyl-spinosyn biosynthesis.In the current study, we aimed to explore the effect of metabolic differences on strain growth, glucose consumption, phosphate utilization, and antibiotic biosynthesis by comparing the phylogenetically coherent and functionally similar strains Escherichia coli strains were grown at 37°C in Luria-Bertani broth . The bacteria were cultured at 30°C for 48 h, and a 2.5 mL aliquot of the seed culture was transferred into a 300 mL baffled flask containing 50 mL of synthetic fermentation medium  and incubated at 30°C  was analyzed to determine its cell concentration during fermentation. The cells were collected every 12 h for strain growth curve measurement by UV scanning. Supernatants were collected every 12 h during fermentation to determine the glucose concentration by using glucose assay kit  until the glucose was used up. Supernatants were collected every 48 h during fermentation to determine the phosphate concentration by phosphomolybdenum blue spectrophotometry  after 4 days of culture, washed four times by resuspending the cell pellet in 20 mL of fresh PBS , and quickly frozen in liquid nitrogen. Protein extraction was performed as described previously . The lyophilized SCX fractions were re-dissolved in 2% acetonitrile and 0.1% formic acid and then loaded on a ChromXP C18  nanoLC trap column. Online trapping and the desalting were performed at 2 μL/min for 10 min with 100% solvent A. The solvents were composed of water/acetonitrile/formic acid . An elution gradient of 5–38% acetonitrile (0.1% formic acid) in 70 min gradient was then employed on an analytical column . LC-MS/MS analysis was performed using a TripleTOF 5600 System  fitted with a Nanospray III source. Data were acquired at an ion spray voltage of 2.4 kV, a curtain gas of 30 PSI, a nebulizer gas of 5 PSI, and an interface heater temperature of 150°C. MS was operated with TOF-MS scans. For information dependent acquisition (IDA), the survey scans were acquired in 250 ms, and up to 30 product ion scans (80 ms) were collected if the threshold of 260 counts was exceeded and with a + 2 to + 5 charge state. Rolling collision energy setting was applied to all precursor ions for collision-induced dissociation. Dynamic exclusion was set to 16 s.2D chromatography was conducted on an Eksigent nanoLC-UltraSaccharopolyspora strains. False discovery rate (FDR) was estimated via a reverse database strategy, and only proteins below the threshold of 1% of FDR were further considered. The differential abundant proteins (DAPs) were defined in the iTraq experiment on the basis of the following criteria: unique peptides ≥ 1, P-value < 0.05, and fold change > 1.33 or < 0.75. Only spots present in all the replicate gels were considered for subsequent analysis. The DAPs were annotated using the GO database1, and proteins were classified based on functional annotations using the GO terms for cellular components, biological processes, and molecular functions. The metabolic pathway analysis of DAPs was conducted using the KEGG database2.MS/MS data were analyzed for protein identification and quantification using the ProteinPilot software v.4.5 . The utilized protein database was the protein sequence set of all For metabolomic analysis, samples were collected at 4 days and from three biological replicates. The sample treatment methods used were as described by Wentzel et al. with some modifications . 10 mL oMetabolic profiling of samples was performed on an Agilent 1290 Infinity LC system  coupled with an AB SCIEX Triple TOF 5600 System . Chromatographic separation was conducted on ACQUITY HSS T3 1.8 μm (2.1 × 100 mm) columns for both positive and negative models. The column temperature was set at 25°C for operation. The mobile phases of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) were used in positive ionization mode, while 0.5 mM ammonium fluoride in water (C) and acetonitrile (D) were used in negative ionization mode. In the positive (negative) model, the elution gradient initially started with 1% B (D) for 1 min, linearly increased to 100% B (D) at 8 min, was maintained for 2 min, and then returned to 1% B (D) for approximately 2 min of equilibrium. The delivery flow rate was 300 μL/min, and a 2 μL aliquot of each sample was injected onto the column. TOF/MS was performed on both ionization modes. Electrospray ionization source conditions on triple TOF were set as follows: ion source gas 1, 40 psi; ion source gas 2, 60 psi; curtain gas, 30 psi; source temperature, 650°C; ion spray voltage floating, 5000 V(+) and −4500 V (−). Information-dependent acquisition, an artificial intelligence-based product ion scan mode, was used to detect and identify MS/MS spectra. The parameters were set as follows: declustering potential, 60 V (+) and −60 V (−); collision energy, 50 V (+) and −20 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10. The analysis process was conducted with the assistance of Applied Protein Technology Co., Ltd. .3. The data were subsequently processed using XCMS software for peak alignment and data filtering. For peak picking, the following parameters were used:centWave m/z = 25 ppm, peak width = c, prefilter = c. For peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used. Compound identification of metabolites was performed by comparing of accuracy m/z value (<25 ppm), and MS/MS spectra with an in-house database established with available authentic standards. After normalized to total peak intensity, the processed data were uploaded into SIMCA-P  for statistical analysis. Partial-least squares discrimination analysis (PLS-DA) was conducted as a supervised method to identify the important variables with discriminative power. PLS-DA models were validated based on the multiple correlation coefficient (R2) and cross-validated R2 (Q2) in cross-validation and permutation tests by applying 200 iterations (P > 0.001). The significance of the biomarkers was evaluated by calculating the variable importance in projection (VIP) score (>1) from the OPLS-DA model. For the univariate analysis, specific biomarkers were compared between S. pogona and S. spinosa by employing Student’s T-test. A p-value less than 0.05 indicated a significantly different metabolite.The raw data generated by UPLC-Q-TOF/MS were converted into mzML format files using the Proteo Wizard MS converter tool and then subjected to data processing using XCMS online softwaregdh + kre, gtt, and epi were amplified from the genomic DNA of S. spinosa by using primer pairs P1/P2, P3/P4, and P5/P6  in accordance with manufacturer’s instructions. qRT-PCR amplification was performed using Power SYBRR Green PCR Master Mix (Applied Biosystems), as previously described (For RNA isolation for quantitative reverse transcription-PCR (qRT-PCR), all strains were cultivated in SFM at 30°C. Four-day cultures (1 mL) were collected, and the cells were harvested via centrifugation at 8,000 g for 5 min at 4°C. Total RNAs were separately isolated using TRIzol Reagent (Invitrogen). RNA concentration and purity were determined by measuring the ratio of ODescribed . The prit-test, with ∗p < 0.05, ∗∗p < 0.01, ns, no significant.All data in this study were stated as means ± Standard deviation (SD), and analysis by Student’s S. pogona and S. spinosa and then examined through HPLC and LC-MS/MS in accordance with the method described above , followed by rapid entry into the decline phase was shown by S. pogona. For S. spinosa, its growth rate was slower than that of S. pogona early in growth. However, its bacterial density decreased slowly during the recession, which the final bacterial density of S. spinosa was 1.65-fold that of S. pogona  to analyze the key factors affecting the phenotypic changes. Proteins isolated from three independent samples within the same phase were mixed and fragmented using trypsin. The resulting mass spectrum data were searched against the Saccharopolyspora proteome database. A total of 5,207 proteins were identified, 2,894 of which were identified in all of the samples, which were common proteins identified by S. pogona and S. spinosa, representing approximately 29.80 and 32.15% of the predicted proteome for S. pogona and S. spinosa, respectively  (To further understand the causes of the phenotypic differences between roteome) , which wS. pogona and S. spinosa from the biological system level, we estimated the abundance value of all proteins identified in this study, and the protein abundance scales are listed in Dataset S1. Some similarities and differences existed in the most highly expressed proteins between the two strains  can catalyze L-methionine conversion into S-adenosyl-L-methionine, which is an important source for butenyl-spinosyn and spinosyn methylation (S. spinosa was remarkably higher than that of S. pogona (2.41-fold), which may ultimately affect the biosynthesis of target products. These high-abundance proteins play a role in key metabolic pathways, such as glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and RNA degradation, as well as basic cell function and maintenance.To evaluate the possible function of these proteins, we carried out GO and KEGG functional annotation analysis . The ana strains . The mosh strain . These phylation . Howeverp < 0.05) for further investigation. Abundances of all the metabolites are shown in S. pogona was mostly higher than that of the S. spinosa, such as phosphorylcholine (4.57), 1-oleoyl-sn-glycero-3-phosphocholine (6.77), glycerophosphocholine (3.42), phosphatidylethanolamine (1.83), etc. (Data Set S3). These detection results effectively reflected the difference in metabolic pathways between the S. pogona and S. spinosa.In order to analyze whether the changes of metabolite abundance related to strain growth development and target products biosynthesis were consistent with the changes of proteins, detection of intracellular metabolome was carried out by UHPLCQ-TOF/MS . In thisptsI), phosphocarrier protein Hpr (ptsH), PTS glucose transporter IIA/IIBC (PTS IIA/IIBC), multiple sugar transport system ATP-binding protein (msmX), and glucokinase (ppgK) were identified in S. pogona and S. spinosa during glucose fermentation. The abundance of HPr and multiple sugar transport system ATP-binding protein in S. pogona was higher than that of S. spinosa, indicating that both strains have developed ways of glucose transport by PTS and the ABC transport system. Furthermore, the ability of S. pogona to transport and phosphorylate glucose was stronger than that of S. spinosa, which was kept in line with the rapid glucose consumption rate and the improvement of cell growth. In addition, the expression abundance of the proteins related to the transport of fructose, mannose and other sugars in S. pogona was also higher than that of S. spinosa, indicating that the potential ability of S. pogona to transport other sugars was probably stronger than that of S. spinosa (Data Set S1).Central carbon metabolism is one of the most basic cellular pathways that occur in all living organisms, and metabolite interconversion at the phosphoenolpyruvate–pyruvate–oxaloacetate node involves a set of reactions that connect the major pathways of carbon metabolism . These rS. pogona and S. spinosa could catabolize glucose into acetyl-CoA via the complete glycolysis and PP pathways, and all of the proteins involved in these two pathways could be detected. Throughout the glycolysis process, the abundance of 6-phosphofructokinase (pfkA) in S. pogona was lower than that of S. spinosa; this enzyme is involved in the conversion of fructose-6-phosphate to fructose-1,6-bisphosphate and is a rate-limiting enzyme for glycolysis (glpX) was higher than that of S. spinosa; this enzyme is involved in the conversion of fructose-1,6-bisphosphate to fructose-6-phosphate and reduce substrate metabolism flow to glycolysis  and enolase (eno)  and pyruvate carboxylase (pyc), was higher than that of S. spinosa, indicating that S. pogona has a strong ability to metabolize pyruvate. The PP pathway is the second important pathway for glucose metabolism, and its initial substrate is glucose 6-phosphate. In fast-growing cells, the PP pathway plays crucial roles in anabolic reactions, converting glucose 6-phosphate into glyceraldehyde 3-phosphate and ribose 5-phosphate or producing NADPH for the synthesis of acetyl-CoA, nucleotides, and other metabolites  and 6-phosphogluconate dehydrogenase (zwf3) are the rate-limiting enzymes of this stage (tktA2) and transaldolase  play important roles in the above reactions  and 2-dehydro-3-deoxy-D-gluconate aldolase (kdgA) in the proteomic data of S. pogona and S. spinosa, which were characteristic enzymes of the ED pathway that takes only four steps to convert glucose to pyruvate  pathway, Entner-Doudoroff (ED) pathway, and pentose phosphate (PP) pathway . The glyycolysis . The abuycolysis . Meanwhise (eno) , were alabolites . The patis stage . Howevereactions . Surprispyruvate . MoreoverbsK) was also identified in our proteomic data. Ribokinase is one of the principal enzymes in carbohydrate metabolism, catalyzing D-ribose and 2-deoxy-D-ribose to produce ribose-5-phosphate and 2-deoxy-D-ribose-5-phosphate, respectively, eventually generating glyceraldehyde-3-phosphate into glycolysis; this pathway may be used as an alternative route in central carbon metabolism  in S. pogona was higher than that of S. spinosa; this enzyme is involved in the conversion of glucose-6-phosphate to glucose-1-phosphate, and its catalyzed product, glucose 1-phosphate, also has a high abundance in S. pogona. gtt), dTDP-glucose-4,6-dehydratase (gdh), dTDP-4-keto-6-deoxy-D-glucose epimerase (epi) and dTDP-4-dehydrorhamnose reductase (kre), and can also synthesize UDP-D-glucuronate successively with the participation of UTP-glucose-1-phosphate uridylyltransferase  and UDP-N-acetyl-D-glucosamine dehydrogenase (rkpK), in which dTDP-L-rhamnose is an important precursor of target products and cell wall synthesis, while UDP-D-glucuronate is an important substrate for amino sugar and nucleotide sugar metabolism. In the above biological processes, the expression abundance of dTDP-glucose-4,6-dehydratase, dTDP-4-keto-6-deoxy-D-glucose epimerase and UDP-N-acetyl-D-glucosamine dehydrogenase in S. pogona was higher than that in S. spinosa. In addition, UDP-glucose and UDP-D-glucuronate also have a high detection abundance in S. pogona, indicating that S. pogona has a strong ability to use glucose for anabolism during the analysis period. We also found that enzymes and intermediate metabolites associated with the metabolism of mannose, galactose, and glycerol 3-phosphate had high abundance in S. pogona, such as mannose-6-phosphate isomerase (manA), UDP-glucose 4-epimerase , dihydroxyacetone kinase (dak2), D-mannose 6-phosphate, galactose 1-phosphate, UDP-galactose and glycerol 3-phosphate, which effectively reflected the difference on metabolic capacity between the two strains.In addition to catabolism through glycolysis and PP pathway, glucose-6-phosphate can also be converted into glucose-1-phosphate for anabolism, including polyketide sugar unit biosynthesis, N-glycan biosynthesis and peptidoglycan biosynthesis . In thisfadD), acyl-CoA dehydrogenase (acdA-3), enoyl-CoA hydratase (fadB), and acetyl-CoA acyltransferase  in S. pogona were higher than that in S. spinosa. Meanwhile, hexadecanoyl-CoA, tetradecanoyl-CoA and decanoyl-CoA had a higher abundance in S. pogona, which was consistent with the abundance variation of acyl-CoA synthetase and acetyl-CoA acyltransferase in S. pogona. Since acyl-CoA synthetase is the core enzyme for fatty acid degradation metabolism, this result suggests the strong ability of S. pogona to metabolize exogenous fatty acids by β-oxidation. The acetyl-CoA produced from fatty acid β-oxidation and pyruvate metabolism will enter the TCA cycle to produce energy and several metabolic precursors for cell growth and the synthesis of other metabolites. Citrate synthase (gltA), isocitrate dehydrogenase (icd-2), and 2-oxoglutarate dehydrogenase (sucB) are the rate-limiting enzymes of TCA cycle and were detected in our proteomic data. The abundance of these three rate-limiting enzymes in S. pogona was higher than that in S. spinosa. The same finding was observed for other enzymes involved in the TCA cycle, such as aconitate hydratase (acn), succinyl-CoA synthetase (sucC), and succinate dehydrogenase . In addition, we also found that 2-oxoglutarate has a high abundance in S. pogona. This result suggests the strong ability of S. pogona to produce energy and metabolic precursors by using acetyl-CoA, although the concentration of fumarate hydratase (fumB) and its catalytic product malate is low. S. pogona showed stronger metabolic capacity than S. spinosa in fatty acid metabolism and TCA cycle because it rapidly absorbs and utilizes nutrients in the environment early in growth. It suggests that the metabolic flux flowing through the TCA cycle in S. pogona is increased compared with the S. spinosa, which may cause the acetyl-CoA produced by the glycolysis, fatty acid degradation and other metabolic pathways to flow more into the TCA cycle, and ultimately limits the biosynthesis of butenyl-spinosyn. These results can be used to explain why S. pogona exhibited a rapid growth rate but low butenyl-spinosyn production.Fatty acid degradation metabolism and TCA cycle are important metabolic pathways for the synthesis of acetyl-CoA and other intermediate precursors, respectively . BacteriS. pogona overall was stronger than that of S. spinosa, indicating that S. pogona demonstrated a stronger synthetic ability to convert amino acids into acetyl-CoA and other key precursors compared with S. spinosa. For example, the abundance of enzymes  involved in the leucine to acetyl-CoA, valine to (R)-methylmalonyl-CoA, and isoleucine to (S)-methylmalonyl-CoA pathways in S. pogona was remarkably higher than that in S. spinosa  and methionine adenosyltransferase (metK), two key enzymes involved in the S-adenosine methionine synthesis , succinate dehydrogenase (sdhA1/B/B1/C), polyphosphate kinase (ppk), inorganic pyrophosphatase (ppa), and ATP synthase (atpA/C/H), was higher than that in S. spinosa and showed an increased energy production capacity. NAD + /NADH ratio and total NAD(H) play important roles for whole-cell biochemical redox transformations. As shown in the previously described central carbon metabolism process, four reactions that generate reducing power (NADH and FADH2) occur in the TCA cycle: isocitrate to 2-oxo-glutarate (NADH), 2-oxo-glutarate to succinyl-CoA (NADH), succinate to fumarate (FADH2), and malate to oxaloacetate (NADH)  involved in the conversion of isocitrate to 2-oxo-glutarate, 2-oxo-glutarate to succinyl-CoA, and succinate to fumarate in S. pogona was remarkably higher than that in S. spinosa, implying the strong ability of S. pogona to produce reducing power. We hypothesized that the increased NADH level induces the elevated expression of oxidative phosphorylation-related enzymes in S. pogona, which was consistent with the quantitative proteomic analysis.Most of the intermediate precursors and reducing power generated by central carbon and amino acid metabolism follow the oxidative phosphorylation pathway for ATP synthesis . We detee (NADH) . The abufdhA), formate dehydrogenase (fdhF), and sulfate adenylyltransferase (cysD), which are involved in methane metabolism and sulfur metabolism, were identified, indicating that in addition to using organic carbon to generate energy, utilizing inorganic carbon (methane) and inorganic sulfur to produce energy in S. pogona and S. spinosa is highly possible.Unexpectedly, methane and sulfur metabolism are the other energy metabolism mechanisms found in our proteomic data based on KEGG pathway analysis  Hakobya. The enzde novo synthesis (primary) and the other is remediation synthesis (S. pogona. IMP dehydrogenase (guaB3) catalyzes the redox reaction between inosine monophosphate (IMP) and xanthosine 5′-phosphate (XMP), carbamoyl-phosphate synthase (carA/carB) catalyzes the synthesis of carbamoyl-phosphate, which belongs to the rate-limiting enzyme of purine and pyrimidine nucleotide metabolism respectively , ribokinase and transketolase, had a higher expression abundance in S. pogona, making its ribose 5-phosphate abundance much higher than that of S. spinosa, so as to meet the needs of its vigorous nucleotide metabolism.Nucleotide metabolism plays an important role in the synthesis of genetic materials, energy supply, metabolic regulation, as a messenger molecule and the formation of coenzymes, including purine nucleotide metabolism and pyrimidine nucleotide metabolism . The synynthesis . Figure ectively . The twobusJ, L), rhamnose attachment and methylation , forosamine biosynthesis , and rhamnose biosynthesis   . Howevergtt, gdh, epi, and kre) and metK involved in the biosynthesis of rhamnose and S-adenosyl methionine was low compared to other high-abundance proteins (metK in S. pogona, respectively, and then evaluated the resulting mutants SPOG-RM and SPOG-ME for discernable phenotypes (metK in SPOG-ME. The qRT-PCR results showed that the transcript levels of gdh, kre, epi, and gtt in SPOG-RM were 14. 0-, 11. 9-, 26. 8-, and 54.1-fold higher than those in S. pogona, respectively, and the transcript level of metK in SPOG-ME was 7.3-fold higher than that in S. pogona (Rhamnose and S-adenosyl methionine are the precursors of butenyl-spinosyn biosynthesis. Meanwhile, the former is also participated in cell wall synthesis, and the latter is a direct donor of methyl groups. However, the proteomic results in the present study showed that the expression abundance of rhamnose synthetic genes (proteins . Therefoenotypes –8. The benotypes . We also. pogona . The finS. pogona and S. spinosa. The results may contribute to our understanding of the biosynthetic mechanism of butenyl-spinosyn and spinosyn. Strain OD600 value, glucose consumption, phosphate utilization, and target product yield analysis indicated that S. pogona exhibited faster growth rate and glucose and phosphate utilization rate but lower target product biosynthetic ability compared with S. spinosa. Comparative proteomic and metabolomic analysis revealed the difference on central carbon metabolism, amino acid metabolism, energy metabolism, purine and pyrimidine metabolism, and the biosynthetic gene cluster expression of the target products between S. pogona and S. spinosa. These results suggest that S. pogona demonstrates high-efficiency glucose transport ability via the PTS and ABC transporter system, allowing its faster glucose utilization rate compared with that of S. spinosa. S. pogona showed strong ability for fatty acid metabolism, TCA cycle, amino acid metabolism, energy metabolism, purine and pyrimidine metabolism, allowing its vigorous basic metabolism to promote its rapid growth. However, this metabolic feature also led to insufficient precursors and energy supply of S. pogona during the butenyl-spinosyn biosynthesis, which not only caused its premature entry to the stationary and decline growth periods but also reduced its butenyl-spinosyn biosynthetic ability. The ability of S. pogona to participate in the synthesis of precursors, such as rhamnose and S-adenosyl methionine, was weaker than that of S. spinosa, thus affecting the precursor supply of butenyl-spinosyn biosynthesis. Proteomic data also showed that the overall expression level of the butenyl-spinosyn synthetic gene cluster was very low, which may greatly affect biosynthetic ability. The over-expression experiment of rhamnose synthetic genes and metK confirmed the reliability of this conjecture. We propose that the metabolic pathways associated with the supplies of rhamnose and S-adenosyl methionine, which were selected based on the results of quantitative proteomic and metabolomic data, require further research on their regulatory mechanism on butenyl-spinosyn biosynthesis. Studies on the genetic modification of their corresponding genes are useful to understand the key nodes and regulatory networks of primary metabolism and butenyl-spinosyn and spinosyn biosynthesis.This study was the first to conduct a comparative analysis of All datasets generated for this study are included in the article/S. pogona and S. spinosa iTraq-based quantitative proteomic sample preparation and bioinformatics analysis. JR, HH, ZL, and ZX conducted mutants construction. JR, XD, HH, and ZY employed butenyl-spinosyn and spinosyn HPLC and MS analysis. HH, SH, TK, YH, and WH performed mutant strain physiological and biochemical assay. JR and LX wrote the manuscript. All authors discussed the results and approved the final manuscript.LX conceived the project. LX and JR generated the concepts and designed the research. JR, HH, SY, JT, and SH performed 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.


The human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health.Video abstractThe online version contains supplementary material available at 10.1186/s40168-020-00955-1. The gut microbiota is the community of microorganisms residing in the gut and include commensal, symbiotic, and pathogenic bacteria. Under normal circumstances, the gut microbiota and its host are in symbiosis . DisruptMetabolic networks can be used to predict the effect of alterations in the metabolic network in silico. A constraint-based reconstruction and analysis (COBRA) approach is often used to simulate the operation of a metabolic network under different external nutrient conditions. A COBRA method suitable for investigating the metabolism of the microbiota is flux balance analysis (FBA) . A flux (S) represents a stoichiometric matrix, and (v) represents the flux distribution. The formula describing a biomass function contains all the metabolites in the system that are required to build a new cell. In other words, a biomass function simulates cell growth. Importantly, since Eq. FBA works at steady-state, i.e., the amount of the metabolite produced is equal to the amount of the metabolite consumed. Accordingly, the set of linear equations is formulated as:mentclasspt{minima(Z) is the solution of the OF, (C) is the vector of weights, indicating how much each reaction contributes to the OF and (T) represents the matrix transpose. For example, when a single flux is maximized or minimized, C is a vector of 0s with a single 1 [Similarly, the OF is formulated as follows:single 1 , 15. Howsingle 1 , 17.Bifidobacterium adolescentis shows a reduction in the production of formate, ethanol and acetate as well as a reduction in biomass formation if lactate production is manually increased. The OF in this example is maximizing biomass production , also known as genomic-scale metabolic reconstructions (GENREs), consist of all the metabolic capacities of a bacterium , and areordingly . The AGOordingly . For exaern diet  to the dern diet , 48–50.Citrobacter sedlakii, a non-pathogenic bacterium found in human stool. The authors investigated the metabolic output of C. sedlakii by taking samples at different growth stages and analyzing the extracts using two liquid chromatography mass spectrometry (LCMS) approaches, reverse phase (RP) and hydrophilic interaction liquid chromatography (HILIC). The obtained data was compared to a predicted list of metabolites generated via FBA using a tool called MS_FBA. The comparison showed that the metabolic output of the GEM of C. sedlakii did not cover all the metabolites measured with LCMS, highlighting the need to determine the accuracy of bacterial GEMs and more precise genome annotation [Nevertheless, community efforts are being taken to standardize GEMs and assure the quality of the models . To optinotation . Lastly,notation . To answnotation  using thnotation . In addinotation . CollectTo understand the effect of the microbiota on the host, microbial metabolic networks can be extrapolated to include metabolic networks of the host. This enables predicting the effects of gut microbiota on the host and suggesting possible interventions to promote host health. Diener et al. constructed microbial metabolic networks from the metagenome data of a cohort of Swedish people with and without diabetes mellitus and used FBA to analyze SCFA production. In this study, 16S abundance data was integrated with metabolic fluxes to construct personalized predictions of metabolic output and interventions such as dietary changes and medical treatment. The authors used GEMs from the AGORA database, however the AGORA database did not cover all the species found in the Swedish cohort. Instead, the authors performed FBA on the genus and species level, whereby AGORA models were pooled together into higher phylogenetic ranks. This resulted in networks containing between 12 and 30 GEMs at the genus level and 23 and 81 GEMs at the species level. The GEMs were placed in a shared metabolic environment for each sample in the cohort representing the gut lumen. The relative biomass for each GEMs was estimated from the relative read distribution in each sequenced sample. The authors found a minimal overlap of resource utilization between microbes in different niches, suggesting an upper bound on alpha diversity in the gut. Next to ecological insights, the authors concluded from their model that SCFA production is highly specific per individual. Nonetheless production of butyrate and propionate was reduced in diabetic subjects compared to healthy controls and the overall SCFA production profile could be restored upon metformin treatment . The autLactobacillus plantarum with metabolic reactions commonly found in the gut such as butyrate and propionate fermentation. Next, the authors placed multiple expanded GEMs of L. plantarum randomly in a tube-like grid environment and used the SOA to simulate the network in 80,000 time steps. For every cell in the grid the OF is maximizing the rate of ATP production, which is an alternative to biomass formation. Periodically, glucose was added to the system at the proximal side of the tube and concentrations of metabolites in each compartment were shifted to the right until they leave the system at the distal side of the tube. Together, this represents metabolic gradients that occur in the gut. The authors then included growth, removal, movement and evolution of single organisms in the network and showed that diversification can be an emergent property of cross feeding among microbial communities. When diarrhea was simulated by increasing the flux speed through the system, the microbial diversity was destroyed [Currently, several tools are available for modeling bacterial communities, such as OptCom, BacArena, MiMoSa, COMETS, FLYCOP and MICOM , 61–65. estroyed , which iestroyed . Chan etestroyed . These eestroyed . HoweverAnother problem in combining bacterial GEMs and host GEMs is formulating an appropriate OF. In the majority of metabolic networks, the total biomass growth, or metabolic output is optimized. In the gut, the host benefits the most from a balance between the metabolic output of all bacteria, which requires a balance among the distribution of the different microbial species. To address this problem OptCom was developed. This approach uses two layers of OFs. The first layer maximizes the biomass formation of each individual species. The second layer maximizes the growth of the whole community, resulting in a more realistic growth distribution in a bacterial community . The extAkkermansia muciniphila, and Bilophila wadsworthia in PD patients, and identified a new research target in PD research with the use of metabolic networks [. confirmed these results by using the same approach as Baldini et al. [B. wadsworthia and A. muciniphila [, which will improve the process of choosing the best treatment, when prescribing medication.While the composition of the gut microbiota fluctuates over time due to, among others, the diet, the overall composition is more or less stable  and is anetworks . Hertel i et al.  to consti et al. . Simulatiniphila . Both spiniphila . The aboiniphila . Membersiniphila , 93. On iniphila , causes iniphila . The aboClostridium difficle infection [Currently, dysbiosis can be treated in several ways such as a fecal matter transplantation (FMT), antibiotic use and using pro-, pre-, or psychobiotics. FMT has been used successfully in recurrent nfection , but notnfection . Interesnfection . This suBifidobacterium species are widely used as probiotics [Bifidobacteria in the AGORA database are not condition-specific and do not give the metabolic behavior in detail. For example the GEMs of Bifidobacteria in the AGORA database do not show growth on starch, whereas most Bifidobacteria can metabolize starch [Bifidobacteria including probiotic strains used in commercialized products. The authors compared the metabolic capabilities of the GEMs under 30 different environmental conditions, with maximizing the biomass function as the OF. Based on metabolic capabilities in the different environments the Bifidobacteria could be divided into three groups. Based on the metabolic capabilities of the GEMs the authors hypothesized that the protective effect of the probiotic candidate Bifidobacterium thermophilum RBL67 on Salmonella and Listeria species comes from the production of SCFAs. Furthermore the authors hypothesize that Bifidobacterium gallicum DSM20093 and Bifidobacterium kashiwanohense DSM21854 can help relieve constipation via production of acetate [Another possible treatment for dysbiosis is the use of probiotics. Probiotics are defined as live microorganisms that, when administered in adequate amounts, confer a health benefit on the host . Numerouobiotics . As mente starch . Therefo acetate , showingMetabolic networks and FBA are promising tools to elucidate the precise interactions among bacteria, between bacteria and the host, and can be used to test the effectiveness of probiotic treatment before clinical trials Furthermore, metabolic networks can help point at knowledge gaps. For example, if there is a discrepancy between the outcome of model simulations, and experimental work, a more thorough understanding of the metabolic capabilities of the organism of interest is needed, which leads to new hypotheses and more experiments. Next, metabolic networks can elucidate causality in the microbiota research, because unlike in experimental work, numerous alterations can be implemented in metabolic networks . CurrentInteractions within the complex microbiota community and between the microbiota and the host are challenging to elucidate but understanding these interactions is vital for successful intervention in the microbiota. This review shows that the use of computational methods based on flux balance analysis provides novel understanding of the microbial interactions, helps formulating new testable hypotheses, and enables the investigation of the effectiveness of probiotic administration in a personalized manner.


Here, we use a constraints-based integrated approach to delineate the interplay between metabolism and epigenetics, hardwired in the genome, to shape temozolomide (TMZ) resistance. Differential metabolism was identified in response to TMZ at varying concentrations in both the resistant neurospheroidal (NSP) and the susceptible (U87MG) glioblastoma cell-lines. The genetic basis of this metabolic adaptation was characterized by whole exome sequencing that identified mutations in signaling pathway regulators of growth and energy metabolism. Remarkably, our integrated approach identified rewiring in glycolysis, TCA cycle, malate aspartate shunt, and oxidative phosphorylation pathways. The differential killing of TMZ resistant NSP by Rotenone at low concentrations with an IC Genome scale measurements allow cataloging drug and therapeutic candidates7. However, their integration with constraints-based metabolic modeling presents a paradigm shift in understanding homeostasis, disease, and treatment strategies8. Cancer cells are punctuated by their characteristic ability to proliferate and for unchecked proliferation in part due to mutation and in part due to disruption of homeostatic control coordinated by metabolic networks14 and unchecked signaling pathways16. In this work, we showcase unique therapeutic windows that exploit metabolic pathway vulnerabilities during cell growth to tackle chemotherapeutic resistance.The grand challenge of designing new therapies is the cellular complexity of a multi-hit, multifactorial disease like cancer and the rapid emergence of chemo-resistance19. Temozolomide (TMZ), an alkylating agent is the most effective chemotherapeutic agent against GBM. However, TMZ resistance is increasing creating an urgent need to identify new therapeutic strategies23. Our previous study24 differentially analyzed growth limiting metabolites and nutrient preferences for respiration in TMZ resistant neurospheroidal cells (NSP) isolated from an authenticated cell-line population of U87MG cells. In this study, we have identified the Complex I inhibitor, rotenone as an alternate drug to induce killing of the drug resistant NSP. Our work highlights the use of constraints-based modeling integrated with exome, limited transcriptome and metabolome data to identify vulnerabilities of a chemo-resistant glioblastoma cell to identify differential response of an Electron Transfer Chain (ETC) inhibitor rotenone. We pioneer a scalable systems biology workflow from isolation of resistant heterogenous populations to molecular profiling/measurements integrated with metabolic modeling to rationally identify reprogramed pathways resulting in identifying potential alternate drugs.Glioblastoma Multiforme (GBM), is an aggressive brain cancer with inherent heterogeneity14 and efforts towards anabolic macromolecule biosynthesis. The drug dose dependent 3-fold higher lactate-to-pyruvate ratios in U87MG also indicated differential cytoplasmic NADH/ NAD+ ratios and altered redox state  ratios were observed with increasing TMZ concentration and thus was a potential index of higher methylation and drug action resulting in lowered cell growth Fig. , and 6. els Fig.  were druels Fig. . Higher els Fig. . Figure ons Fig.  to identTM phenotype microarray testing also identified differential coupling between growth and respiration on 57% of the C/N sources tested including glutamine in NSP24  and Complex III CYC1). ATPase (ATP4A) harbored unique mutations Fig.  in NSP a. ATPase 28. Such a disruption could result in an increase in AMP to ATP ratio, thus activating AMPK. Mutations in Adenylate kinase ADK could modify the ratios of ATP, AMP, and ADP resulting in impact of p53 and AMPK signaling. A differential upregulation or expression of genes from these pathways24 is an indication of the known crosstalk between AMPK and the cellular survival axis of PI3K/Akt/mTOR pathwayThere is a mutation in Cytochrome C-1 (Cyc1 gene) one of the 11 genes in the mitochondrial respiratory chain (MRC) complex III (Supplementary File OAZ1 the controller of the enzyme Ornithine Decarboxylase (ODC1) regulating polyamine synthesis and associated cell growth was mutated in U87MG alone  . The model was validated by estimating uptake rates of primary nutrients with more than 90% accuracy , Nitrogen metabolism , and energy metabolism (oxidative phosphorylation and ETC)  Fig. . The int50 values were identified as lower by an order of magnitude calculates as 5 nM for NSP as compared to 1.8 mM for U87MG , towards a behavior and metabolism closer to the normal cell function indicated by increased oxidative phosphorylation and the balance between glycolysis and mitochondrial oxidative phosphorylation is essential. Such integrated systems level approaches, are essential to unraveling the tacit connections between epigenetics, metabolism and genotyping and are scalable to the clinic to fill a critical need for predictive models in individualized therapy. Regardless of the requirement of rigorous characterization in animal models and clinically derived cell lines to extend the promise of this study, we foresee the capability of these approaches to expedite choices for personalized medicine.TM, ThermoFisher Scientific) and 1% non-essential amino acids (Sigma-Aldrich) was used additionally for growth. Cell lines were maintained at 37 °C in a humidified atmosphere of 5% CO2/95% air. For the separation of NSP, the sub-population sorting assay was performed for Fluorescence-activated cell sorting (FACS) with cells at 70–80% confluency using BD FACSAria III (BD biosciences Pvt. Ltd) and separated the populations using Hoechst 33342 staining procedure24. The detailed protocol is available in our previous study24. After separation using FACS, NSPs were initially maintained in neurobasal medium  supplemented with B27 supplement , 0.2 μg per mL of epidermal growth factor, EGF (ThermoFisher Scientific) and 0.2 μg per mL of basic fibroblast growth factor, bFGF (ThermoFisher Scientific). Further sub-culturing and passaging of NSP was carried out using a similar medium as U87MG to avoid any contribution from different micro-environments in delineating heterogeneity of molecular signatures. NSP were cultured as free-floating spheres in appropriate low attachment T-75 flasks or 6 well or 24 well plates  for this study.U87MG cell line  was cultured in DMEM  with Glucose (1 mg per mL) and L-glutamine (0.584 mg per mL). 10% fetal bovine serum  at ~10,000 cells per well. The growth profile was studied in a 24 well plate (Nunc tissue culture-treated) for ease of harvesting. Both U87MG and NSP cells were harvested every 24 h and counted using a hemocytometer based on the trypan blue dye exclusion assay. Before counting, the NSP population was also disaggregated by trypsinization. For dose-response experiments, cells were plated in replicates at ~20,000 cells per well in 24-well plates  in full growth medium for 24 h and then treated them with different doses of TMZ . Three biological replicates were performed with three technical replicates in each biological replicate on a 24-well plate . Growth and temozolomide response curves were graphed with the number of cells on the Y axis and time on the X axis. The data was fitted using a logistic Gompertz function using GraphPad Prism software and the growth parameters were calculated.Growth of the cells (U87MG and NSP) were studied by monitoring their proliferation via cell count over a period of 168 h (7 days). The initial seeding set had a starting population  from each experiment setup  were harvested over a period of five days and used for the metabolic profiling to understand nutrient uptake and release kinetics. A sample pooling strategy33 was applied to reduce the number of samples and for high-throughput quantification. A viable cell count was performed to count the number of cells in each sample from growth curves. After cell count, the samples were centrifuged at 2795 × g (4 °C). The supernatant was used for extracellular analysis and the pellet was used for intracellular analysis.Extracellular samples. Each replicate sample was prepared and stored at −80 °C; thawed on an ice bath to aliquot 100 µL of sample for extraction. The aliquot was transferred into a fresh 1.5 mL centrifuge tube. Four hundred microliter of chilled methanol (previously stored in −80 °C) was added. The solution was thoroughly mixed for 2 min followed by centrifugation for 15 min at 2795 × g (4 °C). The tubes were carefully removed, 300 µL of supernatant was withdrawn and transferred into a fresh tube (Dilution level: 5×).Dilutions. A two-step serial dilution of supernatant was performed using 50% acetonitrile in water. In the first step, 50 µL of sample was thoroughly mixed with 450 µL of diluent (Dilution level: 50×). This solution was further diluted by mixing 100 µL of sample solution with 400 µL of diluent (Dilution level: 250×). Before injection, 100 µL of sample solution was mixed with equal volume internal standard solution containing 4.4 µM verapamil in 50% acetonitrile in water with 0.2% formic acid.Intracellular samples. The cell pellet was washed with PBS  (pH–7.4) by centrifugation at 2795 × g (4 °C). This step was repeated twice to remove any media residues. In the subsequent steps, HPLC grade methanol  software platform, whereas HESI source parameters were set using Tune module . Samples were stored in temperature controlled Accela autosampler maintained at 4 °C during LC-HRMS analysis. A reverse-phase C18 hypersil gold column (10 cm × 2.1 mm × 3.0 μm) was used for chromatography. The mobile phase consisted of 0.1% formic acid in deionized water (Mobile phase ‘A’) and 0.1% formic acid in acetonitrile (Mobile phase ‘B’). The elution gradient was set as 70% of mobile phase A (0.0–2.5 min), 10% A (3.5–5.5 min), 70% A (5.5–8.0 min) with a constant flow rate at 1000 μL per min. Five microliter of samples was injected for analysis using the auto-sampler unit. The data was acquired in both positive and negative ion mode in two separate batches. Metabolomics data analysis was carried out by the Qual and Quan browser modules of Xcalibur (Thermo Fisher Scientific Pvt. Ltd.). The same protocol was followed in our previous study24.http://biit.cs.ut.ee/clustvis/). Consumption and release concentrations were calculated by subtracting the concentration at 0 and 96 h. These values were used as inputs for generating heat maps and for PCA. Heat maps and clustering was performed with the criteria of unit variance scaling (applied to rows) where all 35 metabolites were the rows in the heat maps. Both rows and columns were clustered using correlation distance and average linkage with the tightest cluster first in tree ordering. PCA was carried out by using SVD with the imputation algorithm specified in the ClustVis tool. In some specific cases (BIOLOGTM experiments), the Euclidean algorithm was used for clustering to interpret the data. Variable Importance in Projection (VIP) scores and PLS-DA were calculated using MetaboAnalyst, a web-based metabolomic analysis tool37.The concentrations for all 35 metabolites identified from LC-MS/MS analysis were further clustered using ClustVis web tool  was used to remove any RNA contamination from the extracted DNA by incubating for 5 min at room temperature. ~15 µg of genomic DNA was extracted from each sample. This extracted DNA was used for the exome sequencing. Exome workflow of Ion ProtonTM systems  was used to obtain the exome sequences of U87MG and NSP cells. Ion TargetSeqTM Exome kit and Ion ProtonTM sequencer was used for acquiring the exome data that was further processed through TorrentSuite and Ion Reporter software to identify the variants and for the coverage analysis . A total of 4 × 10https://portals.broadinstitute.org/oncotator/). Oncotator is a web-based application commonly used for annotating human genomic point mutations including Single Nucleotide Polymorphisms (SNPs) and Insertions and Deletions (INDELS). This includes genomic annotations , protein annotations , and common SNP annotations from dbSNP database. The input file contained the details of the position in chromosomes, reference and its corresponding variants identified that was uploaded in the web tool for analysis. The output file had the results with details of the gene name, gene IDs, HUGO symbol, variant classifications , gene description, protein/amino acid change and its biological functions. These details helped further analysis and interpretation  consist of panels of PMM screening assays—(i) Energy metabolism pathways; (ii) Ion and hormone effects on cells and (iii) Sensitivity to anti-cancer agents. In PM-M1 to PM-M4, the metabolic pathway activities were assayed by using the cell suspension  prepared in an inoculating fluid (IF-M1 or IF-M2) that lacks carbon and energy sources (provided with the BIOLOGTM plates). Biolog Redox Dye Mix MA or Biolog Redox Dye Mix MB, was added to all wells. This measurement employs a tetrazolium dye that can be reduced to a purple formazan that can be measured at 590 nm with a microplate reader. The redox energy produced when a cell metabolizes a substrate is used to convert the color from yellow to purple formazan. The rate of formazan production changes linearly with time and corresponds to the number of viable cells. iMark™ Microplate Absorbance Reader (Bio-Rad), with a wavelength range of 400–750 nm, was used in our study to measure the absorbance. Cell suspension  of cell growth, followed by adding the dye and reading for 24 h of study .Biolog Phenotype MicroArrays™ (PM-M1 to PM-M14) from Biolog, Inc. USA  were used to estimate IC50 dose value. The concentrations of rotenone used in the IC50 estimation for U87MG cells were 0.5 mM, 0.1 mM, 1 mM, 2 mM, 3 mM, 4 mM, and 5 mM. The concentrations of rotenone used in the IC50 estimation for NSP cells were 5 nM, 10 nM, 20 nM, 50 nM, 100 nM, 0.5 mM, and 1 mM. The results were graphed and drug dose response parameters were calculated using Graph Pad Prism.For dose response experiments, four replicates at 20,000 cells per well were plated in 96 well Nunc32 consisting of 386 reactions  and v represent feasible flux range in each reaction from an unaltered model system, identified using flux variability analysis (FVA). For estimation of fluxes, these models were constrained using exchange rate for twenty-one metabolites including glucose, lactate and amino acids. (Supplementary File 32 and on oxygen uptake rate (OUR) based on estimations following flux balance estimations for maximal OUR feasible by the models. These contextualized models were evaluated following their in silico phenotypic predictions using constraints-based methods such as, flux balance analysis and uniform random flux sampling of flux solution space. We also added an in silico pseudo-hypoxia reaction (NAD [c] + H2[c] → NADH [c] + H[c]) involving the conversion of cytosolic NADH to NAD+ according to the previous study of testing the pseudo-hypoxia in cancer cells39, to estimate the levels of NADH and NAD+ in the cell-specific contextualized models.In Eq. vobj is an optimal solution for (Eq. 1) or alternate sub-optimal network states (0 ≤ 1) for objective function. The non-uniqueness of the FBA solution allows calculation of a range of flux that is feasible for each reaction, thus defining the rigidity and plasticity of the network.Plurality of solutions exists for the FBA problem, since the cell can choose multiple flux distributions to result in a unique objective function. FVA identifies the set of feasible fluxes at the optimal objective. The method calculates the minimum and maximum allowable fluxes through each reaction using a double optimization linear programming approach for each reaction of interest. The FVA problem, an extension of the FBA, is set up asmentclass2pt{minim38. This can be achieved by choosing a random point uniformly along each edge of parallelepiped following Monte Carlo sampling. Equation Similar to FVA, properties of metabolic flux states can be deciphered by random sampling of feasible flux space within the enclosing parallelepiped solution spaceRn is a random number chosen between 0 and 1 while αi,min and αi,max defines the flux range of feasible flux state along each reaction vector identified using FVA. These points can then be further compared to the set of constraints imposed on a constrained based metabolic model, in order to verify whether the random point falls in solution space.In Eq. 38.Solution sampling in this manner not only offers insights about plasticity of the metabolic network but offers latent information about metabolic flux states such as, coregulated list of trans-acting metabolic reactions. In addition, information about rewiring of the metabolic network imposed by system specific constraints can also be elucidated by inspecting the population distribution of random sampling for each reactionFlux sampling for U87MG and NSP cell line models was carried out using a Markov Chain Monte Carlo method of Artificial Centering Hit-and-Run (ACHR) sampler from COBRA toolbox. The initial point for the sampler was chosen amongst 1000 warmup points identified by combining random and orthogonal points. A total of 50,000 randomly distributed sampling points were computed with 1000 iterations between each stored point. Distribution of individual reaction flux values across the sampling population was represented as a histogram of feasible flux value and associated frequency in the convex polytope of solution space.Further information on experimental design is available in the Supplementary FileSupplementary File 2Supplementary File 3Supplementary File 4Supplementary File 5Reporting Summary


Schistosoma. Praziquantel (PZQ) is today the only drug used in humans and animals for the treatment of schistosomiasis but unfortunately it is poorly effective on larval and juvenile stages of the parasite. Therefore, it is urgent the discovery of new drug targets and compounds. We have recently showed that the anti-anginal drug perhexiline maleate (PHX) is very active on multiple developmental stages of Schistosoma mansoni in vitro. It is well known that PHX impacts the lipid metabolism in mammals, but the final target on schistosomes still remains unknown. The aim of this study was to evaluate the ability of 1H nuclear magnetic resonance (NMR) spectroscopy in revealing metabolic perturbations due to PHX treatment of S. mansoni adult male worms. The effects of PHX were compared with the ones induced by vehicle and gambogic acid, in order to detect different metabolic profiles and specificity of the PHX action. Remarkably a list of metabolites associated to PHX-treatment was identified with enrichment in several connected metabolic pathways including also the Kennedy pathway mediating the glycerophospholipid metabolism. Our study represents the first 1H-NMR metabolomic approach to characterize the response of S. mansoni to drug treatment. The obtained “metabolic fingerprint” associated to PHX treatment could represent a strategy for displaying cellular metabolic changes for any given drug and to compare compounds targeting similar or distinct biochemical pathways.Schistosomiasis is one of the most devastating neglected tropical parasitic diseases caused by trematodes of the genus  Schistosoma. The control and treatment of the disease rely almost exclusively on praziquantel (PZQ), poorly effective on some developmental stage of the parasite. Identification of novel targets and drugs is required. The aim of this study was to use a 1H-NMR metabolomic approach to characterize the response of Schistosoma mansoni to perhexiline maleate (PHX), a multi-stage schistosomicidal drug previously investigated by our group. Remarkably we identified a metabolic signature specifically associated to drug-treatment in adult male parasites. This approach could contribute to the identification of novel targets and biochemical pathways implicated in parasite development and survival.Schistosomiasis is a chronic and debilitating neglected tropical parasitic disease caused by the helminth  Schistosoma is the causing agent of one of the most devastating parasitic neglected disease, the schistosomiasis. More than 200 million people are infected and 800 million are at risk of infection in endemic countries. The three main species infecting humans are S. haematobium, S. japonicum, and S. mansoni [ mansoni . The curin vitro [in vitro on larval (schistosomula), juvenile, and adult parasites [Schistosoma metabolism showed that this parasite lacks CPT-1 and CPT-2 [We recently discovered multiple series of compounds able to kill the parasite in vitro ,4. Amongarasites . In humaarasites . In addiarasites ,7. PHX, arasites . PHX is arasites ,10. In cnd CPT-2 –13 and tnd CPT-2 ,14, ther1H Nuclear Magnetic Resonance (1H-NMR) are the main tools. While the former is able to quantify metabolites even at very low concentrations by extracting them via liquid chromatography, the latter is suitable for untargeted qualitative and quantitative analysis of metabolites in complex mixture without extraction [Metabolomics is a powerful technique, which simultaneously detects metabolites in biological fluids, cells, tissues, and whole organisms to obtain information about metabolic processes at baseline and treated conditions. Liquid Chromatography Mass Spectrometry (LC-MS) and traction . NMR bastraction ,17.Metabolomics in the field of parasitosis is a remarkable tool of investigation to shed light onto inner metabolism, host–parasite interaction , and ult1H NMR spectroscopy to analyze the metabolic status of S. mansoni and to identify potential treatment-associated signatures. We examined the metabolic changes that occur within time when S. mansoni adult male parasites are treated with vehicle or two different concentrations of PHX or gambogic acid (GA). The levels of some metabolites and the enrichment of some metabolic pathways associated to PHX or GA treatment allowed to differentiate the effects of the two compounds.In the present study, for the first time, we set out to use S. mansoni for a given drug and comparing compounds targeting similar or distinct biochemical pathways.This study provides new, to our knowledge, insights for displaying cellular metabolic changes in Chemical reagents, Dimethyl sulphoxide (DMSO), fetal bovine serum (FBS), gambogic acid (GA), perhexiline maleate (PHX) were purchased from Sigma-Aldrich ; Dulbecco-Modified Eagle’s Medium (DMEM), HEPES, L-glutamine from Lonza ; antibiotic-antimycotic reagent (100×) from Thermo Fisher Scientific .Animal work was approved by the National Research Council animal welfare committee (OPBA) and by the competent authorities of the Italian Ministry of Health (authorizations no. 25/2014-PR and no. 336/2018-PR). All experiments were conducted in respect to the 3R rules according to the ethical and safety rules and guidelines for the use of animals in biomedical research provided by the relevant national and international laws.S. mansoni was maintained by passage through albino Biomphalaria glabrata, as the intermediate host, and ICR (CD-1) outbred female mice as definitive host as previously described [A Puerto Rican strain of escribed . Female S. mansoni adult male worms, isolated from infected mice, were cultured in complete DMEM tissue culture medium and allowed to recover for 24 hours at 37°C in 5% CO2 atmosphere. 1H-NMR can detect metabolic changes associated within a shorter time frame, so that the status of parasites viability should be compatible with the analysis. Therefore, the worms were treated with vehicle (DMSO), PHX , or GA  for short time: compound concentrations were chosen according to what previously observed in terms of parasites viability [S. mansoni adult male worms was assessed under a Leica MZ12 stereomicroscope by a multiple phenotypical score as previously reported [In order to evaluate the minimum number of parasites to consistently detect a reasonable number of metabolites, 10, 25, 50, and 90 worms were employed for metabolites extraction. Following spectra analysis, the sample of 25 parasites showed to give appropriate spectra in terms of quality and reproducibility. Therefore, for all compound treatments, 75 parasites/sample were placed in 90 mm dish culture plates with 20 ml of DMEM complete medium. Six and 24 hours after drug exposure parasites were harvested, divided into 25 worms per sample , washed extensively with saline solution, and the pellets stored a -80°C until metabolites extraction. Three technical replicates, for at least two biological experiments, were processed.2 prior each agitation. At the end of each extraction method, sample were put on a laboratory shaker featuring a horizontal (left to right) linear action to provide vigorous shaking for 1 hour at 4° C in a final volume of 1 mL of extraction buffer. Finally, all extract mixtures were centrifuged , supernatants collected, dried under vacuum conditions, and stored at -80°C until NMR analyses.Metabolites extraction protocol from whole parasites was adapted from tissue extraction techniques previously described . In partWorm extracts were dissolved in 100 μL of phosphate buffer 50 mM pH 7.4 in D2O solvent, with the addition of trimethylsilylpropionic acid (TSP) (0.5 mM) as internal standard solution. After centrifugation , a volume of 50 μL of this solution was inserted in a 1.7 mm NMR tube.1H-NMR experiments were performed at 25°C on Bruker Avance 600 MHz equipped with a triple resonance 1.7 mm TXI probe and a SampleJet autosampler, using a noesypr1d (1D Nuclear Overhauser effect spectroscopy with water pre-saturation) pulse sequence with acquisition time of 2 s, relaxation delay of 3 s, 4096 transients, 4 dummy scans, tm = 100 ms, spectral width of 20 ppm, for a total acquisition time of 6 hours.All 1H-1H TOCSY , 1H-13C and 1H-31P HSQC (heteronuclear single quantum coherence). For a detailed table and experimental conditions see Supplementary Information . All data were normalized, to account for different extraction efficiency, by using probabilistic quotients  and Unit is good . In the Heatmap was drawn by R ggplot2  applyingNetwork analysis was performed in Cytoscape . In partMetScape builds a network from a list of query compounds and extends the connections also to compounds that are taking part in the reactions in which query compounds are involved.S. mansoni. The reference was based on the S. mansoni metabolome and pathways were selected according to p-value < 0.05 and impact > 0.3.MSEA was performed using MetaboAnalyst pathway analysis tool . In detaS. mansoni adult male worms, we first assayed different disruption methods  to obtain 1H-NMR spectra with sufficiently good signal to noise allowing the quantification of the highest number of metabolites. We found that the glass tissue Dounce homogenizer and TissueLyser led to the identification of essentially the same number of metabolites, even though the latter gave a higher concentration thus allowing us to reduce the number of parasites up to 25 worms/sample. Since parasites preparation is a labor-intensive procedure, the reduction of the number of parasites is advantageous especially in terms of number of host animals (mice) to be used in these studies.In order to investigate the effect of PHX-treatment on the metabolic profile of 1H-NMR spectrum of the extracts obtained from S. mansoni worms is shown in A representative S. mansoni adult male worms in vitro. GA (0.25 and 1 μM) was used as reference compound to evaluate the specificity of the PHX associated variations with respect to generalized metabolic changes possibly due to toxic effects of a compound. The concentrations of PHX and GA were chosen in order to affect viability to a similar phenotypical extent. The viability was assessed by a multiple parameter score system as previously described [In order to identify metabolomic signatures associated to PHX treatment, two concentrations of PHX (5 and 10 μM) previously shown to impair viability  were useescribed ,4. The p1H-NMR. The variation of metabolite concentrations was analyzed using an orthogonal partial least squares (OPLS) Y model [In each experiment, metabolites were extracted and quantified by  Y model , in whicThe best metabolic profile separation was obtained between worms treated with 10 μM PHX or vehicle at both time points whereas the separation was less pronounced at 5 μM PHX both at 6 h and 24 h treatment .With regard to the GA-treated samples, a marked metabolic separation was seen at 1 μM at both time points, while the lowest concentration did not seem to produce clearly distinct profiles .The corresponding loading plots show the variation of metabolites concentration upon PHX  and GA  treatmenSince 10 μM PHX and 1 μM GA, both at 6 and 24 h, yielded a clear perturbation of the metabolic profile, they represent the most reasonable experimental conditions to reveal a possible effect on parasite metabolome due either PHX or GA treatment.To compare the effects of PHX and GA on metabolite concentrations, a multivariate discriminant analysis (OPLS-DA)  among coIn order to investigate which pattern of metabolites was perturbed after 6 and 24 h compound treatment four supervised models were applied  Figs. MeWe observed that the concentration levels of 12 metabolites resulted selectively altered in samples treated for 6 h with GA, whereas 8 were modulated in PHX-treated samples. Seven metabolites were found to change irrespective of the PHX or GA treatment . Upon 24Interestingly, the levels of choline and 3-glycerophosphocholine varied in an opposite way between PHX- and GA-treated samples at 24 h, suggesting that metabolic pathways regulation of these metabolites are differently impacted by the two treatments . A complThe 35 metabolites emerged from the discriminant analysis  represenTo gain insight into pathways possibly modulated upon PHX and GA treatments we applied a network analysis. To this aim metabolites modulated by each treatment and involved in a common reaction were linked in order to identify those metabolites whose modulation is highly related upon treatment. The approach was based on the assumption that the specific effect of a treatment might impact metabolites residing in close proximity and participating to common metabolic axis.Homo sapiens biochemical pathways.Starting from the list of metabolites modulated by PHX and GA (hereafter “seed metabolites”) a network was built by linking in “connected components” also metabolites participating to the same biochemical reaction not present in the seed metabolites list . The net1H-NMR, but that are part of the same reaction in which the seed metabolites participate (1H-NMR sensitivity and ii) lack of accumulation of some of the neighbor metabolites. Then we analyzed the extended list of metabolites starting from the hypothesis that whenever the concentration of a metabolite is perturbed, its variation can also affect metabolites that are partners into a common reaction.The small number of specific modulated metabolites makes the identification of pathways associated to a specific compound-treatment not an easy task. For these reasons we widened the networks by adding the “neighbor metabolites”, i.e. metabolites which were not detected by ticipate . PathwayThe size of our “neighbor-extended” networks ranges from 141 to 225 connected nodes including the majority of the seed metabolites (78% on average). Noteworthy, PHX treatment at 6 h showed the highest number of connected seed metabolites (87%) . SimilarS. mansoni  using the metabolite lists of the expanded networks generated via MetScape . The libThe majority of the PHX enriched pathways  were repThey are involved in the Kennedy pathway and in the betaine and folate metabolism, which in turn are linked together by the glycine-serine interconversion reaction. Side pathways like cysteine and methionine metabolism and, glyoxalate metabolism are also depicted . The cenUnderstanding how small molecules interfere with cellular metabolism is a critical part of modern drug development. Untargeted metabolomics offers a rapid and unbiased approach for the investigation of metabolic pathways, the discovery of drug modes of action, and of potential drug targets.1H-NMR spectroscopy on S. mansoni adult male worms treated with schistosomicidal compounds. As initial point, we focused our attention on setting the best technical procedure for the extraction of worm soluble metabolites. TissueLyser produced the best results in terms of qualitative and quantitative level of metabolite extraction and the minimum number of parasites required for the identification of a reproducible and accurate set of metabolites.In this study, we applied for the first time a metabolomic analysis based on 1H-NMR approach we succeeded into identifying 43 soluble metabolites of S. mansoni adult male worms. OPLS analysis allowed us to select the best experimental conditions to identify variation in metabolic profiles associated to PHX or GA treatments, thus demonstrating that the 1H-NMR approach could be effective in detecting metabolic perturbations induced by different compound treatments.Using the 1H-NMR technique; in addition, some metabolites might not accumulate in a pathway for the peculiar metabolic assets of a given species. Therefore, to overcome this limit, we took advantage of a neighbor-extended network analysis starting from the metabolites identified by 1H-NMR and building a network including the neighbor metabolites residing in their close proximity and participating to common metabolic axes.Finally, we attempted to ascribe metabolic pathways associated to parasite-treatments. Low-sensitivity is one of the drawbacks of the The glycerophospholipids pathway is among the pathways enriched in PHX-treated samples and this was an intriguing, though not totally unexpected finding. Indeed, PHX has been largely recognized to perturb the lipid metabolism in mammals; and it seems that an effect on parasite’s lipids and choline metabolism could be also ascribed now to PHX. Importantly the lipid metabolism has for parasite biology a remarkable role, in particular for tegumental development and its turnover, and eggs production. Noteworthy, treatment with PHX or GA appears to modulate choline and 3-GPC in an opposite way: these metabolites were decreased in PHX-treated samples and increased in GA-treated ones, suggesting that the fate of both metabolites is differentially impacted by compound treatment.Schistosoma genome [in vitro in agreement with what previously reported on studies with the CPT-1 inhibitor etomoxir, that inhibited depletion of lipid reserves in fecund females worms in vitro and impaired schistosome egg production as well [In mammals, PHX is thought to impact the lipid metabolism by acting as inhibitors of CPT-1 and CPT-2 enzymes , whose ra genome –13, in o as well .S. mansoni worms can exploit the oxidative route of choline bringing to betaine formation as well as to incorporate choline within PC, hence initiating the de novo synthesis of phosphatidylcholine via Kennedy pathway [In the schematic representation of the PHX-associated enriched pathways where the metabolite variations observed in this study are highlighted , the cho pathway .S. mansoni couples reside in the mesenteric veins where male and female worms live paired and acquire nutrients directly from the blood of the host including among others carbohydrates, amino acids, lipids, and choline [S. mansoni biology, mainly due to its role in development and survival of parasites; therefore, we concentrate our attention on the Kennedy pathway, relying on the uptake of exogenous choline into the cell for phosphatidylcholine synthesis. Choline is needed for the biosynthesis of phosphatidylcholine, the most predominant cell membrane phospholipid in S. mansoni cercariae, schistosomula, and adult worms [Within the mammalian host, adult  choline . Parasit choline ; ii) the choline . Overalllt worms , as a meS. mansoni sporocysts [Overall the modulation of metabolism due to PHX treatment appears also to affect the ability of the parasite to utilize glutamine. Interestingly glutamine is amongst the most dysregulated metabolites detected in our analyses  and expeorocysts . TherefoPlasmodium falciparum and Trypanosoma brucei, the Kennedy pathway has already been proposed as a potentially relevant drug target [S. mansoni could be important for drug discovery and drug repositioning (i.e. PHX).Remarkably in protozoan parasites such as g target –48. Furt1H-NMR metabolomic approach to characterize the response of S. mansoni metabolome to a drug-treatment. The workflow proceeded through an optimization of sample preparation , 1H-NMR data acquisition, signal preprocessing, data analysis, and then to interpretation of the results. Multiple factors, such as dosage and exposure time, were extensively considered and tested in pilot studies before assessing the final conditions of both time points and drug concentration. The untargeted metabolic comparisons were always performed between two groups  in order to extract useful information. The “metabolic fingerprints” associated to PHX or GA treatment in vitro were distinct and they could represent a strategy of displaying cellular metabolic changes for any given drug and to compare compounds targeting similar or distinct biochemical pathways.In conclusion our study represents the first S1 Fig2X: 0.637; R2Y: 0.965; Q2: 0.923; CV Anova: 1.2E-06 and (b) 24 h N: 18; A: 1+1+0; R2X: 0.625; R2Y: 0.984; Q2: 0.970; CV Anova: 1.1E-09(a) OPLS-DA score plots of GA (1μM) treated-sample vs control after 6 h N: 21; A: 1+2+0; R(TIF)Click here for additional data file.S2 Fig2X: 0.477; R2Y: 0.960; Q2: 0.943; CV Anova: 2.2E-09 and (b) 24 h N: 21; A: 1+1+0; R2X: 0.339; R2Y: 0.938; Q2: 0.827; CV Anova: 2.5E-05(a) OPLS-DA score plots of PHX (10μM) treated-sample vs control after 6 h N: 24; A: 1+2+0; R(TIF)Click here for additional data file.S3 FigNetworks are showed before (left panel) and after (right panel) neighbor-extension. Green and red colors represent respectively an increase or a decrease of the seed metabolite level in compound treated samples. Gray nodes represent “neighbor” metabolites.(PDF)Click here for additional data file.S1 Table1H and 13C chemical shifts are reported with respect to the TSP signal, and 31P chemical shifts are reported relative to 85% inorganic orthophosphoric acid.(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableMetabolite annotations are reported according to MetScape library.(XLSX)Click here for additional data file.S4 TableEnriched pathways of neighbor-extended networks upon PHX and GA treatment. Green: p-value < 0.05; Yellow: impact > 0.3(XLSX)Click here for additional data file.