Fucosylated Proteome Profiling Identifies a Fucosylated, Non-Ribosomal, Stress-Responsive Species of Ribosomal Protein S3.
Gregory WatsonDaniel LesterHui RenConnor M ForsythElliot MedinaDavid Gonzalez PerezLancia DarvilleJiqiang YaoVince LucaJohn M KoomenLing CenEric LauPublished in: Cells (2021)
Alterations in genes encoding for proteins that control fucosylation are known to play causative roles in several developmental disorders, such as Dowling-Degos disease 2 and congenital disorder of glycosylation type IIc (CDGIIc). Recent studies have provided evidence that changes in fucosylation can contribute to the development and progression of several different types of cancers. It is therefore important to gain a detailed understanding of how fucosylation is altered in disease states so that interventions may be developed for therapeutic purposes. In this report, we find that fucosylation occurs on many intracellular proteins. This is an interesting finding, as the fucosylation machinery is restricted to the secretory pathway and is thought to predominately affect cell-membrane-bound and secreted proteins. We find that Ribosomal protein S3 (RPS3) is fucosylated in normal tissues and in cancer cells, and that the extent of its fucosylation appears to respond to stress, including MAPK inhibitors, suggesting a new role in posttranslational protein function. Our data identify a new ribosome-independent species of fucosylated RPS3 that interacts with proteins involved in posttranscriptional regulation of RNA, such as Heterogeneous nuclear ribonucleoprotein U (HNRNPU), as well as with a predominance of non-coding RNAs. These data highlight a novel role for RPS3, which, given previously reported oncogenic roles for RPS3, might represent functions that are perturbed in pathologies such as cancer. Together, our findings suggest a previously unrecognized role for fucosylation in directly influencing intracellular protein functions.
Keyphrases
- protein protein
- binding protein
- amino acid
- genome wide
- electronic health record
- physical activity
- squamous cell carcinoma
- transcription factor
- machine learning
- big data
- oxidative stress
- signaling pathway
- small molecule
- dna methylation
- single cell
- deep learning
- artificial intelligence
- cancer therapy
- genome wide identification