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Influenza A matrix protein M1 is sufficient to induce lipid membrane deformation

The matrix protein M1 of the Influenza A virus is considered to mediate viral assembly and budding at the plasma membrane (PM) of infected cells. In order for a new viral particle to form, the PM lipid bilayer has to bend into a vesicle towards the extracellular side. Studies in cellular models have proposed that different viral proteins might be responsible for inducing membrane curvature in this context (including M1), but a clear consensus has not been reached. In this study, we use a combination of fluorescence microscopy, cryogenic transmission electron microscopy (cryo-TEM), cryo-electron tomography (cryo-ET) and scanning fluorescence correlation spectroscopy (sFCS) to investigate M1-induced membrane deformation in biophysical models of the PM. Our results indicate that M1 is indeed capable to cause membrane curvature in lipid bilayers containing negatively-charged lipids, in the absence of other viral components. Furthermore, we prove that simple protein binding is not sufficient to induce membrane restructuring. Rather, it appears that stable M1-M1 interactions and multimer formation are required in order to alter the bilayer three-dimensional structure, through the formation of a protein scaffold. Finally, our results suggest that, in a physiological context, M1-induced membrane deformation might be modulated by the initial bilayer curvature and the lateral organization of membrane components (i.e. the presence of lipid domains).

Introduction
The influenza A virus (IAV) buds from the plasma membrane (PM) of infected cells and, while doing so, acquires a lipid envelope from its host (1, 2). During this step, the PM lipid bilayer is bent into a vesicle, towards the extracellular side (3). Assembly of viral components, bending of the lipid bilayer and the resulting budding of virions are essential parts of the IAV replication cycle and, therefore, constitute potential targets for antiviral therapies. IAV assembly is thought to be orchestrated by its matrix protein M1, which is capable of mediating the interactions among other viral components and the lipids in the PM (4). In spite of its fundamental importance, this process is still not fully understood. We and others have investigated M1-M1 and M1-lipid interactions, both in model membranes and cellular system (5)(6)(7). It appears clear that M1 interacts via its N-terminal domain with acidic lipids and this interaction modulates protein multimerization (5,7,8). On the other hand, the process by which M1 or other viral components can induce membrane curvature during IAV budding is less clear. This issue has been investigated in cellulo without reaching a clear consensus (9). In some cases, it was demonstrated that M1 alone is sufficient to induce budding at the PM, without the need for other viral components (10,11). Other studies have shown instead that IAV hemagglutinin and neuraminidase are needed to produce viral particles, while M1 might not play a significant role (12)(13)(14). Another group suggested that, in the presence of significant M1-PM binding, the protein might indeed be capable of inducing budding (15). In light of these observations, it would be therefore very interesting to investigate M1-lipid interactions in a controlled environment and clarify whether the protein has indeed the ability to induce curvature in lipid bilayers on its own. In this context, giant unilamellar vesicles (GUVs) provide an ideal model for the PM, consisting of a free-standing lipid bilayer with well-defined composition and physical properties (16)(17)(18)(19). GUVs have been used in the past to investigate the interplay between viral proteins and lipid membranes and, specifically, protein-induced alterations in the three-dimensional structure of the bilayer (20)(21)(22)(23). In this work, we used an analogous approach to investigate the interaction between IAV M1 and lipid membranes, using a combination of fluorescence microscopy imaging, scanning fluorescence correlation spectroscopy (sFCS) (24), cryogenic transmission electron microscopy (cryo-TEM), as well as cryo-electron tomography (cryo-ET). Our results indicate that M1 is capable to cause membrane deformation, also in the absence of other proteins. Furthermore, we quantitatively demonstrate that M1 multimerization, rather than simple binding, is necessary for the induction of membrane curvature.

Preparation of giant unilamellar vesicles (GUVs)
GUVs were prepared using the electroformation method (25,26). Different lipid compositions were used to prepare the vesicles, as specified in each case in the Results section. Typically, DOPC and cholesterol were mixed with various amounts of negatively charged lipids (e.g. DOPS) at molar ratios between 10 mol% and 50 mol%. 0.05 or 0.1 mol% Rhodamine-DOPE was added as a tracer to allow GUV membrane visualization by confocal microscopy. The GUV electroformation chamber consisted of two conductive ITO coverslips facing each other and separated by a 3 mm thick teflon spacer. The total volume of the chamber was ~300 µL. 30 µL of a 3 mM lipid solution in chloroform and/or ethanol were spread on a preheated ITO coverslip, forming a very thin lipid film. The solvent was evaporated by nitrogen flow for 5 min at room temperature (note that solvent evaporation under vacuum for 1 h did not show a difference in vesicle behavior). After assembly, the chamber was filled with a sucrose solution in deionized water (e.g. 150mM) and connected to a voltage generator (AC generator FG 250 D, H-Tronic, Hirschau, Germany). For experiments at low pH, the chamber was filled with a 150 mM sucrose, 10 mM acetate buffer solution at pH 5. A sinusoidal electric field of 1.4 V at 10 Hz was applied for 1.5 h at room temperature. To facilitate the detachment of GUV from the slides, the voltage was decreased to 0.5 V for 30 minutes.

Preparation of GUVs showing phase separation
Vesicles were prepared as described in the previous paragraph, using a lipid mixture consisting of 10 mol% cholesterol, 45 mol% DOPC, 15 mol% DPPG and 30 mol% DSPC. Additionally, 0.1 mol% TopFluor-cholesterol and 0.05 mol% Rhodamine-DOPE were added to visualize the different lipid domains. Lipids were dissolved in chloroform/methanol 9:1 v:v (5 mM, prepared freshly and kept under a nitrogen atmosphere). The obtained lipid film was subjected to the electroformation procedure (see previous paragraph) at 50°C for 1.5 h. The chamber was then cooled down slowly at room temperature before imaging.

Expression and purification of recombinant matrix protein (M1) constructs
6xHis-tag M1 protein from Influenza A/FPV/Rostock/34 was expressed and purified, using a protocol adapted from Hilsch et al. (7). Rosetta E. coli (DE3) pLysS-competent cells were grown in 1 liter of medium (containing 0.2% glucose, 50 mg/mL chloramphenicol and 50 mg/mL ampicillin) until reaching OD 600 ∼0.7 at 37 °C. Then, protein expression was induced by addition of IPTG (0.4 mM) while shaking for 3 h at 37°C. Bacteria were harvested by centrifugation for 10 min at 4800 rpm (Thermo Fisher Scientific, Waltham, MA, United States). The obtained pellets were stored at -80 °C until needed. On a different day, pellets were resuspended in lysis buffer (16 mM Na 2 HPO 4 , 146 mM KH 2 PO 4 , 500 mM NaCl, 5.4 mM KCl, EDTA-free protease inhibitor cocktail, 1 mM PMSF, 200 µg/ml DNAse), quickly frozen (15 min at -80°C) and incubated on a rotation shaker at 4°C for 30-60 min. All the following steps were performed at 4 °C. The bacteria were completely lysed using a French press (Glen Mills, NJ, USA) run at 1000 psi. Finally the obtained bacterial lysate was clarified by centrifugation at 21,000 rpm for 30 minutes (Thermo Lynx 4000, A27-8 x 50mL fixed-angle rotor, Thermo Fisher Scientific, Waltham, MA, United States). The supernatant was incubated with 2 mL TALON resin (Takara, Saint-Germain-en-Laye, France) in a tube using a rotation shaker for 30-60 minutes. The resin was then washed with an equilibration buffer (8 mM Na 2 HPO 4 , 1.5 mM KH 2 PO 4 , 500 mM NaCl, 2.7 mM KCl, pH 7.4) and incubated on shaker for 15 min. The resin was washed again 2-3 times with an intermediate washing buffer consisting of the equilibration buffer with additional 60 mM imidazole, pH 7.2. Finally, M1 was eluted with an elution buffer (2-fold concentrated PBS with additional 180 mM imidazole, pH 7.4). Typical (maximal) protein concentrations were ~60 µM. SDS-PAGE was performed to verify protein purity. Protein concentration was determined using UV absorbance at 280 nm on an Agilent 8453 UV/Vis spectrophotometer (Agilent Technologies, Milford, MA, USA).
For the experiments performed at pH 5, the protein was purified directly at low pH. In particular, the intermediate washing buffer contained 20 mM imidazole and had a pH of 6.5. The elution buffer contained 50 mM Sodium Acetate and 300 mM NaCl (pH 5). M1-derived constructs (i.e. N-terminal domain 1-164 aa (NM1) and C-terminal domain 165-252 aa (CM1)) were purified at pH 7.4 as previously described (8). The elution buffer used for NM1 was the same as the one used for M1. The elution buffer used for CM1 was PBS with additional 150 mM imidazole (pH 7.4).

Protein labelling for fluorescence microscopy
The purified protein was conjugated, if needed, with the primary amine-reactive dye Alexa Fluor 488 succinimidyl ester. Freshly purified protein (in elution buffer) was incubated with the reactive dye (10:1 molar ratio) for 2 h at 4 °C, at the desired pH (7.4 or pH 5).
For simple imaging experiments, the protein-dye mixture was directly used, without further separation, as described in the next paragraph. For quantitative experiments (e.g. Par. 3.6), the labeled protein (M1-Alexa488) was separated from the free dye using size-exclusion chromatography (PD-10, GE Healthcare, Munich, Germany). The labeled protein was eluted with diluted PBS (pH 7.4), matched to the desired final osmolarity, as described in the next paragraph. Protein concentration and labeling efficiency were determined by absorbance at 280 nm and 490 nm, respectively. The maximum protein concentrations were typically around 40 μM, while labeling efficiencies ranged between 0.1 and 0.15 dye/protein.

Protein-GUV samples and imaging
Before mixing with GUVs, the protein solution (i.e. M1 in elution buffer) was diluted with deionized water to reach the target approximate osmolarity. For example, for experiments in which GUVs were prepared in 150 mM sucrose (Figs. 1, 3 and 4), the purified M1 solution (with or without Alexa Fluor 488 succinimidyl ester) was diluted ca. 5-fold. Typical protein concentrations at this step were ~12 µM. The protein solution was then mixed with the GUV suspension with different volume ratios, in order to obtain the final desired protein concentration (usually between 5 and 10 µM) in a total 300 µL volume, and transferred to 35mm dishes (CellVis, Mountain View, CA) with optical glass bottoms, previously passivated with a 1% bovine serum albumin solution.
For the experiments carried in the presence of the M1-multimerization inhibitor PHE, M1 (~40 µM) was incubated with PHE (100 µM, unless differently stated) for 30 minutes directly in the elution buffer. Subsequently, the mixture was incubated with 4 µM Alexa Fluor 488 succinimidyl ester, for 2 hours at 4 °C. Finally, the protein solution was diluted with deionized water to approximatively match the osmolarity of the vesicle suspension and mixed with the GUVs, so to obtain a final protein concentration of 10 µM (and ~25 µM PHE). Experiments carried at pH 5 were carried out as follows: M1 was purified and labelled as described in Par. 2.4 and 2.5 directly at pH 5. The protein-dye solution in elution buffer (50 mM Sodium Acetate and 300 mM NaCl, pH 5) was subsequently diluted ~3 fold using deionized water, to a final protein concentration of ~13 µM (in ~16 mM sodium acetate, ~90 mM NaCl). This solution was finally mixed with the GUV suspension, so to obtain a final protein concentration of 10 µM. We have verified that the presence of imidazole does not affect membrane curvature for at least 2 hours and that Alexa Fluor 488 succinimidyl ester does not significantly interact with GUVs (data not shown). Nevertheless, in the case of samples that required higher protein concentrations (i.e. >10 µM M1) or extensive removal of free dye, the elution buffer was exchanged after protein labelling, using a PD-10 column. The protein was eluted using 2-fold diluted PBS (pH 7.4), to approximatively match the osmolarity of GUV samples prepared in 150 mM sucrose (e.g. for the sFCS experiments described in Par. 3.6). Confocal fluorescence laser scanning microscopy (LSM) imaging was performed on a Zeiss LSM780 system (Carl Zeiss, Oberkochen, Germany) using a 40× 1.2 numerical aperture water-immersion objective. Samples were excited with a 488 nm argon laser (for the fluorophores Alexa Fluor 488 succinimidyl ester and TopFluor-cholesterol) or a 561 nm diode laser (for the fluorophore Rhodamine-DOPE). For measurements performed using 488 nm excitation, fluorescence was detected between 499 and 695 nm, after passing through a 488 nm dichroic mirror, using GaAsP detectors. For measurements performed 561 nm excitation, fluorescence emission was separated using a 488/561 nm dichroic mirror and was detected between 570 and 695 nm.

Liposome preparation for cryo-transmission electron microscopy
Liposomes were prepared by extrusion. The desired lipids mixture (DOPC + 50 mol% DOPS) was dissolved in chloroform and then evaporated under nitrogen stream at room temperature to form a lipid film. Films were then rehydrated in PBS (pH 7.4). The obtained dispersion (2 mM total lipid concentration) was vigorously vortexed for 2-5 minutes. The diameter of the obtained multilamellar vesicles was reduced by serially extruding the suspension 12 times through a 100 nm pore diameter polycarbonate membrane (Whatman, Maidstone, UK) with a hand-held extruder (Avanti Polar Lipids, AL, USA). Liposome size (ca. 130 nm diameter in average) was measured via dynamics light scattering, using a Zetasizer HS 1000, (Malvern, Worcestershire, UK). The purified M1 (unlabeled) in elution buffer was diluted with deionized water to approximatively match the osmolarity of the liposome suspension (ca. 2.6-fold dilution). Liposomes and protein solution were mixed in a total volume of 800 µL in volume ratios calculated to obtain a 10 µM final M1 concentration. Typical final lipid concentrations were ~0.5-1 mM. Perforated carbon film-covered microscopical 200 mesh grids (R1/4 batch of Quantifoil, MicroTools GmbH, Jena, Germany) were cleaned with chloroform and hydrophilised by 60 s glow discharging at 8 W in a BAL-TEC MED 020 device (Leica Microsystems, Wetzlar, Germany), before 5 µl aliquots of the liposome/protein solution were applied to the grids. The samples were vitrified by automatic blotting and plunge freezing with a FEI Vitrobot Mark IV (Thermo Fisher Scientific, Waltham, USA) using liquid ethane as cryogen.

Cryo-transmission electron microscopy
The vitrified specimens were transferred under liquid nitrogen into the autoloader of a FEI TALOS ARCTICA electron microscope (Thermo Fisher Scientific, Waltham, USA). This microscope is equipped with a high-brightness field-emission gun (XFEG) operated at an acceleration voltage of 200 kV. Micrographs were acquired on a FEI Falcon 3 4k × 4k direct electron detector (Thermo Fisher Scientific, Waltham, USA) using a 70 µm objective aperture at a primary magnification of 28 k or 45 k, corresponding to a calibrated pixel size of 3.69 or 2.29 Å/pixel, respectively.

Cryo-electron tomography
Single axis tilt series (±60° in 2° tilt angle increments) were recorded with the Falcon 3 direct electron detector at full resolution (28 K primary magnification) with a total dose lower than 70 e-/Å 2 . Tomogram reconstruction was performed using ThermoFisher Inspect3D software. Amira, Version 6.0 (FEI, Oregon, USA) was used for visualization.

Scanning fluorescence correlation spectroscopy (sFCS)
sFCS measurements were performed on a Zeiss LSM780 system (Carl Zeiss, Oberkochen, Germany) using a 40× 1.2 NA water-immersion objective. M1-Alexa488 was excited with a 488 nm argon laser. Fluorescence was detected after passing through a 520/35 nm bandpass filter. Data acquisition and analysis were performed as described in Dunsing et al. (24,27). Briefly, line scans of 128 × 1 pixels (pixel size 160 nm) were performed perpendicular to the GUV membrane with a 472.73 µs scan time. Typically, 600,000 lines were acquired (total scan time ~4 min) in photon counting mode. Low laser power was used (∼1 µW) to avoid photobleaching and fluorescence saturation effect. Data were exported as TIF files, imported and analyzed in Matlab (MathWorks, Natick, MA) using custom-written code (24,27). The analysis of the thus obtained auto-correlation curves with a two-dimensional diffusion model resulted in the determination of the number of fluorophores in the confocal volume (N) and their apparent diffusion times. Furthermore, total fluorescence intensity at the GUV membrane and M1-Alexa488 brightness were determined. Protein brightness and fluorescence intensity were normalized to account for day-to-day variations in laser power, optics alignment and degree of protein labelling, as described in Hilsch et al. (7). Statistical significances of differences among sFCS data sets were determined using a two-sided t-test with distinct variances (ttest2 routine, Matlab).

M1 induces membrane deformation in GUVs containing negatively charged lipids
In order to clarify whether M1 is sufficient to induce membrane deformation in protein-free lipid bilayers, we incubated GUVs of different compositions in the presence of 5 µM M1-Alexa448. The freshly-prepared lipid vesicles included increasing amounts of DOPS (from 0 to 50 mol%), as it was shown that this lipid promotes M1 binding via electrostatic interactions (5)(6)(7)(8)28). GUVs were stained with trace amounts of a fluorescent lipid (Rhodamine-DOPE) and observed via confocal LSM. According to Fig. 1 A-D and G, significant membrane deformation was observed for GUVs containing high amounts of DOPS (i.e. ≥30 mol%). GUVs containing low amounts of PS (e.g. 10 mol%, Fig. 1 B) or in control samples without protein (Fig. S1 A) remained mostly spherical. We did not observe significant changes in the shape of deformed GUVs during the measurement time.
Although alterations in membrane shape could be obtained by using also non-fluorescent M1 ( Fig. S1 B), labelling of M1 was instrumental to directly visualize protein binding and organization. Figures 1 E-F and the inset in panel D show the spatial distribution of M1-Alexa488 typically observed in the samples represented in panels A, B and D, respectively. As expected, in the absence of DOPS, very little binding of M1 to the membrane was observed and most of the protein could be found in solution outside the GUVs (Fig. 1 E). In the case of GUVs containing 10 mol% DOPS, M1 bound homogeneously to the lipid membrane ( Fig. 1 F). M1 binding appears therefore necessary but, in general, not sufficient to induce membrane deformation. Interestingly, we noticed that the protein bound homogeneously also to non-spherical GUVs (e.g. Figure 1 D and inset). Also, the protein fluorescence intensity inside and outside deformed GUVs was, in general, not distinguishable (see e.g. inset in Fig. 1 D). This indicates that M1 crossed the membrane into the lumen of the vesicles in many cases. Nevertheless, we observed that the shape of deformed GUVs was qualitatively reproducible also whenever M1 was more clearly excluded from the lumen of the vesicles (see Fig. S1 B and S1 C).
Additionally, an increase in membrane acyl-chain order (by increasing cholesterol concentration to 40 mol%, Fig. 1 H) did not appear to suppress membrane deformation. GUV shape alterations could also be observed if DOPS was substituted by other negatively-charged lipids, e.g. PIP 2 or PG (data not shown). Finally, we observed that the N-terminal domain of M1 (M1N aa. 1-164) is sufficient to induce membrane deformation. The C-terminal domain (M1C aa. 165-252) shows no effect on GUV shape and, as previously reported (8), has a low degree of membrane binding (Fig. S2).
Table 1 (row "M1 pH 7.4") shows a quantitative overview regarding the amounts of deformed GUVs observed for different compositions, in conditions similar to those relative to the samples shown in Fig. 1. In order to clearly detect protein binding and, thus, include in the quantification only larger GUVs (>⁓ 10µm diameter) that displayed a significant amount of bound M1, we increased the protein concentration to 10 µM (18 µM for GUVs with only 10 mol% DOPS). Visual inspection of several vesicles confirmed that membrane deformation is facilitated by higher PS concentrations. A quantitative characterization of M1 binding to deformed membranes is described below (Par 3.6)

M1-induced membrane bending in SUVs observed at high resolution
Characterization of bilayer deformation in GUVs via confocal LSM is limited by optical resolution. In order to obtain high-resolution information regarding the interplay between M1 binding and membrane curvature, we used cryo-TEM to observe SUVs in the presence of M1. Lipid vesicles containing DOPC and 40 mol% DOPS were incubated with 10 µM (unlabeled) M1 before freezing. The representative results shown in Fig. 2 A and B indicate that M1 binds to a large fraction of the lipid bilayers. By observing the apparent bilayer thickness, it is possible to distinguish regions of the bilayer with bound M1 (red and yellow arrows) from those devoid of protein (green arrows). In order to obtain a better insight into the conditions at the membrane, we have employed cryo-electron tomography (cryo-ET). Slices of the final 3D reconstruction, calculated from tilting series of images of the SUV embedded in vitreous ice, provide a more accurate representation of the membrane dimensions, compared to individual cryo-TEM projection images. Fig. 2 C shows such a 15 nm thick section through the 3D volume just in the middle of a SUV with partially attached M1 protein. The protein-free bilayer has a thickness of about 4 nm. Bilayer regions which appear to be covered with M1 have a thickness between ca. 8 and 9 nm. Interestingly, the presence of M1 on the vesicles is clearly correlated to changes in vesicle shape. Protein-free SUV surface regions display a positive mean curvature (referred to the membrane monolayer exposed to the protein solution) similar to that observed for control samples (Fig. S3). Membrane regions to which M1 has bound display various spatial features. Most often, we observed outward tubulation, i.e. membrane surfaces with zero Gaussian curvature, as indicated by the red arrows in Fig. 2. In other cases, we observed inward vesiculation or membrane regions with negative Gaussian curvature (yellow arrows in Fig. 2).

PS-rich domains promote localized inwards membrane binding upon binding of M1
It is assumed that Influenza virus assembly and budding occurs in correspondence of confined membrane domains (9). Furthermore, it has been shown that M1 binds to negatively charged lipids (5,29). To investigate whether the spatial confinement of acidic lipids within membrane domains can modulate M1-induced membrane deformation, we produced GUVs displaying phase separation in ordered domains (enriched in saturated phosphatidylcholine and saturated phosphatidylglycerol) and disordered domains (enriched in unsaturated phosphatidylcholine). We have produced similar (i.e. supported) lipid bilayers in the past by using a mixture of DOPC, DSPS, DSPC and cholesterol (30). When producing GUVs, we have noticed that exchanging the saturated phosphatidylserine with saturated phosphatidylglycerol (i.e., dipalmitoylphosphatidylglycerol, DPPG) improved the yield of phase-separated GUVs. Fig. 3 A-B shows an example of such GUVs observed via confocal microscopy. The red channel (panel A) refers to the lateral distribution of a fluorescent unsaturated lipid analogue, which strongly partitions into the disordered bilayer phase. The green channel (panel B) refers to the lateral distribution of a fluorescent cholesterol analogue, which significantly partitions into both ordered and disordered domains. Fig. 3 C-D shows a typical GUV with the same composition, after addition of 10 µM M1. Compared to GUVs observed in the absence of the protein, the ordered domains display deviation from spherical shape and inwards curvature, as also shown in Fig. 3 E-H. We are currently investigating whether M1 is confined exclusively to the outer milieu of these GUVs exhibiting phase separation (compared to the samples described in Par. 3.1).

Membrane deformation is accompanied by the formation of a stable M1-M1 network
So far, we have shown that M1-lipid interactions appear to causes alterations in the spatial structure of the bilayer. We next investigated whether M1-M1 interactions are also involved in the membrane deformation process. Fig. 4 A-B shows a typical GUV containing 30 mol% DOPS in the presence of 5 µM M1-Alexa488. As expected, within 30 minutes of incubation, deformed vesicles could be observed by monitoring the spatial distribution of a fluorescent lipid analogue (Fig. 4 B) or the labeled protein itself (Fig. 4 A). It is worth noting that the alteration of membrane shape is, in these conditions, specific to M1. We have verified that extensive binding of another protein with high affinity to PS (i.e. Annexin V) does not cause significant membrane deformation (see Fig. S4).
We then proceeded to dissolve the GUVs, by adding 1.7 mM Triton X-100. Most of the lipids were effectively dissolved by the treatment, as demonstrated by the strong decrease of the fluorescence signal of the lipid analogue (Fig. 4 D, for an exemplar GUV). Interestingly, we observed that the protein was not affected by the detergent treatment, as it formed an apparently stable lipid-free three-dimensional structure (Fig. 4 C). We obtained similar results if lipid were dissolved using a mixture of different detergents (Triton X-100 1.7mM, n-Dodecylphosphocholine 1mM, CHAPS 10mM and n-Dodecyl-beta-Maltoside 1mM).

M1-M1 interactions are needed for membrane deformation
The previous results indicate that M1 forms structures around GUVs that remain stable even after lipid removal. This observation suggests the presence of significant inter-protein interactions. To verify whether such M1-M1 interactions have a specific role in altering membrane curvature, we investigated conditions that inhibited protein multimerization, while not completely abolishing membrane binding. First, we incubated GUVs containing 40 mol% DOPS in the presence of 10 µM M1-Alexa488 pre-treated with 100 µM PHE. PHE is a small molecule that disrupts M1-M1 interactions (31). The above-mentioned DOPS and M1 concentrations were chosen so that a strong deformation of the GUV bilayer would have been expected (compare to e.g. Fig. 1). Strikingly, most of the GUVs appeared spherical in the presence of PHE, as shown in Fig. 5 A. The same observation was made also under slightly different conditions (i.e. PS concentrations between 30 and 50 mol% and M1-Alexa488 incubated with 75-150 µM PHE, see Table 1).

M1 exhibits reduced dynamics and high degree of multimerization in deformed vesicles
The results described in the previous paragraphs strongly suggest that M1 multimerization plays a determinant role in causing bilayer deformation. As an alternative approach to characterize protein-protein interactions, we performed scanning fluorescence correlation spectroscopy (sFCS) measurements on protein-bound GUVs. We have already used fluorescence fluctuation analysis methods to characterize M1 multimerization upon interaction with lipid bilayers (7). In this study, we used a sFCS approach to directly quantify protein binding, dynamics and multimerization in spherical or deformed GUVs. It is worth noting that the sFCS variant (compared to e.g. point FCS) is particularly suitable to investigate free-standing membranes. We have incubated GUVs containing 30 mol% DOPS with 10 µM M1-Alexa488, at pH 7.4. More precisely, one every ca. 10 proteins was labelled with a fluorophore. Fig. 6 A shows, for different experimental conditions, the measured average normalized molecular brightness, i.e. a parameter proportional to the clustering degree of the membrane-bound protein (7,8). Our results indicate that the M1 bound to deformed vesicles (at pH 7.4, in the absence of PHE) is characterized by a significantly higher degree of multimerization, compared to the protein bound to spherical GUVs within the same samples. As reported in Table 1, such samples (i.e. 30 mol% DOPS, 10 µM M1) contain in average ca. 50% deformed GUVs. Furthermore, we have measured M1 normalized brightness in samples that were treated with the M1 multimerization inhibitor PHE (31) or that were prepared at pH 5. Both conditions supposedly decrease M1 multimerization and membrane deformation, as reported in the previous paragraph. As expected, in both cases M1 multimerization quantified via sFCS resulted significantly lower than that measured for deformed vesicles.
Additionally, sFCS provided quantitative information about M1-Alexa488 average diffusion dynamics. More precisely, we report the typical time needed by the protein to diffuse through a membrane area intersected by the detection volume. Such diffusion time is inversely proportional to the Brownian diffusion coefficient. The results shown in Fig. 6 B indicate, as expected, that M1 dynamics in deformed vesicles is slower than that observed in all other cases. It is worth noting that protein diffusion is, in general, dependent on both the size of protein assemblies and protein-membrane interaction.
In conclusion, these data indicate that M1-induced membrane deformation is clearly accompanied by a significant increase in protein multimerization and reduced protein dynamics. On the other hand, we did not observe a simple correlation between total amount of bound protein and the induction of membrane deformation, as the total fluorescence signal measured in deformed or spherical GUVs was very similar (see Fig. S5).

Discussion
The protein M1 plays a fundamental role in the assembly of IAV (4). In order to release a new virion from the PM of an infected cell, the lipid bilayer has to undergo a shape change. In particular, some viral or cellular component(s) must induce a negative curvature on the inner leaflet of the PM, so that a virion can bud out from the cell. Previous studies have suggested that different viral proteins might be responsible for the reshaping of the PM (9). M1 has also been suggested to be capable of inducing curvature in the bilayer, but no unequivocal evidence has been presented yet. While investigations in cellulo provide information in a biologically relevant context (e.g. (10,11)), the concurrent presence of several cellular and viral proteins does not allow the isolation and characterization of specific protein-protein or protein-lipid interactions. For this reason, we have applied here a bottom up approach and modelled the interaction between M1 and the PM in a controlled environment. In particular, we have characterized the interaction between M1 and physical models of the PM (i.e. GUVs or SUVs) using several microscopy methods. A similar approach has been recently used to investigate the interaction between the matrix protein from the Influenza C virus and lipid membranes (20). It was shown that this specific matrix protein forms elongated structures on lipid membranes and can also cause the formation of lipid tubules (by inducing membrane curvature). Nevertheless, the low sequence similarity between the two proteins and the morphologically different membrane structures observed in cells infected by the two viruses (38,39) do not allow extending the conclusions regarding the matrix protein of Influenza C directly to Influenza A M1. In the current work we have investigated for the first time the interaction between IAV M1 and free-standing model membranes containing different amounts of PS. The protein concentration (much larger than the reported K d values (8,33)) was chosen so to take into account that, in vivo, viral proteins might be recruited in small PM domains (40) and, therefore, reach a high local concentration (41). Previous investigations on solid-supported bilayers have demonstrated that M1 binds to lipid membranes containing negatively-charged lipids and that M1-lipid binding is accompanied by extensive protein multimerization (7). In agreement with these observations, we have observed that M1 binds effectively to GUVs containing DOPS. Surprisingly, M1 binding induced a significant alteration in the shape of the vesicles, especially at higher PS concentrations (e.g. >30 mol%, see Fig. 1). Specifically, the N-terminal domain of M1 is sufficient to induce membrane shape changes, in line with previous findings suggesting that this domain of the protein is responsible for M1-lipid and M1-M1 interactions (5,8). Interestingly, the N-terminal domain of Influenza C M1 is not sufficient to cause membrane curvature (20). Observation via optical microscopy indicates that M1 binds homogeneously to the deformed (i.e. non-spherical) vesicles and, in general, both regions with positive and negative curvature can be observed. It reasonable to assume that GUVs might become (temporarily) unstable, due to the significant deformation and volume decrease, thus allowing the internal solution to leak out. Accordingly, we have observed that M1 might indeed be bound to both membrane leaflets in many vesicles. Nevertheless, reproducible membrane deformation is observed also in the cases in which the protein seems to bind only to the outer leaflet (Fig. S1).
Alteration in bilayer curvature was also observed if M1 was incubated with SUVs with diameter ⁓100 nm. Thanks to the high spatial resolution of cryo-TEM and cryo-ET, M1-lipid interaction could be observed with remarkable detail. On a nanometer scale, binding of the protein to the vesicles appears not homogeneous. M1 seems capable to induce tubulation and, in fewer cases, vesiculation. The protein is found also concentrated in membrane regions with negative Gaussian curvature (i.e. at the neck of tubes) (Fig. 2). While comparing these results to the possible membrane-bending properties of M1 in a physiological context, it must be kept in mind that SUVs are highly curved compared to GUVs or the PM. The specific protein-lipid arrangements which are observed for SUVs (e.g. the ⁓20 nm radius observed for tubular structures) might therefore also be influenced by the initial membrane curvature. Furthermore, the protein-lipid ratio used in this study for GUV samples was ca. 10-100 times lower than the one used for SUV sample, and this difference might play a role in the formation of protein-lipid structures. Nevertheless, taken together, the experiments performed in GUVs and SUVs suggest that M1 is sufficient to induce membrane deformation. While no other membrane component appears necessary for M1-induced membrane bending, the possibility that e.g. other viral proteins might modulate this effect (by, e.g., making negative curvature of the leaflet interacting with M1 energetically favorable over positive curvature) is currently under investigation. Furthermore, it is worth mentioning that M1 was reported to form various multimeric arrangements (e.g. helical (36)) that, in vivo, might result in a different remodeling of lipid membranes, compared to the one presented in the current work. The physical properties of the lipid bilayer do not appear to play a significant role in M1induced membrane bending, since we observed no effect induced by e.g. increasing membrane bending rigidity (by increasing cholesterol content (42)), or due to changes in membrane lateral tension (due to varying osmotic pressure). Additionally, our observation that significant shape alteration is observed only in bilayers containing higher amounts of PS is likely not connected to an effect of this lipid on the physical properties of the membrane. It was shown in fact that PS does not decrease the bending stiffness of a lipid bilayer (43,44). In conclusion, the observed alterations in bilayer shape are brought about specifically by the binding of M1 to the membrane. Similar results are observed if M1 binds to GUVs via interactions with other lipids (i.e. in the absence of PS), such as PG, PIP 2 or metal-ionchelating lipids bound to the His-tag of M1 (data not shown). A further aspect that was examined in these experiments regarded the lateral organization of the bilayer, prior to protein binding. Previous investigations have shown that M1 multimerization and binding to lipid membranes are modulated by the presence of lipid domains containing negatively-charged lipids (30). In infected cells, M1 clusters are observed in correspondence of PS-rich membrane regions (30). In order to clarify whether M1-induced membrane restructuring is also affected by the lateral organization of the lipid bilayer, we investigated lipid mixtures displaying phase separation. In particular, GUVs contained ordered domains (enriched in cholesterol, saturated lipids and, reasonably, negativelycharged lipids) in a disordered bilayer (enriched in unsaturated lipids). In the presence of M1, the ordered domains showed inward budding and, in general, variations from the original shape (see e.g. Fig. 3 E). This observation demonstrates, on one hand, that these ordered domains are indeed enriched in acidic lipids to which M1 can effectively bind. Second, it confirms that the membrane restructuring effect of M1 appears significant even at relatively high values of membrane bending rigidity (cfr. ⁓65 kT for a DPPC:cholesterol 80:20 bilayer (45)). Third, it suggests that the local membrane restructuring induced by M1 might be modulated by the spatial properties of the bilayer region in which M1 is concentrated. This phenomenon might be relevant in vivo, since M1 is supposed to be confined in budozones, i.e. small domains of the PM of infected cells from which IAV budding takes place (46). Finally, we aimed to clarify the molecular mechanism driving the deformation of the membrane induced by M1. Several mechanisms for the induction of membrane curvature driven by (non trans-membrane) proteins were described, including: membrane insertion, protein crowding and scaffolding (47,48). In order to distinguish among these possibilities, we observed M1-GUV interaction in conditions in which M1 could bind to the membrane but protein multimerization was hindered (i.e. low pH or in the presence of a multimerization inhibitor, see Fig. 5). These experiments revealed that protein binding to the bilayer per se did not induce significant alterations in membrane shape. The reduced ability of M1 to form large multimers in these conditions was confirmed via sFCS measurements (Fig. 6). Interestingly, the total amounts of M1 bound to spherical or deformed vesicles did not differ significantly (Fig. S5). Finally, we verified that binding of another protein to GUVs (in comparable amounts) did not affect membrane shape (Fig. S4). Taken together, these observations suggest that membrane insertion or protein crowding are not the main factors driving M1-induced curvature. On the other hand, we observed that M1 forms a protein network which is characterized by slow dynamics and seems to impose its shape on the underlying bilayer (Fig. S4, Fig. 6 B). The stability of M1-M1 interactions allows the protein shell to remain stable even after removal of the lipid membrane (Fig. 4). Additionally, we were able to quantitatively verify that M1 bound to deformed vesicles forms in general larger multimers, compared to the case of spherical vesicles (Fig. 6). At this regard, the limitations of the sFCS approach in this context should be mentioned: First, the reported brightness/multimerization and diffusion values refer to an average of different multimeric species that might be present in the sample. Second, membrane geometry and the detection area are not well-defined in the case of deformed membranes with large curvature (compared to the typical size of the detection volume). In other words, a larger than expected bilayer surface (due to ruffling within the detection volume) might be observed during our experiments. As a consequence, protein diffusion times might be overestimated in deformed vesicles. On the other hand, protein brightness and multimerization would be underestimated, thus confirming our main finding that M1 bound to deformed vesicles is indeed characterized by a high degree of multimerization.
In conclusion, our data show that IAV M1 binds to vesicles containing negatively-charged lipids, forming a stable protein layer. This M1-M1 network seems to be characterized by both regions of negative and positive curvature, although it appears that its precise spatial structure might be modulated by the initial curvature of the bilayer and the lateral organization of the lipids. Our results support the model according to which the M1 shell, in vivo, might drive the IAV budding (likely in concert with other membrane and/or viral components determining a specific curvature) and provides mechanical stability to the newly formed virion (see e.g. (49)).


extfieldsofstudy: Medicine Biology

s2fieldsofstudy: Biology Medicine Chemistry

text:
CRISPR Loss-of-Function Screen Identifies the Hippo Signaling Pathway as the Mediator of Regorafenib Efficacy in Hepatocellular Carcinoma

Regorafenib is used for hepatocellular carcinoma (HCC), but its response does not last long, partly due to chemoresistance acquisition. We performed a clustered regularly interspaced short palindromic repeats (CRISPR)-based loss-of-function genetic screen and aimed to discover molecules involved in regorafenib resistance in HCC. Xenograft tumors established from Cas9-expressing HCC cells with pooled CRISPR kinome libraries were treated with regorafenib or a vehicle. Sequencing analysis identified 31 genes, with the abundance of multiple guide RNAs increased in regorafenib-treated tumors compared to that in vehicle-treated tumors, including 2 paralogues, LATS2 and LATS1, core components of the Hippo signaling pathway. Notably, all eight designed guide RNAs targeting LATS2 increased in regorafenib-treated tumors, suggesting that LATS2 deletion confers regorafenib resistance in HCC cells. LATS2 knockdown significantly mitigated the cytotoxic and proapoptotic effects of regorafenib on HCC cells. LATS2 inhibition stabilized the Hippo signaling mediator YAP, leading to the upregulation of antiapoptotic Bcl-xL and the multidrug resistance transporter ABCB1. Among 12 hepatoma cell lines, the half maximal inhibitory concentration (IC50) values of regorafenib were positively correlated with any of YAP, Bcl-xL and ABCB1 levels. The inhibition of YAP or Bcl-xL in regorafenib-insensitive HCC cells restored their susceptibility to regorafenib. In conclusion, our screen identified the Hippo signaling pathway as the mediator of regorafenib efficacy in HCC.

Introduction
Hepatocellular carcinoma (HCC) is the primary histological type of liver cancer and the most common cause of cancer-related deaths, especially in Asia and Africa [1]. Surgical resection or transplantation are the curative therapeutic options, but they can be applied to only 10 to 20% of HCC patients because of donor shortage, liver dysfunction or detection at an advanced tumor stage [1]. Furthermore, even after curative resection, HCC often recurs at a very high rate, since the liver is already predisposed to tumor development due to sustained chronic damage caused by viral infection, alcohol, lipids, etc. For unresectable or recurrent advanced HCC, therapeutic options are very limited, and sorafenib has been the only Food and Drug Administration (FDA)-approved drug for approximately a decade. However, the RESORCE (regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment) trial, a randomized, double-blind, placebo-controlled, phase 3 trial of regorafenib, was recently conducted for patients with HCC who progressed on sorafenib treatment and showed statistically significant improvement of overall survival compared to patients who received the placebo. Since 2017, regorafenib can be used for advanced HCC as the second line of therapy after the failure of sorafenib treatment [2,3].
Regorafenib is a multiple tyrosine kinase inhibitor that blocks the activity of a variety of kinases, including VEGFR, PDGFR, RAF1, KIT, and RET, leading to potent antitumor activity, such as the suppression of cell proliferation, apoptosis induction and the inhibition of angiogenesis [3]. Although this drug shows statistically significant prolongation of progression-free survival, its benefit does not last long, suggesting that tumor cells acquire chemoresistance to this compound. However, its molecular mechanism is mostly not yet clarified [3].
Recent progress in genetic perturbation technologies including the small interfering RNA (siRNA)/small hairpin RNA (shRNA) library and the clustered regularly interspaced short palindromic repeats (CRISPR) library, as an arrayed or pooled format, have enabled us to screen a large number of genes simultaneously. In particular, a pooled CRISPR/Cas9 library-based genetic screen is a versatile and convenient tool that can simultaneously assess the biological consequences of genetic loss-of-function for thousands of genes in a single experiment. Technically, the CRISPR library is introduced into the cell population under conditions of one gene perturbation per cell, and the entire cell population is exposed to selective pressure, which is determined based on the phenotype of interest. The cells that acquire adaptation to the applied environment upon gene knockout become enriched and are identified by high-throughput sequencing. While we and others have previously performed this screen for the discovery of cancer genes involving cell proliferation or tumor growth [4,5], it has also been applied to identify genes involved in drug susceptibility/resistance in several cancer types, including the BRAF inhibitor for melanoma and the FLT3 inhibitor for acute myeloid leukemia [6,7].
In this study, to discover genes involved in chemoresistance to regorafenib in HCC, we performed an in vivo pooled CRISPR library screen. Our screen successfully identified two paralogous kinases, LATS2 and LATS1, which are major components of the Hippo signaling pathway, as positive regulators of its antitumor effect. For the validation study, we showed that LATS2 inactivation dephosphorylated YAP, which in turn activated antiapoptosis and multidrug transporter machineries, mitigating the antitumor effect of regorafenib against HCC. Consistent with these results, we found that regorafenib efficacy was inversely correlated with the expression of YAP and downstream target genes in a variety of HCC cell lines. We also showed that the inhibition of YAP or its downstream antiapoptotic gene sensitized regorafenib-insensitive HCC cells to regorafenib. Our data suggest that the Hippo signaling pathway is the mediator of regorafenib efficacy in HCC.

Regorafenib Suppresses Xenograft Tumor Growth of the Cas9-Expressing HCC Cell Line
To perform a CRISPR screen, we first generated a Cas9-expressing human HCC cell line by lentiviral transduction of a Cas9-expressing vector into HLF cells and subsequent isolation of the Cas9-positive clone by the limiting dilution method. We confirmed that propagated clone cells expressed Cas9 protein at a similar level as those from transduced polyclonal cells ( Figure 1A). We assessed the in vitro cytotoxic effect of regorafenib on these cells and confirmed that the half maximal inhibitory concentration (IC50) values of regorafenib were not affected by the presence of Cas9 ( Figure 1B). Next, we examined the in vivo tumorigenic potential of the Cas9-positive HLF cell line and observed measurable xenograft tumors in the flanks of NOD/Shi-scid/IL-2Rγ null (NOG) mice two months after inoculation. We then treated these mice with regorafenib or vehicle and confirmed that regorafenib significantly suppressed the growth of these tumors ( Figure 1C). These data suggested that our in vivo experimental setting using the HCC xenograft model of Cas9-positive HLF clone cells, at least in part, mimicked the clinical antitumor effect of regorafenib on HCC patients. Therefore, we performed pooled CRISPR library screening using this model and aimed to discover genes involved in chemoresistance to regorafenib in HCC. (C) Nine million Cas9-transduced HLF clone cells per flank were subcutaneously injected into NOD/Shi-scid/IL-2Rγ null (NOG) mice. Once the tumor volume reached 200 mm 3 , mice were treated with either vehicle or 20 mg/kg regorafenib, and tumor volume was measured at various times (N = 4 for each, * p < 0.05).

In Vivo Crispr Library Screen Identifies Lats1/2 Genes as Candidates Involved in Regorafenib Resistance in HCC
For the CRISPR screen, we focused on the kinome. Lentiviral particles of the CRISPR pooed library containing 6104 gRNAs targeting 763 human kinases [8] were transduced into 2 × 10 7 Cas9positive HLF cells. After puromycin selection, in order to determine the initial distribution of gRNA abundance in the library-transduced cells, 9.0 × 10 6 cells were collected and gRNA sequences were PCR amplified and sequenced by next-generation sequencing. A total of 6084 out of 6104 gRNAs were detected in transduced cancer cells with uniform distribution of their abundance, indicating the successful preparation of library-transduced cells (Supplementary Figure S1A). Then, 9.0 × 10 6 transduced cancer cells per flank were subcutaneously inoculated into 6 NOG mice (Figure 2A). Once the tumor volume reached 200 mm 3 , tumors were randomly divided into two groups and treated with either vehicle or regorafenib for three weeks (Supplementary Figure S1B). After the treatment, all the tumors were collected and gRNA sequences integrated into the tumor genome were sequenced. An average of approximately 1.6 × 10 6 gRNA sequences were obtained per tumor sample. Different from the distribution of gRNA abundance in library-transduced cells, the distribution in each tumor was uneven and some of gRNAs were highly enriched, suggesting the existence of strong in vivo selective pressure (Supplementary Figure S1A). We then expected that inside regorafenibtreated tumors, cancer cells that acquired resistance to regorafenib by CRISPR-mediated gene knockout would have a growth advantage and become enriched, while these cells would not become enriched inside vehicle-treated tumors. To identify genes targeted by CRISPR in those enriched cancer cells, we calculated the ratio of each gRNA abundance in regorafenib-treated tumors compared to that in vehicle-treated tumors and extracted 1048 gRNAs that showed greater than or equal to a 1.5-fold increase in ratio. We then examined genes with greater than or equal to 4 corresponding gRNAs identified among them and selected 31 genes, the knockout of which was expected to confer a growth advantage in cancer cells under regorafenib treatment ( Figure 2B).

In Vivo Crispr Library Screen Identifies Lats1/2 Genes as Candidates Involved in Regorafenib Resistance in HCC
For the CRISPR screen, we focused on the kinome. Lentiviral particles of the CRISPR pooed library containing 6104 gRNAs targeting 763 human kinases [8] were transduced into 2 × 10 7 Cas9-positive HLF cells. After puromycin selection, in order to determine the initial distribution of gRNA abundance in the library-transduced cells, 9.0 × 10 6 cells were collected and gRNA sequences were PCR amplified and sequenced by next-generation sequencing. A total of 6084 out of 6104 gRNAs were detected in transduced cancer cells with uniform distribution of their abundance, indicating the successful preparation of library-transduced cells (Supplementary Figure S1A). Then, 9.0 × 10 6 transduced cancer cells per flank were subcutaneously inoculated into 6 NOG mice ( Figure 2A). Once the tumor volume reached 200 mm 3 , tumors were randomly divided into two groups and treated with either vehicle or regorafenib for three weeks (Supplementary Figure S1B). After the treatment, all the tumors were collected and gRNA sequences integrated into the tumor genome were sequenced. An average of approximately 1.6 × 10 6 gRNA sequences were obtained per tumor sample. Different from the distribution of gRNA abundance in library-transduced cells, the distribution in each tumor was uneven and some of gRNAs were highly enriched, suggesting the existence of strong in vivo selective pressure (Supplementary Figure S1A). We then expected that inside regorafenib-treated tumors, cancer cells that acquired resistance to regorafenib by CRISPR-mediated gene knockout would have a growth advantage and become enriched, while these cells would not become enriched inside vehicle-treated tumors. To identify genes targeted by CRISPR in those enriched cancer cells, we calculated the ratio of each gRNA abundance in regorafenib-treated tumors compared to that in vehicle-treated tumors and extracted 1048 gRNAs that showed greater than or equal to a 1.5-fold increase in ratio. We then examined genes with greater than or equal to 4 corresponding gRNAs identified among them and selected 31 genes, the knockout of which was expected to confer a growth advantage in cancer cells under regorafenib treatment ( Figure 2B). Among them, LATS2, one of the Hippo signaling pathway components, drew our attention most because not only it showed biggest increase of gRNA abundance in regorafenib-treated tumors compared to that in vehicle-treated tumors ( Figure 2B), but also the abundance of all designed 8 gRNAs was increased in regorafenib-treated tumors ( Figure 2C). These data strongly suggest that HCC cells with the CRISPR-mediated knockout of LATS2 acquire regorafenib resistance. In addition, we also identified that the abundance of gRNAs targeting LATS1, another Hippo signaling pathway components and the paralogue of LATS2, was increased in regorafenib-treated tumors ( Figure 2B,C), suggesting that the Hippo signaling pathway may be involved in the regorafenib resistance. Among them, LATS2, one of the Hippo signaling pathway components, drew our attention most because not only it showed biggest increase of gRNA abundance in regorafenib-treated tumors compared to that in vehicle-treated tumors ( Figure 2B), but also the abundance of all designed 8 gRNAs was increased in regorafenib-treated tumors ( Figure 2C). These data strongly suggest that HCC cells with the CRISPR-mediated knockout of LATS2 acquire regorafenib resistance. In addition, we also identified that the abundance of gRNAs targeting LATS1, another Hippo signaling pathway components and the paralogue of LATS2, was increased in regorafenib-treated tumors ( Figure 2B,C), suggesting that the Hippo signaling pathway may be involved in the regorafenib resistance.  Note that the fold change of gRNA6-8 in LATS1 was not calculated because of the absence of these gRNAs in vehicle-treated tumors.

Inhibition of LATS2 Confers Regorafenib Resistance to HCC Cell Lines
To validate the gene candidates identified in the CRISPR screen, we investigated the effect of LATS2 silencing on regorafenib-induced cytotoxicity and apoptosis in HCC cell lines in vitro. We first confirmed that the LATS2 siRNA significantly suppressed the mRNA and protein levels of LATS2 in 2 HCC cell lines, Hep3B and Huh7 ( Figure 3A,C). While regorafenib treatment induced apoptosis, as exhibited by the marked increase in caspase-3/7 activity, it was significantly suppressed by LATS2 knockdown in both cell lines ( Figure 3D,E). Similarly, while regorafenib treatment significantly impaired cell viability, as examined by the WST-1 assay, it was significantly alleviated by LATS2 knockdown in these cells ( Figure 3F,G). These data indicate that the inhibition of LATS2 confers resistance to regorafenib in HCC cell lines.

Inhibition of LATS2 Inactivates the Hippo Signaling Pathway, Leading to the YAP-Mediated Activation of Downstream Machineries
We then examined the molecular mechanism of regorafenib resistance in HCC upon LATS2 inhibition. LATS2 and LATS1 are core components and positive regulators of Hippo signaling. When Hippo signaling is active, LATS2 and LATS1 phosphorylate the Hippo signaling mediator YAP, leading to its cytoplasmic retention and subsequent degradation [9]. Indeed, the siRNA-mediated knockdown of LATS2 reduced the levels of phosphorylated YAP in 4 human liver cancer cell lines ( Figure 4A), proving that LATS2 negatively regulates YAP activity in liver cancer cell lines. Once YAP is activated by dephosphorylation, the YAP protein translocates to the nucleus and forms a complex with the transcriptional factor TEAD, leading to the upregulated expression of a variety of target genes [10]. We found that LATS2 inhibition activated YAP and transcriptionally upregulated the downstream targets of YAP/TEAD, the antiapoptotic gene Bcl-xL and the multidrug resistance transporter ABCB1 in Hep3B and HLF cells ( Figure 4B-E). We also showed the upregulation of these proteins by western blot (WB) analysis ( Figure 4F). A similar finding was observed, when LATS2 gene was depleted by CRISPR in Cas9-positive HLF cells using two LATS2-targeting gRNAs enriched in our CRISPR library screen ( Figure 4G). We also found that regorafenib treatment reduced the levels of Bcl-xL and ABCB1 ( Figure 4H). These data suggest that LATS2 silencing inactivates the Hippo signaling pathway, represented by YAP stabilization, and subsequently activates antiapoptotic and drug transporter machineries, which may contribute to regorafenib resistance in HCC cell lines.

Hippo Signaling May Mediate the Susceptibility of HCC Cells to Regorafenib
To further examine the involvement of the Hippo signaling pathway in regorafenib efficacy in HCC, we investigated the association between YAP levels and regorafenib susceptibility in human liver cancer cell lines. We first determined the each IC50 values of regorafenib toward commercially available 12 human liver cancer cell lines ( Figure 5A) and examined the correlation with their YAP protein levels. Interestingly, YAP protein levels showed a significant positive correlation with the IC50 values of regorafenib ( Figure 5B). We also assessed the expression levels of CYR61, known downstream transcriptional target of YAP/TEAD component [11], as the marker of YAP activation. The mRNA levels of CYR61 show strong positive correlation with the IC50 values of regorafenib in 12 HCC cell lines (Supplementary Figure S2), suggesting that YAP activation may contribute to the regorafenib resistance. Furthermore, the mRNA levels of its downstream target genes, Bcl-xL and ABCB1, were also significantly positively correlated with the IC50 values of regorafenib in these HCC cell lines ( Figure 5C,D). Collectively, these data suggest that Hippo signaling may mediate regorafenib susceptibility in HCC cells.

Hippo Signaling May Mediate the Susceptibility of HCC Cells to Regorafenib
To further examine the involvement of the Hippo signaling pathway in regorafenib efficacy in HCC, we investigated the association between YAP levels and regorafenib susceptibility in human liver cancer cell lines. We first determined the each IC50 values of regorafenib toward commercially available 12 human liver cancer cell lines ( Figure 5A) and examined the correlation with their YAP protein levels. Interestingly, YAP protein levels showed a significant positive correlation with the IC50 values of regorafenib ( Figure 5B). We also assessed the expression levels of CYR61, known downstream transcriptional target of YAP/TEAD component [11], as the marker of YAP activation. The mRNA levels of CYR61 show strong positive correlation with the IC50 values of regorafenib in 12 HCC cell lines (Supplementary Figure S2), suggesting that YAP activation may contribute to the regorafenib resistance. Furthermore, the mRNA levels of its downstream target genes, Bcl-xL and ABCB1, were also significantly positively correlated with the IC50 values of regorafenib in these HCC cell lines (Figure 5C,D). Collectively, these data suggest that Hippo signaling may mediate regorafenib susceptibility in HCC cells.

Suppression of YAP or Its Downstream Antiapoptotic Machinery Restores the Sensitivity of HCC Cells to Regorafenib
To clarify the causal relationship between YAP activation and regorafenib susceptibility, we examined the effect of suppressing YAP on the cytotoxicity of regorafenib in HCC cells. We selected HLF and SNU-475 cells, which showed the highest IC50 values of regorafenib and YAP among the

Suppression of YAP or Its Downstream Antiapoptotic Machinery Restores the Sensitivity of HCC Cells to Regorafenib
To clarify the causal relationship between YAP activation and regorafenib susceptibility, we examined the effect of suppressing YAP on the cytotoxicity of regorafenib in HCC cells. We selected HLF and SNU-475 cells, which showed the highest IC50 values of regorafenib and YAP among the 12 HCC cell lines, and treated them with the YAP inhibitor verteporfin. While verteporfin treatment alone did not affect cell viability, YAP inhibition significantly augmented the cytotoxicity of regorafenib in these cells ( Figure 6A,B). We also confirmed that siRNA-mediated YAP knockdown also significantly augmented the cytotoxicity of regorafenib (Supplementary Figure S3A). These findings demonstrated that YAP activation contributed to chemoresistance to regorafenib in HCC cells. We also examined the involvement of the YAP downstream target gene Bcl-xL in regorafenib efficacy. Similar to YAP inhibition, Bcl-xL inhibition by ABT-737, a Bcl-xL/Bcl-2/Bcl-w inhibitor, significantly enhanced cytotoxicity induced by regorafenib ( Figure 6C,D) in HLF and SNU-475 cells. We also observed a significant elevation in caspase-3/7 activity in both cell lines when treated with both regorafenib and ABT-737 compared to treatment with either single agent ( Figure 6E,F). We also confirmed that siRNA-mediated Bcl-xL knockdown significantly enhanced the cytotoxicity of regorafenib with marked elevation of caspase-3/7 activity (Supplementary Figure S3B,C). Collectively, these data also suggest that the suppression of YAP or its downstream antiapoptotic machinery in combination with regorafenib could be a potential therapeutic for regorafenib-insensitive HCC cell lines.

Discussion
In the present study, through CRISPR-based high-throughput screening, we discovered that Hippo signaling was involved in chemoresistance to regorafenib in HCC. As a mechanism of action, we showed that inactivation of the Hippo signaling pathway activated YAP, upregulating machineries involved in antiapoptosis and drug resistance transporters. Last, we proposed the potential combination therapy of regorafenib and the inhibition of YAP or Bcl-xL for regorafenib-insensitive HCC patients.
Although regorafenib is also used for advanced colorectal cancer and gastrointestinal stromal tumors (GISTs) other than HCC, there are only a few reports regarding its chemoresistance mechanism. Mirone G. et al. reported that Notch-1 was upregulated in regorafenib-resistant colon cancer cells and that its inhibition partially restored regorafenib sensitivity [12]. Tong J. et al. reported that the inactivating mutation in FBW7, an E3 ubiquitin ligase, contributes to regorafenib resistance in colorectal cancer cells [13]. They also demonstrated that it is caused by stabilization of the antiapoptotic protein Mcl-1 due to the lack of FBW7-mediated proteasomal degradation. For HCC, Wang J. et al. recently reported that Pin1 expression was increased in a regorafenib-resistant HCC cell line and that its suppression reversed regorafenib resistance [14]. Mechanistically, they showed that Pin1 positively regulated EMT and the migration/invasion capacity of HCC cells. In the current paper, we newly identified that the Hippo signaling pathway is involved in regorafenib chemoresistance in HCC. In addition, similar to Tong's study, we found that the activation of antiapoptotic Bcl-2 family proteins contributed to its chemoresistance mechanism.
The Hippo signaling pathway is the master regulator of organ development and also plays a critical role in liver size control [15,16]. In addition, a substantial number of papers has elucidated the important role of Hippo signaling in tumorigenesis in a variety of cancers, including HCC [15,16]. Genetic studies have shown that the liver-specific deletion of MST1/2, another core component of the Hippo signaling pathway, or YAP overexpression induces liver overgrowth and the development of HCC in mice [17,18]. We also previously demonstrated that the liver-specific deletion of Sav1, an adaptor protein of MST1/2, activated YAP and significantly accelerated liver tumor development in the Pten-deficient liver [9]. The Hippo signaling pathway is now appreciated as one of the major gatekeepers of hepatocarcinogenesis. Moreover, the association between Hippo signaling and resistance to chemotherapeutic drugs has been recently received great attention [19][20][21]. YAP is involved in resistance to a variety of chemotherapeutic drugs in several cancer types, including molecular targeted drugs such as inhibitors of CDK4/6, EGFR and BRAF [22][23][24], as well as classical cytotoxic drugs such as 5-FU, cisplatin (CDDP) and doxorubicin [25][26][27]. Regarding HCC, YAP contributes to chemoresistance to doxorubicin [28], CDDP [29], and irinotecan [30]. Gao J, et al. also recently reported that tissue stiffness observed in cirrhotic liver induced sorafenib resistance via the upregulation of YAP [31]. In regard to its mechanism of action, YAP contributed to chemoresistance through the induction of autophagy, cell cycle, antiapoptosis, and EMT [21]. In the current paper, we showed for the first time that the Hippo pathway and its effector YAP contributed to regorafenib resistance in HCC in vitro. We found that it was in part through the induction of antiapoptosis and multidrug transporter machineries. But other chemoresistance mechanisms could be also involved and need further investigation. In addition, considering that our screening was done in vivo and regorafenib affects VEGF/PDGF signaling, YAP activation might confer regorafenib resistance in HCC cells through affecting tumor vasculature and microenvironment. An in vivo study to validate our finding and pursue these interactions is desired and will be performed.
In addition to LATS1 and LATS2 proteins, MST1 and MST2 proteins, known as STK3 and STK4, are also core components of the Hippo signaling pathway. They are upstream kinases of LATS1/2 and thus physiologically suppress YAP activity same as LATS1/2 do. In our screen, although all four kinases were targeted by the CRISPR kinome library, we only identified the enrichment of gRNAs targeting LATS1/2 but not MST1/2 in regorafenib-treated tumors. The definitive reason of discordancy is unclear but there could be due to the distinct strength of these proteins regulating YAP activity. It was shown that in MST1/2 knockout hepatocytes, there is residual phosphorylation of YAP at LATS1/2 phosphorylation sites suggesting that LATS1/2 is still active and suppress YAP activity in the absence of MST1/2 [32]. Moreover, YAP induces a negative feedback loop through directly enhancing the transcription of LATS1/2 [33]. As LATS1/2 deletion would lose this negative feedback loop, higher levels of YAP activation in LATS1/2-deficient cells would be anticipated relative to depletion of other upstream Hippo pathway components such as MST1/2. Although there is no direct experimental evidence to support, such difference between LATS1/2 and MST1/2 molecules could affect the result of our CRISPR screen.
In addition to the limited survival benefit of regorafenib, its response rate is unsatisfactory. Therefore, it is urgent to identify efficacious biomarkers for the selection of good responders. Teufel M. et al. recently analyzed plasma and tumor samples from HCC patients who participated in the RESORCE trial [34]. Among 266 evaluable proteins in the plasma of 499 patients, the levels of 5 (ANG-1, cystatin-B, LAP TGF-β1, LOX-1, and MIP-1α) were negatively associated with an enhanced treatment benefit with regorafenib, with a hazard ratio between 1.02 and 1.46. They also showed that some of the circulating miRNA levels were also significantly correlated with overall survival. Although very important results were obtained from a large cohort of patients, their predictive value is still uncertain until proven by further validation studies. In our current study, we revealed that tumor Hippo signaling activity was closely associated with the regorafenib response. Therefore, it might be worth further investigation to identify surrogate serum/plasma biomarkers for Hippo signaling pathway activity in HCC tissue, which could potentially be used for the identification of efficacious biomarkers of regorafenib in HCC. Meanwhile, although we could show that LATS2 depletion functionally confers regorafenib resistance in the HCC cells, it does not necessarily mean that HCC cells acquire chemoresistance by inactivating LATS2 in the clinical setting. It could be achieved by activation of YAP by other mechanism. Therefore, future studies are also important to assess the alteration of these signals in HCC specimens from patients who become refractory to regorafenib and/or regorafenib-resistant HCC cell lines.
In conclusion, in the current study, we discovered an unprecedented link between the Hippo signaling pathway and regorafenib efficacy in HCC. Further studies are required to evaluate the potential of Hippo signaling as a candidate biomarker for efficacy against regorafenib in HCC patients and as a therapeutic target for patients with regorafenib-insensitive HCC.

Materials and Methods
All methods were performed in accordance with the relevant guidelines and regulations of our institution.

Cell Lines and Reagents
Human liver cancer cell lines Hep3B, PLC/PRF/5, HepG2, SNU-182, SNU-387, SNU-398, SNU-423, SNU-449, and SNU-475, HLE and HLF were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). The Huh-7 human liver cancer cell line was obtained from the Japanese Cancer Research Resource Bank (JCRB, Ibaraki, Japan). These cell lines were cultured in RPMI-1640 medium or Dulbecco's modified eagle medium (DMEM) supplemented with 10% fetal calf serum and antibiotics. All cells were confirmed to be free from pathogens and mycoplasma. Regorafenib was provided by Bayer AG (Leverkusen, Germany). Regorafenib was dissolved in the mixture of PEG400, propylene glycol and pluronic F68. ABT-737 and Verteporfin were purchased from Cayman Chemical (Ann Arbor, MI, USA). These compounds were dissolved in dimethyl sulfoxide (DMSO) to prepare stock solutions and diluted in cell growth medium for assays.

Production of Lentiviral Particles Containing the Human Kinome CRISPR Knockout Library (Brunello) or Lentiviral Vectors
We obtained the Brunello library from Addgene. The two halves of the library were combined and used as one pool containing 6104 gRNAs targeting 763 genes (approximately 8 gRNAs per gene), it was electroporated and transformed following the protocol from the Broad institute [8]. To produce lentivirus, 180 µg of the lentiCRISPR plasmid library, 90 µg of pVSVg (Addgene, Watertown, MA, USA) and 126 µg of psPAX2 (Addgene) in OptiMEM (Thermo Fisher Scientific, Waltham, MA, USA) were mixed with 900 µL of Lipofectamine 2000 (Thermo Fisher Scientific). Then, the mixture was added to 2.5 × 10 8 HEK293 cells. Sixty-four hours later, the supernatant was collected and concentrated using a Lenti-X Concentrator (Takara Bio, Kusatsu, Japan) and stored at −80 • C.
The production of lentiviral particles containing a single vector was performed as follows: For the Cas9-expressing vector, the lentiCas9-Blast vector was obtained from Addgene. To produce lentivirus, 5 µg of lentiGuide-Puro or lentiCas9-Blast, 2.5 µg of pVSVg and 3.5 µg of psPAX2 in OptiMEM were mixed with 25 µL of Lipofectamine 2000. Then, the mixture was added to 6.5 × 10 6 HEK293 cells. Forty-eight hours later, the supernatant was collected and concentrated using a Lenti-X Concentrator and stored at −80 • C.

Generation of Cas9-Positive HLF Monoclonal Cells and LATS2-Depleted Cells by CRISPR
HLF cells were plated at a density of 1.5 × 10 5 cells per 6-well plate one day before infection. The next day, the medium was replaced with serum-free medium containing 4 µg/mL polybrene (Merck Millipore, Burlington, MA, USA) and transduced with lentivirus containing lentiCas9-Blast at a multiplicity of infection (MOI) of 10. The following day, cells were supplemented with complete medium containing 4.0 µg/mL blasticidin (Fujifilm Wako pure chemical corporation, Osaka, Japan) for 7 days. Then, several single clones were established by the limiting dilution method, and Cas9 expression in these cells was confirmed by WB analysis and flow cytometry.
To generate LATS2-depleted cells by CRISPR, Cas9-positive HLF cells were plated at a density of 1.5 × 105 cells per 6-well plate one day before infection. The next day, the medium was changed into serum-free medium containing 8 µg/mL of polybrene and transduced with lentivirus containing lentiguide-puro vector cloned with gRNA targeting LATS2 (gRNA-1: ACCAGCAGAAGGTTAACCGG, gRNA-2: ATAAGGTCCGAACTTTGGGG) at an MOI of 2-3. The following day, cells were supplemented with complete medium containing 1.0 µg/mL puromycin (Thermo Fisher Scientific) for 7 days.

In Vivo Pooled CRISPR Kinome Library Screening
Based on library size, all the following steps including infection, puromycin selection, injection and gDNA isolation were performed with enough cells to maintain a representation of more than 200 cells per gRNA in the library. Twenty million HLF cells expressing Cas9 were transduced with the Brunello library [8] at an MOI of 0.2. After 7 days of the selection of transduced cells by puromycin, 9.0 × 10 6 library-transduced cells were injected into the flanks of NOG mice and also stocked for subsequent gDNA isolation. Mice were monitored for tumor growth at least twice a week. Tumor volume was calculated by the following formula: volume (mm 3 ) = length × width × width × 0.52. Once each tumor volume reached 200 mm 3 , tumors were randomly divided into two groups and treated with either vehicle or 20 mg/kg regorafenib for three weeks. Then, all the tumors (three tumors from the vehicle group and three tumors from the regorafenib group) were collected. All xenograft procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at Osaka University Graduate School of Medicine (30-015-023).
Genomic DNA from transduced cells and xenografted tumors was extracted using a DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD, USA) with RNase treatment. gRNA sequences integrated into the gDNA were amplified using illumina-adapted forward and reverse indexed PCR primers according to the protocol from the Broad institute [8] and purified with a PCR purification kit (Qiagen). A total of 8.0 µg of gDNA per sample was used for the PCR. Purified amplicons were multiplexed and sequenced in Illumina HiSeq. We obtained an average of 1.6 × 10 6 assigned gRNA sequences per cells or tumor, indicating that the library complexity was maintained more than 200× throughout the entire experiment. Log2 normalized read counts of each gRNA were plotted to determine the distribution of gRNA abundance inside cells and tumors (Supplementary Figure S1A). We then calculated the ratio of each gRNA abundance in regorafenib-treated tumors compared to that in vehicle-treated tumors (Supplementary Table S1).

siRNA Transfection
The siRNA oligonucleotides were purchased from Thermo Fisher Scientific. The silencer select siRNA IDs are as follows; negative control No.1 siRNA (Catalog number 4390843) and LATS2(s25504). The siRNA was transfected into human liver cancer cell lines using Lipofectamine RNAiMAX reagent (Thermo Fisher Scientific) according to the manufacturer's protocol. Two to three days after transfection, knockdown efficiency was assessed by qRT-PCR.

Cell Viability Assay
Cell viability was assessed using WST-1 reagent (Takara Bio) according to the manufacturer's protocol. Briefly, 3 × 10 3 to 8 × 10 3 cells were seeded into 96-well plates with or without siRNA transfection and cultured with medium containing 2-10% fetal calf serum with or without the compounds of interest for 1-5 days. WST-1 reagent was added to each well and incubated for 1.5 h at 37 • C. The absorbance at 450 nm and 650 nm (reference) was measured using a micro plate reader SH-9000Lab (CORONA).

Real-Time PCR
Total RNA was extracted from cells or tumor tissues using a RNeasy Plus Mini Kit (Qiagen) according to the manufacturer's instructions. RNA was reverse transcribed using ReverTra Ace qPCR Master Mix (TOYOBO, Osaka, Japan). qPCR was performed using the QuantStudio 6 Flex RT-PCR system with TaqMan Gene Expression Assay probes (Thermo Fisher Scientific). The TaqMan probe IDs are as follows; ABCB1 (Hs00184500_m1), ACTB (Hs01060665_g1), BCL2L1 (Hs00236329_m1), and LATS2 (Hs01059009_m1).

Western Blot Analysis
The cell pellet or xenograft tissue was lysed in lysis buffer (1% Nonidet P-40, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate, 1 × protease inhibitor cocktail (Nacalai Tesque, Kyoto, Japan), 1 × phosphatase inhibitor cocktail (Nacalai Tesque) and phosphate-buffered saline, pH 7.4). Protein concentrations were measured using a bicinchoninic acid protein assay kit (Pierce). Equal amounts of protein lysates were electrophoretically separated using sodium dodecyl sulfate (SDS) polyacrylamide gels and transferred onto a polyvinylidene fluoride membrane. After incubation in 5% dry milk in Tris-buffered saline (TBS) and 0.1% Tween 20 for 1 h at room temperature (RT), the membranes were incubated with the primary antibodies overnight at 4 • C. The following primary antibodies were used: Rabbit polyclonal antibody to Bcl-xL (Santa Cruz Biotechnology), rabbit polyclonal antibody to phospho-YAP (Cell Signaling Technology), rabbit polyclonal antibody to YAP (Cell Signaling Technology), rabbit polyclonal antibody to TAZ (Cell Signaling Technology), rabbit polyclonal antibody to ABCB1 (Cell Signaling Technology), rabbit polyclonal antibody to Cas9 (Cell Signaling Technology), rabbit polyclonal antibody to LATS2 (Abcam), and rabbit polyclonal antibody to ACTB (Sigma-Aldrich, St. Louis, MO, USA). After incubation with the secondary antibody for 1 hour, immunolabelled proteins were detected using a chemiluminescent substrate (Pierce). Intensity of each band is measured by densitometry and normalized by intensity of corresponding β-actin band using Image-J software (version 1.52). Detailed information can be found at Figure S4 The IC50 values of regorafenib in 12 human liver cancer cell lines were calculated based on the WST-1 cell viability assay. Microarray data of cancer cell lines using Affymetrix U133 + 2 arrays obtained in the Cancer Cell Line Encyclopedia (CCLE) project [36] are publicly available and were downloaded from the CCLE (https://portals.broadinstitute.org/ccle). The Pearson correlation coefficient between the IC50 value and each mRNA level was calculated and is presented as the r value.

Statistics
All data are presented as the mean ± standard deviation (S.D.). Statistical analyses were performed using an unpaired Student's t test or one-way analysis of variance (ANOVA) in GraphPad Prism ® 6 software (version 6.0f). When ANOVA was applied, the differences in the mean values among the groups were examined by post hoc correction. The Pearson correlation coefficient was calculated in GraphPad Prism ® 6, and p < 0.05 was considered statistically significant.

Conclusions
CRISPR loss-of-function screen identified the Hippo signaling pathway as the mediator of regorafenib efficacy in HCC.
Supplementary Materials: The following are available online at http://www.mdpi.com/2072-6694/11/9/1362/s1. Figure S1: The distribution of gRNA abundance and tumor growth curve during CRISPR library screening, Figure S2: Correlation between YAP activation and regorafenib resistance, Figure S3: Genetic suppression of YAP or Bcl-xL restores regorafenib sensitivity in HCC cells, Figure S4: Original images of western blot with molecular weight markers, Table S1: The ratio of each gRNA abundance in regorafenib-treated tumors compared to that in vehicle-treated tumors