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. 2020 Jul 10;9(7):1666.
doi: 10.3390/cells9071666.

Disclosing the Interactome of Leukemogenic NUP98-HOXA9 and SET-NUP214 Fusion Proteins Using a Proteomic Approach

Adélia Mendes  1 , Ramona Jühlen  1   2 , Sabrina Bousbata  1 , Birthe Fahrenkrog  1
Affiliations

Disclosing the Interactome of Leukemogenic NUP98-HOXA9 and SET-NUP214 Fusion Proteins Using a Proteomic Approach

Adélia Mendes et al. Cells. .

Abstract

The interaction of oncogenes with cellular proteins is a major determinant of cellular transformation. The NUP98-HOXA9 and SET-NUP214 chimeras result from recurrent chromosomal translocations in acute leukemia. Functionally, the two fusion proteins inhibit nuclear export and interact with epigenetic regulators. The full interactome of NUP98-HOXA9 and SET-NUP214 is currently unknown. We used proximity-dependent biotin identification (BioID) to study the landscape of the NUP98-HOXA9 and SET-NUP214 environments. Our results suggest that both fusion proteins interact with major regulators of RNA processing, with translation-associated proteins, and that both chimeras perturb the transcriptional program of the tumor suppressor p53. Other cellular processes appear to be distinctively affected by the particular fusion protein. NUP98-HOXA9 likely perturbs Wnt, MAPK, and estrogen receptor (ER) signaling pathways, as well as the cytoskeleton, the latter likely due to its interaction with the nuclear export receptor CRM1. Conversely, mitochondrial proteins and metabolic regulators are significantly overrepresented in the SET-NUP214 proximal interactome. Our study provides new clues on the mechanistic actions of nucleoporin fusion proteins and might be of particular relevance in the search for new druggable targets for the treatment of nucleoporin-related leukemia.

Keywords: BioID; NUP98-HOXA9; SET-NUP214; gene ontology; interactome; leukemia.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proximity-dependent biotin identification (BioID) fusion protein expression and biotinylation of endogenous proteins. (A) Localization of NHA9-BioID, SN214-BioID, and BirAR118G was evaluated by immunostaining with anti-HA antibody. (B) Localization of GFP-tagged NUP98-HOXA9 and SET-NUP214 was evaluated by green fluorescent protein (GFP) fluorescence. NHA9-BioID and SN214-BioID exhibit the same distribution pattern in nuclear foci and at the nuclear envelope as their GFP-tagged counterparts. (C) Detection of protein biotinylation by Streptavidin-488. Cells were transfected with NHA9-BioID, SN214-BioID, and BirAR118G and probed with Streptavidin-Alexa Fluor™ 488 conjugate. Shown are representative confocal images. DNA was visualized with DAPI (blue). Scale bars, 10 µm. (D) For detection of protein biotinylation by NHA9-BioID, SN214-BioID, and BirAR118G, corresponding cell lysates were enriched on Streptavidin-coated magnetic beads and whole protein lysates and the bound fractions were analyzed by immunoblotting. Note, virtually no specific bands were detected in the bound fraction of BirAR118G, in contrast to the bound fractions of NHA9-BioID and SN214-BioID, which exhibited patterns of differentially biotinylated proteins.
Figure 2
Figure 2
Gene ontology (GO) of NHA9-BioID proximal interactors. Most represented (A) cellular compartments (GOCC), (B) biological processes (GOBP), and (C) molecular functions (GOMF) among NHA9-BioID proximal interactors. Statistical analysis of the overrepresented proteins in the NHA9-BioID fraction (total 131 proteins) was performed with the PANTHER classification online software (v14.1) using Fisher’s exact test. Results are displayed for FDR p < 0.05.
Figure 3
Figure 3
Clustered pathway analysis of NHA9-BioID proximal interactors. (A) Functionally grouped network of NHA9-BioID proximal interactors. Nodes correspond to functional clusters, resulting from term grouping based on their overlapping level. (B) Overview pie-chart showing functional groups. Statistical analysis was performed using the Cytoscape plugin ClueGo (v.2.5.5) using the hypergeometric test and the following parameters: p < 0.01, kappa-score (k) = 0.4; (min/%) genes = 3/4%, GO tree levels: 3-11. Ontology databases: GOBP, GOCC, and GOMF, Reactome Pathways and KEGG.
Figure 4
Figure 4
Screening of nuclear export signals (NESs) in NHA9-BioID proximal interactors. The amino acid sequences of the 131 NHA9-BioID proximal interactors were analyzed by two different algorithms (NES Finder 0.2 and LocNES) to evaluate the presence of classical NES. (A) The results from both algorithms show that the majority of NHA9-BioID proximal interactors have at least one putative classical NES, with a higher percentage of NES+ proteins determined by the LocNES algorithm. (B) GO analysis of NES+ and NES- proteins identified by NES Finder 0.2 shows an overrepresentation of proteins from cytoplasmic RNP granules and the microtubule organizing center. NES- proteins are mostly nucleoplasmic and are associated with the ß-catenin and the AP-1 transcription factor complexes. Statistical analysis was performed with the PANTHER classification online software (v14.1), using Fisher’s exact test. Results are displayed for FDR p < 0.05.
Figure 5
Figure 5
Gene ontology (GO) of SN214-BioID proximal interactors. Most represented (A) cellular compartments (GOCC), (B) biological processes (GOBP), and (C) molecular functions (GOMF) among SN214-BioID proximal interactors. Statistical analysis of the overrepresented proteins in the SN214-BioID fraction (total 1125 proteins) was performed with the PANTHER classification online software (v14.1) using Fisher’s exact test. Results are displayed for FDR p < 0.05. Summarized graphic representation of the significantly enriched GO terms among SN214-BioID proximal interactors. The detailed results of GOBP and GOCC analysis can be consulted in Figures S3 and S4.
Figure 6
Figure 6
Clustered pathway analysis of SN214-BioID proximal interactors. (A) Functionally grouped network of SN214-BioID proximal interactors. Nodes correspond to functional clusters resulting from term grouping based on their overlapping level. (B) Overview pie-chart with functional groups. Statistical analysis was performed with the Cytoscape plugin ClueGo (v2.5.5) using the hypergeometric test and the following parameters: p < 0.01, kappa-score (k) = 0.5; (min/%) genes = 40/4%, GO tree levels: 13-15. Ontology databases: GOBP, GOCC, and GOMF, Reactome Pathways and KEGG.
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