SSBP1 Upregulation In Colorectal Cancer Regulates Mitochondrial Mass

Abstract

Background: Colorectal cancers (CRC) are one of the most common forms of cancer seen worldwide, and also remain difficult to treat despite recent advances in chemotherapy. Although significant progress has been made in recent years towards precision medicine and mutation-guided therapy, common mechanisms that underlie tumor growth and progression remain incompletely understood.

Methods: Tumor tissue and nearby unaffected tissue were collected from >15 patients at each stage of CRC, from which we generated representative proteomics profiles of three stages. Bioinformatics analysis was performed to discover common differences that may be shared between the representative profiles and across larger cohorts. Flow cytometry was then used to identify functional consequences of SSBP1 depletion in cell lines, since its expression level was consistently increased in tumor cells across all of the datasets analyzed.

Results: Direct comparison of CRC tumor and unaffected tissue at each stage demonstrated that a number of proteins involved in mitochondrial function displayed significantly altered expression patterns. Depletion of SSBP1 in colon cancer cell lines was able to trigger loss of mitochondrial mass and an increase in tumor cell death, and this effect that was further accentuated in the presence of the common chemotherapy drug cisplatin.

Conclusion: Mitochondrial biogenesis and maintenance may play an important part in tumor cell survival during CRC progression, and may be a useful target for directed inhibition or adjuvant targeting in the cases of cisplatin resistance.

Keywords: SSBP1; colorectal cancer; proteomics.

Figure 1

Proteomic profiling of colorectal cancer. (A) Resected samples from a pool of patients were pooled together and run through mass spectrometry, yielding a collection of 2,968 peptides that match to 1071 distinct proteins shared among all the samples. (B) Correlation matrix computed using Pearson correlation between each of the samples run. NA = normal stage II, NB = normal stage III, NC = normal stage IV, TA = tumor stage II, TB = tumor stage III, TC = tumor stage IV. ND and TD samples corresponded to mucinous adenocarcinoma sample. (C) Principal component analysis of the samples confirms the stratification of tumor and normal samples seen in simple correlation analysis, while also separating out the mucinous adenocarcinoma tumor samples from the rest. (D) Volcano plot visualization of the differentially expressed genes between normal and tumor samples reveals an upregulation in some factors associated with basic cellular processes, such as PCNA and EIF3b. (E) Network analysis of proteins upregulated in tumor samples using ReactomeFI as visualized in Cytoscape identifies several classes of proteins that are highly related. (F) Gene set enrichment analysis for KEGG pathways that are differentially expressed across all normal vs tumor samples. Positive enrichment score indicates enrichment in tumor samples. The top 3 varied pathways in either direction are shown here, and are statistically significant with FDR <10% and p-value <0.05.

Figure 2

Changes in mitochondrial gene expression show sharp differences between samples derived from healthy tissue and tumor tissue. (A) Correlation of all mitochondrial protein expression levels according to Pearson’s R. (B) Heatmap visualization of the expression profiles for each gene. (C) Volcano plot highlighting the most variably expressed genes in between all of the tumor and healthy samples. (D) Network analysis through ReactomeFI based on the full list of mitochondrial proteins identified. Genes in red are key nodes identified.

Figure 3

Paired analyses with TCGA and single-cell RNAseq. (A) tSNE visualization of the TCGA colon cancer cohort shows clear separation of normal solid tissue from tumor samples. (B) Overlaid gene expression visualization of the expression profiles of mitochondrial genes identified in Figure 2 shows that the proteomics results we observed could also be matched with larger cohort-level data. (C) Violin plots computing the expression levels between normal and primary tumor samples. All of the visualizations shown were significant at a p-value <0.01 according to Wilcoxian testing. (D) tSNE visualization of a single-cell sequencing dataset of a colorectal cancer sample. (E–F) tSNE visualization and violin plots of the same genes in (B) show similar effects in terms of expression differences, albeit with significant levels of dropout.

Figure 4

SSBP1 regulates mitochondrial mass and cell viability. Depletion of SSBP1 via lipofectamine transfection of targeted siRNA or scramble induces an increase in cell death when assessed in flow cytometry via 7-AAD staining in SW480 cells. (B–C) Assessment of mitochondrial mass and potential in two cell lines via flow cytometry based on mitotracker dyes shows a decrease in mean fluorescence intensity following knockdown. (D) Wound healing assay did not observe significant differences in the rates of recovery of SSBP1-knockdown cells. (E) Viability assay as in (A) but performed in the additional presence of 1uM cisplatin for 24 hrs.