The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non‐alcoholic fatty liver disease
2020; Springer Nature; Volume: 16; Issue: 4 Linguagem: Inglês
10.15252/msb.209495
ISSN1744-4292
AutoresCheng Zhang, Elias Björnson, Muhammad Arif, Abdellah Tebani, Alen Lovrić, Rui Benfeitas, Mehmet Özcan, Kajetan Juszczak, Woonghee Kim, Jung Tae Kim∥, Gholamreza Bidkhori, Marcus Ståhlman, Per‐Olof Bergh, Martin Adiels, Hasan Türkez, Marja‐Riitta Taskinen, Jim Bosley, Hanns‐Ulrich Marschall, Jens Nielsen, Mathias Uhlén, Jan Borén, Adil Mardinoğlu,
Tópico(s)Alcohol Consumption and Health Effects
ResumoArticle27 April 2020Open Access Transparent process The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alcoholic fatty liver disease Cheng Zhang Cheng Zhang orcid.org/0000-0002-3721-8586 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China†Joint first authors Search for more papers by this author Elias Bjornson Elias Bjornson Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden†Joint first authors Search for more papers by this author Muhammad Arif Muhammad Arif Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden†Joint first authors Search for more papers by this author Abdellah Tebani Abdellah Tebani orcid.org/0000-0002-8901-2678 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Alen Lovric Alen Lovric Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Rui Benfeitas Rui Benfeitas orcid.org/0000-0001-7972-0083 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Mehmet Ozcan Mehmet Ozcan orcid.org/0000-0002-1222-2802 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Kajetan Juszczak Kajetan Juszczak Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Woonghee Kim Woonghee Kim Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Jung Tae Kim Jung Tae Kim Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Gholamreza Bidkhori Gholamreza Bidkhori Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK Search for more papers by this author Marcus Ståhlman Marcus Ståhlman Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Per-Olof Bergh Per-Olof Bergh orcid.org/0000-0001-9993-6965 Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Martin Adiels Martin Adiels Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Hasan Turkez Hasan Turkez Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey Search for more papers by this author Marja-Riitta Taskinen Marja-Riitta Taskinen Research Programs Unit, Diabetes and Obesity, Department of Internal Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, Finland Search for more papers by this author Jim Bosley Jim Bosley Clermont, Bosley LLC, Gothenburg, Sweden Search for more papers by this author Hanns-Ulrich Marschall Hanns-Ulrich Marschall Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Jens Nielsen Jens Nielsen orcid.org/0000-0002-9955-6003 Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Search for more papers by this author Mathias Uhlén Mathias Uhlén orcid.org/0000-0002-4858-8056 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Jan Borén Corresponding Author Jan Borén [email protected] orcid.org/0000-0003-0786-8091 Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Adil Mardinoglu Corresponding Author Adil Mardinoglu [email protected] orcid.org/0000-0002-4254-6090 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK Search for more papers by this author Cheng Zhang Cheng Zhang orcid.org/0000-0002-3721-8586 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China†Joint first authors Search for more papers by this author Elias Bjornson Elias Bjornson Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden†Joint first authors Search for more papers by this author Muhammad Arif Muhammad Arif Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden†Joint first authors Search for more papers by this author Abdellah Tebani Abdellah Tebani orcid.org/0000-0002-8901-2678 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Alen Lovric Alen Lovric Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Rui Benfeitas Rui Benfeitas orcid.org/0000-0001-7972-0083 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Mehmet Ozcan Mehmet Ozcan orcid.org/0000-0002-1222-2802 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Kajetan Juszczak Kajetan Juszczak Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Woonghee Kim Woonghee Kim Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Jung Tae Kim Jung Tae Kim Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Gholamreza Bidkhori Gholamreza Bidkhori Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK Search for more papers by this author Marcus Ståhlman Marcus Ståhlman Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Per-Olof Bergh Per-Olof Bergh orcid.org/0000-0001-9993-6965 Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Martin Adiels Martin Adiels Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Hasan Turkez Hasan Turkez Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey Search for more papers by this author Marja-Riitta Taskinen Marja-Riitta Taskinen Research Programs Unit, Diabetes and Obesity, Department of Internal Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, Finland Search for more papers by this author Jim Bosley Jim Bosley Clermont, Bosley LLC, Gothenburg, Sweden Search for more papers by this author Hanns-Ulrich Marschall Hanns-Ulrich Marschall Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Jens Nielsen Jens Nielsen orcid.org/0000-0002-9955-6003 Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Search for more papers by this author Mathias Uhlén Mathias Uhlén orcid.org/0000-0002-4858-8056 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Jan Borén Corresponding Author Jan Borén [email protected] orcid.org/0000-0003-0786-8091 Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden Search for more papers by this author Adil Mardinoglu Corresponding Author Adil Mardinoglu [email protected] orcid.org/0000-0002-4254-6090 Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK Search for more papers by this author Author Information Cheng Zhang1,2, Elias Bjornson3,4, Muhammad Arif1, Abdellah Tebani1, Alen Lovric1,9,10, Rui Benfeitas1,11, Mehmet Ozcan1, Kajetan Juszczak1, Woonghee Kim1, Jung Tae Kim1, Gholamreza Bidkhori5, Marcus Ståhlman3, Per-Olof Bergh3, Martin Adiels3, Hasan Turkez6, Marja-Riitta Taskinen7, Jim Bosley8, Hanns-Ulrich Marschall3, Jens Nielsen4, Mathias Uhlén1, Jan Borén *,3 and Adil Mardinoglu *,1,5 1Science for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden 2School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China 3Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Gothenburg, Sweden 4Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden 5Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK 6Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey 7Research Programs Unit, Diabetes and Obesity, Department of Internal Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, Finland 8Clermont, Bosley LLC, Gothenburg, Sweden 9Present address: Division of Clinical Physiology, Department of Laboratory Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden 10Present address: Unit of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden 11Present address: Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden (NBIS), Stockholm University, Stockholm, Sweden *Corresponding author. Tel: +46 31 342 2949; E-mail: [email protected] *Corresponding author. Tel: +46 8 524 820 20; E-mail: [email protected] Molecular Systems Biology (2020)16:e9495https://doi.org/10.15252/msb.209495 Lead Contact. Adil Mardinoglu PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase dramatically, and there is no approved medication for its treatment. Recently, we predicted the underlying molecular mechanisms involved in the progression of NAFLD using network analysis and identified metabolic cofactors that might be beneficial as supplements to decrease human liver fat. Here, we first assessed the tolerability of the combined metabolic cofactors including l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine by performing a 7-day rat toxicology study. Second, we performed a human calibration study by supplementing combined metabolic cofactors and a control study to study the kinetics of these metabolites in the plasma of healthy subjects with and without supplementation. We measured clinical parameters and observed no immediate side effects. Next, we generated plasma metabolomics and inflammatory protein markers data to reveal the acute changes associated with the supplementation of the metabolic cofactors. We also integrated metabolomics data using personalized genome-scale metabolic modeling and observed that such supplementation significantly affects the global human lipid, amino acid, and antioxidant metabolism. Finally, we predicted blood concentrations of these compounds during daily long-term supplementation by generating an ordinary differential equation model and liver concentrations of serine by generating a pharmacokinetic model and finally adjusted the doses of individual metabolic cofactors for future human clinical trials. Synopsis An animal toxicity study identifies changes in human plasma metabolome and inflammatory protein markers associated with the supplementation of metabolic cofactors. Global metabolic changes are identified by integrating this data using genome-scale metabolic modelling. None of the administered doses of metabolic cofactors caused any detectable hematological, plasma chemistry or tissue effects in animals. The acute changes associated with the supplementation are analysed by plasma profiling in humans. Metabolic cofactor supplementation significantly affects the global human lipid, amino acid and anti-oxidant metabolism. The doses of the individual metabolic cofactors are adjusted for future human clinical trials. Introduction Hepatic steatosis (HS) is defined as the accumulation of large vacuoles of triglycerides in the liver (> 5.5% tissue weight) due to an imbalance between lipid deposition and lipid removal from the liver (Solinas et al, 2015; Francque et al, 2016; Samuel & Shulman, 2018). Non-alcoholic fatty liver disease (NAFLD) encompasses a broad spectrum of pathological conditions, ranging from simple HS to various degrees of liver inflammation such as non-alcoholic steatohepatitis (NASH), which can progress to severe liver diseases, including cirrhosis and hepatocellular carcinoma (HCC). The prevalence of NAFLD continues to increase dramatically, and it has reached 25% at the population level (Estes et al, 2018) with the progressive epidemics of obesity and type 2 diabetes mellitus (T2DM). There are at present no approved effective medications for treating NASH. In order to resolve the pathophysiology of NAFLD and to reveal the underlying molecular mechanisms involved in the progression towards NASH, a systems biology approach which enables integration and analysis of multi-layer omics data through the use of biological networks has been employed (Mardinoglu & Nielsen, 2015; Mardinoglu & Uhlen, 2016; Bosley et al, 2017; Mardinoglu et al, 2018a,b). Earlier, we generated a functional liver-specific genome-scale metabolic model (GEM) (Mardinoglu et al, 2014) and an integrated network (IN) (Lee et al, 2016) by merging GEMs with regulatory and protein–protein interaction networks. This integrative approach allows not only to reveal the key pathways, metabolites, and genes involved in the progression of liver diseases, but also to make solid predictions that can be experimentally tested due to the known regulatory effect of other proteins on metabolism. Recently, we have combined clinical studies with stable isotopes, in-depth multi-omics profiling, and liver-specific networks to clarify the underlying mechanisms of NAFLD and develop strategies for prevention and treatment (Mardinoglu et al, 2017). Our integrative multi-tissue analysis has indicated that NAFLD patients have reduced de novo synthesis of glutathione (GSH) due to limited availability of serine and glycine, resulting in altered GSH and NAD+ metabolism, which is a prevailing feature of NAFLD. To test the model-based predictions, we assessed the effect of short-term serine supplementation in NAFLD patients by providing an oral dose of ~ 20 g of l-serine (200 mg/kg) per day for 14 days showing that liver enzymes (ASAT, ALAT, ALP) and plasma triglycerides, as well as the amount of fat in liver were significantly decreased after supplementation of serine (Mardinoglu et al, 2017). Our model also indicated that supplementation of l-carnitine as well as precursors of GSH and NAD+ including l-serine, N-acetyl-l-cysteine (NAC), and nicotinamide riboside (NR) would decrease liver fat accumulation by promoting the fat uptake and its oxidation in the mitochondria as well as generation of GSH required in the liver (Mardinoglu et al, 2017). These predictions were further tested in a mouse study and supplementation with such a formulation decreased the amount of liver fat (Mardinoglu et al, 2017). Here, we first performed a 7-day rat toxicology to study tolerability of the combined metabolic cofactors and measured clinical parameters to identify potential side effects. Next, we performed a 5-day human calibration study by supplementing naturally occurring metabolic cofactors including 20 g l-serine, 3 g l-carnitine, 5 g NAC, and 1 g NR and a control study to reveal the acute global effect of the supplementation of combined metabolic cofactors by eliminating the effect of the fasting. We generated plasma metabolomics and inflammatory protein markers data to reveal the changes associated with the supplementation of these metabolic cofactors and measured the kinetics of the metabolites and proteins in the plasma of the healthy subjects. Next, we predicted altered pathways, reactions, and metabolites in liver due to the supplementation of the metabolic cofactors using metabolomics data and personalized genome-scale metabolic modeling. Moreover, we developed an ordinary differential equation (ODE) model to predict blood concentrations of each metabolic cofactor during daily long-term supplementation and adjusted their doses. Finally, we analyzed literature data and the data generated in the supplementation study using pharmacokinetic (PK) modeling and statistical analysis. Results Seven-day oral (gavage) tolerability study in the rat We performed a 7-day oral (gavage) toxicology study in the Wistar Hannover rats (without blinding) and assessed the tolerability of the combined metabolic cofactors including l-serine, NAC, NR, and l-carnitine tartrate (salt form of l-carnitine) at intended human clinical doses (Formulation I) and 10-fold (Formulation II) and 30-fold (Formulation III) dose levels. We assessed body weight, hematology, plasma chemistry, and gross pathology during the study. Three groups of rats, each consisting of three females to acquire minimal required statistical power (without randomization), were given combined metabolic cofactors orally, once a day for 7 days. The dose levels administered are presented in Table EV1. From available data on the individual components, Formulation I was not expected to produce any adverse effects. In Formulations II and III, the levels of some of the components were approaching known tolerability levels; hence, adverse effects could be expected. Several observations (e.g., plowing with the nose in the bedding material and excessive chewing) were made in all groups in connection with, or shortly after dosing, and are not considered of toxicological significance. Most severe observations, such as ataxia, cyanosis, irregular and/or respiration and decreased motor activity, were observed in Group 3 at Days 1 and 2. This led to a lowering of the dose (33%) in this group on Day 3. The new dose level was well tolerated for the remainder of the study. In all groups, some milder signs of discomfort (eyes half-shut and pilo-erection) were observed at Day 1, but were not present from Day 2 in Groups 1 and 2. This may only indicate a reaction to a new, unknown treatment, but may also indicate a tolerability buildup after repeated exposure. We observed that none of the doses administered in this study caused any significant changes (Appendix Table S1) in hematological (Appendix Table S2) and plasma chemistry parameters (Appendix Table S3). We also did not detect any significant changes by the pathology analysis or during the macroscopic analysis at necropsy. Human calibration study with natural metabolic cofactors We performed a 5-day calibration study by recruiting nine healthy male subjects without any medication (age 26–36 years, BMI 19.4–34.5 kg/m2) to identify the acute global effect of metabolic cofactors supplementation (Fig 1A, Table EV2). The subjects stayed in the same hotel, had the same breakfast, and did not eat/drink anything until the end of the study in each day. We supplemented each of the four metabolic cofactors NR, l-carnitine, NAC, and l-serine to all subjects at separate days as well as the combined metabolic cofactors (i.e., a cocktail of the substances) at another day. Based on literature information, we supplemented 20 g l-serine, 3 g l-carnitine, 5 g NAC, and 1 g NR per day (Hurd et al, 1996; Hathcock & Shao, 2006; Garofalo et al, 2011). The study started at 8:00 every day and blood samples were collected before and 4 h after the supplementation of individual metabolic cofactors. At the day we supplemented the combined metabolic cofactors, eight blood samples were collected during the day (Fig 1A). We measured the plasma level of glucose, insulin, free fatty acids, triglycerides, total cholesterol, HDL, LDL, and known liver markers including gamma GT, bilirubin, ASAT, ALAT, ALP before and after the study, and observed no significant differences (Table EV2). Subjects involved in the study did not report any side effect. Our analysis below focused on the day, where we supplemented the combined metabolic cofactors. Figure 1. Calibration study with the supplementation of metabolic cofactors Summary of the metabolic cofactor supplementation and control study as well as the dosage of the metabolic cofactors before (left regimen) and after (right regimen) dosage adjustment based on this study. Changes in plasma level of each cocktail substances in both supplementation and control studies (NR is detected in control study) compared to time baseline based on untargeted metabolomics measurement. The gray shaded area represents the 95% confidence level interval. For boxplots limits, the middle line represents the median. The upper and lower box limits represent the 25% quantiles. The upper and lower error bars correspond to 75% quantiles. The P-values are derived from one-way ANOVA (FDR < 0.05). Download figure Download PowerPoint After 21 months, we asked the subjects involved in the study to run a follow-up control study and five of the subjects responded positively. We recruited another five healthy subjects and run a 1-day control study with 10 male subjects without any medication (age 26–36 years, BMI 19.8–35.8 kg/m2) following the same study design in the metabolic cofactor supplementation study. Subjects stayed in the same hotel, had the same breakfast, and did not eat/drink anything until the end of the study. The study started at 8:00, and six blood samples were collected during the day after drinking a glass of water (Fig 1A). Plasma metabolites associated with the supplementation of metabolic cofactors We first quantified the plasma levels of l-serine, l-carnitine, and NAC using targeted metabolomics on the day we supplemented the combined metabolic cofactors and observed that supplementation of each metabolite increased the plasma levels proportionally (Appendix Fig S1 and Table EV3). We also generated untargeted metabolomics data using the plasma samples (Table EV4) and confirmed that plasma level of l-serine, l-carnitine, cysteine, and nicotinamide is increased after supplementation (Fig 1B). We observed a high degree of correlation between the serine (Pearson, r = 0.99) and l-carnitine (Pearson, r = 0.95) levels detected using targeted and untargeted metabolomics analysis, as expected. We have earlier shown that plasma levels of kynurenine, kynurenate, pyruvate, and ornithine are significantly positively associated with increased liver fat (Mardinoglu et al, 2017). We first investigated whether supplementation of the combined metabolic cofactors affected the plasma level of these substances (Table EV5). We found that plasma levels of kynurenine (Fig 2A), kynurenate (Fig 2B), pyruvate (Fig 2C), and ornithine (Fig 2D) were significantly decreased after the supplementation compared to baseline. In addition to the decrease in the plasma level of lipid structures (Appendix Figs S2 and S3), we also found that plasma levels of branch chain amino acids (BCAAs) including leucine (Fig 2E), isoleucine (Fig 2F), and valine (Fig 2G) which are significantly associated with insulin resistance and future incidence of T2D were significantly decreased after supplementation compared to baseline. In order to investigate if the decrease in the plasma levels of these metabolites was associated with the supplementation of the metabolic cofactors, we performed a correlation analysis between these metabolites and supplemented metabolic cofactors (Table EV6, Appendix Figs S4 and S5). We found that the decreased plasma level of kynurenine is significantly negatively correlated with carnitine and NR; kynurenate is significantly negatively correlated with serine; pyruvate is significantly negatively correlated with NR and cysteine; and ornithine is significantly negatively correlated with serine (Fig 2H). We also observed that the decreased plasma level of BCAAs was significantly negatively correlated with plasma level of serine. Figure 2. The changes in the plasma level of NAFLD associated metabolites due to the supplementation A–G. Changes in plasma level of key metabolites compared to time baseline (solid line) based on untargeted metabolomics measurement in metabolic cofactor supplementation study. The upper and lower box limits represent the 25% quantiles. The upper and lower error bars correspond to 75% quantiles. *Denotes significance (one-way ANOVA; FDR < 0.05). H. Spearman correlation (visualized by Circlize) between plasma levels of supplemented metabolic cofactors and key metabolites associated with high liver fat and insulin resistance. *Denotes significance (FDR < 0.05). Download figure Download PowerPoint In order to eliminate the fasting effect on the kinetics of the plasma metabolites, we also generated untargeted metabolomics data using the plasma samples from the control study and detected the plasma level of 630 metabolites (Table EV7) of which 96 metabolites were detected in both studies (Table EV8). We observed that none of the significant correlations between the metabolites associated with increased liver fat and supplemented metabolic cofactors were identified in the control study. Next, we studied the kinetics of the plasma metabolites by comparing to the baseline in the supplementation and control study (Fig 3A). We observed that the plasma levels of l-serine, l-carnitine, and cysteine were significantly different and showed different dynamics, as expected. For instance, we found that both the l-serine and l-carnitine levels kept almost constant in the control study which are opposite to their rapid increase exhibited in the supplementation study (Fig 1C). Interestingly, we found that the plasma cysteine level between the two studies has been shifted during the experiment, where it is significantly higher in the first 2 h but much lower in the last 2 h in the supplementation study (Fig 1C). This indicated that the supplementation not only boosted the cysteine level in the first half of the experiment, but also increased its consumption during the whole study. We also identified the metabolites showing the same and opposite trends in two studies. In addition to the plasma level of supplemented metabolic cofactors, directly associated metabolites to these cofactors including betaine, choline, glycine, cystine, carnitine derivatives; as well as other metabolites including citrulline, trimethylamine N-oxide (TMO), homoarginine, cortisone, and dimethylarginine showed opposite trend in two different studies. Figure 3. The effect of supplementation on plasma metabolites A. A summary of significantly different plasma metabolites compared to time baseline in metabolic cofactor supplementation and control studies based on untargeted metabolomics measurements. Red and blue colors denote the increased and decreased plasma levels compared to baseline, respectively. *Denotes significance (one-way ANOVA; FDR < 0.05). B. Metabolites significantly different between supplementation and control studies. Red and blue colors denote the increased and decreased plasma level in supplementation study compared to control study, respectively. *Denotes significance (one-way ANOVA; FDR < 0.05). C–E. Changes in plasma level of key metabolites compared to time baseline based on untargeted metabolomics measurement in supplementation and control studies. The gray shaded area represents the 95% confidence level interval. Download figure Download PowerPoint Finally, to systematically evaluate the differences between two the studies and to eliminate the fasting effect, we compared the plasma levels of these metabolites between the subjects for each time point and identified 18 metabolites that were significantly (ANOVA test, adjusted P < 0.05) altered in at least three time points (Fig 3B). We observed the plasma level of citrulline (Fig 3C), TMO (Fig 3D), and dimethylarginine (Fig 3E) showed consistently significant differences. Citrulline is a key metabolite in the urea cycle, and
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