Effect of the identified siRNAs on autophagy and dextran uptake.

<p>(A–D) tfLC3-MCF7 cells (A, B) or MCF7 cells (C, D) were left untreated, treated with Oligofectamine (Oligo) or transfected with control siRNA (CT) or indicated siRNA pools (3×6.67 nM). (A, B) After 48 h, tfLC3-MCF7 cells were analyzed by confocal microscopy. Representative images (A; <i>Bars</i>, 10 µm) and quantification of puncta (B) are shown. Raptor siRNA (RPTOR) served as a control for increased autophagic flux. Closed arrows indicate AVd, open arrows indicate AVi. (C) After 60 h, the level of p62/SQSTM1 (p62), which is degraded by autophagy, was examined by Western blot. Rapamycin (20 nM, 4 h) was used to induce autophagy. Numbers represent p62 levels as percentage of the level in untreated control siRNA-transfected cells. (D) <i>Top</i>, After 60 h, MCF7 cells were treated with 100 µg/ml Alexa Fluor 488-dextran (dextran-488) for 1 h and analyzed by flow cytometry (FL1-H). The threshold for high intensity staining was defined so that 88% of control siRNA-transfected cells were below. <i>Bottom</i>, Example histograms showing dextran-488 content of cells transfected with control or KIF20A siRNA. M1 = gate for high intensity staining. Values represent means + SEM of 20 cells in one representative experiment (B, n = 3) or means + SD of three independent experiments (D). *<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001, vs. control siRNA-transfected cells.</p

Identification of cytoskeleton-associated proteins whose depletion induces non-apoptotic cancer cell death.

<p>(A, B) MCF7 (A), HeLa (B) and U-2-OS (B) cells were left untreated, treated with Oligofectamine (Oligo) or transfected with control siRNA (CT) or three independent siRNAs against the indicated targets individually (8 nM; A) or in pools (3×6.67 nM; B). Cell density was measured after 72 h by the MTT reduction assay. (C) MCF7-Bcl-2 or MCF7-pCEP (control) cells were transfected as in (B). <i>Left bottom</i>, After 96 h, cell death was determined by counting Hoechst 33342-stained cells with condensed nuclei (three random fields of 100 cells). TNF (20 ng/ml, 24 h) served as a positive control for Bcl-2 sensitive apoptotic cell death. <i>Left top</i>, Western blot confirming overexpression of Bcl-2 in untreated MCF7-Bcl-2 cells. <i>Right</i>, Examples of images of Hoechst 33342-stained nuclei of MCF7-pCEP and MCF7-Bcl-2 cells 96 h after transfection with indicated siRNAs. (D) MCF7 cells were treated as in (B). <i>Left</i>, After 60 h, DNA was stained with propidium iodide and cell cycle distribution analyzed by flow cytometry (FL-2A). <i>Right</i>, Examples of histograms showing cell cycle distribution of cells 60 h after transfection with indicated siRNAs. Values represent means + SD of three independent experiments (A, C, D) or triplicates in one representative experiment (B, n = 3). *<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001, vs. control siRNA-transfected cells or as indicated (C).</p

Identification of Cytoskeleton-Associated Proteins Essential for Lysosomal Stability and Survival of Human Cancer Cells

<div><p>Microtubule-disturbing drugs inhibit lysosomal trafficking and induce lysosomal membrane permeabilization followed by cathepsin-dependent cell death. To identify specific trafficking-related proteins that control cell survival and lysosomal stability, we screened a molecular motor siRNA library in human MCF7 breast cancer cells. SiRNAs targeting four kinesins (KIF11/Eg5, KIF20A, KIF21A, KIF25), myosin 1G (MYO1G), myosin heavy chain 1 (MYH1) and tropomyosin 2 (TPM2) were identified as effective inducers of non-apoptotic cell death. The cell death induced by KIF11, KIF21A, KIF25, MYH1 or TPM2 siRNAs was preceded by lysosomal membrane permeabilization, and all identified siRNAs induced several changes in the endo-lysosomal compartment, <em>i.e.</em> increased lysosomal volume (KIF11, KIF20A, KIF25, MYO1G, MYH1), increased cysteine cathepsin activity (KIF20A, KIF25), altered lysosomal localization (KIF25, MYH1, TPM2), increased dextran accumulation (KIF20A), or reduced autophagic flux (MYO1G, MYH1). Importantly, all seven siRNAs also killed human cervix cancer (HeLa) and osteosarcoma (U-2-OS) cells and sensitized cancer cells to other lysosome-destabilizing treatments, <em>i.e.</em> photo-oxidation, siramesine, etoposide or cisplatin. Similarly to KIF11 siRNA, the KIF11 inhibitor monastrol induced lysosomal membrane permeabilization and sensitized several cancer cell lines to siramesine. While KIF11 inhibitors are under clinical development as mitotic blockers, our data reveal a new function for KIF11 in controlling lysosomal stability and introduce six other molecular motors as putative cancer drug targets.</p> </div

Summary of cellular changes induced by the depletion of survival-associated motor proteins.

1<p>CC, cell cycle.</p>2<p>VAC, volume of the acidic compartment (late endosomes and lysosomes).</p>3<p>photo-oxidation-induced lysosomal leakage.</p>4<p>−, decrease, <i>P</i><0.05; +, increase, <i>P</i><0.05; ±, no change; () 0.05<<i>P</i><0.10.</p>5<p>It should be noted that these data might be affected by cell death that starts already 50 hours after the transfection with KIF21A siRNAs.</p

Effect of the identified siRNAs on lysosomes and cytoskeleton.

<p>(A–D) MCF7 cells were left untreated, treated with Oligofectamine (Oligo) or transfected with control siRNA (CT) or indicated siRNA pools. (A) After 60 h, cells with enlarged acidic compartment (late endosomes and lysosomes) were identified by flow cytometry (FL-2A) of LysoTracker Red-stained cells. The threshold for high intensity staining was defined so that 90% of control siRNA-transfected cells were below. (B) After 72 h, total cysteine cathepsin activity (zFR-AFC cleavage) was determined. HSPA1 and CTSB siRNAs served as internal controls. (C, D) After 60 h, cells were stained for Lamp-2 (C) or F-actin (D) and analyzed by confocal microscopy. Representative images are shown. <i>Arrows</i>, aggregation of lysosomal structures in cell protrusions/periphery. <i>Bars</i>, 20 µm. Values represent means + SD of a minimum of three independent experiments. *<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001, vs. control siRNA-transfected cells.</p

Reduction of lysosomal stability by the identified siRNAs and monastrol.

<p>(A–C) MCF7 cells were left untreated, treated with Oligofectamine (Oligo) or transfected with control siRNA (CT) or indicated siRNA pools (3×6.67 nM). (A and B) After 60 h, cells were treated with acridine orange and analyzed by live cell imaging to measure the loss of lysosomal integrity (increased green fluorescence) upon laser treatment. A miminum of 25 cells from pre-defined areas was examined for each experiment. Three independent experiments are shown in A and values in B represent means + SD of these experiments at the 60 sec time point. (C) After 72 h, cytosolic and total cysteine cathepsin activities were measured by analyzing the cleavage of zFR-AFC. The activities in cytosolic extracts are shown as percentages of the activities in the corresponding total extracts. HSPA1 and CTSB siRNAs served as controls for the induction of lysosomal leakage and transfection efficacy, respectively. (D) Cytosolic cysteine cathepsin activities in MCF7 cells left untreated or treated with vehicle (2% dimethyl sulfoxide, DMSO) or indicated concentrations of monastrol for 72 h were determined as in (C). Values represent means + SD of five (C) or three (D) independent experiments. *<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001, vs. control siRNA-transfected (B, C) or vehicle-treated cells (D).</p

Sensitization to lysosome-disrupting drugs by the identified siRNAs and monastrol.

<p>(A, B) MCF7 cells were left untreated, treated with Oligofectamine (Oligo) or transfected with control siRNA or indicated siRNA pools (3×6.67 nM). After 48 h, cells were left untreated or treated with 2 µM siramesine (<i>top</i>), 50 µM etoposide (<i>middle</i>) or 10 µM cisplatin (<i>bottom</i>) for additional 48 h before light microscopy pictures were taken (representative images in B) and cell death was quantified by the LDH release assay (A). (C) MCF7 cells were left untreated or treated with 2 µM siramesine together with indicated concentrations of monastrol for 72 h and cell death was quantified by the LDH release assay. Values represent means + SD of a minimum of three independent experiments. *<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001, vs. control siRNA-transfected cells (A) or cells treated with 2 µM siramesine alone (C).</p

DNA-dependent protein kinase regulates lysosomal AMP-dependent protein kinase activation and autophagy

Macroautophagy/autophagy is a central component of the cytoprotective cellular stress response. To enlighten stress-induced autophagy signaling, we screened a human kinome siRNA library for regulators of autophagic flux in MCF7 human breast carcinoma cells and identified the catalytic subunit of DNA-dependent protein kinase PRKDC/DNA-PKcs as a positive regulator of basal and DNA damage-induced autophagy. Analysis of autophagy-regulating signaling cascades placed PRKDC upstream of the AMP-dependent protein kinase (AMPK) complex and ULK1 kinase. In normal culture conditions, PRKDC interacted with the AMPK complex and phosphorylated its nucleotide-sensing γ1 subunit PRKAG1/AMPKγ1 at Ser192 and Thr284, both events being significantly reduced upon the activation of the AMPK complex. Alanine substitutions of PRKDC phosphorylation sites in PRKAG1 reduced AMPK complex activation without affecting its nucleotide sensing capacity. Instead, the disturbance of PRKDC-mediated phosphorylation of PRKAG1 inhibited the lysosomal localization of the AMPK complex and its starvation-induced association with STK11 (serine/threonine kinase 11). Taken together, our data suggest that PRKDC-mediated phosphorylation of PRKAG1 primes AMPK complex to the lysosomal activation by STK11 in cancer cells thereby linking DNA damage response to autophagy and cellular metabolism. Abbreviations: AXIN1: axin 1; 3-MA: 3-methyladenine; 5-FU: 5-fluorouracil; AA mutant: double alanine mutant (S192A, T284A) of PRKAG1; ACACA: acetyl-CoA carboxylase alpha; AICAR: 5-Aminoimidazole-4-carboxamide ribonucleotide; AMPK: AMP-activated protein kinase; ATG: autophagy-related; ATM: ataxia telangiectasia mutated; ATR: ATM serine/threonine kinase; AV: autophagic vacuole; AVd: degradative autophagic vacuole; AVi: initial autophagic vacuole; BECN1: beclin 1; BSA: bovine serum albumin; CBS: cystathionine beta-synthase; CDK7: cyclin dependent kinase 7; CDKN1A: cyclin dependent kinase inhibitor 1A; EGFP: enhanced green fluorescent protein; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GST: glutathione S transferase; H2AX/H2AFX: H2A.X variant histone; HBSS: Hanks balanced salt solution; IP: immunopurification; IR: ionizing radiation; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MAP3K9: mitogen-activated protein kinase kinase kinase 9; mRFP: monomeric red fluorescent protein; mCh: mCherry; MCM7: minichromosome maintenance complex component 7; MTORC1: mechanistic target of rapamycin kinase complex 1; NHEJ: non-homologous end joining; NRBP2: nuclear receptor binding protein 2; NTC: non-targeting control; NUAK1: NUAK family kinase 1; PBS: phosphate-buffered saline; PIK3AP1: phosphoinositide-3-kinase adaptor protein 1; PIK3CA: phosphatidylinositol-4,5-biphosphate 3-kinase catalytic subunit alpha; PIKK: phosphatidylinositol 3-kinase-related kinase; PRKAA: protein kinase AMP-activated catalytic subunit alpha; PRKAB: protein kinase AMP-activated non-catalytic subunit beta; PRKAG: protein kinase AMP-activated non-catalytic subunit gamma; PRKDC: protein kinase, DNA-activated, catalytic subunit; RLuc: Renilla luciferase; RPS6KB1: ribosomal protein S6 kinase B1; SQSTM1: sequestosome 1; STK11/LKB1: serine/threonine kinase 11; TP53: tumor protein p53; TSKS: testis specific serine kinase substrate; ULK1: unc-51 like autophagy activating kinase 1; WIPI2: WD repeat domain, phosphoinositide interacting 2; WT: wild type.</p

Unraveling membrane properties at the organelle-level with LipidDyn.

Cellular membranes are formed from different lipids in various amounts and proportions depending on the subcellular localization. The lipid composition of membranes is sensitive to changes in the cellular environment, and its alterations are linked to several diseases. Lipids not only form lipid-lipid interactions but also interact with other biomolecules, including proteins. Molecular dynamics (MD) simulations are a powerful tool to study the properties of cellular membranes and membrane-protein interactions on different timescales and resolutions. Over the last few years, software and hardware for biomolecular simulations have been optimized to routinely run long simulations of large and complex biological systems. On the other hand, high-throughput techniques based on lipidomics provide accurate estimates of the composition of cellular membranes at the level of subcellular compartments. Lipidomic data can be analyzed to design biologically relevant models of membranes for MD simulations. Similar applications easily result in a massive amount of simulation data where the bottleneck becomes the analysis of the data. In this context, we developed LipidDyn, a Python-based pipeline to streamline the analyses of MD simulations of membranes of different compositions. Once the simulations are collected, LipidDyn provides average properties and time series for several membrane properties such as area per lipid, thickness, order parameters, diffusion motions, lipid density, and lipid enrichment/depletion. The calculations exploit parallelization, and the pipeline includes graphical outputs in a publication-ready form. We applied LipidDyn to different case studies to illustrate its potential, including membranes from cellular compartments and transmembrane protein domains. LipidDyn is available free of charge under the GNU General Public License from https://github.com/ELELAB/LipidDyn

Sensitive detection of lysosomal membrane permeabilization by lysosomal galectin puncta assay

Lysosomal membrane permeabilization (LMP) contributes to tissue involution, degenerative diseases, and cancer therapy. Its investigation has, however, been hindered by the lack of sensitive methods. Here, we characterize and validate the detection of galectin puncta at leaky lysosomes as a highly sensitive and easily manageable assay for LMP. LGALS1/galectin-1 and LGALS3/galectin-3 are best suited for this purpose due to their widespread expression, rapid translocation to leaky lysosomes and availability of high-affinity antibodies. Galectin staining marks individual leaky lysosomes early during lysosomal cell death and is useful when defining whether LMP is a primary or secondary cause of cell death. This sensitive method also reveals that cells can survive limited LMP and confirms a rapid formation of autophagic structures at the site of galectin puncta. Importantly, galectin staining detects individual leaky lysosomes also in paraffin-embedded tissues allowing us to demonstrate LMP in tumor xenografts in mice treated with cationic amphiphilic drugs and to identify a subpopulation of lysosomes that initiates LMP in involuting mouse mammary gland. The use of ectopic fluorescent galectins renders the galectin puncta assay suitable for automated screening and visualization of LMP in live cells and animals. Thus, the lysosomal galectin puncta assay opens up new possibilities to study LMP in cell death and its role in other cellular processes such as autophagy, senescence, aging, and inflammation.</p