Literature DB >> 31235856

A novel potent autophagy inhibitor ECDD-S27 targets vacuolar ATPase and inhibits cancer cell survival.

Jiraporn Paha1, Phongthon Kanjanasirirat2, Bamroong Munyoo2,3, Patoomratana Tuchinda2,3, Naravut Suvannang2, Chanin Nantasenamat4, Kanokthip Boonyarattanakalin5, Prasat Kittakoop6,7,8, Sirawit Srikor1, Gunganist Kongklad9, Noppawan Rangkasenee2, Suradej Hongeng2,10, Pongsak Utaisincharoen1, Suparerk Borwornpinyo2,11, Marisa Ponpuak12.   

Abstract

Autophagy is a conserved lysosomal-dependent cellular degradation process and its dysregulation has been linked to numerous diseases including neurodegeneration, infectious diseases, and cancer. Modulation of autophagy is therefore considered as an attractive target for disease intervention. We carried out a high-content image analysis screen of natural product-derived compounds to discover novel autophagy modulating molecules. Our screen identified ECDD-S27 as the most effective compound for increasing the number of autophagic vacuoles inside cells. The structure of ECDD-S27 revealed that it is a derivative of cleistanthin A, a natural arylnaphthalene lignan glycoside found in plants. ECDD-S27 increases the number of autophagic vacuoles by inhibiting the autophagic flux and is able to restrict the survival of different cancer cells at low nanomolar concentrations. Molecular docking and SERS analysis showed that ECDD-S27 may potentially target the V-ATPase. Upon treatment of various cancer cells with ECDD-S27, the V-ATPase activity is potently inhibited thereby resulting in the loss of lysosomal acidification. Taken together, these data indicated that ECDD-S27 retards the autophagy pathway by targeting the V-ATPase and inhibits cancer cell survival. The observed antitumor activity without cytotoxicity to normal cells suggests the therapeutic potential warranting further studies on lead optimization of the compound for cancer treatment.

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Year:  2019        PMID: 31235856      PMCID: PMC6591302          DOI: 10.1038/s41598-019-45641-x

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


Introduction

Autophagy is an evolutionarily conserved homeostatic degradation process of cytoplasmic substances that normally occurs inside eukaryotic cells for self-cleansing and can be upregulated during time of stresses[1,2]. Autophagy can be induced by various conditions such as starvation, drug exposure, and immune mediators as well as be inhibited by numerous compounds[3,4]. During autophagy, the double-membrane autophagosomes engulf the cytosolic substrates and deliver them to lysosomes for digestion[5]. These substrates include defective macromolecules, defunct organelles, and even invading whole pathogens[6]. Autophagosomes are formed through the co-operative work of various autophagy-related (ATG) proteins organized into complexes which include the initiating complex ULK1/2 protein kinase, the nucleation complex Beclin 1/PI3K/ATG14, and the elongation complex ubiquitin-like conjugating systems ATG12-ATG5-ATG16 and LC3[2,5,7]. Upon closure of the autophagosomes, the Beclin 1/PI3P/UVRAG complex then promotes the fusion of autophagosomes with the acidic lysosomes[8], resulting in the delivery of lysosomal hydrolases to degrade the engulfed contents. For the past decade, autophagy has been increasingly appreciated for its role in various diseases including neurodegeneration[9], aging[10], inflammation[11,12], infectious diseases[4], and cancer[13-16]. Therefore, manipulation of autophagy pathway holds a great promise for new therapeutic applications. In the case of cancer, autophagy has been shown to play two opposing roles. On one hand, autophagy functions as a tumor suppressor mechanism in which it facilitates the clearance of damaged macromolecules and organelles thereby preventing excessive ROS production and genome damage and thus preventing normal cells to become cancerous[17]. However, once the cells become malignant, autophagy plays a pro-survival role in protecting cancer cells from metabolic and therapeutic stresses thereby promoting the growth of the established tumors[17]. Extensive data showed that autophagy is upregulated in different cancer types and autophagy impairment by genetic or pharmaceutical inhibition can limit cancer cell survival, dissemination, and metastasis in in vitro studies and in vivo animal tumor models[17]. Therefore, inhibiting autophagy is currently being developed as a new strategy for cancer treatment. Multiple clinical trials on autophagy inhibitors which include chloroquine (CQ) and its derivative hydroxychloroquine (HCQ) either alone or in combination with other cancer drugs or radiation are now being conducted in a wide range of tumors and the results demonstrated some improving clinical outcomes for cancer patients[14,15]. Both CQ and HCQ block acidification of the lysosomes and thereby inhibiting the autophagosome-lysosome fusion and hence the autophagic flux[18]. As high micromolar concentrations of CQ and HCQ are required to inhibit autophagy which may limit their clinical use[19-23], the continued search for more potent autophagy inhibitors is warranted. Taken together, these findings supported the idea and potential use of autophagy inhibitors for anticancer therapy. As mentioned above, impaired autophagy has been implicated in different pathophysiological conditions and modulation of autophagy is viewed as an attractive new strategy for disease treatment. In this work, we set out to identify autophagy modulating molecules from natural product-derived compounds by using the fluorescently-based high-content (HC) image screen. From the screen, ECDD-S27 was identified as the compound that potently increases the number of autophagic vacuoles in cells. Further characterization on ECDD-S27 mechanism of action revealed that it is an autophagic flux inhibitor that can strongly restrict the viability of different cancer cell types while not toxic to normal cells. Our molecular docking, SERS, and functional analyses identified vacuolar ATPase as the target of ECDD-S27. The lack of synergistic effect between bafilomycin A1, a well-known autophagic flux inhibitor, and ECDD-S27 in cancer cell restriction further supported the involvement of ECDD-S27 in targeting this pathway and thereby inhibits the survival of cancer cells. These data indicated the potential development of ECDD-S27 as a lead compound for cancer.

Results

Identification of natural product-derived autophagy modulating compounds

As defective autophagy has been linked to a number of medical conditions, several drug discovery screens of small compound libraries and FDA-approved drugs have been conducted to identify autophagy modulating compounds[24-30]. Since Thai herbal and natural product-based traditional medicines have been used as therapeutics for diseases, we are interested to see whether autophagy modulating activity could be found in these molecules and their derivatives. We therefore conducted the HC imaging screen by quantitating the number of LC3B puncta, the biological marker for autophagic vacuoles in cells, upon treatment with the Thai natural product-derived compounds deposited into the Excellent Center for Drug Discovery, Mahidol University. In brief, RAW264.7 macrophages expressing mRFP-GFP-LC3B were treated with DMSO (negative control), starvation (positive control), or 50 µM of each compound for 4 h and processed for HC image analysis. The number of total autophagosomes (RFP+GFP+-LC3B) and autolysosomes (RFP+GFP−LC3B) per cell was then quantified. ECDD-S27 was identified as the most effective compound in increasing the number of total LC3B puncta per cell from our screen (Fig. 1a,b).
Figure 1

ECDD-S27 is a potent autophagic flux inhibitor. (a,b) Screening of natural product-derived compounds for their autophagy modulating activity. Raw264.7 macrophages were transfected with cDNAs encoding RFP-GFP-LC3B. At 48 h post transfection, cells were treated with DMSO (negative control), starvation (positive control), or natural product-derived compounds (50 µM) for 4 h. Cells were then fixed and analyzed by HC image analysis to quantify the number of total LC3B puncta per cell. The dashed line represents 3 S.D. above that of the mean of the DMSO treated control. ECDD-S27 was identified as the most effective compound to increase the number of total LC3B puncta per cell from the screen. Representative images of the HC image analysis with boundary of cells (right panels). Bar 5 µm. (c,d) ECDD-S27 inhibits autophagic flux. Raw264.7 macrophages expressing RFP-GFP-LC3B were treated with DMSO, starvation, or 50 µM of ECDD-S27 for 4 h. Cells were then processed for confocal microscopy analysis and the number of RFP+GFP+-LC3B (autophagosomes) and RFP+GFP−-LC3B (autolysosomes) puncta per cell was quantified. Only puncta ≥0.3 µm in size were counted. Data are the means ± SEM from at least three independent experiments. At least 30 cells per condition per independent experiment were quantified; *p < 0.05 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. Bar 5 µm. (e–g) LC3-II and p62 immunoblots confirmed inhibition of autophagic flux by ECDD-S27. Raw264.7 macrophages were treated with DMSO with or without bafilomycin A1 or ECDD-S27 at the indicated concentrations for 4 h. Representative images cropped from the same blot are shown and full images are included in the supplementary information. The intensities of LC3-II, p62, and Actin were quantified using ImageJ. The graphs showed densitometric analysis of p62/Actin and LC3-II/Actin levels. The IC50 value of ECDD-S27 in inhibiting autophagic flux was ≤ 0.016 µM. Data are mean ± SEM; *p < 0.05, all relative to the DMSO control from three independent experiments, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

ECDD-S27 is a potent autophagic flux inhibitor. (a,b) Screening of natural product-derived compounds for their autophagy modulating activity. Raw264.7 macrophages were transfected with cDNAs encoding RFP-GFP-LC3B. At 48 h post transfection, cells were treated with DMSO (negative control), starvation (positive control), or natural product-derived compounds (50 µM) for 4 h. Cells were then fixed and analyzed by HC image analysis to quantify the number of total LC3B puncta per cell. The dashed line represents 3 S.D. above that of the mean of the DMSO treated control. ECDD-S27 was identified as the most effective compound to increase the number of total LC3B puncta per cell from the screen. Representative images of the HC image analysis with boundary of cells (right panels). Bar 5 µm. (c,d) ECDD-S27 inhibits autophagic flux. Raw264.7 macrophages expressing RFP-GFP-LC3B were treated with DMSO, starvation, or 50 µM of ECDD-S27 for 4 h. Cells were then processed for confocal microscopy analysis and the number of RFP+GFP+-LC3B (autophagosomes) and RFP+GFP−-LC3B (autolysosomes) puncta per cell was quantified. Only puncta ≥0.3 µm in size were counted. Data are the means ± SEM from at least three independent experiments. At least 30 cells per condition per independent experiment were quantified; *p < 0.05 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. Bar 5 µm. (e–g) LC3-II and p62 immunoblots confirmed inhibition of autophagic flux by ECDD-S27. Raw264.7 macrophages were treated with DMSO with or without bafilomycin A1 or ECDD-S27 at the indicated concentrations for 4 h. Representative images cropped from the same blot are shown and full images are included in the supplementary information. The intensities of LC3-II, p62, and Actin were quantified using ImageJ. The graphs showed densitometric analysis of p62/Actin and LC3-II/Actin levels. The IC50 value of ECDD-S27 in inhibiting autophagic flux was ≤ 0.016 µM. Data are mean ± SEM; *p < 0.05, all relative to the DMSO control from three independent experiments, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

ECDD-S27 potently inhibits the autophagic flux

Since an increased number of LC3B puncta per cell by a compound could be resulted from either autophagy induction or autophagic flux inhibition activity[31], we performed a more careful analysis of the RFP+GFP+-LC3B versus RFP+GFP–LC3B number per cell using confocal microscopy in order to determine the ECDD-S27 mode of action. The results showed that ECDD-S27 increases the number of autophagosomes but decreases the number of autolysosomes per cell when compared to that of the DMSO treated control, indicating that it acts by inhibiting the autophagic flux (Fig. 1c,d). This is in contrast to the results seen in cells undergoing starvation (autophagy induction control), in which both the number of autophagosomes and autolysosomes are increased when compared to that of the DMSO treated cells (Fig. 1c,d). In addition, to confirm that ECDD-S27 works by inhibiting the autophagic flux, p62 and lipidated LC3 (LC3-II) protein levels were examined by immunoblotting. Both p62 and LC3-II are substrates for autophagic degradation in the autolysosomes and hence their elevated levels are an indication of autophagic flux retardation[31]. When cells were treated with varied concentrations of ECDD-S27, the levels of p62 and LC3-II are increased in a dose dependent manner, confirming that ECDD-S27 functions by hampering the autophagic flux (Fig. 1e–g). ECDD-S27 strongly blocks the autophagic flux at low nanomolar concentrations when compared to that of 100 nM bafilomycin A1 used as the standard autophagic flux inhibitor control (Fig. 1e–g). Thus, ECDD-S27 is a novel potent natural product-derived autophagic flux inhibitor.

ECDD-S27 is a derivative of cleistanthin A

Investigation of ECDD-S27 structure revealed that it is a derivative of cleistanthin A, a well-known natural arylnaphthalene lignan glycoside found in plants Phyllanthus taxodiifolius Beille (Euphorbiaceae) and Cleistanthus collinus[32-36]. ECDD-S27 was derived from the esterification of cleistanthin A with 3,5-dimethoxybenzoic acid in the presence of N,N′-dicyclohexylcarbodiimide (DCC) and a catalytic amount of 4-dimethylaminopyridine (DMAP), resulting in the formation of a new ester, 2-(9-(1,3-benzodioxol-5-yl)-6,7-dimethoxy-1-oxo-1,3-dihydrobenzo[f][2]benzofuran-4-yl)oxy-4,5-dimethoxytetrahydro-2H-pyran-3-yl 3,5-dimethoxybenzoate (ECDD-S27, 1) (Fig. 2). The structure of ECDD-S27 was confirmed by spectroscopic methods (1H-NMR, 13C-NMR, UV, IR and MS analysis) (Please see Methods for more information). Purity of ECDD-S27 was determined to be 97% by using the HPLC technique (column C18 shiseido, 80% MeOH/H2O, 1 mL/min). Note that while ECDD-S27 was identified as a positive compound from our screen that can effectively increase the number of total LC3B puncta per cell (Fig. 1a,b), the parent compound cleistanthin A does not increase the number of LC3B puncta and hence not a positive compound in our screen.
Figure 2

Chemical structure of ECDD-S27. The newly identified potent natural-product derived autophagic flux inhibitor, ECDD-S27, is a derivative of cleistanthin A, a natural arylnaphthalene lignan glycoside found in Phyllanthus taxodiifolius Beille (Euphorbiaceae). ECDD-S27 is derived from the esterification of cleistanthin A with 3,5-dimethoxybenzoic acid, in the presence of N,N′-dicyclohexylcarbodiimide (DCC) and a catalytic amount of 4-dimethylaminopyridine (DMAP), resulting in the formation of a new ester, 2-(9-(1,3-benzodioxol-5-yl)-6,7-dimethoxy-1-oxo-1,3-dihydrobenzo[f][2]benzofuran-4-yl)oxy-4,5-dimethoxytetrahydro-2H-pyran-3-yl 3,5-dimethoxybenzoate (ECDD-S27, 1).

Chemical structure of ECDD-S27. The newly identified potent natural-product derived autophagic flux inhibitor, ECDD-S27, is a derivative of cleistanthin A, a natural arylnaphthalene lignan glycoside found in Phyllanthus taxodiifolius Beille (Euphorbiaceae). ECDD-S27 is derived from the esterification of cleistanthin A with 3,5-dimethoxybenzoic acid, in the presence of N,N′-dicyclohexylcarbodiimide (DCC) and a catalytic amount of 4-dimethylaminopyridine (DMAP), resulting in the formation of a new ester, 2-(9-(1,3-benzodioxol-5-yl)-6,7-dimethoxy-1-oxo-1,3-dihydrobenzo[f][2]benzofuran-4-yl)oxy-4,5-dimethoxytetrahydro-2H-pyran-3-yl 3,5-dimethoxybenzoate (ECDD-S27, 1).

ECDD-S27 restrains autophagic flux and decreases colorectal adenocarcinoma HT-29 cell viability

As numerous tumor cells were found to depend on autophagy for their survival and autophagic flux inhibitors are currently being tested in clinical trials against cancers[13-15], we set out to examine the effect of ECDD-S27 on different cancer cells. To determine this, we first treated the colorectal adenocarcinoma HT-29 cells with various concentrations of ECDD-S27 and measured the alteration in autophagic flux by p62 and LC3-II immunoblotting. ECDD-S27 is able to increase the levels of p62 and LC3-II in a dose dependent manner with the IC50 value of ≤0.016 µM (Fig. 3a–c). In addition, we further confirmed the autophagic flux inhibition activity of ECDD-S27 in these cells by using the LC3B puncta assay. When mRFP-GFP-LC3B expressing HT-29 cells were treated with starvation to induce the autophagic flux, the enhanced number of both autophagosomes and autolysosomes was observed (Fig. 3d,e). In contrast, when cells were treated with low nanomolar concentrations of ECDD-S27, only the number of autophagosomes is increased while the number of autolysosomes is slightly decreased (Fig. 3d,e), confirming the autophagic flux retardation activity of ECDD-S27. To assess the ability of ECDD-S27 in restricting HT-29 cell survival, we treated the cells with different concentrations of ECDD-S27 and determine the cell viability using the MTS assay. ECDD-S27 is able to potently limit HT-29 cell survival with the IC50 value of 0.06 µM (Fig. 3f). The activity of ECDD-S27 in decreasing the HT-29 cell survival is better than that of HCQ (IC50 = 18.4 µM) (Fig. 3f). Thus, ECDD-S27 is able to strongly inhibit the autophagic flux and HT-29 cell viability at low nanomolar concentrations.
Figure 3

ECDD-S27 potently inhibits autophagic flux and restricts colorectal adenocarcinoma HT-29 cell survival. (a–c) Autophagic flux analysis in HT-29 cells by LC3-II and p62 immunoblots. Cells were treated with DMSO with or without bafilomycin A1 or ECDD-S27 at the indicated concentrations for 4 h. Representative images cropped from the same blot are shown and full images are included in the supplementary information. The intensities of LC3-II, p62, and Actin were quantified using ImageJ. The graphs showed densitometric analysis of p62/Actin and LC3-II/Actin expression levels. ECDD-S27 inhibits autophagic flux with the IC50 value of ≤0.016 µM. Data are mean ± SEM; *p < 0.05 and **p < 0.01, all relative to the DMSO control from three independent experiments, were determined by one-way ANOVA with a Tukey’s multiple comparison test. (d–e) RFP-GFP-LC3B puncta analysis confirmed ECDD-S27 autophagic flux inhibition. RFP-GFP-LC3B expressing HT-29 cells were treated with DMSO, starvation, or ECDD-S27 at the indicated concentrations for 4 h and processed for confocal microscopy. The number of LC3B puncta/cell was then analyzed. Only puncta ≥0.3 µm in size were counted. Data are the means ± SEM from at least three independent experiments. At least 30 cells per condition per independent experiment were quantified; **p < 0.01 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. Bar 5 µm. (f) ECDD-S27 inhibits HT-29 cell survival. Cells were treated with DMSO (negative control), ECDD-S27, or HCQ at the indicated concentrations for 72 h and their viability was measured by the MTS assay. Data are mean ± SD from three independent experiments; the results were expressed relative to the DMSO control, defined as 100%. ***p < 0.001, ECDD-S27 vs HCQ treatment at the same concentration, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

ECDD-S27 potently inhibits autophagic flux and restricts colorectal adenocarcinoma HT-29 cell survival. (a–c) Autophagic flux analysis in HT-29 cells by LC3-II and p62 immunoblots. Cells were treated with DMSO with or without bafilomycin A1 or ECDD-S27 at the indicated concentrations for 4 h. Representative images cropped from the same blot are shown and full images are included in the supplementary information. The intensities of LC3-II, p62, and Actin were quantified using ImageJ. The graphs showed densitometric analysis of p62/Actin and LC3-II/Actin expression levels. ECDD-S27 inhibits autophagic flux with the IC50 value of ≤0.016 µM. Data are mean ± SEM; *p < 0.05 and **p < 0.01, all relative to the DMSO control from three independent experiments, were determined by one-way ANOVA with a Tukey’s multiple comparison test. (d–e) RFP-GFP-LC3B puncta analysis confirmed ECDD-S27 autophagic flux inhibition. RFP-GFP-LC3B expressing HT-29 cells were treated with DMSO, starvation, or ECDD-S27 at the indicated concentrations for 4 h and processed for confocal microscopy. The number of LC3B puncta/cell was then analyzed. Only puncta ≥0.3 µm in size were counted. Data are the means ± SEM from at least three independent experiments. At least 30 cells per condition per independent experiment were quantified; **p < 0.01 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. Bar 5 µm. (f) ECDD-S27 inhibits HT-29 cell survival. Cells were treated with DMSO (negative control), ECDD-S27, or HCQ at the indicated concentrations for 72 h and their viability was measured by the MTS assay. Data are mean ± SD from three independent experiments; the results were expressed relative to the DMSO control, defined as 100%. ***p < 0.001, ECDD-S27 vs HCQ treatment at the same concentration, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

Autophagic flux and survival of hepatocellular carcinoma HepG2 cells are blocked by ECDD-S27

To investigate whether ECDD-S27 can arrest autophagic flux in other cancer cell types, we performed the Western blot analysis of p62 and LC3-II levels in the hepatocellular carcinoma HepG2 cells upon treatment with different concentrations of ECDD-S27. The elevated p62 and LC3-II protein levels were observed starting at low nanomolar concentrations of ECDD-S27 and increased in a dose dependent manner (IC50 = 0.016–0.080 µM) (Fig. 4a–c). In addition, the mRFP-GFP-LC3B puncta assay was conducted and ECDD-S27 can effectively increase the number of autophagosomes but not that of autolysosomes, demonstrating the autophagic flux inhibition activity of ECDD-S27 in these cells (Fig. 4d,e). Moreover, ECDD-S27 can strongly reduce HepG2 cell survival as determined by the MTS assay (IC50 = 0.03 µM) and its activity is more potent than that observed when cells were treated with HCQ (IC50 = 11.44 µM) (Fig. 4f). These results confirmed that ECDD-S27 can limit the autophagic flux and cell viability of the hepatocellular carcinoma HepG2 cells.
Figure 4

Autophagic flux arrest and survival inhibition by ECDD-S27 in hepatocellular carcinoma HepG2 cells. (a–c) LC3-II and p62 Western blot analysis to assess autophagic flux in HepG2 cells. Cells were treated with DMSO with or without bafilomycin A1 or ECDD-S27 at the indicated concentrations for 4 h. Representative images cropped from the same blot are shown and full images are included in the supplementary information. The intensities of LC3-II, p62, and Actin were quantified using ImageJ. p62/Actin and LC3-II/Actin levels were then determined. The IC50 value of ECDD-S27 for autophagic flux inhibition was 0.016–0.080 µM. Data are mean ± SEM; *p < 0.05, all relative to the DMSO control from three independent experiments, was determined by one-way ANOVA with a Tukey’s multiple comparison test. (d–e) Confirmation of ECDD-S27 autophagic flux inhibition by RFP-GFP-LC3B puncta assay. HT-29 cells expressing RFP-GFP-LC3B were treated with DMSO, starvation, or ECDD-S27 at the indicated concentrations for 4 h, processed for confocal microscopy, and analyzed for the number of LC3B puncta/cell. Only puncta ≥0.3 µm in size were counted. Data are the means ± SEM from at least three independent experiments. At least 30 cells per condition per independent experiment were quantified; *p < 0.05 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. Bar 5 µm. (f) ECDD-S27 decreases HepG2 cell survival. Cells were treated with DMSO (negative control), ECDD-S27, or HCQ at the indicated concentrations for 72 h and their viability was measured by the MTS assay. Data are mean ± SD from three independent experiments; the results were expressed relative to the DMSO control, defined as 100%. ***p < 0.001, ECDD-S27 vs HCQ treatment at the same concentration, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

Autophagic flux arrest and survival inhibition by ECDD-S27 in hepatocellular carcinoma HepG2 cells. (a–c) LC3-II and p62 Western blot analysis to assess autophagic flux in HepG2 cells. Cells were treated with DMSO with or without bafilomycin A1 or ECDD-S27 at the indicated concentrations for 4 h. Representative images cropped from the same blot are shown and full images are included in the supplementary information. The intensities of LC3-II, p62, and Actin were quantified using ImageJ. p62/Actin and LC3-II/Actin levels were then determined. The IC50 value of ECDD-S27 for autophagic flux inhibition was 0.016–0.080 µM. Data are mean ± SEM; *p < 0.05, all relative to the DMSO control from three independent experiments, was determined by one-way ANOVA with a Tukey’s multiple comparison test. (d–e) Confirmation of ECDD-S27 autophagic flux inhibition by RFP-GFP-LC3B puncta assay. HT-29 cells expressing RFP-GFP-LC3B were treated with DMSO, starvation, or ECDD-S27 at the indicated concentrations for 4 h, processed for confocal microscopy, and analyzed for the number of LC3B puncta/cell. Only puncta ≥0.3 µm in size were counted. Data are the means ± SEM from at least three independent experiments. At least 30 cells per condition per independent experiment were quantified; *p < 0.05 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. Bar 5 µm. (f) ECDD-S27 decreases HepG2 cell survival. Cells were treated with DMSO (negative control), ECDD-S27, or HCQ at the indicated concentrations for 72 h and their viability was measured by the MTS assay. Data are mean ± SD from three independent experiments; the results were expressed relative to the DMSO control, defined as 100%. ***p < 0.001, ECDD-S27 vs HCQ treatment at the same concentration, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

ECDD-S27 inhibits autophagic flux and survival of cervical adenocarcinoma HeLa cells

We also examined the activity of ECDD-S27 in the cervical adenocarcinoma HeLa cells. Immunoblot analysis showed a dose dependent enhancement levels of p62 and LC3-II in these cells upon treatment with varied concentrations of ECDD-S27 (IC50 = 0.016–0.080 µM) (Fig. 5a–c). In addition, the augmented number of autophagosomes but not that of autolysosomes was observed in mRFP-GFP-LC3B expressing ECDD-S27-treated HeLa cells, thus confirming the blockage of autophagic flux by ECDD-S27 (Fig. 5d,e). Furthermore, the viability of HeLa cells was greatly suppressed by ECDD-S27 (IC50 = 0.04 µM) when compared to that of the HCQ treated cells (IC50 = 13.56 µM) (Fig. 5f). Altogether, these data demonstrated that ECDD-S27 is a potent inhibitor of the autophagic flux and able to restrict cell survival of different cancer cell types. To also assess whether ECDD-S27 is toxic to normal cells, we performed the toxicity test using the MTT assay upon treatment of the human kidney normal cell HK-2 cells with varied concentrations of ECDD-S27. The results showed that ECDD-S27 is nontoxic to HK-2 cells (IC50 > 50 µM) (Supplementary Fig. S1).
Figure 5

Decrease in autophagic flux and cell survival in cervical adenocarcinoma HeLa cells by ECDD-S27. (a–c) ECDD-S27 inhibits autophagic flux in HeLa cells. Cells were treated with DMSO with or without bafilomycin A1 or ECDD-S27 at the indicated concentrations for 4 h. The expression levels of p62/Actin and LC3-II/Actin were then assessed using immunoblotting followed by the densitometric analysis using ImageJ. Representative images cropped from the same blot are shown and full images are included in the supplementary information. ECDD-S27 inhibits autophagic flux with the IC50 of 0.016–0.080 µM. Data are mean ± SEM from three independent experiments. (d–e) ECDD-S27 autophagic flux inhibition in HeLa cells was confirmed by RFP-GFP-LC3B puncta assay. RFP-GFP-LC3B expressing HeLa cells were treated with DMSO, starvation, or ECDD-S27 at the indicated concentrations for 4 h and analyzed by confocal microscopy. The number of LC3B puncta/cell was then quantified. Only puncta ≥0.3 µm in size were counted. Data are the means ± SEM from at least three independent experiments. At least 30 cells per condition per independent experiment were quantified; *p < 0.05, **p < 0.01, and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. Bar 5 µm. (f) HeLa cell survival is restricted by ECDD-S27. Cells were treated with DMSO (negative control), ECDD-S27, or HCQ at the indicated concentrations for 72 h and their viability was measured by the MTS assay. Data are mean ± SD from three independent experiments; the results were expressed relative to the DMSO control, defined as 100%. ***p < 0.001, ECDD-S27 vs HCQ treatment at the same concentration, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

Decrease in autophagic flux and cell survival in cervical adenocarcinoma HeLa cells by ECDD-S27. (a–c) ECDD-S27 inhibits autophagic flux in HeLa cells. Cells were treated with DMSO with or without bafilomycin A1 or ECDD-S27 at the indicated concentrations for 4 h. The expression levels of p62/Actin and LC3-II/Actin were then assessed using immunoblotting followed by the densitometric analysis using ImageJ. Representative images cropped from the same blot are shown and full images are included in the supplementary information. ECDD-S27 inhibits autophagic flux with the IC50 of 0.016–0.080 µM. Data are mean ± SEM from three independent experiments. (d–e) ECDD-S27 autophagic flux inhibition in HeLa cells was confirmed by RFP-GFP-LC3B puncta assay. RFP-GFP-LC3B expressing HeLa cells were treated with DMSO, starvation, or ECDD-S27 at the indicated concentrations for 4 h and analyzed by confocal microscopy. The number of LC3B puncta/cell was then quantified. Only puncta ≥0.3 µm in size were counted. Data are the means ± SEM from at least three independent experiments. At least 30 cells per condition per independent experiment were quantified; *p < 0.05, **p < 0.01, and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. Bar 5 µm. (f) HeLa cell survival is restricted by ECDD-S27. Cells were treated with DMSO (negative control), ECDD-S27, or HCQ at the indicated concentrations for 72 h and their viability was measured by the MTS assay. Data are mean ± SD from three independent experiments; the results were expressed relative to the DMSO control, defined as 100%. ***p < 0.001, ECDD-S27 vs HCQ treatment at the same concentration, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

ECDD-S27 suppresses autophagic flux by targeting the vacuolar ATPase

To investigate the molecular mechanism of ECDD-S27 in autophagic flux arrest, we looked at the substructural components of ECDD-S27 (Fig. 2). The core structure of ECDD-S27 consists of diphyllin [1-hydroxy-2-(hydroxymethyl)-6,7-dimethoxy-4-(3,4-methylenedioxy-phenol)-3-naphthoic acid-γ-lactone], which has previously been shown to inhibit the vacuolar ATPase (V-ATPase) activity and thereby the lysosomal acidification in osteoclasts and cancer cells[37-39]. Thus, it is plausible that ECDD-S27 may block the V-ATPase activity and thereby retarding the autophagic flux in our cells. In order to determine whether ECDD-27 could potentially target the V-ATPase, we performed a protein-ligand interaction study that constitutes an initial molecular dynamics simulation of the protein structure in a lipid bilayer water box followed by molecular docking. Representative docking pose from the highest populated cluster having the lowest energy was selected for post-docking analysis. As shown in Fig. 6a, ECDD-S27 binds to the eukaryotic V-ATPase with a binding energy of −7.8 kcal/mol. Hydrogen bond interaction is formed between the ether oxygen atom from ECDD-S27 with the amide nitrogen atom of Gln55 on subunit e along with hydrophobic interaction formed with Ser122, Leu119, and Glu211 on subunit c″, Arg153 on subunit c, and Phe531, Ser534, Met537, Lys538, Trp598, Pro606 and Leu608 on subunit a (Fig. 6b,c).
Figure 6

Molecular simulation for elucidating the potential binding between ECDD-S27 and V-ATPase. (a) Molecular dynamics simulation of the protein-lipid membrane system. (b) Side, top, and bottom views of the V-ATPase protein structure. (c) Left panel illustrates the location and zoom-up view of the binding pocket showing the hydrogen bond interaction between the ether oxygen atom from ECDD-S27 with the amide nitrogen atom from Gln55. Right panel shows the two-dimensional schematic diagram of the protein-ligand interaction.

Molecular simulation for elucidating the potential binding between ECDD-S27 and V-ATPase. (a) Molecular dynamics simulation of the protein-lipid membrane system. (b) Side, top, and bottom views of the V-ATPase protein structure. (c) Left panel illustrates the location and zoom-up view of the binding pocket showing the hydrogen bond interaction between the ether oxygen atom from ECDD-S27 with the amide nitrogen atom from Gln55. Right panel shows the two-dimensional schematic diagram of the protein-ligand interaction. In addition to the molecular docking results described above, we further examined the interaction of ECDD-S27 with V-ATPase by conducting surface-enhanced Raman substrates (SERS) analysis. Firstly, the human V-ATPase was isolated from HeLa cells by immunoprecipitation using the anti-V1A1 antibody. Western blot analysis of the precipitated immune complex showed that the anti-V1A1 antibody can isolate V1A1 and V0a3 proteins, both of which are components of the V1 and V0 domains of the V-ATPase respectively, from the HeLa cell lysate while the normal rabbit IgG antibody that was used as the negative control could not (Fig. 7a). This indicated that our immunoprecipitation can successfully isolate the endogeneous human V-ATPase protein complex from HeLa cells. Afterwards, the interaction between the isolated human V-ATPase protein complex and ECDD-S27 was examined by SERS analysis. This was conducted by treating the isolated human V-ATPase with 5 mM ECDD-S27 at 4 °C overnight followed by multiple washes in PBS containing 0.5% NP-40 as to remove the unbound compound. Then, the V-ATPase alone, the ECDD-S27 alone, and the ECDD-S27 treated V-ATPase samples were subjected to SERS analysis. Figure 7b showed SERS spectrum of the V-ATPase, ECDD-S27, and ECDD-S27 treated V-ATPase. The Raman pattern of ECDD-S27 is composed of peaks at 1389 and 1600 cm−1 as observed as high intensity peaks in the ECDD-S27 alone sample in addition to the lower intensity peaks at 334, 670, and 765 cm−1. Interestingly, in the ECDD-S27 treated V-ATPase sample, the ECDD-S27 characteristic peaks at 1389 and 1600 cm−1 as well as peaks at 237, 276, 315 cm−1 corresponding to those identified in the V-ATPase alone sample can be observed. The weakening of the ECDD-S27 characteristic peaks at 1389 and 1600 cm−1 in concurrent with the disappearance of peaks at 334, 670, and 765 cm−1 in the ECDD-S27 treated V-ATPase sample suggested that ECDD-S27 could directly interact with the V-ATPase. In addition, no new peaks were observed in the ECDD-S27 treated V-ATPase sample, thereby confirming our molecular docking results which showed no new covalent bond formed between ECDD-S27 and the V-ATPase.
Figure 7

ECDD-S27 targets the vacuolar ATPase. (a) Isolation of human V-ATPase by immunoprecipitation. HeLa cells were lysed in RIPA buffer supplemented with protease inhibitors. Anti-V1A1 antibody was used to isolate the V-ATPase from the lysate using the normal rabbit IgG antibody as the negative control. The precipitated immune complex was then analyzed by Western blot analysis for the presence of V1A1 and V0a3, markers for V1 and V0 domains of the V-ATPase, respectively. (b) SERS analysis of the ECDD-S27 and V-ATPase interaction. The isolated human V-ATPase was treated with 5 mM ECDD-S27 at 4 °C overnight followed by washing three times with PBS containing 0.5% NP-40 to wash out the unbound compound. The sample was then subjected to SERS analysis. The V-ATPase alone, ECDD-S27 alone, and buffer alone were used as controls. HT-29 (c,d), HepG2 (e–f), and HeLa (g,h) cells were treated with DMSO or ECDD-S27 at the indicated concentrations for 2 h. Acidification of lysosomes was then determined by addition of the Lysotracker Red (LTR) dye to these cells in the presence of DMSO or ECDD-S27 and incubation was continued for 2 h. Cells were then fixed and processed for HC image analysis. The number of LTR+ puncta per cell was then quantified. Bar 10 µm. Data are shown as mean ± SEM; ***p < 0.001, all relative to the DMSO control, was determined by one-way ANOVA with a Tukey’s multiple comparison test.

ECDD-S27 targets the vacuolar ATPase. (a) Isolation of human V-ATPase by immunoprecipitation. HeLa cells were lysed in RIPA buffer supplemented with protease inhibitors. Anti-V1A1 antibody was used to isolate the V-ATPase from the lysate using the normal rabbit IgG antibody as the negative control. The precipitated immune complex was then analyzed by Western blot analysis for the presence of V1A1 and V0a3, markers for V1 and V0 domains of the V-ATPase, respectively. (b) SERS analysis of the ECDD-S27 and V-ATPase interaction. The isolated human V-ATPase was treated with 5 mM ECDD-S27 at 4 °C overnight followed by washing three times with PBS containing 0.5% NP-40 to wash out the unbound compound. The sample was then subjected to SERS analysis. The V-ATPase alone, ECDD-S27 alone, and buffer alone were used as controls. HT-29 (c,d), HepG2 (e–f), and HeLa (g,h) cells were treated with DMSO or ECDD-S27 at the indicated concentrations for 2 h. Acidification of lysosomes was then determined by addition of the Lysotracker Red (LTR) dye to these cells in the presence of DMSO or ECDD-S27 and incubation was continued for 2 h. Cells were then fixed and processed for HC image analysis. The number of LTR+ puncta per cell was then quantified. Bar 10 µm. Data are shown as mean ± SEM; ***p < 0.001, all relative to the DMSO control, was determined by one-way ANOVA with a Tukey’s multiple comparison test. Moreover, to functionally verify that ECDD-S27 can inhibit the V-ATPase activity, we treated HT-29, HepG2, and HeLa cells with varied concentrations of ECDD-S27 and measured the lysosomal acidification by staining the acidic organelles with the LysoTracker Red (LTR) dye. The HC image analysis was then used to quantify the number of LTR+ vacuoles per cell. The results showed that ECDD-S27 can markedly decrease the number of acidified organelles in HT-29 (Fig. 7c,d), HepG2 (Fig. 7e,f), and HeLa (Fig. 7g,h) cells in a dose dependent manner when compared to that of the DMSO treated control cells. Thus, these data supported that ECDD-S27 targets the V-ATPase and thereby deacidifies the lysosomes resulting in impaired autophagic flux.

Causal relationship between autophagic flux inhibition by ECDD-S27 and cancer cell inhibition

To investigate the causal relationship between autophagic flux inhibition by ECDD-S27 and cancer cell inhibition, we first measured whether autophagic flux inhibition by ECDD-S27 is correlated with cancer cell inhibition in GFP-LC3B expressing HT-29, HepG2, and HeLa cells treated with different concentrations of ECDD-S27 by HC image analysis. An increase in the number of GFP+-LC3B puncta (autophagosomes) in cells is used as a marker for autophagic flux inhibition. Results showed a dose-dependent increase in autophagic flux inhibition of GFP-LC3B expressing HT-29 (Fig. 8a), HepG2 (Fig. 8b), and HeLa (Fig. 8c) cells while there is a dose-dependent decrease in the survival of these cells upon treatment with increasing concentration of ECDD-S27. These data thus confirmed the correlation between autophagic flux inhibition by ECDD-S27 and cancer cell inhibition. To further examine the aforementioned relationship, we also treated HT-29, HepG2, and HeLa cells with bafilomycin A1, a standard autophagic flux inhibitor, and measured the cancer cell viability by means of the MTS assay. Upon treatment with the standard autophagic flux inhibitor bafilomycin A1, the survival of HT-29 (Fig. 8d), HepG2 (Fig. 8e), and HeLa (Fig. 8f) cells were found to be greatly inhibited at a similar level to that of ECDD-S27 treatment, thereby confirming that autophagic flux inhibition can result in cancer cell inhibition. In addition, the co-treatment of bafilomycin A1 with ECDD-S27 did not result in a further increase in cancer cell inhibition in these cells, thereby indicating that ECDD-S27 and bafilomycin A1 target the same pathway. Altogether, these data strongly suggested that autophagic flux inhibition by ECDD-S27 resulted in subsequent cancer cell inhibition.
Figure 8

ECDD-S27 inhibits autophagic flux and decreases cancer cell survival. HT-29 (a), HepG2 (b), and HeLa (c) cells were transfected with cDNAs encoding GFP-LC3B. At 48 h after transfection, cells were treated with DMSO (negative control) or ECDD-S27 at indicated concentrations for 72 h. Cells were then stained with Hoechst and processed for HC image analysis. Percent cell viability and autophagic flux-inhibited cells were then analyzed. Data are shown as mean ± SEM. **p < 0.01 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. HT-29 (d), HepG2 (e), HeLa (f) cells were treated with DMSO (negative control), ECDD-S27, bafilomycin A1, or bafilomycin A1 in combination with ECDD-S27 at the indicated concentrations for 72 h and their viability was measured by the MTS assay. Data are shown as mean ± SD from at least two independent experiments; results were expressed relative to the DMSO control, defined as 100%. †p ≥ 0.05 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test.

ECDD-S27 inhibits autophagic flux and decreases cancer cell survival. HT-29 (a), HepG2 (b), and HeLa (c) cells were transfected with cDNAs encoding GFP-LC3B. At 48 h after transfection, cells were treated with DMSO (negative control) or ECDD-S27 at indicated concentrations for 72 h. Cells were then stained with Hoechst and processed for HC image analysis. Percent cell viability and autophagic flux-inhibited cells were then analyzed. Data are shown as mean ± SEM. **p < 0.01 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. HT-29 (d), HepG2 (e), HeLa (f) cells were treated with DMSO (negative control), ECDD-S27, bafilomycin A1, or bafilomycin A1 in combination with ECDD-S27 at the indicated concentrations for 72 h and their viability was measured by the MTS assay. Data are shown as mean ± SD from at least two independent experiments; results were expressed relative to the DMSO control, defined as 100%. †p ≥ 0.05 and ***p < 0.001, all relative to the DMSO control, were determined by one-way ANOVA with a Tukey’s multiple comparison test. In summary, our study showed that ECDD-S27 is a novel potent natural product-derived autophagic flux inhibitor that can restrict cancer cell viability at low nanomolar concentrations. It does so by targeting the V-ATPase resulting in the decrease in lysosomal acidification and thereby hampering the autophagosome-lysosome fusion. The ability of ECDD-S27 to potently decrease the viability of cancer cells while sparing normal cells indicated a great promise of this compound as a lead anti-cancer molecule.

Discussion

Due to the involvement of autophagy in several pathophysiological conditions, the search for novel autophagy modulating small molecules is of high interest[40-44]. As many herbal and natural product-derived compounds have been used in traditional medicines to treat diseases, they hold a great pharmacological potential, though the underlying mechanisms possessed by these compounds are largely uncharacterized. The aim of this study was to identify natural product-derived compounds that exhibit autophagy modulating activity by using a phenotypic-based screening. Our high-content image analysis screen identified ECDD-S27, a derivative of cleistanthin A found in plants[32-36], to potently increase the number of autophagic vacuoles in cells. Upon further characterization into its mechanism of action, we discovered that ECDD-S27 does so by targeting the V-ATPase and hence deacidifies the lysosomes leading to the block in autophagosome-lysosome fusion and thereby inhibiting the autophagic flux. As autophagic flux inhibitors, CQ and HCQ, are currently investigated in clinical trials either alone or in combination with chemotherapy and radiotherapy for cancers[13-15], we tested the ability of ECDD-S27 in suppressing the viability of cancer cells. Our results conducted in different cancer cell types showed that ECDD-S27 can potently restrict cancer cell survival at low nanomolar concentrations while it is not toxic to normal cells, establishing the therapeutic potential of this compound to be further developed for cancer treatment. Unlike CQ and HCQ in which the autophagy inhibition and anti-tumor activity were observed when used at high concentrations[19-23], ECDD-S27 exhibits a potent inhibition of the autophagic flux and cancer cell survival at low nanomolar concentrations. The IC50 values of ECDD-S27 for suppression of cell viability are 0.03–0.06 µM for cancer cells tested in our study while the IC50 values of HCQ are 11.44–18.40 µM, demonstrating that ECDD-S27 is >300 fold more potent than that of HCQ. Our results are consistent with other studies in which the IC50 values of HCQ in restricting various cancer cells were reported to be in the range of 8–72 µM[45]. In addition to their high concentration requirement, results from preclinical models and clinical trials have also demonstrated that while CQ and HCQ can be used to synergistically enhance the efficacy of other anti-cancer drugs and radiation, both have low efficacy when used as a single agent[15]. Due to the aforementioned limitations of CQ and HCQ and potent activity of ECDD-S27 as a single agent against cancer cells while sparing the normal cells, ECDD-S27 appears attractive for future therapeutic development. Therefore, studies to determine ECDD-S27 safety and efficacy in cancer animal models are warranted and require further investigation. Although both CQ and HCQ have been shown to impair lysosomal acidification and hence the autophagic flux, their definitive molecular targets are unclear[46]. CQ and HCQ are lysomotropic weak base agents that accumulate in lysosomes resulting in the increased pH and diminished activity of the lysosomal enzymes[47]. However, protein or nonprotein targets of CQ and HCQ within the lysosomes remain to be identified. Better understanding of the underlying molecular mechanism elicited by CQ and HCQ and identifying their targets are keys to aid in improving their efficacy through structural modifications. In contrast, our work by the molecular docking, SERS, and functional study analyses identified the V-ATPase as the ECDD-S27 target. The eukaryotic V-ATPase is a multi-subunit protein complex which functions as a motor for pumping H+ [48]. It is composed of the peripheral catalytic V1 and membrane-bound V0 domains, in which the former functions in hydrolyzing ATP while the latter translocates the proton across membrane[49]. Our SERS analysis confirmed that ECDD-S27 can interact with the V-ATPase while our molecular docking analysis revealed that ECDD-S27 can form a stable interaction with the membrane-bound subunit a, c, c″, and e of the V0 domain. Particularly, ECDD-S27 forms a hydrogen bond interaction between its ether oxygen atom with the amide nitrogen atom of Gln55 on the subunit e. In addition, several hydrophobic interactions are formed between ECDD-S27 and amino acid residues within the subunit a, c, and c″. As previous studies have demonstrated the importance of subunit a, c, cʹ, and c″ in the proton transport and release into the lumen[50], thus ECDD-S27 may inhibit the translocation of H+ and thereby inhibiting the lysosomal acidification. Indeed, our functional study confirmed this idea in which treatment of ECDD-S27 to different cancer cells demonstrated a dose-dependent decrease in the number of acidic organelles inside cells. Nevertheless, although the molecular docking, SERS, and functional studies strongly support that the V-ATPase is a target of ECDD-S27, additional target cannot be excluded and is thus a subject for future investigations.

Methods

Cells, inhibitors, antibodies, plasmid construct and fluorescent dye

Raw264.7, HT-29, HepG2, and HeLa cells were obtained from the American Type Culture Collection. Cells were maintained in DMEM (Gibco), 10% FBS (Gibco), 0.37% sodium bicarbonate (Sigma), and 4 mM -glutamine (Hyclone) (full medium) at 37 °C and 5% CO2. Earle’s balanced salt solution (EBSS; starvation medium) and hydroxychloroquine sulfate (HCQ) were obtained from Sigma. Bafilomycin A1 (Baf; LC laboratories) was used at 0.1 µM. The plasmid constructs encoding RFP-GFP-LC3B and GFP-LC3B used in this study have been described previously[51]. For immunoblotting, polyclonal antibodies against p62 (Progen) were used at 1: 3,000 dilutions, polyclonal antibodies against LC3 (MBL International) were used at 1: 2,000 dilutions, polyclonal antibodies against V0a3 (Thermo Fisher) were used at 1: 2,000 dilutions, polyclonal antibodies against V1A1 (Thermo Fisher) were used at 1: 2,000 dilutions, and monoclonal antibody against Actin (Abcam) was used at 1: 10,000 dilutions. The fluorescent dye Hoechst 33342 (Invitrogen) was used at 1:500 and LysoTracker Red (LTR; Invitrogen) dye was used at 0.25 µM.

Screening of natural product-derived compounds

Screening of natural product-derived compounds for their autophagy modulating activity was conducted by using the Operetta HC imaging analysis system (PerkinElmer) to quantitate the number of RFP-GFP-LC3B puncta (a cellular marker for autophagosomes/autolysosomes) in Raw264.7 macrophages. In brief, Raw264.7 macrophages were transfected with 5 µg cDNAs encoding RFP-GFP-LC3B in 100 µL of electroporation buffer V (Amaxa) using the Amaxa Nucleofector apparatus and the program D-032. The cells were then transferred to a new flask containing complete medium. At 24 h after transfection, 3 × 104 cells were then plated into each well of 96-well plates. At 48 h after transfection, cells were treated with starvation (EBSS media; positive control; 10 wells per plate), DMSO (negative control; 10 wells per plate), or 50 µM final concentration of each natural product-derived compounds for 4 h. Cells were then fixed with 4% paraformaldehyde for 10 min and stained with Hoechst for 15 min. Cells were then analyzed by HC image analysis to quantify the number of fluorescent autophagic puncta, which are either RFP+GFP+-LC3B (autophagosomes) or RFP+GFP−LC3B (autolysosomes). Compounds that can up-regulate the number of autophagic puncta to be more than 3 S.D. above that of the mean of the negative control in the same plate were identified as positive compounds for autophagy inducers or autophagy flux inhibitors.

ECDD-S27 synthesis and structure characterization

To synthesize ECDD-S27, cleistanthin A was first extracted from the aerial parts of Phyllanthus taxodiifolius using the previously described procedure[36]. All solvents used for extraction and isolation were distilled prior to use at their boiling point ranges. Cleistanthin A (306.5 mg, 0.568 mmol) was then reacted with 3,5-dimethoxybenzoic acid (155.1 mg, 0.852 mmol) in the presence of N,N′-dicyclohexylcarbodiimide (175.8 mg, 0.852 mmol) and 4-dimethylaminopyridine (cat.) at room temperature for 12 h. The reaction mixture was purified by column chromatography, eluting with 50% ethyl acetate-hexanes (1:1), followed by recrystallization from MeOH-CH2Cl2 to yield ECDD-S27 (1) (308.4 mg, 77% yield) as yellow powder. Purity of ECDD-S27 was determined to be 97% by using HPLC technique (Agilent 1200 series; column C18 shiseido, 80% MeOH/H2O, 1 mL/min). Melting points were measured using a digital Electrothermal melting point apparatus. Optical rotations were determined on JASCO DIP 370 digital polarimeter, using a 50 mm microcell (1 mL). UV (EtOH) and IR (KBr) spectra were recorded on a JASCO 530 and Perkin-Elmer spectrophotometers, respectively. The 400 MHz 1H and 100 MHz 13C NMR spectra were recorded on Bruker Ascend-400 spectrometer in CDCl3 using TMS as internal standard. EIMS were recorded at 70 eV (probe) on a Thermo Finnigan Polaris Q mass spectrometer. The HRMS were recorded on HR-TOF-MS (Micromass model VQ-Tof-2 spectrometer). Silica gel 60 (70−230 mesh ASTM) was used for column chromatography, and preparative TLC was carried out with silica gel 60 PF254 (5–40 μm, 0.5 mm). ECDD-S27 (1): m.p. 142.3–143.5 °C (MeOH-CH2Cl2); TLC (50% EtOAc-hexanes): Rf = 0.33; −42.6 (c 0.5 in CHCl3); UV (EtOH): λmax (log ε): 206 (4.88), 261 (4.61), 308 (4.13), 355 (3.67) nm; CD (1.99 × 10−5 M, EtOH): λmax (Δε): 314 (−2.58), 268 (+6.49), 250 (−2.94), 210 (+4.00), 200 (−0.82), 194 (+4.06); IR (KBr):3450, 2938, 1763, 1729, 1596, 1507, 1432, 1390, 1351, 1264, 1227, 1158, 1101, 1062, 1040, 931, 860, 770 cm−1; EI-MS m/z (rel. int.): 380 [M-Glucose]+ (25), 325 (6), 261 (28), 233 (6), 165 (100), 137 (14); HRMS (m/z): [M + Na]+ calcd. for C37H36O14Na, 727.2003; found, 727.2002; 1H NMR (400 MHz, CDCl3): 7.53 (d, J = 2.7 H, 1H), 7.22 (s, 2H), 7.02 (s, 1H), 6.94 (d, J = 7.9 Hz, 1 H), 6.80 (s, 1 H), 6.67 (s, 1 H), 6.09 (s, 1 H), 6.04 (s, 1 H), 5.58 (brt, J = 7.0 Hz, 1 H), 5.46 (d, J = 14.8 Hz, 1H), 5.39 (dd, J = 14.8, 2.7 Hz, 1 H), 5.19 (d, J = 6.6 Hz, 1 H), 4.21 (brdd, J = 11.8, 3.4 Hz, 1H), 3.87 (s, OCH3), 3.82 (s, 2 x OCH3), 3.77 (s, OCH3), 3.66 (s, OCH3), 3.58 (obsc, 1H), 3.56 (obsc, 1H), 3.55 (s, OCH3), 3.38 (m, 1H); 13C NMR (100 MHz, CDCl3): 169.6 (C), 165.3 (C), 160.8 (2 x C), 151.8 (C), 150.3 (C), 147.5 (2 x C), 144.0 (C), 135.8 (C), 131.3 (C), 130.6 (C), 128.4 (C), 127.3 (C), 126.4 (C), 123.6 (CH), 119.2 (C), 110.7 (CH), 108.2 (CH), 107.6 (2 x CH), 106.0 (CH), 105.6 (CH), 101.29 (CH), 101.23 (CH2), 100.8 (CH), 82.1 (CH), 78.3 (CH), 72.6 (CH), 67.1 (CH2), 63.2 (CH2), 60.4 (OCH3), 58.7 (OCH3), 56.0 (OCH3), 55.8 (OCH3), 55.6 (2 x OCH3).

Immunofluorescence confocal microscopy and HC image analysis

Cells were transfected with 5 µg of cDNAs encoding RFP-GFP-LC3B or GFP-LC3B as described above. For confocal microscopy, at 24 h post transfection, cells were plated onto 12-well plate containing coverslips at a density of 3 × 105 cells/well (Raw264.7 macrophages), 2.5 × 105 cells/well (HT-29 or HepG2 cells), and 8 × 104 cells/well (HeLa cells). At 48 h after transfection, cells were treated with DMSO (negative control), starvation (EBSS media; positive control), or ECDD-S27 at indicated final concentrations for 4 h. Cells were then fixed with 4% paraformaldehyde for 10 min and mounted with ProLong Gold antifade mountant (Invitrogen). The number of RFP+GFP+-LC3B or RFP+GFP−LC3B puncta per cell were quantified using Zeiss LSM-700 laser scanning confocal microscope. At least 30 cells per experimental condition in three independent experiments were analyzed. For HC image analysis of GFP-LC3B expressing cells, at 24 h post transfection, cells were plated onto 96-well black plate at a density of 1 × 104 cells/well (HT-29 or HepG2 cells) and 2 × 103 cells/well (HeLa cells). At 48 h after transfection, cells were treated with DMSO (negative control) or ECDD-S27 at indicated final concentrations for 72 h. Cells were then fixed with 4% paraformaldehyde for 10 min and stained with Hoechst for 15 min. Percent cell viability and autophagic flux-inhibited cells (defined as cells with more than 5 GFP+-LC3B puncta) were analyzed by HC image analysis.

SDS-PAGE and immunoblotting

Cells were lysed in lysis buffer containing 62.5 mM Tris-HCl (pH 6.8), 10% glycerol, 2% SDS, 5% β-mercaptoethanol, and 0.01% bromophenol blue. The cell lysates were then separated by 15% polyacrylamide gels and transferred onto nitrocellulose membranes (Amersham Biosciences). The membranes were then blocked with 5% blocking solution (Roche Diagnostics) for 1 h at room temperature followed by incubation with primary antibodies at 4 °C overnight. The membranes were washed 4 times with 0.1% PBST and incubated with appropriate horseradish peroxidase-conjugated secondary antibodies for 1 h at room temperature. The membranes were then washed 4 times and the expression levels of the proteins were detected using the chemiluminescence method (Roche Diagnostics). The band intensities were quantified using the Image J 1.47 v software (NIH, USA). At least three independent experiments were performed and analyzed. IC50 values were the concentrations at which 50% increase in the p62/Actin or LC3II/Actin levels could be observed, relative to that seen in the bafilomycin A1 treated control set to 100%.

MTS cell viability assay

HT-29 (1 × 104 cells), HepG2 (1 × 104 cells), and HeLa (2 × 103 cells) were plated onto 96-well plates for 16 h. Cells were treated with DMSO (negative control), ECDD-S27, HCQ, or bafilomycin A1 at the indicated concentrations and incubated for 72 h. The cell viability was measured by MTS assay using CellTiter 96 AQueous One Solution Reagent (Promega) following the manufacturer’s instruction. The percent cell viability was then calculated by using % cell viability = [(Absorbance of treated cells − Absorbance of blank)/(Absorbance of DMSO control cells − Absorbance of blank)] × 100. The percent cell cytotoxicity was calculated by using % cell cytotoxicity = 100 − % cell viability. IC50 values were then determined by plotting the dose response curve between Log [M] concentrations of the compound and % cell cytotoxicity and the values were determined using nonlinear regression analysis by GraphPad Prism 5 (GraphPad Software Inc.). All assays were performed in triplicate with at least three independent experiments.

Molecular simulation

The X-ray crystal structure of the eukaryotic V-ATPase (PDB id 5TJ5)[52] from Saccharomyces cerevisiae was used as the docking target. Prior to the docking simulation, the protein structure was subjected to molecular dynamics simulation. Briefly, molecular dynamics simulation was performed using the AMBER14 force field[53] in YASARA, version 18.4.24[54]. The simulation was performed using default parameters in md_runmembrane.mcr for protein-lipid membrane system in YASARA. Briefly, the protein was placed in a lipid membrane (i.e. phosphatidyl-ethanolamine) corresponding to the XZ plane in which the membrane spans 15 Å on each side of the protein. This lipid-protein system is immersed in water that spans 10 Å on each side of the protein. The simulation was performed for 30 ns at pH 7.4, temperature of 298 K and 0.9% NaCl solution. Docking calculation was performed using AutoDock, version 4.2[55], by means of the Lamarckian Genetic Algorithm[56] while the grid box was generated using AutoGrid[57]. The protein structure was prepared for docking by first adding essential hydrogen atoms, Kollman united atom charges and solvation parameters using AutoDockTools[55] and PyRx0.3[58]. The ligand structure was geometrically optimized via molecular mechanic force field using the Python package, RDKit. Next, non-polar hydrogen atoms were merged, Gasteiger partial charges were added and rotatable bonds were defined. As there was no reported information on binding site residues, therefore a box was generated to cover all residues of the protein structure. AutoDock parameters pertaining to set- and distance-dependent dielectric functions were used for calculating van der Waals and electrostatic terms, respectively. The initial position, orientation and torsions of the ligand was set randomly. The docked compound was derived from 100 independent docking runs using a mutation rate of 0.02 and a crossover rate of 0.8 and the calculations were set to terminate after a maximum of 2.5 × 106 energy evaluations were reached. Furthermore, the population size was set to use 250 randomly placed individual. The Lamarckian genetic algorithm was employed to search for low-energy binding orientations. A translational step of 0.2 Å, a quaternion step of 5 Å and a torsion step of 5 Å were used. The best docked conformations as deduced from the clustering histogram were those with low binding energy. Docking results were analyzed using AutoDockTools[55] and PyMOL[59]. These tools help to shed light on the interaction type (i.e. hydrogen-bond, π-π interaction and cation-pi interaction) contributing to the ligand binding. Favorable ligand binding poses (i.e. deduced by clustering histograms) along with their corresponding binding energy were obtained from AutoDockTools[55]. Moreover, PyMOL[59] was used to provide complementary information on ligand-receptor interaction. All molecular graphics were rendered and ray-traced using PyMOL[59].

Immunoprecipitation and surface-enhanced Raman substrates (SERS) analysis

HeLa cells were lysed in RIPA buffer supplemented with the protease inhibitor cocktail tablet (Roche) on ice for 20 min. The lysate was then pass through 27 gauge needle 9 times and then spun down at 10,000 × g at 4 °C for 10 min. The supernatant was transferred to a new tube. Protein concentration was then determined using RC-DC kit (Biorad) and 2.5 mg of total lysate was aliquoted into new tubes. Rabbit anti-V1A1 antibodies were added to the lysate at 2 µg per mg protein using normal rabbit IgG (Santa Cruz Biotechnology) as the control. The samples were then incubated at 4 °C for 4 h. Protein A sepharose beads (Santa Cruz Biotechnology) were then added and incubated at 4 °C for 4 h. The immunoprecipitated proteins were then washed with PBS containing 0.5% NP-40 three times. The immune complex was then subjected for WB analysis to check for the presence of V1A1 and V0a3 proteins in the immune complex as described above. For SERS analysis, the anti-V1A1 precipitated immune complex was treated with 5 mM ECDD-S27 at 4 °C overnight. The sample was then washed three times in 1 mL PBS containing 0.5% NP-40 in order to wash out the unbound ECDD-S27. The washed sample was then spotted onto the surface-enhanced Raman substrates (SERS) ONSPEC Chips (NECTEC, Thailand), which is fabricated in silver nanorod structure. All the SERS experiments were performed with the Raman spectrometer (Rigaku Analytical Devices, Inc.). As the controls, the ECDD-S27 alone, buffer alone, and anti-V1A1 immune complex alone were spotted onto the ONSPEC Chips followed by the Raman analysis. All spectra were taken in an automatic mode by the Raman spectroscope with 1064 nm laser wavelength and TE cooled InGaAs 512 pixel detector. The range of spectrum cover 200–2500 with resolution 8–11 and adjustable power laser 30–490 mW.

LysoTracker Red staining

HT-29 (2.5 × 104 cells), HepG2 (2.5 × 104 cells), or HeLa (6 × 103 cells) were plated onto 96-well black plates for 16 h. Cells were then treated with DMSO (negative control) or ECDD-S27 at the indicated concentrations for 2 h. LysoTracker Red (LTR) dye was then added and the incubation was continued for 2 h. Cells were then fixed with 4% paraformaldehyde for 10 min and stained with Hoechst for 15 min. The LTR+ puncta/cell were analyzed by HC image analysis. At least 30 cells per experimental condition were analyzed.

Statistical analysis

Unless otherwise stated, all experiments were conducted at least three times and the data were pooled for determination of the mean ± standard error of the mean (S.E.M.). All data were analyzed by the Prism software (GraphPad) using one-way ANOVA with a Tukey’s multiple comparison test. p values less than 0.05 were considered to indicate statistical significance.
  51 in total

Review 1.  Application and interpretation of current autophagy inhibitors and activators.

Authors:  Ya-ping Yang; Li-fang Hu; Hui-fen Zheng; Cheng-jie Mao; Wei-dong Hu; Kang-ping Xiong; Fen Wang; Chun-feng Liu
Journal:  Acta Pharmacol Sin       Date:  2013-03-25       Impact factor: 6.150

Review 2.  A brief history of autophagy from cell biology to physiology and disease.

Authors:  Noboru Mizushima
Journal:  Nat Cell Biol       Date:  2018-04-23       Impact factor: 28.824

3.  Diphyllin, a novel and naturally potent V-ATPase inhibitor, abrogates acidification of the osteoclastic resorption lacunae and bone resorption.

Authors:  Mette G Sørensen; Kim Henriksen; Anita V Neutzsky-Wulff; Morten H Dziegiel; Morten A Karsdal
Journal:  J Bone Miner Res       Date:  2007-10       Impact factor: 6.741

4.  Phase I clinical trial and pharmacodynamic evaluation of combination hydroxychloroquine and doxorubicin treatment in pet dogs treated for spontaneously occurring lymphoma.

Authors:  Rebecca A Barnard; Luke A Wittenburg; Ravi K Amaravadi; Daniel L Gustafson; Andrew Thorburn; Douglas H Thamm
Journal:  Autophagy       Date:  2014-05-20       Impact factor: 16.016

5.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

Authors:  Garrett M Morris; Ruth Huey; William Lindstrom; Michel F Sanner; Richard K Belew; David S Goodsell; Arthur J Olson
Journal:  J Comput Chem       Date:  2009-12       Impact factor: 3.376

Review 6.  Mechanisms of autophagy and relevant small-molecule compounds for targeted cancer therapy.

Authors:  Jin Zhang; Guan Wang; Yuxin Zhou; Yi Chen; Liang Ouyang; Bo Liu
Journal:  Cell Mol Life Sci       Date:  2018-02-07       Impact factor: 9.261

Review 7.  Autophagy: machinery and regulation.

Authors:  Zhangyuan Yin; Clarence Pascual; Daniel J Klionsky
Journal:  Microb Cell       Date:  2016-12-01

8.  Effects of diphyllin as a novel V-ATPase inhibitor on TE-1 and ECA-109 cells.

Authors:  Haijiao Chen; Pengfei Liu; Ting Zhang; Yi Gao; Yingdi Zhang; Xiuyun Shen; Xiao Li; Weidong Shen
Journal:  Oncol Rep       Date:  2018-01-04       Impact factor: 3.906

9.  Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition).

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Elaine Gutierrez; Maximiliano G Gutierrez; Ho-Shin Gwak; Albert Haas; James E Haber; Shinji Hadano; Monica Hagedorn; David R Hahn; Andrew J Halayko; Anne Hamacher-Brady; Kozo Hamada; Ahmed Hamai; Andrea Hamann; Maho Hamasaki; Isabelle Hamer; Qutayba Hamid; Ester M Hammond; Feng Han; Weidong Han; James T Handa; John A Hanover; Malene Hansen; Masaru Harada; Ljubica Harhaji-Trajkovic; J Wade Harper; Abdel Halim Harrath; Adrian L Harris; James Harris; Udo Hasler; Peter Hasselblatt; Kazuhisa Hasui; Robert G Hawley; Teresa S Hawley; Congcong He; Cynthia Y He; Fengtian He; Gu He; Rong-Rong He; Xian-Hui He; You-Wen He; Yu-Ying He; Joan K Heath; Marie-Josée Hébert; Robert A Heinzen; Gudmundur Vignir Helgason; Michael Hensel; Elizabeth P Henske; Chengtao Her; Paul K Herman; Agustín Hernández; Carlos Hernandez; Sonia Hernández-Tiedra; Claudio Hetz; P Robin Hiesinger; Katsumi Higaki; Sabine Hilfiker; Bradford G Hill; Joseph A Hill; William D Hill; Keisuke Hino; Daniel Hofius; Paul Hofman; Günter U Höglinger; Jörg Höhfeld; Marina K Holz; Yonggeun Hong; David A Hood; Jeroen Jm Hoozemans; Thorsten Hoppe; Chin Hsu; Chin-Yuan Hsu; Li-Chung Hsu; Dong Hu; Guochang Hu; Hong-Ming Hu; Hongbo Hu; Ming Chang Hu; Yu-Chen Hu; Zhuo-Wei Hu; Fang Hua; Ya Hua; Canhua Huang; Huey-Lan Huang; Kuo-How Huang; Kuo-Yang Huang; Shile Huang; Shiqian Huang; Wei-Pang Huang; Yi-Ran Huang; Yong Huang; Yunfei Huang; Tobias B Huber; Patricia Huebbe; Won-Ki Huh; Juha J Hulmi; Gang Min Hur; James H Hurley; Zvenyslava Husak; Sabah Na Hussain; Salik Hussain; Jung Jin Hwang; Seungmin Hwang; Thomas Is Hwang; Atsuhiro Ichihara; Yuzuru Imai; Carol Imbriano; Megumi Inomata; Takeshi Into; Valentina Iovane; Juan L Iovanna; Renato V Iozzo; Nancy Y Ip; Javier E Irazoqui; Pablo Iribarren; Yoshitaka Isaka; Aleksandra J Isakovic; Harry Ischiropoulos; Jeffrey S Isenberg; Mohammad Ishaq; Hiroyuki Ishida; Isao Ishii; Jane E Ishmael; Ciro Isidoro; Ken-Ichi Isobe; Erika Isono; Shohreh Issazadeh-Navikas; Koji Itahana; Eisuke Itakura; Andrei I Ivanov; Anand Krishnan V Iyer; José M Izquierdo; Yotaro Izumi; Valentina Izzo; Marja Jäättelä; Nadia Jaber; Daniel John Jackson; William T Jackson; Tony George Jacob; Thomas S Jacques; Chinnaswamy Jagannath; Ashish Jain; Nihar Ranjan Jana; Byoung Kuk Jang; Alkesh Jani; Bassam Janji; Paulo Roberto Jannig; Patric J Jansson; Steve Jean; Marina Jendrach; Ju-Hong Jeon; Niels Jessen; Eui-Bae Jeung; Kailiang Jia; Lijun Jia; Hong Jiang; Hongchi Jiang; Liwen Jiang; Teng Jiang; Xiaoyan Jiang; Xuejun Jiang; Xuejun Jiang; Ying Jiang; Yongjun Jiang; Alberto Jiménez; Cheng Jin; Hongchuan Jin; Lei Jin; Meiyan Jin; Shengkan Jin; Umesh Kumar Jinwal; Eun-Kyeong Jo; Terje Johansen; Daniel E Johnson; Gail Vw Johnson; James D Johnson; Eric Jonasch; Chris Jones; Leo Ab Joosten; Joaquin Jordan; Anna-Maria Joseph; Bertrand Joseph; Annie M Joubert; Dianwen Ju; Jingfang Ju; Hsueh-Fen Juan; Katrin Juenemann; Gábor Juhász; Hye Seung Jung; Jae U Jung; Yong-Keun Jung; Heinz Jungbluth; Matthew J Justice; Barry Jutten; Nadeem O Kaakoush; Kai Kaarniranta; Allen Kaasik; Tomohiro Kabuta; Bertrand Kaeffer; Katarina Kågedal; Alon Kahana; Shingo Kajimura; Or Kakhlon; Manjula Kalia; Dhan V Kalvakolanu; Yoshiaki Kamada; Konstantinos Kambas; Vitaliy O Kaminskyy; Harm H Kampinga; Mustapha Kandouz; Chanhee Kang; Rui Kang; Tae-Cheon Kang; Tomotake Kanki; Thirumala-Devi Kanneganti; Haruo Kanno; Anumantha G Kanthasamy; Marc Kantorow; Maria Kaparakis-Liaskos; Orsolya Kapuy; Vassiliki Karantza; Md Razaul Karim; Parimal Karmakar; Arthur Kaser; Susmita Kaushik; Thomas Kawula; A Murat Kaynar; Po-Yuan Ke; Zun-Ji Ke; John H Kehrl; Kate E Keller; Jongsook Kim Kemper; Anne K Kenworthy; Oliver Kepp; Andreas Kern; Santosh Kesari; David Kessel; Robin Ketteler; Isis do Carmo Kettelhut; Bilon Khambu; Muzamil Majid Khan; Vinoth Km Khandelwal; Sangeeta Khare; Juliann G Kiang; Amy A Kiger; Akio Kihara; Arianna L Kim; Cheol Hyeon Kim; Deok Ryong Kim; Do-Hyung Kim; Eung Kweon Kim; Hye Young Kim; Hyung-Ryong Kim; Jae-Sung Kim; Jeong Hun Kim; Jin Cheon Kim; Jin Hyoung Kim; Kwang Woon Kim; Michael D Kim; Moon-Moo Kim; Peter K Kim; Seong Who Kim; Soo-Youl Kim; Yong-Sun Kim; Yonghyun Kim; Adi Kimchi; Alec C Kimmelman; Tomonori Kimura; Jason S King; Karla Kirkegaard; Vladimir Kirkin; Lorrie A Kirshenbaum; Shuji Kishi; Yasuo Kitajima; Katsuhiko Kitamoto; Yasushi Kitaoka; Kaio Kitazato; Rudolf A Kley; Walter T Klimecki; Michael Klinkenberg; Jochen Klucken; Helene Knævelsrud; Erwin Knecht; Laura Knuppertz; Jiunn-Liang Ko; Satoru Kobayashi; Jan C Koch; Christelle Koechlin-Ramonatxo; Ulrich Koenig; Young Ho Koh; Katja Köhler; Sepp D Kohlwein; Masato Koike; Masaaki Komatsu; Eiki Kominami; Dexin Kong; Hee Jeong Kong; Eumorphia G Konstantakou; Benjamin T Kopp; Tamas Korcsmaros; Laura Korhonen; Viktor I Korolchuk; Nadya V Koshkina; Yanjun Kou; Michael I Koukourakis; Constantinos Koumenis; Attila L Kovács; Tibor Kovács; Werner J Kovacs; Daisuke Koya; Claudine Kraft; Dimitri Krainc; Helmut Kramer; Tamara Kravic-Stevovic; Wilhelm Krek; Carole Kretz-Remy; Roswitha Krick; Malathi Krishnamurthy; Janos Kriston-Vizi; Guido Kroemer; Michael C Kruer; Rejko Kruger; Nicholas T Ktistakis; Kazuyuki Kuchitsu; Christian Kuhn; Addanki Pratap Kumar; Anuj Kumar; Ashok Kumar; Deepak Kumar; Dhiraj Kumar; Rakesh Kumar; Sharad Kumar; Mondira Kundu; Hsing-Jien Kung; Atsushi Kuno; Sheng-Han Kuo; Jeff Kuret; Tino Kurz; Terry Kwok; Taeg Kyu Kwon; Yong Tae Kwon; Irene Kyrmizi; Albert R La Spada; Frank Lafont; Tim Lahm; Aparna Lakkaraju; Truong Lam; Trond Lamark; Steve Lancel; Terry H Landowski; Darius J R Lane; Jon D Lane; Cinzia Lanzi; Pierre Lapaquette; Louis R Lapierre; Jocelyn Laporte; Johanna Laukkarinen; Gordon W Laurie; Sergio Lavandero; Lena Lavie; Matthew J LaVoie; Betty Yuen Kwan Law; Helen Ka-Wai Law; Kelsey B Law; Robert Layfield; Pedro A Lazo; Laurent Le Cam; Karine G Le Roch; Hervé Le Stunff; Vijittra Leardkamolkarn; Marc Lecuit; Byung-Hoon Lee; Che-Hsin Lee; Erinna F Lee; Gyun Min Lee; He-Jin Lee; Hsinyu Lee; Jae Keun Lee; Jongdae Lee; Ju-Hyun Lee; Jun Hee Lee; Michael Lee; Myung-Shik Lee; Patty J Lee; Sam W Lee; Seung-Jae Lee; Shiow-Ju Lee; Stella Y Lee; Sug Hyung Lee; Sung Sik Lee; Sung-Joon Lee; Sunhee Lee; Ying-Ray Lee; Yong J Lee; Young H Lee; Christiaan Leeuwenburgh; Sylvain Lefort; Renaud Legouis; Jinzhi Lei; Qun-Ying Lei; David A Leib; Gil Leibowitz; Istvan Lekli; Stéphane D Lemaire; John J Lemasters; Marius K Lemberg; Antoinette Lemoine; Shuilong Leng; Guido Lenz; Paola Lenzi; Lilach O Lerman; Daniele Lettieri Barbato; Julia I-Ju Leu; Hing Y Leung; Beth Levine; Patrick A Lewis; Frank Lezoualc'h; Chi Li; Faqiang Li; Feng-Jun Li; Jun Li; Ke Li; Lian Li; Min Li; Min Li; Qiang Li; Rui Li; Sheng Li; Wei Li; Wei Li; Xiaotao Li; Yumin Li; Jiqin Lian; Chengyu Liang; Qiangrong Liang; Yulin Liao; Joana Liberal; Pawel P Liberski; Pearl Lie; Andrew P Lieberman; Hyunjung Jade Lim; Kah-Leong Lim; Kyu Lim; Raquel T Lima; Chang-Shen Lin; Chiou-Feng Lin; Fang Lin; Fangming Lin; Fu-Cheng Lin; Kui Lin; Kwang-Huei Lin; Pei-Hui Lin; Tianwei Lin; Wan-Wan Lin; Yee-Shin Lin; Yong Lin; Rafael Linden; Dan Lindholm; Lisa M Lindqvist; Paul Lingor; Andreas Linkermann; Lance A Liotta; Marta M Lipinski; Vitor A Lira; Michael P Lisanti; Paloma B Liton; Bo Liu; Chong Liu; Chun-Feng Liu; Fei Liu; Hung-Jen Liu; Jianxun Liu; Jing-Jing Liu; Jing-Lan Liu; Ke Liu; Leyuan Liu; Liang Liu; Quentin Liu; Rong-Yu Liu; Shiming Liu; Shuwen Liu; Wei Liu; Xian-De Liu; Xiangguo Liu; Xiao-Hong Liu; Xinfeng Liu; Xu Liu; Xueqin Liu; Yang Liu; Yule Liu; Zexian Liu; Zhe Liu; Juan P Liuzzi; Gérard Lizard; Mila Ljujic; Irfan J Lodhi; Susan E Logue; Bal L Lokeshwar; Yun Chau Long; Sagar Lonial; Benjamin Loos; Carlos López-Otín; Cristina López-Vicario; Mar Lorente; Philip L Lorenzi; Péter Lõrincz; Marek Los; Michael T Lotze; Penny E Lovat; Binfeng Lu; Bo Lu; Jiahong Lu; Qing Lu; She-Min Lu; Shuyan Lu; Yingying Lu; Frédéric Luciano; Shirley Luckhart; John Milton Lucocq; Paula Ludovico; Aurelia Lugea; Nicholas W Lukacs; Julian J Lum; Anders H Lund; Honglin Luo; Jia Luo; Shouqing Luo; Claudio Luparello; Timothy Lyons; Jianjie Ma; Yi Ma; Yong Ma; Zhenyi Ma; Juliano Machado; Glaucia M Machado-Santelli; Fernando Macian; Gustavo C MacIntosh; Jeffrey P MacKeigan; Kay F Macleod; John D MacMicking; Lee Ann MacMillan-Crow; Frank Madeo; Muniswamy Madesh; Julio Madrigal-Matute; Akiko Maeda; Tatsuya Maeda; Gustavo Maegawa; Emilia Maellaro; Hannelore Maes; Marta Magariños; Kenneth Maiese; Tapas K Maiti; Luigi Maiuri; Maria Chiara Maiuri; Carl G Maki; Roland Malli; Walter Malorni; Alina Maloyan; Fathia Mami-Chouaib; Na Man; Joseph D Mancias; Eva-Maria Mandelkow; Michael A Mandell; Angelo A Manfredi; Serge N Manié; Claudia Manzoni; Kai Mao; Zixu Mao; Zong-Wan Mao; Philippe Marambaud; Anna Maria Marconi; Zvonimir Marelja; Gabriella Marfe; Marta Margeta; Eva Margittai; Muriel Mari; Francesca V Mariani; Concepcio Marin; Sara Marinelli; Guillermo Mariño; Ivanka Markovic; Rebecca Marquez; Alberto M Martelli; Sascha Martens; Katie R Martin; Seamus J Martin; Shaun Martin; Miguel A Martin-Acebes; Paloma Martín-Sanz; Camille Martinand-Mari; Wim Martinet; Jennifer Martinez; Nuria Martinez-Lopez; Ubaldo Martinez-Outschoorn; Moisés Martínez-Velázquez; Marta Martinez-Vicente; Waleska Kerllen Martins; Hirosato Mashima; James A Mastrianni; Giuseppe Matarese; Paola Matarrese; Roberto Mateo; Satoaki Matoba; Naomichi Matsumoto; Takehiko Matsushita; Akira Matsuura; Takeshi Matsuzawa; Mark P Mattson; Soledad Matus; Norma Maugeri; Caroline Mauvezin; Andreas Mayer; Dusica Maysinger; Guillermo D Mazzolini; Mary Kate McBrayer; Kimberly McCall; Craig McCormick; Gerald M McInerney; Skye C McIver; Sharon McKenna; John J McMahon; Iain A McNeish; Fatima Mechta-Grigoriou; Jan Paul Medema; Diego L Medina; Klara Megyeri; Maryam Mehrpour; Jawahar L Mehta; Yide Mei; Ute-Christiane Meier; Alfred J Meijer; Alicia Meléndez; Gerry Melino; Sonia Melino; Edesio Jose Tenorio de Melo; Maria A Mena; Marc D Meneghini; Javier A Menendez; Regina Menezes; Liesu Meng; Ling-Hua Meng; Songshu Meng; Rossella Menghini; A Sue Menko; Rubem Fs Menna-Barreto; Manoj B Menon; Marco A Meraz-Ríos; Giuseppe Merla; Luciano Merlini; Angelica M Merlot; Andreas Meryk; Stefania Meschini; Joel N Meyer; Man-Tian Mi; Chao-Yu Miao; Lucia Micale; Simon Michaeli; Carine Michiels; Anna Rita Migliaccio; Anastasia Susie Mihailidou; Dalibor Mijaljica; Katsuhiko Mikoshiba; Enrico Milan; Leonor Miller-Fleming; Gordon B Mills; Ian G Mills; Georgia Minakaki; Berge A Minassian; Xiu-Fen Ming; Farida Minibayeva; Elena A Minina; Justine D Mintern; Saverio Minucci; Antonio Miranda-Vizuete; Claire H Mitchell; Shigeki Miyamoto; Keisuke Miyazawa; Noboru Mizushima; Katarzyna Mnich; Baharia Mograbi; Simin Mohseni; Luis Ferreira Moita; Marco Molinari; Maurizio Molinari; Andreas Buch Møller; Bertrand Mollereau; Faustino Mollinedo; Marco Mongillo; Martha M Monick; Serena Montagnaro; Craig Montell; Darren J Moore; Michael N Moore; Rodrigo Mora-Rodriguez; Paula I Moreira; Etienne Morel; Maria Beatrice Morelli; Sandra Moreno; Michael J Morgan; Arnaud Moris; Yuji Moriyasu; Janna L Morrison; Lynda A Morrison; Eugenia Morselli; Jorge Moscat; Pope L Moseley; Serge Mostowy; Elisa Motori; Denis Mottet; Jeremy C Mottram; Charbel E-H Moussa; Vassiliki E Mpakou; Hasan Mukhtar; Jean M Mulcahy Levy; Sylviane Muller; Raquel Muñoz-Moreno; Cristina Muñoz-Pinedo; Christian Münz; Maureen E Murphy; James T Murray; Aditya Murthy; Indira U Mysorekar; Ivan R Nabi; Massimo Nabissi; Gustavo A Nader; Yukitoshi Nagahara; Yoshitaka Nagai; Kazuhiro Nagata; Anika Nagelkerke; Péter Nagy; Samisubbu R Naidu; Sreejayan Nair; Hiroyasu Nakano; Hitoshi Nakatogawa; Meera Nanjundan; Gennaro Napolitano; Naweed I Naqvi; Roberta Nardacci; Derek P Narendra; Masashi Narita; Anna Chiara Nascimbeni; Ramesh Natarajan; Luiz C Navegantes; Steffan T Nawrocki; Taras Y Nazarko; Volodymyr Y Nazarko; Thomas Neill; Luca M Neri; Mihai G Netea; Romana T Netea-Maier; Bruno M Neves; Paul A Ney; Ioannis P Nezis; Hang Tt Nguyen; Huu Phuc Nguyen; Anne-Sophie Nicot; Hilde Nilsen; Per Nilsson; Mikio Nishimura; Ichizo Nishino; Mireia Niso-Santano; Hua Niu; Ralph A Nixon; Vincent Co Njar; Takeshi Noda; Angelika A Noegel; Elsie Magdalena Nolte; Erik Norberg; Koenraad K Norga; Sakineh Kazemi Noureini; Shoji Notomi; Lucia Notterpek; Karin Nowikovsky; Nobuyuki Nukina; Thorsten Nürnberger; Valerie B O'Donnell; Tracey O'Donovan; Peter J O'Dwyer; Ina Oehme; Clara L Oeste; Michinaga Ogawa; Besim Ogretmen; Yuji Ogura; Young J Oh; Masaki Ohmuraya; Takayuki Ohshima; Rani Ojha; Koji Okamoto; Toshiro Okazaki; F Javier Oliver; Karin Ollinger; Stefan Olsson; Daniel P Orban; Paulina Ordonez; Idil Orhon; Laszlo Orosz; Eyleen J O'Rourke; Helena Orozco; Angel L Ortega; Elena Ortona; Laura D Osellame; Junko Oshima; Shigeru Oshima; Heinz D Osiewacz; Takanobu Otomo; Kinya Otsu; Jing-Hsiung James Ou; Tiago F Outeiro; Dong-Yun Ouyang; Hongjiao Ouyang; Michael Overholtzer; Michelle A Ozbun; P Hande Ozdinler; Bulent Ozpolat; Consiglia Pacelli; Paolo Paganetti; Guylène Page; Gilles Pages; Ugo Pagnini; Beata Pajak; Stephen C Pak; Karolina Pakos-Zebrucka; Nazzy Pakpour; Zdena Palková; Francesca Palladino; Kathrin Pallauf; Nicolas Pallet; Marta Palmieri; Søren R Paludan; Camilla Palumbo; Silvia Palumbo; Olatz Pampliega; Hongming Pan; Wei Pan; Theocharis Panaretakis; Aseem Pandey; Areti Pantazopoulou; Zuzana Papackova; Daniela L Papademetrio; Issidora Papassideri; Alessio Papini; Nirmala Parajuli; Julian Pardo; Vrajesh V Parekh; Giancarlo Parenti; Jong-In Park; Junsoo Park; Ohkmae K Park; Roy Parker; Rosanna Parlato; Jan B Parys; Katherine R Parzych; Jean-Max Pasquet; Benoit Pasquier; Kishore Bs Pasumarthi; Daniel Patschan; Cam Patterson; Sophie Pattingre; Scott Pattison; Arnim Pause; Hermann Pavenstädt; Flaminia Pavone; Zully Pedrozo; Fernando J Peña; Miguel A Peñalva; Mario Pende; Jianxin Peng; Fabio Penna; Josef M Penninger; Anna Pensalfini; Salvatore Pepe; Gustavo Js Pereira; Paulo C Pereira; Verónica Pérez-de la Cruz; María Esther Pérez-Pérez; Diego Pérez-Rodríguez; Dolores Pérez-Sala; Celine Perier; Andras Perl; David H Perlmutter; Ida Perrotta; Shazib Pervaiz; Maija Pesonen; Jeffrey E Pessin; Godefridus J Peters; Morten Petersen; Irina Petrache; Basil J Petrof; Goran Petrovski; James M Phang; Mauro Piacentini; Marina Pierdominici; Philippe Pierre; Valérie Pierrefite-Carle; Federico Pietrocola; Felipe X Pimentel-Muiños; Mario Pinar; Benjamin Pineda; Ronit Pinkas-Kramarski; Marcello Pinti; Paolo Pinton; Bilal Piperdi; James M Piret; Leonidas C Platanias; Harald W Platta; Edward D Plowey; Stefanie Pöggeler; Marc Poirot; Peter Polčic; Angelo Poletti; Audrey H Poon; Hana Popelka; Blagovesta Popova; Izabela Poprawa; Shibu M Poulose; Joanna Poulton; Scott K Powers; Ted Powers; Mercedes Pozuelo-Rubio; Krisna Prak; Reinhild Prange; Mark Prescott; Muriel Priault; Sharon Prince; Richard L Proia; Tassula Proikas-Cezanne; Holger Prokisch; Vasilis J Promponas; Karin Przyklenk; Rosa Puertollano; Subbiah Pugazhenthi; Luigi Puglielli; Aurora Pujol; Julien Puyal; Dohun Pyeon; Xin Qi; Wen-Bin Qian; Zheng-Hong Qin; Yu Qiu; Ziwei Qu; Joe Quadrilatero; Frederick Quinn; Nina Raben; Hannah Rabinowich; Flavia Radogna; Michael J Ragusa; Mohamed Rahmani; Komal Raina; Sasanka Ramanadham; Rajagopal Ramesh; Abdelhaq Rami; Sarron Randall-Demllo; Felix Randow; Hai Rao; V Ashutosh Rao; Blake B Rasmussen; Tobias M Rasse; Edward A Ratovitski; Pierre-Emmanuel Rautou; Swapan K Ray; Babak Razani; Bruce H Reed; Fulvio Reggiori; Markus Rehm; Andreas S Reichert; Theo Rein; David J Reiner; Eric Reits; Jun Ren; Xingcong Ren; Maurizio Renna; Jane Eb Reusch; Jose L Revuelta; Leticia Reyes; Alireza R Rezaie; Robert I Richards; Des R Richardson; Clémence Richetta; Michael A Riehle; Bertrand H Rihn; Yasuko Rikihisa; Brigit E Riley; Gerald Rimbach; Maria Rita Rippo; Konstantinos Ritis; Federica Rizzi; Elizete Rizzo; Peter J Roach; Jeffrey Robbins; Michel Roberge; Gabriela Roca; Maria Carmela Roccheri; Sonia Rocha; Cecilia Mp Rodrigues; Clara I Rodríguez; Santiago Rodriguez de Cordoba; Natalia Rodriguez-Muela; Jeroen Roelofs; Vladimir V Rogov; Troy T Rohn; Bärbel Rohrer; Davide Romanelli; Luigina Romani; Patricia Silvia Romano; M Isabel G Roncero; Jose Luis Rosa; Alicia Rosello; Kirill V Rosen; Philip Rosenstiel; Magdalena Rost-Roszkowska; Kevin A Roth; Gael Roué; Mustapha Rouis; Kasper M Rouschop; Daniel T Ruan; Diego Ruano; David C Rubinsztein; Edmund B Rucker; Assaf Rudich; Emil Rudolf; Ruediger Rudolf; Markus A Ruegg; Carmen Ruiz-Roldan; Avnika Ashok Ruparelia; Paola Rusmini; David W Russ; Gian Luigi Russo; Giuseppe Russo; Rossella Russo; Tor Erik Rusten; Victoria Ryabovol; Kevin M Ryan; Stefan W Ryter; David M Sabatini; Michael Sacher; Carsten Sachse; Michael N Sack; Junichi Sadoshima; Paul Saftig; Ronit Sagi-Eisenberg; Sumit Sahni; Pothana Saikumar; Tsunenori Saito; Tatsuya Saitoh; Koichi Sakakura; Machiko Sakoh-Nakatogawa; Yasuhito Sakuraba; María Salazar-Roa; Paolo Salomoni; Ashok K Saluja; Paul M Salvaterra; Rosa Salvioli; Afshin Samali; Anthony Mj Sanchez; José A Sánchez-Alcázar; Ricardo Sanchez-Prieto; Marco Sandri; Miguel A Sanjuan; Stefano Santaguida; Laura Santambrogio; Giorgio Santoni; Claudia Nunes Dos Santos; Shweta Saran; Marco Sardiello; Graeme Sargent; Pallabi Sarkar; Sovan Sarkar; Maria Rosa Sarrias; Minnie M Sarwal; Chihiro Sasakawa; Motoko Sasaki; Miklos Sass; Ken Sato; Miyuki Sato; Joseph Satriano; Niramol Savaraj; Svetlana Saveljeva; Liliana Schaefer; Ulrich E Schaible; Michael Scharl; Hermann M Schatzl; Randy Schekman; Wiep Scheper; Alfonso Schiavi; Hyman M Schipper; Hana Schmeisser; Jens Schmidt; Ingo Schmitz; Bianca E Schneider; E Marion Schneider; Jaime L Schneider; Eric A Schon; Miriam J Schönenberger; Axel H Schönthal; Daniel F Schorderet; Bernd Schröder; Sebastian Schuck; Ryan J Schulze; Melanie Schwarten; Thomas L Schwarz; Sebastiano Sciarretta; Kathleen Scotto; A Ivana Scovassi; Robert A Screaton; Mark Screen; Hugo Seca; Simon Sedej; Laura Segatori; Nava Segev; Per O Seglen; Jose M Seguí-Simarro; Juan Segura-Aguilar; Ekihiro Seki; Christian Sell; Iban Seiliez; Clay F Semenkovich; Gregg L Semenza; Utpal Sen; Andreas L Serra; Ana Serrano-Puebla; Hiromi Sesaki; Takao Setoguchi; Carmine Settembre; John J Shacka; Ayesha N Shajahan-Haq; Irving M Shapiro; Shweta Sharma; Hua She; C-K James Shen; Chiung-Chyi Shen; Han-Ming Shen; Sanbing Shen; Weili Shen; Rui Sheng; Xianyong Sheng; Zu-Hang Sheng; Trevor G Shepherd; Junyan Shi; Qiang Shi; Qinghua Shi; Yuguang Shi; Shusaku Shibutani; Kenichi Shibuya; Yoshihiro Shidoji; Jeng-Jer Shieh; Chwen-Ming Shih; Yohta Shimada; Shigeomi Shimizu; Dong Wook Shin; Mari L Shinohara; Michiko Shintani; Takahiro Shintani; Tetsuo Shioi; Ken Shirabe; Ronit Shiri-Sverdlov; Orian Shirihai; Gordon C Shore; Chih-Wen Shu; Deepak Shukla; Andriy A Sibirny; Valentina Sica; Christina J Sigurdson; Einar M Sigurdsson; Puran Singh Sijwali; Beata Sikorska; Wilian A Silveira; Sandrine Silvente-Poirot; Gary A Silverman; Jan Simak; Thomas Simmet; Anna Katharina Simon; Hans-Uwe Simon; Cristiano Simone; Matias Simons; Anne Simonsen; Rajat Singh; Shivendra V Singh; Shrawan K Singh; Debasish Sinha; Sangita Sinha; Frank A Sinicrope; Agnieszka Sirko; Kapil Sirohi; Balindiwe Jn Sishi; Annie Sittler; Parco M Siu; Efthimios Sivridis; Anna Skwarska; Ruth Slack; Iva Slaninová; Nikolai Slavov; Soraya S Smaili; Keiran Sm Smalley; Duncan R Smith; Stefaan J Soenen; Scott A Soleimanpour; Anita Solhaug; Kumaravel Somasundaram; Jin H Son; Avinash Sonawane; Chunjuan Song; Fuyong Song; Hyun Kyu Song; Ju-Xian Song; Wei Song; Kai Y Soo; Anil K Sood; Tuck Wah Soong; Virawudh Soontornniyomkij; Maurizio Sorice; Federica Sotgia; David R Soto-Pantoja; Areechun Sotthibundhu; Maria João Sousa; Herman P Spaink; Paul N Span; Anne Spang; Janet D Sparks; Peter G Speck; Stephen A Spector; Claudia D Spies; Wolfdieter Springer; Daret St Clair; Alessandra Stacchiotti; Bart Staels; Michael T Stang; Daniel T Starczynowski; Petro Starokadomskyy; Clemens Steegborn; John W Steele; Leonidas Stefanis; Joan Steffan; Christine M Stellrecht; Harald Stenmark; Tomasz M Stepkowski; Stęphan T Stern; Craig Stevens; Brent R Stockwell; Veronika Stoka; Zuzana Storchova; Björn Stork; Vassilis Stratoulias; Dimitrios J Stravopodis; Pavel Strnad; Anne Marie Strohecker; Anna-Lena Ström; Per Stromhaug; Jiri Stulik; Yu-Xiong Su; Zhaoliang Su; Carlos S Subauste; Srinivasa Subramaniam; Carolyn M Sue; Sang Won Suh; Xinbing Sui; Supawadee Sukseree; David Sulzer; Fang-Lin Sun; Jiaren Sun; Jun Sun; Shi-Yong Sun; Yang Sun; Yi Sun; Yingjie Sun; Vinod Sundaramoorthy; Joseph Sung; Hidekazu Suzuki; Kuninori Suzuki; Naoki Suzuki; Tadashi Suzuki; Yuichiro J Suzuki; Michele S Swanson; Charles Swanton; Karl Swärd; Ghanshyam Swarup; Sean T Sweeney; Paul W Sylvester; Zsuzsanna Szatmari; Eva Szegezdi; Peter W Szlosarek; Heinrich Taegtmeyer; Marco Tafani; Emmanuel Taillebourg; Stephen Wg Tait; Krisztina Takacs-Vellai; Yoshinori Takahashi; Szabolcs Takáts; Genzou Takemura; Nagio Takigawa; Nicholas J Talbot; Elena Tamagno; Jerome Tamburini; Cai-Ping Tan; Lan Tan; Mei Lan Tan; Ming Tan; Yee-Joo Tan; Keiji Tanaka; Masaki Tanaka; Daolin Tang; Dingzhong Tang; Guomei Tang; Isei Tanida; Kunikazu Tanji; Bakhos A Tannous; Jose A Tapia; Inmaculada Tasset-Cuevas; Marc Tatar; Iman Tavassoly; Nektarios Tavernarakis; Allen Taylor; Graham S Taylor; Gregory A Taylor; J Paul Taylor; Mark J Taylor; Elena V Tchetina; Andrew R Tee; Fatima Teixeira-Clerc; Sucheta Telang; Tewin Tencomnao; Ba-Bie Teng; Ru-Jeng Teng; Faraj Terro; Gianluca Tettamanti; Arianne L Theiss; Anne E Theron; Kelly Jean Thomas; Marcos P Thomé; Paul G Thomes; Andrew Thorburn; Jeremy Thorner; Thomas Thum; Michael Thumm; Teresa Lm Thurston; Ling Tian; Andreas Till; Jenny Pan-Yun Ting; Vladimir I Titorenko; Lilach Toker; Stefano Toldo; Sharon A Tooze; Ivan Topisirovic; Maria Lyngaas Torgersen; Liliana Torosantucci; Alicia Torriglia; Maria Rosaria Torrisi; Cathy Tournier; Roberto Towns; Vladimir Trajkovic; Leonardo H Travassos; Gemma Triola; Durga Nand Tripathi; Daniela Trisciuoglio; Rodrigo Troncoso; Ioannis P Trougakos; Anita C Truttmann; Kuen-Jer Tsai; Mario P Tschan; Yi-Hsin Tseng; Takayuki Tsukuba; Allan Tsung; Andrey S Tsvetkov; Shuiping Tu; Hsing-Yu Tuan; Marco Tucci; David A Tumbarello; Boris Turk; Vito Turk; Robin Fb Turner; Anders A Tveita; Suresh C Tyagi; Makoto Ubukata; Yasuo Uchiyama; Andrej Udelnow; Takashi Ueno; Midori Umekawa; Rika Umemiya-Shirafuji; Benjamin R Underwood; Christian Ungermann; Rodrigo P Ureshino; Ryo Ushioda; Vladimir N Uversky; Néstor L Uzcátegui; Thomas Vaccari; Maria I Vaccaro; Libuše Váchová; Helin Vakifahmetoglu-Norberg; Rut Valdor; Enza Maria Valente; Francois Vallette; Angela M Valverde; Greet Van den Berghe; Ludo Van Den Bosch; Gijs R van den Brink; F Gisou van der Goot; Ida J van der Klei; Luc Jw van der Laan; Wouter G van Doorn; Marjolein van Egmond; Kenneth L van Golen; Luc Van Kaer; Menno van Lookeren Campagne; Peter Vandenabeele; Wim Vandenberghe; Ilse Vanhorebeek; Isabel Varela-Nieto; M Helena Vasconcelos; Radovan Vasko; Demetrios G Vavvas; Ignacio Vega-Naredo; Guillermo Velasco; Athanassios D Velentzas; Panagiotis D Velentzas; Tibor Vellai; Edo Vellenga; Mikkel Holm Vendelbo; Kartik Venkatachalam; Natascia Ventura; Salvador Ventura; Patrícia St Veras; Mireille Verdier; Beata G Vertessy; Andrea Viale; Michel Vidal; Helena L A Vieira; Richard D Vierstra; Nadarajah Vigneswaran; Neeraj Vij; Miquel Vila; Margarita Villar; Victor H Villar; Joan Villarroya; Cécile Vindis; Giampietro Viola; Maria Teresa Viscomi; Giovanni Vitale; Dan T Vogl; Olga V Voitsekhovskaja; Clarissa von Haefen; Karin von Schwarzenberg; Daniel E Voth; Valérie Vouret-Craviari; Kristina Vuori; Jatin M Vyas; Christian Waeber; Cheryl Lyn Walker; Mark J Walker; Jochen Walter; Lei Wan; Xiangbo Wan; Bo Wang; Caihong Wang; Chao-Yung Wang; Chengshu Wang; Chenran Wang; Chuangui Wang; Dong Wang; Fen Wang; Fuxin Wang; Guanghui Wang; Hai-Jie Wang; Haichao Wang; Hong-Gang Wang; Hongmin Wang; Horng-Dar Wang; Jing Wang; Junjun Wang; Mei Wang; Mei-Qing Wang; Pei-Yu Wang; Peng Wang; Richard C Wang; Shuo Wang; Ting-Fang Wang; Xian Wang; Xiao-Jia Wang; Xiao-Wei Wang; Xin Wang; Xuejun Wang; Yan Wang; Yanming Wang; Ying Wang; Ying-Jan Wang; Yipeng Wang; Yu Wang; Yu Tian Wang; Yuqing Wang; Zhi-Nong Wang; Pablo Wappner; Carl Ward; Diane McVey Ward; Gary Warnes; Hirotaka Watada; Yoshihisa Watanabe; Kei Watase; Timothy E Weaver; Colin D Weekes; Jiwu Wei; Thomas Weide; Conrad C Weihl; Günther Weindl; Simone Nardin Weis; Longping Wen; Xin Wen; Yunfei Wen; Benedikt Westermann; Cornelia M Weyand; Anthony R White; Eileen White; J Lindsay Whitton; Alexander J Whitworth; Joëlle Wiels; Franziska Wild; Manon E Wildenberg; Tom Wileman; Deepti Srinivas Wilkinson; Simon Wilkinson; Dieter Willbold; Chris Williams; Katherine Williams; Peter R Williamson; Konstanze F Winklhofer; Steven S Witkin; Stephanie E Wohlgemuth; Thomas Wollert; Ernst J Wolvetang; Esther Wong; G William Wong; Richard W Wong; Vincent Kam Wai Wong; Elizabeth A Woodcock; Karen L Wright; Chunlai Wu; Defeng Wu; Gen Sheng Wu; Jian Wu; Junfang Wu; Mian Wu; Min Wu; Shengzhou Wu; William Kk Wu; Yaohua Wu; Zhenlong Wu; Cristina Pr Xavier; Ramnik J Xavier; Gui-Xian Xia; Tian Xia; Weiliang Xia; Yong Xia; Hengyi Xiao; Jian Xiao; Shi Xiao; Wuhan Xiao; Chuan-Ming Xie; Zhiping Xie; Zhonglin Xie; Maria Xilouri; Yuyan Xiong; Chuanshan Xu; Congfeng Xu; Feng Xu; Haoxing Xu; Hongwei Xu; Jian Xu; Jianzhen Xu; Jinxian Xu; Liang Xu; Xiaolei Xu; Yangqing Xu; Ye Xu; Zhi-Xiang Xu; Ziheng Xu; Yu Xue; Takahiro Yamada; Ai Yamamoto; Koji Yamanaka; Shunhei Yamashina; Shigeko Yamashiro; Bing Yan; Bo Yan; Xianghua Yan; Zhen Yan; Yasuo Yanagi; Dun-Sheng Yang; Jin-Ming Yang; Liu Yang; Minghua Yang; Pei-Ming Yang; Peixin Yang; Qian Yang; Wannian Yang; Wei Yuan Yang; Xuesong Yang; Yi Yang; Ying Yang; Zhifen Yang; Zhihong Yang; Meng-Chao Yao; Pamela J Yao; Xiaofeng Yao; Zhenyu Yao; Zhiyuan Yao; Linda S Yasui; Mingxiang Ye; Barry Yedvobnick; Behzad Yeganeh; Elizabeth S Yeh; Patricia L Yeyati; Fan Yi; Long Yi; Xiao-Ming Yin; Calvin K Yip; Yeong-Min Yoo; Young Hyun Yoo; Seung-Yong Yoon; Ken-Ichi Yoshida; Tamotsu Yoshimori; Ken H Young; Huixin Yu; Jane J Yu; Jin-Tai Yu; Jun Yu; Li Yu; W Haung Yu; Xiao-Fang Yu; Zhengping Yu; Junying Yuan; Zhi-Min Yuan; Beatrice Yjt Yue; Jianbo Yue; Zhenyu Yue; David N Zacks; Eldad Zacksenhaus; Nadia Zaffaroni; Tania Zaglia; Zahra Zakeri; Vincent Zecchini; Jinsheng Zeng; Min Zeng; Qi Zeng; Antonis S Zervos; Donna D Zhang; Fan Zhang; Guo Zhang; Guo-Chang Zhang; Hao Zhang; Hong Zhang; Hong Zhang; Hongbing Zhang; Jian Zhang; Jian Zhang; Jiangwei Zhang; Jianhua Zhang; Jing-Pu Zhang; Li Zhang; Lin Zhang; Lin Zhang; Long Zhang; Ming-Yong Zhang; Xiangnan Zhang; Xu Dong Zhang; Yan Zhang; Yang Zhang; Yanjin Zhang; Yingmei Zhang; Yunjiao Zhang; Mei Zhao; Wei-Li Zhao; Xiaonan Zhao; Yan G Zhao; Ying Zhao; Yongchao Zhao; Yu-Xia Zhao; Zhendong Zhao; Zhizhuang J Zhao; Dexian Zheng; Xi-Long Zheng; Xiaoxiang Zheng; Boris Zhivotovsky; Qing Zhong; Guang-Zhou Zhou; Guofei Zhou; Huiping Zhou; Shu-Feng Zhou; Xu-Jie Zhou; Hongxin Zhu; Hua Zhu; Wei-Guo Zhu; Wenhua Zhu; Xiao-Feng Zhu; Yuhua Zhu; Shi-Mei Zhuang; Xiaohong Zhuang; Elio Ziparo; Christos E Zois; Teresa Zoladek; Wei-Xing Zong; Antonio Zorzano; Susu M Zughaier
Journal:  Autophagy       Date:  2016       Impact factor: 16.016

10.  A phase I/II trial of hydroxychloroquine in conjunction with radiation therapy and concurrent and adjuvant temozolomide in patients with newly diagnosed glioblastoma multiforme.

Authors:  Myrna R Rosenfeld; Xiaobu Ye; Jeffrey G Supko; Serena Desideri; Stuart A Grossman; Steven Brem; Tom Mikkelson; Daniel Wang; Yunyoung C Chang; Janice Hu; Quentin McAfee; Joy Fisher; Andrea B Troxel; Shengfu Piao; Daniel F Heitjan; Kay-See Tan; Laura Pontiggia; Peter J O'Dwyer; Lisa E Davis; Ravi K Amaravadi
Journal:  Autophagy       Date:  2014-05-20       Impact factor: 16.016

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  1 in total

Review 1.  Molecular Mechanisms Underlying Autophagy-Mediated Treatment Resistance in Cancer.

Authors:  Cally J Ho; Sharon M Gorski
Journal:  Cancers (Basel)       Date:  2019-11-11       Impact factor: 6.639

  1 in total

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