Project

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Personalized longitudinal metabolomic and microbiomic profiling of diabetes progression

English title Personalized longitudinal metabolomic and microbiomic profiling of diabetes progression
Applicant Röst Steiner Hannes
Number 164703
Funding scheme Advanced Postdoc.Mobility
Research institution Department of Genetics Stanford University School of Medicine
Institution of higher education Institution abroad - IACH
Main discipline Genetics
Start/End 01.01.2017 - 31.08.2017
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Keywords (4)

Personalized Medicine; Systems Biology; Genetics; Diabetes Type 2

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: 28.10.2016

Responsible applicant and co-applicants

Publications

Publication
Initial guidelines for manuscripts employing data-independent acquisition mass spectrometry for proteomic analysis
Chalkley R.J., MacCoss M.J., Jaffe J.D., Rost H.L. (2019), Initial guidelines for manuscripts employing data-independent acquisition mass spectrometry for proteomic analysis, in Molecular and Cellular Proteomics, 18(1), 1-2.
Longitudinal multi-omics of host–microbe dynamics in prediabetes
Zhou W., Sailani M.R., Contrepois K., Zhou Y., Ahadi S., Leopold S.R., Zhang M.J., Rao V., Avina M., Mishra T., Johnson J., Lee-McMullen B., Chen S., Metwally A.A., Tran T.D.B., Nguyen H., Zhou X., Albright B., Hong B.-Y., Petersen L., Bautista E., Hanson B., Chen L., Spakowicz D., Bahmani A., Salins D., Leopold B., Ashland M., Dagan-Rosenfeld O., Rego S., Limcaoco P., Colbert E., Allister C., Perelman D., Craig C., Wei E., Chaib H., Hornburg D., Dunn J., Liang L., Rose S.M.S.-F., Kukurba K., Piening B., Rost H., Tse D., McLaughlin T., Sodergren E., Weinstock G.M., Snyder M. (2019), Longitudinal multi-omics of host–microbe dynamics in prediabetes, in Nature, 569(7758), 663-671.
High-frequency actionable pathogenic exome variants in an average-risk cohort
Rego S., Dagan-Rosenfeld O., Zhou W., Reza Sailani M., Limcaoco P., Colbert E., Avina M., Wheeler J., Craig C., Salins D., Röst H.L., Dunn J., McLaughlin T., Steinmetz L.M., Bernstein J.A., Snyder M.P. (2018), High-frequency actionable pathogenic exome variants in an average-risk cohort, in Cold Spring Harbor Molecular Case Studies, 4(6), a003178.
Integrative Personal Omics Profiles during Periods of Weight Gain and Loss
Piening B.D., Zhou W., Contrepois K., Röst H., Gu Urban G.J., Mishra T., Hanson B.M., Bautista E.J., Leopold S., Yeh C.Y., Spakowicz D., Banerjee I., Chen C., Kukurba K., Perelman D., Craig C., Colbert E., Salins D., Rego S., Lee S., Zhang C., Wheeler J., Sailani M.R., Liang L., Abbott C., Gerstein M., Mardinoglu A., Smith U., Rubin D.L., Pitteri S., Sodergren E., McLaughlin T.L., Weinstock G.M., Snyder M.P. (2018), Integrative Personal Omics Profiles during Periods of Weight Gain and Loss, in Cell Systems, 6(2), 157.
Automated swath data analysis using targeted extraction of ion chromatograms
Röst H.L., Aebersold R., Schubert O.T. (2017), Automated swath data analysis using targeted extraction of ion chromatograms, in Parag Mallick (ed.), Springer Protocols, New York, 1550, 289-307.
BioContainers: an open-source and community-driven framework for software standardization
da Veiga Leprevost Felipe, Grüning Björn A, Alves Aflitos Saulo, Röst Hannes L, Uszkoreit Julian, Barsnes Harald, Vaudel Marc, Moreno Pablo, Gatto Laurent, Weber Jonas, others (2017), BioContainers: an open-source and community-driven framework for software standardization, in Bioinformatics, 192-192.
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.), Royal Society of Chemistry, online, 2017-January(5), 200-227.
Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide
Shi Z., Fujii K., Kovary K.M., Genuth N.R., Röst H.L., Teruel M.N., Barna M. (2017), Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide, in Molecular Cell, 67(1), 71.
Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide
Shi Z., Fujii K., Kovary K.M., Genuth N.R., Röst H.L., Teruel M.N., Barna M. (2017), Heterogeneous Ribosomes Preferentially Translate Distinct Subpools of mRNAs Genome-wide, in Molecular Cell, 67(1), 71.
Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS
Rosenberger George, Liu Yansheng, Rost Hannes L, Ludwig Christina, Buil Alfonso, Bensimon Ariel, Soste Martin, Spector Tim D, Dermitzakis Emmanouil T, Collins Ben C, Malmström Lars, Aebersold Ruedi (2017), Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS, in Nature Biotechnology, 781-788.
Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS
Rosenberger George, Liu Yansheng, Röst Hannes L, Ludwig Christina, Buil Alfonso, Bensimon Ariel, Soste Martin, Spector Tim D, Dermitzakis Emmanouil T, Collins Ben C, Malmström Lars, Aebersold Ruedi (2017), Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS, in Nature Biotechnology, 35(8), 781-788.
Quantitative proteomics: Challenges and opportunities in basic and applied research
Schubert O.T., Röst H.L., Collins B.C., Rosenberger G., Aebersold R. (2017), Quantitative proteomics: Challenges and opportunities in basic and applied research, in Nature Protocols, 12(7), 1289-1294.
Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses
Rosenberger G., Bludau I., Schmitt U., Heusel M., Hunter C.L., Liu Y., Maccoss M.J., Maclean B.X., Nesvizhskii A.I., Pedrioli P.G.A., Reiter L., Röst H.L., Tate S., Ting Y.S., Collins B.C., Aebersold R. (2017), Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses, in Nature Methods, 14(9), 921-927.

Awards

Title Year
Canada Research Chair in mass spectrometry-based personalized medicine. 2018
Gilbert S. Omenn Computational Proteomics Award. US HUPO 2018. 2018

Associated projects

Number Title Start Funding scheme
162268 Personalized longitudinal metabolomic and microbiomic profiling of diabetes progression 01.07.2015 Early Postdoc.Mobility

Abstract

Diabetes mellitus type 2 is a global epidemic affecting over 200 million people, and there are currently no truly curative and effective interventions. To improve diagnosis and prognosis, and thus reduce diabetes incidence, 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 diabetes using longitudinal high throughput measurements of the human microbiome and metabolome. This will allow us to apply a personalized systems biology approach to investigate the host-microbiome interaction and observe dynamic changes of the microbiome during disease progression. Using network-based analysis on the molecular level, we expect to obtain functional insights into the microbial community structure and explore its temporal evolution on an individual basis. The proposed large-scale profiling and the longitudinal nature of the analysis will by 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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