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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
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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.