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Burtscher, M. L.

Publications and source records attributed to Burtscher, M. L..

2 recordsLinked to original sources

Deep quantitative glycoproteomics reveals gut microbiome induced remodeling of the brain glycoproteome

HighlightsO_LIHigh throughput glycoproteomics method with multiplexed quantification C_LIO_LI25-fold improvement of the mouse brain glycoproteome coverage C_LIO_LIStructural features dictate level of glycosite micro-heterogeneity C_LIO_LIGut microbiome composition extensively impacts the brain glycoproteome C_LIO_LIModulation of glycosylation is site-specific C_LI Protein glycosylation is a highly diverse post-translational modification, modulating key cellular processes such as cell signaling, adhesion and cell-cell interactions. Its deregulation has been associated with various pathologies, including cancer and neurological diseases. Methods capable of quantifying glycosylation dynamics are essential to start unraveling the biological functions of protein glycosylation. Here we present Deep Quantitative Glycoprofiling (DQGlyco), a method that combines high-throughput sample preparation, high-sensitivity detection, and precise multiplexed quantification of protein glycosylation. We used DQGlyco to profile the mouse brain glycoproteome, in which we identify 158,972 and 15,056 unique N- and O-glycopeptides localized on 3,199 and 2,365 glycoproteins, respectively - this amounts to 25-fold more glycopeptides identified compared to previous studies. We observed extensive heterogeneity of glycoforms and determined their functional and structural preferences. The presence of a defined gut microbiota resulted in extensive remodeling of the brain glycoproteome when compared to that of germ-free animals, exemplifying how the gut microbiome may affect brain protein functions.

molecular biology↗

Integration of Thermal Proteome Profiling with phosphoproteomic and transcriptomic data via mechanistic network models decodes the molecular response to PARP inhibition

The deregulation of complex diseases often spans multiple molecular processes. A multimodal functional characterization of these processes can shed light on the disease mechanisms and the effect of drugs. Thermal Proteome Profiling (TPP) is a mass-spectrometry based technique assessing changes in thermal protein stability that can serve as proxies of functional changes of the proteome. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a framework for causal integration of multi-omics, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover known mechanistic consequences of PARP inhibition in ovarian cancer cells on cell cycle and DNA damage response in detail and to uncover new insights into drug response mechanisms related to interferon and hippo signaling. We found that TPP complements the other omics data and allowed us to obtain a network model with higher coverage of the main underlying mechanisms. These results illustrate the added value of TPP, and more generally the power of network models to integrate the information provided by different omics technologies. We anticipate that this strategy can be used to broadly integrate functional proteomics with other omics to study complex molecular processes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/553354v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@af952eorg.highwire.dtl.DTLVardef@16b1d1borg.highwire.dtl.DTLVardef@1440787org.highwire.dtl.DTLVardef@14a4227_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗