Proteome profiling of triple negative breast cancer cells overexpressing NOD1 and NOD2 receptors unveils molecular signatures of malignant cell proliferation

Abstract

Background: Triple negative breast cancer (TNBC) is a malignancy with very poor prognosis, due to its aggressive clinical characteristics and lack of response to receptor-targeted drug therapy. In TNBC, immune-related pathways are typically upregulated and may be associated with a better prognosis of the disease, encouraging the pursuit for immunotherapeutic options. A number of immune-related molecules have already been associated to the onset and progression of breast cancer, including NOD1 and NOD2, innate immune receptors of bacterial-derived components which activate pro-inflammatory and survival pathways. In the context of TNBC, overexpression of either NOD1or NOD2 is shown to reduce cell proliferation and increase clonogenic potential in vitro. To further investigate the pathways linking NOD1 and NOD2 signaling to tumorigenesis in TNBC, we undertook a global proteome profiling of TNBC-derived cells ectopically expressing each one of these NOD receptors.

Results: We have identified a total of 95 and 58 differentially regulated proteins in NOD1- and NOD2-overexpressing cells, respectively. We used bioinformatics analyses to identify enriched molecular signatures aiming to integrate the differentially regulated proteins into functional networks. These analyses suggest that overexpression of both NOD1 and NOD2 may disrupt immune-related pathways, particularly NF-κB and MAPK signaling cascades. Moreover, overexpression of either of these receptors may affect several stress response and protein degradation systems, such as autophagy and the ubiquitin-proteasome complex. Interestingly, the levels of several proteins associated to cellular adhesion and migration were also affected in these NOD-overexpressing cells.

Conclusions: Our proteomic analyses shed new light on the molecular pathways that may be modulating tumorigenesis via NOD1 and NOD2 signaling in TNBC. Up- and downregulation of several proteins associated to inflammation and stress response pathways may promote activation of protein degradation systems, as well as modulate cell-cycle and cellular adhesion proteins. Altogether, these signals seem to be modulating cellular proliferation and migration via NF-κB, PI3K/Akt/mTOR and MAPK signaling pathways. Further investigation of altered proteins in these pathways may provide more insights on relevant targets, possibly enabling the immunomodulation of tumorigenesis in the aggressive TNBC phenotype.

Keywords: Hs578T; MAPK triple negative breast cancer; NF-κB; NLR; NOD1; NOD2; Proteome.

Fig. 1

Volcano plots showing detected peptides (represented by annotated Entrez gene names) in samples overexpressing NOD1 (HS578T/NOD1) (a) and NOD2 (HS578T/NOD2) (b). Visualization in Spotfire® (TIBCO® Software). Thresholds for differentially expressed gene inclusion were established at + 1 or − 1 log₂ fold-change (x axis), from the unmodified HS578T cells (P). Similarly, a threshold for inclusion was set at p-value 0.05 (y axis). Circles representing each identified protein are colored according to Standard Error (SE) calculated by MSstats and circle size according to the number of peptides identified in each protein. Lists of differentially regulated proteins in HS578T/NOD1 (c) and HS578T/NOD2 (d) cell populations. Inclusion criteria: log₂ fold-change ≥ + 1 or ≤ − 1 and p-value ≤0.05. Proteins with log₂ fold-change ≥ + 0.5 or ≤ − 0.5 and p-value ≤0.01 were also included in the lists. Proteins are ranked and color-coded according to their log₂-fold-change relative to their expression in the unmodified HS578T cells (P). For each protein, Entrez gene name, Uniprot accession number, protein name and fold change in both experimental groups are reported. Top 30 differentially expressed proteins are shown, complete lists are available as Additional file 1: Figure S1. Color-coding carried out using MS Office Excel, Red: upregulated. Blue: downregulated

Fig. 2

Heatmaps showing clustering of differentially expressed proteins found in NOD1 (HS578T/NOD1) (a) and NOD2 (HS578T/NOD2) (b) experimental groups. Pearson clustering for rows and columns was carried out according to the log₂-transformed intensity values (calculated by MaxQuant software) for each of three replicates (R) of unmodified HS578T cells (P), HS578T/NOD1 (NOD1) and HS578T/NOD2 (NOD2) groups in MORPHEUS (Versatile matrix visualization and analysis software; https://software.broadinstitute.org/morpheus) [47]. Rows are Identified by Entrez gene names. Intensities are shown by a color range, from red (row max) to white (row average) and blue (row minimum). c Venn diagram showing the distribution of differentially regulated proteins found in NOD1 (HS578T/NOD1; Blue) and NOD2 (HS578T/NOD2; Red) groups, generated in Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny) [137]. Circles in scale to group size

Fig. 3

Subcellular localization and cellular component distribution. Subcellular localization and interactions of differentially expressed proteins in groups NOD1 (HS578T/NOD1) (a) and NOD2 (HS578T/NOD2) (b), obtained via Qiagen Ingenuity® Pathway Analysis (IPA®). Direct protein interactions are represented by continuous lines, while indirect relationships are represented by dotted lines. Gene Ontology (GO) cellular component term assignment for NOD1 (c) and NOD2 (d) experimental groups, obtained via EnrichR (http://amp.pharm.mssm.edu/Enrichr) membership analysis (GO database version 2017b). The length of the bar represents the statistical significance of the combined score in Fisher exact test for that specific gene-set or term. In addition, the brighter the color, the more significant that term is. Top supporting protein evidence for each GO cellular component term presented at the left of horizontal bars

Fig. 4

Networks representing protein-protein interactions in differentially regulated proteins from the NOD1 (HS578T/NOD1) (a) and NOD2 (HS578T/NOD2) (b) experimental groups. Networks generated via Qiagen Ingenuity® Pathway Analysis (IPA®) and organized in radial distribution to emphasize major regulatory nodes. Direct protein interactions are represented by continuous lines, while indirect relationships are represented by dotted lines

Fig. 5

Gene Ontology (GO) pathway enrichment analysis for identified differentially expressed proteins in NOD1 (HS578T/NOD1) (a) and NOD2 (HS578T/NOD2) (b) groups, generated by EnrichR (http://amp.pharm.mssm.edu/Enrichr) membership analysis, using the KEGG 2016 database. The length of the bar represents the statistical significance of the combined score in Fisher exact test for that specific gene-set or term. In addition, the brighter the color, the more significant that term is. Top supporting protein evidence for each GO cellular component term is presented at the left of horizontal bars. Heatmap (c) of GO enriched terms for identified differentially expressed proteins in HS578T/NOD1 and HS578T/NOD2 groups, generated in Metascape (http://metascape.org/gp), using KEGG, Reactome and GO databases. GO terms in heatmap are color coded from grey to brown according to log₁₀(p) of the standard accumulative hypergeometric statistical test

Fig. 6

Molecular pathways enriched in HS578T/NOD1 and HS578T/NOD2 according to both EnrichR/Metascape (Blue boxes) and GSEA (Red boxes) Bioinformatics analyses, with examples of upregulated or downregulated proteins supporting each enriched pathway. Red and blue filled boxes represent pathways detected by all methodologies, while green-circled boxes represent cellular component distribution molecular signatures. Pathways are organized under major cellular processes and linked by putative pathway interaction crosstalks