Profilin 1 and its role in proteasome inhibitors resistance

Published on May 19th, 2022
Written by Axel Martinelli
⏱ 2 min read

Most negatively correlated expression profiles. Highlighted in blue are proteasome-related genesets, highlighted in red are proteasome inhibitors profiles

Introduction

Browsing through the most recent proteomics dataset on the public PRIDE database, I came across this study (PXD033148) from Wang and colleagues at Xiangya Hospital of Central South University in China. In this study, they overexpressed Profilin 1 (PFN1), a protein that has been implicated in metastasis and drug resistance in cancer, in H1299 cells.

I decided to upload the dataset on the Omics Playground platform and look at the differences between cells overexpressing PFN1 and empty vector controls.

GSE Analysis

The GSE analysis module revealed a striking negative correlation between PFN1 overexpression and gene sets associated with the proteasome (Fig.1, highlighted in blue).

Most negatively correlated expression profiles. Highlighted in blue are proteasome-related genesets, highlighted in red are proteasome inhibitors profiles
Figure 1. Most negatively correlated expression profiles. Highlighted in blue are proteasome-related genesets, highlighted in red are proteasome inhibitor profiles.

As it happens, the same group recently published an article implicating PFN1 over-expression at the transcriptome level with bortezomib resistance in multiple myeloma. 

It is thus interesting to observe that the overexpression of PFN1 induces a protein expression pattern that is strongly opposed not only to the profile generated by bortezomib, but also that of a different proteasome inhibitor, carfilzomib (Fig.1, highlighted in red).

In particular, PFN1 overexpression appears to inhibit the expression of most of the subunits of the proteasome S20 particle (Fig. 2), which also happens to be the binding target of carfilzomib. 

Differentially expressed proteins in the KEGG proteasome pathway following PFN1 overexpression.
Figure 2. Differentially expressed proteins in the KEGG proteasome pathway following PFN1 overexpression. Proteins in blue are downregulated, proteins in red upregulated.

The data was produced as part of the following published study: https://doi.org/10.3389/fphar.2022.890891.

Conclusion

In conclusion, the Omics Playground platform has proven to be an invaluable tool in analyzing complex proteomics datasets, as demonstrated in this exploration of the PFN1 overexpression study.

By uploading the dataset to the platform, I was able to efficiently conduct a gene set enrichment analysis (GSEA) that revealed important insights into PFN1’s role in inhibiting proteasome activity, specifically in relation to cancer drug resistance.

The platform enabled me to uncover patterns and correlations that align with previous findings in the literature. 

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About the Author

Axel Martinelli

Axel Martinelli’s academic background is in molecular biology and parasitology. He earned a Ph.D. on the genetics of strain-specific immunity against malaria infections and a master’s degree in bioinformatics with specialization in the analysis of omics data. During his postdoctoral career, he worked on genomics and transcriptomics studies and is currently the head of biology at Bigomics Analytics.