Christine Vogel

The paper investigates the multi-layered repression system controlling C9ORF72 translation and identifies strategies to rescue loss-of-function in disease.

The paper identifies a stem-loop and modified adenines in the uORF2/ATF4 overlap that regulate ATF4 translation under stress, revealing a more complex mechanism than previously thought.

The paper re-mines serum proteomics data to identify post-translational modifications associated with Zika and dengue infections, highlighting specific modifications linked to each virus.

The paper identifies molecular signatures of proteostasis differences between ALS-resistant and -sensitive motor neurons using transcriptome and proteome analysis.

The paper identifies Rnf149-related as a regulator of pharyngeal muscle fate specification in the cardiopharyngeal lineage, acting independently of the FGF/MAPK pathway.

The paper identifies start-stop elements in 5' UTRs that repress translation by halting ribosomes, showing their prevalence in human genes and impact on cellular processes.

This paper develops a proteomics method to quantify protein abundances from as few as 1,000 mammalian cells with high reproducibility and accuracy.

This study uses high-resolution proteomics to identify 26 differentially expressed proteins that can predict Zika virus infection with 72% accuracy using a machine learning model.

The paper identifies proteostatic differences between ALS-resistant and sensitive motor neurons using stem cell-derived models.

The paper identifies K63 ubiquitination sites on ribosomes in yeast under oxidative stress using mass spectrometry, revealing their role in post-initiation translation control.

PTMscape predicts post-translational modification sites across the proteome and identifies hotspots of modification crosstalk using a unified framework.

The paper quantifies gene expression regulation at multiple layers in response to ER stress in cervical cancer cells over time, identifying shared and stress-specific regulatory events.

iSUMO integrates sequence and protein features to predict functional SUMOylation events, identifying more targets in human and yeast than existing tools at the same false positive rate.

The paper re-analyzes protein half-life data in yeast to provide consistent estimates, finding that protein stability correlates with abundance and function.

The paper improves transcription regulatory network inference in yeast by explicitly modeling RNA stability and half-lives, leading to better predictions and new insights.