An Investigation into the Temporal Reproducibility of Tryptophan Metabolite Networks Among Healthy Adolescents

Tryptophan and its bioactive metabolites are associated with health conditions such as systemic inflammation, cardiometabolic diseases, and neurodegenerative disorders. There are dynamic interactions among metabolites of tryptophan. The interactions between metabolites, particularly those that are strong and temporally reproducible could be of pathophysiological relevance. Using a targeted metabolomics approach, the concentration levels of tryptophan and 18 of its metabolites across multiple pathways was quantified in 24-hours urine samples at 2 time-points, age 17 years (baseline) and 18 years (follow-up) from 132 (52% female) apparently healthy adolescent participants of the DOrtmund Nutritional and Anthropometric Longitudinally Designed (DONALD) Study. In sex-specific analyses, we applied 2 network approaches, the Gaussian graphical model and Bayesian network to (1) explore the network structure for both time-points, (2) retrieve strongly related metabolites, and (3) determine whether the strongly related metabolites were temporally reproducible. Independent of selected covariates, the 2 network approaches revealed 5 associations that were strong and temporally reproducible. These were novel relationships, between kynurenic acid and indole-3-acetic acid in females and between kynurenic acid and xanthurenic acid in males, as well as known relationships between kynurenine and 3-hydroxykynurenine, and between 3-hydroxykynurenine and 3-hydroxyanthranilic acid in females and between tryptophan and kynurenine in males. Overall, this epidemiological study using network-based approaches shed new light into tryptophan metabolism, particularly the interaction of host and microbial metabolites. The 5 observed relationships suggested the existence of a temporally stable pattern of tryptophan and 6 metabolites in healthy adolescent, which could be further investigated in search of fingerprints of specific physiological states. The metabolites in these relationships may represent a multi-biomarker panel that could be informative for health outcomes.

[1]  Francesco Valeri,et al.  How biological sex of the host shapes its gut microbiota , 2021, Frontiers in Neuroendocrinology.

[2]  K. Greathouse,et al.  Targeting Dietary and Microbial Tryptophan-Indole Metabolism as Therapeutic Approaches to Colon Cancer , 2021, Nutrients.

[3]  H. Soininen,et al.  Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer’s disease , 2021, Alzheimer's research & therapy.

[4]  G. Mondanelli,et al.  The double life of serotonin metabolites: in the mood for joining neuronal and immune systems. , 2020, Current opinion in immunology.

[5]  K. Maheswari,et al.  A cross-sectional study on the correlation between postnatal foot length and various other anthropometric parameters along with the gestational age , 2020, Pediatric Review: International Journal of Pediatric Research.

[6]  K. Clément,et al.  Gut microbiota-derived metabolites as central regulators in metabolic disorders , 2020, Gut.

[7]  H. Zabed,et al.  An Insight into the Roles of Dietary Tryptophan and its Metabolites in Intestinal Inflammation and Inflammatory Bowel Disease. , 2020, Molecular nutrition & food research.

[8]  W. D. de Jonge,et al.  Nutritional Therapy to Modulate Tryptophan Metabolism and Aryl Hydrocarbon-Receptor Signaling Activation in Human Diseases , 2020, Nutrients.

[9]  P. Kris-Etherton,et al.  Intestinal microbiota-derived tryptophan metabolites are predictive of Ah receptor activity , 2020, Gut microbes.

[10]  D. Fuchs,et al.  Editorial: Immunomodulatory Roles of Tryptophan Metabolites in Inflammation and Cancer , 2020, Frontiers in Immunology.

[11]  L. Romani,et al.  Tryptophan as a Central Hub for Host/Microbial Symbiosis , 2020, International journal of tryptophan research : IJTR.

[12]  U. Nöthlings,et al.  Longitudinal relationship of amino acids and indole metabolites with long-term body mass index and cardiometabolic risk markers in young individuals , 2020, Scientific Reports.

[13]  Alisdair R Fernie,et al.  Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation , 2020, Expert review of proteomics.

[14]  T. Nabeshima,et al.  Serum Metabolic Profiles of the Tryptophan-Kynurenine Pathway in the high risk subjects of major depressive disorder , 2020, Scientific Reports.

[15]  U. Nöthlings,et al.  Metabolic Profiling of Human Plasma and Urine, Targeting Tryptophan, Tyrosine and Branched Chain Amino Acid Pathways , 2019, Metabolites.

[16]  Z. Ma,et al.  How and Why Men and Women Differ in Their Microbiomes: Medical Ecology and Network Analyses of the Microgenderome , 2019, Advanced science.

[17]  G. Guillemin,et al.  Kynurenine Pathway Metabolites as Biomarkers for Amyotrophic Lateral Sclerosis , 2019, Front. Neurosci..

[18]  S. Taleb.  Tryptophan Dietary Impacts Gut Barrier and Metabolic Diseases , 2019, Front. Immunol..

[19]  T. Hendrikx,et al.  Indoles: metabolites produced by intestinal bacteria capable of controlling liver disease manifestation , 2019, Journal of internal medicine.

[20]  J. Gostner,et al.  Tryptophan Metabolism and Related Pathways in Psychoneuroimmunology: The Impact of Nutrition and Lifestyle , 2019, Neuropsychobiology.

[21]  A. Badawy.  Tryptophan Metabolism: A Versatile Area Providing Multiple Targets for Pharmacological Intervention , 2019, Egyptian journal of basic and clinical pharmacology.

[22]  Hamed Kazemi Shariat Panahi,et al.  Microorganisms, Tryptophan Metabolism, and Kynurenine Pathway: A Complex Interconnected Loop Influencing Human Health Status , 2019, International journal of tryptophan research : IJTR.

[23]  Jinde Cao,et al.  Preface , 2019, Math. Comput. Simul..

[24]  Abhijeet R. Sonawane,et al.  A Network Analysis of Biomarkers for Type 2 Diabetes , 2018, Diabetes.

[25]  Jian-Min Yuan,et al.  A prospective evaluation of serum kynurenine metabolites and risk of pancreatic cancer , 2018, PloS one.

[26]  Antonio Rosato,et al.  From correlation to causation: analysis of metabolomics data using systems biology approaches , 2018, Metabolomics.

[27]  A. Hoeflich,et al.  Kynurenic Acid: The Janus-Faced Role of an Immunomodulatory Tryptophan Metabolite and Its Link to Pathological Conditions , 2018, Front. Immunol..

[28]  L. Tenori,et al.  Age and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects. , 2018, Journal of proteome research.

[29]  David Hevey,et al.  Network analysis: a brief overview and tutorial , 2018, Health psychology and behavioral medicine.

[30]  K. Engedal,et al.  The Relationships among Tryptophan, Kynurenine, Indoleamine 2,3-Dioxygenase, Depression, and Neuropsychological Performance , 2017, Front. Psychol..

[31]  Yang Li,et al.  Regulating the balance between the kynurenine and serotonin pathways of tryptophan metabolism , 2017, The FEBS journal.

[32]  Eiko I. Fried,et al.  Mental disorders as networks of problems: a review of recent insights , 2016, Social Psychiatry and Psychiatric Epidemiology.

[33]  Eiko I. Fried,et al.  A Tutorial on Regularized Partial Correlation Networks , 2016, Psychological methods.

[34]  C. Lim,et al.  Kynurenines, Gender and Neuroinflammation; Showcase Schizophrenia , 2016, Neurotoxicity Research.

[35]  Denny Borsboom,et al.  Generalized Network Psychometrics: Combining Network and Latent Variable Models , 2016, Psychometrika.

[36]  A. Rossi,et al.  Tryptophan Biochemistry: Structural, Nutritional, Metabolic, and Medical Aspects in Humans , 2016, Journal of amino acids.

[37]  G. Perdew,et al.  Indole and Tryptophan Metabolism: Endogenous and Dietary Routes to Ah Receptor Activation , 2015, Drug Metabolism and Disposition.

[38]  P. Vineis,et al.  Reliability of Serum Metabolites over a Two-Year Period: A Targeted Metabolomic Approach in Fasting and Non-Fasting Samples from EPIC , 2015, PLoS ONE.

[39]  Jean-Baptiste Denis,et al.  Bayesian Networks , 2014 .

[40]  P. Brennan,et al.  Most blood biomarkers related to vitamin status, one-carbon metabolism, and the kynurenine pathway show adequate preanalytical stability and within-person reproducibility to allow assessment of exposure or nutritional status in healthy women and cardiovascular patients. , 2014, The Journal of nutrition.

[41]  J. Pedraza-Chaverri,et al.  Kynurenines with Neuroactive and Redox Properties: Relevance to Aging and Brain Diseases , 2014, Oxidative medicine and cellular longevity.

[42]  N. Tatonetti,et al.  Connecting the Dots: Applications of Network Medicine in Pharmacology and Disease , 2013, Clinical pharmacology and therapeutics.

[43]  C. Patten,et al.  Activity, distribution and function of indole-3-acetic acid biosynthetic pathways in bacteria , 2013, Critical reviews in microbiology.

[44]  Mark E. Borsuk,et al.  Using Bayesian networks to discover relations between genes, environment, and disease , 2013, BioData Mining.

[45]  H. Flint,et al.  Major phenylpropanoid-derived metabolites in the human gut can arise from microbial fermentation of protein. , 2013, Molecular nutrition & food research.

[46]  Jin-Tai Yu,et al.  The kynurenine pathway in neurodegenerative diseases: Mechanistic and therapeutic considerations , 2012, Journal of the Neurological Sciences.

[47]  Changhe Yuan,et al.  Empirical evaluation of scoring functions for Bayesian network model selection , 2012, BMC Bioinformatics.

[48]  Fabian J Theis,et al.  Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers , 2011, PLoS genetics.

[49]  T. Pischon,et al.  Reliability of Serum Metabolite Concentrations over a 4-Month Period Using a Targeted Metabolomic Approach , 2011, PloS one.

[50]  Korbinian Strimmer,et al.  Introduction to Graphical Modelling , 2010, 1005.1036.

[51]  B. Graubard,et al.  Energy density of diets reported by American adults: association with food group intake, nutrient intake, and body weight , 2005, International Journal of Obesity.

[52]  Andrei S. Rodin,et al.  Mining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma apoE levels) , 2005, Bioinform..

[53]  M. Kersting,et al.  The DONALD Study , 2004, European journal of nutrition.

[54]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[55]  T. Remer,et al.  Anthropometry-based reference values for 24-h urinary creatinine excretion during growth and their use in endocrine and nutritional research. , 2002, The American journal of clinical nutrition.

[56]  G. D. Tjipta,et al.  Correlation between several anthropometric measurements to birth weight , 2001 .

[57]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[58]  D. Borsboom,et al.  Comparing network structures on three aspects: A permutation test , 2017 .

[59]  R. Parr,et al.  Bayesian networks , 2015 .

[60]  Radhakrishnan Nagarajan,et al.  Bayesian Networks in R , 2013 .

[61]  A. Smilde,et al.  Reflections on univariate and multivariate analysis of metabolomics data , 2013, Metabolomics.

[62]  Marek J. Druzdzel,et al.  A comparison of structural distance measures for causal Bayesian network models , 2009 .

[63]  Vincenzo Politi,et al.  The enol tautomer of indole-3-pyruvic acid as a biological switch in stress responses. , 2003, Advances in experimental medicine and biology.

[64]  R A Goldbohm,et al.  Validation of a dietary questionnaire used in a large-scale prospective cohort study on diet and cancer. , 1994, European journal of clinical nutrition.