Abstract Understanding the molecular mechanisms underlying cell migration, which plays an important role in tumor growth and progression, is critical for the development of novel tumor therapeutics. Overexpression of transmembrane protein 30A (TMEM30A) has been shown to initiate tumor cell migration, however, the molecular mechanisms through which this takes place have not yet been reported. Thus, we propose the integration of computational and experimental approaches by first predicting potential signaling networks regulated by TMEM30A using a) computational biology methods, b) our previous mass spectrometry results of the TMEM30A complex in mouse tissue, and c) a number of migration-related genes manually collected from the literature, and subsequently performing molecular biology experiments including the in vitro scratch assay and real-time quantitative polymerase chain reaction (qPCR) to validate the reliability of the predicted network. The results verify that the genes identified in the computational signaling network are indeed regulated by TMEM30A during cell migration, indicating the effectiveness of our proposed method and shedding light on the regulatory mechanisms underlying tumor migration, which facilitates the understanding of the molecular basis of tumor invasion. Introduction Migration and invasion are key behaviors that distinguish benign from malignant tumors, enabling cell metastasis across tissue boundaries from the primary tumor location to a distant secondary site [[42]1], thereby increasing disease severity and therapeutic challenges. Great effort, therefore, has been exerted to elucidate the mechanisms underlying cell migration and invasion [[43]2–[44]4]. Numerous studies have confirmed that invasive carcinoma cells acquire a migratory phenotype associated with various molecular and cellular mechanisms involved in cancer cell invasion [[45]5, [46]6]. Tumor cell invasion is initiated by the loss of the cell-cell adhesion capacity from the primary tumor mass, and subsequent, changes in cell-matrix interactions enable the cells to invade surrounding tissue [[47]4, [48]7]. Pathology observations in vivo indicate that tumor cells migrate in two main ways: individually and collectively [[49]4, [50]8], knowledge of which has attracted efforts focusing on cell adhesion, epithelial to mesenchymal transition, angiogenesis, lymphangiogenesis and organ-specific metastasis [[51]7]. Moreover, related molecules including cell adhesion factors [[52]9, [53]10], growth factors [[54]11], microRNA [[55]3, [56]12], and lncRNA [[57]13, [58]14] has been reported to be active during the metastatic cascade. Mounting evidence also indicates that inaddition to internal molecules, external influences modulate tumor migration and invasion [[59]1, [60]2, [61]7, [62]15], such as chemical signals [[63]16] and excessive amounts of exosomes released by tumor cells [[64]17]. Nevertheless, the elaborate and complex regulatory mechanisms involved in the control of tumor cell migration remain unclear [[65]1, [66]2, [67]7, [68]18]. TMEM30A is a terminally-glycosylated membrane protein that is ubiquitously expressed in mouse tissue [[69]19]. The TMEM30A phospholipid flippase complex is known to play a role in cell migration [[70]20] via the formation of membrane ruffles as a result of phospholipid translocation. However, it remains to be determined which molecules within the migratory machinery coordinate functions with this complex. In the present study, we aim to explore the molecular signaling mechanisms of TMEM30A using a biosystems approach to identify the signaling networks involved in tumor migration ([71]S1 Fig). Our proposed method integrates computational and experimental approaches by first predicting potential signaling networks regulated by TMEM30A using computational biology methods, and subsequently performing molecular biology experiments including the in vitro scratch assay and qPCR. We assume that, in order to affect tumor migration, TMEM30A must regulate the expression of migration-related genes through certain pathways. The migration signaling network was constructed based on the STRING database together with our previous mass spectrometry results of the TMEM30A complex in mouse tissue and a number of migration-related genes manually collected from the literature. Subsequently, using both published data and our experimental results, the genes in the computationally-identified signaling network were validated and indeed shown to be regulated by TMEM30A during tumor migration, laying the foundation for the development of a potential cancer therapy. Materials and methods Computational prediction of the molecular mechanisms involved in migration To identify signaling pathways regulated by TMEM30A during tumor migration, a number of migration-related genes were first manually collected from the literature. Subsequently, we obtained the protein-protein interaction network (PPIN) for Mus musculus from the STRING 10.0 database containing all known and predicted protein-protein interactions. In this version, there are 22668 distinct protein-encoding genes and 5109107 interactions, with weights between 1 and 999. The greater the weight, the higher the confidence, and the stronger the interaction. Subsequently, our previous mass spectrometry results of the TMEM30A complex in mouse tissue and the known migration-related genes from the literature were superimposed onto the PPIN from the STRING database. The sub-network of these selected proteins were extracted from the whole PPIN in STRING, which includes 377 proteins and 14161 interactions. DAVID was used to conduct the function and pathway enrichment analysis and the signaling network was visualized with Cytoscape [[72]21]. [73]http://dx.doi.org/10.17504/protocols.io.iapcadn [PROTOCOL DOI] Cell culture Human hepatocellular carcinoma, SMMC-7721, and cervical adenocarcinoma, HeLa, cell lines were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The cells were cultured in RPMI 1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (Hyclone, USA) and 1% penicillin/streptomycin (Gibco, USA). Maintenance was carried out under 5% CO[2], in a 95% humidified atmosphere at 37°C. When the cells reached approximately 90% confluency, they were detached with 0.1% trypsin—ethylenediamine tetraacetic acid (Gibco, USA), seeded onto appropriate dishes, and incubated overnight. [74]http://dx.doi.org/10.17504/protocols.io.iaqcadw [PROTOCOL DOI] Gene transfection Transfection was carried out using Lipofectamine^™ 2000 transfection reagent (Invitrogen, 12566014), according to the protocol provided. Briefly, cells were seeded in 24-well plates at a density of 1×10^5 cells/500 μl and cultured overnight. 0.5 μg pcDNA3-ATP11A and 0.5 μg pcDNA3-TMEM30A plasmids in Lipofectamine^™ 2000-RPMI 1640 medium (W/V, 1:1) (50 μl) were added dropwise to the cells, which were subsequently incubated under 5% CO[2] in a 95% humidified atmosphere at 37°C for 6 h, following which the medium was replaced with fresh. [75]http://dx.doi.org/10.17504/protocols.io.iarcad6 [PROTOCOL DOI] Measurement of cell migration Cells were seeded at a density of 1×10^5 cells/well in a 24-well plate and cultured under 5% CO[2] at 37°C for 24 h. Following 6 h transfection, changes in the rate of cell migration were measured using a wound healing assay. Briefly, cells in each well were separated by a scratch-wound, a standardized scratch made with a P-20 pipette tip, and cells were observed every 12 h for 48 h using a Nikon Ti-S fluorescence microscope. The data were analyzed using the Image-Pro Plus software. [76]http://dx.doi.org/10.17504/protocols.io.iascaee [PROTOCOL DOI] Total RNA extraction, cDNA synthesis, and qPCR Cells were cultured in 24-well plates. 48 h post-transfection, the total cellular RNA was extracted using a Total RNA Extraction Kit (Promega, USA), according to the manufacturer’s protocol. The concentration of RNA was determined by measuring the absorbance at 260 nm, and 2 μg RNA was used for cDNA synthesis using an RT Master Mix (TaKaRa, Japan). qPCR amplification was performed using a mixture of Top Green qPCR Super Mix (Transgen, China), cDNA samples, and designated primers ([77]Table 1). Relative gene expression was calculated by comparison of the CT value of the gene of interest with that of GAPDH, the internal control. [78]http://dx.doi.org/10.17504/protocols.io.iaucaew [PROTOCOL DOI] Table 1. List of primers used in qPCR. Gene name Primer sequence (5’ to 3’) GAPDH Upstream: TCACCACCATGGAGAAGGC Downstream: GCTAAGCAGTTGGTGGTGCA SRC Upstream: GAACCCGAGAGGGACCTTC Downstream: GAGGCAGTAGGCACCTTTTGT CDC42 Upstream: CCATCGGAATATGTACCGACTG Downstream: CTCAGCGGTCGTAATCTGTCA WASL Upstream: CCCCAAATGGTCCTAATCTACCC Downstream: TGGAAATTGCTTGGTGTTCCTAT RHO Upstream: GGAAAGCAGGTAGAGTTGGCT Downstream: GGCTGTCGATGGAAAAACACAT SUB1 Upstream: GAAGGTGAAATGAAACCAGGAAG Downstream: ACAGCTTTCTTACTGCGTCATC SLC2A1 Upstream: GGCCAAGAGTGTGCTAAAGAA Downstream: CGATACCGGAGCCAATGGT CTNNB1 Upstream: TGATGGAGTTGGACATGGCCATGGA Downstream: TGGCACCAGAATGGATTCCAGA ACTB Upstream: GTGACGTTGACATCCGTAAAGA Downstream: ATGAAGATCAAGATCATTGCTCCT HSP90B1 Upstream: CAGAGAGAGGAAGAAGCTATTCAG Downstream: TTAAAAACTCGCTTGTCCCAGAT CLTC Upstream: AATGAAGGCCCATACCATGACT Downstream: TTATCCGTAACAAGAGCAACCG [79]Open in a new tab Statistical analysis All data were analyzed using the GraphPad Prism software and are presented as the mean ± SEM. Average gaps in the cell migration assay were measured using the Image-Pro Plus software, and the rate of cell migration was analyzed by a two-way ANOVA. The mRNA level was analyzed by a one-way ANOVA. P < 0.05 is considered statistically significant. Results Construction of the molecular signaling networks involved in the TMEM30A complex In order to identify the molecular signaling networks involved in TMEM30A, our previous mass spectrometry results of the TMEM30A complex in mouse tissue and the known migration-related genes from the literature were superimposed onto the PPIN from the STRING database, since PPIN is widely used for the identification of signal transduction pathways. The largest connected component in the PPIN, consisting of 347 migration-related genes and 14161 interactions, was constructed for further analysis. To elucidate the most-related genes, the first neighbors of TMEM30A were selected, which formed a small network of 49 genes with 493 interactions ([80]Fig 1). Moreover, migration-related genes were manually collected from the literature, of which 14 are found in the above PPIN ([81]Table 2). Fig 1. The migration-related signaling network regulated by TMEM30A. [82]Fig 1 [83]Open in a new tab The largest connected components in the PPIN consisting of 347 genes expressed during migration and 14161 interactions. The first neighbors of TMEM30A were selected and a small network including 49 genes and 493 interactions was formed. The inner circles denote migration-related genes manually collected from the literature. The outer circles denote the genes predicted to interact with TMEM30A. The purple nodes were randomly-selected for experimental validation by qPCR. Table 2. The 14 known migration-related genes. Gene symbol Description References (PIMID)