Abstract

   Hepatocellular carcinoma (HCC) typically develops in a chronic
   inflammatory setting causal to release of a plethora of growth factors
   and cytokines. However, the molecular effect of these cytokines on HCC
   progression is poorly understood. In this study, we exposed HCC cells
   to TGF-β2 (Transforming Growth Factor-β2), which resulted in a
   significant elevation of EMT (Epithelial to Mesenchymal Transition)
   like features. Molecular analysis of EMT markers showed an increase at
   both RNA and protein levels upon TGF-β2 administration along with
   up-regulation of TGF-β-induced Smad signaling. Induction of EMT was
   associated with a simultaneous increase in reactive oxygen species
   (ROS) and cytostasis of TGF-β2-treated cells. Importantly, quenching of
   ROS resulted in a significant promotion of TGF-β2-induced EMT.
   Furthermore, cells treated with TGF-β2 also showed an enhanced
   autophagic flux. Interestingly, inhibition of autophagy by
   chloroquine-di-phosphate (CQDP) or siRNA-mediated ablation of ATG5
   drastically inhibited TGF-β2-induced EMT. Autophagy inhibition
   significantly increased ROS levels promoting apoptosis. It was further
   observed that pro-inflammatory cytokine like, TNF-α (Tumor Necrosis
   Factor-α) can antagonize TGF-β2-induced response by down-regulating
   autophagy, increasing ROS levels and thus inhibiting EMT in HCC cells.
   This inhibitory effect of TNF-α is serum-independent. Transcriptomic
   analysis through RNA sequencing was further performed which validated
   that TGF-β2-induced autophagic genes are inhibited by TNF-α treatment
   suppressing EMT. Our study suggests that autophagy plays a
   pro-metastatic role facilitating EMT by regulating ROS levels in HCC
   cells and TNF-α can suppress EMT by inhibiting autophagy. We provide
   unique mechanistic insights into the role of TGF-β2 in HCC cells, along
   with appropriate cues to effectively control the disease.

   Keywords: autophagy, ROS, EMT, TGF-β2, TNF-α

INTRODUCTION

   Autophagy is an evolutionarily conserved lysosome-mediated degradation
   pathway by which cells self-digest selected cellular macromolecules and
   maintain homeostasis [[32]1]. Autophagy also facilitates cellular
   survival under various stresses which may include, nutrient
   deprivation, growth factor depletion and hypoxia [[33]2]. In recent
   years, autophagy has emerged as a focus of studies in cancer research,
   as increasing data indicate that autophagy functions as a central step
   allowing tumor cells to survive under drug/metabolic stress [[34]3]. In
   contrary, a host of other studies also describe the role of autophagy
   in antagonizing cell survival and promoting cell death [[35]4]. We
   assume that autophagy and cell death phenomenon like, apoptosis in
   cancer cells, might be linked to each other and occur in a cell type,
   stimulus and context-dependent manner, which needs to be further
   explored.

   Interestingly, cancer develops in a complex milieu and almost
   invariably in an inflammatory setting. Under such circumstances, the
   cellular niche or tumor microenvironment (TME) can contribute
   substantially to the development, metastasis and therapy resistance of
   tumors. A plethora of cytokines, in the TME, play a crucial role in
   shaping tumor progression [[36]5]. Many cytokines are also potent
   modulators of autophagy suggesting that autophagy-cytokine interaction
   has evolved as an important mechanism regulating pathogenesis of
   cancer. While, both archetypal cytokines, like, IFN-γ (Interferon) and
   TNF-α are known to induce autophagy, the classical cytokines like, IL-4
   and IL-13 (Interleukins) are known to suppress it [[37]6].
   Additionally, recent studies have also shown that autophagy, apart from
   being regulated by a host of cytokines, can itself directly control
   secretion of a number of cytokines. Particularly, a perturbation of
   regular autophagic pathways has been associated with increased
   secretion of pro-inflammatory cytokines, like, IL-1α, IL-1β and IL-18
   [[38]7]. Activated inflammasome complexes are often engulfed by
   autophagosomes reducing IL-1β secretion and thus acting as an important
   homeostatic regulator [[39]8]. Also, autophagy has been reported to
   positively regulate the transcription and secretion of IL-8 and IL-6
   [[40]6]. This establishes a strong connection between autophagy and
   cytokines and enforces the need to study further details of the
   interaction to facilitate better understanding of the disease.

   In this regard, the cytokine, TGF-β has been shown to induce
   autophagosome formation and enhanced expression of autophagic markers
   in multiple cancer types, like, human hepatoma, breast cancer cells,
   etc. [[41]9]. However, similar to the dual role of autophagy, the role
   of TGF-β in cancers is controversially discussed. It is proposed to
   function both as a tumor suppressor and also a tumor promoter in a
   cellular and context dependent manner. At early stages of oncogenesis,
   it is known to serve as tumor suppressor, in contrary its role in
   facilitating EMT and metastasis of tumors in advanced cancers is well
   acclaimed [[42]10]. Also, a recent study by Jiang et al. in 2016 shows
   that sustained TGF-β treatment in mammary epithelial cells can result
   in induction of autophagy and reversal of EMT [[43]11]. This endorses
   the contrasting role played by this cytokine purely in a context
   dependent fashion. Given that, TGF-β is a multifunctional cytokine with
   multidirectional role extending from inhibition of growth, induction of
   apoptosis, triggering of EMT, to senescence, all the more emphasizes
   the need for further investigating the molecular effects of this
   cytokine in various cancer cells. Also, whether TGF-β induced autophagy
   in cancer cells facilitates one or more of the diverse TGF-β-mediated
   cellular functions remain to be explored.

   Another important cytokine, with role in autophagy and other varied
   cellular processes is tumor necrosis factor α (TNF-α). It is reported
   to induce autophagy in various cancer cells, like, Ewing sarcoma cells
   [[44]12], human breast cancer [[45]13] and human T lymphoblastic
   leukemia cells [[46]14]. However, how TNF-α is connected to autophagy
   is not fully understood and actually differs across various cell types.
   TNF-α-induced autophagy has been found to be JNK-dependent in vascular
   smooth cells, ERK-mediated in human breast cancer cells and reactive
   oxygen species (ROS)-induced in intestinal epithelial cells [[47]12,
   [48]14].

   All these extensive links, between cytokines like, TGF-β, TNF-α and
   autophagy has made this field an attractive area of future research.
   However, the role of TGF-β and TNF-α in autophagy and possible cross
   talk between the two cytokines in relation to autophagy needs to be
   further investigated. Also, whether the activation of autophagy in
   response to TGF-β enhances cancer cell killing or is a counter stress
   mechanism is still an open-ended question. Here in, our study shows
   that TGF-β2 treatment leads to a simultaneous induction of cytostasis
   and EMT like phenotype in Huh7 cells. The cells undergoing EMT were
   found to utilize autophagy as a pro-survival strategy, as inhibition of
   later abrogated EMT-like features. Furthermore, we observed that
   simultaneous exposure of TNF-α with TGF-β2 antagonize its function and
   attenuate TGF-β2-induced Smad signaling and EMT. Our study addresses
   the link between EMT, autophagy and functioning of two important
   cytokines with respect to their role in autophagy regulation, which can
   be of potential significance to the understanding of the complex
   cancerous milieu.

RESULTS

TGF-β2 induces Smad-dependent EMT

   TGF-β is a multi-functional cytokine that is known to be involved in
   tumor suppression, cancer invasion and also for its pro-fibrogenic role
   in almost all fibrotic diseases [[49]15]. It can effectively
   orchestrate diverse cellular effects depending on the cell type and
   context. One of the primary established functions of TGF-β is to
   promote EMT of cancer cells [[50]16]. EMT can be described as the
   process promoting metastasis where epithelial cells undergo
   trans-differentiation by shedding off their polarity and epithelial
   characteristics, which facilitate their migration into neighboring
   tissues; and TGF-β is a well-known inducer of it. Taking this into
   consideration we were interested in exploring TGF-β2-mediated EMT
   induction in HCC cell type (Huh7) and the signaling associated with it.
   A distinct change in morphology, marked by extended cellular phenotype
   was observed in cells exposed to TGF-β2, when compared to untreated
   control (Figure [51]1A). Huh7 cells treated separately with an
   unrelated cytokine e.g., IL-6 in this case, for a similar time period
   failed to show any change in morphology ([52]Supplementary Figure 1A).
   During EMT, the down-regulation of E-cadherin is stabilized by the
   up-regulation of N-cadherin, which connects to the cytoskeleton through
   β-catenin resulting in a cadherin switch that controls cell adhesion
   [[53]17]. Another crucial marker for EMT is Vimentin which facilitates
   invasion of cells into surrounding tissues [[54]18]. In our study, the
   expression of these key genes was monitored post TGF-β2 exposure
   through real time PCR and immunoblot. A significant increase in
   transcript level of N-cadherin and Vimentin was observed along with a
   concurrent reduction of E-cadherin mRNA (Figure [55]1B). Immunoblot
   analysis further revealed a significant increase in Vimentin,
   N-cadherin and also β-catenin protein levels and a simultaneous
   decrease in E-cadherin protein levels in Huh7 cells upon 48 h of
   exposure to TGF-β2 (Figure [56]1C). Additionally, dose kinetics studies
   were also performed which showed similar trend in increased expression
   of N-cadherin and Vimentin at different doses of TGF-β2 with respect to
   untreated control; similarly, E-cadherin showed a significant decrease
   in protein levels at higher doses of TGF-β2 (Figure [57]1D). The
   altered regulation of the above markers signified an EMT like
   transition in the epithelial Huh7 cells after TGF-β2 administration.
   Investigation of the upstream signaling leading to EMT revealed that
   the canonical TGF-β signaling pathway was activated following TGF-β2
   exposure. A high level of phospho-Smad-2 protein was observed in cells
   undergoing EMT (Figure [58]1E). To further validate a Smad-dependent
   effect, TGF-β-induced Smad signaling was inhibited with SIS3, an
   inhibitor of TGF-β and Activin signaling that acts through specific
   suppression of Smads without affecting the MAPK/p38, ERK or PI3-kinase
   signaling pathways [[59]19]. A reduction in Smad levels was observed
   upon treatment with SIS3, as analyzed by immunoblot analysis
   ([60]Supplementary Figure 1B). Importantly, a significant reduction in
   Vimentin protein levels was observed upon Smad inhibition suggesting
   the role of Smad signaling in TGF-β2-induced EMT (Figure [61]1F). Taken
   together, these observations suggest that TGF-β2 treatment induces EMT
   by activating Smad signaling pathway in Huh7 cells.

Figure 1. TGF-β2 induces EMT through Smad signaling in Huh7 cells.

   [62]Figure 1
   [63]Open in a new tab

   (A) Phase-contrast microscopic images of Huh7 cells post 48 h of 5
   ng/ml TGF-β2 treatment; white circles indicate elongated cells. The
   scale bar represents 50 μm, images taken at 40×. (B) Quantitative
   RT-PCR result showing expression of Vimentin, N-cadherin and E-cadherin
   after 48 h of TGF-β2 (5 ng/ml) treatment. (C) Immunoblot assay
   depicting expression levels of proteins like, Vimentin, N-cadherin,
   β-catenin and E-cadherin with and without addition of TGF-β2 (5 ng/ml).
   (D) Immunoblot assay showing protein expression of Vimentin, N-cadherin
   and E-cadherin post TGF-β2 exposure for 48 h at varied concentrations
   (2, 5, 10 ng/ml). (E) Immunoblot assay showing an up-regulation of
   p-Smad-2 after TGF-β2 treatment (5 ng/ml) for 48 h. (F) Immunoblot
   assay showing protein expression of Vimentin post p-Smad-2 inhibition
   with SIS3 in TGF-β2 (5 ng/ml; 48 h) treated cells. SIS3 was added 1 h
   before cytokine treatment. Expression in untreated control was taken as
   arbitrary unit “1”. β-actin served as a loading control.

TGF-β2 induced EMT is dependent on ROS levels

   Reactive oxygen species (ROS)-induced signaling has often been linked
   to the diverse activities of TGF-β [[64]20, [65]21]. TGF-β-induced
   signaling can lead to redox unevenness through mitochondrial damage or
   by inhibiting cellular anti-oxidant activities. Recent evidences
   indicate that TGF-β and ROS can have opposing roles in pro- as well as
   anti-tumor effects [[66]22, [67]23]. For these reasons, understanding
   the interplay between them is important for elucidating TGF-β–mediated
   effects in cancer progression. In this study, we observed that exposure
   of Huh7 cells to different doses of TGF-β2 led to a significant
   increase in intra-cellular ROS levels when compared to un-treated
   control as analyzed through H2DCF-DA fluorimetric assay (Figure
   [68]2A). Time kinetics analysis further showed significantly elevated
   levels of ROS after 48 h and 72 h of TGF-β2 (5 ng/ml) treatment (Figure
   [69]2B). Interestingly, an increase in ROS was associated with
   TGF-β2-induced cytostatic effect. As compared to untreated control,
   significantly less number of viable cells was observed upon treatment
   with different doses of TGF-β2 for varied time points (Figure [70]2C
   and [71]2D). However, treatment with an unrelated cytokine, IL-6 failed
   to show any cytostatic effect on Huh7 cells ([72]Supplementary Figure
   2). To further verify that the reduced viable cell number is not due to
   cytotoxicity induced by TGF-β2, we performed apoptosis assay with
   AnnexinV/PI. TGF-β is reported to induce apoptosis in various cell
   types like, NIH3T3 and AML12 [[73]24]. In our study, at the stipulated
   dose and time (5 ng/ml; 48 h), TGF-β2 failed to show an induction of
   apoptosis (Figure [74]2E). The conventionally used anti-cancer drug,
   cisplatin (35 µM) was taken as a positive control. To further validate
   the cytostatic effect, cell cycle analysis was also performed using
   Propidium Iodide (PI) dye through flow cytometry after TGF-β2 exposure.
   A significant shift in number of cells in G1 phase of the cell cycle,
   indicative of arrest at G1/S juncture was observed establishing the
   cytostatic effect of TGF-β2 in the hepatocellular carcinoma cells
   studied (Figure [75]2F). An inhibition of ROS, with the widely used ROS
   quencher, NAC (N-acetyl cysteine) caused attenuation in ROS levels
   (Figure [76]2G) and significant reversal of cytostatic effect induced
   by TGF-β2 as analyzed by MTT assay (Figure [77]2H). An increased number
   of cells was also observed when Huh7 cells were exposed to both NAC and
   TGF-β2 when compared to only TGF-β2 treatment suggesting a rescue of
   the cytostatic effect upon quenching of TGF-β2-induced ROS (Figure
   [78]2I). This tempted us to investigate the role of ROS in
   TGF-β2-induced EMT. Interestingly a significant increase in EMT markers
   like N-cadherin and Vimentin was observed upon NAC-mediated quenching
   of TGF-β2-induced ROS (Figure [79]2J). These results suggest that
   generation of ROS was acting as a limiting factor attenuating EMT
   induction in Huh7 cells upon exposure to TGF-β2.

Figure 2. TGF-β2 induced EMT is ROS dependent.

   [80]Figure 2
   [81]Open in a new tab

   (A) H2DCF-DA fluorimetric assay showing intra-cellular accumulation of
   ROS after treatment of Huh7 cells with TGF-β2 at the following
   concentrations (2, 5, 10, 20, 50 ng/ml; 48 h). Untreated control was
   taken as arbitrary unit “1”. (B) Bar graph showing time dependent
   increase in intracellular ROS accumulation in Huh7 cells after
   treatment with TGF-β2 (5 ng/ml; 48 h). (C) Viability of Huh7 cells
   measured through MTT assay upon exposure to varied doses of TGF-β2 for
   48 h. (D) Time dependent alterations in cell viability as analyzed
   through MTT assay upon treatment with TGF-β2 (5 ng/ml). (E) Detection
   of apoptotic cells after TGF-β2-treatment (5 ng/ml) for 48 h, as
   analyzed through flow cytometry using AnnexinV-FITC/Propidium Iodide
   (PI) staining. Cisplatin (35 μm, 24 h) treated cells were taken as
   positive control. (F) Cell cycle analysis of TGF-β2-exposed cells (48
   h) using PI dye by flow cytometry. A shift in G1 peak is marked by an
   arrow. Percentage of cells in each phase of cell cycle is represented
   by bar diagram. (G) Intracellular levels of ROS upon addition of NAC in
   TGF-β2 (5 ng/ml) exposed Huh7 cells as analyzed through H2DCF-DA
   fluorimetric assay. (H) MTT assay showing percentage cell viability
   upon inhibition of ROS (5 ng/ml) for 48 h; 5 mM of NAC was added 1 h
   prior to TGF-β2 exposure (5 ng/ml; 48 h). (I) Phase-contrast
   microscopic images following TGF-β2 (5 ng/ml, 48 h) or TGF-β2 plus NAC
   treatment for 48 h in Huh7 cells. The scale bar represents 50 μm,
   images were taken at 40× (J) Immunoblot analysis showing expression of
   N-cadherin and Vimentin upon quenching of ROS with NAC treatment.

TGF-β2-induced autophagy limits intra-cellular ROS levels

   TGF-β has been previously reported to induce autophagy in various
   mammalian cancer cell types, extending from normal bovine mammary
   epithelial cells to mammary carcinoma cells in vitro [[82]25].
   Autophagy triggered, in turn can play a dual and paradoxical role
   either in tumor progression or suppression. It is known to mediate a
   cytoprotective phenomenon under nutrient deprivation [[83]26], while,
   there are other evidences which document its role in promoting cell
   death [[84]27]. The above observations prompted us to investigate
   whether autophagy is activated by TGF-β2 administration in Huh7 cells,
   and if so, what role it plays in such context. Exposure of Huh7 cells
   to TGF-β2 led to an increase in intracellular monodansylcadaverine
   (MDC) fluorescence indicative of autophagosome formation which was
   measured fluorometrically (Figure [85]3A). Microscopic analysis further
   revealed an increase in the number of MDC labelled spherical autophagic
   vacuoles distributed in the cellular cytoplasm, when compared to
   untreated control (Figure [86]3B). A significant increase in specific
   autophagic markers like, microtubule-associated protein light chain
   3B-II (LC3B-II) and ATG5, indicative of enhanced autophagic activity
   was also observed by immunoblot analysis upon exposure to TGF-β2 (5
   ng/ml; 48 h) (Figure [87]3C). A lower dose of TGF-β2 (2 ng/ml) for a
   similar time period also stimulated LC3B-II accumulation indicative of
   enhanced autophagy ([88]Supplementary Figure 3). Simultaneously, a
   decrease in p62, the turnover of which serves as a useful marker for
   induction of autophagy was obtained following exposure of Huh7 cells to
   TGF-β2 (Figure [89]3C). Since, the accumulation of autophagosomes is
   not always indicative of autophagy induction and may just represent
   either the increased generation of autophagosomes and/or a block in
   autophagosomal maturation, we hence checked for the difference in the
   amount of LC3B-II in the presence or absence of the lysosomal
   inhibitor, chloroquine di-phosphate (CQDP). The difference in the
   amount of LC3B-II in the presence and absence of CQDP represents the
   amount of LC3 that is delivered to lysosomes for degradation (i.e.,
   autophagic flux) [[90]23]. An increase in LC3B-II levels, indicative of
   autophagic flux, was observed in cells treated with CQDP plus TGF-β2
   when compared to only CQDP-treated cells (Figure [91]3D). Considering
   the induction of autophagy and the context-dependent growth-inhibitory
   effect of TGF-β2, we were interested to investigate the role of
   autophagy. Interestingly, the inhibition of autophagy with CQDP caused
   a significant increase in intracellular ROS levels and also an
   associated shift in fluorescence of JC-1 dye indicative of a
   significant dip in mitochondrial membrane potential (Figure [92]3E and
   [93]3F). This was an important observation, given that we previously
   observed that ROS limits TGF-β2-induced EMT; however how, inhibition of
   autophagy and hence increased ROS effects EMT remained to be addressed.

Figure 3. TGF-β2 exposure induces pro-survival autophagy in Huh7 cells.

   [94]Figure 3
   [95]Open in a new tab

   (A) MDC fluorimetric assay showing autophagosome accumulation in Huh7
   cells post 48 h of TGF-β2 treatment (5 ng/ml). (B) Fluorescent
   microscopy images of MDC fluorescence accumulation in TGF-β2-treated (5
   ng/ml, 48 h) Huh7 cells. The scale bar represents 50 μm, images were
   taken at 100×. (C) Immunoblot analysis showing expression of autophagic
   markers- LC3-BII, ATG5 and p62 in TGF-β2-treated (5 ng/ml, 48 h) cells.
   Expression in untreated control was taken as arbitrary unit “1”.
   β-actin served as loading control. (D) Determination of autophagic flux
   through immunoblot analysis of LC3B-II accumulation after TGF-β2
   exposure (5 ng/ml, 48 h), in presence or absence of CQDP (10 µM); the
   autophagy inhibitor was added 1 h before addition of TGF-β2. The
   expression level of TGF-β2 was taken as arbitrary unit “1”. (E)
   H2DCF-DA fluorimetric assay showing fold increase in ROS levels, upon
   inhibition of autophagy by CQDP (10 µM) in TGF-β2-treated cells (5
   ng/ml, 48 h). ROS level in only TGF-β2 treated cells was taken as
   arbitrary unit “1”. (F) JC-1 fluorescence estimation by flow cytometry,
   showing a decrease in red fluorescence indicative of reduction of
   mitochondrial membrane potential after exposure to TGF-β2 (5 ng/ml, 48
   h) and CQDP (10 µM; added 1 h before cytokine addition), compared to
   only TGF-β2

TGF-β2-induced autophagy facilitates EMT by regulating ROS levels

   Cancer cells are known to generate moderate to high levels of ROS to
   aid their need for enhanced proliferation, migration and metastasis;
   however, resistance to chemotherapeutic drugs can also be attributed to
   the higher intra-cellular levels of ROS in cancer cells. In contrary,
   increasing the level of ROS has often been utilized as a strategy to
   tip the balance of cancer cells from proliferation towards cell death.
   Under the above circumstances, the generation of enhanced intracellular
   ROS plays a pivotal role in initiation of cell death. In corroboration
   to above, in our study, inhibition of autophagy with CQDP resulted in
   an increased ROS and an associated cell death of Huh7 cells. Phase
   contrast images and cell viability analyzed through MTT assay provided
   evidences for the same (Figure [96]4A and [97]4B). To further validate
   the induction of cell death by autophagy inhibition, apoptosis was
   analyzed through AnnexinV/PI staining (Figure [98]4C). A significantly
   increased cell death was observed in cells treated with TGF-β2 and
   CQDP, compared to only TGF-β2. At 10 µM concentration, for the studied
   time period, only CQDP treatment did not show a significant cell death,
   when analyzed through MTT assay ([99]Supplementary Figure 4). We
   speculate that TGF-β2-induced autophagy restricts intra-cellular ROS
   levels to go up substantially probably by clearing off damaged
   mitochondria; hence, an inhibition of lysosomal activity by CQDP
   results in a significant boost in intra-cellular ROS levels triggering
   cell death. However further studies are required to confirm the
   hypothesis of mitophagy.

Figure 4. TGF-β2-induced autophagy suppresses ROS and promotes EMT.

   [100]Figure 4
   [101]Open in a new tab

   (A) Phase contrast microscopic images showing increase in cell death
   upon inhibition of TGF-β2 (5 ng/ml, 48 h)-induced autophagy with CQDP
   (10 µM) addition. The scale bar represents 50 μm, images were taken at
   40×. (B) MTT assay showing percentage cell viability upon inhibition of
   TGF-β2-induced autophagy by CQDP addition prior to cytokine treatment.
   The percentage viability of cells after TGF-β2 treatment was taken as
   arbitrary unit “1”. (C) Detection of apoptotic cells following addition
   of CQDP (10 µM), 1 h prior to TGF-β2 -treatment (5 ng/ml, 48 h) as
   analyzed through flow cytometry using AnnexinV-FITC/Propidium Iodide
   (PI) staining. Percentage apoptotic cells are plotted in bar diagram.
   (D) Real time PCR analysis showing expression of N-cadherin and
   E-cadherin upon autophagy inhibition by CQDP (10 µM) in TGF-β2-treated
   cells. (E) Immunoblot analysis showing Vimentin and E-cadherin protein
   expression upon autophagy inhibition by CQDP (10 µM) in TGF-β2-treated
   cells. (F) Immunoblot analysis showing p-Smad-2 protein expression upon
   inhibition of autophagy by CQDP (10 µM) in TGF-β2-treated cells. (G)
   Immunoblot analysis showing knockdown of ATG5 in Huh7 cells transiently
   transfected with siATG5. (H) Immunoblot analysis showing protein
   expression of Vimentin upon inhibition of autophagy using siATG5. (I)
   Immunoblot analysis showing protein expression of autophagic markers-
   LC3-BII and ATG5 in untreated control, TGF-β2-treated (5 ng/ml, 48 h)
   and TGF-β2 plus NAC (5 mM) treated cells. Expression in untreated
   control was taken as arbitrary unit “1”. GAPDH expression was used as
   housekeeping control for RT-PCR and β-actin served as loading control
   for immunoblot.

   Interestingly, inhibition of autophagy with CQDP resulted in a
   significant decrease in EMT markers, as verified both at the RNA and
   protein levels (Figure [102]4D and [103]4E). Protein levels of Vimentin
   were significantly reduced upon inhibition of autophagic flux by CQDP
   (Figure [104]4E). Furthermore, p-Smad2 levels were found to be
   significantly going down post CQDP treatment in TGF-β2 exposed Huh7
   cells (Figure [105]4F). To further confirm that inhibition of autophagy
   has an effect on EMT, we transiently transfected Huh7 cells with siRNA
   against ATG5. In corroboration to above, knock down of ATG5 (Figure
   [106]4G) significantly reduced TGF-β2-induced Vimentin expression level
   (Figure [107]4H). The above results are conclusive of the fact that
   TGF-β2-induced autophagy facilitates EMT by suppressing and thus
   regulating intra-cellular ROS levels; with inhibition of autophagy ROS
   levels shoot up suppressing EMT as well as survival of Huh7 cells. To
   understand, what happens to autophagy levels if ROS is quenched, we
   checked for autophagic markers post NAC treatment. Interestingly,
   LC3B-II protein levels significantly went down upon addition of the ROS
   quencher (Figure [108]4I). This suggests that autophagy was
   instrumental in limiting ROS levels, and when intra-cellular ROS was
   quenched, autophagy was dispensable for EMT, as demonstrated in Figure
   [109]2J. Although the role of TGF-β–induced autophagy remains unclear
   in majority of cell types, and might be different in certain stages and
   aspects of tumor development, here we establish that autophagy supports
   TGF-β2–mediated cell survival and induction of EMT in Huh7 cells by
   controlling intra-cellular ROS.

TGF-β2–induced EMT is antagonized by TNF-α

   Few studies have previously addressed the antagonistic activities
   exerted by pro-inflammatory cytokines and TGF-β. The opposing roles
   exerted by pro-inflammatory cytokines against TGF-β are known to play
   an essential role in context to maintaining tissue homeostasis and
   extracellular matrix deposition [[110]28]. We therefore wondered
   whether the functionally opposing nature of these cytokines can
   represent a useful paradigm in the study of complex cellular signals
   regulating HCC pathogenesis. In this context, we explored the effect of
   TNF-α administration on TGF-β2–induced effects described above. TNF-α,
   generally released by activated macrophages, is a key player modulating
   inflammatory responses in the HCC tumor microenvironment [[111]29]. Our
   findings reflect that Huh7 cells when exposed to TGF-β2 and TNF-α
   simultaneously, showed a significant reduction of TGF-β2-induced EMT
   features. A significant attenuation in mRNA expression of the EMT
   markers like, Vimentin and N-cadherin in TGF-β2 and TNF-α-treated
   samples was observed through RT-PCR (Figure [112]5A). Furthermore,
   immunoblot analysis also revealed a marked reduction in Vimentin,
   N-cadherin and β-catenin protein levels upon simultaneous exposure to
   TGF-β2 and TNF-α compared to only TGF-β2 treatment (Figure [113]5B). At
   the same time, E-cadherin expression substantially recovered in cells
   treated with TGF-β2 and TNF-α, when compared to only TGF-β2-treated
   cells (Figure [114]5B). Similar results were obtained at different time
   points studied where TNF-α antagonized TGF-β2 induced effects (Figure
   [115]5C and [116]5D). Immunoblot analysis showed a significant
   reduction in Vimentin and N-cadherin protein levels analyzed after 72 h
   and 96 h of TGF-β2 plus TNF-α exposure (Figure [117]5C and [118]5D).
   Moreover, a significant down-regulation of N-cadherin and Vimentin
   along with an up-regulation of E-cadherin in TGF-β2 and TNF-α-treated
   samples, was observed, even after serum rescue emphasizing the fact
   that TNF-α-mediated antagonistic effects are serum independent (Figure
   [119]5E). Similar results demonstrating an antagonistic effect of TNF-α
   over TGF-β2-mediated EMT were obtained in other epithelial cell types
   studied, like, lung cancer (H1299) and other HCC cell type (HepG2)
   suggesting a probable universal effect across different cell types
   ([120]Supplementary Figure 5A and 5B).

Figure 5. TNF-α suppresses TGF-β2-induced EMT.

   [121]Figure 5
   [122]Open in a new tab

   (A) Real time PCR results showing expression of Vimentin and E-cadherin
   mRNA levels, upon simultaneous exposure of Huh7 cells to TGF-β2 (5
   ng/ml) and TNF-α (20 ng/ml) for 48 h. (B) Immunoblot analysis showing
   protein expression of Vimentin, N-cadherin, β-catenin and E-cadherin
   upon simultaneous exposure of TGF-β2 (5 ng/ml) and TNF-α (20 ng/ml) for
   48 h in Huh7 cells. (C) Immunoblot analysis showing protein expression
   of Vimentin, N-cadherin and β-catenin upon 72 h of simultaneous
   exposure to TGF-β2 (5 ng/ml) and TNF-α (20 ng/ml) in Huh7 cells. (D)
   Immunoblot analysis showing protein expression of N-cadherin and
   β-catenin upon 96 h of exposure to TGF-β2 (5 ng/ml) and TNF-α (20
   ng/ml). (E) Immunoblot analysis showing protein expression of Vimentin,
   N-cadherin and E-cadherin upon exposure to TGF-β2 (5 ng/ml) and TNF-α
   (20 ng/ml) following rescue of serum with 10% FBS. Expression in
   untreated control was taken as arbitrary unit “1”. GAPDH expression was
   used as housekeeping control for RT-PCR and β-actin served as loading
   control for immunoblot.

TNF-α inhibits TGF-β2–induced autophagy and signaling

   More recently, existing studies have shown that several signaling
   pathways, activated in response to various stimuli, can lead to an
   increased expression of inhibitory Smad-7, which, in turn, prevents
   TGF-β-induced signaling [[123]30]. Alternatively, specific cytokines
   have also been shown to directly interfere with Smad-2/3 functioning
   resulting in similar effects. Therefore, we checked for Smad-7
   expression and activation of Smad-2, upon exposure to TGF-β2 and TNF-α.
   Interestingly, we observed a striking increase in Smad-7 levels and a
   stark decrease in phospho-protein levels of Smad-2 upon simultaneous
   exposure of both the cytokines (Figure [124]6A). Additionally, an
   attenuation in protein expression of TGF-β2-induced autophagic markers-
   LC3B-II and ATG5 was also observed in TGF-β2 and TNF-α-treated samples
   suggesting that a reduction of EMT like features could probably be
   attributed to inhibition of autophagy by TNF-α (Figure [125]6B).
   Similar results depicting a significant reduction of LC3B-II were
   obtained when cells were exposed to TGF-β2 for prolonged time periods
   like, 72 h and 96 h as well (Figure [126]6C and [127]6D). LC3B-II
   levels were found to go down significantly even after serum rescue
   suggesting TNF-α-mediated autophagy inhibition is a serum independent
   effect (Figure [128]6E). Interestingly, a significant elevation in ROS
   levels was also observed when Huh7 cells were exposed to both TGF-β2
   and TNF-α (Figure [129]6F). To explore the effect of increased ROS on
   fate of cells, we analyzed viability of cells upon exposure to TGF-β2
   and TNF-α. This resulted in an increased cytotoxicity of Huh7 cells
   when compared to any of the cytokine exposure independently (Figure
   [130]6G). Collectively our results suggest that TNF-α can abrogate
   TGF-β-induced metastatic spreading and proliferation of HCC cells by
   targeting autophagy signaling and regulating intra-cellular ROS levels.

Figure 6. TNF-α antagonizes TGF-β2-induced signaling and autophagy.

   [131]Figure 6
   [132]Open in a new tab

   (A) Immunoblot analysis showing smad7 and p-Smad-2 protein expression
   in Huh7 cells exposed to TNF-α (20 ng/ml, 48 h) along with TGF-β2 (5
   ng/ml) for 48 h. (B) Immunoblot analysis of autophagic markers- LC3B-II
   and ATG5 in the presence or absence of TNF-α (20 ng/ml, 48 h) along
   with TGF-β2 (5 ng/ml). (C) Immunoblot analysis of LC3B-II post 72 h of
   TNF-α (20 ng/ml) and TGF-β2 (5 ng/ml) treatment. (D) Immunoblot
   analysis of LC3B-II post 96 h of TNF-α (20 ng/ml) and TGF-β2 (5 ng/ml)
   treatment. (E) Immunoblot analysis of LC3B-II upon simultaneous
   exposure to TGF-β2 (5 ng/ml) and TNF-α (20 ng/ml) for 48 h when serum
   was rescued with 10% FBS. (F) H2DCF-DA fluorimetric analysis showing
   elevated ROS levels upon simultaneous exposure to TGF-β2 (5 ng/ml) and
   TNF-α (20 ng/ml) for 48 h. (G) MTT assay showing percentage cell
   viability upon TGF-β2 (5 ng/ml) and TNF-α (20 ng/ml) treatment for 48
   h, when compared to independent cytokine treatment or untreated cells.

Transcriptomic analysis depicting TNF-α-mediated antagonism of TGF-β2-induced
effects

   Illumina paired end reads (150*2) were generated for untreated control,
   TGF-β2, TNF-α and TGF-β2 plus TNF-α exposed Huh7 cells and reference
   based transcriptome analysis was carried out. An average of 91.7% of
   the reads were aligned to the reference genome. Approximately, 89 K
   transcripts covering ∼22 K genes were found to be expressed across all
   the samples. On an average, approximately 32% of the transcripts were
   differentially regulated in cytokine-treated samples of which 6.2 K
   transcripts were found to be uniquely expressed and 6029 transcripts
   were found to be common across all the samples, which is represented in
   the form of a Venn diagram (Figure [133]7A). Top 40 de-regulated
   transcripts in TGF-β2 compared to control, and in TGF-β2 plus TNF-α
   compared to TGF-β2-exposed samples are represented in the form of a
   Heatmap (Figure [134]7B). The key genes regulating EMT, like,
   N-cadherin, Vimentin and β-catenin were found to be significantly
   up-regulated in TGF-β2-treated samples when compared to control with a
   log2fold change value of 2.28, 1.10281 and 1.15 respectively.
   Substantiating our previously observed results, the expression of
   N-cadherin, Vimentin and β-catenin were either neutrally regulated or
   significantly down-regulated in TGF-β2 plus TNF-α treated samples
   (N-cadherin-0.092569, Vim-0.174, β-catenin-0.98). Also, in
   corroboration to results described earlier, E-cadherin and Smad7 were
   found to be drastically going up with log2fold change values of 2.14
   and 6.4 respectively following TGF-β2 plus TNF-α exposure in comparison
   to only TGF-β2-treated samples. To have a holistic idea on
   transcriptomic alterations associated with intracellular signaling,
   pathway enrichment analysis was performed. Amongst the top pathways
   that were severely de-regulated post exposure to cytokines, we observed
   that pathways related to autophagy signaling, lysosome and protein
   degradation machinery were the ones that were pre-dominantly altered
   (Figure [135]7C). This is suggestive of the fact that there is a
   significant alteration of intra-cellular material or protein turn-over
   post exposure to cytokines which might determine cellular fate.
   Importantly, in accordance to earlier results, a significant
   down-regulation of autophagic genes was observed in TGF-β2 plus
   TNF-α-treated samples when compared to only TGF-β2-exposed cells. The
   log2fold change in some of the key autophagy-related genes in response
   to cytokines is represented in the form of a graph (Figure [136]7D).
   Two key genes involved in autophagy which were found to be
   significantly down-regulated in TGF-β2 plus TNF-α-treated samples
   included, Atg16L1 and WIPI2. Atg16L1 along with other Atgs are some of
   the final Atgs recruited that determines the site of LC3-II formation;
   LC3 family of proteins are required for phagophore expansion, closure,
   and cargo recruitment. Whereas, WIPI2 acts immediately upstream of
   Atg16L1 and is responsible for Atg12-5-16L1 recruitment to omegasomes
   (autophagosomes arise from endoplasmic reticulum-derived omegasomes),
   resulting in LC3 lipidation and subsequent autophagy. Furthermore, KEGG
   autophagy pathway graph rendered by Pathview also shows an
   up-regulation of genes (in red) (Figure [137]8A) involved in autophagic
   pathway in TGF-β2-treated samples compared to control; however, the
   same genes did not show a significant up-regulation in TGF-β2 plus
   TNF-α-treated samples compared to only TGF-β2 (Figure [138]8B).
   Overall, the transcriptomic analysis authenticates and emphasizes on
   the up-regulation of autophagy supporting TGF-β2-induced EMT and
   attenuation of the same upon addition of TNF-α in Huh7 cells; we also
   provide valuable information on critical genes that are key to
   regulation of autophagy and EMT upon cytokine exposure.

Figure 7. Transcriptomic analysis showing TNF-α-mediated antagonism of TGF-β2
induced effects.

   [139]Figure 7
   [140]Open in a new tab

   (A) Venn diagram showing total number of transcripts and common
   transcripts across four samples sequenced- untreated control (C48),
   TGF-β2 (5 ng/ml; 48 h), TNF-α (20 ng/ml; 48 h) and TGF-β2 (5 ng/ml; 48
   h) plus TNF-α (20 ng/ml; 48 h). (B) Heatmap demonstrating top 40
   de-regulated transcripts in TGF-β2 (5 ng/ml; 48 h) treated samples when
   compared to untreated control; and in TGF-β2 (5 ng/ml; 48 h) plus TNF-α
   (20 ng/ml; 48 h) treated samples when compared to only TGF-β2
   segregated based on their log2 fold change values. (C) Pathway
   enrichment analysis showing the top 10 pathways getting de-regulated
   post TGF-β2 treatment. (D) Graph showing up or down regulation of
   transcripts involved in autophagy, upon exposure to TGF-β2 (5 ng/ml; 48
   h) in comparison to untreated control; and in TGF-β2 (5 ng/ml; 48 h)
   plus TNF-α (20 ng/ml; 48 h) treated samples when compared to only
   TGF-β2.

Figure 8. KEGG pathway graph rendered by Pathview.

   [141]Figure 8
   [142]Open in a new tab

   KEGG Pathview showing autophagy pathway genes up or down regulated in
   TGF-β2 (5 ng/ml; 48 h) treated samples with respect to control (A), and
   in TGF-β2 (5 ng/ml; 48 h) plus TNF-α (20 ng/ml; 48 h) treated samples
   compared to only TGF-β2 (5 ng/ml; 48 h) (B).

DISCUSSION

   HCC is one of the most prevalent cancers worldwide with a relative
   5-year survival rate as low as 15% [[143]31]. This authenticates the
   need for more studies related to the understanding of the disease. The
   tumor microenvironment is known to play a critical role in the complex
   etiology of HCC where an intricate interplay exists between the
   hepatocytes and surrounding cells [[144]32]. Cytokines like, TGF-β and
   TNF-α are secreted in the HCC tumor milieu, which alongside other
   cytokines, growth factors and tumor infiltrating leukocytes invariably
   create a chronic inflamed state contributing significantly to the
   progression of the disease [[145]32]. Hence, in this study we
   investigated the effect of the above cytokines in HCC cells and their
   functional co-relation with respect to disease progression.

   TGF-β, as a cytokine has a plethora of biological functions. TGF-β
   signaling is known to play a tumor suppressive function, attenuating
   cell growth and inducing apoptosis. On the contrary, TGF-β is one of
   the most powerful activator of EMT during tumor progression. TGF-β in
   late stages of cancers induces EMT, promoting cellular migration and
   anoikis resistance [[146]33]. This complicates the biological
   understanding of the spectrum of its functional diversity encouraging
   more studies elucidating its role. Interestingly, autophagy seems to be
   amongst the wide plethora of cellular processes that is under the
   control of TGF-β. It is known to activate autophagy in both normal and
   cancer cells [[147]9]. It has been observed that cancer cells exposed
   to TGF-β can result in a strong activation of autophagy marked by an
   increased expression of pro-autophagic genes. This effect is primarily
   facilitated by Smad signaling. Functional exploration of TGF-β-induced
   autophagy supports its role primarily in induction of apoptosis.
   Additional supportive evidences also emphasize on the fact that
   TGF-β-induced autophagy inhibits metastatic progression of cancer cells
   [[148]34]. However, TGF-β can probably trigger both pro-tumorigenic and
   anti-tumorigenic signals and the choice may be completely dependent on
   the cellular context and the stage of tumor progression [[149]33].
   Interestingly, in our results, we observed that TGF-β2 treatment led to
   simultaneous induction of cytostasis and EMT like phenotype in Huh7
   cells. The cells utilized autophagy as a pro-survival strategy, as
   inhibition of it by CQDP or siRNA-mediated ablation of ATG5, abrogated
   EMT-like features. Interestingly, Yang et al. in 2006 suggested that
   TGF-β, being a pleiotropic molecule can not only induce EMT, but can
   also simultaneously induce other cellular processes like, apoptosis in
   mouse hepatocyte cells [[150]35]. This endorses the fact that TGF-β can
   concurrently regulate multiple biological functions. In corroboration
   to above, we observed that pro-survival autophagy was required for the
   Huh7 cells to promote EMT features. Our results are in corroboration to
   Pang et al. (2016) who showed that TGF-β can induce both autophagy and
   EMT in mouse tubular epithelial cells and inhibition of autophagy
   reduced TGF-β-induced EMT [[151]36]. We speculate that, during
   metastatic spreading, a massive reorganization of cellular interaction
   properties and loss of the adhesion, can render cancer cells without an
   effective anchorage thus inducing apoptosis. Under these circumstances,
   autophagy can come to the rescue and induce resistance to cell death.
   In our study, pre-treatment of Huh7 cells with CQDP or siRNA against
   ATG5, resulted in down-regulation TGF-β-induced metastatic features,
   enhanced cytotoxicity and apoptotic cell death thus proving that
   autophagy was acting as a survival response facilitating EMT. Similar
   pro-survival role of autophagy has been observed before but it is
   completely context and cell type dependent [[152]19].

   Since, recent studies suggest that mitochondrial ROS are essential for
   TGF-β-mediated gene expression [[153]37], we explored ROS levels upon
   TGF-β exposure as well. Significantly increased ROS levels were
   observed in cells exposed to TGF-β. Interestingly, quenching of ROS by
   NAC resulted in an increased expression of EMT markers. In contrary,
   inhibition of autophagy resulted in a significant enhancement in ROS
   levels suggesting that TGF-β-induced autophagy limits ROS facilitating
   EMT. We assume that this increase in ROS levels, upon autophagy
   inhibition, was the trigger to tip the balance of Huh7 cells from
   survival towards cell death. This role of ROS and autophagy in
   TGF-β-induced EMT is demonstrated through a schematic diagram (Figure
   [154]9).

Figure 9. A schematic representation of the effect of TGF-β2 and TNF-α on
Huh7 cells.

   [155]Figure 9
   [156]Open in a new tab

   Previous report suggests that the synthesis of type-I collagen is
   regulated by cytokines especially, TGF-β [[157]38], and the
   matrix-remodeling function of the pro-inflammatory cytokine, TNF-α is
   opposite to TGF-β [[158]39]. We hence explored the effect of TNF-α
   addition on TGF-β-induced effects in Huh7 cells. Interestingly, we
   observed that simultaneous exposure of TNF-α with TGF-β antagonized the
   function of later and attenuated TGF-β-induced Smad signaling and EMT.
   Similar results were obtained upon transcription profiling of
   TGF-β-treated samples and its comparison to TGF-β and TNF-α exposed
   cells. Apart from the EMT-specific genes like N-cadherin, Vimentin and
   others, which were significantly inhibited, two of the major autophagy
   genes which showed reduced expression on TNF-α exposure were Atg16L1
   and WIPI2. These genes are required during autophagosome maturation and
   lipidation [[159]40, [160]41], and with both the genes significantly
   going down in TGF-β2 plus TNF-α-treated samples we assume that TNF-α
   inhibits autophagy and hence EMT by suppressing the final steps of
   autophagosome maturation and lipidation.

   Several pharmacological agents have till date been tested as a
   treatment modality to attenuate the effect of TGF-β, specifically, with
   respect to collagen deposition in fibrotic diseases. One approach has
   been to reduce TGF-β gene expression, either by suppressing
   transcription or by altering RNA stability; or by the use of
   antioxidants such as α-tocopherol [[161]42]; or through administration
   of anti-TGF-β antiserum [[162]43]. However, the use of antagonistically
   functioning cytokines to reduce TGF-β-induced effects in cancer cells
   is still lacking. In this regard, our study is unique and provides a
   novel and promising alternative for the treatment of HCC. Here, we
   provide concrete evidences that autophagy, a genetically regulated and
   finely orchestrated process of selective cell survival, induced by
   TGF-β2 can play a critical role in EMT sustenance of HCC cells and
   TNF-α antagonizes this effect by suppression of autophagy.

MATERIALS AND METHODS

Chemicals and reagents

   TGF-β2 (#SRP3170), TNF-α (# H8916), 2′,7′-dichlorofluorescin diacetate
   (DCFDA, # D6883), monodansylcadaverine (MDC, # D4008), chloroquine di
   phosphate (CQDP, #C6528), propidium iodide (PI; #P4864) were purchased
   from Sigma; N-Acetyl-L-cysteine (NAC, #47866) and
   3-(4,5-dimethylthiazol-2-yl)-2,5-di-phenyltetrazolium bromide (MTT,
   #33611) were obtained from SRL. FITC conjugated Annexin-V (#A13199),
   Annexin-binding buffer (#1796344) were procured from Thermo Fisher
   Scientific and JC-1 was bought from Santacruz (#sc-364116A). SIS3 was
   procured from (Bio vision # 2227-1), siATG5 was from Dharmacon #
   M-004374-04). Lipofectamine 3000 was from Invitrogen (#L3000-001).
   Unless otherwise mentioned, gene specific primary and secondary (goat
   anti-rabbit IgG) antibodies were obtained from Cell Signaling
   Technology (CST, USA).

Cell culture

   Human hepatocellular carcinoma cell line (Huh7) was cultured at 37°C,
   5% CO[2] in Dulbecco’s modified minimal essential medium (DMEM; Gibco,
   #12800-017) supplemented with 10% fetal bovine serum (FBS; Invitrogen,
   #26140-079) and 1% penicillin-streptomycin mixture (Invitrogen,
   #10378-016). Cells were grown till 50% confluency in DMEM with 10% FBS
   and rinsed with phosphate-buffered saline (PBS) and replaced with serum
   starved medium (DMEM containing 2% FBS) and kept for 12 h prior to
   cytokine treatments. For performing serum rescue experiment, 2 h post
   cytokine treatment, serum (10%) was added and cells were incubated with
   cytokine for 48 h.

Cell viability assay

   Assessment of cell viability was performed by MTT assay. Briefly, cells
   growing in log phase were cultured in 96-well plates overnight. The
   following day, cells were washed with PBS and starved for 12 h as
   mentioned above, post which cytokines at desired concentrations were
   added. Thereafter, MTT (20 µl; stock concentration- 5 mg/ml) was added.
   Cells were then incubated for 4 h; post which formazan crystals were
   solubilized in DMSO and readings were obtained at 495 nm with a
   differential filter at 630 nm using an enzyme-linked immune-sorbent
   assay (ELISA) micro-plate reader (Start-fax 2100). Percentage of viable
   cells were calculated using the formula: viability (%) = (mean
   absorbance value of drug-treated cells)/(mean absorbance value of
   control) *100 [[163]44].

RNA isolation and real-time PCR (RT-PCR)

   Total RNA was isolated using TRIzol reagent (Sigma, #T9424).
   Complementary DNA (cDNA) was synthesized using GeneSure First Strand
   cDNA Synthesis kit (Genetix, # PGK162-B) with oligodT, as per
   manufacturer’s protocol. Templates were amplified using gene specific
   primers for Vimentin, N-cadherin and E-cadherin taking GAPDH as
   housekeeping control and detected using SYBR Green Supermix (Bio-Rad,
   #170-8882AP) in CFX Connect RT-PCR System (Bio-Rad). The primers used
   and their sequences are given below: - Vimentin- forward 5′-
   TCTACGAGGAGGAGATGCGG-3′, reverse 5′-GGTCAAGACGTGCCAGAGAC-3′;
   E-cadherin-forward 5′-TACACTGCCCAGGAGCCAGA-3′, reverse
   5′-TGGCACCAGTGTCCGGATTA-3′; N-cadherin-forward
   5′-CGAATGGATGAAAGACCCATCC-3′, reverse 5′-GGAGCCACTGCCTTCATAGTCAA-3′;
   GAPDH- forward 5′-GCACCGTCAAGGCTGAGAAC-3′, reverse 5′-TGGTGA
   AGACGCCAGTGG A-3′. The relative RNA expression was calculated using
   Livak method [[164]45]. The melting temperature for the PCR reactions
   were 53°C for Vimentin and E-cadherin and 55°C for N-cadherin and the
   template was amplified for 30 cycles in each case.

Immunoblotting

   Immunoblotting was performed following protocols as described
   previously [[165]46]. Cells were lysed in modified RIPA buffer
   (Sigma-Aldrich) and protein content was measured using Bradford reagent
   (Thermo Scientific). The cellular protein lysates were run in
   denaturing polyacrylamide gels and thereafter transferred to PVDF
   membrane (Thermo Fisher Scientific, #88518) for blocking with 5%
   skimmed milk (HiMedia). The blots were then probed or re-probed with
   specific primary antibodies and detected using enhanced
   chemi-luminescence (ECL; Thermo Fisher Scientific, #3210) detection
   system following the manufacturer’s protocol. The primary antibodies
   used were as follows: anti-E-cadherin (CST, 24E10), N-cadherin (CST,
   D4R1H), Vimentin (CST, D21H3), β-catenin (CST, D10A8), ATG5 (CST,
   D1G9), LC3-II (CST, D11), phospho-total Smad-2/3 (CST) and Smad-7
   (Santacruz, #sc-365846). The secondary antibodies were horseradish
   peroxide-conjugated goat anti-rabbit IgG. Expression was quantitated
   using ImageJ and analyzed through Graph-pad Prism software [[166]47].

Determination of reactive oxygen species (ROS) levels

   ROS levels were measured using 2, 7-dichlorofluorescein diacetate
   (H2DCF-DA) (Sigma) which measures intracellular generation of hydrogen
   peroxide, a procedure widely used for estimation of ROS. The H2DCF-DA
   passively enters the cell, where it reacts with ROS to form the highly
   fluorescent compound dichloro-fluorescein (DCF). Briefly, cells were
   seeded at a density of 6000 cells / well in 96 well plates, allowed to
   grow till 50% confluency. The ROS scavenger, N-acetyl cysteine (NAC, 5
   mM) was added wherever mentioned, 1 h prior to treatment with cytokine
   to inhibit ROS. Following exposure, cells were washed with PBS and then
   incubated in 100 μl of working solution of H2DCF-DA (stock solution was
   diluted to yield a 10 μM working solution) at 37°C for 30 min. The
   fluorescence was measured at 485 nm excitation and 530 nm emission
   using a microplate reader (Fluoroskan Ascent) [[167]48].

Determination of mitochondrial membrane potential

   Flow cytometric analysis of mitochondrial membrane potential was done
   using the cyanine dye JC-1
   (5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethylbenzimi-dazolylcarbocyanine
   iodide). A shift in fluorescence intensity from red to green is an
   indication of mitochondrial depolarization. Huh7 cells were seeded at a
   density of 1 × 10^6 cells/plate, grown overnight and then treated with
   specific cytokine alone or in combination with CQDP (added 1 h before
   cytokine addition). Cells were then harvested in 1ml of complete media.
   5 μg/ml of JC-1 was added to cell suspension and vortexed vigorously.
   After incubating for 20 min at room temperature, samples were acquired
   using flow cytometer (Cytoflex, Beckmann Coulter) and analysis of
   acquired data was performed using CytExpert software [[168]44].

Analysis of autophagy using monodansylcadaverine (MDC)

   The compound MDC, a specific autophago-lysosomal marker was used to
   analyze induction of autophagy [[169]49–[170]51]. For visualization of
   the autophagic vacuoles by fluorescence microscopy, cells were seeded
   on sterile cover slips and grown overnight. Following cytokine
   treatment, the cells were incubated for 10 min at 37°C with 0.05 mM MDC
   dissolved in PBS. The cover slips containing the cells were then washed
   with PBS and mounted with anti-fade mountant (containing DAPI).
   Intracellular MDC in the form of punctate dots were visualized using
   fluorescence microscopy. The images were taken at 100× under upright
   fluorescence microscope (Olympus, U-25ND25). For, fluorimetric
   measurement, cells after treatment were labeled with MDC for 10 min,
   washed with PBS and collected in 10 mM Tris-HCl (pH 8) containing 0.1%
   TritonX-100. Intracellular MDC was measured by fluorescence photometry
   (excitation 380 nm and emission 525 nm) in a microplate reader
   (Fluoroskan Ascent). An increase in MDC fluorescence upon treatment was
   expressed as fold change with respect to control.

Flow cytometric analysis of cell cycle

   For DNA content analysis, cells were seeded in 6 cm dishes at a density
   of 2 × 10^6 cells/plate and grown overnight. After 48 h of treatment
   with cytokine, the cells were harvested, washed with PBS and
   centrifuged at 2000 rpm for 5 min at 4°C. The pellet was then
   re-suspended in 100 µL of PBS and 900 µL of ice cold 70% ethanol, used
   as a fixative. The fixed cells were incubated at 4°C overnight. The
   next day, cells were centrifuged and the pellet was re-suspended in 450
   µL PBS with 10 µL of propidium iodide (PI; 2 mg/ml) containing solution
   [[171]52]. The samples were then incubated in dark for 10 min followed
   by event acquisition using flow cytometer (Cytoflex, Beckmann Coulter)
   and analysis using CytExpert software.

Flow cytometric detection of apoptosis

   During apoptosis phosphatidylserine (PS) which under normal
   physiological conditions remain on the cytoplasmic surface of the lipid
   bi-layer gets exposed, which can be detected by fluorochrome-tagged
   protein AnnexinV that binds to phosphatidylserine residues in the
   presence of calcium ions. Hence, for determination of apoptosis, cells
   were seeded in 6 cm dishes at a density of 1 × 10^6 cells/dish. The
   following day, the cells were treated with specific cytokines and
   incubated for 48 h. Thereafter, the cells were harvested, washed with
   PBS and re-suspended in 500 µL of 1× binding buffer (Thermo Fisher
   Scientific). To detect both early and late apoptotic cells, 4 µL of
   AnnexinV and 10 µl of PI were added to the cells in binding buffer,
   followed by incubation in dark for 20 min [[172]52]. The samples were
   then acquired using flow cytometer (Cytoflex, Beckmann Coulter) and
   analysis of acquired data was performed using CytExpert software.
   Percentage of apoptotic cells was calculated by adding up percentage of
   cells in upper and lower right quadrant (UR and LR) is represented
   through bar diagram.

ATG5 knockdown and SMAD inhibition studies

   To inhibit macroautophagy, cells were stably transfected with siATG5 (a
   kind gift from Prof. Santosh Chauhan, ILS, Bhubaneswar) a crucial
   regulator of macro-autophagy using lipofectamine 3000. Studies were
   performed in Huh7 cells pre-incubated with 20 nM siATG5 for 6 hours
   followed by exposure to TGF-β2 for 48 h. For Smad inhibition, Huh7
   cells were treated with 2.5 µM of SIS3 one hour prior to TGF-β2
   exposure for 48 h.

Transcriptomic analysis through RNA sequencing

   Briefly, for transcriptomic sequencing total RNA was isolated and taken
   for fragmentation and priming. The fragmented and primed mRNA was
   further subjected to first strand synthesis followed by second strand
   synthesis. The double stranded cDNA was purified using HighPrep PCR
   magnetic beads (Magbio Genomics Inc, USA). The purified cDNA was
   end-repaired, adenylated and ligated to Illumina multiplex barcode
   adapters as per NEBNext^® Ultra™ Directional RNA Library Prep Kit
   protocol. The adapters used in the study were Illumina Universal
   Adapter:
   5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′ and
   Index Adapter: 5′-GATCGGAAGAGCACACGTCTGAACTCCAGTCAC
   ATCTCGTATGCCGTCTTCTGCTTG-3′. The adapter-ligated cDNA was purified
   using HighPrep beads and was subjected to Indexing-PCR to enrich. The
   final PCR product was purified with HighPrep beads. The sequencing
   library was initially quantified by Qubit fluorimeter (Thermo Fisher
   Scientific, USA) and its fragment size distribution was analyzed on
   Agilent Bio-analyzer. Finally, the sequencing library was quantified by
   qPCR using Kapa Library Quantification Kit (Kapa Biosystems, USA). The
   quantified libraries were pooled in equimolar amounts to create a final
   multiplexed library pool for sequencing on Illumina sequencer for 150bp
   paired-end reads. The cleaned reads were aligned to the reference
   genome using Tophat2. Transcript quantification was done using
   Cufflinks. Differential expression analysis was performed using
   Cuffdiff. Transcripts with log2fold change of 1 and above were
   considered as up regulated and those below -1 as down regulated. The
   raw reads were submitted to NCBI as BioProject PRJNA395629. Expression
   of some of the key genes regulating EMT was validated by RT-PCR, the
   conditions and primer sequences of which are described before.

Statistical analysis

   Tukey tests was used as a follow up to one way or two way ANOVA to
   compare every mean to a control mean and every mean with every other
   mean using Graph Pad Prism software version 5.0. The Bonferroni method
   was used to analyse multiple comparisons using Graph Pad Prism software
   version 5.0. The tests compute a confidence interval for the difference
   between the two means. Throughout the text the representative images
   are of experiments done in triplicates. Data represented in mean ± SEM
   (n = 6). The symbols in parenthesis denotes the following: ^*p < 0.001
   Vs Cntrl, ^#p < 0.001 Vs TGF-β2, ns P > 0.05,^*P ≤ 0.05, ^**P ≤ 0.01,
   ^***P ≤ 0.001.

SUPPLEMENTARY MATERIALS FIGURES

   [173]oncotarget-09-6433-s001.pdf^ (899.2KB, pdf)

Acknowledgments