Master of Science, Technische Universitat Munchen (2010)
Bachelor of Science, Technische Universitat Munchen (2008)
Doctor of Philosophy, Ludwig Maximilian Universitat Munchen (2015)
Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2Dbetter, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host-microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.
View details for DOI 10.1038/s41586-019-1236-x
View details for PubMedID 31142858
View details for Web of Science ID 000459610400452
Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n?=?109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8?years (median, 2.8?years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.
View details for PubMedID 31068711
Lipidomics - the global assessment of lipids - can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner.
View details for PubMedID 30532037
The bioconversion of lignocellulosic biomass in various industrial processes, such as the production of biofuels, requires the degradation of hemicellulose. Clostridium stercorarium is a thermophilic bacterium, well known for its outstanding hemicellulose-degrading capability. Its genome comprises about 50 genes for partially still uncharacterised thermostable hemicellulolytic enzymes. These are promising candidates for industrial applications.To reveal the hemicellulose-degrading potential of 50 glycoside hydrolases, they were recombinantly produced and characterised. 46 of them were identified in the secretome of C. stercorarium cultivated on cellobiose. Xylanases Xyn11A, Xyn10B, Xyn10C, and cellulase Cel9Z were among the most abundant proteins. The secretome of C. stercorarium was active on xylan, ?-glucan, xyloglucan, galactan, and glucomannan. In addition, the recombinant enzymes hydrolysed arabinan, mannan, and galactomannan. 20 enzymes are newly described, degrading xylan, galactan, arabinan, mannan, and aryl-glycosides of ?-d-xylose, ?-d-glucose, ?-d-galactose, ?-l-arabinofuranose, ?-l-rhamnose, ?-d-glucuronic acid, and N-acetyl-?-d-glucosamine. The activities of three enzymes with non-classified glycoside hydrolase (GH) family modules were determined. Xylanase Xyn105F and ?-d-xylosidase Bxl31D showed activities not described so far for their GH families. 11 of the 13 polysaccharide-degrading enzymes were most active at pH 5.0 to pH 6.5 and at temperatures of 57-76 °C. Investigation of the substrate and product specificity of arabinoxylan-degrading enzymes revealed that only the GH10 xylanases were able to degrade arabinoxylooligosaccharides. While Xyn10C was inhibited by ?-(1,2)-arabinosylations, Xyn10D showed a degradation pattern different to Xyn10B and Xyn10C. Xyn11A released longer degradation products than Xyn10B. Both tested arabinose-releasing enzymes, Arf51B and Axh43A, were able to hydrolyse single- as well as double-arabinosylated xylooligosaccharides.The obtained results lead to a better understanding of the hemicellulose-degrading capacity of C. stercorarium and its involved enzyme systems. Despite similar average activities measured by depolymerisation tests, a closer look revealed distinctive differences in the activities and specificities within an enzyme class. This may lead to synergistic effects and influence the enzyme choice for biotechnological applications. The newly characterised glycoside hydrolases can now serve as components of an enzyme platform for industrial applications in order to reconstitute synthetic enzyme systems for complete and optimised degradation of defined polysaccharides and hemicellulose.
View details for PubMedID 30159029