Biology subjects

Bludau, I.

Publications and source records attributed to Bludau, I..

23 records · Page 2Linked to original sources

bioRxiv2019

Integration of transcriptome, proteome and phosphoproteome data elucidates the genetic control of molecular networks

Genomic variation affects cellular networks by altering diverse molecular layers such as RNA levels, protein abundance, and post-translational protein modifications. However, it remains unclear how these different layers are affected by genetic polymorphisms and give rise to complex physiological phenotypes. To address these questions, we generated high-quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genetically diverse yeast strains. While genetic effects on transcript abundances were generally transmitted to the protein level, we found a significant uncoupling of the transcript-protein relationship for certain protein classes, such as subunits of protein complexes. The additional phosphoproteomics data suggests that the same genetic locus often affects distinct sets of genes within each of these layers. In particular, QTLs tended to affect upstream regulatory proteins at the phosphorylation layer, whereas downstream pathway targets were typically affected at the transcript and protein abundance layers. Underscoring the importance of regulatory protein phosphorylation in linking genetic to phenotypic variation is the finding that the number of protein phosphosites associated with a given genetic locus was more predictive for its influence on cellular growth traits than the number of transcripts or proteins.\n\nThis study shows how multi-layered molecular networks mediate the effects of genomic variants to more complex physiological traits and highlights the important role of protein phosphorylation in mediating these effects.

systems biology↗

bioRxiv2019

A global screen for assembly state changes of the mitotic proteome by SEC-SWATH-MS

Living systems integrate biochemical reactions that determine the functional state of each cell. Reactions are primarily mediated by proteins that have in systematic studies been treated as independent entities, disregarding their higher level organization into complexes which affects their activity and/or function and is thus of great interest for biological research. Here, we describe the implementation of an integrated technique to quantify cell state-specific changes in the physical arrangement of protein complexes, concurrently for thousands of proteins and hundreds of complexes. Applying this technique for comparison of human cells in interphase and mitosis, we provide a systematic overview of mitotic proteome reorganization. The results recall key hallmarks of mitotic complex remodeling and discover new events, such as a new model of nuclear pore complex disassembly, validated by orthogonal methods. To support the interpretation of quantitative SEC-SWATH-MS datasets, we extend the software CCprofiler and provide an interactive exploration tool, SECexplorer-cc.\n\nHighlightsO_LIQuantification of proteome assembly state changes using SEC-SWATH-MS\nC_LIO_LISystems-wide analysis of assembly state changes in the mitotic proteome\nC_LIO_LIDiscovery and validation of a novel mitotic disassembly intermediate of the nuclear pore complex\nC_LIO_LIHigher sensitivity and information content compared to thermostability-based approaches for global measurement of proteome states\nC_LIO_LISECexplorer, an online platform to browse results and investigate proteins newly implicated in cell division\nC_LI

systems biology↗

bioRxiv2018

Complex-centric proteome profiling by SEC-SWATH-MS

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance.\n\nIn this paper we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction and the computational framework CCprofiler to detect and quantify protein complexes by error-controlled, complex-centric analysis using prior information from generic protein interaction maps.\n\nOur analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2127 protein subunits. The technique identifies novel subcomplexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible, and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.\n\nHighlightsO_LIIntroduction of the concept of complex-centric proteome profiling\nC_LIO_LIDevelopment of CCprofiler, a software framework for complex-centric data analysis\nC_LIO_LIDetection and quantification of subunit distribution of 462 distinct protein complexes containing 2127 proteins from a SEC-SWATH-MS dataset of HEK293 cells, and identification of novel complex variants such as assembly intermediates\nC_LIO_LIStatistical target-decoy model to estimate accurate false discovery rates for complexes quantified by complex-centric analysis\nC_LIO_LISECexplorer, an online platform to support custom complex-centric exploration of SEC-SWATH-MS datasets.\nC_LI

systems biology↗

bioRxiv2018

Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results

The independent reproduction of research results is a cornerstone of experimental research, yet it is beset by numerous challenges, including the quality and veracity of reagents and materials. Much of life science research depends on life materials, including human tissue culture cells. In this study we aimed at determining the degree of variability in the molecular makeup and the ensuing phenotypic consequences in commonly used human tissue culture cells. We collected 14 stock HeLa aliquots from 13 different laboratories across the globe, cultured them in uniform conditions and profiled the genome-wide copy numbers, mRNAs, proteins and protein turnover rates via genomic techniques and SWATH mass spectrometry, respectively. We also phenotyped each cell line with respect to the ability of transfected Let7 mimics to modulate Salmonella infection.\n\nWe discovered significant heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto variety. We also observed progressive divergence within a specific cell line over 50 successive passages. From the aggregate multi-omic datasets we quantified the response of the cells to genomic variability across the transcriptome and proteome. We discovered organelle-specific proteome remodeling and buffering of protein abundance by protein complex stoichiometry, mediated by the adaptation of protein turnover rates. By associating quantitative proteotype and phenotype measurements we identified protein patterns that explained the varying response of the different cell lines to Salmonella infection.\n\nAltogether the results indicate a striking degree of genomic variability, the rapid evolution of genomic variability in culture and its complex translation into distinctive expressed molecular and phenotypic patterns. The results have broad implications for the interpretation and reproducibility of research results obtained from HeLa cells and provide important basis for a general discussion of the value and requirements for communicating research results obtained from human tissue culture cells.

systems biology↗

bioRxiv2017

ACEseq - allele specific copy number estimation from whole genome sequencing

ACEseq is a computational tool for allele-specific copy number estimation in tumor genomes based on whole genome sequencing. In contrast to other tools it features GC-bias correction, unique replication timing-bias correction and integration of structural variant (SV) breakpoints for improved genome segmentation. ACEseq clearly outperforms widely used state-of-the art methods, provides a fully automated estimation of tumor cell content and ploidy, and additionally computes homologous recombination deficiency scores.

bioinformatics↗