Biology subjects

Rogowska-Wrzesinska, A.

Publications and source records attributed to Rogowska-Wrzesinska, A..

3 recordsLinked to original sources

bioRxiv2021

Distinct and diverse chromatin-proteomes of ageing mouse organs reveal protein signatures that correlate with physiological functions

Temporal molecular changes in ageing mammalian organs are of relevance to disease etiology because many age-related diseases are linked to changes in the transcriptional and epigenetic machinery that regulate gene expression. We performed quantitative proteome analysis of chromatin-enriched protein extracts to investigate the dynamics of the chromatin-proteomes of the mouse brain, heart, lung, kidney, liver, and spleen at 3, 5, 10, and 15 months of age. Each organ exhibited a distinct chromatin-proteome and sets of unique proteins. The brain and spleen chromatin-proteomes were the most extensive, diverse, and heterogenous among the six organs. The spleen chromatin proteome appeared static during the lifespan, presenting a young phenotype that reflects the permanent alertness state and important role of this organ in physiological defense and immunity. We identified a total of 5928 proteins, including 2472 nuclear or chromatin associated proteins across the six mouse organs. Up to 3125 proteins were quantified in each organ demonstrating distinct and organ-specific temporal protein expression timelines and regulation at the post-translational level. Bioinformatics meta- analysis of these chromatin proteomes revealed distinct physiological and ageing- related features for each organ. Our results demonstrate the efficiency of organelle specific proteomics for in vivo studies of a model organism and consolidate the hypothesis that chromatin-associated proteins are involved in distinct and specific physiological functions in ageing organs. HIGHLIGHTSO_LIQuantitative chromatin-proteome analysis during mouse lifespan; C_LIO_LIChromatin analysis in vitro and in vivo mouse models; C_LIO_LIDistinct chromatin proteomes of six organs during mouse lifespan; C_LIO_LICorrelations between ageing and chromatin regulation in mammalian lifespan. C_LI

cell biology↗

bioRxiv2019

PolySTest: Robust statistical testing of proteomics data with missing values improves detection of biologically relevant features

Statistical testing remains one of the main challenges for high-confidence detection of differentially regulated proteins or peptides in large-scale quantitative proteomics experiments by mass spectrometry. Statistical tests need to be sufficiently robust to deal with experiment intrinsic data structures and variations and often also reduced feature coverage across different biological samples due to ubiquitous missing values. A robust statistical test provides accurate confidence scores of large-scale proteomics results, regardless of instrument platform, experimental protocol and software tools. However, the multitude of different combinations of experimental strategies, mass spectrometry techniques and informatics methods complicate the decision of choosing appropriate statistical approaches. We address this challenge by introducing PolySTest, a user-friendly web service for statistical testing, data browsing and data visualization. We introduce a new method, Miss Test, that simultaneously tests for missingness and feature abundance, thereby complementing common statistical tests by rescuing otherwise discarded data features. We demonstrate that PolySTest with integrated Miss Test achieves higher confidence and higher sensitivity for artificial and experimental proteomics data sets with known ground truth. Application of PolySTest to mass spectrometry based large-scale proteomics data obtained from differentiating muscle cells resulted in the rescue of 10%-20% additional proteins in the identified molecular networks relevant to muscle differentiation. We conclude that PolySTest is a valuable addition to existing tools and instrument enhancements that improve coverage and depth of large-scale proteomics experiments. A fully functional demo version of PolySTest and Miss Test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.

bioinformatics↗

bioRxiv2019

ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets

We have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomics data that focuses on protein complexes. The software uses information from CORUM and Complex Portal databases to identify protein complex components. Based on the expression changes of individual complex subunits across the proteomics experiment it calculates Complex Fold Change (CFC) factor that characterises the overall protein complex expression trend and the level of subunit co-regulation. Thus up- and down-regulated complexes can be identified. It provides interactive visualisation of protein complexes composition and expression for exploratory analysis. It also incorporates a quality control step that includes normalisation and statistical analysis based on Limma test. ComplexBrowser performance was tested on two previously published proteomics studies identifying changes in protein expression in human adenocarcinoma tissue and during activation of mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found co-ordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues and identification of chromatin remodelling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and also provide novel biological details about protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.\n\nA fully functional demo version of ComplexBrowser v1.0 is available online via http://computproteomics.bmb.sdu.dk/Apps/ComplexBrowser/\n\nThe source code can be downloaded from: https://bitbucket.org/michalakw/complexbrowser\n\nHighlightsO_LIAutomated analysis of protein complexes in proteomics experiments\nC_LIO_LIQuantitative measure of the coordinated changes in protein complex components\nC_LIO_LIInteractive visualisations for exploratory analysis of proteomics results\nC_LI\n\nIn briefComplexBrowser is capable of identifying protein complexes in datasets obtained from large scale quantitative proteomics experiments. It provides, in the form of the CFC factor, a quantitative measure of the coordinated changes in complex components. This facilitates assessing the overall trends in the processes governed by the identified protein complexes providing a new and complementary way of interpreting proteomics experiments.

bioinformatics↗