Papillary thyroid cancer (PTC), the most common thyroid malignancy, has a strong propensity for cervical lymph node metastasis (LNM), which increases the risk of locoregional recurrence and decreases survival probability in some high-risk groups. Hence, there is a pressing requirement for a reliable biomarker to predict LNM in thyroid cancer. In the present study, MKL1 (also known as MRTFA) expression was significantly increased in PTC patients with LNM compared with those without. Further receiver operating characteristic (ROC) analysis showed that MKL1 expression had a diagnostic value in the differentiation of LNM in PTC. Furthermore, Kaplan–Meier analysis revealed that high MKL1 expression was associated with significantly decreased survival in PTC. Additionally, our study indicated that MKL1 promoted the migration and invasion of PTC cells. MKL1 interacted with and recruited Smad3 to the promoter of MMP2 to activate MMP2 transcription upon treatment with TGF-β. Moreover, there was significant correlation between expression of TGF-β, MKL1 and MMP2 in our clinical cohort of specimens from individuals with PTC. Our results suggest that the detection of MKL1 expression could be used to predict cervical LNM and inform post-operative follow-up in individuals with PTC.
Thyroid cancer is the most common type of endocrine carcinoma, and has demonstrated increasing incidence and mortality over the past decades (Lim et al., 2017). Papillary thyroid cancer (PTC) is the major form of thyroid cancer. Most patients with PTC have a good prognosis after conventional treatments including surgery, thyroid-stimulating hormone suppression and radiotherapy (Haugen, 2017). However, PTC cells have a propensity to penetrate the lymphatic vessel in the form of small clumps and, therefore, cause lesions in the thyroid nodule. This biological process leads to a high incidence of regional lymph node metastasis (LNM). Furthermore, occult lymph node metastasis leads to regional recurrence (Lee et al., 2017) and an increased risk of mortality prominently among older patients (Lundgren et al., 2006).
It has been reported that at the time of diagnosis, cervical lymph node metastasis (CLNM) is present in 12.3–64.1% of patients with PTC from different clinical centers (Aydin et al., 2016). The primary imaging examinations, such as ultrasonography and computed tomography, play a limited role in CLNM assessment because of their low sensitivity (Lee et al., 2018). Accordingly, it is important to identify the risk factors for CLNM so that proper clinical procedures regarding central lymph node dissection (CLND) can be followed. Various factors have been found to be correlated with LNM, including clinicopathological features like tumor size, multifocality and extracellular extensions (Joseph et al., 2018). Moreover, the value of mutations in BRAF, TERT promoter (TERTp) and the genes encoding the RAS family of proteins, the common somatic mutations in PTC, as prognostic biomarkers of LNM has been assessed in several studies (Melo et al., 2017; Shen et al., 2017). In the era of personalized medicine for advanced disease, a comprehensive understanding of the molecular alterations present in metastatic tissues is an urgent requirement.
Megakaryocytic leukemia 1 (MKL1), also known as megakaryocytic acute leukemia (MAL), BSAC, or MRTFA, was first identified as a co-activator of serum response factor (SRF) in the transactivation of target genes (Selvaraj and Prywes, 2004). Further studies revealed that MKL1 reinforced the recruitment and assembly of other transcriptional machines, including the H3K4 methyl transferase COMPASS family (Yu et al., 2017a) and acetyltransferase PCAF (also known as KAT2B), to the promoter of genes specific to muscle lineage (Yu et al., 2017b). Subsequent studies unveiled that MKL1 also served as an oncogene in tumorigenesis of some tumors, for instance, hematological malignancies (Ma et al., 2001), solid carcinoma lung cancer (Cheng et al., 2015) and anaplastic thyroid cancer (Zhang et al., 2015). However, whether MKL1 is involved in the tumorigenesis of PTC is still unclear. Here, we demonstrated that MKL1 was overexpressed in tissue samples from PTC patients with LNM. We further evaluated the possibility of the clinical application of MKL1 in the differentiation of LNM. MKL1 promoted the migration and invasion ability of human papillary thyroid carcinoma BCPAP cells by targeting the TGF-β–Smad3–MMP2 signaling pathway.
Elevated expression of MKL1 is correlated with LNM and predicts a poor prognosis in patients with PTC
In order to characterize the clinicopathological significance of MKL1 in thyroid cancer, we first examined MKL1 mRNA expression levels in PTC patient tissues. MKL1 expression was significantly upregulated in PTC malignant thyroid tumors (n=14) compared with benign thyroid nodules (n=14) (Fig. 1A). To confirm the high expression of MKL1 in thyroid tumor tissues, we next performed immunohistochemistry (IHC) for MKL1 on 32 paraffin-embedded PTC samples. Examples of hematoxylin–eosin (H&E) and immunostaining for MKL1 on three tumor tissue samples and one normal thyroid tissue sample are presented in Fig. 1B. Of note, normal thyroid tissues were completely negative for MKL1 staining. In PTC samples, MKL1 expression was increased in tumor tissues, compared with adjacent non-tumoral tissues (Fig. 1B). Moreover, correlative analysis of MKL1 protein levels with clinicopathologic features suggested a significant association between increased MKL1 expression and LNM (P=0.001) and age at diagnosis (P<0.001), but not between sex, tumor size and multifocality (Table 1). MKL1 staining intensity progressed as the disease progressed (Fig. 1C). That is, 66.7% of samples from PTC patients with LNM (LNM+, n=18) exhibited strong staining for MKL1, whereas only 7.1% of samples from PTC patients without LNM (LNM−, n=14) showed strong MKL1 staining. Furthermore, increased MKL1 mRNA expression levels were detected in PTC samples when paired with the corresponding adjacent normal tissues from the same individual patient. MKL1 mRNA expression was increased in all five LNM+ samples, but somewhat decreased in four out of five LNM− samples (Fig. 1D). The differences in MKL1 protein levels between the tumor and non-tumoral thyroid tissues from PTC patients with LNM were further confirmed by western blot assay. MKL1 was upregulated in four out of six tissue samples from PTC patients with LNM (Fig. 1E). On this basis, a receiver operating characteristic (ROC) curve was plotted to estimate the diagnostic performance of MKL1 in lymph node metastasis according to IHC assay results (n=32). The area under the ROC curve was up to 0.87 (95% CI: 0.722–1.008; P<0.001; Fig. 1F). ROC analysis with a cutoff value of MKL1 staining intensity score at five differentiated LNM with a sensitivity of 0.78 and a specificity of 0.86. Finally, Kaplan–Meier analysis revealed that patients with high MKL1 expression levels in tissues had significantly lower overall survival rates than those with low MKL1 expression (P=0.02; Fig. 1G). Collectively, all these data indicate that MKL1 expression is upregulated in tissue samples from PTC patients with LNM and thus high expression of MKL1 predicts a poor prognosis in PTC.
MKL1 promotes the migration and invasiveness of human papillary thyroid carcinoma BCPAP cells
Next, protein expression levels of MKL1 in human thyroid cell lines were detected. Compared with normal human thyroid follicular epithelial cells (Nthy-ori-3.1), the expression level of MKL1 was higher in three different thyroid cancer cell lines, including a PTC cell line (BCPAP) (P<0.05), a follicular thyroid cancer (FTC) cell line (FTC-133), as well as an anaplastic thyroid cancer (ATC) cell line (8505C) (Fig. 2A). Subsequently, two small interfering RNAs (siRNAs) targeting different coding sequence regions of MKL1 (siMKL1#1 and siMKL1#2) were designed and synthesized to downregulate MKL1 expression in BCPAP cells. These two siRNAs specifically downregulated MKL1 expression at both mRNA and protein levels (Fig. 2B–D). We noted that MKL1 downregulation did not affect cell proliferation capability (Fig. 2E). As expected, MKL1 downregulation markedly inhibited the migration ability of BCPAP cells (Fig. 2F). Correspondingly, exogenously overexpressed MKL1 increased the migration ability of BCPAP cells (Fig. 2G,H). Next, the involvement of MKL1 in regulating cell migration and invasion was further confirmed in another PTC cell line, KTC-1. Accordantly, exogenously overexpressed MKL1 increased the migration ability of KTC-1 cells (Fig. S1A,B), whereas downregulation of MKL1 by siRNA interference decreased KTC-1 cell wound-healing ability, migration and invasion (Fig. S1C–F).
TWIST1, SNAI1 and ZEB1 with zinc finger domains have not only been shown as regulators of epithelial-to-mesenchymal transition (EMT), which is a critical process occurring in tumor metastasis, but have also been regarded as biomarkers indicative of thyroid cancer metastasis (Wu et al., 2019). Consistent with increased cell migration ability, the mRNA and protein levels of TWIST1, SNAI1 and ZEB1 were upregulated in MKL1-overexpressed BCPAP cells (Fig. 2I,J). Overall, these results show that MKL1 is overexpressed in thyroid cancer cells and able to promote cell migration.
MKL1 is required for TGF-β-induced migration and invasion of BCPAP cells
Transforming growth factor beta (TGF-β) is a major driver of tumor metastasis (Zhang et al., 2016). A profound increase in MKL1 mRNA expression was observed on treatment with 5 ng/ml TGF-β in BCPAP cells (Fig. 3A); therefore, this dosage was used in subsequent experiments. Next, it was found that 5 ng/ml TGF-β increased MKL1 mRNA expression in a time-dependent manner within 36 h of treatment (Fig. 3B). As expected, TGF-β also enhanced MKL1 protein expression in BCPAP cells in a dose-dependent manner (Fig. 3C). As a transcription co-factor, nuclear translocation is a key step for the transcriptional activity of MKL1 (Olson and Nordheim, 2010). Interestingly, TGF-β treatment not only upregulated MKL1 expression at the mRNA and protein levels, but also enhanced the translocation of MKL1 from cytoplasm to nucleus (Fig. 3D). Agreeing with previous reports that TGF-β induces EMT (Heldin et al., 2012), TGF-β alone was capable of enhancing cell migration and invasion. However, MKL1 downregulation by siRNA transfection significantly inhibited TGF-β-induced cell migration and invasion in BCPAP cells (Fig. 4A–C). Meanwhile, mRNA expression levels of EMT regulators, TWIST1, SNAI1 and ZEB1 were evaluated in BCPAP cells upon TGF-β stimulation. TGF-β treatment upregulated SNAI1 and ZEB1 mRNA levels, but downregulated TWIST1 in BCPAP cells (Fig. 4D). Knockdown of MKL1 using siRNA led to a significant reduction in ZEB1 mRNA expression but had no effect on SNAI1. Taken together, these results demonstrate that MKL1 is required for the cell migration and invasion induced by TGF-β in BCPAP cells.
MKL1 recruits Smad3 to the MMP2 promoter upon TGF-β stimulation in BCPAP cells
One important step in tumor invasion is the penetration of the basement membrane (Mori et al., 2019). Matrix metalloproteinase 2 (MMP2) is an important member of the MMP family of secreted and membrane-associated neutral endopeptidases (Hendrix and Kheradmand, 2017). MMP2 is able to degrade collagen IV, a basic component of constitutive basement membranes (Hendrix and Kheradmand, 2017). Indeed, it has been reported that MMP levels are correlated with LNM in thyroid cancer (Ren et al., 2017). TGF-β, which is overexpressed in the invasive front of thyroid tumors, has been reported to increase MMP expression and secretion (Moore-Smith et al., 2017; Riesco-Eizaguirre et al., 2009). Intriguingly, treatment with TGF-β upregulated the protein level of MKL1, accompanied by an increase in MMP2 expression in BCPAP cells (Fig. 5A). Next, the protein levels of both MKL1 and MMP2 were examined in three paired tissues from PTC patients with LNM. In line with the results obtained from cell lines, tissues with strong MKL1 expressions also exhibited high levels of MMP2 (Fig. 5B). Therefore, we examined whether the increase in MMP2 protein in these tumor tissues was associated with the increased MKL1 expression. Expression of MMP2 increased in TGF-β-treated BCPAP cells and, strikingly, knockdown of MKL1 using siRNAs led to a significant reduction in MMP2 expression at both mRNA and protein levels (Fig. 5C,D). Upon ectopic overexpression of MKL1 in BCPAP cells, as expected, the expression of MMP2 was significantly upregulated at the mRNA (Fig. 5E) and protein level (Fig. 5F) compared with mock vector, confirming the impact of MKL1 on translational activation of MMP2. MKL1 is known as a transcription co-activator in the transcription of its downstream targets (Li et al., 2018). To address whether MKL1 could regulate MMP2 transcription, a ChIP assay was performed. MKL1 recruitment at the promoter of MMP2 was seen to be enhanced under TGF-β exposure (Fig. 6A,B). Smad3, one of the intracellular mediators that transduces signals from TGF-β, is phosphorylated and forms a complex with Smad4. The Smad3–Smad4 complex then translocates to the nucleus where it regulates the transcription of target genes (Yang et al., 2018). To uncover details of the components of the transcription machine recruited at the MMP2 promoter under TGF-β stimulation, we employed anti-Smad3 antibody to perform a second round of ChIP using the precipitate recovered from the first round of ChIP against MKL1, and analyzed the resulting second-round precipitate for the presence of DNA fragments of the MMP2 promoter. A reciprocal re-ChIP assay, in which anti-Smad3 was used for the first immunoprecipitation and anti-MKL1 was then used for the second immunoprecipitation, was also performed. Such sequential ChIP assays revealed that the two factors, MKL1 and Smad3, localized at the same genomic region of the MMP2 promoter (Fig. 6C). To confirm these results, we further extracted the total proteins of BCPAP cells, and performed co-immunoprecipitation experiments with antibodies detecting the endogenous proteins. As shown in Fig. 6D, MKL1 interacted with Smad3 under basal condition. Of note, an increase in the amount of co-immunoprecipitated Smad3 in the lysates of cells treated with TGF-β was observed. Taken together, these findings show that MKL1 is able to recruit Smad3 to the MMP2 promoter, exerting a role in gene activation as a component of the Smad3–Smad4 co-activator complex in the presence of TGF-β.
TGF-β, MKL1 and MMP2 co-expression profiles correlate with lymph node metastasis in PTC
To determine the clinical relevance and validity of our findings, we examined the expression of TGF-β and MMP2 and their relationship with MKL1 in the clinical setting. IHC analysis further revealed significant co-expression correlations between TGF-β, MKL1 and MMP2 in a clinical cohort of PTC specimens (n=21) (Fig. 7A,B). Furthermore, there was a clear overexpression of MKL1 in PTC cells in both primary tumors and in metastatic nodes from the same patient (Fig. 7C). In summary, our findings provide strong clinical evidence for a critical role of a TGF-β–MKL1–MMP2-mediated pathway in lymph node metastasis of PTC.
The incidence of thyroid cancer is increasing rapidly, with the most common histology being papillary thyroid carcinoma (PTC) (Siegel et al., 2017). The most life-threatening aspects of the tumors are invasion and metastasis. In PTC, the primary metastasis route is through lymph node metastasis (LNM), which often occurs at an early stage and initially appears in the central region (Mulla and Schulte, 2012). Although PTC is generally indolent with excellent prognosis, the reported incidence of cervical LNM (CLNM) was relatively high at 50% (Sturgeon et al., 2016). CLNM is known to be related to locoregional recurrence, which is a major predictor of poor prognosis (Lan et al., 2015). Moreover, CLNM might lead to further surgery for nodal recurrence, which would increase the risk of recurrent laryngeal nerve injury and hypoparathyroidism (Aydin et al., 2016). Thus, CLNM is one of the important factors in determining the extent of surgery.
Finding sensitive predicting factors for CLNM remains a challenging task in thyroid cancer treatment. It has been reported that clinicopathological features including sex, age, tumor diameter and extrathyroidal invasion are independent risk factors of CLNM (Liu et al., 2017). Besides, immune response of thyroid tissue, such as Hashimoto's thyroiditis, may protect against central and lateral LNM in PTC (Zhu et al., 2016). Recently, relying on molecular and genetic analysis, BRAF-only mutations have been found as the most frequent molecular alterations in LNM, whereas TERTp-only mutations are the most frequent alterations in distant metastasis (Melo et al., 2017). In addition, several miRNAs, such as miRNA-146b and miR-221/222, have also been shown to be potential biomarkers in predicting LNM (Mutalib et al., 2016). To date, studies focusing on the involvement of protein biomarkers in PTC with LNM are quite limited.
MKL1, a ubiquitously expressed transcription co-factor, has been shown to promote tumorigenesis in different cancer types (Cheng et al., 2015; Xu et al., 2019). In our present study, we demonstrated that MKL1, a transcription co-factor, was correlated with LNM in PTC. An upregulation of mRNA and protein expression of MKL1 was further observed in tissue samples from PTC patients with LNM but not in those without LNM. Furthermore, ROC analysis in our clinical setting showed that MKL1 had a diagnostic value in the differentiation of LNM. In addition, high MKL1 expression was found to be associated with a worse survival rate in PTC. In line with the clinical performance, laboratory testing of the biological function of MKL1 demonstrated that MKL1 promoted the migration and invasiveness of PTC cells. This study, for the first time, identified MKL1 as a valuable diagnostic biomarker that could be used for the prediction of CLNM in PTC. Consistent with our findings, it has been reported that downregulation of MKL1 by miR-260 impeded the migration of an ATC cell line (Zhang et al., 2015), which further verified the important role of MKL1 in thyroid cancer progression.
Next, we aim to investigate the mechanism by which MKL1 regulates PTC metastasis. Numerous lines of evidence show that tumor microenvironment is a niche favoring tumor cell survival and metastasis. TGF-β, which performs complex functions in cancer biology, is believed to be an important modulator of the tumor microenvironment. Consistent with this notion, the expression of TGF-β was found to be stronger in the invasive regions versus the central regions of tumors, and high TGF-β and Smad protein activity is associated with PTC invasion and CLNM (Riesco-Eizaguirre et al., 2009). Our results showed that TGF-β treatment increased MKL1 expression at both the mRNA and protein levels. Indeed, it has been reported that MKL1 responds to various stimuli within the tumor microenvironment, including TNF-α (Xu et al., 2019), endothelin-1 (Yang et al., 2014), LPS and hypoxia (Yu et al., 2014). In addition, TGF-β induces phosphorylation of Smad2 and Smad3, after which phosphorylated Smad2 and Smad3 form dimers or trimers with Smad4. Thereafter, the Smad complexes translocate to the nucleus, where they may interact with specific DNA-binding proteins and direct transcription of target genes, such as MMP family genes (Feng et al., 2018). Previous studies have reported that MKL1 interacts with Smad3 in epithelial and fibroblast cells (Fan et al., 2015; Morita et al., 2007). In the present study, we found that MKL1 interacted with Smad3 under basal or TGF-β stimulation conditions, serving as a component of the Smad3–Smad4 co-activator complex. Our results demonstrate that MKL1 is a downstream effector of TGF-β, which directly interacts with Smad3 and binds to the MMP2 promoter in thyroid cancer cells.
In summary, our study demonstrated that MKL1 was positively associated with LNM in PTC, evidenced by promoting migration and invasiveness of thyroid cancer cells. There are, however, some questions remaining. First, the mechanisms underlying the upregulation of MKL1 expression under TGF-β exposure in PTC cells are unclear. Of note, our results showed that TGF-β induced the upregulation of MKL1. Moreover, we found that MKL1 was overexpressed in BCPAP and 8505C cells that harbored BRAF and TERTp mutations. MKL1 expression levels were also high in FTC-133 cells with a TERTp mutation, but not in normal thyroid cell line Nthy-ori-3.1 cells. It has been reported that mutations in the BRAF gene are most commonly associated with PTC and closely related to lymph node metastasis (Song et al., 2018). Additionally, it has been reported that the coexistence of TERTp and BRAF V600E mutations is associated with a poor prognosis for patients with PTC (Xing et al., 2014). Whether these mutations enhance the expression of MKL1 remains to be discovered. Second, in the present study, we demonstrated that MKL1 bound with transcription factor Smad3 to mediate the migration and invasion of PTC cells. In fact, MKL1 can also enhance the recruitment of other epigenetic regulators. For example, regulators including acetyl transferase p300 (also known as EP300) (He et al., 2015), methyl transferase COMPASS proteins (Yang et al., 2014) and nucleosome regulators SWI/SNF complex subunits and Brg1 (also known as SMARCA4) (Menendez et al., 2017) can be recruited to targeted genes with the help of MKL1. All these findings suggest a key epigenetic mediator role of MKL1 in modulating the transcription of target genes. Epigenetic alterations were reported to function in multiple processes of thyroid cancer progression including tumorigenesis, lymph node metastasis and distant metastasis (Asa and Ezzat, 2018; Sasanakietkul et al., 2018). However, it still remained unknown whether MKL1 functions as a central regulator through recruiting other factors to form epigenetic machines and thus promote the migration of PTC cells. Furthermore, as mentioned above, although ultrasonography is the most convenient way to identify and locate metastasis in the neck, its sensitivity remains low, especially for lymph nodes without typical signs of metastasis (e.g. microcalcifcation, cystic changes) (Ito et al., 2005). Thus, it is difficult to formulate a surgery schedule based on ultrasonography alone. Considering that the immunohistochemical detection of MKL1 in PTC cells collected by ultrasonography-mediated fine needle aspiration (FNA) biopsy is feasible, the combined diagnostic value of ultrasonography and evaluation of MKL1 expression, or evaluation of TERTp mutation and MKL1 expression, in the differentiation of LNM in PTC should be assessed in future studies.
MATERIALS AND METHODS
Chemicals, reagents and antibodies
Lipo6000 was purchased from Beyotime Institute of Biotechnology (Shanghai, China). Opti-MEM was purchased from Gibco (Gibco-Invitrogen, CA, USA). UltraSYBR mixture was purchased from CWBIO (Beijing, China). Diaminobenzidine (DAB) substrate kit was obtained from ZSGB-BIO (Beijing, China). Matrigel Basement Membrane Matrix was purchased from Becton Dickinson (San Jose, CA, USA). Transwell inserts (8.0 μm) were purchased from Merck Millipore (MA, USA). Crystal Violet staining solution was purchased from Beyotime Biotechnology (Nantong, China). Bead homogenizer was purchased from Scientz Biotechnology (Scientz-48, Ningbo, China). Protein A/G agarose was purchased from Santa Cruz Biotechonology (Santa Cruz, CA, USA). ChIP assay kit was purchased from Beyotime Biotechnology (P2078, Shanghai, China). TGF-β was purchased from Peprotech (Rocky Hill, NJ, USA). Primary antibodies used were as follows: anti-MKL1 (sc-398675; 1:1000), anti-PARP (H-250, sc-7150; 1:1000) and anti-β-actin (sc-47778; 1:1000) were purchased from Santa Cruz Biotechonology. Anti-Smad3 (cst-9523; 1:1000) was purchased from Cell Signaling Technology (Beverly, MA, USA). Anti-MMP2 (AF-0234; 1:1000) and anti-Tubulin (AF-819; 1:1000) were purchased from Beyotime Biotechnology (Shanghai, China). Anti-SNAI1 (Cat. no. 13099-1-AP; 1:1000), anti-TWIST1 (Cat. no. 25465-1-AP; 1:1000) and anti-ZEB1 (Cat. no. 21544-1-AP; 1:1000) were purchased from Proteintech Group (Chicago, IL, USA). All primers were synthesized by Sangon Biotech Co. Ltd (Shanghai, China). Other chemicals were analytical reagents and purchased from Sinopharm Chemical Reagent Co. Ltd (Shanghai, China).
Human PTC sample collection
Human PTC samples were collected from patients who received thyroidectomy surgery in 2017 at Jiangyuan Hospital, Wuxi, Jiangsu, China. The study methodologies were approved by the ethics committee of Jiangyuan Hospital. Written informed consent was obtained from all patients and tissue donors, and all clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki.
Those tissues with diameter >2 cm were divided into tumor (T) and adjacent non-tumoral tissues (NT) by experienced pathologists. Tissues collected from patients with thyroid nodular goiter were defined as ‘benign’. Tissues collected from patients with papillary thyroid cancer were defined as ‘malignant’. According to the lymph node metastasis (LNM) status of these patients, collected malignant tissues were further grouped as ‘LNM−’ (without LNM) and ‘LNM+’ (with LNM). Detailed pathological information of these patients is summarized in Table S1.
Cell culture and drug treatments
Papillary thyroid cancer BCPAP cells were obtained from the German Collection of Micro-organisms and Cell Cultures (Braunschweig, Germany). Nthy-ori-3.1, FTC-133 and 8505C cell lines were obtained from the European Collection of Cell Cultures (Salisbury, UK). KTC-1 cells were obtained from Shanghai Cell Bank of Chinese Academy of Sciences (Shanghai, China). Cells were maintained at 37°C with atmosphere of 5% CO2 in a humidified incubator (Thermo Electron Corporation, USA). Standard medium, which consists of RPMI 1640 (Gibco, USA), 10% calf serum (Sijiqing, Hangzhou, China), 100 U/ml penicillin, and 100 mg/l streptomycin, was used throughout the experiments. The BCPAP cells used in this study were at early passage (passage 3–5) and the short tandem repeat (STR) profiles of BCPAP cells were tested by the Bio-Research Innovation Center Suzhou, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences. All cell lines used in this study were negative for mycoplasma. For TGF-β treatment, cells were treated with TGF-β at a concentration of 5 ng/ml for 24 h unless otherwise indicated.
Kaplan–Meier survival plot
The Kaplan–Meier survival data of patients with PTC presented in this study were collected and processed using the UALCAN database (http://ualcan.path.uab.edu/index.html) (Chandrashekar et al., 2017). Briefly, survival data were obtained from The Cancer Genome Atlas (TCGA) Level 3 RNA-seq information and corresponding patient information was transferred from Genomic Data Commons (GDC, https://gdc.cancer.gov/). The expression value of each gene was defined as transcripts per million (TPM), generated using PERL language. The prognosis of each group of patients was examined using Kaplan–Meier survival estimators, and survival outcomes of the two groups were compared using log-rank tests.
Plasmids construction and siMKL1 transient transfection
MKL1 plasmid construction has been described previously (Cen et al., 2003), pcDNA3.1 was used as a negative control. Two siRNA sequences targeting different coding sequence regions of MKL1 were as follows: MKL1#1 (targeting region: residues 562 to 580) sense: 5′-GAAUGUGCUACAGUUGAAA-3′; MKL1#2 (targeting region: residues 3650 to 3668) sense: 5′-GUGUCUUGGUGUAGUGUAA-3′. siRNAs were chemically synthesized by Gene Pharma (Shanghai, China). The negative control scramble (SCR) siRNA sequence 5′-UUCUCCGAACGUGUCACGUTT-3′ was used. The indicated siRNAs and plasmids were transfected into BCPAP cells using Lipofectamine 2000 (Invitrogen). RNA and protein were extracted 24 h and 48 h after cell transfection, respectively.
Whole-cell proteins were extracted with ice-cold lysis buffer [150 mM NaCl, 50 mM Tris-HCl pH 7.5, 2 mM ethylene diamine tetraacetic acid (EDTA), 1% (w/v) Nonidet P-40, 0.02% (w/v) sodium azide] which contained a protease-inhibitor cocktail (1%, v/v). Collected proteins were quantified with a Bradford protein assay and equivalent amount of proteins were separated by SDS-PAGE. Proteins were then transferred to a nitrocellulose filter membrane and blocked with 5% fat-free milk. To detect protein bands, membranes were incubated with indicated primary antibodies at 4°C overnight. After washing in Tris-buffered saline containing 0.1% Tween 20, membranes were incubated with corresponding HRP-conjugated secondary antibodies anti-rabbit or anti-mouse antibody (Santa Cruz Biotechnology; 1:1000) for another 1 h and then visualized using an ECL western blot kit (Abxbio Biotechnology Co., Ltd., Beijing, China).
Cell proliferation assay
After transfection for 24 h with control siRNA or siRNA against MKL1, BCPAP cells were seeded in a 24-well plate and cultured for another 7 days. Every day, cell numbers were counted and Typan Blue staining was performed to distinguish dead cells from living cells. The mean cell number value was calculated from three replicated parallel wells and a proliferation curve was plotted for each group.
Scratch assay was carried out in a 35-cm dish. After transfection with control siRNA or siMKL1, BCPAP cells at the stage of maximal cell confluence were wounded in a 4×4 cross pattern using a 10 μl pipette tip. Wound healing at matching cross points was observed across treatments and images were taken at indicated time points. Wound healing ratio was calculated according to the formula: wound healing ratio (%)=[(gap width at 0 h−the remaining gap width)/gap width at 0 h]×100.
Boyden chamber migration and invasion assay
Boyden chamber migration and invasion assays were executed in an 8 μm Boyden chamber according to the manufacturer's instructions. Briefly, for migration assays, BCPAP cells were detached and resuspended in RPMI 1640 containing 10% BSA, and planted into Boyden chambers inserted into a 24-well plate. The pore size of the transwell apparatus is 8.0 μm. Complete culture medium with 10% FBS was added to the 24-well plate. Cells that had moved to the lower surface of the chamber were fixed with methanol for 15 min and subsequently stained with 500 μl of 0.2% Crystal Violet. Cells were photographed under a microscope followed by washing with PBS three times. For invasion assay, extracellular matrix (ECM) Matrigel was first diluted tenfold with RPMI 1640 and added to the Boyden chamber before use, after which the procedure was the same as for the cell migration assay. Ten random fields were photographed, and cells were quantified by Image-Pro Plus software (Media Cybernetics). The migration and invasion rates were calculated according to the formula: relative migration or invasion=staining area of experimental group/staining area of control group.
Cytoplasmic and nuclear protein extraction
Cytoplasmic and nuclear proteins were extracted using NE-PER nuclear and cytoplasmic extraction reagents according to the manufacturer's instructions (Thermo Fisher Scientific). Briefly, cells were collected and washed with ice-cold PBS. In order to thoroughly resuspend pellets in cytoplasmic extraction reagent I (CER I), cells were vortexed vigorously for 15 s and then incubated on ice for 10 min. Then, cytoplasmic extraction reagent II (CER II) was added into cell lysates and the mixed lysates were vortexed for another 5 s. After centrifugation at 16,000 g for 5 min, supernatant was collected for the analysis of cytoplasmic proteins. Next, pellets were lysed with nuclear extraction reagent (NER) and vortexed 15 s per minute for a total of four rounds. After centrifugation, the supernatant was collected as nuclear extract. Protein levels of MKL1 in the nuclear and cytoplasmic fractions were analyzed by SDS-PAGE, with tubulin used as a loading control of cytoplasmic proteins and PARP used as an internal control of nuclear proteins.
Reverse transcriptase PCR and real-time PCR
For reverse transcriptase PCR (RT-PCR), total cellular RNA was extracted using TRIzol reagent (Life Technologies) and cDNA was synthesized using M-MLV reverse transcriptase and oligo (dT)18 primers. PCR reaction was initialized with a step of denaturation at 95°C for 5 min, and then 30 cycles of amplification with denaturation at 95°C for 30 s, annealing at 60°C for 30 s and elongation at 72°C for 30 s and a final extension at 72°C for 5 min. PCR products were then separated by 1.5% agarose gel and visualized by ethidium bromide (20 µg/ml) staining. The primer sequences for PCR were as follows: MKL1 forward: 5′-TTAAGCAAAGCCAACCCAA-3′ and MKL1 reverse: 5′-GCCCGAGACAGGCAGTGAT-3′; Actin forward: 5′-GCCGGGACCTGACTGACTAC-3′ and Actin reverse: 5′-CGGATGTCCACGTCACACTT-3′. Real-time PCR (qPCR) was performed as follows. Briefly, cDNA was amplified using a SYBR mixture (TaKaRa, Japan), and triplicate experiments were performed. Unless otherwise indicated, the 2−ΔΔCT method was used to calculate the relative expression level of genes of interest. Actin (ACTB) was used as an internal control. The primer sequences for qPCR were as follows: MKL1 forward: 5′-GGCGAAAATGATGATGAACCA-3′ and MKL1 reverse: 5′-TCATTGGCAACAGCTTCACTCT-3′; TWIST1 forward: 5′-TACATCGACTTCCTCTACCAGGTC-3′ and TWIST1 reverse: 5′-TAGTGGGACCGCCAGATGGA-3′; SNAI1 forward: 5′-TCGGAAGCCTAACTACAGCGA-3′ and SNAI1 reverse: 5′-GCTGGAAGGAAACTCTGGATTA-3′; ZEB1 forward: 5′-GCACAAGAAGAGCCACAAGTA-3′ and ZEB1 reverse: 5′-GCAAGACAAGTTCAAGGGTTC-3′; MMP2 forward: 5′-TTTGACGGTAAGGACGGACTC-3′ and MMP2 reverse: 5′-CCTGGAAGCGGAATGGAA-3′; Actin forward: 5′-GACTTAGTTGCGTTACACCCTTTCT-3′ and Actin reverse: 5′-GCTGTCACCTTCACCGTTCC-3′.
Co-immunoprecipitation was performed as previously described with minor modifications (Zhang et al., 2014). Briefly, 1×107 cells were lysed with 500 µl of RIPA lysis buffer (50 mM Tris-HCl, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100) containing a complete protease cocktail, and kept on ice for 10 min. After centrifugation, cell lysates were collected and pre-cleared with protein A/G plus agarose for 30 min to eliminate non-specific binding proteins. Supernatant was then collected by centrifugation at 10,000 g and incubated with 4 µg of anti-MKL1 primary antibody or mouse IgG with gentle agitation at 4°C overnight. The mixture was then mixed with protein A/G agarose for another 3 h. After repeat washes with RIPA lysis buffer, agarose pellets were then centrifuged and extracted with 50 µl of 1×SDS sample buffer by heating at 95°C for 5 min. Then the samples were subjected to SDS-PAGE followed by immunoblotting.
Chromatin immunoprecipitation (ChIP) and re-ChIP assay
The experiment was performed using a ChIP Assay Kit according to the manufacturer's instructions (Beyotime Biotechnology, Nantong, China) with some modifications. Briefly, 2×107 cells cultured in a 10-mm dish were cross-linked with 275 µl of fresh 37% paraformaldehyde (PFA) for 15 min at room temperature. Then the cell culture was added with 1.1 ml of glycine solution (10×) and incubated for a further 5 min. After that, cells were collected and lysed with 1×SDS on ice. Next, DNA–protein complexes were sonicated using a Scientz-IID sonicator, and DNA was broken into ∼500 bp fragments. For each IP experiment, cell lysate containing 200 µg of protein was incubated with 5 µg of anti-MKL1 or 1 µg of mouse anti-IgG antibodies. After incubation with 30 μl of protein A/G agarose and salmon sperm DNA, cross-linked DNA was washed, eluted and subjected to PCR and qPCR assay. For re-ChIP, immune complexes were eluted with the elution buffer (1% SDS, 100 mM NaCO3), diluted with the re-ChIP buffer (1% Triton X-100, 2 mM EDTA, 150 mM NaCl, 20 mM Tris pH 8.1), and subjected to immunoprecipitation with a primary antibody against the protein of interest. The primer sequences for MMP2 promoter were as follows: MMP2 promoter-1 (position −394 to −169) forward primer: 5′-GACCATTCCTTCCCGTTCC-3′, and reverse primer: 5′-TTTCCCCGGCCGCCTGC-3′; MMP2 promoter-2 (position −681 to −525) forward primer: 5′-CCCCTGTTCAAGATGGAGTC-3′, and reverse primer: 5′-CCCAGGTTGCTTCCTTACCT-3′.
Hematoxylin–eosin and immunohistochemistry staining and quantification
Paraffin-embedded tissue samples from patients with PTC were obtained from the Pathology Department of Jiangyuan Hospital. Samples were deparaffinized and rehydrated through a series of graded dewaxing and ethanol solutions. After that, samples were subjected to hematoxylin–eosin (H&E) and immunohistochemistry (IHC) assays. For H&E staining, rehydrated tissues were stained with eosin and hematoxylin. For IHC assays, tissues were incubated with indicated primary antibodies at 4°C overnight. Tissues were then probed using secondary anti-rabbit or anti-mouse antibodies (Santa Cruz Biotechnology, Berverly, MA, USA; 1:200), followed by peroxidase detection using a DAB kit. Staining results were assessed by at least two pathologists, using an immunoreactive scoring (IRS) system which provides a range of 1 to 12 qualitative scores based on staining intensity per tissue sample. Categories for the IRS include 1–4 (weak), 5–8 (moderate) and 9–12 (strong).
All data were shown as means±s.d. of three independent experiments. For data showing a Gaussian distribution, significant difference was determined by Student's t-test for comparing two groups, and one-way or two-way ANOVA for more than two groups. ROC curve was calculated and assayed by SigmaPlot 12.5 (Systat Software Inc.). Correlations between MKL1 expression and clinicopathological features were analyzed by Chi-square test, correlation between MKL1 expression and overall survival rate was calculated by the Kaplan–Meier method, and the correlation between TGF-β, MKL1 and MMP2 protein expression levels in PTC samples was analyzed by performing Spearman’s correlation using GraphPad Prism software. P-values with P<0.05 were considered statistically significant.
We thank Ms Jing Wu for assistance with the H&E and IHC assays.
Conceptualization: X.C., L.Z.; Methodology: X.C., S.X., L.Z.; Validation: X.C., L.Z.; Formal analysis: X.C., S.X., H.Y., L.Z.; Investigation: X.C., S.X., J.P., J.Z., X.W.; Resources: H.Y., J.B., Y.X., H.G., L.Z.; Data curation: X.C., S.X.; Writing - original draft: X.C., L.Z.; Writing - review & editing: X.C., S.X., J.P., J.Z., X.W., H.Y., J.B., Y.X., H.G., L.Z.; Visualization: X.C., L.Z.; Supervision: X.C., L.Z.; Funding acquisition: X.C., L.Z.
This study was supported by grants from the National Natural Science Foundation of China (81602352 and 81602353), the Science and Research Foundation of the Health Bureau of Jiangsu Province (H2017032), the Natural Science Foundation of Jiangsu Province (BK20171145), the Jiangsu Provincial Key Medical Discipline Laboratory (ZDXKA2016017) and the Wuxi Municipal Commission of Health and Family Planning (Q201608 and Q201836).
The authors declare no competing or financial interests.