p42.3 gene expression in gastric cancer cell and its protein regulatory network analysis
- Jianhua Zhang†1,
- Chunlei Lu†2,
- Zhigang Shang1,
- Rui Xing3,
- Li Shi1Email author and
- Youyong Lv3Email author
© Zhang et al.; licensee BioMed Central Ltd. 2012
Received: 20 November 2012
Accepted: 4 December 2012
Published: 11 December 2012
To analyze the p42.3 gene expression in gastric cancer (GC) cell, find the relationship between protein structure and function, establish the regulatory network of p42.3 protein molecule and then to obtain the optimal regulatory pathway.
The expression of p42.3 gene was analyzed by RT-PCR, Western Blot and other biotechnologies. The relationship between the spatial conformation of p42.3 protein molecule and its function was analyzed using bioinformatics, MATLAB and related knowledge about protein structure and function. Furthermore, based on similarity algorithm of spatial layered spherical coordinate, we compared p42.3 molecule with several similar structured proteins which are known for the function, screened the characteristic nodes related to tumorigenesis and development, and established the multi variable relational model between p42.3 protein expression, cell cycle regulation and biological characteristics in the level of molecular regulatory networks. Finally, the optimal regulatory network was found by using Bayesian network.
(1) The expression amount of p42.3 in G1 and M phase was higher than that in S and G2 phase; (2) The space coordinate systems of different structural domains of p42.3 protein were established in Matlab7.0 software; (3) The optimal pathway of p42.3 gene in protein regulatory network in gastric cancer is Ras protein, Raf-1 protein, MEK, MAPK kinase, MAPK, tubulin, spindle protein, centromere protein and tumor.
It is of vital significance for mechanism research to find out the action pathway of p42.3 in protein regulatory network, since p42.3 protein plays an important role in the generation and development of GC.
Keywordsp42.3 Gastric cancer Protein structure Bayesian regulatory network
Gastric cancer (GC) is one of the most common malignant tumors in China and also in the world. Data showed that the new increasing GC patients are more than one million annually, with China accounts for 42%. About 0.8 million people dead of GC and 44% of them are in China. As one of the high GC incidence rate and death rate countries, the morbidity and mortality of China are more than twice of the world average level . The tumorigenesis and development of GC is a complex issue involving genetic variation. Existing studies have demonstrated that genes, such as erbB-2, c-met, p53, cadherin, APC and RUNX3 gene, may be involved in the development and progression of GC [2, 3].
The object of this research is the c9orf140 gene, which is located in the 9q34.3 site of the human chromosome, also a novel gene called p42.3 (DQ150361) which was cloned by synchronization, mRNA differential display and bioinformatics . The full-length cDNA of p42.3 is approximately 4.0 kb, and the gene encodes a 389 amino acid protein that is estimated to have a molecular mass of 42.3 kDa. Further study found that its expression is cell cycle-dependent in GC cell lines. p42.3 protein expression peaks during the M phase of the cell cycle, then gradually declines after cell division; this indicates that p42.3 may be involved in cell cycle regulation. Furthermore, silencing of p42.3 by small interfering RNA results in the upregulation of CHK2 and the downregulation of cyclin B1, which are two key proteins involved in cell cycle regulation [5, 6]. However, the mechanism of its action needs further exploration.
The biological function of protein is largely determined by its spatial structure. The research on the relationship between structure and function is the basis of protein function prediction and protein design. With the development of bioinformatics, mathematical method and computor technology are widely applied to protein structure prediction for less time-consuming and free from the constraints of experimental condition.
On the basis of establishing the model of protein structure domain spatial conformation and functional information, this study analyzed the regulatory function and mechanism of p42.3 protein in the malignant cell proliferation and tumor generation, it will provide theoretical basis for further experimental and clinical application.
Cell lines and tissue samples
GC cell line BGC823, MGC803, SGC7901, PAMC82, MKN45, SNU1, SNU5, SNU16, RF1, RF48, AGS and N87 are provided by Beijing Tumor Hospital.
Reagents and main instruments
DMEM medium (Invitrogen, USA), Nocodazole (Sigma, USA), inverse transcription kit (Promega, USA), flow cytometry (Becton Dickinson, Flanklin Lakes, NJ, USA).
Cell culture and synchronization
GC cell lines were cultured in DMEM medium supplemented with 5% fetal bovine serum at 37°C in 5% CO2 incubator. The GC BGC823 cells were synchronized to G1, S, G2 and M phases by Nocodazole (mitotic inhibitor), then all synchronized cells were analyzed by FACS.
Detection of the p42.3 mRNA expression level
The total RNA was extracted from each cell line and synthesized into cDNA through reverse transcription. Using β-actin as the control, PCR amplification was performed, and the product of which was conducted agarose gel electrophoresis detection. Moreover, the total RNA of BGC823 cells synchronized into each cell cycle was also extracted in order to detect the expression level in each phase by reverse transcription detection.
Detection of the p42.3 protein expression
The law of p42.3 protein expression changes with cell cycle was analyzed by Western blotting.
Analysis of protein structure
Protein folding was predicted by prediction tool Phyre in threading method. And Phyre2 was utilized to predict the tertiary protein structure and carry on the analysis for the secondary structure. The spatial conformation of protein was displayed by Swiss-Pdb Viewer 3.7 software.
Protein similarity comparison
The similarity algorithm based on the spatial layered spherical coordinate: (1) A coordinate was built with the gravity center of the molecule as the coordinate origin. All atoms are transferred to this coordinate via the same transfer vector, and the conversion formula was 1.1, 1.2 and 1.3.
(2) Equidistant spherical shell layering was performed by the radius component in the spheric polar coordinate system. (3) The number of same C atom in each spatial layer is calculated separately. The summation of the number of C atom is computed and saved in the multi-dimensional vector α i and b i . (4) The vector obtained in step 3 is substituted into function (1.4) in order to obtain the similarity value focused on the atom that composed by the weight. Then compute the weighted average; finally, the similarity value of protein molecule is carried out.
Optimization of the regulatory network
Where, Pa (Xi) represents the parent node of Xi. In order to verify the above joint probability, all the conditional probabilities in formula 1.3 should be defined. The core of theBayesian network is that this condition-independent probability relationship is interpreted as causal relationship, representing the causal regulatory relation between genes . In this study, we utilized Bayesian network to find the optimal p42.3 regulatory pathway.
Detection results of mRNA expression
Test results of p42.3 protein expression
Analytical results of p42.3 protein structure
Establishment of the regulatory network of p42.3 protein molecule functional domain group
Similarity comparison result in EF-hand data set
Calcium and integrin binding protein
calcium and integrin binding protein
Stromal cell-derived factor-1 Leukemia
calcium binding protein
calcium binding protein
a kind of small G-protein
RAS and EF-hand domain containing
calcium binding protein
calcium binding protein
calcium binding protein
calcium binding protein
Similarity comparison result in CC-domain data set
monomer yeast transcriptional activator
Rho-associated,coiled-coil containing protein kinase 1
P21 activated kinase
Optimization of the p42.3 regulatory network
As shown in Figure 6, we reversely seeked for the most possible reason leading to the final subevent – malignant cell proliferation, that is, the optimal regulatory pathway as the best prediction of the mechanism of action of p42.3 protein. After analysis, the optimal pathway marked by red was selected, that is, Ras protein–Raf-1 protein–MEK–MAPK kinase–MAPK–Tubulin–spindle protein– centromere protein–tumor showing in Figure 2, which is the most possible action pathway of p42.3.
P42.3 gene is highly reserved in mammalian. As an oncogene, p42.3 plays an important role in the transformation process from normal gastric epithelial cell to cancer cell. Cui Y et al.  found that MiR-29a can target at p42.3 gene. The p42.3 gene silencing can change the expression of two key genes -- CHK2 and cyclin B1, which would further inhibit cell proliferation and the advances of cell cycle. The above results verify that p42.3 plays an important role in cell cycle regulation.
Based on the theory that the protein’s function is determined by its spatial structure, we found molecules similar to p42.3 protein by bioinformatic software in related databank, and then predicted the spatial structure of p42.3 protein and analyzed the structure-function relation. Threading method was adopted to predict the spatial conformation of p42.3 gene for there is no homologous protein with p42.3 at present. Analysis of the structure data indicated that EF-hand structure domain existed in the N-end of the p42.3 protein. It has been reported that this conformation exists in the S100 family. A CC domain exists in the C-end, the three-dimentional conformation of which has high homology with the CC domain in the C-end of the APC molecule amino acid (95%). Study verified that the deactivation of APC gene plays an important role in the genesis and development of GC, which indicated that p42.3 protein may interfere related cell signal transduction pathway and biological function by influencing the active site of APC protein .
In protein molecule, easily distinguishable 3D structure is folded by two or more independent structure domains [11, 12]. 3D structure exploring of protein molecule enables people to organize the rapidly growing set of thousands of known protein shapes, to identify new types of protein architecture, and to discover unexpected evolutionary relations . The measurement of protein structure similarity influences the prediction and evaluation of the protein structure . Hu min et al.  proposed a method for measuring protein structure similarity based on the molecular inner spatial density distribution, which the protein molecule space is performed concentric spherical shell division and got many shell structure space units with a certain radial depth, then the number of C atom in each spherical shell is counted. At last the spatial structure similarity of the two proteins is calculated by similarity calculation function. Zou BJ et al.  proposed a new method for measuring protein structure similarity based on spatial density characteristic, which is regarding spatial spherical polar coordinate as the presentation model. The model based on spherical polar coordinate can provide theoretical basis for protein classification and protein function prediction. We transfer the protein 3D structure information into distance sequence information which is then performed fast Fourier transform, and the spatial domain information of protein structure is transformed into frequency domain information. At last, the similarity of spatial domain information is determined by the similarity of frequency domain information .
The construction of gene regulatory network is based on the gene expression data with every gene as a random variable and the expression level in different conditions as its value. Moreover, gene regulatory network is sparsity , which means, one a few genes directly affect the transcription process. Having the characteristic of illustrating the probability dependence of variables in the form of possibility, Bayesian Network is very suitable for analyzing and predicting this kind of sparse network.
Based on the two characteristic structures (EF-hand structure domain and CC domain) of p42.3 protein, we collected a large amount of similar protein. The similar protein set was screened by protein structure similarity comparison method based on spherical polar coordinate. The constructed regulatory network was optimized by Bayesian Network in order to obtain the optimal regulatory pathway. The possible action way of p42.3 gene in tumorigenesis was explored, which provided powerful means for the further study on mechanism of action. Applying mathematics and computer technolgoy in protein structure prediction and function analysis is a new trend in the field of biomedical research.
This research was supported by the National Natural Science Foundation of China (No. 60971110) and the cooperation project for Academicians from Henan province and other provinces (No.122106000042).
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