Global analysis of protein arginine methylation

Zhang, F.; Kerbl-Knapp, J.; Rodriguez-Colman, M. J.; Macher, T.; Vujic, N.; Fasching, S.; Jany-Luig, E.; Korbelius, M.; Kuentzel, K. B.; Mack, M.; Akhmetshina, A.; Paar, M.; Rinner, B.; Hoerl, G.; Steyrer, E.; Stelzl, U.; Burgering, B.; Eisenberg, T.; Pertschy, B.; Kratky, D.; Madl, T.

doi:10.1101/2021.01.25.428036

Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.01.25.428036

Global analysis of protein arginine methylation

View original source ↗Export metadata ↓

Abstract

Quantitative information about the levels and dynamics of post-translational modifications (PTMs) is critical for an understanding of cellular functions. Protein arginine methylation (ArgMet) is an important subclass of PTMs and is involved in a plethora of (patho)physiological processes. However, due to the lack of methods for global analysis of ArgMet, the link between ArgMet levels, dynamics and (patho)physiology remains largely unknown. We utilized the high sensitivity and robustness of Nuclear Magnetic Resonance (NMR) spectroscopy to develop a general method for the quantification of global protein ArgMet. Our NMR-based approach enables the detection of protein ArgMet in purified proteins, cells, organoids, and mouse tissues. We demonstrate that the process of ArgMet is a highly prevalent PTM and can be modulated by small-molecule inhibitors and metabolites and changes in cancer and during ageing. Thus, our approach enables to address a wide range of biological questions related to ArgMet in health and disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/428036v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@37a0f1org.highwire.dtl.DTLVardef@273ebdorg.highwire.dtl.DTLVardef@87fcd6org.highwire.dtl.DTLVardef@1e84c07_HPS_FORMAT_FIGEXP M_FIG C_FIG

Source connections

Published article ↗

Explore related subjects

systems biology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Zhang, F., Kerbl-Knapp, J., Rodriguez-Colman, M. J., Macher, T., Vujic, N., Fasching, S., Jany-Luig, E., Korbelius, M., Kuentzel, K. B., Mack, M., Akhmetshina, A., Paar, M., Rinner, B., Hoerl, G., Steyrer, E., Stelzl, U., Burgering, B., Eisenberg, T., Pertschy, B., Kratky, D., Madl, T.. 2021-01-26. Global analysis of protein arginine methylation. https://doi.org/10.1101/2021.01.25.428036

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Three-dimensional mapping of human lung fibrosis identifies spatial coupling between tissue microarchitecture and maladaptive repair states

Human lung fibrosis progressively destroys three-dimensional tissue architecture, yet how the structural microenvironment shapes cellular states remains unclear. Integrating 3D X-ray histology with spatial omics, we generate a volumetric multimodal atlas of human lung fibrosis that enables direct interrogation of relationships between tissue microarchitecture and spatial molecular programmes. We identify collapse of the alveolar-capillary unit - the coupled loss of alveolar type 1 epithelial cells, capillary endothelial cells, and basement membrane integrity - as a structural transition associated with early fibrogenesis. Surrounding transitional zones exhibit aberrant epithelial differentiation and proteolytic matrix remodelling, with epithelium shifting toward bronchiolised, airway-like states. Stromal activation converges on spatially restricted fibrogenic cores, sites of maximal collagen maturation and matrix stabilisation that function as signalling hubs within surrounding perifocal and vascular microenvironments. Molecular signatures of atlas-defined tissue states were associated with lung function and mortality across independent patient cohorts. Together, these data support a model in which fibrosis emerges as a spatial tissue-state process, with alveolar-capillary collapse redirecting repair towards mechanically stabilised niches that reinforce pathological remodelling.

systems biology↗

bioRxiv2026

A three-factor model can explain symmetry breaking and the n-1 rule during X-chromosome inactivation

X-chromosome inactivation (XCI) is a mammalian gene regulatory process that via chromosome-scale transcriptional silencing, leaves active a single X chromosome per diploid genome complement. The mechanisms that enable cells to select the appropriate number of X chromosomes to inactivate while leaving others active remain unclear. We demonstrate that all known chromosomal counting scenarios during XCI can be accounted for by a three-factor mathematical model that includes (1) Xist, the long noncoding RNA (lncRNA) responsible for gene silencing during XCI in eutherians, (2) an X-linked transcriptional activator of Xist, and (3) the autosomally-encoded SPEN, an Xist RNA-binding protein that is essential for gene silencing during XCI. In our model, symmetry between active X chromosomes is broken by stochastic differences in the number of Xist molecules that associate with the X chromosomes at the onset of XCI. Subsequently, integrated positive and negative feedback loops amplify then propagate superstoichiometric assemblies of SPEN complexed with Xist on the inactive chromosome(s), and select for levels of X-linked activator that enable only one X chromosome per diploid genome complement to remain active. Thus, asymmetric propagation of the SPEN-Xist ribonucleoprotein complex may play an essential role in symmetry breaking and chromosomal counting during XCI, independently of or in concert with cis-acting networks of DNA regulatory elements. Our results also have conceptual implications for gene regulatory circuits on autosomes.

systems biology↗

bioRxiv2026

stem.MAP: Identification of cell-type-specific protein complexes in mammalian embryonic stem cells.

Protein complexes carry out critical functions during development. Mapping cell-type-specific protein complexes in early embryogenesis is essential for uncovering the molecular mechanisms of embryonic development. We unfortunately currently lack a catalog of protein complexes specific for embryogenesis including embryonic stem cells. Here we develop a proteomics-based machine learning workflow to predict protein complexes present in mouse embryonic stem cells (mESC). Our method integrates data from a cell-type agnostic protein complex resource with co-fractionation mass spectrometry (CF-MS) experiments on mESC. Our resource, stem.MAP, identifies 3,986 complexes encompassing ~133,000 unique interactions, and 8,616 proteins. stem.MAP captures variant complexes critical for stem cell function including the PRC2, BAF and NuRD complexes. stem.MAP also discriminates mutually exclusive interactions specific to embryonic stem cells (e.g. Eif4ebp2 - Eif4e). Finally we observe 1,388 high confidence stem.MAP interactions found only in mESC CF-MS data, many with support from AlphaFold modeling and a gastruloid coregulation network. We make stem.MAP protein complexes available through our web resource: stem.proteincomplexes.org. We anticipate stem.MAP will be beneficial to the study of early development and we expect our proteomics-based machine learning workflow to be applicable to the creation of any context-specific protein interaction network.

systems biology↗