Gut Metabotypes as Biomarkers for Nutrition and Health

ERA-HDHL cofunded call “Biomarkers for Nutrition and Health” (BioNH 2016)
Gut Metabotypes as Biomarkers for Nutrition and Health
BioNUGUT
2017-09-05
2020-11-30
Prof. Dr. Matthias Laudes
University Hospital of Schleswig Holstein
Germany

Consortium

Partner Organization Partner Country
Human Nutrition and Food ScienceGermany
University of CalgaryCanada
Medizinische Universität GrazAustria

1. Overall project description


1.1 Summary

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.


1.2 Highlights

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. Impact


4.1 List of publications

AuthorsTitleYear, Issue, PPPartners NumberDoiPdf
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 2310.3389/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 infections2020 Nov;44(11):2335-233810.1038/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-e60110.1210/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, 1171-117410.1038/nmeth.4470
Hartler, J. et al. Automated Annotation of Sphingolipids Including Accurate Identification of Hydroxylation Sites Using MS(n) Data2020, 14054-1406210.1021/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 202010.3390/metabo10090342
Leithner, K. et al.The glycerol backbone of phospholipids derives from noncarbohydrate precursors in starved lung cancer cells2018, 15, 6225-623010.1073/pnas.1719871115
Lesko, J. et al.Phospholipid dynamics in ex vivo lung cancer and normal lung explants.2021, 53, 81-9010.1038/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. 202110.1016/j.jhep.2021.02.027
Zullig, T*., Trotzmuller, M. & Kofeler, H. C*. Global Lipidomics Profiling by a High Resolution LC-MS Platform. 20201, 2306, 39-510.1007/978-1-0716-1410-5_3
Zullig, T*., Trotzmuller, M. & Kofeler, H. C*. Lipidomics from sample preparation to data analysis: a primer. 2020, 412, 2191-220910.1007/s00216-019-02241-y
Zullig, T*. & Kofeler, H. C*. High Resolution Mass Spectrometry in Lipidomics.2021, 40, 162-17610.1002/mas.21627
Liebisch, G. et al. Lipidomics needs more standardization. 2019, 1, 745-74710.1038/ s42255-019-0094-z
Liebisch, G. et al. Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures.2020, 61, 1539-155510.1194/jlr.S120001025
Parey, K. et al. High-resolution cryo-EM structures of respiratory complex I: Mechanism, assembly, and disease. 2019, 5, eaax948410.1126/sciadv.aax9484
Zhang, Y. et al. Asymmetric opening of the homopentameric 5-HT3A serotonin receptor in lipid bilayers. 2021, 12, 107410.1038/s41467-021-21016-7

4.2 Presentation of the project

Target groupAuthorsMeans of communicationHyperlinkPdf

4.3 List of submitted patents and other outputs

Patent licencePartners involvedYearInternational eu or national patentCommentPdf

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