Light signal provides the spatial and temporal information for plants to adapt to the prevailing environmental conditions. Alterations in light quality and quantity can trigger robust changes in global gene expression. In
Ubiquitination is widely involved in protein homeostasis and cell signaling. Ubiquitin E3 ligases are critical regulators of ubiquitination that recognize and recruit specific ubiquitination targets for the final rate-limiting step of ubiquitin transfer reactions. Understanding the ubiquitin E3 ligase activities will provide knowledge in the upstream regulator of the ubiquitination pathway and reveal potential mechanisms in biological processes and disease progression. Recent advances in mass spectrometry-based proteomics have enabled deep profiling of ubiquitylome in a quantitative manner. Yet, functional analysis of ubiquitylome dynamics and pathway activity remains challenging.
Here, we developed a UbE3-APA, a computational algorithm and stand-alone python-based software for Ub E3 ligase Activity Profiling Analysis. Combining an integrated annotation database with statistical analysis, UbE3-APA identifies significantly activated or suppressed E3 ligases based on quantitative ubiquitylome proteomics datasets. Benchmarking the software with published quantitative ubiquitylome analysis confirms the genetic manipulation of SPOP enzyme activity through overexpression and mutation. Application of the algorithm in the re-analysis of a large cohort of ubiquitination proteomics study revealed the activation of PARKIN and the co-activation of other E3 ligases in mitochondria depolarization-induced mitophagy process. We further demonstrated the application of the algorithm in the DIA (data-independent acquisition)-based quantitative ubiquitylome analysis.
Source code and binaries are freely available for download at URL: https://github.com/Chenlab-UMN/Ub-E3-ligase-Activity-Profiling-Analysis, implemented in python and supported on Linux and MS Windows.
Supplementary data are available at Bioinformatics online.
- Award ID(s):
- 1753154
- NSF-PAR ID:
- 10394807
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 38
- Issue:
- 8
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- p. 2211-2218
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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