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Personalized longitudinal metabolomic and microbiomic profiling of diabetes progression

Applicant Röst Steiner Hannes
Number 162268
Funding scheme Early Postdoc.Mobility
Research institution Departments of Pediatrics and Genetics Stanford University School of Medicine
Institution of higher education Institution abroad - IACH
Main discipline Genetics
Start/End 01.07.2015 - 31.12.2016
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Keywords (3)

Genetics; Personalized Medicine; Microbiome

Lay Summary (German)

Lead
Um die Früherkennung und Prävention bei Diabetes Typ 2 zu verbessern, ist ein besseres Verständnis der molekularen Mechanismen, welche zur Krankheit führen, essentiell. Insbesondere ist die Rolle des humanen Mikrobiomes (die Gesamtheit aller Mikroorganismen im menschlichen Verdauungstrakt) bei der Krankheitsentwicklung momentan ungenügend verstanden.
Lay summary

Inhalt und Ziele des Forschungsprojekts

Das Projekt hat das Ziel, eine mehrjährige molekulare Analyse des Krankheitsverlaufes von Diabetes Typ 2 in einer Gruppe von 100 Personen mit erhöhtem Diabetes Typ 2-Risiko durchzuführen. Dabei wird ein personalisierter Ansatz verwendet, welcher viele tausend molekulare Messpunkte über mehrere Jahre hinweg verfolgen kann. Diese Herangehensweise erlaubt, die Interaktion zwischen den im menschlichen Verdauungstrakt lebenden Mikroorganismen und dem menschlichen Wirt mittels eines ganzheitlichen systemischen Ansatz zu analysieren. Die Ergebnisse werden danach mit weiteren molekularen Daten und bereits bekannten Interaktionsnetzwerken integriert. Dies wird unser Verständnis der molekularen Abläufe bei Diabetes Typ 2 verbessern und neue Erkenntnisse bezüglicher der Rolle des humanen Mikrobiomes liefern.

Wissenschaftlicher und gesellschaftlicher Kontext des Forschungsprojektes

Einerseits werden im Verlauf des Projekts neue Auswertungsmethoden und Software entwickelt, welche allgemein in der personalisierten Medizin, der mikrobiellen und metabolomischen Analyse verwendet werden können. Weiterhin können die Ergebnisse der Studie direkte Anhaltspunkte für mögliche Ansätze in der Prävention und Diagnostik von Diabetes Typ 2 liefern.

Direct link to Lay Summary Last update: 04.07.2015

Responsible applicant and co-applicants

Publications

Publication
High-frequency actionable pathogenic exome variants in an average-risk cohort
Rego Shannon, Dagan-Rosenfeld Orit, Zhou Wenyu, Sailani M. Reza, Limcaoco Patricia, Colbert Elizabeth, Avina Monika, Wheeler Jessica, Craig Colleen, Salins Denis, Röst Hannes L., Dunn Jessilyn, McLaughlin Tracey, Steinmetz Lars M., Bernstein Jonathan A., Snyder Michael P. (2018), High-frequency actionable pathogenic exome variants in an average-risk cohort, in Molecular Case Studies, 4(6), a003178-a003178.
Integrative Personal Omics Profiles during Periods of Weight Gain and Loss
Piening Brian D., Zhou Wenyu, Contrepois Kévin, Röst Hannes, Gu Urban Gucci Jijuan, Mishra Tejaswini, Hanson Blake M., Bautista Eddy J., Leopold Shana, Yeh Christine Y., Spakowicz Daniel, Banerjee Imon, Chen Cynthia, Kukurba Kimberly, Perelman Dalia, Craig Colleen, Colbert Elizabeth, Salins Denis, Rego Shannon, Lee Sunjae, Zhang Cheng, Wheeler Jessica, Sailani M. Reza, Liang Liang, et al. (2018), Integrative Personal Omics Profiles during Periods of Weight Gain and Loss, in Cell Systems, 6(2), 157-170.e8.
Chapter 10: Data Analysis for Data Independent Acquisition
Navarro P., Trevisan-Herraz M., Röst H.L. (2017), Chapter 10: Data Analysis for Data Independent Acquisition, in Conrad Bessant (ed.), RSC, Unknown, 200-227.
A multicenter study benchmarks software tools for label-free proteome quantification
Navarro P., Kuharev J., Gillet L.C., Bernhardt O.M., MacLean B., Röst H.L., Tate S.A., Tsou C.-C., Reiter L., Distler U., Rosenberger G., Perez-Riverol Y., Nesvizhskii A.I., Aebersold R., Tenzer S. (2016), A multicenter study benchmarks software tools for label-free proteome quantification, in Nature Biotechnology, 34(11), 1130-1136.
OpenMS: A flexible open-source software platform for mass spectrometry data analysis
Röst H.L., Sachsenberg T., Aiche S., Bielow C., Weisser H., Aicheler F., Andreotti S., Ehrlich H.-C., Gutenbrunner P., Kenar E., Liang X., Nahnsen S., Nilse L., Pfeuffer J., Rosenberger G., Rurik M., Schmitt U., Veit J., Walzer M., Wojnar D., Wolski W.E., Schilling O., Choudhary J.S., Malmström L., Aebersold R. (2016), OpenMS: A flexible open-source software platform for mass spectrometry data analysis, in Nature Methods, 13(9), 741-748.
TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics
Röst Hannes L, Liu Yansheng, D'Agostino Giuseppe, Zanella Matteo, Navarro Pedro, Rosenberger George, Collins Ben C, Gillet Ludovic, Testa Giuseppe, Malmström Lars, Aebersold Ruedi (2016), TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics, in Nature Methods, 777-783.
Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms
Röst Hannes, Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms, in Methods in Molecular Biology (Springer), 0.

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
HUPO 2016 Talk given at a conference TRIC: An automated alignment strategy for reproducible protein quantification in targeted proteomics. 18.09.2016 Taipeh, Taiwan Röst Steiner Hannes;
ASMS 2016 Poster organised the DIA workshop and had a poster on TRIC 05.06.2016 San Antonio, Texas, United States of America Röst Steiner Hannes;
Dagstuhl Seminar Individual talk Participation in various workshops 23.08.2015 Dagstuhl, Germany Röst Steiner Hannes;


Awards

Title Year
HUPO 2016 Early Career Researcher Manuscript Competition, Finalist 2016

Associated projects

Number Title Start Funding scheme
164703 Personalized longitudinal metabolomic and microbiomic profiling of diabetes progression 01.01.2017 Advanced Postdoc.Mobility

Abstract

Diabetes mellitus type 2 is a global epidemic that affects over 200 million people worldwide and its prevalence is expected to increase by more than 50 % during the next two decades. Currently, early diagnosis and prevention programs are the most effective method to reduce diabetes incidence and comorbidities. However, to improve diagnosis and prognosis, a better understanding of the molecular mechanisms underlying diabetes is needed. Specifically, the role of the human microbiome in diabetes is currently poorly understood but potentially plays a large role in disease progression. Here, we propose to use personalized omics profiling of 100 subjects with high risk of developing diabetes to investigate the molecular causes of and events associated with diabetes progression. Our study will focus on longitudinal high throughput measurements of the human microbiome and metabolome, which will allow us to investigate host-microbiota interactions on a systems level. Our approach will enable us to observe dynamic changes in the microbiome during the course of disease progression, providing molecular insight into ongoing changes in each subject. The proposed large-scale profiling and the longitudinal nature of the analysis will far exceed any previous study to date both in number of time points as well as in number of molecular components measured. The deep personalized profiling will likely reveal novel interactions between microbial pathways and host metabolism, which can contribute to our understanding of diabetes and serve as biomarker signature that may be useful for the prevention of the disease.
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