extfieldsofstudy: Medicine Chemistry

s2fieldsofstudy: Biology Chemistry Medicine

text:
Marsdenia tenacissima extract induces apoptosis and suppresses autophagy through ERK activation in lung cancer cells

Background Marsdenia tenacissima is an herb medicine which has been utilized to treat malignant diseases for decades. The M. tenacissima extract (MTE) shows significant anti-proliferation activity against non-small cell lung cancer (NSCLC) cells, but the underlying mechanisms remain unclear. In this study, we explored the potential anti-proliferation mechanisms of MTE in NSCLC cells in relation to apoptosis as well as autophagy, which are two critical forms to control cancer cell survival and death. Methods The proliferation of H1975 and A549 cells was evaluated by MTT assay. Cell apoptosis was assessed by Annexin V and PI staining, Caspase 3 expression and activity. Autophagy flux proteins were detected by Western blot with or without autophagy inducer and inhibitor. Endogenous LC3-II puncta and LysoTracker staining were monitored by confocal microscopy. The formation of autophagic vacuoles was measured by acridine orange staining. ERK is a crucial molecule to interplay with cell autophagy and apoptosis. The role of ERK on cell apoptosis and autophagy influenced by MTE was determined in the presence of MEK/ERK inhibitor U0126. Results The significant growth inhibition and apoptosis induction were observed in MTE treated NSCLC cells. MTE induced cell apoptosis coexisted with elevated Caspase 3 activity. MTE also impaired autophagic flux by upregulated LC3-II and p62 expression. Autophagy inducer EBSS could not abolish the impaired autophagic flux by MTE, while it was augmented in the presence of autophagy inhibitor Baf A1. The autophagosome–lysosome fusion was blocked by MTE via affecting lysosome function as evidenced by decreased expression of LAMP1 and Cathepsin B. The molecule ERK became hyperactivated after MTE treatment, but the MEK/ERK inhibitor U0126 abrogated autophagy inhibition and apoptosis induction caused by MTE, suggested that ERK signaling pathways partially contributed to cell death caused by MTE. Conclusion Our results demonstrate that MTE caused apoptosis induction as well as autophagy inhibition in NSCLC cells. The activated ERK is partially associated with NSCLC apoptotic and autophagic cell death in response to MTE treatment. The present findings reveal new mechanisms for the anti-tumor activity of MTE against NSCLC. Electronic supplementary material The online version of this article (10.1186/s12935-018-0646-4) contains supplementary material, which is available to authorized users.

Background
Lung cancer remains one of the leading causes of cancerrelated deaths worldwide. It can be divided into small-cell lung cancer (SCLC, 15%) and non-small cell lung cancer (NSCLC, 85%) according to the histologic features. In patients with advanced NSCLC who generally have a poor prognosis [1], new strategies to improve survival are urgently required.
Aberrant signal transduction pathways often occur in tumorigenesis and progress. Studies demonstrated that autophagy and apoptosis play central roles during lung cancer initiation and progression [2]. Fundamental cellular physiological activities such as apoptosis and autophagy are critical to control cell survival and cell death [2]. Apoptosis is one form of programmed cell death with the function of removing damaged cells. Resistance to apoptosis is regarded as one of the hallmarks of cancer [3], thus targeting apoptosis in cancer is a practicable therapy with the suggest of many studies [4].
Autophagy is a self-degradation process to keep constant supply of cellular energy [5]. The relationship between autophagy and cell death is subtle and intricate, and it may promote or inhibit cell death in different contexts. The role of autophagy in tumor initiation and progression is multifaceted and complicated. It has been reported that autophagy inhibits tumorigenesis in some circumstances but promotes carcinogenesis under most conditions [6]. Through upregulating autophagy, cancer cells can survive, growth and become aggressive under pressured microenvironment [6]. Therefore, it makes autophagy as an attractive therapeutic target for effective treatment of tumors including lung cancer [7,8].
Traditional Chinese Medicine has been used extensively to treat diseases from ancient time. The stem of Marsdenia tenacissima (Roxb.) Wight et Arn. is mainly produced in Yunnan (China), and its medical use was firstly recorded in "Dian Nan Ben Cao", a medical literature written by Mao Lan in Ming Dynasty with the activity of expectorant, diuresis, eliminating heat and purging fire, lactating. M. tenacissima has long been used as a remedy to treat malignant diseases, tracheitis, and pneumonia in China [9,10].
Due to the vital role of apoptosis and autophagy in cell death, in the present study, we evaluated the influence of MTE on cell apoptosis and autophagy in NSCLC cell lines A549 and H1975. Meanwhile, the molecular mechanisms of MTE treatment shared by both apoptosis and autophagy were also explored and elucidated.
The stem of M. tenacissima was collected from Yunnan, China. A voucher specimen (200907-T009-05) was deposited in the herbarium of SanHome Pharmaceutical Co., Ltd (NanJing, China) and was identified by Professor De-Kang Wu (Nanjing University of Chinese Medicine). The preparation of MTE is described as previously [21]. 1 kg powder of the stem of M. tenacissima was extracted with water for three times which is 1.5 h, 1 h and 0.8 h, respectively. The combined extracts were filtered, concentrated, and then precipitated with 8 times 85% ethanol at 4 °C for 24 h. The ethanol was recovered and new 85% ethanol was added to cause further precipitation. The ethanol in extract was recovered thoroughly and the insoluble precipitate was removed by filtration. Finally, the extract was concentrating to 200 ml. This condensed extract was dilute with water for injection, added 0.3% polysorbate 80, and adjust pH to 5.5-6.0 to get Xiao-Ai-Ping injection following the standard of State Food and Drug Administration (SFDA) of China.

Cell viability assays
A549 or H1975 cells were suspended in complete RPMI-1640 medium and plated at a density of 5 × 10 3 cells/ well in 96-well culture dishes (Costar, Cambridge, MA, USA). Following 24 h of culture, the medium was replaced with complete culture medium supplemented with various concentrations of drugs. On the collection time points, cells were incubated with MTT at 37 °C for 4 h, and the precipitate was dissolved in DMSO. Subsequently, the absorbance (optical density, OD) at 570 nm was measured using a microplate reader (Model 680; Bio-Rad Laboratories, Hercules, CA, USA) and cell viability was calculated according to the following formula: (OD sample − OD blank )/(OD control − OD blank ) × 100%.

Western blot analysis
For immunoblot analysis, cells were harvested and lysed in RIPA lysis buffer (WB0002, TDYBio). Protein concentrations were determined using the BCA protein assay kit (Thermo Fisher). Protein samples (20 μg per lane) were separated on the 8-15% SDS-polyacrylamide gel electrophoresis (PAGE) and blotted onto polyvinylidene difluoride membranes (Immobilon-P, Millipore, Bedford, MA, USA). Following transfer, the membranes were blocked in 5% nonfat milk or bovine serum albumin (BSA) (for phosphorylated proteins) in phosphatebuffered saline (PBS) with 0.1% Tween-20, probed with primary antibodies overnight at 4 °C. After washing, the membranes were incubated with appropriate horseradish peroxidase-conjugated secondary antibodies. Visualization of the protein bands was accomplished using an Immobilon Western Chemiluminescent HRP substrate (Millipore). Image J software was used to calculate the expression of each protein, which was normalized by β-actin.

Apoptosis analysis
Cell apoptosis was assayed by Annexin V and PI staining (AD10, Dojindo, Kumamoto, Japan). Cells were treated with different concentrations of MTE for 24 h, without or with MEK/ERK inhibitor U0126 (50 µM for A549, 20 µM for H1975). Then cells were collected and incubated with the buffer containing FITC-conjugated Annexin V and PI for 15 min at room temperature, and then analyzed by FACScan flow cytometry (Bection Dikinson, USA). Quantification of early apoptotic cells (Annexin V + /PI − cells) and late apoptotic cells (Annexin V + /PI + cells) was calculated by CellQuest software.

Caspase 3 activity assay
The activity of Caspase 3 was determined using a kit from Beyotime Institute of Biotechnology (C1116, Beijing, China). The activity of Caspase 3 was based on its ability to change acetyl-Asp-Glu-Val-Asp p-nitroanilide (Ac-DEVD-pNA) into a yellow formazan product (p-nitroaniline (pNA)). An increase in absorbance at 405 nm was used to quantify Caspase 3 activity. After 24 h exposure, cells with various designated treatments were collected and rinsed with cold PBS, and then lysed by lysis buffer (60 μL) for 15 min on ice, respectively. Cell lysates were centrifuged at 16,000×g for 15 min at 4 °C. The detail analysis procedure was described in the manufacturer's protocol. The Caspase 3 activity was shown as fold change of enzyme activity compared to control. All the experiments were carried out in triplicates.

Immunofluorescence, fluorescence, and confocal microscopy
Cells were seeded to cover glasses in 24-well plates and treated as indicated, fixed with 4% paraformaldehyde and permeabilized with 0.2% Triton X-100 (ST795, Beyotime). The cells were then blocked with 5% FBS for 1 h and exposed to anti-LC3-II (PM036, MBL, Nagoya, Japan) antibody overnight at 4 °C. After washing three times with PBS, cells were incubated with FITC-conjugated secondary antibody solution. After staining nuclei with DAPI, cells were observed under a confocal microscope (Leica, Welzler, Germany). For each group, the number of endogenous LC3-II puncta per cell was assessed in 100 cells, and statistical data were obtained from three independent experiments.
For LysoTracker staining, A549 or H1975 cells with stable expression of GFP-LC3 were cultured in confocal dishes and incubated for 90 min in complete RPMI-1640 medium supplemented with 500 nM LysoTracker Red.
The colocalization of LC3 and LysoTracker was analyzed by the confocal microscopy.

Acridine orange (AO) staining
Autophagy is a lysosomal degradation pathway for cytoplasmic material and organelles. The acidic intracellular compartments were visualized by supravital AO staining. After the treatment with MTE (0, 20, 40 mg/ml) for 6 h, cells were washed with PBS and stained with 1 μg/ ml AO (318337, Sigma-Aldrich) for 20 min at 37 °C. Subsequently, cells were analyzed under the confocal microscopy.

Statistical analysis
All experiments were repeated at least three times and values are expressed as the mean ± standard error of mean (SEM). Student's t-test was used to determine the difference between two independent groups. All data were analyzed using SPSS statistical software 16.0 (SPSS Inc., Chicago, IL, USA). P < 0.05 was considered statistically significant difference between values.

MTE suppressed lung cancer cell proliferation in vitro
MTE is a Chinese medicine used to treat lung cancer, gastric cancer and other cancers with a good therapeutic efficacy. Firstly, we examined the IC50 (half maximal inhibitory concentration) in A549 and H1975 using MTT assay. The results demonstrated that MTE significantly inhibited NSCLC cells proliferation in a dosedependent manner after 24, 48 and 72 h treatment. The IC50 values of A549 cells were 92.5 ± 4.3 mg/ml at 24 h, 69.0 ± 4.8 mg/ml at 48 h and 48.9 ± 5.1 mg/ml at 72 h, separately (Fig. 1a). The IC50 values of H1975 cells were 82.5 ± 4.9 mg/ml at 24 h, 56.3 ± 6.2 mg/ml at 48 h and 40.5 ± 3.0 mg/ml at 72 h, respectively (Fig. 1b). Such findings demonstrated that MTE can significantly suppress the growth of NSCLC cells.
Next, proteins associated with apoptosis were examined by Western blot. Treatment of MTE for 24 h decreased Caspase 3 zymogens expression (Fig. 2c) and increased Caspase 3 activities (Fig. 2d) in both cell lines. However, the cleaved PARP was raised in H1975 cells after MTE treatment, while only non-active PARP was reduced in A549 cells. In addition, mitochondrial associated apoptosis was involved in MTE-induced apoptosis, as evidenced by increased Bax and declined Bcl-2 protein expression (Additional file 1: Fig. S1A, B). These data indicated that cell apoptosis may contribute to cell growth suppression by MTE in NSCLC.

MTE treatment disrupted autophagic flux in NSCLC cells
Recent reports showed that cell apoptosis and autophagy are often affected by anticancer agents [22]. In order to investigate the effect of MTE on autophagy, firstly we monitored the classic autophagic marker LC3-II by Western blot. As shown in Fig. 3a, b, LC3-II increased in a dose-and time-dependent manner after MTE treatment in both A549 and H1975 cells, indicating MTE influenced the process of cell autophagy. MTE treatment also raised p62 protein level in a dose-and time-dependent manner (Fig. 3a, b). p62 is an adaptor protein that serves as a link between LC3 and ubiquitinated substrates; its increase suggested the substrate degradation was blocked and the autophagic flux was impaired after MTE treatment.
The autophagy flux after MTE treatment was further determined through the addition of autophagy inducer EBSS or inhibitor Baf A1. As shown in Fig. 3c (lane 6 vs lane 5), compared with EBSS alone, the co-treatment of MTE exerted an enhanced increase of LC3-II and p62 expression in A549 and H1975 cells, indicating MTE still suppressed cell autophagy even concurrent with EBSS treatment. Moreover, a synergistically impaired autophagy flux was observed with the combined treatment of MTE and Baf A1, which is an inhibitor to block the fusion of autophagosome with lysosome (Fig. 3c, lane 4 vs lane 3). In addition, we monitored LC3-II puncta in cells by immunofluorescence. In Fig. 3d, e, compared with control group, MTE treatment augmented the LC3-II puncta distribution in the presence or absence of EBSS or Baf A1, which is similar to the results of Western blot. These data suggested that the increased LC3-II and p62 in MTE treated NSCLC cells were due to suppression of autophagic flux in the late stage of autophagy.

MTE suppressed autophagy by affecting lysosomal function
In order to further investigate the influence of MTE on the late stage of autophagy, we focused on the lysosomal function, which is critical for the maturation of autophagosomes and the degradation of their contents. The intralysosomal pH plays an important role in affecting the Cathepsin enzymatic activity and lysosomal functions. So, firstly we performed AO staining to visualize acidic vesicles after MTE treatment. AO is a fluorescent The percentage of early and late apoptotic cells were quantified, respectively. Early-stage apoptotic cells in the figures were shown in red, and late-stage apoptotic cells were shown in blue. c The protein levels of Caspase 3 and PARP were determined by Western blot, and the expression ratio was counted. β-Actin was used as loading control. Data are one representative experiment performed in triplicate. d The Caspase 3 activities in MTE treated cells were detected. A549 cells were shown in red, and H1975 cells were shown in blue. *P < 0.05, **P < 0.01 vs control group weak base that fluoresced bright red when accumulating in acidic compartments such as autolysosome and lysosome, whereas fluoresced bright green in cytoplasm and nucleolus [23]. As shown in Fig. 4a, b, MTE treatment for 6 h resulted in visible and increased bright red vacuoles compared with control groups both in A549 and H1975 cells, suggesting MTE treatment significantly decreased intralysosomal pH. Next, we tested whether MTE can affect lysosomal function through detecting LAMP1 and activated Cathepsin B by Western Blot. LAMP1 is located in lysosomal membrane involving in lysosomal motility, and Cathepsin B is one of the most important proteases inside lysosome [24]. As shown in Fig. 4c, MTE treatment reduced LAMP1 and Cathepsin B protein expression with increased MTE concentration, confirming that MTE impaired lysosomal function.
Furthermore, we examined the fusion between autophagosome and lysosome by monitoring LysoTracker to detect its colocalization with the autophagosomal marker LC3 in GFP-LC3 stable cells. Presence of MTE in GFP-LC3 stable cells led to an increase of GFP-LC3/ LysoTracker colocalization compared to control cells The protein levels of LC3-II and p62 in treated cells were determined by Western blot, and the ratio of protein levels was counted. a Cells were treated with MTE at the concentration of 0, 10, 20, 40, 60 and 80 mg/ml, respectively. b Cells were treated with MTE at 0, 4, 8, 12 and 24 h, respectively. c Cells were pretreated with 40 mg/ml MTE for 24 h following by Baf A1 (5 nM) or EBSS for another 4 h. β-Actin was used as loading control. d Endogenous LC3 levels (green fluorescence) were visualized on confocal microscope by immunofluorescence staining. e LC3 dots in A549 and H1975 cells were counted. Data represents mean ± SEM of at least 100 cells scored. *P < 0.05 for compared two groups (Fig. 4d for A549 and e for H1975, group 2 vs. group 1 in d-f ). Such changes were similar to that cells treated with Baf A1 (Fig. 4d-f, group 4 vs. group 3). In EBSS treated A549 cells, GFP-LC3 punctation in MTE group was a little more colocalized with LysoTracker compare to control group, while no obvious change in H1975 cells (Fig. 4d-f, group 6 vs. group 5) was observed. Taken together, these results indicated that MTE impaired the fusion of autophagosome and lysosome, further confirming that MTE inhibits autophagy at the late stage in A549 and H1975 cells.

ERK activation is required for apoptosis and autophagy regulation by MTE treatment
It has been reported that both autophagy and apoptosis are regulated by the MEK/ERK pathway [25,26]. Thus, we examined whether this pathway accounts for MTE caused cell apoptosis induction and autophagy inhibition. Western blot results in Fig. 5a, b (left panel) showed that MTE treatment upregulated phosphorylated ERK with a dose-dependent manner in both NSCLC cells. The role of ERK was further determined by using MEK/ERK inhibitor U0126, and we found that the activation of ERK by MTE was obviously attenuated via co-treatment with U0126 in A549 and H1975 cells (Fig. 5a, b, right panel). The above results showed that MTE activated MEK/ERK signaling in NSCLC.
Next, we examined the influence of ERK on autophagy associated molecules by MTE treatment. As shown in Fig. 5c, d, pre-treatment with U0126 deteriorated the autophagy inhibition caused by MTE, resulted in autophagy induction with increased LC3-II and decreased p62 in both cells. The function of lysosomes was damaged by MTE with down-regulated expression of LAMP1 and Cathepsin B. However, in presence of U0126, LAMP1 and Cathepsin B were upregulated, implying partly recovery of lysosomal function (Fig. 5c, d). These results demonstrated that MTE-caused autophagy inhibition was reversed by U0126, suggesting that MEK/ERK pathway contributed to the autophagy inhibition caused by MTE.
Finally, we evaluated the association between MTEinduced apoptosis and MEK/ERK pathway; and found cell apoptosis, especially late-stage apoptosis was significantly decreased in the presence of U0126. As shown in Fig. 6a-d, MTE-caused late apoptotic A549 cells ranged from 4.5 ± 0.7% to 12.7 ± 1.6% in U0126 pretreated group, while it was 3.3 ± 0.5% to 18.6 ± 0.8% in no U0126 group. The ratio of MTE-induced late apoptotic H1975 cells was from 4.7 ± 0.4% to 13.5 ± 1.3% in U0126 pretreated group, compared with 2.5 ± 0.5% to 17.7 ± 2.1% in no U0126 group. There is no obvious difference for the early apoptotic cells in each group. The results suggested that MTE-induced apoptosis was dramatically attenuated by U0126. As shown in Fig. 6e, f, MTE treatment relieved the protein levels of zymogens of Caspase 3 in U0126 group compared with no U0126 group in both cell lines. Caspase 3 activity induced by MTE was decreased significantly after pretreated with U0126 (Fig. 6g, h). These data indicated that ERK activation is required for MTEinduced apoptosis.

Discussion
Studies showed C21 steroidal glycosides are major components in M. tenacissima. Compounds such as Tenacigenoside A [13], 11α-O-benzoyl-12β-O-acetyl tenacigenin B [27], tenacissoside C, tenacissoside B, tenacissoside C, Tenacissoside I and marsdenoside K [28] etc. are demonstrated to possess anti-cancer activity. According to our previous HPLC-MS analysis, 13 compounds including most of compounds mentioned above were identified from MTE by HPLC-MS analysis [12]. In recent years, M. tenacissima has attracted extensive interest in cancer research area with multiple effects, such as inhibiting tumor growth and angiogenesis, reversing anti-tumor drug resistance [12,14,17]. However, the reasons why MTE treatment resulted in the inhibition on cancer cell growth still remain largely unknown. In the present study, we found MTE significantly induced cell apoptosis, suppressed cell autophagy through impairing lysosomes function in A549 and H1975 NSCLC cells. Our results also indicated that ERK may mediate autophagy inhibition and apoptosis induction effect of MTE in NSCLC cells.
Programmed forms of cell death pathway at least include apoptosis and autophagy. Apoptosis is a physiological process to eliminate damaged, mutant or aged cells to maintain cellular homeostasis in normal tissue [29]. The inhibition of apoptosis is regarded as one of the hallmarks of cancer [3], and apoptosis-inducing has been exploited as an indispensable anticancer therapeutic strategy. Approaches targeting apoptotic pathway can result in cancer cell death, increasing sensitivity to current treatments or reversing drug resistance, thus may bring promising clinical benefits. Till now, different apoptosis targeted therapies have entered clinical trials for efficacy evaluation in various tumor types including lung cancer [30]. In the present study, MTE induced significant cell apoptosis in both A549 and H1975 cells along with Caspase 3 activation. Although cleaved PARP was not observed in A549 cells with MTE treatment, remarkable apoptotic cells presented after stained with Annexin V-FITC for flow cytometry analysis. The above results indicated that apoptosis-inducing may contribute to the cell death caused by MTE treatment.
Autophagy has complicated functions on cell death, as it may promote or inhibit cell death under certain circumstances. Although autophagy may offer tumor suppressive function in some conditions [31], it is mainly a cytoprotective process to facilitate cancer cells survive under stressful environments [32]. Studies showed that autophagy suppression in NSCLC cells resulted in cell proliferation suppression [33] and cell apoptosis increase [34]. In addition, constitutive activation of autophagy is also associated with anti-cancer therapeutic resistance [35], and inhibiting autophagy may overcome drug resistance in tumors [36]. Therefore, targeting autophagy is considered as a potential therapeutic strategy for cancer treatment.
LC3-II and p62 serve as marker of autophagic flux. The level of p62 increased when autophagy inhibition occurred; and decreased when autophagy is induced. In our study, MTE treatment caused accumulation of both LC3-II and p62, which means the substrate degradation was blocked and autophagic flux was impaired. The autophagy inhibitory effect of MTE was further confirmed by adding autophagy inhibitor Baf A1 and autophagy inducer EBSS. Next, GFP-LC3 stable NSCLC cells labeled with LysoTracker showed MTE suppressed the fusion of lysosomes with autophagosome at the late stage. This effect was further confirmed by detecting lysosomal marker LAMP1 and lysosomal protease Cathepsin B, indicating MTE impaired lysosomal function. Consistent with our results, other study demonstrated that inhibition of the fusion between lysosomes and autophagosomes leading to accumulated LC3-II and increased acidic vacuolar compartment [37]. Our results demonstrated that MTE can target both apoptosis and autophagy leading to NSCLC cells death. In fact, the molecular connections exist between apoptosis and autophagy, . e, f The protein level of Caspase 3 in treated cells was detected by Western blot, and the expression ratio was counted. Cells were treated with 20, 40, 80 mg/ml MTE for 24 h. g, h The caspase 3 activities in treated cells were detected. *P < 0.05, **P < 0.01, ***P < 0.005; Groups with U0126 vs groups without U0126 and some regulators are shared to maintain a subtle and complicated balance with each other [38][39][40]. ERK is a crucial molecule to control diverse cell responses including proliferation, migration, and differentiation [25]. High levels of ERK has been found in many malignant tumors, but ERK activation is not always correlated with cell survival protection, it can also interplay with cell death including apoptosis, autophagy, and senescence [25,26]. Growing evidence demonstrated that activated ERK has positive contribution to cancer treatment, such as induced cell apoptosis and cell death [41,42]. BPIQ, a synthetic quinoline analog, upregulated ERK phosphorylation leading to H1299 cell death, and this can be abrogated by ERK inhibitor [43]. In consistent with other studies, our results demonstrated that ERK activation plays important roles in drug-induced cancer cells apoptotic death.
Accumulated evidence demonstrated activated ERK is also involved in autophagic cell death [26]. 8-CEPQ, a novel quercetin derivative, inhibited colon cancer cell growth by inducing autophagic cell death through ERK activation [44]. Tan IIA induces autophagic cell death via activation of AMPK and ERK in KBM-5 cells, and ERK inhibitor PD184352 suppressed LC3-II expression induced by Tan IIA [45]. Rhuscoriaria induced autophagic cell death through p38 and ERK1/2 activation in breast cancer cells [46]. All the above studies link ERK activation with autophagic cell death. In our study, MEK/ERK inhibitor U0126 effectively abrogated the impaired autophagy flux caused by MTE. Taken together, our results revealed the effect of MTE on cell apoptosis-induction and autophagy-inhibition can partly ascribe to ERK activation.

Conclusion
A Chinese herb preparation, MTE, induced significant cell apoptosis and impaired the fusion of lysosomes with autophagosome in NSCLC cells A549 and H1975. The molecule ERK, which links the crosstalk between apoptosis and autophagy, partly accounts for the underlying mechanisms of cell death caused by MTE. As cancer has complex networks of signaling pathways, thus multiple targeting autophagy and apoptosis by Chinese medicine may shed some light on the way for NSCLC cancer treatment.


extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy: Biology Medicine

text:
Using CRISPR/Cas9 to Knock out Amylase in Acinar Cells Decreases Pancreatitis-Induced Autophagy

Pancreatic cancer is a malignant neoplasm that originates from acinar cells. Acinar cells get reprogrammed to become duct cells, resulting in pancreatic cancer. Pancreatitis is an acinar cell inflammation, leading to “impaired autophagy flux”. Pancreatitis promotes acinar-to-ductal transdifferentiation. Expression of amylase gets eliminated during the progression of pancreatic cancer. Amylase is considered as an acinar cell marker; however, its function in cells is not known. Thus, we investigated whether amylase affects the acinar cell autophagy and whether it plays any role in development of pancreatitis. Here, we knocked out ATG12 in a pancreatic cancer cells and acinar cells using CRISPR/Cas9. Autophagy inhibition led to an increase in the expression of duct cell markers and a simultaneous decrease in that of acinar cell markers. It also caused an increase in cell viability and changes in mitochondrial morphology. Next, we knocked out amylase in acinar cells. Amylase deficiency decreased autophagy induced by pancreatitis. Our results suggest that amylase controls pancreatitis-induced autophagy. We found that eliminating amylase expression contributes to pancreatic cancer etiology by decreasing autophagy. Furthermore, our results indicate that amylase plays a role in selective pancreatitis-induced autophagy of pancreatic enzyme vesicles.

Introduction
The pancreas is primarily composed of acinar cells, which are parenchymal cells, but is also made up of other cells such as duct cells. Acinar cells produce digestive enzymes, including pancreatic alpha amylase (AMY2) and trypsin. Pancreatic cancer (pancreatic ductal adenocarcinoma) is the most frequent type of malignant pancreatic neoplasm [1,2]. Morphologically, pancreatic cancer exhibits distinctive features of duct cells. Thus, scientists previously hypothesized that pancreatic cancer originates from duct cells. However, pancreatic cancer was found to be an acinar cell-derived malignant neoplasm [3]. Acinar cells undergo reprogramming known as "acinar-to-ductal metaplasia" because of inflammation caused by pancreatitis and genetic mutations [3][4][5][6][7][8]. During this reprogramming process, acinar cells lose their acinar cell phenotype and acquire a duct cell phenotype. Reprogrammed acinar cells develop into pancreatic cancer. These processes are important in pancreatic cancer etiology, with pancreatitis contributing to the first step of this process.
Pancreatitis is a risk factor for pancreatic cancer in humans and is mainly characterized by acinar cell inflammation [1,9]. Acinar cell inflammation is activated by trypsinogen inside of cells [10,11]. Trypsinogen activation occurs via "impaired autophagy flux", which causes a disease state similar to pancreatitis in mice [12][13][14]. Furthermore, autophagy deletion increases acinar-to-ductal metaplasia in mice [15,16]. Autophagy leads to the development of pancreatitis and contributes to pancreatic cancer development via acinar-toductal metaplasia.
During autophagy, cells degrade and recycle long-lived proteins and dysfunctional mitochondria [17]. A reduction in autophagy leads to accumulation of dysfunctional mitochondria and alters cellular metabolism. In humans, an increase in autophagy corresponds to the poor prognosis of pancreatic cancer patients [18]. However, there is no evidence that targeting autophagy is effective for pancreatic cancer treatment. Previously, two major studies employed knockdown of autophagy in a human cell line and autophagy knockout in a genetically engineered mouse model (GEMM). Autophagy knockdown in the human cell line suppressed the progression of pancreatic cancer [19]. Thus, an autophagy-inhibiting agent as a drug for pancreatic cancer treatment was clinically tested [20]. However, autophagy knockout in the GEMM was associated with increased tumor-related death [16]. To reevaluate the relationship between autophagy and pancreatic cancer, we used the CRISPR/Cas9 system [21,22] to knock out autophagy in a human pancreatic cancer cell line.
Next, we focused on loss of the acinar cell phenotype in the progression from pancreatitis to pancreatic cancer, specifically the loss of amylase expression. Pancreatic alpha amylase (AMY2) is an acinar cell marker and diagnostic marker for pancreatitis [23][24][25]. Pancreatic alpha amylase is a pancreatic enzyme produced only in acinar cells. The substrate of pancreatic alpha amylase is starch derived from plants, but its intracellular function is unknown. During reprogramming of acinar cells into duct cells in pancreatic cancer, pancreatic alpha amylase expression is lost. We hypothesized that the loss of pancreatic alpha amylase expression not only is a marker of loss of the acinar phenotype, but also plays a role in reprogramming these cells, particularly for autophagy. For immunoblotting, antibodies against amylase (sc-12821; Santa Cruz Biotechnology, Santa Cruz, CA, USA), LC3 (sc-271625; Santa Cruz Biotechnology), ATG12 (#2010; Cell Signaling Technology), and -actin (013-24553; Wako Pure Chemical, Osaka, Japan) were used. Anti-mouse (sc-2031; Santa Cruz Biotechnology) and anti-goat (sc-2020; Santa Cruz Biotechnology) peroxidase-conjugated immunoglobulin were used as secondary antibodies. For immunofluorescence, an anti-LC3 monoclonal antibody (clone: #-1703, CTB-LC#-2-IC; Cosmo Bio, Tokyo, Japan), anti-tom20 (D8T4N; Cell Signaling Technology, Danvers, MA, USA), and Alexa 488-conjugated goat anti-mouse IgG (A11029; Thermo Fisher, Waltham, MA, USA) were used.
MIA PaCa-2 and AR42J were transfected with pX458 with the above gRNAs inserted using Lipofectamine 2000 (Thermo Fisher). After 48 h, green fluorescent protein positive cells were isolated using a cell sorter (BD FACSARIA 3; BD Biosciences, Franklin Lakes, NJ, USA) and single clones were obtained. Clones with mutations in both alleles were identified by immunoblotting and confirmed by sequencing of genomic DNA.

Quantitative
Reverse Transcription PCR. mRNA was extracted using Rneasy Mini Kit (Qiagen, Hilden, Germany). cDNA was produced using PrimeScript RT Master Mix (Takara, Shiga, Japan). The reaction conditions were 50 min at 50 ∘ C, followed by 5 min at 85 ∘ C. Quantitative analysis of expression was conducted using SYBR Premix Ex Taq (Takara, Shiga, Japan). The reaction conditions were 40 cycles for 15 s at 95 ∘ C and 1 min at 60 ∘ C. Expression was quantified by the ΔΔCT method using a 7500 fast (Applied Biosystems, Foster City, CA, USA). The primers used were human gapdh-ACATGTTCCAATATGATTCCA and TGGACTCCACGACGTACTCAG, human CK19-GCA-GGTCCGAGGTTACTGAC and CCAGTGTGTCTTCCA-AGGCA, rat gapdh-GTTACCAGGGCTGCCTTCTC and GGGTTTCCCGTTGATGACC, rat PTF1-CAGGTAACC-AGGCCCAGAAG and TTTCATCAGCCCAGGAAAGG, and rat amylase-GCAACCAAGTGGCTTTTAGC and CAGTATGTGCCAGCAGGAAG.

Immunofluorescence
Staining. Cells were fixed in 4% paraformaldehyde, washed three times with phosphatebuffered saline (PBS) and 50 g/mL digitonin/PBS for 5 min at room temperature, and blocked with 3% bovine serum albumin/PBS. Cells were then incubated for 1 h with a primary antibody at room temperature, followed by incubation for 1 h with a secondary antibody conjugated to green fluorescent protein at room temperature. Images were acquired using a Zeiss LSM 700 confocal microscope (Carl Zeiss, Oberkochen, Germany) using the ×63 objective. The pinhole size was 1 AU.

Cell Viability Assay.
Cells were incubated with 10% ala-marBlue for 1 h at 37 ∘ C in the presence of 5% CO 2 . After each stimulation, the same well was incubated again in medium containing 10% alamarBlue for 1 hr. Fluorescence was measured at an excitation/emission wavelength of 570/590 nm. "Δ fluorescent intensity" is defined as basal fluorescent intensity minus fluorescent intensity after stimulation. "Fluorescent intensity change (%)" is defined as fluorescent intensity after stimulation divided basal fluorescent intensity.

Statistical
Analysis. An unpaired two-tailed -test was conducted. Variance was confirmed to be roughly equal in both groups by an test. Statistical processing was conducted using GraphPad Prism 7 software (GraphPad, Inc., Chicago, IL, USA).

Autophagy Deletion Pancreatic Cancer Cells.
To clarify the role of autophagy in pancreatic cancer, we established an autophagy deletion MIA PaCa-2 cell line. Autophagy consists of a conjugation system that requires LC3 (ATG8) and ATG12 [27,28]. We edited exon 1 of ATG12 in MIA PaCa-2 cells using CRISPR/Cas9. The edited genome sequence is shown in Figure 1(a). The western blotting results for the ATG5-ATG12 complex are shown in Supplemental Figure  1A. ATG12-deficient MIA PaCa-2 cells were subjected to LC3 western blotting and immunofluorescence staining to confirm autophagy deficiency. MIA PaCa-2 cells have increased LC3-II, an autophagy marker under normal conditions [19]. ATG12-deficient cells exhibited no conversion from LC3-I to LC3-II (Figure 1(b)). In addition, during starvation, a state that generally induces autophagy, no conversion to LC3-II was observed (Figure 1(b)). LC3 was stained diffusely and LC3 puncta decreased in ATG12-deficient cells (Supp. Figure  1B). Next, to evaluate the influence of autophagy deletion on inducing the duct phenotype, we measured the mRNA expression of the duct cell marker CK19 (KRT19). CK19 mRNA expression in autophagy-deleted pancreatic cancer cells was increased compared to wild-type CK19 mRNA expression (Figure 1(c)). This suggests that autophagy deletion strengthened the duct phenotype of pancreatic cancer. Next, we evaluated whether autophagy deletion influences pancreatic cancer cell viability using alamarBlue. Autophagy deletion increased cell viability during starvation, which is a common stimulus that induces autophagy (Figure 1(d)). Next, we investigated the relationship between autophagy deletion and the mitochondria. Autophagy suppresses cellular metabolism by degrading uncoupled mitochondria during mitophagy. Administration of the mitochondria uncoupler CCCP increased cell viability (Figure 1(e)). alamarBlue staining, which reveals mitochondrial metabolism, suggested a deficiency in the degradation of mitochondria. To evaluate morphological changes in the mitochondria, we performed immunostaining of TOM20, which is present inside the mitochondrial inner membrane. MIA PaCa-2 cell mitochondria were localized in the perinuclear region and fused (Figure 1(g)). CCCP administration resulted in fission of the mitochondria in wild-type pancreatic cancer cells and their subsequent diffusion throughout the cytoplasm. However, in autophagy deletion pancreatic cancer cells, mitochondria remained localized in the perinuclear region and were generally fused (Figure 1(g)). This suggests that autophagy in pancreatic cancer cells is necessary for morphological changes in uncoupled mitochondria. Next, to investigate the differences in responsiveness to anticancer agents following autophagy deletion, a common anti-pancreatic cancer drug, gemcitabine, was administered. Cell viability increased in autophagy-deleted pancreatic cancer cells (Figure 1(f)). Additionally, wild-type mitochondria exhibited fission and diffusion throughout the cytoplasm. However, we observed no morphological changes in the mitochondria due to gemcitabine in autophagy-deleted cells (Figure 1(g)). Pancreatic cancer showed increased cell viability after gemcitabine treatment because of autophagy deletion and the altered mitochondria morphology. This suggests that the response of pancreatic cancer to gemcitabine involves mitochondrial uncoupling and that mitophagy occurs during this process. It also suggests that the response of pancreatic cancer to gemcitabine requires the involvement of mitophagy.

Autophagy Deletion Acinar
Cells. Next, we used AR42J cells to investigate the relationship between the acinar phenotype and autophagy. AR42J is the only cell line that produces pancreatic enzyme proteins and possesses an acinar cell phenotype. We knocked out Atg12 in AR42J cells using CRISPR/Cas9 (Figure 2(a)). Western blotting and immunostaining of LC3 were performed to demonstrate that Atg12-deficient AR42J was deficient in autophagy (Supp. Figures 2A, and 2B). Accumulation of LC3-II due to starvation, a general autophagy-induced stimulus, was decreased in Atg12-deficient cells. During immunostaining, LC3 also remained diffuse, and LC3 puncta decreased due to starvation. Autophagy deletion decreased the mRNA expression of Ptf1, an acinar cell marker [29], compared to the expression in wild-type cells (Figure 2(b)). Loss of Ptf1 causes the loss of acinar cells in vivo [30]. We evaluated amylase, a pancreatic enzyme and acinar cell phenotype marker, in the same manner. Amylase mRNA expression was decreased in autophagy deletion cells compared to that in wild-type cells (Figure 2(c)). Western blotting revealed decreased amylase protein levels (Figure 2(d)), suggesting that autophagy deletion weakens the acinar cell phenotype. The results in AR42J and MIA PaCa-2 cells suggest that autophagy deletion is necessary to strengthen the duct phenotype in pancreatic cancer cells and decrease the acinar phenotype in acinar cells.

Amylase Deletion Acinar Cells.
Next, to investigate the functional role of amylase, we knocked out amylase (Figures  3(a) and 3(b)). Amylase is an acinar cell marker; however, its functional role in cells is unknown. We established 4 independent amylase deletion clones. In normal culture, amylase deficiency did not lead to an increase in LC3-II, an autophagy marker (Figure 3(b)). LC3-I levels varied before being converted to LC3-II, but LC3-I is not an autophagy marker. On the other hand, amylase-deficient cells exhibited autophagy induction equivalent to that in wild-type cells during starvation (data not shown). Next, we observed an increase in LC3-II, an autophagy marker, in wildtype AR42J under starvation conditions as well as following treatment with rapamycin, a common autophagy-inducing agent (Figure 3(c)). Cerulein is the most common drug used to experimentally induce pancreatitis and induced autophagy in wild-type AR42J cells (Figure 3(c)). Cerulein-induced autophagy in wild-type AR42J is consistent with observations in a previous study [31].

Inducing Autophagy in Amylase Deletion Acinar Cells.
Next, we investigated whether amylase deletion influences the increase in autophagy caused by rapamycin and cerulein. We investigated the effect of rapamycin, a common autophagy-inducing agent. Compared to the wild-type, the Amy2-deficient clone showed decreased levels of LC3-II (Figure 4(a)). Next, we evaluated autophagy by morphological analyses. We examined LC3 by confocal microscopy. We evaluated autophagy by assessing LC3 puncta. Compared to the wild-type, the amylase deletion clone showed a decrease in LC3 puncta (Figure 4(b), Supp. Figure 3C). These findings indicate that amylase increases rapamycin-induced autophagy. Next, we investigated the effect of cerulein, a pancreatitis-inducing agent. We observed less LC3-II in Amy2-deficient clones than in wild-type clones (Figure 4(c)).
Finally, we evaluated whether attenuation of autophagy by Amy2 deficiency affects cell function. Cell viability was measured using alamarBlue (Supp. Figures 3A, and 3B). Cell viability, following rapamycin and cerulean exposure, was lower with respect to amylase deficiency than that in wild-type cells. Based on these findings, amylase deficiency attenuates cerulein-induced autophagy and decreases cell viability.

Conclusions
In this study, we found that autophagy deletion in a pancreatic cancer cell line increased the duct phenotype in pancreatic cancer cells and decreased the acinar phenotype in acinar cells; morphological changes in the mitochondria were also observed. This is consistent with previous studies of autophagy deletion using a GEMM. To further elucidate the influence of autophagy deletion on pancreatic cancer cells in vivo, xenograft experiments and assessments of mitochondrial metabolism need to be performed. Cell line knockdown experiments showed that autophagy-inhibiting agents are useful for pancreatic cancer treatment, and clinical trials are being conducted. However, in studies using GEMM knockouts, autophagy-inhibiting agents increased the rate of tumor-related death. Our results using cell line autophagy knockouts support the efficacy of therapeutic drugs targeting pancreatic cancer mitophagy. A limitation of this study is that because the CRISPR/Cas9 system was used, off-target effects may have occurred. Although we created 4 clones of amylase-deficient cells, only a single ATG12-deficient cell clone could be produced. At least 2 clones are desirable for confirmation purposes. Additionally, expression of CK19 was not observed in Atg12deficient AR42J cells. This was because of a decrease in the acinar phenotype marker, but we could not confirm that a shift to the duct phenotype had occurred.
We showed that amylase is necessary for the induction of autophagy by pancreatitis in acinar cells. Autophagy of organelles such as the mitochondria and the endoplasmic reticulum occurs in a selective manner, known as organellophagy. In the most well-known organellophagy, mitophagy, the mitochondrial protein PINK is important for selectivity [32]. In pancreatitis, selective autophagy occurs in pancreatic enzyme vesicles [31]. However, the specific proteins among those present in the enzyme vesicles, involved in pancreatitisinduced autophagy are not clearly known. As amylase is the most abundant protein in pancreatic enzyme vesicles, our results suggest the possibility that amylase selectively contributes to the pancreatitis-induced autophagy of pancreatic enzyme vesicles.
Amylase expression is eliminated during pancreatic cancer development. We found that autophagy in acinar cells is controlled by amylase, suggesting that loss of amylase expression contributes to pancreatic cancer development through autophagy.

Conflicts of Interest
There are no conflicts of interest.


extfieldsofstudy: Biology Medicine

text:
ANALYSIS OF AN HIV INFECTION MODEL INCORPORATING LATENCY AGE AND INFECTION AGE

. There is a growing interest to understand impacts of latent infection age and infection age on viral infection dynamics by using ordinary and partial diﬀerential equations. On one hand, activation of latently infected cells needs speciﬁcity antigen, and latently infected CD4+ T cells are often het- erogeneous, which depends on how frequently they encountered antigens, how much time they need to be preferentially activated and quickly removed from the reservoir. On the other hand, infection age plays an important role in modeling the death rate and virus production rate of infected cells. By rigorous analysis for the model, this paper is devoted to the global dynamics of an HIV infection model subject to latency age and infection age from theoretical point of view, where the model formulation, basic reproduction number computation, and rigorous mathematical analysis, such as relative compactness and persis- tence of the solution semiﬂow, and existence of a global attractor are involved. By constructing Lyapunov functions, the global dynamics of a threshold type is established. The method developed here is applicable to broader contexts of investigating viral infection subject to age structure.

1.
Introduction. Determining the threshold dynamics of infection-free and infection equilibrium in viral infection model has made great progress in the last decades [1,2,4,8,9,10,16,17,24,31,36,32,33,34,35,40,44]. A key insight in this progress is that if threshold value (named, the basic reproduction number) is less than one then the infection-free equilibrium is globally asymptotically stable otherwise the endemic equilibrium attracts all solutions (is globally asymptotically stable) whenever it exists. One method adopted here is due to the classical Volterra type Lyapunov function, which was discovered by Volterra [30]. These confirmed global stability properties of steady states for within host virus model establish our understanding the virus dynamical behaviors, that is, whether the viruses die out or not.
Even large discrete and continuous delay differential equations of viral infection models have been successfully treated by Volterra type Lyapunov function, (i) nonlinear incidence rate functions [8,24,31]; (ii) discrete delays [10,16,35] and finite distributed delays [17,31,36], and infinite distributed delays [8,24,34]; (iii) immune responses [24,35,44,39,41]; and (iv) additional infection processes [31]. It is still a hot topic in in-host model to determine how these factors affect the virus dynamical behaviors. We also refer the reader to see these citations for more references.
Recently, age-structured viral infection model has attracted much attentions of researchers. HIV latency remains a major obstacle to viral elimination. Although HIV-1 replication can be controlled by antiretroviral therapy in suppress the plasma viral load to below the detection limit, the time spent in this progress may last half life of months or years [21]. Virus persisting in reservoirs, such as latency infected CD4+ T cells, may the reason that long-term low viral load persistence in patients on antiretroviral therapy and keeping the virus from being eliminated. These latency infected CD4+ T cells are not affected by immune responses but can produce virus once activated by relevant antigens.
Some recent studies reveals the decay dynamics of the latent reservoir. For example, a model has been developed by Muller et al. [11] to describe the heterogeneity of latent cell activation. An ordinary differential equations (ODEs) model has been studied by Kim and Perelson [7] to include decreasing activation of latently infected cells. Activation of these latently infected cells needs specificity antigen. A recent study by Strain et al. [22] reveals that the dynamics of latently infected CD4+ T cells are often heterogeneous. They argued that cells specific to frequently encountered antigens are activated soon while cells specific to rare antigens need more time to be activated. Thus, the activation rate depends on the time spent since the cell is latently infected (that is the time elapsed since the establishment of latency), which we refer as latency age for short. A recent paper by Alshorman et al. [1] introduced a latency age model to mathematically analyze the dynamics of the latent reservoir under combination therapy. They give an affirmed answers that the long-term activation rate of latently infected cells plays an important role in determining the dynamics.
Taking into account the picture that the mortality rate and viral production rate of infected cells may depend on the infection age of cells, Nelson et al. [15], Huang et al. [4] and Wang et al. [37] have studied age-structured model of HIV infection by considering age to be a continuous variable rather than be constant in ODEs models. These assumptions lead to a hybrid system of ODEs and partial differential equations (PDEs) formulation and allow us to have a good understanding on productively infected cells. Together with the infinite-dimensional nature of system, this formulation creates some mathematical difficulties in establishing the existence of a global compact attractor, even in other epidemic models (see some relevant references for our discussion on age-structured models, [6,43,42,25,27,26,14]).
Denote by T (t), e(a, t), i(a, t), V (t) the concentration of uninfected CD4+ T cells at time t, the concentration of latently infected T cells with latency age a at time t, the concentration of productively infected cells, and the concentration of virions in plasma at t, respectively. The parameter h is the production rate of uninfected CD4+ T cells, d is the per capita death rate of uninfected cells, and β is the infection rate of the target cell by virus. c is the viral clearance rate.
The following assumptions are a compromise between generality and simplicity.
There is a small fraction (f ) of infected cells lead to latency and that the remaining become productively infected cells [1,40]. (ii) When latently infected cells are activated to become productively infected cells, an age-dependent remove rate −θ 1 (a) is used to illustrate the decreasing effect of the pool size of latent reservoir. The integral term ∞ 0 ξ(a)e(a, t)da describes he total number of productively infected cells gained per unit time from the activation of latently infected cells, where ξ(a) denotes the activation AGE-STRUCTURED HIV MODEL 571 rate of latently infected T cells with latency age a. Biologically, we omit the proliferation rate [25] and the death rate of latently infected cells, which is assumed to be included in the removal rate. (iii) We assume that production rate of viral particles p(b) and the death rate of productively infected cells θ 2 (b) wiht infection age b are two continuous functions of age (the time passed since infection), see, e.g., [15,4,32].
Biologically, (i) of Assumption 1.1 comes from the evidences that a very small fraction of CD4+ T cell infection leads to HIV latency. They don't produce new virus unless activated by antigens, please see [18,19,20]. (ii) of Assumption 1.1 based on the fact the latently infected cell population is very likely to be heterogeneous [22,23]. Cells specific to frequently encountered antigens may be preferentially activated and quickly removed from the reservoir. It may depends on the time elapsed since latent infection and affect the activation rate, that is the reason why we are interested in the latent infection age. (iii) of Assumption 1.1 it is known that viral proteins and unspliced viral RNA accumulate within the cytoplasm of an infected cell, and thus, they actually ramps up [3,12,29]. Therefore, infection age should be incorporated into the model.
In this paper, we introduce the following HIV infection model with latency and infection age, with boundary and initial conditions is the set of all integrable nonnegative functions on R + := [0, ∞). Mathematically, for the ease of simplicity, we make the following assumptions.
Our goal of the present paper is to adopt previous model in [32,33] by incorporating the latency age for infected cells as discussed in [1], and to study the threshold dynamics of infection-free and infection equilibrium in viral infection model subject to latently age and infection age. We will show the existence of a compact attractor of all compact sets of nonnegative initial data and use the Lyapunov function to show that this attractor is the singleton set containing the endemic equilibrium. Roughly speaking, if the basic reproduction number is less than one then the infection-free equilibrium is globally asymptotically stable otherwise the endemic equilibrium attracts all solutions with active infection at some time.
The remaining part of this paper is organized as follows. In Section 2, we present some preliminary results including model formulation (equivalent integrated semigroup formulation and Volterra formulation), properties of solutions and existence of equilibria. Then we show the asymptotic smoothness of Φ(t, X 0 ) of orbits in Section 3, where we arrive at a key result on the existence of global compact attractor. In section 4, we prove that system (1) is the uniformly persistent. The Section 5 is devoted to local stability analysis of the infection-free equilibrium and the infection equilibrium. Then we establish their global attractivity in Section 6 by constructing Lyapunov functions.
2.1. Integrated semigroup formulation. Following the line of [27], we reformulate the model (1) as a semilinear Cauchy problem. Taking into account the boundary conditions, we consider the following state space, , R) × R + , endowed with the usual product norm, and set We consider the linear operator A : where W 1,1 is a Sobolev space. Note that Dom(A) = X 0 is not dense in X . Define nonlinear operator F : Dom(A) → X by where Then by setting u(t) = T (t), 0 e(·, t) , , we can reformulate system (1) with the boundary and initial conditions as the following abstract Cauchy problem If any initial value X 0 = (T 0 , e 0 (·), i 0 (·), V 0 ) ∈ Y satisfies the coupling equations then (1) is well-posed under Assumption 1.2 due to Iannelli [6] and Magal [14]. Denote In fact, for such solutions, it is not difficult to show that (T (t), e(·, t), i(·, t), V (t)) ∈ Y for each t ≥ 0. In the sequel, we always assume that the initial values satisfy the coupling equations.

Volterra formulation.
According to the Volterra formulation (see Webb [42] and Iannelli [6]), integrating the second and third equations of (1) along the characteristic lines t − a = const. and t − b = const. respectively yields and Thus system (1) can be rewritten as the following Volterra-type equations,

Boundedness of solutions.
Proposition 2. Define Moreover, Φ is point dissipative and Ξ attracts all points in Y.
Proof. By (4) and changes of variables, we have We derivative this equality, By (2) and changing of variables, we have By the first equation in (1), (iv) of Assumption 1.2, and use f < 1, We integrate this differential inequality and obtain the a priori estimate, This implies that We add the two equations, and since (ii) and (iv) of Assumption 1.2, obtain the estimates We integrate this differential inequality and obtain the a priori estimate, It follows (iv) of Assumption 1.2, we have that

JINLIANG WANG AND XIU DONG
Consequently, from (6), (7) and (8), we conclude that if X 0 ∈ Ξ, then for t ≥ 0, In summary, we have shown that Ξ is positively invariant with respect to Φ. Lastly, it follows from (9) that lim sup for any X 0 ∈ Y, that is, Φ is point dissipative and Ξ attracts all points in Y. This completes the proof.
As a consequence of Proposition 2, we have the following result. (1) always has an infection-free equilibrium The equations for an equilibrium are obtained from (1) by setting the time derivatives equal to 0 with boundary conditions, that is, infection equilibrium P * = (T * , e * (·), i * (·), V * ) ∈ Y of (1) satisfies Denote Biologically, K is the total number of infected cells activated by latency infected cells. J accounts for the total number of virus particles produced by an infected cell during its life-span, i.e., the burst size. We define basic reproduction number, 0 of (1) as which accounts for the total number of virons resulted from a single viron through the virus-to-cell infection mod. 1−f is the fraction of productive infection that leads to viral production, and f K represents the contribution to productively infected cells from activation of latently infected cells. 0 will serves as threshold value for (1), which completely determines the global behaviors of equilibria of (1). Direct calculation yields that if 0 > 1, then (1) admits a unique infection In summary, we have shown the following result. (1) always has an infection-free equilibrium P 0 .
(ii) If 0 > 1, then (1) admits a unique infection equilibrium P * , which is defined by (12). 3. Asymptotic smoothness of Φ(t, X 0 ) . By Proposition 2 and 3, the semiflow is point-dissipative and Φ(R + × B) is bounded for every bounded subset B of Y. By Theorem 3.4.6 in [5], the semiflow has a compact attractor of bounded sets if it is asymptotically smooth. To give the existence of compact attractor, we follow the approach in [43, Theorem 4.2 of Chapter IV].
Recall that M (t) and N (t) are defined by ( Proof. Let t ≥ 0 and h > 0. We can check that By applying ξ(a) ≤ξ, e(0, t) ≤ f βA 2 and Ω(a) ≤ 1 for the first integral, and making the substitution σ = a − h for the second integral to (13), we get

JINLIANG WANG AND XIU DONG
Thus, From (iii) of Assumption 1.2, we obtain the following estimate,
has a compact closure in Y if the following two conditions hold, (i) There exists a function ∆ : R + × R + → R + such that, for any r > 0, lim t→∞ ∆ (t, r) = 0 and if X 0 ∈ Ω with X 0 Y ≤ r then Θ (t, X 0 ) Y ≤ ∆ (t, r) for t ≥ 0; (ii) For t ≥ 0, Ψ (t, ·) maps any bounded sets of Ξ into sets with compact closure in Y.
Proof. Proof of (i) of Theorem 3.2. Let ∆ (t, r) = e −µ0t r, then lim t→∞ ∆(t, r) = 0. By (4) and (5),φ Then, for X 0 ∈ Ξ satisfying X 0 Y ≤ r and for t ≥ 0, we have Proof of (ii) of Theorem 3.2. It is sufficient to show that Ψ (t, ·) maps any bounded sets of Ξ into sets with compact closure in Y. From Proposition 2, T (t) and V (t) remains in the compact Thus it remains unknown that whether e (a, t) andĩ (b, t) remain in a precompact subset of L 1 + (0, ∞), which is independent of X 0 ∈ Ξ. To this end, we next to verify the following conditions forẽ (a, t) and similar ones forĩ (b, t) (see, for example, [25,Theorem B.2]).
Next we verify condition (iii). For sufficiently small h ∈ (0, t), we have

JINLIANG WANG AND XIU DONG
We first get an estimate of ∆ 1 . Since it follows from Proposition 3 that Next we estimate ∆ 2 . We rewrite ∆ 2 as It is easy to see that T (t) and V (t) are both Lipschitz continuous on R + with Lipschitz constants M T = h + dA + βA 2 and M V = (p + c)A, respectively. According to [13, Proposition 6], we conclude that T (t)V (t)is Lipschitz continuous with Lipschitz constants M T V = AM V + AM T . Denote that G = f βM T V . This estimate immediately yields and condition (iii) directly follows. As toĩ (b, t), we have Similarly, we have Hence from Proposition 3, we can conclude that Next we estimate Υ 2 . Firstly, we have By a zero-trick, then we have thus condition (iii) directly follows. This completes the proof.
Consequently, we have the following theorem for the semi-flow {Φ(t)} t≥0 , which establish the existence of global attractors by Smith and Thieme [25]. 4. The uniform persistence. This section is spent on proving that (1) is uniformly persistent under the condition 0 > 1, which indicates that 0 > 1 is a threshold index for infection persistence.
Letê(t) := e(0, t) andî(t) := i(0, t). We rewrite the first three equations of (1) as whereê Proof. We first get an estimate onî(t) as follows. By (16), we havê Solving the fourth equation of (1) with initial condition V (0) = V 0 , we have that This, combined with (18), gives uŝ Since 0 > 1, there exists a sufficiently small We claim that (17) holds for this ε 0 .Suppose that there exists a T > 0 such that Then it follows from (14) and hence (19) becomeŝ (20) for all t ≥T . Without loss of generality, we can assume that (20) holds for all t ≥ 0 (just replace X 0 by Φ(T , X 0 )). Then taking the Laplace transforms of both sides of (20), we obtain Here L[î] denotes the Laplace transform ofî, which is strictly positive because of (15) and Assumption 1.2. Dividing both sides of the above inequality by L[î] and letting λ → 0 give us which yields a contradiction.
In order to apply a technique used by Smith and Thieme [25,Chapter 9] (see also McCluskey [13, Section 8]), we consider a total Φ-trajectory of (1) in space Y. A total trajectory of Φ is a function X : R → Y such that Φ s (X(t)) = X(t + s) for all t ∈ R and all s ≥ 0. For a non-empty compact setÃ, it is said to be a compact attractor of a class C of set ifÃ is invariant and d Φ t (C),Ã → 0 for each C ∈ C. For each X 0 ∈Ã, there exists a total trajectory X such that X(0) = X 0 and X(t) ∈Ã for all t ∈ R.
When 0 > 1, the uniform persistence of (1) immediately follows from Theorem 4.2. In fact, it follows from (14) that e(·, t) L 1 ≥ whereê ∞ = lim inf t→∞ê (t). Under Theorem 4.2, there exists a positive constant > 0 such thatê ∞ > if 0 > 1 and hence the persistence of e(a, t) with respect to · L 1 follows. By a similar argument, we can prove that T (t) and V (t) are persistent with respect to | · | and i(a, t) is persistent with respect to · L 1 . In a summary, we get the following result.

5.
The local stability of equilibria. This section is devoted to investigate the local stability of equilibria of (1).
Proof of (ii) of Theorem 5.1. Linearizing the system (1) at P * by using y 1 (t) = T (t) − T * , y 2 (a, t) = e(a, t) − e * (a), where y 0 1 , y 0 2 (a), y 0 3 (b), y 0 4 are to be determined. Substituting (31) into (30) yields and We integrate the first equation of (33), (34) from 0 to a, y 0 2 (a) = y 0 2 (0)e −λa− a 0 θ1(s)ds , and y 0 and from (35), we have Combining (32), (33) into (36), yields the characteristic equation at P * that where It is sufficient to show that (37) has no roots with non-negative real parts. Suppose that it has a root λ = µ + νi with µ ≥ 0. Then we have Separating the real part of the above equality gives Noticing that W 1 (0) = T * 0 T0 = 1 and W 1 is a decreasing function, we have which yields a contradiction. This completes the proof. 6. Global stability of equilibria. This section is devoted to investigate the global stability of the equilibria by using Lyapunov functionals under the threshold value.
In what follows, we introduce an important function g on (0, ∞) defined by g(x) = x−1−ln x for x ∈ (0, ∞). This function is continuous and concave up with g(1) = 0. By Theorem 5.1, it is suffice to show that equilibria of (1) are globally attractive in Y.
Proof. Considering the candidate Lyapunov function as follows, Here the nonnegative kernel functions φ(a) and ψ(b) will be determined later. Firstly, we calculate the derivative of L i , i = 1, 2, 3, 4, respectively, By integration by parts, we calculate the derivative of L 2 ,

JINLIANG WANG AND XIU DONG
An argument similar to the one used in calculating the derivative of L 2 , we get We calculate the derivative of L 4 , Secondly, we have Then it is easy to see that Notice that dL IF E (t) dt = 0 implies that T = T 0 . It can be verified that the largest invariant set where dL IF E (t) dt = 0 is the singleton {P 0 }. Therefore, by the invariance principle, P 0 is globally attractive when 0 ≤ 1.
It is easy to see that G is non-negative on (0, ∞) × (0, ∞) with the minimum value 0 only when x = y. Furthermore, it is easy to verify that xG Considering the following candidate Lyapunov function, where e(a, t), e * (a) da, We define φ 1 (a) and ψ 1 (b) as Firstly, we calculate the derivative of H i , i = 1, 2, 3, 4, respectively, By using (4),  + e * (t + r)G y e 0 (r)e − t+r r θ1(ω)dω , e * (t + r) dr.
We obtain the derivative of H 2 (t),

Recall that
(1 − f )(βT * V * − βT V ) + Recall that Lemma 6.2 holds. Putting (39) into (42), we have ξ(a)e * (a)g e(a, t)i * (0) e * (a)i(0, t) da It is not difficult to check that the largest invariant subset { dL EE (t) dt = 0} is the singleton {P * }. By the invariance principle, P * is globally attractive and hence the proof is complete.
7. Discussion. This paper is devoted to the global dynamics of an HIV infection model subject to latency age and infection age, where the model formulation, basic reproduction number computation, and rigorous mathematical analysis, such as relative compactness and persistence of the solution semi-flows, and existence of a global attractor are involved. We have shown that the existence of a compact attractor of all compact sets of nonnegative initial data and used the Lyapunov functional to show that this attractor is the singleton set containing the equilibrium. Given that the model is so complex, the proof does require some rigorous calculation. The dynamics (at least the long-term dynamics) of the model do not appear to have been altered by adding the e(a, t) component. We hope the model studied here have a contribution to improve the broader contexts of investigating viral infection subject to age structure.


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ZNF424 Induces Apoptosis and Inhibits Proliferation in Lung Carcinoma Cells

Background: Previously, we showed that the Zinc finger-containing transcription factor ZNF424 inhibits p21 transcription, which has been widely associated with various cancers. However, because the roles of ZNF424 in tumorigenesis have not been characterized, we correlated ZNF424 expression with tumorigenesis in lung cancer. Results: The present immunohistochemical analyses show significantly lower ZNF424 expression levels in 43 of 60 lung cancer tissues compared with adjacent tissues. Moreover, flow cytometry assays indicated that overexpression of ZNF424 induces apoptosis in A549 human lung carcinoma cells, and overexpression of ZNF424 significantly increases numbers of G1 phase cells and decreases numbers of S phase cells, suggesting that ZNF424 inhibits proliferation. Western Blot analyses show that overexpression of ZNF424 decreases protein expression levels of the mitogen-activated protein kinase (MAPK) signaling proteins P-P38 and P-ERK in A549 cells. Conclusion: These are the first data to associate ZNF424 with tumorigenesis and demonstrate an inhibitory role in lung cancer, indicating the potential of ZNF424 expression as a diagnostic marker of lung tumorigenesis.

INTRODUCTION
Zinc-finger (ZNF) proteins containing the Krüppelassociated box (KRAB-containing proteins) have been investigated since 1991 [1], and the KRAB-ZNF family is the largest family of transcription factors (TFs) in mammals [2,3]. Accordingly, among various functions, KRAB-ZNF genes maintain embryonic stem cells in an undifferentiated state [4] and regulate the cell cycle [5] during brain and neuron development [6,7]. Lung cancer is the leading cause of cancer-related death worldwide, and non-small-cell lung cancer (NSCLC) comprises about 80% of all lung cancers. Among tumorigenic regulatory networks, KRAB-ZNF genes have been associated with various pivotal biochemical functions in lung cancers. However, the biochemical functions of ZNF424 have not been characterized in lung cancer. Interestingly, our screening with the lymph cancer cell lines HL-60, K-562, Jurkat, Reh and the lung cancer A549 cell line suggested that ZNF424 had negative effects on prohibiting proliferation and promoting apoptosis only in the lung cancer A549 cell line, so we determined ZNF424 expression in A549 cells, and investigated the downstream effects on apoptotic signaling.
Previously, we cloned the novel KRAB-related zinc finger gene ZNF424 and showed that it acts as a transcriptional repressor in the NFAT-p21 pathway in HEK-293 cells [8]. Few studies report biological functions of ZNF424 in tumor cells. Thus, we collected 60 lung cancer specimens from the Hunan Cancer Hospital and revealed associations of ZNF424 using immunohistochemical analyses. Subsequently, we transfected A549 cells with ZNF424-containing plasmid vectors and evaluated the effects of ZNF424 on tumor proliferation, apoptosis. Our data suggested that ZNF424 overexpression in A549 cells induced apoptosis, and promoted cell cycle arrest at the G1/S transition. Finally, we showed that ZNF424 suppressed proliferation and induced apoptosis via the mitogenactivated protein kinase (MAPK) signaling pathway in A549 cells.

Cells and Reagents
A549 cells (epidermal growth factor receptor wild type cells, WELL BIOLOGICAL SCIENCE) were cultured in RPMI1640 medium containing 10% fetal bovine serum (FBS) and 100-U/ml streptomycin/ penicillin (Trans Gen Biotech) at 37°C in a humidified atmosphere containing 5% CO 2 . Cells were subcultured using 0.25% trypsin and were seeded into 10-cm plates for protein and nucleic acid extraction, and into 12-well plates for Dual-Luciferase reporter assays.

Immunohistochemistry
Samples fixed in 10% neutral formalin were embedded in paraffin and were then sliced into 4-μm thick sections. Immunostaining was performed using the streptavidin-peroxidase method. Briefly, sections were incubated with a monoclonal mouse anti-ZNF424 antibody (1: 200; Absin121843) at 4°C overnight, followed by biotinylated goat anti-mouse IgG secondary antibody. After washing, sections were incubated with horseradish peroxidase-conjugated streptavidin-biotin (Service bio G1211) and signals were developed using 3, 3-diaminobenzidine tetrahydrochloride (Sino pharm Chemical Reagent Co., Ltd.).

Flow Cytometry
More than 1 × 10 5 cells were harvested and analyzed using a FACSCalibur flow cytometer (Becton Dickinson, USA). Apoptotic cells were identified using flow cytometry analyses with FITC-Annexin V Staining Detection Kits. Cells in culture flasks were transfected with plasmid containing ZNF424 for 48 h and were then trypsinized and resuspended in 500 μl of binding buffer containing 10-mM HEPES, (pH 7.4), 140-mM NaCl, 1-mM MgCl 2 , 5-mM KCl, 2.5-mM CaCl2, 5 μl of FITCconjugated Annexin V, and 5 μl of propidium iodide (PI). After a 10-min incubation at room temperature in the dark, cells were harvested and analyzed on a FACSCalibur flow cytometer. Separate cells were transfected with a plasmid vector containing ZNF424 for 48 h, were washed three times with ice-cold PBS prior to fixing in 70% ice-cold ethanol overnight at 4°. Before testing, cells were stained with 50-μg/ml PIin the presence of RNase (100 μg/ml). Cells were then harvested within 30 min and cell cycle phases were determined using flow cytometry. PI was excited at 488 nm using an argon ion laser and emissions were quantitated at 630 nm. To eliminate adherent cells and cell debris, 10 000 cells were collected through the FSC/SSC scatterplot using Gate technology.

Protein Extraction and Western Blotting
Total protein extracts were lysed in cold RIPA buffer containing freshly added proteinase and phosphatase inhibitors. Lysates were then centrifuged at 12,000 × g for 15 min, and supernatants were collected. Equal amounts of protein lysates were then separated using SDS-PAGE and were then transferred onto nitrocellulose membranes. Membranes were blocked using 5% non-fat dried milk in TBS with containing 0.1% Tween 20 for 2 h at room temperature, and were then incubated overnight at 4°C with primary antibodies against human cyclinE1, cyclinD1, CDK2, CDK4, P16, P27Kip1, P-ERK/ERK, P-P38/P38, P-JNK/JNK, cleaved-caspase3, which were purchased from UNIV (Shanghai, China). Membranes were then incubated with horseradish peroxidase-conjugated secondary antibody (Vazyme) at room temperature for 2 h and were finally washed with TBS containing 0.1% Tween 20 and scanned using an Odyssey Infrared Imaging System. Scanned bands were quantified using ImageJ software, and were normalized to β-actin protein expression levels. The phosphorylated proteins p-p38, p-erk, and p-jnk were normalized against their nonphosphorylated counterparts p38, erk, and jnk, respectively. Presented blots are representative of at least three experiments.

Statistical Analyses
Data are presented as the means ± standard deviations (S.D.) and statistical analyses were performed using GraphPad Prism 5 (GraphPad Software, Inc., LaJolla, CA, USA). Immunohistochemical differences and clinical correlations were identified using Chi-square tests. Differences in continuous variables between groups were analyzed using Student's t-test and were considered significant when P < 0.05. All reported P-values are two-tailed.

ZNF424 Protein Expression was Downregulated in Lung Carcinoma
To identify clinically relevant correlations between ZNF424 expression and lung cancer progression, 60 samples of lung carcinoma were collected from Hunan Cancer Hospital and were analyzed using immunohistochemistry (IHC). The ensuing data show significantly lower ZNF424 protein expression levels in lung cancer tissues than in adjacent tissues (Fig. 1a,  1b), as identified using chi-square tests. Moreover, decreased ZNF424 protein expression was found in 43 tumor tissue specimens compared with that in para-carcinoma tissues, suggesting associations between, ZNF424 and lung cancer.

Overexpression of ZNF424 Induces Apoptosis in A549 Cells
A549 cells were transiently transfected with ZNF424 and the effects of increased ZNF424 were examined in A549 cells. In flow cytometry analyses, 21.92% of A549 cells overexpressing ZNF424 were apoptotic, whereas only 11.63% of control A549 cells were apoptotic (Fig.  2a). Western blotting analyses showed increased cleaved caspase 3 protein expression in ZNF424 overexpressing A549 cells (Fig. 2b), suggesting that ZNF424 induces apoptosis.
Exogenously expressed ZNF424 in A549 cells induced G1/S cell cycle arrest. Moreover, assessments of cell cycle distributions in A549 cells transfected with pCMV-ZNF424-tag2B plasmid or pcDNA3.1 (negative control; Fig. 3a) showed an 18.52% increase in numbers of ZNF424-overexpression cells in the G1 phase (Fig. 3b). To define the mechanisms behind these changes in numbers of G1 phase cells, we examined protein expression levels of CyclinD1, CyclinE1, CDK2, CDK4, P16, P27, and Kip1 in ZNF424 overexpressing A549 cells using Western blotting. In accordance with immunohistochemical and flow cytometry data, protein expression levels of CyclinD1, CyclinE1, CDK2, and CDK4 were remarkably decreased, whereas those of P27 Kip1 were augmented in A549 cells. However, expression levels of P16 were not significantly changed (Fig. 3c, 3e), suggesting that ZNF424 overexpression leads to G1/S cell cycle arrest and inhibits proliferation in A549 cells.

ZNF424 Overexpression in A549 Cells was Associated with the p38/ERK MAPK Pathway
Multiple extracellular growth regulatory signals are transduced from cell surfaces to the nucleus via MAPK signaling cascades. Thus, to determine whether ZNF424 expression inhibits A549 cell proliferation via the MAPK signaling pathway, we examined expression levels of the related proteins P38, ERK, and JNK. In these analyses, P38 and ERK were suppressed whereas JNK was upregulated in ZNF424 overexpressing A549 cells (Fig. 4a). Hence, overexpression of ZNF424 influences the upstream p38/ERK pathway. Taken together, our data show that ZNF424 inhibits proliferation of A549 cells, in part by inhibiting MAPK signaling.

DISCUSSION
Zinc-finger-containing transcription factors are the largest known single family of transcriptional regulators in mammalian cells, and play essential roles in differentiation, proliferation, apoptosis, and neoplastic transformation. ZNF424 is expressed in multiple human tissues and is localized primarily in the nucleus, where it acts as a transcriptional repressor [8]. Previous reports show that zinc finger proteins containing KRAB and C2H2 domains play important roles in lung carcinoma. Moreover, numerous zinc finger family proteins have been associated with lung cancers. For example, knockdown of the zinc finger protein snail increases the sensitivity of A549 cells to cisplatin [9], and inhibition of ZNF746 suppressed invasive activities and blocked the epithelial to mesenchymal transition in H460 non-small cell lung cancer cells [10]. Although Kruppel-Like Factor 6 was frequently downregulated and induced apoptosis in non-small cell lung cancer cells [11], no previous studies report the roles of ZNF424 in lung cancer. In our study, 60 specimens   were collected from lung cancer patients for immunohistochemical analyses, which showed that ZNF424 expression was significantly lower in tumor tissues than in adjacent tissues, indicating a negative correlation between ZNF424 expression and lung cancer. Hence, ZNF424 may provide a diagnostic marker for lung tumorigenesis, as indicated in ROC analyses (data not shown).
The present negative correlation with lung cancer suggests a negative regulatory function of ZNF424. Accordingly, data from ZNF424-overexpressing A549 cells show increased numbers of cells in the G1 phase and decreased numbers of S-phase cells, and CyclinD1, CyclinE1, CDK2, CDK4 protein levels were concomitantly decreased. In addition, ZNF424-induced apoptosis was observed in FACS analyses, with significant increases in cleaved caspase-3 protein levels. Collectively, these data suggest that ZNF424 functions as an inhibitor of lung cancer by causing G1/S arrest and inducing apoptosis. To our knowledge, this is the first study to show that ZNF424 induces apoptosis in a human cancer cell line. However, previous studies have identified zinc finger transcription factors that inhibit tumor cell proliferation by inducing apoptosis. In particular, the von kruppel-like factor 6 was frequently downregulated in non-small cell lung cancer cells, and induced apoptosis when overexpressed [11]. ZNF family members have been shown to promote or inhibit cell growth by activating or inactivating MAPK and various other signaling pathways. The actions of Kruppel-like factor 4 on non-small cell lung cancer cell growth and aggressiveness were related to transforming growth factor-β1-mediated ERK/JNK/NF-κB signaling [12], and the novel human KRAB/C2H2 zinc finger protein ZNF418 suppressed MAPK signaling [13].
In our studies, ZNF424 promoted JNK phosphorylation but decreased phosphorylation levels of P38 and ERK. Previous studies demonstrate Ras/Raf/MEK/ERK signaling from cell surface receptors to transcription factors, and Raf reportedly acts via MEK-and ERK-dependent and independent mechanisms to induce the phosphorylation of apoptosis-related proteins [14]. Furthermore, Ras and its downstream effectors altered the expression of multiple cell cycle regulators, including p16 Ink4a , p15 Ink4b , and p21 Cip1 , and caused premature cell cycle arrest at the G1 phase. Because this process was shown to be dependent on the Raf/MEK/ERK pathway [15][16][17], the effects of ZNF424 on proliferation and apoptosis are likely mediated by P38/ERK/JNK and MAPK, although further studies are required to confirm the roles of these cascades.

CONCLUSION
In conclusion, ZNF424 induces apoptosis and inhibits proliferation in A549 cells, in part by acting through the MAPK signaling pathway. These data warrant consideration of ZNF424 as a target for small molecule inhibitors of the MAPK signal pathway, and for the treatment of lung cancer.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE
Not applicable.

HUMAN AND ANIMAL RIGHTS
No humans/animals were used for studies that are the basis of this research.

CONSENT FOR PUBLICATION
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