Alantolactone (ALT) (>98% purity) was obtained from Tauto-Biotech (Shanghai, China) while Brevilin A (Brv-A) (purity >98%) and Paclitaxel (PTX) (purity >99%) was obtained from Selleck (China). DMEM (Dulbecco’s modified Eagle’s Medium), trypsin without or with EDTA, Streptomycin-Penicillin (10,000 U/ml), and fetal bovine serum (FBS) were all purchased from Gibco (Thermo Fisher Scientific, Inc.). Cell counting kit-8 (CCK-8) was purchased from KeyGen Biotech (Nanjing, China) and Protease-inhibitor cocktail from Sigma-Aldrich (St. Louis, MO). Dimethyl Sulfoxide (DMSO), Phenyl methyl sulfonyl fluoride (PMSF), Propidium Iodide (PI), crystal violet stain, Calcein acetoxymethyl ester (Calcein-AM), and annexin V-FITC kit for apoptosis detection were obtained from Beyotime Biotechnology (China). The primary antibodies for cleaved caspase-3, cleaved caspase-9 and cleaved PARP as well as p-STAT3, and STAT3 (Tyr 705) were obtained from Cell-Signaling Technology (Beverly, MA). FUT4 from Proteintech (Wuhan, China) while P-GP was obtained from abcam (Cambridge, MA). GAPDH was procured from Beyotime Biotechnology (China), while the HRP (Horseradish Peroxidase)-conjugated secondary type of antibodies (anti-mouse and anti-rabbit) were obtained from Sigma-Aldrich (St. Louis, MO).

Human non-small cell lung cancer cell line (A549), and paclitaxel-resistant A549 (A549/T) cell line was purchased from iCell Biosciences Inc. (Shanghai, China). Both A549/T and A549 cell lines were cultured in 10% FBS containing DMEM, accompanied 100 μg/ml streptomycin and 100 U/ml concentration of penicillin. The cell lines were maintained in a logarithmic phase of growth for all experiments in humidified atmosphere with 5% CO₂ at 37°C.

DMSO (0.5% final concentration) was used to dissolve ALT, Brv-A, and PTX while the same DMSO concentration was kept as a control. CCK-8 kit was used to find out the percentage of cells viability. Briefly, cells (with density of 3 × 10³) were added in 96-well culture plates for 24 h and treated with different concentrations of ALT and Brv-A (2.5, 5, 10, 15, 20, 40, 80 and 120 μM) for 24, 48, and 72 h, while various concentrations of PTX (0.0125, 0.25, 0.5, 1, 2, 4, 8, and 16 μM) were used to check the cell viability at 24, 48, and 72 h in A549 and A549/T lung cancer cells. The resistance index was calculated at 72 h time point. Afterwards, CCK-8 solution (10 μl) was added to each well and 96-well culture plate was kept at 37°C for 3 h. Optical density was measured at 450 nm wavelength through fluorescence-microplate reader (Synergy neo HTS multimode-microplate reader, BioTek). Following formula was used to calculate the cell viability in experiments performed in triplicate. C e l l   v i a b i l i t y   ( % ) = ( A 450   s a m p l e − A 450   b l a n k ) / ( A 450   c o n t r o l − A 450   b l a n k )   ×   100 where “sample” characterizes the optical density value of treated cells containing CCK-8 solution and culture media whereas “blank” displays the optical density of CCK-8 solution containing media without cells. IC₅₀ values were calculated by SPSS 16.0 version.

A549/T cells were taken for transfection with specific siRNA-MALAT1. The sequence of the sense strand used was MALAT1, 5′-CCC​UCU​AAA​UAA​GGA​AUA​ATT-3′ obtained from GenePharma (Shanghai, China). For transfection, lipofectamine 2000 was used along with siRNA-MALAT1 followed by manufacturer’s protocol. Cells were kept for 4–6 h, later, DMEM was removed and fresh DMEM was added. Transfected cells were used for multiple experiments after 48 h of transfection.

Total RNAs were extracted and isolated through Trizol reagent (Invitrogen) as guided by the manufacturer’s instructions. For reverse transcription, Prime Script TMRT-reagent kit (TransGen Biotech, China) was used while Trans Start Tip Top Green qPCR Super Mix (TransGen Biotech, China) was used for qPCR according to the instructions given by the manufacturer. The primers sequences were as follows. MALAT1: 5′-GCA​TTA​ATT​GAC​AGC​TGA​CCC​A-3′ (F), 5′-GCT​TGC​TCC​TCA​GTC​CTA​GCT​T-3′ (R); FUT4: 5′-ACAACTGTTCCCGATTCACG-3′(F), 5′-TGCCCTCCTCACCTTTCTC-3′(R); STAT3: 5′-CTT​TGA​GAC​CGA​GGT​GTA​TCA​CC-3′ (F), 5′-GGTCAGCATGTTGTACCACAGG-3′(R); GAPDH: 5′-AGC​CCA​TCA​CCA​TCT​TCC​AG-3′ (F), 5′-ACC​CAT​CAC​AAA​CAT​GGG​GG-3′. GAPDH was used to normalize the data and the relative measurements were determined using the 2^−ΔΔCt method. All the experiments were performed independently in triplicate.

Chemical structures of ALT and Brv-A were acquired from SciFinder (http://scifinder.cas.org) by using Chem Bio Draw. Afterward, the data was downloaded in the “mol2” format.

To predict the targets of ALT and Brv-A, Pharm Mapper (http://lilabecust.cn/pharmmapper/), was used that contains a pharmacophore mapping method to identify potential drug targets (Liu et al., 2010). Due to the non-standard names of protein of the extracted targets, the official identifiers were corrected and obtained via UniProtKB (http://www.uniprot.org/).

DAVID (version 6.8), accessed at https://david-d.ncifcrf.gov, was used for ontologies (GO) and KEGG pathway as mentioned earlier (Sherman and Lempicki, 2009) while Cytoscape program (version 3.4.0) was used to create pathway networks (Missiuro et al., 2009).

A549/T and A549 cancer cells were added in 96-well culture plates at 37°C overnight and designated treatment of ALT, Brv-A, and PTX was given for 24 h. To determine and photograph the morphological changes in the cells, Phase-contrast microscope (Leica, DMIL LED) was used.

To perform live/dead assay, A549/T cells were treated with 5, 10, and 15 µM of ALT and Brv-A for 24 h. After washing the cells with PBS, calcein-AM (2 µM) and PI (4 µM) were added to each sample for 15–20 min and kept at room temperature in the dark. The extra stain was removed by washing with PBS. Later, cells were mixed in PBS, and photographed using fluorescence microscope (Leica, DMI 4000B). To evaluate the live and dead cells, three different fields were selected and 100 cells in each field were calculated.

Colony formation assay was done to determine the cytotoxic and antiproliferative effect of ALT and Brv-A. Briefly, A549/T cells were cultured and treated with 5, 10, and 15 µM concentrations of ALT and Brv-A for 24 h. Afterwards, PBS was used to wash the cells, trypsinized, and were seeded in six-well culture plates with cells density of 500 per well. Cells were kept at 37°C for next 7 days to form colonies and given media was changed after each 48 h. After colony formation, cells were washed, and 4% paraformaldehyde (PFA) was used to fix colonies for 15–20 min. Colonies were further stained using crystal violet for 25–30 min. Extra stain was washed by PBS and colonies were photographed. The rate of proliferation was also evaluated by the addition of methanol to dissolve the stain taken by the colonies. Optical density at 595 nm wavelength was determined by fluorescent-spectrophotometer (Synergy neo HTS multi-mode microplate-reader; BioTek).

The impact of ALT and Brv-A in the induction of apoptosis was determined through flow cytometry where apoptosis detection kit was used. Annexin V-FITC/PI double staining was performed as directed by the manufacturer’s instruction. In brief, A549/T cells were seeded in six-well culture plates and treated with 5, 10, and 15 µM ALT or Brv-A for 24 h. After washing with PBS, cells were mixed in the provided 1X binding-buffer. Each sample was incorporated with 5 µl annexin V-FITC and 10 µl PI and kept in the dark for 20 min, filtered and subjected to the flow cytometry (Beckmen Coulter, Life Sciences, United States) to investigate apoptotic rate. NovoExpress (version 1.3.0) was used for the analysis of data.

Anti-migration and anti-invasive capabilities of ALT and Brv-A in A549/T lung cancer cells (transfected with or without siRNA MALAT1) were determined by transwell migration and invasion assays. In brief, cells were added in the transwell chamber without/with Matrigel with density of 3 × 10³/chamber. The lower section contained DMEM (10% FBS) as chemoattractant while the DMEM (2% FBS) was poured in the upper chamber. To achieve the rate of migration and invasion among treated and untreated cells, the Transwell chamber was kept at 37°C. Next, the adhesive cells to the inner side of the upper chamber were detached with a soft cotton swab and the cells at the lower side of the chamber were fixed using 4% PFA for 15–20 min. Cells were stained for 30 min through 1% crystal violet, and later PBS was used to wash and remove the extra stain. The chambers were left air dried, photographed by using a fluorescence microscope (Leica, DMI 4000B), and quantified by counting in at least three random fields.

To prepare FUT4 and P-GP proteins for molecular docking, dock prep was performed by using USCF Chimera (Pettersen et al., 2004; Yang et al., 2012). ALT and Brv-A were selected for docking purpose and compound’s charges were minimized, and converted into PDBQT format before docking. The partial charges were added and non-polar hydrogen atoms were merged to the ligand atom by PYRX software (Dallakyan and Olson, 2015). Further, docking affinities were carried by selection of predicted ligand binding residues of FUT4 and P-GP proteins. Results were visualized by Discovery Studio software (Dassault Systemes BIOVIA, BIOVIA Discovery Studio Visualizer, v16).

The Search Tool for the Retrieval of Interacting Genes (STRING) was used to evaluate and integrate the protein-protein interactions containing both physical (direct) and functional (indirect) interconnections.

Cells, after washing with chilled PBS, were lysed by using RIPA-lysis buffer (Beyotime Biotechnology) carrying 1% PMSF on ice for 30 min. Sodium fluoride (NaF) (2%) was also supplemented to RIPA buffer during extraction of phosphorylated proteins. Later, centrifuged the cells for 15 min at 12,000 rpm and the supernatant was transferred to ice-chilled Eppendorf tubes. The BCA protein assay kit (Beyotime Biotechnology) was used to determine the concentration of protein. Furthermore, by using 10–12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels, an equal amount of 30–40 µg protein was separated, transferred to the PVDF (polyvinylidene difluoride) membranes, and kept in 5% skim milk for 2 h at room temperature. The membranes were further washed with TBST (Tris-buffered saline-tween) three times and incubated in specific primary antibodies overnight at 4°C on a shaker. Next, the membranes were washed by TBST three times and kept in HRP-conjugated goat anti-mouse IgG or goat anti-rabbit IgG secondary antibodies at room temperature for 2 h. After washing by TBST, antibody binding was detected in DNR-bioimaging system MicroChemi 4.2 using ECL plus chemiluminescence kit. GAPDH was internal control for all triplicate experiments performed in the study. ImageJ software was used to analyze the variations in protein expression.

Among three different performed experiments, all values are depicted as Mean ± SD and compared statistically with untreated, non-resistant, non-transfected samples as control. Student t-test was used to compare only two groups while one-way ANOVA followed by Tukey’s Multiple Comparison Test was used for comparison between three or more groups. p < 0.05 was considered to be statistically significant.

To evaluate the significance of MALAT1, STAT3, and FUT4 in paclitaxel-resistance, initially, we compared the expression levels of lncMALAT1, STAT3, and FUT4 of A549/T cells with non-resistant A549 cells. We quantified the expressions at mRNA and protein level using qPCR and western blotting respectively. As indicated in Figures 1A–C, the mRNA expression of lncMALAT1, STAT3, and FUT4 was significantly high in A549/T cells as compared with the non-resistant A549 lung cancer cells. Similarly, the higher protein level of STAT3 and FUT4 was found in A549/T cells than A549 cells (Figures 1D,E). After establishing the expected role of MALAT1, STAT3, and FUT4 in resistant and non-resistant lung cancer cells, we further studied the association of MALAT1 with STAT3 and FUT4 expression in A549/T cells. Prior to find this association, we first measured the mRNA level for lncMALAT1 in siRNA MALAT1 transfected A549/T resistant cells to establish a control for our further readings. The expression of MALAT1 was downregulated successfully in siRNA MALAT1 transfected A549/T cells (Figure 1F). Later, we assessed the expression of STAT3 and FUT4 in A549/T cells transfected with siRNA-MALAT1. We detected a significant decrease in STAT3 activation and FUT4 expression in resistant cells transfected with siRNA-MALAT1 (Figures 1G,H). Collectively, our data indicate that STAT3 and FUT4 are overexpressed in A549/T cells and this overexpression is associated to the upregulation of lncRNA MALAT1.

Prior to the mechanistic study, we validated the targets of SLs by compound-target networking analysis. Among SLs family, we chose ALT and Brv-A (Figures 2A,B) for the current study, as 1) the anti-cancer effect and their pivotal role in the inhibition of STAT3 signaling has been extensively reported 2) we confirmed that STAT3, FUT4, and P-GP were the common targets of ALT and Brv-A. There were 536 nodes (two compound nodes and 534 compound-targeted nodes) and 560 edges throughout this network (Figure 2C). The central nodes of this network, 128 nodes in blue, were found to be controlled by both ALT and Brv-A, whereas the external nodes, in green and red, are regulated individually by ALT and Brv-A, respectively. All such controlled nodes could be the core or central nodes in the treatment of lung cancer. As shown in this network, ALT and Brv-A would interact with these targets and exert a significant pharmacological effect.

The potential effect of ALT and Brv-A was further investigated using KEGG pathway evaluation. The analysis represented the enriched pathways in cancer affected by ALT and Brv-A include EGFR-tyrosine kinase inhibitor, PI3k-Akt signaling pathway, RAS signaling pathway, RAP1 signaling pathway, IL-17 signaling pathway, and breast cancer pathway along with EGFR tyrosine kinase inhibitor (Figure 2D). Lung cancer is associated with the aforementioned biological processes. As a result, these findings provided a theoretical support that the anti-cancer activity of ALT and BRV-A is closely connected to the above-mentioned cancer pathways.

We used GO enrichment to further investigate the molecular functions associated with ALT and BRV-A targets. These targets were found not only to be involved into the biosynthesis of small molecules, alcohol metabolism, regulation of ERK1 and ERK2 cascade, but also implicated in regulation of phosphatidyl-tyrosine phosphorylation and the positive regulation of MAPK cascade (Figure 2E).

First, we determined the resistance index of paclitaxel among A549 and A549/T cells at 72 h time point. As presented in Figure 3A, the IC₅₀ of paclitaxel among A549 cells was approximately 0.195 µM while that in A549/T cells was 6.183 µM on average (almost 31 times higher in A549/T cells) at 72 h indicating that A549/T cells are resistant to paclitaxel. Following that, different concentrations of ALT and Brv-A were used to treat A549/T cells for 24, 48, and 72 h to assess cell viability. We detected a halted cell growth in presence of these two drugs followed by a substantial dose-dependent decrease in A549/T cells viability. The IC₅₀ values for ALT were 20, 16, and 15 μM at 24, 48, and 72 h respectively. Moreover, the IC₅₀ values for Brv-A at 24, 48, and 72 h were 18.31, 14.84, and 10.45 µM respectively representing the effectiveness of these natural compounds against paclitaxel resistance in lung cancer cells (Figures 3B,C). The most promising concentrations along the suggested concentration gradient of 2.5–120 µM were 5, 10, and 15 µM. The cytotoxic effect of ALT and Brv-A was further examined by observation of cell-morphological changes. Cells, treated with ALT and Brv-A for 24 h, developed a round shape and lost anchorage and cellular geometry (Figures 3D,E). We further confirmed the cell death via live/dead assay. PI and Calcein-AM were used to stain the dead (red) and live (green) cells respectively. Results showed that ALT and Brv-A treatment enhanced the proportion of PI intake among A549/T cells in concentration-dependent manner representing the dead cells (Figures 3F,G). Next, we performed colony forming assay to evaluate the anti-proliferative efficacy of ALT and Brv-A. After exposing the A549/T cells to ALT and Brv-A for 24 h, we noted that the capability of resistant cells to form colonies was reduced. We assessed the growth constraints in treated cells by liquifying the crystal violet stain (taken by the colonies) in methanol. Consistent with the reported alterations in cell viability and morphology, we observed lower uptake of crystal violet stain in ALT (Figure 3H) and Brv-A (Figure 3I) treated A549/T cells as compared to untreated cells. The collective data show that ALT and Brv-A has a considerable inhibitory effect over the growth and proliferation rate of A549/T cells.

To ascertain the mode of cell death, we performed Annexin V-FITC and PI staining and subjected the cells to flow cytometric analysis. As presented in Figures 4A,B, ALT and Brv-A influenced the apoptotic event in dose-dependent way. The percentage of early and late apoptosis (average) was increased from 3.2 to 6.45, 12.9, and 29.08%, after treating the A549/T cells with 5, 10, and 15 µM concentration of ALT respectively (Figure 4C). Similarly, 5, 10, and 15 µM doses of Brv-A increased the rate of apoptosis from 2.65 to 3.68, 8.78, and 32.18% respectively (Figure 4D). Moreover, ALT and Brv-A increased the expression of cleaved PARP, cleaved caspase-9, and cleaved caspase-3 in dose-dependent manner which are the markers of apoptotic processes (Figures 4E,F).

Our previous studies established the direct binding of Brv-A to the STAT3 with a considerable effect compared to commercially available STAT3 inhibitors (S31-201) (Khan et al., 2020). According to the findings of compound networking constructions and previous experimental research regarding anticancer activity of ALT and Brv-A (Maryam et al., 2017; Khan et al., 2020), we chose STAT3, FUT4, and P-GP (with a diverse role in chemoresistance) as the targets. In current study, molecular docking for FUT4 and P-GP was made to assess the effectiveness of primary drug-protein associations and to examine the reliable binding interfaces, though the molecular docking of these two compounds for STAT3 has already been reported in previous studies (Chun et al., 2015; Khan et al., 2020). The molecular docking (protein-ligand complexes analysis) of FUT4 and P-GP with ALT and Brv-A were performed using PyRx software program. First, the validation method was conducted to ensure the capability of docking machine and the RMSD values were ≤2.0 Å. As presented in Figure 5A, the interaction of FUT4 structure with ALT forms a complex via carboxylic group of ALT interacted by hydrogen bonding with attractive charged residue Arg-201, pi-alkyl group bonded with residue Trp-31, and pi-sigma interacted to Trp-50 amino acid. As shown in Figure 5B, Brv-A moieties form a complex with FUT4 via alkyl and pi-alkyl groups interacting with Phe-215, Val-328, Pro-205, and conventional hydrogen bond to Tyr334. These results showed that FUT4 had the binding affinity of −12.4 Kcal/mol and −8.1 Kcal/mol with ALT and Brv-A respectively. ALT also showed good interaction to P-GP binding site, where hydrogen bond forms interaction between pi-alkyl group and Phe-299 as well as Phe-766 residues (Figure 5C). Brv-A was found to be in interaction with P-GP residues Tyr-306, Phe-332, Leu 335 by alkyl group and pi-alkyl group interacts with amino acids Phe-979, Phe-974, Phe728, Phe-339, and Ile-336 (Figure 5D). On the basis of the binding energy scores of ALT (−10.5 Kcal/mol) and Brv-A (−8.7 Kcal/mol) with P-GP, it is estimated that P-GP has a better binding affinity with ALT.

On the basis of previously reported and obtained results from molecular docking, we were interested to analyze the effect of ALT and Brv-A over STAT3 activation, and FUT4 and P-GP expression via western blotting. As expected, ALT and Brv-A inhibited activation of STAT3 in A549/T cells in dose-dependent fashion (Figures 6A,B). Furthermore, ALT and Brv-A also inhibited FUT4 expression in dose-dependent manner (Figures 6C,D). P-GP, also known as MDR1, ABCB1 is a transmembrane transporter that works for the efflux of various anticancer drugs across the cell membrane via ATP hydrolysis and has been implicated in the promotion of drug resistance in various cancer cells. Additionally, STAT3 plays significant role in transcriptional regulation of P-GP expression (Wang R. et al., 2020). As mentioned in previous studies, targeting P-GP transporter is a viable strategy to overcome paclitaxel resistance in A549 cells (Lin et al., 2020). Therefore, we were interested in determining whether these two SLs members have the capability to downregulate P-GP expression among A549/T cells. To begin, we assessed and compared the expression of P-GP between A549 and A549/T cells. We distinguished a higher expression of P-GP in A549/T cells in contrast to A549 cells confirming the P-GP immersion in paclitaxel resistance (Figure 6E). Next, we determined the expression level of P-GP in A549/T cells treated with 5, 10, and 15 µM concentrations of ALT and Brv-A. Both of these drugs had a dose-dependent inhibitory effect on P-GP expression (Figures 6F,G). The purpose of inhibiting STAT3 activation, P-GP and FUT4 expressions in A549/T cells was to overcome the resistivity, as these proteins are considered to be the major contributors in developing preventive strategies by cancer cells against paclitaxel. Our study shows that ALT and Brv-A has a potential inhibitory effect over STAT3 activation, FUT4, and P-GP all of which are known to be linked to the drug resistance in cancer.

After targeting three important modules responsible for paclitaxel drug resistance in cancer i.e., STAT3, FUT4, and P-GP, we were interested to determine the role of MALAT1 in this scenario. Prior to establish the effectiveness of ALT and Brv-A over the functional role of MALAT1, we predicted the protein-protein interactions (PPIs) by using STRING database. Our analysis revealed that MALAT1 interacts with 20 different types of proteins which included FUT4, STAT3, JAK1, JAK2, JAK3, EGFR, MAPK1, FOXP3, FOS, LEPR, PTPN2, HIF-1α, PIAS3, RELA, HSP90AA1, EP300, AR, CREBBP, IL10RA, and NANOG (Figure 7A). As given in Figure 7B, it is estimated that MALAT1 may interact with FUT4 via STAT3 that needs further experimental validation. Moreover, previous research has established that MALAT1 modulates STAT3, FUT4, and P-GP (Cheng et al., 2013; Fang et al., 2018; Yang et al., 2018; Wang R. et al., 2020; Xu et al., 2020). On the basis of MALAT1/STAT3, MALAT1/FUT4 and MALAT1/STAT3/P-GP interactions, we hypothesized that ALT and Brv-A may influence the expression of MALAT1. We assessed the MALAT1 levels in drug treated (ALT, Brv-A) groups and compared to the untreated and found a significant decrease in dose-dependent fashion (Figure 8A). Due to strong interactive properties of MALAT1 to STAT3, FUT4, and P-GP (selected markers to be targeted in current study) we were interested to investigate the impact of knockdown MALAT1 over further improving the efficacy of ALT and Brv-A. This was accomplished by determining and comparing the viability of A549/T cells treated with different concentrations of ALT and Brv-A with and without siRNA MALAT1 transfection. As presented in Figure 8B, siRNA MALAT1 transfected cells were more sensitive to ALT and Brv-A at various dosages. Previously, some studies have confirmed the role of MALAT1 in the migration and invasive properties of various cancer cells (Chen et al., 2018; Tang et al., 2018; Yuan et al., 2019). Our data suggested that the inhibition of MALAT1 restricts A549/T cells from migration and invasion individually as well as in combination with treatment of ALT and Brv-A (Figures 8C,D). These results suggested that targeting the STAT3, FUT4, and P-GP and MALAT1 (by ALT and Brv-A as well as via gene knockdown) reduce the migration and invasion of paclitaxel-resistant A549 cells and enhance the competence of these two compounds significantly. These findings propose that along with targeting STAT3, FUT4, and P-GP transporters, inhibiting MALAT1 response would promote the rate of apoptosis in drug resistant cancer cells.