Gut Metabotypes as Biomarkers for Nutrition and Health
|Partner Organization||Partner Country|
|Human Nutrition and Food Science||Germany|
|University of Calgary||Canada|
|Medizinische Universität Graz||Austria|
1. Overall project description
In recent decades, nutritional science and biomedicine have made enormous progress in the development of modern molecular and cellular research technologies. This has led to the awareness that health is a state of homeostasis, not only for our various eukaryotic cells, but also for the millions of living microorganisms that live in symbiosis on (e.g. skin) or in (e.g. gut) our human bodies. The aim of the project described here was to identify useful microbiome-host metabolic axes and to use biological patterns in the gut microbiome and of metabolites in human serum as markers for nutrition and health. Identified patterns reflecting a symbiotically balanced state will provide a better indication of "good" nutrition and health compared to a single molecule biomarker.
The gut microbiome is essential for human health and gastrointestinal tract homeostasis as it plays a role in the development and activity of the immune system, regulates the renewal of the intestinal epithelium and the maintenance of mucosal integrity, and is essential for dietary energy production. It is now widely recognised that disorders in the gut microbiome are associated with many different diseases, including:
1. metabolic disorders (e.g. type 2 diabetes and obesity),
2. cardiovascular diseases (e.g. atherosclerosis and heart failure),
3. chronic inflammatory diseases (e.g. rheumatoid arthritis and chronic inflammatory bowel diseases) and
4. defined malignancies (e.g. stomach and colon cancer).
Because disorders of the gut microbiome are associated with so many different disease entities, a health-promoting microbiome is likely to be a reliable reflection of the state of symbiotic homeostasis associated with health. Furthermore, it is known that human health status and the gut microbiome are strongly influenced by different diets. The gut microbiome intervenes in human metabolism through the uptake and exchange of certain dietary components and the production of new metabolites.
Various bioinformatic methods were used to evaluate the data generated in the project. The classification of probands according to health status and the identification of relevant biological patterns was done using random forests, among other statistical methods. Multi-Omics analysis of metabolome, lipidome and dietary and microbiome data led to the identification of a biopattern consisting of 10 microbiome ASVs, several hydrophilic and lipophilic metabolites and few nutrition components that is indicative of a homeostatic and healthy human state.
The consortium successfully established a human dietary intervention study as well as recalls of the FoCus and ATP Tomorrow cohorts. A biopattern which is associated with human health and consists of 10 microbiome ASVs, several hydrophilic and lipophilic metabolites and few nutrition components has been identified. Multi- Omics and machine learning analysis revealed that the gut microbiome influences human health more than other omics levels. A variety of secondary research projects have been achieved within this platform, for example regarding gut microbial community metabolism and bile acids, secondary plant compounds, the gut microbial species Parasutterella as a biomarker for metabolic disease and the development of a new platform for Lipidomics data analysis.
4.1 List of publications
|Authors||Title||Year, Issue, PP||Partners Number||Doi|
|Demetrowitsch TJ*, Schlicht K*, Knappe C, Zimmermann J, Jensen-Kroll J*, Pisarevskaja A, Brix F, Brandes J, Geisler C, Marinos G, Sommer F, Schulte DM, Kaleta C, Andersen V, Laudes M*, Schwarz K*, Waschina S. Precision||Precision Nutrition in Chronic Inflammation.||2020 Nov 23||10.3389/fimmu.2020.587895. PMID: 33329569; PMCID: PMC7719806|
|Schlicht K*, Rohmann N, Geisler C, Hollstein T, Knappe C, Hartmann K, Schwarz J, Tran F, Schunk D, Junker R, Bahmer T, Rosenstiel P, Schulte D, Türk K, Franke A, Schreiber S, Laudes M*.||Circulating levels of soluble Dipeptidylpeptidase-4 are reduced in human subjects hospitalized for severe COVID-19 infections||2020 Nov;44(11):2335-2338||10.1038/s41366-020-00689-y. PMID: 32958905; PMCID: PMC7503441.|
|Rohmann N*, Schlicht K*, Geisler C, Hollstein T, Knappe C, Krause L, Hagen S, Beckmann A, Seoudy AK, Wietzke- Braun P, Hartmann K, Schulte D, Türk K, Beckmann J, von Schönfels W, Hägele FA, Bosy-Westphal A, Franke A, Schreiber S, Laudes M*.||Circulating sDPP-4 is Increased in Obesity and Insulin Resistance but Is Not Related to Systemic Metabolic Inflammation.||2021 Jan 23;106(2):e592-e601||10.1210/clinem/ dgaa758. PMID: 33084870|
|Hollstein T; Schlicht K*; Krause L; Hagen S; Rohmann N*; Schulte DM, Türk K, Beckmann A, Ahrens M, Franke A, Schreiber S, Becker T, Beckmann J, Laudes M*.||Differential effects of various weight loss interventions on serum NT- proBNP concentration in severe obese subjects without clinical manifest heart failure|
|Hartler, J. et al.||Deciphering lipid structures based on platform-independent decision rules.||2017, 1171-1174||10.1038/nmeth.4470|
|Hartler, J. et al.||Automated Annotation of Sphingolipids Including Accurate Identification of Hydroxylation Sites Using MS(n) Data||2020, 14054-14062||10.1021/acs.analchem.0c03016|
|Zullig, T*. et al.||A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches||2020||10.3390/metabo10090342|
|Leithner, K. et al.||The glycerol backbone of phospholipids derives from noncarbohydrate precursors in starved lung cancer cells||2018, 15, 6225-6230||10.1073/pnas.1719871115|
|Lesko, J. et al.||Phospholipid dynamics in ex vivo lung cancer and normal lung explants.||2021, 53, 81-90||10.1038/s12276-020-00547-x|
|Geidl-Flueck, B. et al.||Fructose- and sucrose- but not glucose-sweetened beverages promote hepatic de novo lipogenesis: A randomized controlled trial.||2021||10.1016/j.jhep.2021.02.027|
|Zullig, T*., Trotzmuller, M. & Kofeler, H. C*.||Global Lipidomics Profiling by a High Resolution LC-MS Platform.||20201, 2306, 39-51||0.1007/978-1-0716-1410-5_3|
|Zullig, T*., Trotzmuller, M. & Kofeler, H. C*.||Lipidomics from sample preparation to data analysis: a primer.||2020, 412, 2191-2209||10.1007/s00216-019-02241-y|
|Zullig, T*. & Kofeler, H. C*.||High Resolution Mass Spectrometry in Lipidomics.||2021, 40, 162-176||10.1002/mas.21627|
|Liebisch, G. et al.||Lipidomics needs more standardization.||2019, 1, 745-747||10.1038/ s42255-019-0094-z|
|Liebisch, G. et al.||Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures.||2020, 61, 1539-1555||10.1194/jlr.S120001025|
|Parey, K. et al.||High-resolution cryo-EM structures of respiratory complex I: Mechanism, assembly, and disease.||2019, 5, eaax9484||10.1126/sciadv.aax9484|
|Zhang, Y. et al.||Asymmetric opening of the homopentameric 5-HT3A serotonin receptor in lipid bilayers.||2021, 12, 1074||10.1038/s41467-021-21016-7|
4.2 Presentation of the project
|Target group||Authors||Means of communication||Hyperlink|
4.3 List of submitted patents and other outputs
|Patent licence||Partners involved||Year||International eu or national patent||Comment|