As industrial bioprocesses primarily depend on food-competing organic feedstocks or fossil raw materials, CO2 co-assimilation or the use of CO2-derived methanol or formate as carbon sources are considered pathbreaking contributions to solving this global problem. The number of industrially-relevant microorganisms that can use these two carbon sources is limited, and even fewer can concurrently co-assimilate CO2. Here, we search for alternative native methanol and formate assimilation pathways that co-assimilate CO2 in the industrially-relevant methylotrophic yeast Komagataella phaffii (Pichia pastoris). Using 13C-tracer-based metabolomic techniques and metabolic engineering approaches, we discover and confirm a growth supporting pathway based on native enzymes that can perform all three assimilations: namely, the oxygen-tolerant reductive glycine pathway. This finding paves the way towards metabolic engineering of formate and CO2 utilisation to produce proteins, biomass, or chemicals in yeast.The current climatic change is predominantly driven by excessive anthropogenic CO 2, methanol and formate are cost-effective and environmentally friendly microbial feedstocks for biomanufacturing. Here, the authors report the oxygen tolerant reductive glycine pathway in Komagataella phaffii can co-assimilate CO2, methanol and formate.One carbon compounds such as CO Regarding this global problem, CO2-fixation is of utmost importance. The use of CO2-derived renewable and green feedstocks as carbon sources for bioprocesses has increased in importance over the last years. This is not only because of climate change concerns, but also as a consequence of the problems emerging from food-competing bio-industrial feedstock, e.g., glucose. Green, non-food-competing carbon sources such as methanol (MeOH) and formate (FA) can be electrochemically produced from CO23. Still, the number of organisms that can produce biomass, proteins, or chemicals from these green carbon sources is limited4.The combustion of fossil fuels and the associated increase in atmospheric COKomagataella phaffii  is a methylotrophic yeast6 that is used industrially in the heterologous protein production of enzymes and biopharmaceuticals and the methanol-inducible alcohol oxidase promotor system10 of this yeast is well-known. Synthetic biology tools  have simplified metabolic engineering of this methylotrophic host14, which was demonstrated by altering a heterotrophic organism in an auxotroph that can grow on CO215. The main methanol assimilation pathway, the xylulose 5-phosphate pathway (XuMP), is well understood and has been investigated in detail16. A hitherto unknown metabolic route was recently discovered in K. phaffii. Although the flux is too low to support growth17, this route is active. In the synthetic autotrophic strain expressing a heterologous Calvin-Benson-Bassham cycle 15 we discovered a pathway dealing with 2-phosphoglycolate, the side product of RuBisCO oxygenation reaction18. Such findings were the motivation behind our search for other undiscovered pathways that focus on methanol, formate, and CO2 assimilation routes that are naturally active.Bacillus methanolicus, is a formaldehyde-fixation pathway  and phosphohexose isomerase (Phi) are of central importance in RuMP. After adaptive laboratory evolution, Saccharomyces cerevisae could also grow on methanol via a formaldehyde-fixation pathway that involves the PPP24. In the serine cycle pathway of e.g. Methylorubrum extorquens (formerly Methylobacterium extorquens25), methanol is dissimilated to formaldehyde and then to formate via a formate-fixation pathway. Subsequently, formate enters the enzymatic tetrahydrofolate (THF) pathway leading to methylene-THF27. In addition to this formate-fixation pathway, to a minor extent, it may also fixate formaldehyde because of the spontaneous in vivo condensation reaction of THF with formaldehyde-forming methylene-THF , we find that there is an alternative route of methanol assimilation in yeast, and identify it as a variant of the reductive glycine pathway. GC-TOFMS methods43 allow for the verification of the carbon transition from methanol via methylene-THF plus CO2 to glycine, and from glycine and methylene-THF to serine, from where the carbon enters the central metabolism. We show that formate is assimilated via the same route. Deletion of the mitochondrial isogene of serine hydroxymethyltransferase (SHM1) relieves the competition of this enzyme with the mitochondrial glycine cleavage system for methylene-THF and enables a strain to assimilate methanol or formate, respectively, together with CO2 at a rate that is fast enough to sustain growth and cell division.In this study, employing metabolic tracer analysis with 2 are present in eukaryotes as well and can be awakened by metabolic engineering, and form a basis for sustainable single-carbon bioprocesses with industrially-relevant yeasts in the future.Our work points out that such naturally encoded pathways for methanol or formate co-assimilation with CO13C-methanol without any other carbon source, the presence of an alternative methanol assimilation pathway became apparent as observed by a temporal increase in the 13C content of various metabolites  isotopologue fraction. This M + 0 fraction is also indicated as a numerical value in the corresponding bar for all forward labelling approaches. Generally, an upstream metabolite of any active pathway contains a higher fraction of 13C than the corresponding downstream metabolites. Further parameters like metabolite pool sizes, reaction rates and compartmentalisation of reactions need to be considered as well. Compounds that have the fastest and highest incorporation of the 13C label are indicative at the beginning of an active inherent pathway.The XuMP knockout strain  as these contained the C1 and C2 carbons of the amino acids only , α-ketoglutarate (AKG) and glutamate. The labelling pattern of oxaloacetate was not determined because the concentration in the extracts was below the limit of detection of the method. Pyruvate was observed as a degradation product of both oxaloacetate and phosphoenolpyruvate (PEP). These metabolites originate from two different pathways, thus pyruvate could not be evaluated. Generally, such metabolic interconversion reactions (non-enzymatic in vitro reactions) can falsify results and consequently, pathway interpretation. Therefore, critical analytes of the hypothesised pathways were assessed . Alternative to the XuMP pathway, these data clearly showed that methanol is primarily assimilated via the tetrahydrofolate pathway because methionine and serine are produced from this pathway. Serine showed an increase in the isotopologue M + 2 after 24 h. This indicated that a second labelled carbon atom originating from methylene-THF is incorporated via glycine. Glycine increased in  and 72 he.g., sedoheptulose 7-phosphate (S7P), ribose 5-phosphate (R5P), 2&3-PG would show an earlier and higher degree of 13C labelling than methionine, serine and glycine, as well as the downstream metabolites aspartate, malate and fumarate. Labelling data of wild type K. phaffii cannot directly be compared with the das1Δdas2Δ strain used here, as the growing cells incorporate 13C much faster. Therefore, we used an aox1Δaox2Δ (methanol utilisation negative or Mut-) strain as a control that still dissimilates methanol to formaldehyde via ADH217 and has the native xylulose 5-phosphate pathway but does not grow on methanol. The labelling patterns of metabolites in this control strain matches the expected patterns described above  cycle metabolites isocitrate (I-Cit) and α-ketoglutarate (AKG) would be expected. These are the first metabolites that can be measured after the acetyl-CoA fixation point in the TCA cycle.If formaldehyde fixation pathways acting through the pentose phosphate pathway (PPP) and glycolysis would be active, then a different pattern would be expected. Namely, metabolites of these pathways, ove Fig. . The lownatC glycine was supplied to the medium and the methanol labelling was performed, the same labelling pattern was observed although the intensity of the labels was reduced. This is a consequence of the reaction of glycine to methylene-THF resulting in a mixed feed of methanol and glycine; and additionally provides evidence for the general activity of the glycine cleavage system  is an intermediate metabolite of methanol dissimilation. If the oxygen-tolerant reductive glycine pathway is the alternative methanol assimilation pathway in 13C label would be incorporated at this stage. The pool of unlabelled metabolites of the biomass is a likely source of 12C, leading to the lower degree of labelling at 72 hours compared to 24 hours. Compared to the wildtype strain, formate-labelling of the DasKO strain resulted in similar patterns at all time points and for all metabolites  which obviously leads to severe starvation after 24 hours of cultivation. A limitation of supply with ATP and reducing equivalents impairs further assimilation of formate into methylene-THF so that no further 2 via the reductive glycine pathway, or more specifically, via the glycine cleavage system, a CO2 reverse labelling experiment was performed. By avoiding the use of 13C-CO2, the DasKO strain was fully labelled with 13C-glycerol followed by reverse labelling with natC-CO2 under 13C-methanol addition  was firstly considered deleted. The natively-encoded enzymes and resultant pathways for the assimilation of methanol were then compared to the pathways of other methylotrophic organisms. For the ribulose 5-phosphate pathway (RuMP), only 3-hexulose-6-phosphate synthase (Hps) and phosphohexose isomerase (Phi) are absent in K. phaffii  is the most active methanol assimilation pathway in DAS1, DAS was firs2 assimilation pathways in silico, a search for enzymes involved in CO2 fixation and pathways inspired by organisms that grow on CO2 as the sole carbon source was initiated. As observed in the oxygen-sensitive reductive glycine pathway of D. desulfuricans, the oxygen-sensitive glycine reductase complex to acetyl-phosphate and the phosphate acetyltransferase, or the acetate kinase, to acetyl-CoA are not encoded in the genome of K. phaffii  was split into two genes. Subsequently, there is a higher expression of the formate-tetrahydrofolate ligase subunit compared to the methenyltetrahydrofolate cyclohydrolase and dehydrogenase subunits, based on transcriptome dataMIS genes (MisKO strain). Formate- and methanol-labelling experiments with this strain led to the following result. After 24 h, the fraction of unlabelled isotopologue M + 0 from methionine and serine was only slightly reduced indicating that a minor amount of 13C-label was incorporated . Due to the fact that the dissimilation pathway to CO2 is still active, 13C-CO2 is produced intracellularly and re-fixation via the Gcv system can occur. Consequently, the labels detected in the unfragmented amino acids originate from the refixation of 13C-CO2, and not from the direct fixation of formate or methanol. To remove any doubt as to whether the labelling patterns derived from the experiments with the DasKO strain and the wildtype (described in the prior sections) stem from intracellular 13C-CO2, the decarboxylated amino acid fragments from these experiments were also evaluated  cannot grow on methanol as the only carbon source Fig. . Neverth2 which was even outperformed when exchanging methanol with 30 mM formate  to enable more of the carbon flux from methylene-THF to glycine, while serine synthesis is still enabled by cytosolic Shm2. This strain can grow on methanol and formate, respectively, both with elevated CO2 concentrations, but without addition of any further carbon source  had no positive effect on growth rate, indicating that the main limit is the flux competition at the GCV/SHM node.The addition of glycine to DasKO cultures enabled growth without a further carbon source Fig. , therefoate Fig. . This inrce Fig. . Growth ine Fig.  in compasee Fig. . Co-overK. phaffii, the canonical methanol assimilation pathway is initiated by alcohol oxidase (Aox) to formaldehyde and enters the XuMP cycle to generate glyceraldehyde 3-phosphate. Recently, we showed that Aox is not the only enzyme that catalyses the first reaction step in native methanol metabolism. Known for both synthesis and consumption of ethanol, the cytosolic alcohol dehydrogenase (Adh2) also oxidises methanol to formaldehyde17. This reaction conserves energy by the reduction of NAD+ to NADH; but there appears to be some limitations because with Adh only, growth was not observed. Here, we demonstrate that K. phaffii harbours a complete methanol, or formate and CO2 co-assimilation pathway that firstly leads to pyruvate and then to oxaloacetate. This oxygen-tolerant reductive glycine pathway provides precursors for all metabolic routes in yeast  system is reversible and that native Gcv and Mis enzymes are active in yeast. The metabolic activity of pyruvate carboxylase in K. phaffii is obvious and was metabolically verified by reverse labelling of fully 13C-labelled biomass with natC CO215. Our DasKO strain confirmed these findings.Although the native metabolic activity of the oxygen-tolerant reductive glycine pathway from formate or methanol to pyruvate or oxaloacetate, respectively, has not been observed in nature it is not completely unknown. The pathway has been reported on a metagenomic level for the anaerobic bacterium K. phaffii has the metabolic and genomic capability to assimilate and grow on methanol via the formate-fixing reductive glycine pathway, the organism has evolved to use the formaldehyde-fixing xylulose 5-phophate pathway as the main route for methanol utilisation. Where growth was achieved via the Gcv system, all experiments described in the literature39 show that elevated CO2 concentrations are a prerequisite and we could confirm that increased CO2 levels boost the pathway activity. K. phaffii was isolated from trees5, so it has obviously evolved under atmospheric CO2 concentrations that appear to be too low to achieve sufficient glycine synthesis via the Gcv system for C1-mixotrophic growth.Although 23. The co-assimilation of CO2 with methanol or formate would even enable direct carbon capture and reduce greenhouse gas emissions. The discovery of a native functional methanol, formate and CO2 assimilation pathway in K. phaffii bears huge potential for the design of a chassis cell that can convert C1 substrates to pyruvate and oxaloacetate. From these two central metabolites, all carbon backbones of metabolites and microbial biomass are generated. Co-assimilation of CO2 with a reduced substrate such as methanol is of special interest for the production of oxidised molecules (such as organic acids) because the reducing equivalents for CO2 assimilation in the synthesis pathway are thus directly provided50. The industrial relevance of the oxygen-tolerant pathway in K. phaffii can be pathbreaking, as it can lead to improved product yields on methanol compared to the wildtype while enabling net CO2 fixation. Due to better energy efficiency it bears the potential to outperform the recently described CBB cycle strain15.Enabling methanol oxidation to formaldehyde by ADH217, theoretically up to 2/3 moles of methanol and 1/3 mole of CO2 are fixed into 1 mole carbon at the degree of reduction of pyruvate  from wildtype genomic DNA of CBS7435 used previously15. The ligated homologous regions, the natMX-splitmarker for MIS1-2 & 3 and the overexpression cassette for CRISPR integration were also amplified for transformation with PCR. The single guide RNA recognition site . For Fhs, FchA and MtdA the native M. extorquens sequence was codon optimized (see Supplementary Data All plasmids were constructed by Golden Gate cloning14. 500 ng guide RNA plasmid and 3-5 µg linear homologous DNA sequence were transformed in K. phaffii (P. pastoris) CBS743547 by electroporation10. The DasKO strain was constructed within a single transformation with both guide RNA plasmids and linear homologous DNA sequences. This strain served as the foundation for all further strain constructions in this study, and additional knockouts were performed consecutively. The mis1-2 & 3 knockout was conducted by combining a splitmarker-method10 with CRISPR/Cas9 and supplementing YPD plates with 10 mmol L-1 hypoxanthine (further details in Supplementary Method SmaI or AscI (New England Biolabs) and transformed consecutively into the DasKO strain as mentioned above. Integration in the correct locus was verified with the primer pair listed in Supplementary Table All knockouts in this study were constructed by CRISPR/Cas9-based homology-directed recombination-1 yeast extract, 20 g L-1 soy peptone, 22 g L-1 D-glucose) precultures were inoculated with a single colony and shaken at 25 °C and 180 rpm overnight. A following 100 mL Yeast nitrogen base without amino acids (YNB) 2SO4, 0.1 mol L-1 potassium-phosphate buffer, pH = 6) batch culture was inoculated to an OD600 of 1 . For methanol and formate forward labelling, 18 g L-1 natC-glycerol (Carl Roth GmbH) and for reverse labelling, 0.79% (v/v) fully labelled 13C-glycerol  were used as carbon sources in the batch cultures. For ShmKO, the batch culture was also conducted on YPD and extended to 48 hours to gain sufficient biomass. The MisKO pre and batch cultures were conducted in YPD with 5 mmol L-1 hypoxanthine (Sigma-Aldrich GmbH) and 100 mg L-1 nourseothricin. Methanol and CO2 reverse labelling cultivations were performed in YNB medium with 1% (v/v) 13C-methanol , formate labelling in YNB medium with 30 mmol L-1 13C-sodium formate . For the labelling cultivations with natC-glycine, glycine (Merck KGaA) was added at a concentration of 20 mmol L-1 in addition to methanol. The biomass of the batch cultures was washed and inoculated at an OD600 of 50 (which corresponds to 10 g L-1 natC-cell dry weight) for methanol and CO2 reverse labelling, or at an OD600 of 25 (which corresponds to 5 g L-1 cell dry weight) for formate labelling. Shm1KO and the corresponding DasKO-control labelling was performed for 24 hours at an OD600 of 25 with the addition of 5% naturally distributed CO2. For experiments with metabolite sampling points at 2 h, 24 h and 72 h, the starting volume was set to 16 mL. 13C-methanol was adjusted to 1% (v/v) after 24 h, 48 h and 58 h. 13C-formate concentration was adjusted to 30 mmol L-1 after 7 h, 17 h, 22 h, 30 h, 41 h and 52 h . Each metabolic sample had 3 mL, additional samples for methanol measurements 1 mL and for formate measurements 300 µL. Experiments with metabolic sampling at 24 h only had an initial volume of 10 mL. Methanol and formate labelling was conducted at 25 °C at atmospheric gas conditions, CO2 reverse labelling at 30 °C in 5% natC-CO2 shaken at 180 rpm. All forward labelling experiments were conducted in biological duplicates with parallel natC-carbon source control experiments.10 mL YPD  was quenched in the 4-fold volume of quenching solution (60% methanol (Sigma-Aldrich GmbH), 125 mmol L-1 TRIS-HCl (Carl Roth GmbH), 55 mmol L-1 NaCl . The mixture was immediately vortexed for 4 s and rapidly filtered through cellulose acetate filters . The biomass was washed on the filter with 10 mL 60% methanol and stored at -70 °C until metabolite extraction.The labelling experiment of the Mut- strain including sampling and extraction was performed within the study of Zavec et al. 17.Metabolic sampling was performed with cold methanol quenchingK. phaffii53. Briefly, 4 mL 75% ethanol  at 85 °C was added to the cell pellet on the filter. Samples were vortexed, heated for 3 min at 85 °C and rapidly cooled on dry ice before centrifugation at 4000 × g and -20 °C for 10 min. The supernatants were dried in a vacuum centrifuge, and dry extracts were stored until analysis at -80 °C. Before analysis, the extracts were reconstituted in 1 mL MS-grade water for 30 min at room temperature.For gas chromatography time-of-flight mass spectrometry (GC-TOFMS) isotopologue distribution analysis, the quenched cells were extracted using boiling ethanol extraction as established in our laboratory for All metabolites were derivatised using a sample preparation robot  for automated just-in-time online derivatisation prior to gas chromatography time-of-flight mass spectrometry .43. This method was originally designed and developed for the K. phaffii wildtype on glucose by Mairinger et al. 43. Minor adaptions at the GC-MS data acquisition level were made and significant changes were necessary for subsequent data analysis. As the cultivation of the XuMP knockout strain (DasKO) on methanol and the use of formate as the carbon source led to profound changes in analyte concentrations and matrix composition of the labelling samples, the chromatographic and mass spectrometry-related background differed significantly from previously analysed K. phaffii samples. Consequently, the method had to be further developed and optimised. Technical changes included: (i) slowing down the GC-temperature program to reduce interferences ; and (ii) the use of an Agilent split/splitless injector with a splitless gooseneck liner with glass wool (Agilent). 350 µL of the reconstituted extract was dried in 400 µL inserts in 1.5 mL chromatography vials after the addition of 40 µL ethoxyamine hydrochloride in pyridine (c = 20 g L-1). Vials were crimped with magnetic caps before placing the samples in the autosampler at 7 °C.For some metabolites  derivatisation followed by GC-EITOFMS in split mode  was used15. For the analysis of metabolites of low concentration , some derivatised samples were reinjected in splitless mode after changing to an Agilent splitless gooseneck liner with glass wool  ion source, an additional GC-MS-based method was implemented, further developed and optimised with respect to measurement and data evaluation  using the ICT correction toolbox v.0.0454. Carbon isotopologue fractions were calculated  according to Eq. For isotopologue distribution analysis, the exact masses of a number of adducts and fragments of all target analytes were calculated and used for data analysis in both profile and centroid mode  were compared with the calculated natural isotopologue fractions . This was achieved in two ways: (i) the uncorrected measured natC isotopologue distributions  were compared with the isotopologue fractions calculated via Envipat for the entire derivatised compound; and (ii) the carbon isotopologue fractions as obtained after ICT correction were compared to the isotopologue fractions calculated via Envipat for the respective number of carbon atoms in the metabolite. Fragments or adducts were only considered for further evaluation if both the deviation between measured and calculated isotopologue fraction and the deviation between measured and calculated carbon isotopologue fraction was less than 5%. For each strain, time point, and metabolite, the fragment or adduct in centroid or profile mode that was selected for further data interpretation had the lowest deviations of the (carbon) isotopologue fractions from the calculated theoretical values and the lowest standard deviation for natC and 13C replicates (chosen adducts/fragments and type of data (profile/centroid) are summarised in Supplementary Data 13C carbon isotopologue fraction data in Fig. 56. natC carbon isotopologue fraction data are presented as mean and standard deviation of n replicates of the selected fragments or adducts from naturally distributed samples.Several fragments and adducts were evaluated in profile and centroid mode for each metabolite, and the corresponding https://www.genome.jp/kegg/pathway.html#metabolism) or alternatively, enzymes were searched via the corresponding metabolites in Expasy´s chemical compound search (https://enzyme.expasy.org/enzyme-bycompound.html); related genes were determined via the Pichia genome database47 (www.pichiagenome.org). Non-annotated enzymes were further assessed for presence in S. cerevisiae (https://www.yeastgenome.org/). The homologous gene was either directly located in the Pichia genome database or identified by NCBI´s BLAST, as shown in Supplementary Fig. https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch).Genes and enzymes for the reactions of interest were either located in the database KEGG , 30 mmol L-1 sodium formate (Carl Roth GmbH) or 4 g L-1 methanol (Carl Roth GmbH) for the first 24 h. The concentration was then maintained at 8 g L-1 methanol or 30 mmol L-1 sodium formate by feeding every 2-3 days. Cultures were initiated with an optical density at 600 nm (OD600) between 0.5 and 1. Flasks were incubated at 25 °C under atmospheric conditions, at 30 °C and 25 °C with 5% CO2, and at 25 °C with 10% CO2 whilst shaking at 180 rpm. The evaporated culture volume between sample points was compensated by the addition of water. OD600 and methanol or formate concentrations (HPLC measurements17) were monitored every 2-3 days. The ShmKO test cultures on 18 g L-1 glycerol, with optional 20 mM glycine, 8 g L-1 methanol or 30 mM sodium formate and 5% CO2 were performed at 30 °C. Batch cultures were omitted and precultures were performed in YPG  at 30 °C. The experiments were conducted in duplicates if not stated differently in the figure legends. 100 mg L-1 nourseothricin, 200 mg L-1 hygromycin, 500 mg L-1 geneticin and 25 mg L-1 or 50 mg L-1 zeocin were added according to the corresponding resistance markers of the overexpression strains to the precultures.Cultivations were conducted in 100 mL shake flasks with working volumes of between 8 and 25 mL, containing YNB medium. Precultures and batch cultures of the knockout strains were conducted as in the labelling experiments above. For the overexpression strains the batch culture was omitted. The carbon sources of the test culture were 18 g LFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementray Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting SummarySource Data


Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated. Type 2 diabetes mellitus is a complex progressive metabolic disease with a heterogeneous etiology. The disease has a high global prevalence and is among the top ten causes of death worldwide. According to the latest international diabetes federation report, the global prevalence of diabetes in 2019 is estimated to be 463 million people. In addition, 374 million people are at greater risk of developing T2DM [Insulin resistance is the main pathophysiologic feature in T2DM. The key insulin-sensitive tissues include skeletal muscle, liver, and adipose tissues. Among these, skeletal muscle is responsible for the clearance of over 75% of glucose from the bloodstream. Thus this tissue plays a great role in lowering the blood glucose level . Therefoin silico exploration of metabolism through a systems biology approach. Since there is no one-to-one relationship between transcription and translation, the study of gene expression data alone does not provide an accurate understanding of cellular metabolism; while, metabolic network simulation gives an in-depth insight into the molecular mechanisms involved in cellular processes. Generic metabolic networks are reconstructed based on the information encoded in the genome. This information includes all possible reactions that can occur in all types of human cells. By having gene expression data from a particular cell or disease state and integrating it into the generic model, active reactions are identified and a context-specific model can be reconstructed. Some efforts have been made to study the molecular mechanisms contributing to muscle insulin resistance using genome-scale metabolic models (GEMs). Bordbar et.al. have reconstructed a multi-tissue type GEMs and integrated gene expression data of obese and type 2 obese gastric bypass patients to study metabolic activity between these states [In complex and multifactorial diseases such as diabetes, systems biology provides a holistic view to study dysregulated subsystems associated with the disease in a cell. Meanwhile, genome-scale metabolic modeling serves as a context for e states . Nogiec e states . Varemo e states . PreviouIn this study, the system-level metabolic analysis on the most comprehensive data from early-stage T2DM patients was performed. The samples include muscle gene expression data from newly diagnosed diabetic patients . We atteIn this study, we utilized transcriptome data from the dbGaP database with the accession code phs001068.v1.p1, which was originally published in reference . The autAs we did not directly perform any experiments on human samples and solely used publicly available data, we deemed it unnecessary to seek additional ethical approval for our study.We searched transcriptome databases for skeletal muscle data from healthy and T2DM individuals. We found the most comprehensive dataset from the dbGaP database with the accession code phs001068.v1.p1. The gene expression data were obtained from vastus lateralis muscle using high-throughput RNA-Seq technology. The data comprise 91 healthy and 63 newly diagnosed subjects . The traFor validation of metabolite markers, the muscle gene expression data of healthy and diabetic subjects were taken from the GEO repository with the accession numbers GSE63887 and GSE81965 [The muscle-specific metabolic model was reconstructed by integration of gene expression data to the generic model. Here, we used the HMR2 model as the generic model , 10. ThePersonalized GEMs were reconstructed by integrating gene expression data from each individual into the muscle-specific metabolic model. Pre-processed gene expression data and E-Flux algorithm were used here. Media conditions were set by body fluid metabolites . We usedTo looks for significantly dysregulated pathways and metabolites, enrichment analyses were performed. Significant perturbed reactions were used as the reaction set to be enriched. Pathway-reaction set and metabolite-reaction sets were created using our reconstructed muscle metabolic model. Metabolites were assigned to the reaction if it participated in the corresponding reaction as either substrate or products. For enrichment analysis, hypergeometric 1-sided test with FDR correction for multiple testing correction was used.Metabolite-centric is one of the most common representations of the metabolic network. Metabolites are considered as the nodes of the network. Then two nodes are linked if both of them participate in the reaction, one as the substrate and the other as the product. We reconstructed the metabolite-centric network from perturbed reactions. For simplicity, transport and exchange reactions were excluded and unique metabolites instead of the compartmentalized ones were used. We also excluded the currency metabolites. To find the significant connected components of dysregulated metabolites, metabolites with an adjusted p-value greater than 0.05 were filtered. Adjusted p-values were obtained from metabolite enrichment analysis. The metabolite-centric subnetwork was visualized using Cytoscape version 3.8.2 .An SVM classifier with a polynomial kernel of order 2 was used for classification. The classifier was evaluated by 10-fold stratified cross-validation and analysis of accuracy, sensitivity, and specificity were reported.True Positives (TP) refers to the T2DM individuals that are correctly classified as patients. False Positives (FP) refers to the healthy individuals that are incorrectly classified as patients. True Negatives (TN) is the number of healthy individuals that correctly classified as healthy, and False Negatives (FN) is the number of T2DM individuals that are incorrectly classified as healthy.For feature selection, a feature selection method, based on a hybrid of genetic algorithm (GA) and support vector machine was employed. In the GA, the feature subset is encoded as the candidate solution on a chromosome-like structure. The population is formed from a set of chromosomes that mutation and crossing over in it occur to generate the next generation. A fitness score is calculated for each chromosome representing adaptation of it to the environment and better feature subsets have more chance of reproducing the next generation. Creating the next generations will be continued until a stopping criterion is satisfied.Here, a binary GA was employed that the binary values of 1 and 0 represent the presence or absence of a specific feature at the particular chromosome, respectively. The chromosome length was set to the number of features. The population size and the maximal number of generations were set to 300 chromosomes and 100 generations, respectively. We used the accuracy of the SVM classifier as the fitness score. The GA was stopped if the fitness score was reached an accuracy higher than 90 percent or the maximum number of generations was reproduced.We looked for the most comprehensive skeletal muscle gene expression profiles. We queried the transcriptome repositories for individuals’ expression profiles with the following criteria: 1) newly diagnosed with T2DM. 2) Taking no medication for diabetes. 3) Do not have any other disease affecting the analysis. This resulted in data with the accession code phs001068.v1.p1 in the dbGaP database. Diabetic patients in this sample have been newly diagnosed as T2DM patients, and in most of them, the fasting blood glucose concentration was around seven mmol/l. Reconstruction of the muscle-specific metabolic model requires the human generic metabolic model as the template. Here, the Human Metabolic Reaction 2 (HMR2)  was emplThe biomass reaction is used in metabolic models to represent all metabolites required for cell growth and maintenance. We included the biomass function in the template model to ensure that the simulated fluxes also assert muscle maintenance and viability. The muscle-specific biomass maintenance function was built using the information obtained from the experimental measurements and literature . The prohttps://github.com/Maryamkhn/Muscle_MetNet_CBB.The process of reconstructing the muscle-specific metabolic model requires the expression data to infer active reactions from the template model. The preprocessed transcriptome data from the healthy group were used to integrate into the model. E-flux algorithm  was applFor quality controls and validation, we first checked for biomass production. This asserts that all the required reactions to having an alive model are satisfied. Then, 56 metabolic tasks known to occur in all types of human cells were checked; Important pathways including glycolysis, TCA cycle, pentose phosphate, cellular respiration, synthesis of nucleotides and lipids, oxidation of fatty acids were checked. The complete list of these metabolic tasks can be found in . MoreoveThe reconstructed muscle metabolic model can be employed as a context to investigate metabolic reprogramming in diabetic muscles. Integrating gene expression data with the metabolic model can unravel pathways implicated in the disease. Here, we applied constraint-based personalized metabolic modeling. We reconstructed personalized metabolic models for healthy and T2DM individuals. Then, using a constraint-based approach, metabolism was simulated at the personalized level. In this approach, the metabolic model is converted to the mathematical format, and several constraints (e.g. adjusting lower and upper bound of fluxes in each reaction) were imposed on the model. Combining high-throughput transcriptional data and constraint-based models provides an opportunity to infer cellular metabolism at the relevant physiological state . Flux vaThis analysis resulted in perturbed reactions involving in several pathways. The perturbations in the glucose to glucose-6-phosphate conversion reaction and the glucose exchange reaction were also observed. Many reactions contributing to fatty acids and lipid metabolism were dysregulated. Also, changes in fluxes of several reactions implicated in inositol phosphate, chondroitin, and heparin sulfate metabolism were observed. To find statistically significant enriched pathways, pathway enrichment analysis was employed. To reveal metabolic alterations at the subcellular compartment level, we classified each perturbed reaction based on the compartment the reaction occurs inside it  indicates insulin resistance in this group, which is compensated by more insulin secretion. Thus, we decided to remove data from NGT/obese individuals and re-evaluated markers in this set. The result significantly altered with 78.50% accuracy, 70.17% specificity, and 82.67% sensitivity. Therefore, the metabolites markers are related to insulin-resistant metabolism and can successfully discriminate insulin-resistant individuals from healthy ones.Here, we aimed to provide a holistic view of the metabolism remodeling in skeletal muscle of newly diagnosed T2DM patients. Since diabetes is a progressive metabolic disease, our goal was to identify the metabolic alterations at the early stages of the disease. We used the gene expression data from the most comprehensive human skeletal muscle transcriptome study to date. The diabetic sample consists of participants who have newly been diagnosed with diabetes (with an average blood glucose concentration of ~ 7.2 mmol/L) and had not taken any medications. For understanding metabolism alterations in T2DM, we first reconstructed our functional muscle-specific metabolic model. We could not utilize Bordbar  or NogieWe employed the reconstructed model for system-level investigation of dysmetabolism in T2DM. Personalized metabolic models with constraint-based simulations were applied to quantitatively compare metabolic capabilities between healthy and newly diagnosed diabetic patients. Perturbed reactions and subsequently affected pathways and metabolites were identified. We found that significant metabolic alterations occurred in several pathways. Pathways implicated in membrane and ECM biosynthesis were dysregulated. Perturbations in the metabolism of keratin sulfate, chondroitin sulfate, and heparan sulfate, inositol phosphate, glycerolipid, glycerophospholipid, and sphingolipid metabolism were observed. Chondroitin sulfate, keratin sulfate, heparan sulfate, and hyaluronic acid are glycosaminoglycans (GAGs). GAGs play a vital role in cell physiology including cell signaling, proliferation, and cell adhesion. The insulin-sensitizing and anti-diabetic impacts of some GAGs have been reported , 24. PerSignificant alterations in lipid metabolism including glycerolipid, glycerophospholipid, sphingolipid, and glycosphingolipid metabolism were found in our analyses. These pathways mediate the production of major constituents of the plasma membrane. These metabolites act as structural components of membranes and as signaling molecules. Dysfunctional lipid metabolism can lead to membrane rearrangement, changing its integrity, and interrupt cell signaling. Perturbation in these pathways also triggers feedback loops resulting in mitochondrial dysfunction, reactive oxygen species production, and inflammation. Studies have found several intermediates in these pathways implicated in insulin resistance .Enrichment analysis also revealed perturbation in amino acids. Previous studies have demonstrated associations between amino acids and T2DM. In particular, the various pieces of evidence have implied the implication of branched-chain amino acids (BCAAs) in diabetes. Studies have shown that dysregulation of BCAAs metabolism results in the serine phosphorylation of insulin receptor substrates and subsequent uncoupling of insulin signaling . Here, iAnalysis of metabolic alteration at the sub-cellular level revealed that Golgi is mainly affected by metabolic remodeling in T2DM. This compartment implicates in building ECM metabolites. Many studies have been performed on the role of mitochondria and endoplasmic reticulum in the development of insulin resistance ; whereasTaken together, the main dysmetabolism was observed in ECM components, lipids, and amino acids related pathways. Muscle dysmetabolism changes the abundance of metabolites involved in the membrane and ECM. This metabolic reprogramming can interrupt the process of sensing insulin and transmitting signals into the cell. Decreased insulin sensitivity results in lower expression of insulin-responsive genes, reduced glucose uptake, and consequently changes in energy, glucose, fatty acids, and amino acid metabolisms.As the final analysis, potential metabolite markers were predicted. Our approach led to the identification of 13 exchange metabolites that could discriminate healthy individuals from T2DM patients with 81% accuracy. We validated these markers using separate gene expression data from the Varemo et.al. study . SeveralS1 File(XLSX)Click here for additional data file.


The identification of significantly altered metabolites was accomplished through MetaboAnalyst (v.5.0) and R packages, which enabled both multivariate and univariate analyses. Out of the 48 metabolites identified, 14 were chosen based on their significant alterations in MCF-7 cells under different glycaemic conditions and PEDF treatment . Dysregulation in pathways associated with amino acid metabolism, intermediates of the TCA cycle, nucleotide metabolism, and lipid metabolism were detected, and they exhibited different responses to PEDF. Our results suggest that PEDF has a diverse influence on the metabolism of MCF-7 cells in both normo- and hyperglycaemic environments, thereby warranting studies using patient samples to correlate our findings with clinical response in the future.Studies have demonstrated that pigment-epithelium-derived factor (PEDF) is a robust inhibitor of tumour growth and development, implying that this may serve as a promising target for therapeutic intervention. However, the precise impact of PEDF on cancerous cell metabolic pathways remains uncertain despite ongoing research. In this light, this study aimed to employ a metabolomics approach for understanding the metabolic reprogramming events in breast cancer across different glycaemic loads and their response to PEDF. Gas chromatography-quadrupole mass spectrometry (GC/Q-MS) analysis revealed metabolic alterations in ER Based o subtype . Despite subtype . ConsequCancerous cells exhibit a unique metabolic phenotype that confers the ability to sustain high energetic demands of vigorous cellular proliferation and growth . A notabPigment-epithelium-derived factor (PEDF) is a secreted glycoprotein of the serpin (serine protease inhibitor) superfamily, which was initially identified as a potent angiogenic inhibitor secreted by retinal pigment epithelium (RPE) cells . PEDF exMetabolomics, a rapidly evolving field of omics sciences, plays a pivotal role in cancer research by providing valuable insights into the metabolic alterations associated with tumourigenesis and disease progression . Untarge2O) were obtained from Sigma-Aldrich, along with alkanes, 4,4′-dibromooctafluorobiphenyl, and hexane. Derivatisation reagents including methoxyamine (MOX), trifluoroacetamide (MSTFA), and 1% trimethylsilyl chloride (TMCS) were purchased from Thermo Fisher Scientific, Waltham, MA, USA.The recombinant PEDF was supplied by MD Bioproducts . The MCF-7 was from the American Tissue Culture Collection, ATCC . Dulbecco’s modified Eagle medium (DMEM), foetal bovine serum (FBS), and antibiotic/antimycotic were all purchased from Sigma-Aldrich, Saint Louis, MO, USA. HPLC-grade isopropanol (IPA), methanol (MeOH), and water (H+/PR+/−/HER2−) breast adenocarcinoma cell line (MCF-7) was cultured in DMEM with 10% foetal bovine serum (FBS) and 1% antibiotics and antimycotics under normal glucose conditions (5 mM). The cultures were grown to 80% confluence under controlled conditions at 37 °C with 5% CO2 and passaged weekly following the established ATCC protocols. For the metabolomics analysis, the MCF-7 cells were seeded in 24-well plates at a density of 4 × 105 cells/well in two distinct media groups with varying glucose concentrations (5 mM and 25 mM). After the cells were cultured for 24 h, they were exposed to a physiologically normal concentration of PEDF (100 nM) [g for 5 min. The culture medium was discarded, and the obtained pellets were promptly frozen at −80 °C until sample preparation. The experiment was carried out with six replicates per media condition.The human luminal-A (ER(100 nM)  and theng for 15 min at 4 °C. Finally, the supernatants containing the cell metabolites were subjected to evaporation to dryness under a nitrogen stream. The dried residues were derivatised using two steps as follows: methoxyamination was performed by adding 30 μL of methoxyamine hydrochloride and heating for an hour at 60 °C. This preceded the addition of MSTFA as trimethylsilylation reagent containing 1% trimethylchlorosilane (TMCS) and a standard mixture of the alkane retention index (50 μL). The mixture was then incubated for 20 min at 45 °C to allow the reaction to take place. Following this, each sample was dissolved with 20 µL of an injection standard that contained 4,4′-dibromooctafluorobiphenyl at a concentration of 10 mg/L in hexane. The supernatant of each sample was immediately transferred to autosampler GC/MS glass vials for GC/MS analysis.Metabolite extraction and derivatisation were performed following a previously described method . In briem/z 50–600.An Agilent 5977B MSD/Agilent 8860 GC system fitted with a Restek Rxi-5-ms column ; 0.25 μm film) was used to analyse the derivatised samples. Approximately 1 µL of each sample was injected at a split ratio of 1:1 in random order. The chromatographic method was conducted at a constant flow rate of helium 1 mL/min as the carrier gas, ramping from 20 °C/min to 320 °C, and then holding at 320 °C for 5 min. The MS source, transfer line, and quadrupole temperature were set at 150 °C, 290 °C, and 250 °C, respectively, operating in electron ionisation mode at −70 eV. After a 5.4 min solvent delay, mass spectrometry data were collected at a scan rate of 20 spectra/sec within the range of p-value < 0.05 and FDR < 0.1.The data were processed using MS-DIAL (version 4.9) to create a data matrix consisting of InChIKey, peak intensity, and the original dataset’s average retention time (RT). Metabolite names were assigned to the GC/MS spectra based on two parameters: mass spectral similarity and retention indices . To confirm the identified metabolites, the GC/MS spectra were compared to several mass spectral libraries, including Fiehn library, MassBank, Golm DB, GNPS, and HMDB. The peak intensity was normalised by sum and scaled using autoscaling with MetaboAnalyst (v5.0) to achieve normal distribution. Multivariate and univariate statistics and visualisation were performed with supervised partial least squares discriminant analysis (PLS-DA) and one-way analysis of variance (ANOVA). The data were displayed as a boxplot by applying the R packages “ggpubr” and “tidyverse”. An investigation into the primary biological pathways was conducted via an enrichment pathway analysis using MetaboAnalyst (v5.0), with a significance threshold of The data were processed by MS-DIAL, resulting in 356 compounds, out of which 48 metabolites were considered reliable for further analysis , and theA supervised PLS-DA model score plot showed a perfect separation among the groups B. The crp-values < 0.05, FDR q < 0.05) coupled with a multivariate test with a VIP score of ≥0.8 revealed that out of 48 metabolites, 14 metabolites exhibited substantial differences among the groups  was used to perform an enrichment analysis of the 14 critical metabolites. The findings indicated that PEDF had a notable impact on three major metabolic pathways. The first pathway affected by PEDF was arginine and proline metabolism, as evidenced by changes in glycine, fumaric acid, aspartic acid, and succinic acid. The second pathway influenced by PEDF was glycine and serine metabolism, which exhibited alterations in glycine, threonine, methionine, and homocysteine. The third significant pathway affected by PEDF was the citric acid cycle with amended metabolites such as fumaric acid, malate, and succinic acid . All of Hyperglycaemia, a metabolic diabetes, has been implicated in recent decades as a risk factor for breast cancer , which cIt has been demonstrated that PEDF is associated with metabolic disorders . On the The metabolomic analysis conducted in this study revealed significant alterations in the metabolic profile of MCF-7 breast cancer cells following PEDF treatment under normal and high-glucose conditions. Several metabolites were found to be differentially regulated by PEDF treatment, including uracil, beta-alanine, methionine, glycine, succinic acid, cholesterol, homocysteine, methylmalonic acid, oleic acid, fumaric acid, threonine, malate, palmitoleic acid, and aspartic acid. These metabolites play crucial roles in various metabolic pathways, including nucleotide, amino acid, lipid, and energy metabolism.The biosynthesis of nucleotides plays a vital role in the metabolic processes necessary for tumour cell replication . AccordiAmino acid metabolism is one of the key pathways that is altered in cancer cells, and changes in the levels of specific amino acids have been linked to tumour cell growth and survival . The pre+ human BC. Elucidating the precise mechanisms underlying these changes is challenging due to metabolism’s complexity and other factors’ influence. There are similarities between the alteration of methionine by PEDF in the glycaemic environment in this study and the one that our group performed on MDA-MB-231; however, the changes were not significant [The reduction in methionine levels observed in MCF-7 cells under high-glucose conditions may be attributed to the upregulation of the transsulfuration pathway, which diverts the methionine pool towards cysteine synthesis ,36. Thisnificant . Exposur+ human BC. This implies that PEDF might directly target beta-alanine biosynthetic pathways or interact with upstream regulators, circumventing the influence of glucose availability. This finding can potentially be elucidated by the biochemical conversion of uracil into beta-alanine, whereby the administration of PEDF triggered a decline in uracil concentration followed by an elevation in beta-alanine levels, facilitating carnosine biosynthesis. The elevated beta-alanine levels induced by PEDF may have broader implications in the context of cancer biology and metabolic disorders. Beta-alanine, as a component of carnosine, can modulate intracellular pH and act as an antioxidant [Our experiment uncovered a noteworthy observation pertaining to the beta-alanine levels. Beta-alanine is a non-proteinogenic amino acid produced in the human body  from theioxidant , potentiioxidant . MoreoveAnother important finding was the significant changes in the metabolites associated with the TCA cycle, particularly malate, fumaric acid, and succinic acid. All three increased under high-glucose conditions, and after exposure to PEDF, their levels increased under normal conditions and decremented under high-glucose conditions. In concordance with the present results, our previous study demonstrated the same trends regarding the mentioned metabolites in MDA-MB-231 in that the malate levels showed more significant differences between them . Under hThe present study observed a noteworthy alteration in lipid metabolism, specifically concerning cholesterol, which agrees with our previous work on MDA-MB-231. Cholesterol plays an integral role in cell membranes  and the However, despite their similarities in metabolite alterations under normal and high-glucose conditions and responses to PEDF, MCF-7 and MDA-MB-231 exhibit distinct differences in their metabolic profiles. The metabolic disparities between MCF-7 and MDA-MB-231 cells are likely attributed to their different genetic backgrounds and molecular characteristics . MCF-7 c+/PR+/−/HER2−) subtype, as well as the significant impact of PEDF on metabolic pathways. The metabolomics data analysis demonstrates that PEDF has a diverse impact on the metabolism of MCF-7 cells under both normo- and hyperglycaemic conditions. More importantly, this study has identified new metabolic markers that are heavily influenced by PEDF, which can play a crucial role in regulating the metabolism of BC cells. Understanding how PEDF affects cellular metabolism provides insights into the complex regulatory mechanisms that cells employ to adapt to changing environmental conditions, such as glucose availability. This knowledge contributes to our understanding of cellular homeostasis and metabolic plasticity. PEDF has been shown to modify the levels of metabolites in essential metabolic pathways such as the TCA cycle, lipid metabolism, and amino acid metabolism. While there are notable similarities in the alteration of metabolites and PEDF responses between MCF-7 and MDA-MB-231 cells, their distinct metabolic profiles highlight the need for a comprehensive understanding of the metabolic heterogeneity within breast cancer subtypes under stress conditions. Unraveling the complexities of their divergent metabolism could provide valuable insights into their respective tumour biology and aid in developing personalised treatment approaches for breast cancer patients. The observed impact of PEDF on diverse metabolic pathways in BC cells raises intriguing questions about the underlying mechanisms. Further investigations into the specific molecular interactions and signaling cascades involved in PEDF-mediated metabolic regulation can provide valuable mechanistic insights. Understanding these mechanisms can uncover novel targets for therapeutic intervention and enhance our knowledge of fundamental cellular processes. Hence, more research must be conducted to fully discover primary mechanisms of these effects, especially at the protein level, to enhance the accuracy of initial therapeutic selection systems and identify new targets to improve breast cancer treatment.After considering the original objective of this study, it is now possible to state that this finding yielded valuable insights into the link between metabolic processes and glycaemic conditions in BC, specifically the luminal-A (ER


These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved ‘normal’ (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised ‘normal’ 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and ‘biomarkers’ featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with ‘normal’ levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan–nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available. In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset , the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set . These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann–Pick class 1 (NPC1) disease ( Niemann–Pick type C disease  is a neurodegenerative lysosomal storage disease that arises from defects in either the type C1 or C2 genes . NiemannN-glycine (known as bile acid B) , and these are relevant to measurements of the quality of the rules obtained with respect to trade-off between generality and precision, and interest. More information about these quality measures can be found in p values were provided after adjusting for multiple testing.For this investigation, a qualitative metabolite set enrichment analysis (ORA) was also performed. Notably, this form of Metabolite Set Enrichment Analysis (MSEA) directly investigates whether or not a series of functionally associated metabolites are represented or over-represented by selected metabolic pathways or routes without any requirement to preselect metabolites on the basis of a superficial, often subjective cutoff threshold value. Hence, it offers much promise for the identification of only slight, but nevertheless consistent, changes amongst a group of related biomolecules, which may indeed not be detected using more conventional arbitrary approaches. Here, ORA was implemented using the hypergeometric test to evaluate whether a particular metabolite set is represented more expectedly than by chance within the provided lists of compounds present in a range of metabolic pathways. One-tailed p(4)-aminobenzoate, hippurate, quinaldate and trigonelline (both R6 and R7), and indoxyl sulphate and 3-methylhistidine (R7 only). This analysis showed that the significant metabolic pathways involved in the NPC1 disorder were methylhistidine metabolism (enrichment ratio (ER) 85.5), β-alanine metabolism (ER 10.0), nicotinate and nicotinamide metabolism (ER 9.3), histidine metabolism (ER 7.9) and finally tryptophan metabolism (ER 5.7); raw p values for these were 0.0117, 0.0996, 0.105, 0.121 and 0.116, respectively; however, all corresponding false-discovery rate (FDR)-corrected p values were, as expected, >0.05).For this purpose, a software option containing 99 metabolite sets based on normal human metabolic pathways was used. Firstly, all biomolecular markers featured in both NPC1-disease-distinguishing rules R6 and R7 were featured; these subgroup biomarkers were xanthurenate and nicotinate , p values for these were 0.0117, 0.00373, 0.0909, 0.0964 and 0.121, respectively . Finally, all metabolites implicated in all rules as directive subgroup variables were included in the enrichment analysis model, and this approach found that, again, methylhistidine metabolism was the most important (ER = 73.26), followed by the nicotinate and nicotinamide (ER = 11.86) > β-alanine (ER = 8.62) > histidine (ER = 6.82) > ammonia recycling (ER = 5.32) > tryptophan (ER = 4.88) > purine (ER = 1.98) metabolic routes  and nicotinate and nicotinamide (p = 0.0675) metabolic pathways.Secondly, from Rule 1 to Rule 7, all metabolites deviating from their “normal” urinary concentration levels were included, and for this model the metabolic pathways featured were methylhistidine metabolism (ER 85.47), nicotinate and nicotinamide metabolism (ER 18.52), ammonia recycling (ER = 10.66), β-alanine metabolism (ER = 10.04), and histidine metabolism (ER = 7.94); raw c routes . This fiHowever, it is important to note that such analysis is based on the conventional number of biomolecular “hits” on individual metabolic pathways and determinations of the statistical significance of these , an apprInterestingly, the enrichment analysis performed also found that there were metabolic connectivities between the β-alanine and histidine pathways, with the latter then feeding into the nicotinate and nicotinamide and/or the ammonia recycling routes.1H NMR-linked urinary dataset was collected, spectrally interpreted and analysed. This methodology is noninvasive, with a cost lower than that associated with other studies such as genomic-based ones. Once samples were prepared, the study from predictive and descriptive induction was carried out. The results of the study showed the importance of the application of intelligence techniques to “real-world” problems and, specifically, in medical domains where the combination between excellent infrastructure equipment and complex computational methodologies can throw new knowledge to researchers on rare cases such as the NPC1 disease.An experimental study using data mining methodology in order to analyse a very complex problem, the CIT-based diagnosis of Niemann–Pick type C1 disease, is presented herein. To achieve this task, an This Discussion section is divided into two parts. Initially, a discussion regarding the SD viewpoint is performed, and then a qualitative over-representation metabolic pathway network enrichment analysis of the results acquired and their potential ramifications are presented in different subsections.All subgroups obtained have a low number of variables. For example, subgroups for the control heterozygous carrier class are between three and four variables, and for the NPC1 class, the subgroups have between six and seven variables. These values are low with respect to the whole dataset, which contains 54 continuous variables. This property shows the advantages of the use of this type of algorithm in order to analyse complex problems such as this one.The unusualness values are very interesting with values in the interval . As we have presented in ref. , we can rateTP and fuzzy confidence is good. For the heterozygote class, this relation is excellent with values in confidence close to 1.0, along with excellent values in general. Nevertheless, in the NPC1 class, it should be noted that all or almost all examples of these collected samples are covered by the subgroups, respectively. However, despite the subgroups obtained in this class being specific, their confidence criterion values are somewhat lower.The relation between TEF p value parameter are lower than the α = 0.10 considered in the experimental study, so all subgroups reject the null hypothesis, i.e., subgroups are interesting because there are significant differences between the proportions of positive and negative examples covered and not covered for each rule.Finally, it should be considered that all values obtained for the As noted in previous sections, the discussion for SD must consider interpretability, interest and good trade-off between generality and precision. In this manner, we observed the following:rateTP and rateFP can be observed in rateTP and rateFP close to the main diagonal. This is primarily attributable to rules with the rateTP/rateFP ratio on the main diagonal having the same percentage distribution of covered positives and negatives  as the distribution in the entire dataset. In this manner, all subgroups obtained in the experimental study are valid and relevant from this point of view. Specifically, we can observe subgroups for the NPC1 class with the highest values in rateTP and good relationships with respect to the rateFP values.The ROC analysis for the space It is very important to note the quality of the NMEEFSD algorithm in this type of complex problem in predicting new instances for the NPC1 disease class. As can be observed, in The valuable diagnostically and/or prognostically trackable disease biomarkers found in the NMR-linked SD investigations performed here may indeed provide options for the design and development of novel, handheld biosensor devices which, at least in principle, can be operated and applied in a PC-controlled multi-analyte format, and which may achieve the detection and quantification of five or six of the most clinically significant biomarkers discovered. This strategy may provide an indispensable technology for the evaluation of NPC1 and other lysosomal storage diseases in an MV context, i.e., that involving rapid urine screening at patient points of contact .in vivo .In addition to the provision of useful diagnostic and disease monitoring biomarker information to clinical and health service staff, the novel SD approaches developed here will potentially also inform researchers of modifications to intracellular metabolic pathways and functions in NPC1 and related diseases, including information regarding the availability of potential interventional sites for drug actions, notably those for therapeutic agents which are either already established or which are newly developing. Indeed, the identification, detection and validation of disease biomarkers, and their metabolic pathway impacts and longevities, are of critical importance for these considerations, as are the identification of cellular activities that are or may be affected by the upstream activation or downstream inhibition of disease-specific target proteins. Of course, also critical is the rich information potentially available on the actions of genes/proteins for drug-targeting technologies in diseases explored, together with the employment of fully validated biomarkers for evaluating dose-dependent drug actions and activities 1H NMR-linked technologies may serve to offer essential supporting diagnostic information.However, it is of much pertinence to note that full validation of such biomarkers may only be accomplished when it is strongly statistically verified that they are able to respond positively to therapeutic avenues with pre-established, already-known and effective disease-modifying regimens for patients with the clinical condition considered. Additionally, it is also recommended that SD- and corresponding metabolomics analysis-identified biomarker strategies should be used only when fully validated, and when this is the case, they should be employed in parallel with other, more conventional or established methods for the clinical diagnosis of diseases, for example, histopathology gradings and supporting microscopic, haematological or microbiological evaluations, etc., where available. Indeed, this approach will permit researchers to explore significant associations between these two diagnostic routes of recognition. If such correlations are strong, these 1H NMR-based SD techniques presented here offer much value, most especially since this HL diagnostic problem again has an imbalanced experimental design. Hence, in this situation, patients presenting with an SD-determined poor prognostic potential may receive more intense treatments, whereas those with a more favourable prognostic status may be spared the harmful adverse side effects experienced with chemotherapeutic treatments.With respect to the prognostic monitoring of disease progression and the potential successes of therapeutic agents applied to differential disease subclasses and stages, currently there is a high level of interest in treatment strategies that feature the prior stratification of patients according to their biological classifiers of disease , or alternatively the degree of their responses to selected drug therapies. One informative example of this is Hodgkin’s lymphoma (HL) ,52; indetrans retinoic acid for treating a high percentage of patients with acute promyelocytic leukaemia who bear translocations in the retinoic acid receptor (RAR) α gene :[tryptophan] concentration ratios, and corresponding depleted tryptophan levels in blood serum, cerebrospinal fluid and/or brain tissue, are actually substantial in neurodegenerative diseases over and above that observed in a normally ageing population ,66; manyHowever, from these considerations, particularly important is the involvement of neurotoxins generated in this critical pathway and also alterations in the levels of neurotransmitters such as melatonin and serotonin. Indeed, selected [metabolite]:[tryptophan] concentration ratios can provide much valuable information regarding the degree of neuroinflammation featured in neurodegenerative diseases .Also notable as key biomarker variables in the current study are further metabolites involved in the tryptophan metabolic pathway, specifically xanthurenate and indoxyl sulfate. The former of these, a quinolone carboxylate derivative, forms part of the pathway subroute from 3-hydroxy-L-kynurenine, its immediate precursor being 4-(2-aminophenyl)-2,4-dioxobutanoate, whereas the latter is the sulphated urinary excretion product of indoxyl (indoxyl sulphate), which arises from tryptophan itself via indole.n = 17 fibromyalgia patients; although these cases reacted similarly to an age-matched healthy control group for most markers evaluated, the fibromyalgia patient cohort had significantly elevated interleukin-6 (IL-6) concentrations. Such results indicated modifications in tryptophan metabolism, since diminishing tryptamine levels activate both 5-hydroxytryptamine (serotonin) metabolism and IL-6 generation. However, in healthy control subjects, a tryptophan depletion-mediated experimental and transient interruption in global monoamine species function was not found to induce IL-6 production :[NADH] molar concentration ratio, which represents an index of redox status within mitochondria. Although not featured as a key discriminatory subgroup metabolite in the human dataset explored in the current study, its creatinine-normalised urinary concentration was significantly lower in a univariate context .3-Hydroxyphenylacetate is involved in the human tyrosine metabolism pathway, in which it is transformed to 3,4-dihydroxyphenylacetate via the enzyme 4-hydroxyphenylacetate 3-mono-oxygenase, a reaction which also requires the NADetonuria . Moreoveinction) . Such peHowever, the potential applications of 3-hydroxyphenylacetate as a biomarker for NPC1 disease remain complex and unclear, since this phenolic acid anion is also generated as a co-metabolite arising from gut microflora . A furthAlthough previous reports available on the value of 3-hydroxyphenylacetate as a biomarker are somewhat limited, Boudonck et al.  found thOf the other non-tryptophan–nicotinamide pathway metabolites featured in the SD models developed here, histidine represents an essential precursor for the biosynthesis of both histamine and carnosine and interestingly is involved in a variety of mental and physical retardation problems, for example, diminished intellectual function, ataxia, tremor and psychoses. However, 3-methylhistidine arises from the methylation of actin and myosin, and peptide bond biosynthesis, and serves as a biomarker for muscle protein degradation , and theMoreover, the purine derivative hypoxanthine is an adenosine intermediate involved in the salvage pathway of nucleic acid production, whereas urinary concentrations of hippurate, an excretion product derived from the conjugation of glycine with benzoate, are elevated with increasing levels of dietary phenolic compound consumption .1H NMR region of (aromatic) metabolites which qualified as significant biomarkers were upregulated urinary quinolinate and 3-methylhistidine and downregulated nicotinate, 1-methyl nicotinamide, N-methyl-2-pyridone- and N-methyl-4-pyridone-5-carboxamides , and the caffeine metabolite trigonelline, although the latter may have arisen from a much-diminished rate of caffeinated beverage consumption in the largely younger or much younger NPC1 disease group evaluated. Together with these aromatic biomarkers, a range of aliphatic ones were also detected, e.g., bile acids, 3-aminoisobutyrate and other branched-chain amino acid (BCAA) intermediates and degradation products, N-acetyl sugars, glutamine, creatine, succinate and trimethylamine, which were all NPC1 disease-upregulated. Extension of these datasets to quantitative metabolite set enrichment and pathway topological analyses revealed that bile acid biosynthesis, BCAA degradation, methylamine metabolism, pyrimidine (thymine) catabolism and the nicotinate and nicotinamide metabolism pathways were imbalanced in NPC1 disease and that the brain, liver, mitochondria, endoplasmic reticulum, and of course the lysosome, represented key tissue, organ and subcellular sites for important disease activities. However, for the current investigation, a qualitative metabolite set enrichment analysis performed revealed that the significant metabolic pathways involved were the methylhistidine, β-alanine, ammonia recycling, nicotinate and nicotinamide, histidine, tryptophan and purine metabolism routes, with methylhistidine metabolism being the most important -corrected p value 0.0234).Our previously conducted metabolomics investigation discovered a wide range of urinary metabolites in patients with NPC1 disease which were significantly modified in concentration when compared to a larger cohort of parental heterozygotic carriers of this disease , althougp(4)-aminobenzoate as a critical feature in rules 6 and 7 for the diagnosis of NPC1 disease  the joint tryptophan–nicotinamide metabolic system, which is known to be affected by physiological homeostasis and selected human disorders, with the essential amino acid tryptophan acting as a neurotransmitter precursor, and quinolinate playing an important role in neurodegenerative diseases in general; (2) the kynurenine pathway, which is efficiently upregulated by immune response activations; (3) tryptophan metabolism, which is of much relevance to the pathogenesis of lysosomal storage diseases; and (4) tyrosine metabolism, which features the metabolite 3-hydroxyphenylacetate, and which has previously been observed to be a potential biomarker for NPC1 disease-driven hepatic dysfunction in a mouse model, although it should be noted that it may also arise from dietary sources. Further metabolic routes implicated are those involving histidine and 3-methylhistidine, and the adenosine intermediate hypoxanthine, along with 4-aminobenzoate and the excretion product hippurate. The novel SD strategy employed for the analysis of MV data from this NPC1 study should be readily translatable to the analysis of other metabolomics datasets with only clinically limited sample sizes. In conclusion, the novel SD techniques applied in this investigation for the first time provided much valuable knowledge regarding biomolecular and metabolic pathway imbalances potentially featured in the pathogenesis of NPC1 disease. This information was derived from an examination of disturbed, NPC1-mediated patterns of urinary metabolites exposed by high-resolution