Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations.IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics. Saccharomyces cerevisiae, which is an extensively used model organism in industrial and medical applications, can exhibit self-sustaining oscillatory patterns in essential cellular processes like the cell division cycle (CDC), glucose metabolism, and respiration  and regulate their physiology according to new conditions . Saccharpiration . These ppiration –3. The yiration –, 5.2) in a yeast culture, two distinct YMC phases (growth-related high-oxygen-consumption [HOC] and stress/quiescence-related low-oxygen-consumption [LOC] phases) were described  is commonly characterized by respiratory oscillations in nutrient-limited chemostat cultures at high cell density . Even thescribed . In the escribed , 5. Diffescribed , 8–12. Rcribed 8–, 13. NADcribed 8–. NADPH acribed 8–, 13. Acccribed 8–, 13. Whicribed 8–, 13–15. bed 8–13–, 16. Thubed 8–13–.S. cerevisiae  , and metabolic regulation of the epigenome is essential to determine eukaryotic transcription levels  models have been used as standard platforms to analyze yeast metabolism since 2003 . GMNs ar18–revisiae . The trarevisiae , 25. At revisiae , iMAT  , 38. Varication) , 40. In ication) , 41, andication) , 42–44. n levels .5 transposase enzymes to insert adapters into accessible (nucleosome-free) regions of the genome prior to high-throughput sequencing (–A positive correlation between transcriptome sequencing (RNA-seq) and transposase-accessible chromatin using sequencing (ATAC-seq) results was reported for a certain part of the genome . ATAC-sequencing . There iquencing –49. Suchuencing –, 41, andS. cerevisiae across YMC phases. Chromatin accessibility and transcriptome levels derived from ATAC-seq and RNA-seq methods were used to achieve this goal. Integration of RNA-seq and microarray data sets into GMN models is an extensively used approach in condition-specific simulations. On the other hand, ATAC-seq data also provide a genome-wide information about dynamic cellular alterations. Therefore, we focused on the incorporation of this information in a generic yeast model for phenotype predictions across YMC phases.In this study, we evaluated the contribution of epigenetic information to the prediction of metabolic profiles of As summarized in the flowchart in S. cerevisiae strain CEN.PK in a comparative manner to examine the alterations in yeast metabolism across diverse YMC phases. We first mapped transcriptomic  RNA-seq data sets matched with Yeast8 genes; (B) ATAC-seq data sets matched with Yeast8 genes. Download Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the In RNA-seq data, cluster I includes 152 genes , which a10.1128/msystems.01347-21.5TABLE S2Table S2, DOCX file, 0.03 MB.Biological process enrichment analysis of the genes in cluster I using Gene Ontology Term Finder for RNA-seq data (A) and ATAC-seq data (B) (FDR at 0.01 level). Download Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 1 cyclin CLN3. In the OX phase, the level of CLN3 peaks in response to fresh medium  and nucleotides were determined as enriched among the 95 common genes in cluster III, whose upregulations were shown in the differential analyses of both chromatin accessibility and gene expression profiles. This gene cluster represents upregulated genes in particularly mid OX phase: 254 genes for the RNA-seq data and 262 genes for the ATAC-seq data  cycle and electron transport chain for ATP production . The enhh medium . Thus, ah medium . In agreseq data . In agreseq data A and B, seq data . In the seq data . Accordiimidines . We idenimidines , 9. Thusimidines A and B.10.1128/msystems.01347-21.6TABLE S3Table S3, DOCX file, 0.03 MB.Biological process enrichment analysis of the genes in cluster II using Gene Ontology Term Finder for RNA-seq data (A) and ATAC-seq data (B) (FDR at 0.01 level). Download Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.01347-21.7TABLE S4Table S4, DOCX file, 0.04 MB.Biological process enrichment analysis of the genes in cluster III using Gene Ontology Term Finder for RNA-seq data (A) and ATAC-seq data (B) (FDR at 0.01 level). Download Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Characterization of the differential metabolic genes through the analysis of both RNA-seq and ATAC-seq data sets was shown to be useful in order to gain a biological understanding of altered processes during progressive YMC phases. This encouraged us to further assess the power of ATAC-seq data. Therefore, we used this epigenomic information in metabolic modeling to examine phase-dependent metabolic changes. Using the ΔFBA approach, the Yeast8 reactions regulated by phase transitions were identified. This facilitated the elucidation of significantly altered KEGG pathways during the metabolic shift from early RC phase to OX and RB phases via corresponding genes.S-adenosylhomocysteine) participating in the regulatory pathways of sulfur metabolism were reported to display robust oscillations during the YMC , from early RC phase to late OX phase (B), from early RC phase to early RB phase (C), and from early RC phase to late RB phase (D) using the g:Profiler web server for ATAC-seq data (FDR at 0.05 level). Download Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the n = 46) associated with the regulated pathways in the late RB phase than in early RB phase (n = 89) and OX phases (n = 83 [mid] and n = 125 [late]). Differential chromatin accessibility and gene expression patterns also confirmed this result. As described in the previous section, early RC and late RB phases were clustered together . A clear relationship between metabolic cycling and G0/G1 phase was reported for slowly growing budding yeasts, while transcriptional cycling was also identified in G2 phase for fission yeasts  and predominantly lower error values (MSE = 0.07 to 0.64), compared to the generic Yeast8 model (r = 0.59 to 0.76 and MSE = 0.18 to 1.38) and RNA-seq-based YMC models (r = 0.61 to 0.77 and MSE = 0.18 to 1.32). The significance of the differences between the correlation coefficients was evaluated using two-sample z test at a P value cutoff of 0.05. Apart from the RC phase , significant correlation differences were found between ATAC-seq- and RNA-seq-based models . Furthermore, integration of ATAC-seq data resulted in significant correlation differences in comparison with the generic Yeast8 model for all YMC phases. Flux prediction capacity was also evaluated for the combinatory YMC models, which were contextualized by both transcriptomic and chromatin accessibility data. Fluxes from intersection models also exhibited higher correlation to experimental values (r = 0.85 to 0.91) than those from RNA-seq-based models. These correlation values were shown to be significantly different at RC phase  and RB phase  from those of the RNA-seq-based models. Similarly, the correlation differences were found to be significant  in comparison with Yeast8 models, although we did not find a significant difference between the performances of OX models  at the significance level of 0.05. On the other hand, we did not observe considerable significant differences between the correlation values calculated for the union models and Yeast8 model. This indicates that the union models are less powerful to represent the condition-specific behavior of yeast cells.Prior to the use of measured fluxes, we matched the YMC phases and sampling time points of flux measurements according to dOC models . Phase-sC models  through C models . Accordi10.1128/msystems.01347-21.1FIG S1FIG S1, TIF file, 0.5 MB.Estimated flux values of central carbon metabolism through RNA-seq-based (A) and ATAC-seq-based (B) metabolic network models. Both flux maps correspond to early reductive charging (RC) phase, and flux values are given in millimoles per gDW per hour. The dashed arrows show blocked (inactive) reactions due to the preference of alternative routes. Abbreviations for the Embden-Meyerhof pathway: Glc, glucose; G6P, glucose-6-phosphate; F6P, fructose-6-phosphate; FBP, fructose-1,6-bisphosphate; DHAP, dihydroxyacetone phosphate; GAP, glyceraldehyde-3-phosphate; G3P, glycerol 3-phosphate; PG3, 3-phosphoglycerate; PG2, 2-phosphoglycerate; PEP, phosphoenolpyruvate; PYR, pyruvate. Abbreviations for the trehalose biosynthesis pathway: T6P, trehalose 6-phosphate; Treh, trehalose. Abbreviations for the glycogen biosynthesis pathway: G1P, glucose 1-phosphate; UDPG, uridine diphosphate glucose; Glyco, glycogen. Abbreviations for the pentose phosphate pathway: PG6, 6-phosphogluconate; Ribu5P, ribulose-5-phosphate; X5P, xylulose 5-phosphate; Rib5P, ribose-5-phosphate; S7P, sedoheptulose-7-phosphate; E4P, erythrose-4-phosphate. Abbreviations for the tricarboxylic acid cycle: AcCoA, acetyl coenzyme A; Cit, citrate; iCit, isocitrate; AKG, α-ketoglutarate; Succ, succinate; Fum, fumarate; Mal, malate; OAA, oxaloacetate. Abbreviation for the glyoxylate bypass: gliox, glyoxylate. Abbreviations for the amino acid synthesis pathway: Ala, alanine; Asp, aspartate; Glu, glutamate. Download Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.01347-21.9TABLE S613C-MFA are mapped to the reactions in the Yeast8 model. (B) Intracellular fluxes measured by 13C-MFA are mapped to the active reactions in all YMC models  for each phase. Flux values are given in millimoles per gDW per hour. Download Table S6, DOCX file, 0.03 MB.(A) Intracellular fluxes measured by Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Compared to RNA-seq-based models, chromatin accessibility further supports to generate accurate YMC models for the inference of carbon metabolism . To conf10.1128/msystems.01347-21.10TABLE S7Table S7, DOCX file, 0.04 MB.Comparison of the predicted and measured fluxes for different thresholds used in the GIMME algorithm (objective fraction: 0.5). Each column is colored by comparing the corresponding metrics within itself. A color scale is used from blue  to white  to represent the magnitude of mean squared errors, while white  to red  scale is used for Pearson’s correlation coefficients. Download Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the r = 0.72 to 0.86 and MSE = 0.22 to 0.87), incorporation of the chromatin accessibility levels was shown to result in higher correlation to the experimental measurements once more in both ATAC-seq-based models (r = 0.85 to 0.91) and intersection models (r = 0.78 to 0.88). For the filtered data sets, union models also exhibited considerably better correlation values than did RNA-seq-based models for RC and RB phases as a result of more stringent constraints imposed by the filtered data. Furthermore, the integration of chromatin accessibility data sets led to a reduction in MSE values in comparison with gene expression data sets  . We subsata sets . Overall2 phase of the CDC for embryonic stem cells (Although it is not intuitive to find a higher-level cellular regulation layer (chromosome accessibility) to better represent a lower layer (metabolism), rather than an intermediate layer (RNA-seq), there may be several reasons for this outcome. One reason may be the lower signal/noise ratio in RNA-seq data than in ATAC-seq data, leading to less accurate metabolic models. Or, the success of ATAC-seq data in the network contextualization for dynamic events may be explained by the fact that mRNA levels reflect a pool of molecules produced in a certain time period, whereas the ATAC-seq profile reflects the momentary regulation state of the gene at a specific time point. Hence, the ATAC-seq profile may be more accurate reflection of the cell state at a given time point. For instance, ATAC-seq and RNA-seq patterns were reported to differ in the S/Gem cells  even if em cells . This phem cells . Similarem cells .S. cerevisiae provides remarkable information on the epigenetic processes underlying the establishment and maintenance of histone modification-dependent chromatin states in higher eukaryotes, this organism has a relatively simple epigenetic system without many mammalian histone variants, repressive histone mark (H3K9 methylation), DNA methylation, and RNA interference machinery . Growth-associated maintenance, referring to the energy required for biomass formation, was set to 55.3 mmol ATP/g dry weight (gDW). Non-growth-associated maintenance responsible for the energy dedicated to cellular functions apart from growth was used as 0.7 mmol ATP/gDW/h  that were generated and processed by Gowans and colleagues for wild-type S. cerevisiae strain CEN.PK  RC (early and mid) and late RB, (ii) RB (early and late) and late OX, and (iii) early RB and OX (mid and late). The clustered genes were characterized through the investigation of the related biological processes using GO Term Finder tool (version 0.86) (Saccharomyces Genome Database (The RNA-seq and ATAC-seq data sets (GEO accession number n CEN.PK . To inveon 0.86)  providedDatabase  at a falDatabase .q value] ≥ 0.01), which were identified through R (version 4.1.0) limma-trend (default settings)  for OX (mid and late) and RB (early and late) phases. For each phase, we discarded the genes with no significant alterations in their accessibility levels  . Using t Toolbox  in MATLA Toolbox . Thus, toduction . We predoduction  with FDRmapExpressionToReactions function. In the GIMME algorithm, the gene levels mapped to the reactions are used as an input. Other inputs are threshold value, GMN model, and objective fraction  through the integration of transcriptome and chromatin accessibility data sets to the generic Yeast8 model. Distinct models were built for three time points  using RNA-seq and ATAC-seq data sets . To do sfraction . We applfraction . We set In addition to the RNA-seq-based and ATAC-seq-based models  reconstructed as described above, we developed mass-balanced combinatory models (intersection and union models) using multi-omics information. To this end, we generated a binary vector to represent the active/inactive state of reactions. Reactions that were active in either of the ATAC-seq and RNA-seq models were represented as 1 in this binary vector for the union model, while the rest of reactions were set to zero. Similarly, another binary vector was created to generate intersection models such that only reactions active in both ATAC-seq and RNA-seq models were represented as 1 ; Table 1We repeated the model reconstruction steps for filtered data sets to test the impact of measured data compositions on the performances of the YMC models. In the RNA-seq data set, there are genes whose levels were not detected in the ATAC-seq data sets. We removed these genes from the data sets. Then, the filtered data sets were integrated to the generic Yeast8 model using the GIMME algorithm as explained above. In other words, only commonly measured genes were considered in the model reconstruction process. Thus, an additional model was developed for each YMC phase. These models are listed in YMC models were simulated under minimal medium , 85 for ijS with the coefficient of metabolite i within a total of m metabolites, and jV represents the flux of the jth reaction within a total of n reactions. In addition to the assumption on mass conservation, an assumption on the reversibility of biochemical reactions is used in the FBA approach. Accordingly, lbj and ubj are the flux boundaries (lower and upper bounds) for the jth reaction as shown in Vopt), which satisfies the given primary objective function. Predicted growth rate was used as a constraint in the next step. In Vopt. A secondary optimization (minimization of the enzyme production) was subsequently applied based on the minimization of sum of absolute intracellular fluxes as formulated in 13C metabolic flux analysis (MFA) for S. cerevisiae strain CEN.PK117-5D  and mean squared error (MSE). We also compared the significance of the differences between correlation coefficients. Using Fisher’s z transformation, each correlation coefficient was first converted to z-score, which is approximately normally distributed .To evaluate the capacity of YMC models in the prediction of flux distributions, we compared predicted fluxes with the measured counterparts derived from PK117-5D . SimilarPK117-5D , the minPK117-5D  was usedPK117-5D  and fluxPK117-5D  were theg points . Here, wg points , 61, 62.FA study  and the FA study . Thus, od fluxes  to the rd fluxes . At leasd fluxes . For thetributed , 93. The10.1128/msystems.01347-21.2FIG S2FIG S2, TIF file, 0.5 MB.Estimated flux values of the central carbon metabolism through RNA-seq-based (A) and ATAC-seq-based (B) metabolic network models. Both flux maps correspond to mid oxidative (OX) phase, and flux values are given in millimoles per gDW per hour. The dashed arrows show blocked (inactive) reactions due to the preference of alternative routes. Abbreviations are as for Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.01347-21.3FIG S3FIG S3, TIF file, 0.6 MB.Estimated flux values of the central carbon metabolism through RNA-seq-based (A) and ATAC-seq-based (B) metabolic network models. Both flux maps correspond to late reductive building (RB) phase, and flux values are given in millimoles per gDW per hour. The dashed arrows show blocked (inactive) reactions due to the preference of alternative routes. Abbreviations are as for Copyright © 2022 Cesur et al.2022Cesur et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases. Here, the authors present a method to build genetically personalised metabolic models across tissues to estimate individualised reaction fluxes. A fluxome-wide association study in UK Biobank identifies fluxes associated with metabolites and coronary artery disease. While the contribution of individual variants to a given phenotype is generally small, the effect of multiple genetic variants can be aggregated into polygenic scores (PGS), which are highly predictive of disease incidence and enhance existing risk models4. However, while GWAS and PGS can be useful for risk stratification7, the mechanisms by which genetic variants influence disease risk, i.e., variant to function (V2F), remain largely unsolved. Addressing V2F is a major challenge in human genetics and has the potential to unveil many new therapeutic targets9.Genome-wide association studies (GWAS) have identified more than 50,000 genetic variants associated with complex traits or diseases12. For example, PredictDB is a database that offers a collection of linear models to impute transcript levels in specific organs of the human body13. PredictDB models were trained in the GTEx dataset, which contains genotype profiling and tissue-specific transcript abundance from post-mortem donors14. Imputed transcript or protein levels can be used to perform transcriptome-wide or proteome-wide association analyses, respectively, to identify gene products associated with disease15. Alternatively, PGSs for disease can be used to identify proteins and other gene products which may disrupt polygenic risk16. However, transcripts and proteins do not exert their effects in isolation but in highly connected and complex biological networks. Indeed, previous studies have shown the merit of analysing genetic variation in the context of gene co-expression and gene interaction networks to characterise how the effects of genetic variants contribute to complex traits or diseases by propagating through biological networks20.An approach to address the V2F challenge is to quantify how genetic variation causes disease through the regulation of molecular traits. To this end, genetic variants affecting gene expression are identified and subsequently aggregated into models that can impute the abundance of transcripts and proteins21. The metabolic phenotype of a given organ is defined by both metabolite concentrations and metabolic fluxes  and emerges from the complex interaction of metabolites, enzymes, and transmembrane carriers23. Metabolite concentrations offer a static snapshot of metabolite distributions, whereas metabolic fluxes provide a map of metabolite traffic through metabolic pathways24.Metabolism is one of the most prominent biological networks and a comparatively tractable experimental setting in which to address the V2F challenge. Essentially, metabolism is a set of interconnected chemical reactions and transport processes occurring in a highly ordered, regulated and coordinated manner across multiple organs in the human body26, simulate steady-state metabolic fluxes by formulating network stoichiometry as sets of linear equations and directionality constraints27. GSMMs have emerged as a useful approach to integrate transcriptomics, proteomics, and metabolomics to characterise metabolic flux maps29. For example, proteomics, metabolomics, and physiological data have been used to build human organ-specific GSMMs30. Similarly, there is increasing interest in integrating individual measures to build personalised GSMMs that reflect the specific metabolic phenotype in each individual, thus facilitating personalised medicine34.Genome-scale metabolic models (GSMMs), mathematical representations of the metabolic reaction network arising from the human genome13, it may be feasible to leverage human genome-scale metabolic networks to analyse the system-wide effects of genetic variants on metabolism and build genetically personalised GSMMs. To this end, we present a framework where transcript levels imputed from genetic data can be used to simulate personalised and organ-specific, genome-scale flux maps using the quadratic metabolic transformation algorithm (qMTA). Such flux maps provide genetically personalised metabolic models at a genome scale for each tissue. As proof of concept, we build personalised organ-specific flux maps for over 520,000 individuals across the INTERVAL36 and UK Biobank (UKB)37 cohorts, then perform a fluxome-wide association study (FWAS) to test the association between organ-specific flux values and directly measured blood metabolite levels. Finally, we apply FWAS to identify fluxes associated with coronary artery disease (CAD), thus demonstrating the potential of genome-scale flux maps for V2F by elucidating intermediary biochemical reactions between genetic variation and common disease.Since gene expression is highly heritable30, which provide a set of curated metabolic networks for the main organs of the human body. Harvey/Harvetta models were built from the Recon3D human GSMM25, which has been superseded by HUMAN126. HUMAN1 shares 97% of reactions with Recon3D, but it incorporates a myriad of improvements in gene-reaction rules, reaction reversibility and stoichiometric consistency compared to the latter. Hence, we performed a liftover of the Harvey/Harvetta organ-specific models to HUMAN1 (Methods).We developed a framework for building personalised organ-specific flux maps from genotype data Fig. ; Methods14 and using them as an input for the GIM3E algorithm38. GIM3E is an algorithm that, subject to fulfilling the organ-specific metabolic objectives, seeks to minimise the overall flux through the network using transcript abundance data to give each reaction a minimisation weight inversely proportional to the expression of the enzymes catalysing it. Subsequently, flux sampling39 is applied to identify a representative flux distribution  in the solution space within 99% of the GIM3E optimal solution. The resulting set of flux values, termed reference flux distribution, is both enzymatically efficient and consistent with the average transcript abundances in each organ , obtaining average organ transcript abundances from GTEx13 are used to impute personalised organ-specific transcript abundances from genotype data. The resulting imputed transcript data are mapped to reactions in the organ-specific subnetworks as putative reaction activity fold changes relative to the average organ-specific transcript expression in GTEx14. The imputed personalised reaction activity fold changes and the reference flux distributions are then utilised by the qMTA to compute genetically personalised organ-specific flux maps. Briefly, qMTA finds the flux distributions most consistent with the putative reaction activity fold changes in each individual  for further analysis  and Metabolon HD4 mass spec assays (N = 8115 participants)50. In UKB, blood samples for 120,266 participants were profiled with Nightingale Health NMR51.We next validated that genetically personalised GSMMs could generate reliable and meaningful flux predictions across cohorts. As phenotypes, organ-specific flux maps are expected to lead to distinct profiles in the blood metabolome. To demonstrate this, we performed an association analysis by individually regressing each measured blood metabolic feature against the 4300 personalised fluxes computed in the INTERVALP < 1.0 × 10−6 was applied to control for all tested pairs (Methods). We identified 4312 significant associations between flux values and blood metabolic features in total, of which 1066 were for the Nightingale platform and 3246 for Metabolon , followed by the heart (1005), skeletal muscle (896), brain (593), and adipose tissue (517)  between INTERVAL and UKB  Fig. . We founheart 100, skeleta30 to compute genetically-personalised fluxes for the INTERVAL cohort36, test their association to blood metabolic features, and compare the results to the above-described FWAS that had used fluxes computed with HUMAN1-based models. We identified 3895 significant associations between blood metabolic features and the genetically personalised flux values computed using Recon3D-based organ-specific models (Supplementary Data ρ = 0.72). However, 2134 associations were only statistically significant on the Recon3D-based analysis and could not be replicated with HUMAN1 models. Likewise, of the 4312 significant associations between blood metabolic features and fluxes computed using HUMAN1 models, 2551 associations could not be detected with Recon3D-based models. Such discrepancy between HUMAN1- and Recon3D-based analyses is not surprising; HUMAN126, which is a newer reconstruction of human metabolism than Recon3D25, expands gene reaction annotations and refines reaction reversibility, both of which can have significant effects on how genetic variation propagates through the network and, thus, can lead to significant differences in the resulting personalised flux maps and the downstream FWAS. Indeed, many discrepancies between the Recon3D and HUMAN1 results are likely artefacts emerging from erroneous or incomplete annotations in Recon3D. Throughout this work, we focus on the analyses and discussion of HUMAN1-based fluxes, as HUMAN1 has been established to be a better representation of human metabolism26, but results obtained with Recon3D-based models will also be provided in the appropriate supplementary data 54.In the liver, we identified 555 associations between fluxes and lipoprotein fractions . Of such associations, 31 could be replicated with genetically personalised fluxes computed with Recon3D-based models, whereas 61 were specific to the HUMAN1-based models. Liver fluxes yielded the largest number of significant associations with CAD (N = 32), followed by fluxes from the adipose tissue (N = 26), heart (N = 15), brain (N = 10), and skeletal muscle (N = 9) , which identified 92 significant associations  and histidine decarboxylation  were associated with increased CAD risk. Histamine is an inflammatory mediator synthesised from histidine primarily in mast cells70, which reside in most tissues, including liver and adipose tissue72. Histamine has been reported to be associated with atherosclerosis via blood lipids and lipoprotein fractions as well as by promoting inflammation76. In adipose tissue, polyamine synthesis was also associated with reduced CAD risk . Notably, polyamine-rich diets have been established to have a protective effect against cardiovascular disease78. Moreover, it has recently been determined that polyamines produced by adipose endothelial cells might protect against obesity, a known risk factor for CAD79, by promoting vascularisation and lipolysis in white adipose tissue80.The flux of histamine synthesis through histidine decarboxylase was shown to be strongly associated with CAD in adipose tissue with a hazard ratio (HR) per s.d. of log-transformed flux value of 1.060 and a P = 6.48 × 10−11), heart  and skeletal muscle  were strongly associated with reduced CAD risk, consistent with the anti-atherogenic effect of lipoprotein lipase activity in these organs81. Similarly, the release of free fatty acids from adipose tissue was also associated with reduced CAD risk . Interestingly, not only is the release of free fatty acids part of normal adipocyte function21, but it is also a key part of the polyamine-driven signalling cascade in adipose tissue80. Conversely, the flux through the phospholipase reaction was associated with increased CAD risk in adipose tissue . Notably, phospholipase activities have been suggested to have a causal role in atherosclerosis and have been investigated as potential pharmacological targets to prevent atherosclerosis and CAD85.Concerning lipid metabolism, the fluxes through the TAG lipase reactions in adipose tissue  and its antiport with glutathione being associated to increased risk . Also in the liver, transport of bilirubin conjugates was associated to decreased CAD risk . Notably, the transport process of bilirubin-monoglucuronoside is mediated by SLCO1B1, which also mediates the hepatic uptake of statins, enhancing their therapeutic efficacy87. Interestingly, high levels of total bilirubin in blood have been associated with decreased risk of CAD89, likely mediated by the modulation of arterial diameter and reactivity90. In both the brain and heart, the flux of prostaglandin E2 transport was also associated with increased CAD risk . Prostaglandin E2 is an inflammatory mediator that promotes inflammation and has been reported to contribute to the development of atherosclerotic lesions92. Additionally, in the brain, the transport of norepinephrine, a neurotransmitter that can increase blood pressure and may play a role in atherosclerosis94, was also associated with increased risk of CAD .The fluxes through several transport processes were also identified as associated with CAD. For instance, histamine transport in the liver appears to be associated with CAD risk in a transport process-specific manner, with histamine transport through uniport being associated with decreased CAD risk  on the personalised flux maps. Additionally, while transcript levels are widely used in genome-scale metabolic modelling38, protein levels have a more direct effect on enzymatic activity, and new methods are being developed to fully integrate them into GSMMs98. With models to impute the levels of proteins becoming increasingly available15, we expect that the framework for computing genetically personalised fluxes will be extended to integrate the protein layer in the future. Finally, an inherent limitation of our analysis is that it is dependent on the quality of the underlying metabolic networks and their gene-reaction annotations. Indeed, we determined that an important number of the associations between fluxes and blood metabolomics or CAD risk could not be replicated with models based on an earlier reconstruction of human metabolism . However, with human GSMMs becoming increasingly more well-annotated26, differences in FWAS results using models built from different genome-scale reconstructions of human metabolism will progressively become more subtle.Our analysis has several limitations. For instance, as a proof of concept, this study focused on modelling only the five most prominent human organs102. Therefore, FWAS may enable identifying fluxes associated with disease states and, by extension, the gene knockdowns or metabolic interventions  to target them. FWAS to blood metabolic features may also help screen for potential adverse side effects of metabolic interventions. For example, we identified that retinyl esters might increase TAG levels and reduce cholesterol esterification in lipoproteins, consistent with reports that administering high doses of vitamin A derivatives results in hypertriglyceridemia and dysregulation of cholesterol levels68. Furthermore, while it is very early days, personalised fluxes associated with disease risk could also be incorporated into existing risk prediction models, potentially enhancing their predictive capacity.Concerning translating genetically personalised models and fluxes to clinical applications, GSMMs have already been established to have utility for drug discovery and repositioningOverall, this work demonstrates that genome-scale metabolic modelling can contribute to addressing the V2F challenge by characterising how the effects of genetic variants propagate through the metabolic networks of specific human organs.https://clinicaltrials.gov/ct2/show/NCT01610635). Participants were blood donors aged 18 years and older  recruited between 2012 and 2014 from 25 NHS Blood and Transplant centres36. Genetically personalised fluxes were computed for the 37,220 individuals with genotype and blood metabolome data that had passed quality control.INTERVAL is a cohort of approximately 50,000 participants nested within a randomised trial studying the safety of varying the frequency of blood donation .Genotyping of INTERVAL samples, their quality control and imputation were performed as previously described:https://www.ukbiobank.ac.uk/). Participants were between age 40 and 69 at recruitment  and accepted an invitation to attend one of the assessment centres that were established across the United Kingdom between 2006 and 201037. Genetically personalised fluxes were computed for the 487,395 individuals in the version 3 release of the UK Biobank genotype data104 (https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=263), which has been imputed to the UK10K/1000 genomes and haplotype reference consortium (HRC)105 panels.UKB is a cohort of approximately 500,000 participants from the general UK population  or female (Harvetta) models was included.For each analysed organ , the set of organ-specific metabolic reactions was extracted from the Harvey/Harvetta models (version 1_03c)25, which has been superseded by HUMAN126. Hence, we performed a liftover to update the Harvey/Harvetta organ-specific models to HUMAN1. Briefly, the IDs of the organ-specific metabolic reactions from the Harvey/Harvetta models were mapped to HUMAN1 (version 1.11.0) using the mapping provided in the HUMAN1 reaction annotations26. Subsequently, the resulting set of HUMAN1 reaction IDs was used to assemble organ-specific models from HUMAN1 reactions. Manual curation was used to identify and, when possible, correct gaps and missmaps. Some reactions in the Harvey/Harvetta models that were not present on the base Recon3D and thus could not be mapped to HUMAN1, were also added to the resulting network. These reactions included phospholipase, cholesterol esterase, and extracellular LCAT. Additionally, the side acyl chains of triglycerides and phospholipids were simplified to a stoichiometric mix of 1/3 oleoyl, 1/6 palmitoleoyl, 1/6 palmitoyl, 1/6 stearoyl, 1/6 myristoyl in line with the ratio used in Harvey/Harvetta for non-essential fatty acids30. Boundaries for the exchange reactions fluxes  between each organ-specific model and blood or bile were set as the average bounds of the corresponding reactions in the Harvey and Harvetta models. In some instances, the ranges of metabolite uptake and secretion were further constrained to ensure that they were physiologically relevant. In the brain-specific model, exchange reactions to blood were mapped to the exchange reactions between blood and cerebrospinal fluid defined in Harvey/Harvetta. Such reactions had been defined, taking into consideration the selective permeability of the blood-brain barrier30. Thus, only metabolites permeable to this barrier can be exchanged between blood and the brain-specific model. Next, metabolites in blood or bile were made boundary conditions , allowing each organ subnetwork to function independently. Finally, given that most HUMAN1 reactions lack a name attribute, unnamed reactions in the resulting network were named using their corresponding name in Recon3D. In a number of instances, ambiguously named reactions were manually renamed.Harvey/Harvetta models were built from the Recon3D human GSMM106 to test the capacity of reactions in the networks to carry a significant amount of flux (>10−6 mol/day), and 93% were shown to be capable of carrying a significant flux. Furthermore, models were also evaluated against a set of essential metabolic tasks  and organ-specific metabolic tasks obtained from the HUMAN1 repository26 Additionally, a set of Recon3D-based organ-specific models were also built. Such models were obtained by applying the steps described above without performing the liftover to HUMAN1.38  and the average abundance of each transcript in each organ was computed. In the heart, adipose tissue, and brain, there were transcripts abundances measured from multiple source sites. Hierarchical clustering analysis indicated that source sites from each organ were clustered together  for the relevant reactions.subject to:38. Finally, the resulting solution space was sampled using the Artificially Centred hit-and-run (ACHR) algorithm39 implemented into COBRApy109. ACHR was run with a thinning factor of 1000, and 1000 sets of steady-state flux distributions were computed. The average of those flux samples was used as each organ’s reference flux map.Subsequently, FVA was used to identify the feasible flux ranges within 99% of the optimal value of the GIM3E objective function25.Following the same approach, reference fluxes were also computed for the Recon3D organ-specific models using the gene reaction annotations of Recon3D13 were used to impute organ-specific gene expression levels from individual-level genotypes. These are well-established models that have been extensively validated112. The latest release of PredictDB models, which had been trained with GTEx v8 data, were obtained from https://predictdb.org/. They were used with PLINK2113 to predict relative transcripts abundances using genotype data from the INTERVAL36 and UKB37 cohorts. For adipose, brain and heart tissue, the average of the imputed abundances in each source site was used.The elastic net models from PredictDB26. Imputed values were expressed as log2 fold changes relative to average gene expression in GTEx and mapped to reactions in the organ-specific model considering the relative transcript abundance of isoenzymes and enzyme subunits in GTEx:g in individual n computed using the elastic net models from PredictDB; g in GTEx; R in the organ-specific network; R in individual n.Imputed individual-specific expression patterns from metabolic genes  were mapped to organ-specific models using the gene reaction annotations of HUMAN125.Reaction activity fold changes were also computed for the Recon3D organ-specific models using the gene reaction annotations of Recon3D102, we developed qMTA. qMTA seeks to identify the flux map most consistent with a set of reaction activity fold changes starting from a reference flux distribution  were tuned using the regression analysis with blood metabolic features in the INTERVAL cohort. For each simulated organ, a grid search  was performed to identify the parameters that resulted in flux maps with the strongest association with both Nightingale Health and Metabolon HD4 metabolic features. This was measured as the summation of the amount of variance explained (R2) for each blood feature-flux value pair when testing associations between metabolic fluxes and blood metabolic features. The resulting parameters were subsequently used in the analysis of the samples from UKB. The process was repeated to identify the best set of hyperparameters for the Recon3D-based models.Additionally, two hyperparameters in qMTA . In UKB, metabolite concentrations were determined in 117,981 participants at baseline assessment and 5141 participants at repeat assessment, among which there were 1427 participants with measurements at both time points. For participants with measurements at both baseline and repeat assessment, the measurement at baseline assessment was used114. Values were adjusted for technical covariates using the ukbnmr R package114 and subsequently regressed for age, sex, BMI, and the first 5 PCs of genetic ancestry. Composite biomarkers and ratios were recomputed after adjustment, including 98 and 76 additional biomarker ratios in INTERVAL and UKB, respectively, not provided by the Nightingale platform. Metabolic features not present in both INTERVAL and UKB were excluded from downstream analyses. Likewise, 68 features with markedly distinct variance between INTERVAL and UKB (|log2(sdINTERVAL/sdUKB)| > log2(2.5)) were also excluded. Finally, acetate was excluded due to a large number of NA (>75%) in INTERVAL. Subsequently, measures were standardised to zero-mean and unit variance.The Nightingale NMR platform quantifies 230 and 249 analytes in INTERVAL and UKB, respectively, including lipoprotein subfractions and ratios, lipids and low molecular weight metabolites The Metabolon HD4 assay measures ~1000 metabolites , including lipids, xenobiotics, amino acids and energy-related metabolites. A subset of INTERVAL participants  had their blood profiled with this assay, predominantly using baseline blood samples. Nineteen features were excluded due to a large number of NA (>75%). Values were regressed against technical covariates age, sex, BMI, and the first 5 PCs of genetic ancestry. Subsequently, measures were standardised to zero mean and unit-variance.ρ > 0.9, the feature with the largest mean absolute correlation to other flux values was removed115. Likewise, both the Nightingale and Metabolon platforms had some metabolic features with strong correlations, and those features with ρ > 0.75 were removed using the same approach used for reaction fluxes. Overall, 4300 scaled flux values and 57 Nightingale Health and 718 Metabolon HD4 blood metabolic features were selected to perform FWAS.Due to the linear nature of many metabolic pathways, some flux values were highly intercorrelated Fig. . To remoi on Then, the association of each metabolic feature to each personalised flux value was evaluated using linear regression  on effect sizes. The resulting P values were adjusted for multiple testing against all evaluated blood metabolic features—reaction flux pairs using the Benjamini and Yosef Hochberg  method.Statistical significance was evaluated with a To evaluate the association between metabolic fluxes computed with Recon3D-based models and blood metabolic features, the set of 4300 uncorrelated flux values in HUMAN1 was mapped to equivalent reactions in the Recon3D-based models. This set of flux values was then used to perform FWAS to the same set of 57 Nightingale Health and 718 Metabolon HD4 blood metabolic features as the HUMAN1 analysis.P < 0.05 was applied to control for all tested classes of blood metabolic features across all organs.Nightingale/Metabolon platforms provide sets of Groups/Sub-pathways to stratify metabolic features. We harmonised both annotations systems to define a set of curated groups that could be applied to both Nightingale and Metabolon features. For instance, the Metabolon features annotated to “Glycerolipid Metabolism” and “Phospholipid metabolism”, and the Nightingale features annotated to “Phospholipids” were all assigned to the curated group “Glycerides and phospholipids”. Some Metabolon features were not annotated  and could not be assigned to any curated group. Unknown features were included in the FWAS but omitted from the enrichment analysis. Fisher’s exact test (one-sided) was used to identify metabolite classes enriched in features with significant association to personalised flux values relative to the set of all uncorrelated blood metabolic features. An FDR-adjusted significance threshold of 26. As some subsystems contained a low number of reactions, functionally related subsystems were merged into larger reaction systems. For instance, the purine metabolism, pyrimidine metabolism and nucleotide metabolism subsystems were aggregated into a reaction system termed nucleotide metabolism. Additionally, transport processes  were assigned a system based on the specific metabolites being transported in each process. Briefly, we first assigned a system to each metabolite based on the most frequent reaction system annotation in the reactions in which it participates. For instance, alanine was assigned to the system “amino acid metabolism” since it was the system annotated most in reactions in which alanine participated. Next, each transport process/exchange reaction in HUMAN1 was assigned the system most numerous in the metabolites being transported. For the purpose of this assignment, metabolites that are often cofactors in transport processes  were set to give less weight than other metabolites. For instance, the alanine-sodium symporter  was assigned to the system “amino acid metabolism” as alanine (system: amino acid metabolism) was given more weight than Na+ (system: Miscellaneous). Reaction systems are solely used as annotations and have no influence on network stoichiometry or genetically personalised flux values.Subsystem annotations for reactions were obtained from the HUMAN1 modelP < 0.05 was applied to control for all tested systems across all organs.Fisher’s exact test (one-sided) was used to identify reaction systems enriched in reactions with significant association with blood metabolic features relative to the set of all evaluated reactions in each organ. An FDR-adjusted significance threshold of Using PheWAS Catalogue (version 1.2), we used the WHO International Classification of Diseases (ICD) diagnosis codes in versions 9 (ICD-9) and 10 (ICD-10) of Phecode 411.4 for CAD case definition in UKB. In detail, we searched for the presence of any of the constituent ICD-9/10 codes in linked health records  and converted the earliest coded date to the age of phenotype onset. Individuals without any codes for CAD were recorded as controls and censored according to the maximum follow-up of the health linkage data  or the date of death.116. The significance of the flux to CAD risk associations was evaluated with a two-tailed Wald test for the flux HRs. The resulting P values were adjusted for multiple testing against all tested fluxes using the Benjamini and Yosef Hochberg  method.We recorded 34,121 events of CAD and 428,669 controls in UKB, which were used to evaluate the association of genetically personalised fluxes to CAD risk. Association was tested using an age-as-time-scale Cox proportional hazards regression. The Cox models were stratified by sex and adjusted by genotyping array, 10 genetic PCs, BMI and smoking status and fitted using the CoxPHFitter function from the lifelines package for pythonFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary


Scopoletin, a typical example of a coumarin compound, exists in several Artemisia species and other plant genera. However, the systemic metabolic effects induced by scopoletin remain unclear. In the present study, we evaluated the metabolic profiles in scopoletin-exposed zebrafish embryos using UHPLC-Q-Obitrap-HRMS combined with multivariate analysis. Compared with the control group, 33 metabolites in scopoletin group were significantly upregulated, while 27 metabolites were significantly downregulated. Importantly, scopoletin exposure affected metabolites mainly involved in phosphonate and phosphinate metabolism, vitamin B6 metabolism, histidine metabolism, sphingolipid metabolism, and folate biosynthesis. These results suggested that scopoletin exposure to zebrafish embryos exhibited marked metabolic disturbance. This study provides a perspective of metabolic impacts and the underlying mechanism associated with scopoletin exposure. These results suggest that there is a need to explore the toxicity of scopoletin.Scopoletin is a coumarin compound, which can be found in several Artemisia species and other plant genera , and alsDanio rerio) has been widely used as a model organism for the evaluation of toxic effects [Zebrafish .Scopoletin (purity ≥ 98%) was purchased from Shanghai Macklin Biochemical Co., Ltd.  Acetonitrile and methanol (HPLC-grade both) were purchased from Merck , while formic acid (≥96%) was provided by Tedia . Dimethyl sulfoxide (DMSO) was purchased from Sigma-Aldrich. Water was purified with a Milli-Q system  and was used throughout the whole experiment, including sample treatment and instrument-based analysis. Internal standard (IS) solution was prepared with 75 ng/mL of 2-Chlorophenylalanine and 15 ng/mL of L-Methionine-d3, Phenylalanine-d5, and Citric-d4 Acid in methanol from Sigma-Aldrich. Fish water was prepared with reverse-osmosis purified water containing 5 mM NaCl, 0.17 mM KCl, 0.33 mM CaClDanio rerio) were purchased from the China Zebrafish Resource Center and cultured in a flow-through system containing fully aerated and filtered tap water at 28 ± 0.5 °C and 14 h light: 10 h dark cycle. Embryos were obtained through the natural mating of 3 males and 2 females on 4-L mesh-bottom breeding tanks in the morning under the light stimulation. Embryos were incubated under the same conditions throughout the exposure. All procedures were conducted in accordance with the guidelines of Institutional Animal Care and Use Committee (IACUC) of the Zhejiang University, Hangzhou of China. The husbandry and treatment of the zebrafish in our study were approved by the Ethics Committee of Zhejiang University (ZJU20220333).Adult 6-month-old zebrafish of wide-type AB strain  using the full scan data-dependent MS/MS (ddMS2) mode. Chromatographic separation was performed on an ACQUTITY UHPLC HSS T3 column . UPLC conditions, including mobile phases and gradients, were optimized based on the necessary sample analysis. Briefly, the flow rate was 0.3 mL/min and the column temperature was set to 35 °C. The mobile phases were 0.1% formic acid in Milli-Q water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The optimized gradient elution was followed: 0–3 min, 2% B; 3–15 min, 2% B to 100% B; 15–17 min, 100% B; 17–17.5 min, 100% B to 2% B; and 17.5–20 min, 2% B. The MS parameters were as follows: a sheath gas flow rate of 10 arbitrary units, a spray voltage of 3.0 kV, a capillary temperature of 320 °C, an auxiliary gas heater temperature of 30 °C, and an S-lens RF level of 50. All samples  were randomly injected. The instrument performance was monitored by the injection of the QC periodically every six samples.p-value < 0.05; and fold change > 2 or < 0.5. Metabolites were aligned according to the positive and negative ion patterns (M + H and M − H) using the Human Metabolome Database (HMDB) (http://www.hmdb.ca/ (accessed on 12 August 2022)), Chemspider (http://www.chemspider.com/Default.aspx (accessed on 12 August 2022)), and mzCloud (https://www.mzcloud.org/ (accessed on 12 August 2022)). Metabolic pathway analysis plots, global KEGG network analysis, and Debiased Sparse Partial Correlation (DSPC) analysis were produced with MetaboAnalyst 5.0 and metabolic pathways were identified based on the Danio rerio KEGG library. The Pearson Correlation and Average Linkage were utilized as the distance metrics for hierarchical clustering.Data processing was performed with Compound Discoverer 3.1 . The parameters were as follows: peak area > 1,000,000; mass tolerance ≤ 5 ppm; retention time tolerance ≤ 0.2 min. The features of a coefficient of variance higher than 30% in the quality control injections were excluded from the metabolomic analyses. After the data were derived, SIMCA software  was used for the multivariate data analyses, including principal-component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The differential metabolites were selected based on following thresholds: Values of variable importance in the projection (VIP) > 1; p values were less than 0.05 and extremely significant when the P values were less than 0.01.The data normalization and validation for score plot analysis was accomplished using the Pareto scaling algorithm, which measured each variable using the square root of its standard deviations. The metabolic expression was determined after a logarithmic transformation. GraphPad Prism 8.0 was employed for the statistical analysis. Data are presented as the mean ± SD. A one-way analysis of variance (ANOVA) with Tukey’s multiple range tests was used to determine the statistical significance of differences in the concentration of the metabolite among groups, which was significant when the After exposure to scopoletin, an untargeted metabolomics approach was used to fingerprint changes in the metabolomes of zebrafish embryos A, which 2X and Q2 parameters. The cumulative R2X (goodness of fit) of the supervised models established PCA model were 0.714 and 0.658 under negative and positive modes, respectively, while the Q2 (goodness of prediction) values were 0.550 and 0.458 given the two modes, respectively. The recorded values of R2X and Q2 for the PCA models well explain the cumulative variation of the data by the principal components and good prediction capability of the models. In addition, we have provided the PCA loading plots as The PCA score plots indicated that the metabolic profiles of MNLC of scopoletin-treated group was clearly separated from the other groups, resulting in different metabolic profiles among the groups B,C. The 2X, R2Y, and Q2 indicates high explained variation and the predictive ability of an OPLS-DA model. The p values of CV-ANOVA indicate the good predictive ability of our established models. From the OPLS-DA models, the MNLC of the scopoletin-treated group was clearly separated from the other two scopoletin-treated groups (1/9 and 1/3 MNLC of scopoletin-treated groups). The 1/9 and 1/3 MNLC dose groups were closer to the fish water and solvent control groups. Then the OPLS-DA model was used in analysis between two groups. The OPLS-DA model demonstrated a significant difference in the metabolic profiles of the solvent control and three scopoletin-treatment groups in both negative and positive modes  indicated high explained variation and predictive ability of an OPLS-DA model, respectively. Moreover, CV-ANOVA was also used to determine the significance of the model and the CV-ANOVA p value for OPLS-DA were all below 0.05, indicating the good predictive ability of our established models. In addition, the values of R2 and Q2  were showed in 2 and Q2 , and eacp-value < 0.05, and the absolute of Log2 (fold change) > 1 were identified as potential different metabolites. As shown in the workflow . Among these metabolites, N-Acetylvanilalani was the most changed metabolite, and the N-Acetylvanilalani was positively changed with N (6)-Methyladenosi, Ptaquilpside, Garcinone D, Probenecid, and so on  and pathway impacted values from the pathway topology analysis (x-axis) [Metabolic profiling not only depicts changes in the level of each metabolite but also provides a thorough assessment of how harmful xenobiotics affect the metabolic process . The met(x-axis) . The nodes present differential metabolites, while the lines represent the associations between these metabolites in the Debiased Sparse Partial Correlation algorithm (DSPC) network . For theIn the present study, we evaluated the metabolic effect of scopoletin in zebrafish embryos using an untargeted metabolomics approach. The significant metabolic profile disturbance under scopoletin exposure were observed. A total of 60 metabolites were identified to be differentially expressed between the solvent control and scopoletin-exposed groups. The significantly changed metabolites between the solvent control and scopoletin-exposed groups were involved in metabolic pathways, such as phosphonate and phosphinate metabolism, vitamin B6 metabolism, histidine metabolism, sphingolipid metabolism, lysine degradation, and folate biosynthesis.Our previous work demonstrated that scopoletin can cause developmental toxicity and affect the behavior of zebrafish embryos. The current study has demonstrated that scopoletin can cause significant metabolic profile disturbance in zebrafish embryos . A totalRecent studies have revealed that vitamin B6 metabolism was involved in chronic inflammation due to its antioxidant defenses in live and heart ,21. A dyC. elegans, such as folate biosynthesis, and these changes are signs of possible oxidative stress. Taken together, these findings indicated that a metabolic disorder caused by scopoletin was likely associated with the oxidative stress of zebrafish embryos induced by scopoletin exposure. In the future, additional studies need to be carried out to confirm its mechanism.Moreover, much research has found the association between sphingolipid metabolism and neurotoxicity ,25,26. HThis study indicates that scopoletin exposure was associated with significant metabolite changes in zebrafish. According to these data, we quantified a set of metabolic pathways, phosphonate and phosphinate metabolism, vitamin B6 metabolism, histidine metabolism, sphingolipid metabolism, lysine degradation, and folate biosynthesis may play critical roles in the scopoletin exposure in zebrafish embryos. Nevertheless, there were also some limitations in this study. Although metabolic profiles alteration was observed in zebrafish induced by scopoletin, the underlying mechanism of these metabolic effects change caused by scopoletin was unclear. Due to the acute exposure to scopoletin in the current study, we could not make robust conclusions about the long-term toxic effects of scopoletin. Therefore, the long-term toxic effects of scopoletin should be explored in further studies.Taken together, the toxicological effects of scopoletin in zebrafish embryos were determined using a UHPLC-Q-Obitrap-HRMS-based untargeted metabolomics approach for the detection of the metabolites. Through PCA and OPLS-DA, the MNLC of scopoletin-treated group was clearly separated from the other two scopoletin treated groups (1/9 and 1/3 MNLC of scopoletin-treated groups). The 1/9 and 1/3 MNLC of scopoletin-treated groups were closer to the control group. Based on our standard for significantly differential metabolites, 60 metabolites were selected as potential biomarkers of scopoletin exposure. Pathway analyses showed that multiple metabolic pathways were affected by scopoletin exposure, including phosphonate and phosphinate metabolism, vitamin B6 metabolism, histidine metabolism, sphingolipid metabolism, lysine degradation, and folate biosynthesis. Metabolite levels in phosphonate and phosphinate metabolism, vitamin B6 metabolism, and histidine metabolism increased, while the metabolite in sphingolipid metabolism was reduced. The involved metabolic pathway network revealed the potential mechanism of the metabolic perturbation induced by scopoletin. This study provides a comprehensive metabolic profiling of scopoletin exposure, and those disturbed pathways could result in a biologically harmful effect on zebrafish embryos.


The increased hyperintense (cystic) areas observed in one PDX mimicked the cystic phenotype typical of some RCC. The negligible hypointense (necrotic) areas of PDX grown under the highly vascularized renal capsule are beneficial for preclinical studies. Mean apparent diffusion coefficient (ADC) values were equivalent to those of ccRCC in human patients. Hyperpolarized (HP) [1-13C]pyruvate MRI of PDX showed high glycolytic activity typical of high-grade primary and metastatic ccRCC with considerable intra- and inter-tumoral variability, as has been observed in clinical HP MRI of ccRCC. Comparison of steady state metabolite concentrations and metabolic flux in [U-13C]glucose-labeled tumors highlighted the distinctive phenotypes of two PDX with elevated levels of numerous metabolites and increased fractional enrichment of lactate and/or glutamate, capturing the metabolic heterogeneity of glycolysis and the TCA cycle in clinical ccRCC. Culturing PDX cells and reimplanting to generate xenografts (XEN), or passaging PDX in vivo, altered some imaging and metabolic characteristics while transcription remained like that of the original PDX. These findings show that PDX are realistic models of ccRCC for imaging and metabolic studies but that the plasticity of metabolism must be considered when manipulating PDX for preclinical studies.Patient-derived xenografts (PDX) are high-fidelity cancer models typically credentialled by genomics, transcriptomics and proteomics. Characterization of metabolic reprogramming, a hallmark of cancer, is less frequent. Dysregulated metabolism is a key feature of clear cell renal cell carcinoma (ccRCC) and authentic preclinical models are needed to evaluate novel imaging and therapeutic approaches targeting metabolism. We characterized 5 PDX from high-grade or metastatic ccRCC by multiparametric magnetic resonance imaging (MRI) and steady state metabolic profiling and flux analysis. Similar to MRI of clinical ccRCC, T In 2022 it is estimated that ~79,000 Americans will be diagnosed with kidney cancer and ~14,000 will die from this disease glucose was evaluated by 1D and 2D high-field nuclear magnetic resonance (NMR) spectroscopy. Measurement of steady state levels and fractional enrichment of metabolites was accompanied by 13C isotopomer modeling. Multiparametric 1H magnetic resonance imaging (MRI), including T2 weighted anatomic and diffusion weighted imaging, characterized architectural features of the PDX and hyperpolarized (HP) [1-13C]pyruvate evaluated glycolytic activity which is associated with cancer progression and therapeutic response pyruvate over 12 s and 15 HP 13C spectra were acquired every 4.2 s beginning 10 s after the start of the injection using a 2D chemical shift imaging pulse sequence with the following parameters: slice thickness of 8 mm, FOV 32 mm × 32 mm, matrix 8 × 8, flip 10°.Cryopreserved or fresh tissue slices of PDX or XEN tumors were implanted under the renal capsule of male or female  8–10-week-old NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) or RAG22-weighted images covering the contralateral kidney and tumor. In this study, we performed histogram analysis to evaluate intra- and inter-tumoral morphology from T2-weighted images. All histograms were plotted in terms of the probability density function (PDF) that displays the relative frequency and probability of a range of values with the AUC being 1. To account for inter-subject variability and enable comparisons among tumors, T2-weighted intensity in the whole tumor was normalized by the mean T2-weighted intensity of the contralateral kidney for each mouse. These T2-weighted normalized intensity values were used for the histogram analysis of tumor morphology, such as the evaluation of the percentage of hypointense (% hypointense) and hyperintense (% hyperintense) in tumors. Pixels with intensities greater than mean + 3 × standard deviation (SD) were considered “hypointense” and those with intensities less than mean—3 × SD were denoted as “hyperintense”. The % hypointense and % hyperintense regions in tumors were calculated as:Analysis of MRI data was performed using MATLAB . Regions of interest (ROI) were manually drawn on T2-weighted images were downsized to a matrix of 96 × 96 and overlaid on DWI. ADC maps were calculated using the following mono-exponential equation:b is the signal intensity for each b value, and S0 is the signal intensity at a b = 0 s/mm2. The ADC mean was calculated for each voxel with a quality of fit (R2) higher than 0.7 and ADC values higher than 0.1 × 10−3 mm2/s for all slices with tumor. For ADC analysis, the ROIs in the TSIVIC package was used to generate quantitative maps for lactate and pyruvate . Only vo2-weighted intensity features, ADC values, L/P AUC ratio, and tumor growth rate among PDX tumors followed by a Tukey’s test for multiple comparisons. Differences between two generations of tumors of the same PDX, or PDX tumors and its corresponding XEN tumors, were assessed with an unpaired t-test. Differences were considered significant at a p-Value < 0.05. All statistical analyses were performed with GraphPad Prism 9 .Data are expressed as mean ± SD. A one-way ANOVA was used to compare differences in T13C]glucose into the tail vein every 15 min for a total labeling time of 45 min HSQC/[total metabolite]{13C}1H. 2D NMR datasets were zero-padded by a factor of 2, manually phased, and peak volumes were integrated using TopSpin (version 4.0.6) . The concentration of 13C-labeled metabolites was quantified using 1H-13C HSQC as previously described glucose-labeled tumor extracts, relative peak volumes of glutamate C2, glutamate C3 and glutamate C4 isotopomer multiplets were quantified from high-resolution 2D 1H-13C HSQC spectra using Topspin (version 4.0.6). 13C isotopomer modeling of [U-13C]glucose-labeled tissues was performed using tcaCALC software (download available at http://invivometabolism.org/tca.html (accessed on 15 September 2020)). This software helps in modeling the active pathways contributing to the carbons in the TCA cycle . The isotopomer model was validated using the following metrics: (1) Akaike Information Criterion (AICc), the metabolic model with lowest AICc is most likely to be the best model for the given data set, (2) Ln , natural logarithms of the sum of squares residual being minimal and (3) ADp, p-Value for the Anderson-Darling test for random residuals. Lower ADp  suggests the residuals are not randomly distributed and the model may be overfitted.For pyruvate MRI was used to evaluate glycolysis. Consistent with the impact of VHL gene aberrations in ccRCC PDX [13C]pyruvate MRI study of colorectal and breast cancer xenografts [13C]pyruvate MRI of RCC was reported in 2019 [13C]pyruvate MRI may potentially discriminate low- from high-grade ccRCC glucose showed a high degree of similarity among 3 of the PDX . In contrast, the other two PDX (047 and 054) were significantly different from the others. The distinguishing metabolites were all higher in PDX047 and PDX054 and included alanine, glutamate, glucose, lactate, creatine, ATP, Kennedy pathway metabolites  and taurine. Steady state levels of metabolites in XEN derived from PDX072 and PDX093 were remarkably like those in the parental PDX, with higher levels of PC and taurine the only significant differences in XEN093 compared to PDX093. In contrast, levels of numerous metabolites differed between XEN047 and XEN054 compared to their parental PDX. All significantly different metabolites were higher in the PDX compared to XEN . For the most part, these same metabolites were higher in XEN047 or XEN054 compared to the other XEN (072 or 093). In this regard, although XEN047 and XEN054 had lower levels of several key metabolites than their parental PDX, they still retained higher levels of metabolites than the other XEN derived from PDX with lower levels of metabolites overall. Metabolic profiling and flux analyses provided deeper insights into the characteristics of ccRCC PDX and XEN. Quantification of steady state levels of metabolites extracted from PDX labeled with pyruvate MRI, and global metabolism by steady state metabolic profiling and flux analysis. We found that imaging and metabolic features of ccRCC PDX are representative of clinical ccRCC, suggesting that PDX will be appropriate models for preclinical studies targeting metabolism for therapy and/or imaging. However, manipulation of PDX, such as by in vitro cell culture and implantation back into mice as xenografts, may alter the metabolic and imaging phenotype. A limitation of our study was the relatively small number of PDX, XEN-PDX pairs, and PDX passages. Further, relatively small numbers of tumors from each PDX or XEN underwent HP MRI, underpowering multivariate comparisons. Nevertheless, it is interesting to compare our results to similar studies of other PDX. Jun et al. compared levels of metabolites in several pancreatic cancer PDX to the patient tumors of origin and found similar patterns, although the site of propagation of PDX (orthotopic versus subcutaneous) influenced metabolite levels [Preclinical studies will continue to inform translation of HP a et al. . In our e levels . A studye levels . ComprehThe metabolic comparisons of the PDX are congruent with the genetic clustering observed among them. However, the genetic stability observed between passages or upon establishment of XEN from cultured cells does not carry over to the metabolomic or imaging features, highlighting the plasticity of the metabolic phenotype and its dependency on microenvironment and growth conditions. These results emphasize the need to understand the irreversible streamlining of metabolic changes conferred upon in vitro culture of cells from PDX and optimal choice of model systems for translational research in RCC including drug screening and biomarker discovery targeting metabolic pathways.


The determination of urine metabolic fingerprint was performed using NMR metabolomics. Multivariate data analyses were performed with SIMCA-P 15.0 software and R language. Non-distinct profiles for both groups (breastfeeding and synbiotic formula) for the two first time points (3rd and 15th days of life) were detected, whereas after the 2nd month of life, a discrimination trend was observed between the two groups, which was further confirmed at the 3rd month of life. A clear discrimination of the synbiotic formula samples was evident when comparing the metabolites taken in the first days of life (3rd day) with those taken in the 2nd and 3rd months of life. In both cases, OPLS-DA models explained more than 75% of the metabolic variance. Non-distinct metabolomic profiles were obtained between breastfed and synbiotic-formula-fed neonates up to the 15th day of life. Discrimination trends were observed only after the 2nd month of the study, which could be attributed to breastfeeding variations and the consequent dynamic profile of urine metabolites compared to the stable ingredients of the synbiotic formula.The aim of this study was to compare the urine metabolic fingerprint of healthy neonates exclusively breastfed with that of neonates fed with a synbiotic-enriched formula (Rontamil Nutritional strategies during the early stages of infant development are considered a pivotal factor in regulating infant metabolism and gut immune system function, thus determining the contingent development of adult metabolic syndromes, i.e., obesity, insulin resistance, gastrointestinal diseases and hypertension ,2.Milk is the first food that is introduced into the gastrointestinal (GI) tract after birth, and the composition of milk is believed to directly impact gut microbiota and affect infant neurodevelopment ,4 througHuman breast milk (HBM) is acknowledged as the ‘gold standard’ for early nutrition (up to 6 months) and the ‘natural fuel’ for infant neurodevelopment due to its high content of essential nutrients , etc.) and small amounts of probiotics and commensal bacteria ,8.Streptococcus thermophilus, Staphylococcus epidermidis and Bifidobacterium longum, to the neonatal gut [Maternally derived microbial metabolites are transmitted to infants via human milk. According to omics studies, breast milk mediates vertical transfer of microbial communities, such as atal gut ,10. Moreatal gut ,12,13. Aatal gut . Furtheratal gut .The infant’s gut microbial composition increases in number and diversity with age and is highly dynamic in the first 6 months of life. A balanced microbiota composition in the first weeks of life predicts microbiota stability throughout the first years of life. Thus, there seems to be a window of opportunity during early infancy that is essentially influenced by the type of feeding and associated with a healthy microbiota profile . At arouDiet is increasingly recognized as a key environmental factor that can modulate the composition and metabolic function of the gut microbiota. Infants can be breastfed, formula-fed or even mixed-fed; these feeding methods influence the gut microbiota at various levels, thus affecting infant health and potentially resulting in consequences later in life . In conclusion, targeted nutritional or environmental interventions and readjustments in obstetrical and neonatal medicine practice, such as vaginal seeding ,20, micrHowever, the decision to practice exclusive or partial breastfeeding is based upon several demographic, socioeconomical, tradition/culture-related and personal criteria ,38. In cUp-to-date advents regarding formula milk composition amend at a rapid pace, focusing on the development of ‘humanized’ formulae that resemble maternal milk, the composition of which is not static but extremely dynamic . Recent Bifidobacterium spp. in C-section-delivered babies [Modification of the gut microbiota by administration of synbiotics, a mixture of prebiotic and probiotic components, might offer a novel and cost-effective strategy in an attempt to reduce the risk of viral infections , to restd babies , to prevd babies ,45, to id babies , to prevd babies .Non-invasively collected urine and fecal samples are considered the most suitable biological substrates for deciphering the relationship between diet and nutritionally driven metabolic changes in groups of breastfed (BF) and formula-fed (FF) newborns, as they represent the final points of gut microbial metabolism occurring inside human intestinal tract (feces) and of the excreted metabolites produced by the host (urine) . Apart fBifidobacterium animalis (strain BB12), as well as prebiotic FOS and polyunsaturated fatty acids (PUFA), such as arachidonic acid (AA), docosahexaenoic acid (DHA) and nucleotides, in comparison with exclusive breastfeeding in terms of any possible correlation with the urine metabolic fingerprint.In the present study, we aspired to determine whether the urine metabolome may act as a functional representative of the gut microbiome as modified by two feeding methods. Therefore, we implemented NMR-based metabolomics with the aim of investigating the effect of exclusive feeding with a synbiotic formula enriched with probiotic 1H NMR spectrum of neonate urine with annotations on the identified metabolites is depicted in A typical standard Several nutritional studies confirmed that the metabolic signature of infants following different feeding practices is strongly associated with their diet ,49. TherA PCA model with two components was computed in the total set of samples to provide an overview and elucidate trends of the sample clustering . Ιn the In the subsequent step, supervised analysis was implemented in the overlapping groups to possibly resolve the metabolic variation. Specifically, an OPLS-DA model was computed, including the breast- and formula-fed samples . This mo2 and Q2  for the models of the first 2 time points of the study  versus the formula-fed groups (FF) by comparing the samples at each time point . The succinct criteria for model validity and reproducibility regarding the values of Rhe study  were nothe study . TherefoThe key metabolites related to the FF group according to the corresponding S-line plots B,D for bFinally, we attempted to frame the evolution of metabolites in relation to the four time points for each of the two groups (BF vs. FF). To this end, supervised models were extracted for all sample groups between time points from day 3 to month 3 .Separation between breastfed samples on collected on the 3rd and 15th days could not be framed in a validated OPLS-DA model , but disInterestingly, the OPLS-DA models of the two different time frames (3rd day versus 2nd and 3rd month) in the BF group and their corresponding S-line plots  identifiWith respect to the formula-fed samples, OPLS-DA models comparing the 3rd day to the 2nd  and 3rd p < 0.05, permutation testing and ROC curves) confirmed that the results of all OPLS-DA models were unbiased and reliable, as described in the Finally, the use of validation steps  was performed using Metaboanalyst 5.0 for the metabolites exhibiting an AUROC  >  0.7 in order to delineate metabolic differences for each feeding method. In the context of pathway analysis, tested whether compounds involved in a particular pathway were enriched compared to random hits. The results in the formula-fed group based on the KEGG database demonstrated alterations in taurine and hypotaurine metabolism, arginine biosynthesis, citric acid cycle, alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism and primary bile acid biosynthesis .Our results pinpointed taurine and hypotaurine metabolism, among other pathways, as being associated with the BF group. In our case, this pathway was significantly upregulated in the FF group relative to the BF group, mainly in relation to the metabolic pathways of fat and protein. This could be explained by the presence of more polyunsaturated fatty acids in infant formula that resembles breast milk, contributing to the digestion and absorption processes .The FF group was also characterized by the citric acid cycle (TCA), a common metabolic pathway for sugars, lipids and amino acids in the mitochondria. On the other hand, pathway analysis using significantly altered metabolites in the breastfed group revealed alterations in alanine, aspartate and glutamate metabolism; glyoxylate and dicarboxylate metabolism; glycine, serine and threonine metabolism; synthesis and degradation of ketone bodies; aminoacyl-tRNA biosynthesis; D-glutamine and D-glutamate metabolism; nitrogen metabolism; valine, leucine and isoleucine biosynthesis; and taurine and hypotaurine metabolism, as presented in Current knowledge confirms time-dependent changes in the metabolic fingerprints and in the biochemical pathways of newborns . SeveralThe metabolic changes in urine from the complete sample set did not exhibit any clustering trends, as shown in Clustering between breast- and formula-fed samples was observed after the third time point, i.e., the second month of the study. As shown in Veillonella, Bacteroides, Clostridium, Enterobacteriaceae and Streptococcus taxa in BF newborns [Bifidobacteria, or bacterial communities, such as Clostridia and Firmicutes, in FF infants from 1st to 3rd month of life [In particular, primary and conjugated bile acids, key regulators in the formation of gut microecology by participating in bacterial metabolism, were detected in high amounts in healthy neonates . Secretinewborns ,56. AccoIn line with our findings , taurineVariations in the levels of citric acid produced during the tricarboxylic acid (TCA) cycle are affected by factors related to gestational age, lactation, height, weight, growth rate and energy demands of the newborn .In the present study, citric acid was found to share a common metabolic fate through time, as its levels increased with age in both groups (3rd month compared to the 3rd day), as supported by Bifidobacteria [Urea is considered the most important source of nitrogen, and its presence in the urine metabolome of newborns is anticipated. A strong correlation of urea with the BF group was closely associated with the modification of their intestinal microbiome and the increase in commensal bacterial populations, such as bacteria . Nonethebacteria ,61,62. Ibacteria .Enterobacteriaceae communities in the FF infant gut [Regarding the assignment of methyl-succinate as a signature marker for the synbiotic formula group , the resfant gut . Apart ffant gut .Our findings indicate creatinine as a discriminant compound, as it was found in higher levels in the FF group than in the BF group after 2 months of life , which mSpecial attention should be paid to dimethylamine, which plays an essential role in the clustering of FF vs. BF urine samples . Recent Proteobacteria, whereas it was negatively correlated with Actinobacteria  [In relation to gut microflora, dimethylamine was found to be positively associated with cterium) . Howevercterium) . Most imcterium) . This phThe lower number of differentiating metabolites in the BF group in the 3rd month compared to 3rd day  may be aAkkermansia abundance) [On the other hand, incomplete kidney maturation and high choline intake appeared to be responsible for the increased production of betaine, a product of choline oxidation. In neonatal urine samples reported in previous study, betaine was increased progressively from the 3rd day to 3rd month before exhibiting a sharp decrease in infants at the age of 6 months ,67. Incrundance) ,74,75. Aundance) . AlthougFinally, threonine was observed in higher concentrations at the beginning of the study compared to the 3rd month in the BF group . A reduc® Complete 1—Bifidobacterium animalis 1.00 × 107 cfu/g, FOS 0.52 g/100 kcal, Rontamil, Zug, Switzerland) (FF). In the FF group, neonates were exclusively breastfed prior to enrolment. In the case of medical indication or parental wish for formula introduction along with breastfeeding, the neonates received Rontamil® Complete 1 infant formula. The average nutritional composition per 100 mL of human breast milk and formula (Rontamil® Complete 1) is presented in Study subjects were allocated into 2 groups: one group exclusively breastfed (BF) and the other group receiving a synbiotic formula  from Aretaieio Hospital Review Board, along with the Ethics committee, as well as approval (No.AΔA: ΩΔΖΧ46906Ψ-450/09-03-2017) from Nikaia General Hospital “Agios Panteleimon”, were received prior to initiation of the study. All experiments were performed in accordance with existing guidelines and regulations. Signed informed consent was obtained from the participating mothers prior to enrolment. The clinical study was registered with 2O containing 0.1% sodium trimethylsilyl propionate (TSP) and sodium azide ; then, samples were transferred to 5 mm NMR tubes [The samples were thawed at room temperature 60 min before performing the NMR experiments. Samples were centrifuged , and 450 µL of urine was mixed with 150 µL of a 1.5 M phosphate buffer (pH 7.4) in DMR tubes .All NMR spectra were acquired on a Varian-600 MHz NMR spectrometer  equipped with a triple-resonance probe {HCN}. One-dimensional 1H-NMR spectra were collected at 25 °C with the 1D NOESYPRESAT pulse sequence for solvent signal suppression.2O region was removed. The spectra were normalized to the standardized area of the reference compound and converted to ASCII format using Mnovav.10.1 software. The ASCII files were imported into MATLAB , and all spectra were aligned using the correlation optimized warping (COW) method [All 1H NMR spectra were phase- and baseline-corrected using Mnovav.10.1 software . The NMR spectrum of each sample was aligned with reference to the TSP signal at δ 0.00 ppm. The 1H NMR spectra were reduced into buckets of 0.0001 ppm, and the D) method .2D NMR spectroscopy was utilized to assist in the assignment of metabolites. Specifically, gCOSY, zTOCSY, gHMBCad and gHSQCad were recorded at 25 °C. The acquisition parameters for gCOSY were: spectral width (SW), 7225.4 Hz; t1 increment, 256; acquisition time, 0.150 s; number of scans, 128; 1084 data points; receiver gain, 30; and relaxation delay, 1 s. The acquisition parameters for zTOCSY were set at: spectral width (SW), 7225.4 Hz; t1 increment, 256; number of scans, 128; acquisition time, 0.283 s; 2048 data points; receiver gain, 30; and relaxation delay, 1 s. The acquisition parameters for gHSQCad were: f2 spectral width (SW), 7225.4 Hz; f1 spectral width (SW), 30165.9 Hz; t1 increment, 256; number of scans, 128; acquisition time, 0.150 s; 1084 data points; receiver gain, 30; and relaxation delay, 1 s. The acquisition parameters for gHMBCad were: f2 spectral width (SW), 7225.4 Hz; f1 spectral width (SW), 36199.1 Hz; t1 increment, 256; number of scans, 128; acquisition time, 0.150 s; 1084 data points; receiver gain, 40; and relaxation delay, 1 s.1H NMR database  and an in-house, fully automated metabolite identification platform [2D spectra were interpreted with MestReNova v.10.1 software . The identification procedure was also assisted by literature data ,83, a replatform .Multivariate statistical data analysis was performed using ASICS and the ropls  R packagSpectral data were mean-centered and Pareto-scaled (Par) . MultivaFeature selection for the OPLS-DA models was based on variable importance in projection (VIP) scores > 0.7 and P(corr)  >  0.2 to reveal the variables with class-discriminating power. S-line plots were facilitated to pinpoint metabolites that contributed to sample discrimination.2 (0 ≤ R2 ≤ 1) and the predictive ability Q2 (0 ≤ Q2 ≤ 1) values. R2 explains the variation, constituting a quantitative measure of the quality of the mathematical reproduction of the training set data. The overall predictive ability of the model was assessed by the cumulative Q2, representing the fraction of the variation of Y that can be predicted by the model, which was extracted according to the internal cross-validation default method of SIMCA-P software, version 14.0 . Q2 is considered a de facto default diagnostic parameter to validate OPLS-DA models in metabolomics. In particular, all OPLS-DA models demonstrated high statistical values (R2 > 0.7 and Q2 ≥ 0.50), the difference between the goodness-of-fit and the predictive ability always remained below 0.3 (R2X(cum) − Q2 (cum) < 0.3) and the goodness of fit was never equal to one (R2X(cum) ≠ 1). Therefore, because the extracted models abided by these rules, their robustness and predictive response were enhanced, and overfitting was avoided.The quality of models (PCA/OPLS-DA) was described by the goodness-of-fit Rp-value < 0.05, as noted in each multivariate statistical model. Furthermore, permutation tests were employed (999 permutations) in order to evaluate whether the specific classification of two classes in a model was significantly better than any other models obtained by randomly permuting the original group attribution. All models were extracted at a confidence level of 95%.Classification models were validated using cross-validation analysis of variance (CV-ANOVA), with a An additional measure of PLS-DA model validity included the extraction of receiver–operator characteristic (ROC) curves to assess the ability of the PLS latent variable Tpred to correctly classify the test set. The area under the ROC (AUROC) was calculated. A perfect discrimination corresponded to an AUROC equal to 1.2Y_ and the goodness-of-prediction coefficient (Q2Y). The prediction was estimated by 7-fold cross validation, and the robustness of the models was measured through permutation testing for 999 iterations. For the PLS models, the optimal number of latent variables used was based on the accumulation of high R2 values without model overfitting.The package ropls (R) was employed for the implementation of the PCA, the partial least squares-discriminant (PLS-DA) and its orthogonal implementation (OPLS-DA). These techniques are used in to maximize the correlation between two sets of variables by reducing the data to a few latent variables. The orthogonal use of this method provides improved interpretation of variations between the discriminated groups. The evaluation of PLS-DA and OPLS-DA models was based on the goodness-of-fit coefficient  for biomarker discovery, classification and pathway mapping. A hypergeometric test using over-representation analysis and pathway topology analysis related these metabolites to metabolic pathways. MetaboAnalyst 5.0  was used for bioinformatics analysis, and the pathway analysis module based on the KEGG database was applied to reveal relevant differential metabolic pathways.Metaboanalyst 5.0 was used . According to established knowledge, we attempted to elucidate alterations in the urine metabolic fingerprint correlating with putative changes in the gut microbiota.In this context, it was evident that the urine metabolic profiles of BF and synbiotic-enriched FF infants were similar during the two first time points of the study. Elucidation of the evolution of metabolites versus time showed that a 2-week period is too short to mirror the effect of different types of feeding in the urine metabolome. An impact on the metabolic fingerprint was evident after the second month, with citric acid, methyl succinate, urea and bile acids, as well as dimethylamine, as discriminant metabolites. Citric acid shared a common metabolic fate through time, as its levels were increased with age in both groups in the 3rd month compared to the 3rd day. With respect to the FF group, the consistent intake of a protein-enriched infant formula for more than 2 months may have led to increased levels of nitrogen metabolites, such as urea and creatine. The increase in methyl-succinate in FF newborns seems to have been driven by the catabolic pathways of amino acids, such as isoleucine, present in protein-rich infant formulae.Although the relationship between the gut microbiota and the effect on the metabolome is not well understood, increasing evidence suggests that the infant’s intestinal microbiome plays a key role in metabolism and immune development, with impacts on lifelong health.In many cases, alterations of metabolites may be attributed to the shaping of the gut microecology over time and by the participation of metabolites in bacterial metabolism. For instance, conjugated bile acids are a key regulator of gut microecology by participating in bacterial metabolism and were correlated with the FF group. Moreover, dimethylamine is an early microflora metabolite of microbiota–host reciprocal interactions; thus, it is present in higher levels in the first days of both in BF and FF infants. Nevertheless, it remains a characteristic metabolite for discrimination between BF and FF infants, even in later stages (month 2). Overall, this study reveals that both investigated feeding types may exert beneficial effects directly or indirectly through alterations in the gut microbiota. However, this study is limited by the use of urine samples as the sole substrate, and further validation is required. The role of metabolomics is also highlighted to provide comparative data regarding the effect of different feeding regimes on infant metabolism. Additionally, despite the short sampling intervals, the metabolic frame at each sample point provides only a snapshot of the constantly changing microbiota ecosystem. Generally, understanding the crosstalk between feeding type, gut microbiota and consequent metabolome will facilitate the development of novel and personalized dietary interventions during early life, which may prove useful to prevent metabolic disorders in later years.