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. 2015 Feb 16;16(2):4209-25.
doi: 10.3390/ijms16024209.

Phosphoproteomic Analysis of the Highly-Metastatic Hepatocellular Carcinoma Cell Line, MHCC97-H

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Free PMC article

Phosphoproteomic Analysis of the Highly-Metastatic Hepatocellular Carcinoma Cell Line, MHCC97-H

Miaomiao Tian et al. Int J Mol Sci. .
Free PMC article

Abstract

Invasion and metastasis of hepatocellular carcinoma (HCC) is a major cause for lethal liver cancer. Signaling pathways associated with cancer progression are frequently reconfigured by aberrant phosphorylation of key proteins. To capture the key phosphorylation events in HCC metastasis, we established a methodology by an off-line high-pH HPLC separation strategy combined with multi-step IMAC and LC-MS/MS to study the phosphoproteome of a metastatic HCC cell line, MHCC97-H (high metastasis). In total, 6593 phosphopeptides with 6420 phosphorylation sites (p-sites) of 2930 phosphoproteins were identified. Statistical analysis of gene ontology (GO) categories for the identified phosphoproteins showed that several of the biological processes, such as transcriptional regulation, mRNA processing and RNA splicing, were over-represented. Further analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations demonstrated that phosphoproteins in multiple pathways, such as spliceosome, the insulin signaling pathway and the cell cycle, were significantly enriched. In particular, we compared our dataset with a previously published phosphoproteome in a normal liver sample, and the results revealed that a number of proteins in the spliceosome pathway, such as U2 small nuclear RNA Auxiliary Factor 2 (U2AF2), Eukaryotic Initiation Factor 4A-III (EIF4A3), Cell Division Cycle 5-Like (CDC5L) and Survival Motor Neuron Domain Containing 1 (SMNDC1), were exclusively identified as phosphoproteins only in the MHCC97-H cell line. These results indicated that the phosphorylation of spliceosome proteins may participate in the metastasis of HCC by regulating mRNA processing and RNA splicing.

Figures

Figure 1
Figure 1
Scheme for sample preparation, HPLC multi-IMAC (Immobilized Metal ion Affinity Chromatography) methods and data processing of MHCC97-H (high metastasis) phosphoproteomics.
Figure 2
Figure 2
Characteristics of the identified unique phosphopeptides in different HPLC fractions and IMAC steps. (A) Distribution of identified unique phosphopeptides in different HPLC fractions; (B) Distribution of identified unique phosphopeptides in different IMAC steps; (C) Distribution of phosphorylated peptides based on their phosphorylation sites (p-sites) in different IMAC steps; (D) The pI value distribution of the identified phosphopeptides in different IMAC steps; (E) The hydrophobicity distribution of the identified phosphopeptides in different IMAC steps.
Figure 3
Figure 3
Characteristics of the identified unique phosphopeptides in the MHCC97-H cell line. (A) Distribution of phosphopeptides based on their length; (B) Distribution of phosphopeptides depending on their number of p-sites; (C) Distribution of phosphorylation proteins based on their number of p-sites; (D) Distribution of phosphorylation serine (p-Ser), phosphorylation threonine (p-Thr) and phosphorylation tyrosine (p-Tyr) sites in the MHCC97-H cell line protein.
Figure 4
Figure 4
Analysis of p-sites by sequence motif, Group-based Prediction System (GPS) algorithm with the interaction filter, or in vivo GPS (iGPS), the distribution of amino acid flanking and structural preferences in the MHCC97-H cell line. (A) The sequence motif analysis of p-sites in the MHCC97-H cell line consisting of 14 residues surrounding the targeted site by Motif-X; (B) The top 10 protein kinases with the most p-sites by the prediction of iGPS; (C) The heatmap for the distribution of amino acids flanking p-sites in the MHCC97-H phosphoproteome; (D) The secondary structural distribution for the p-sites.
Figure 5
Figure 5
Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (top 10) analysis for phosphoproteins. (A) Biological process of GO annotation (top 10); (B) Molecular function of GO annotation (top 10); (C) Cellular component of GO annotation (top 10); (D) The most over-represented KEGG pathways; (E) Comparison of p-sites associated with the spliceosome between MHCC97-H and the normal liver sample; (F) Comparison of phosphoproteins associated with the spliceosome between MHCC97-H and the normal liver sample.

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