The In Situ Structure of Parkinson’s Disease-Linked LRRK2.

RESOURCE AVAILABILITY Lead Contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Elizabeth Villa ( evilla@ucsd.edu ).

Materials Availability

No new unique reagents were generated in this work.

Data and Code Availability

Cryo-ET structures and representative tomograms have been deposited in the Electron Microscopy Data Bank (EMDB) under accession codes EMD-20825, EMD-20826, EMD-20827 and EMD-20828. The corresponding tilt series were deposited in the Electron Microscopy Public Image Archive (EMPIAR) with the accession code EMPIAR-10377 and EMPIAR-10378. The LRRK2 models are deposited in the Protein Data Bank with accession code PDB-6XR4.

EXPERIMENTAL MODEL AND SUBJECT DETAILS Cell culture

HEK-293T cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) containing GlutaMAX-I [Thermo Fisher Scientific, (TFS)], 10% HyClone bovine calf serum (GE Healthcare) and 100 U/mL HyClone penicillin-streptomycin (GE Healthcare) at 37°C and 5% CO 2 .

Plasmids

The plasmid encoding full-length LRRK2(I2020T) fused with YFP at the N-terminus was used as described previously ( Kett et al., 2012 ). The plasmid containing full-length LRRK2 with an N-terminal Halo-tag was cloned using the Gibson Assembly method. The LRRK2 gene was cloned by using the primer sets: 5’-GCGATAACATGGCTAGTGGCAGC-3’ and 5’-GGGGTTATGCTAGTTACTCAACAGATGTTCGTCTC-3’ with pENTR221-LRRK2 (Addgene #39529) as the template. The N-terminal Halo-tag was cloned by using the primer sets: 5’-GAGTAACTAGCATAACCCCTTGGC-3’ and 5’-CACTAGCCATGTTATCGCTCTGAAAGTACAGATC-3’ with the pHTN HaloTag® CMV-neo Vector (Promega) as a template. The two segments were assembled using the NEBuilder® HiFi DNA Assembly Kit following their suggested protocol. The G2358R mutation was made by using the NEB Q5-site directed mutagenesis kit, and the primers were designed based on the online tool NEBBaseChanger. The constructs were sequenced and expressed in HEK-293T cells to confirm the expression of full-length LRRK2(I2020T).

METHOD DETAILS TEM grid preparation

For expression of LRRK2(I2020T), the HEK-293T cells were transfected with a plasmid encoding full-length LRRK2(I2020T) fused with YFP at the N-terminus using a Lipofectamine 3000 transfection kit (TFS) according to the manufacturer’s protocol. After 2 days, 5 µM paclitaxel ( aka Taxol, from Cell Signaling technology) was added. After 16 to 24 hours of Taxol treatment, the cells were detached, and counted with a hemocytometer. Quantifoil 200 mesh holey carbon R4/1 or R2/1 copper grids (Quantifoil Micro Tools) were glow-discharged for 60 s at 0.2 mbar with 20 mA using a PELCO easiGlow glow discharge system (Ted Pella). Just before deposition of cells on TEM grids, 1.5 µl of poly-lysine solution (Sigma) was applied and ~2000–6000 cells were deposited onto a grid by pipetting 3–10 µL of detached cells onto the grid. Blotting and plunging was performed in a humidity-controlled room (~35 % relative humidity) using a custom-made plunger (Max Planck Institute of Biochemistry). Grids were blotted manually using #1 Whatman filter paper on the side of the grid opposite to the Quantifoil carbon foil used as substrate for cell growth, and plunged into a 50/50 mixture of liquid ethane and propane (Airgas) cooled to liquid nitrogen temperature. The grids were clipped onto Autogrids (Thermo Fisher Scientific, hereinafter TFS) and samples were kept at liquid nitrogen temperature throughout the experiments.

Cryo-fluorescence microscopy

For cryo-fluorescence microscopy, grids were observed with a CorrSight microscope (TFS) using EC Plan-Neofluar 5x/0.16NA, EC Plan-Neofluar 20x/0.5NA, and EC Plan-Neofluar 40x/0.9NA air objectives (Carl Zeiss Microscopy), an Oligochrome light-source, which emits in 4 different channels (405/488/561/640 nm) (TFS) and a 1344×1024 px ORCA-Flash 4.0 camera (Hamamatsu). Data acquisition and processing was performed using MAPS 2.1 (TFS). After acquisition of a grid map at 5X magnification, regions of interest were imaged at 20x or 40x magnification, to identify cells with regions containing LRRK2 filamentous structures.

Cryo-focused ion beam milling

Micromachining of frozen hydrated cells was performed in a Scios DualBeam FIB/SEM microscope equipped with a prototype cryo stage (TFS), as detailed previously ( Wagner et al., 2020 ). The sample chamber of the microscope was kept at a pressure below ~1×10 −6 mbar. A low-magnification SEM image encompassing the entire grid using an acceleration voltage of 5 kV, a beam current of 25 pA, and a dwell time of 200 ns was correlated with the grid map from the cryo-light microscope using MAPS 2.1 (TFS) to identify regions of interest on TEM grids. A platinum layer (typically ~2 µm) was deposited on the sample to improve its conductivity and to reduce streaking on the lamella caused by local variations in mass density that result in variations in lamella thickness, known as curtaining ( Mahamid et al., 2016 ; Wagner et al., 2020 ). An integrated gas injection system (GIS) was used to deposit the precursor compound trimethyl(methylcyclopentadienyl)platinum(IV) ( Hayles et al., 2007 ; Schaffer et al., 2017 ). To this end, the grid was placed 11.5 mm away from the GIS with a nominal stage tilt of 7°, and the GIS was opened for 13 seconds. Cells were targeted for FIB-milling if they satisfied the following conditions: (1) They showed a filamentous LRRK2 phenotype; (2) they were centered within a grid square, and (3) they were located on a grid square that was at most five squares from the center of the grid (the central ~1mm 2 area of the grid). The stage was positioned at a nominal tilt of 11–18°, corresponding to a milling angle of 4–11°. FIB milling was performed in three steps with decreasing ion beam currents and a fixed acceleration voltage of 30 kV ( Wagner et al., 2020 ). For a rough milling step, an ion beam current of 0.3 nA was used. The current was reduced to 0.1 nA for the intermediate step and to 30 pA for fine milling. The target lamella thickness was ~100 nm. During the milling process, lamella thickness was estimated utilizing thickness-dependent charging effects observed in the SEM images at different acceleration voltages ( Schaffer et al., 2017 ). At the end of the session, the grids were transferred out of the FIB/SEM chamber under vacuum and stored in liquid nitrogen.

Cryo-electron tomography

Tomographic tilt series were recorded in a Tecnai G2 Polara (TFS) equipped with a field emission gun operated at 300 kV, a GIF Quantum 968 post-column energy filter (Gatan) and a K2 Summit 4k×4k pixel direct electron detector (Gatan). The FIB-milled grids were loaded into the TEM using modified Polara cartridges, which accommodate Autogrids securely ( Rigort et al., 2012 ). The milling slot of each grid was aligned perpendicular to the tilt axis of the microscope ( Wagner et al., 2020 ). Tilt series were acquired at a target defocus of 5 µm and a pixel size of 2.2 or 3.5 Å using the SerialEM software ( Mastronarde, 2005 ) in low-dose mode ( Table S2 ). The dose-symmetric tomography acquisition scheme ( Hagen et al., 2017 ) was modified to account for the pre-tilt of the lamella, and to optimize the dose using a SerialEM algorithm that calculates the exposure according to a target average count per image, and does not take the image if the dose per tilt required would exceed a threshold value. The K2 detector was operated in counting and dose fractionation modes, with 0.075 to 0.1 s frames. The target tilt range was set to 120° (±60° starting at the estimated pre-tilt of the lamellae), with increments of 2 or 3°, and a target total electron dose was of 180 e/Å 2 or 120 e/Å 2 respectively ( Table S2 ).

Tomogram reconstruction and annotation

Alignment of the tilt series and tomographic reconstructions were performed using Etomo, part of the IMOD package ( Kremer et al., 1996 ). Since no fiducial markers were present on the lamellas, tilt-series alignment was performed using patch tracking. Contrast transfer function (CTF) correction was performed in IMOD ( Kremer et al., 1996 ) using defocus values estimated by CTFFIND4 ( Rohou and Grigorieff, 2015 ). Motion correction and dose weighting was performed on each image of the tilt series using MotionCor2 ( Zheng et al., 2017 ); motion-corrected and dose-weighted images were replaced in the aligned tilt series. Tomograms were reconstructed using weighted back-projection. Tomograms were 4x-binned (without CTF correction) and used for segmentation of microtubules using the filament tracing function in Amira (TFS) with the following parameters: cylinder length: 600 Å, angular sampling: 5, mask cylinder radius: 140 Å, outer cylinder radius: 125 Å, inner cylinder radius: 75 Å, and missing wedge according to the individual tilt series. After filament tracing ( Figure S1A ), the resulting center coordinates were re-sampled equidistantly using a script written in MATLAB ( Jasnin et al., 2013 ). For each point along the filaments, initial Euler angles were assigned for subtomogram analysis. Since microtubules are polar, within a filament all angles were assigned such as the direction along the filament axis was the same for all particles. The Euler angle orthogonal to the axis of the filament, i.e., the angle around the microtubule, was randomized to minimize the effect of the missing wedge (see details below).

Subtomogram analysis

To determine the protofilament number of single microtubules, 4x binned tomograms were used for extraction of subtomograms with a box size of 38 nm along the microtubule at 4 nm spacing using Dynamo ( Castano-Diez et al., 2012 ), and an initial average for each microtubule was created using the pre-assigned Euler angles described above. Each filament was separately aligned and averaged using Dynamo, using the initial average as a reference. Three iterations of translational and orientational alignment of the first two Euler angles, followed by five iterations of translational alignment and orientational alignment of the third Euler angle around the axis of the microtubule, with the latter restricted to 18–36 degrees, were performed. Particle shifts were limited to 10–20 nm. As this would allow for the particles to shift by 4 nm and result in duplicate particles, we tested barring particle shifts along the helical axis of the filament, and obtained the same overall geometries. However, we used the shifts as described, as they resulted in better resolved features for the analysis. Since this analysis was performed exclusively to obtain the polarity and geometry of the microtubule and LRRK2 helices as described below and we did not actively exclude potential particle duplication that could cause resolution overestimation, we do not report the resolution of the resulting structures. Particles were low-pass filtered to 22–30 Å for alignment. 2D projections along the microtubule axis were calculated in MATLAB, and the 2D projections as well as the 3D averages were visually inspected in MATLAB and IMOD to determine protofilament number and plus/minus end polarity for each microtubule. Assignment of microtubule polarity can be achieved by inspecting a cross section of the averages ( Figure S1B ) from the plus end (‘anti-clockwise slew’ Figure S1B , left column) or from the minus end (‘clockwise slew’ Figure S1B , right column) respectively ( Bouchet-Marquis et al., 2007 ). Microtubules with unclear or ambiguous protofilament number or polarity were discarded. Then, new particles with a box length of ~70 nm were extracted from 4x binned tomograms, and for each microtubule class (11-, 12- or 13-protofilament microtubules) all particles representing the respective class were submitted to alignment and averaging in Dynamo, using an initial average as a reference. In order to separate decorated microtubules from non-decorated microtubules, multi-reference alignment in Dynamo was conducted using two templates: (1) a LRRK2-decorated microtubule template (the average from the previous step) and (2) a non-decorated microtubule template (the average from the previous step with the density of LRRK2 masked out) using a hollow cylindrical classification mask corresponding to the LRRK2 density ( Figure S1D ). Alignment parameters were used as described above. In order to further improve the structure of LRRK2 bound to microtubules, particles sorted to class 1 (decorated microtubules) were further aligned by using a hollow cylindrical alignment mask that included LRRK2 but excluded the microtubule. For obtaining microtubule averages ( Figure 3B ), all particles (decorated and undecorated) with the same microtubule protofilament number and polarity were aligned by using a cylindrical alignment mask containing only the microtubule region and excluding the LRRK2 helix. The resulting averages ( Figure 3A ) were used to estimate the helical parameters of the LRRK2 double-stranded helix and microtubules as detailed below ( Table S1 ). These averages showed that while the LRRK2 and microtubule helices have different polarities, the number of subunits per turn (11, 12, or 13) is the same for both helices ( Figure 3A ). Because imposing helical symmetry made the LRRK2 helix averages worse (data not shown), we extracted new, smaller subtomograms that would encompass the repeating unit of the LRRK2 helix and the adjacent microtubule region. Since the largest sample corresponded to the 12-protofilament (and 12-LRRK2 repeating units per turn) class, we considered only these helices for further analysis. Coordinates and initial Euler angles of particles along the LRRK2 helical path ( Table S1 ) were used to extract particles from unbinned, dose-weighted and non-CTF-corrected tomograms, and used to create an initial average using orientations ( Figure S1F ). 3D refinement of extracted particles was performed in RELION ( Bharat and Scheres, 2016 ; Bharat et al., 2015 ) to create a template for subsequent classification ( Figure S1G ). The resulting density map was used as a reference for 3D classification into three classes, employing a soft mask covering the LRRK2 protomer and a regularization parameter of 2–4 ( Figure S1H ). Class averages were inspected in UCSF Chimera ( Pettersen et al., 2004 ), and classes with high levels of noise were excluded. A density corresponding to the ring-shaped WD40 domain was clearly resolved in one class (class 1 in Figure S1H ). Particles belonging to this class were subjected to gold-standard refinement using two different masks (masks A and B; Figure S1I , J ) and post-processing without B-factor sharpening in RELION ( Figure S1K , M ).

Gold-standard Fourier Shell Correlation

(FSC) was calculated ( Figure S1L , N ) for resolutions of 14.1 Å and 17.6 Å. Local-resolution maps ( Figure 3D and S1O , P ) were calculated using ResMap ( Kucukelbir et al., 2014 ). Maps and structures were visualized using UCSF Chimera ( Pettersen et al., 2004 ) and VMD ( Humphrey et al., 1996 ). Measuring distances/angles between microtubules To calculate the angles and distances between filaments (microtubules), the distances between points sampled every 4 nm along each filament, and all points on all other filaments within the tomogram, were measured. The resulting distance matrix was then reduced to point pairs with distances of 100 nm or less. For each point along a filament, we calculated (1) a tangent vector along the filament at that point, considering the nearest points along the same filament, (2) the distance to the nearest point in each neighboring filament, and (3) the angle between the vectors at these two points, where an angle of zero corresponds to parallel filaments. A 2-D histogram of the distance between filaments vs angle between filaments was calculated in MATLAB.

Determining helical parameters

Helical parameters of non-symmetrized microtubules and LRRK2 helices were obtained using an autocorrelation function as implemented in Dynamo. To obtain the helical parameters for the microtubule and LRRK2 helices, averages were obtained by masking out the volume corresponding to the other helix, i.e ., microtubule helical parameters were obtained using a map that excluded the LRRK2, and vice versa. Integrative modeling of LRRK2 bound to microtubules LRRK2 is a 286kDa protein formed of seven domains: armadillo (ARM), Ankyrin (ANK) and Leucin-rich repeat (LRR) domains, a Ras of complex proteins (ROC), and C-terminal of ROC domain (COR), kinase (KIN) and a WD40 domains ( Figure 4A ). To determine the architecture of LRRK2(I2020T) bound to microtubules, we used an integrative approach implemented in the open source Integrative Modeling Platform (IMP) package ( Russel et al., 2012 ). The integrative modeling process consists of four stages: (1) gathering data, (2) choosing how to represent the system and translating the information into spatial restraints, (3) determining an ensemble of structures that satisfy these restraints and, (4) validating the model. This approach has been applied to determine the structure of numerous biological complexes including the 26S proteasome ( Forster et al., 2010 ), the mediator complex ( Robinson et al., 2015 ) as well as the nuclear pore complex ( Kim et al., 2018 ). However, this integrative modeling approach has not, to our knowledge, been applied to solve a previously undetermined structure using in situ cryo-ET. Given the resolution of the cryo-ET map, the modeling was limited to the location and orientation of the domains within the density, treated as rigid bodies. The clear identification of the characteristic donut shape of the WD40 in the cryo-ET density map provided the starting point for the LRRK2 domains allocation and allowed us to assess that the assignment of the LRRK2 is possible up to the last four domains (WD40, KIN, COR, ROC) while the density related to the LRR, ANK and ARM is visible only at higher threshold, presumably due to their intrinsic flexibility. Stage 1: Gathering Information. Three different types of data were used for structure determination: Cryo-ET map of LRRK2(I2020T) protomer: a 14 Å in situ cryo-ET map was determined as detailed above (EMDB-20825). The protomer corresponds to a dimer LRRK2 bound to the microtubules ( Fig 3C ). Atomic models of the domains composing LRRK2: X-ray crystallography structure of the human LRRK2 WD40 dimer ( Zhang et al., 2019 ) (PDB: 6DLP) Homology model of the kinase domain of human LRRK2 (Uniprot ID Q5S007 , residue 1883–2135). We generated models for both the active and inactive conformations. The models were generated by homology modeling with SWISS-MODEL ( Benkert et al., 2011 ; Bienert et al., 2017 ; Guex et al., 2009 ; Waterhouse et al., 2018 ). The crystal structure of the Roco4 Kinase domain bound to AppCp from D. discoideum was used as a template for the kinase-active (open) model (PDB: 4F0F, homology 43%), and the the crystal structure Roco4 kinase domain from D. discoideum (PDB: 4F0G, homology 43%) was used as a template for the kinase-inactive (closed) model ( Gilsbach et al., 2012 ). Alignment was generated by ClustalOmega ( Sievers et al., 2011 ) ( Figure S4 ). Our initial integrative modeling used an active conformation of the kinase based on the fact that the LRRK2-specific Type-1 inhibitors MLi-2 and LRRK2-IN-1, which bind to the active form, increase filament formation ( Blanca Ramirez et al., 2017 ; Schmidt et al., 2019 ). A model of the monomeric ROCCOR domain of human LRRK2 protein (Uniprot ID Q5S007 , residue 1332–1838 was obtained by homology modeling with SWISS-MODEL ( Benkert et al., 2011 ; Bienert et al., 2017 ; Guex et al., 2009 ; Waterhouse et al., 2018 ). The structure of C. tepidum Roco protein ( Gotthardt et al., 2008 ) (PDB: 3DPU) was used as template and aligned to the human LRRK2 sequence of domains ROC and COR by using ClustalOmega ( Sievers et al., 2011 ) ( Figure S4 ).

Normal model analysis

(NMA) with Bio3D package in R ( Grant et al., 2006 ) was performed on the ROCCOR template revealing a hinge in the COR domain (L1669-I1689) which corresponds to a missing loop area in the template PDB structure. Therefore, we split the ROCCOR domain into three separate rigid bodies namely, COR C , COR N , and ROC ( Gotthardt et al., 2008 ). (PDB: 3DPU, homology 37%, 37% and 38% respectively; Figure S4 and S5 ). We did not directly use human ROC domain structure ( Deng et al., 2008 ) (PDB: 2ZEJ) due to the potential domain swapping described earlier ( Gotthardt et al., 2008 ). However, we replaced the coordinates of the region not involved in this interface (residues 1425–1441 and 1474–1457) in the final models of the ensemble. Since all the domains are part of a single polypeptide chain of LRRK2, the amino acid stretches between the domains were considered as linkers connecting the N- and C-termini of consecutive domains. A worm-like chain model ( Flory, (1953) ) was used to model the connecting linkers, with an average end-to-end distance of N 2 * l , where N is the number of amino acids and l =3.1Å is the length of one amino acid measured as the typical average distance between alpha carbons of adjacent amino acids ( Figure S5 ). Stage 2: System Representation and Translation of Data into Spatial Restraints. Each domain was represented as a rigid body at atomic resolution. Five types of spatial restraints were used during monomer configuration and dimer refinement in Stage 3 . Cryo-ET density restraint : (a) an IMP scoring term that considers the percentage of atoms that are included in the map, used both in monomer configuration and refinement. (b) Cross correlation of model to map implemented in MDFF ( Trabuco et al., 2008 ), used to select models in monomer configuration. Location of WD40: based on the characteristic donut shapes in the density and a rigid-body fit of the crystal structure of the human LRRK2 WD40 dimer to the density, the WD40 could be unequivocally located in the cryo-ET density map (see below) and thus its location was constrained to that region in the map ( Figure S5 ), used in monomer configuration. Chain connectivity restraints : distance restraints between the C-term and N-term of consecutive domains as described above ( Figure S5 ), used both in monomer configuration and dimer refinement. Excluded volume restraints : Overlap between domains is assessed as an excluded volume restraint (steric clashes of alpha carbons of less than 10% and 5%), used both in monomer configuration and dimer refinement respectively. Dimer symmetry restraints : (a) Overlap between monomers was assessed using excluded volume (steric clashes of less that 10% of alpha carbon atoms between LRRK2 monomers), and used in the final stage of monomer configuration. (b) Symmetry restraints implemented in IMP were used during dimer refinement. Stage 3: Ensemble Sampling: To create an ensemble of LRRK dimers that incorporates the data available and satisfies the restraints, we applied a sampling protocol consisting of two steps: monomer configuration and dimer refinement. A. Monomer configuration. To determine the monomer configuration we used the MultiFit module of IMP ( Lasker et al., 2009 ). First, rigid-body fitting of each of the individual five domains to a dimer protomer map were sampled at 5° resolution using colores ( Chacon and Wriggers, 2002 ). The best 10,000 fits for WD40, ROC, COR N and COR C and 5,000 fits for KIN were considered. Additionally, the crystal structure of the human LRRK2 WD40 dimer (PDB: 6DLP) ( Zhang et al., 2019 ) was rigid body fitted using the same parameters. Rigid-body fitting the WD40 dimer (PDB: 6DLP) into the cryo-ET density using the same parameters as above resulted in an unequivocal best fit that matched the WD40 monomers into the donut shaped regions of the map, revealing the same orientation between the monomers as in the crystal structure ( Figure 3E ). Thus, the WD40 fits for the monomer were filtered to be placed within the donut-shaped density to satisfy constraint ii. From these fits, we selected a single position and orientation that matched that of the dimer in the dimer crystal structure ( Zhang et al., 2019 ). Second, we looked at all 5000 fits of the active kinase and selected those that satisfied the distance linker of 9 Å (restraint iii). Only six fits satisfied this restraint, and they had an RMSD of 4.7Å between them. Considering the resolution of our map, they were equivalent, and thus we used a single kinase fit for the subsequent steps to reduce computational time. Next, the fits for ROC, COR N and COR C were filtered to include only those fitted to the same monomer within the protomer map, and not overlapping with the placed WD40 and kinase, yielding 1188, 1446 and 1148 models respectively. MultiFit was then used to sample all possible combinations of the ROC, COR N and COR C domain fits and scored for spatial restraints i(a), iii and iv. A total of three million models were initially scored. From that, we filtered the solutions based on respecting the N-C distance (restraint iii) and having not major compenetration between domains (restraint iv; corresponding to an EV score of