Assembly and lipid-gating of LRRC8A:D volume-regulated anion channels

Domain: physics
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Volume-regulated anion channels (VRACs) are ubiquitously expressed vertebrate ion channels that open in response to hypotonic swelling. VRACs assemble as heteromers of LRRC8A and LRRC8B-E subunits, with different subunit combinations resulting in channels with different properties. Recent studies have described the structures of LRRC8A:C VRACs, but how other VRACs assemble, and which structural features are conserved or variant across channel assemblies remains unknown. Herein, we used cryo-EM to determine structures of a LRRC8A:D VRAC with a 4:2 subunit stoichiometry, which we captured in two conformations. The presence of LRRC8D subunits widens and increases hydrophobicity of the selectivity filter, which may contribute to the unique substrate selectivity of LRRC8D-containing VRACs. The structures reveal lipids bound inside the channel pore, similar to those observed in LRRC8A:C VRACs. We observe that LRRC8D subunit incorporation disrupts packing of the cytoplasmic LRR domains, increasing channel dynamics and opening lateral intersubunit gaps, which we speculate are necessary for pore lipid evacuation and channel activation. Molecular dynamics simulations show that lipids can reside stably within the pore to close the channel. Using electrophysiological experiments, we confirmed that pore lipids block conduction in the closed state, demonstrating that lipid-gating is a general property of VRACs.

Introduction
Volume-regulated anion channels (VRACs) are a family of ubiquitously expressed osmosensitive vertebrate ion channels that mediate cellular volume regulation and paracrine signaling1,2. VRACs are hexameric large pore channels that assemble as heteromers of LRRC8A and LRRC8B-E subunits3–5. With up to 1934 unique channel assemblies possible, VRAC assembly diversity is thought to account for the observed variability in channel properties, including single channel conductance, open probability, rectification, voltage-dependent inactivation, and substrate selectivity3,5–11. How differences in channel composition are structurally manifested to modulate channel properties remains largely unknown.

Recently, studies from our group12 and Rutz et al.13 described the first high-resolution structures of heteromeric LRRC8A:C VRACs. Both studies identified a similar hexameric channel architecture, but with different LRRC8A:C stoichiometries of 5:1 or 4:2, respectively. We additionally observed lipids bound inside the pore, which occlude the channel in the closed state. How other heteromeric LRRC8 channels assemble and whether they are similarly lipid-gated is unknown. Compared to other characterized VRAC assemblies (i.e., LRRC8A:C/E), LRRC8A:D VRACs have been reported to maintain smaller Cl– conductances, greater open probabilities, and increased permeation of larger and non-anionic substrates, including taurine, γ-aminobutyric acid (GABA), myo-inositol, cisplatin, and blasticidin, although the magnitude of these differences varies between reports3,5–8. By determining cryo-EM structures of a LRRC8A:D VRAC, we aimed to identify which features are shared with LRRC8A:C VRACs, while also establishing a structural basis for their unique channel properties.

Results
To study LRRC8A:D VRACs by cryo-EM, we leveraged our previously described approach for characterizing fiducially-tagged LRRC8A:C VRACs (Supplementary Fig. 1)12. Mouse LRRC8A and LRRC8D subunits were over-expressed in insect cells (which lack endogenous LRRC8s) using a single engineered baculovirus. Each subunit was C-terminally tagged with a fluorescent protein infused through a protease-cleavable linker, with LRRC8A and LRRC8D subunits bearing different fluorescent proteins and protease cleavage tags to enable subunit-specific pulldown. To eliminate channel pseudosymmetry for cryo-EM studies, a BRIL domain14,15 was inserted into the extracellular loop of LRRC8A. Fiducial mass was further increased by adding an α-BRIL antibody fragment (Fab)16 and an α-Fab nanobody (Nb)17 to the purified sample. Detergent solubilization, followed by two sequential rounds of affinity chromatography, allowed us to specifically isolate LRRC8A:D VRACs for cryo-EM single particle analysis.

We initially resolved a consensus map at an overall resolution of 3.1 Å (Supplementary Fig. 2), revealing a hexameric LRRC8 channel. However, poor map quality in three of the six subunits precluded confident assignment of each subunit’s identity. Using 3D variability analysis in cryoSPARC18, we resolved two conformations with improved local map qualities to overall resolutions of 3.3–3.4 Å. These reconstructions allowed us to assign identities to all subunits and to construct atomic models incorporating the transmembrane domains, extracellular regions, and linker regions of all subunits (Fig. 1a, b and Supplementary Fig. 2 and 3). No N-termini (residues 1–14) were resolved inside the channel pore, presumably due to conformational heterogeneity. On the basis of fiducial and subunit-specific side chain densities (Supplementary Fig. 4), we inferred that both conformations correspond to the same channel assembly with a 4:2 LRRC8A:D stoichiometry and adjacent LRRC8D subunits. Our interpretation of these data is that the 4:2 LRRC8A:D assembly is the most prevalent in our purified sample. It is possible that additional assemblies are present, but are too structurally heterogeneous or rare to be resolved. Unless otherwise noted, we focus our analyses on the better resolved conformation 1.

Comparing LRRC8A:D to previously described structures of LRRC8A:C12, LRRC8A homomers19, and LRRC8D homomers20 shows that LRRC8A:D follows a similar overall architecture, with a nearly invariant extracellular region (pairwise backbone r.m.s.d. < 1 Å). Within this rigid extracellular scaffold, differences in selectivity filter residues may help explain differences in substrate selectivity between channel assemblies. The selectivity filter is formed by a single residue from each subunit pointing towards the conduction axis: R103 for LRRC8A, L105 for LRRC8C, and F144 for LRRC8D. Introduction of F144 residues from LRRC8D subunits increases hydrophobicity along one face of the filter compared to LRRC8A homomeric channels (Fig. 1c). The F144 side chain also permits more dilated filters since it lays flatter against the outer vestibule wall compared to L105 in LRRC8C or R103 in LRRC8A (Fig. 2). The structures of LRRC8A:D presented here are similarly constricted at the selectivity filter as LRRC8A:C (to ~5.2 Å in diameter for LRRC8A:D and LRRC8A:C), but this is due to an extended rotamer adopted by one LRRC8A R103 residue. We predicted that assemblies with higher LRRC8D to LRRC8A subunit ratios could house expanded selectivity filters. Indeed, a model of a 1:5 LRRC8A:D selectivity filter shows a predicted minimum pore diameter of ~7.6 Å, only slightly smaller than that observed in a structure of a non-physiological LRRC8D homomer (~8.2 Å). The expanded size and increased hydrophobicity of LRRC8D-containing channel filters are expected to promote, at least in part, their greater permeability to non-anionic or larger substrates, which is consistent with previous electrophysiology experiments that have established the importance of this site for channel selectivity19–22. Other regions along the conduction path may also help determine substrate selectivity, including the N-termini. While N-termini were not resolved in our LRRC8A:D structures, structures of LRRC8A21 and LRRC8D homomers20 have captured N-termini in pore-lining conformations, and prior electrophysiology experiments have evidenced that N-terminal residues influence substrate selectivity21–23. In particular, N-terminal residues 5 (T5 in LRRC8A-C, A5 in LRRC8D-E) and 8 (R8 in LRRC8A/C, K8 in LRRC8B/E, A8 in LRRC8D) may contribute to establishing the unique substrate selectivity of LRRC8D-containing VRACs.

The major structural differences between LRRC8A:D conformations and other VRAC structures arise from the cytoplasmic leucine-rich repeat domains (LRRs). Although poor local resolutions precluded their atomic modeling, the positions of four LRRs were sufficiently resolved to permit docking of LRR models for visualization purposes (Fig. 1d, e). Shared across both conformations, one pair of LRRC8A LRRs adopts an ordered “tight” interaction identical to those observed in structures of LRRC8A homomers19,22,24 and LRRC8A:C heteromers12,13. In conformation 1, two LRRC8D subunits form a second tight LRR pair, similar to those observed in a homomeric LRRC8D structure20, while the two remaining LRRC8A LRRs are disordered (Fig. 1d). The opposite is true in conformation 2, where the LRRC8D LRRs are disordered, but the two remaining LRRC8A subunits order to form a second tight LRR pair (Fig. 1e). These LRR dynamics can be conceptualized as a bistable equilibrium, with stabilization of a LRRC8A LRR pair in competition with, and alternating, with stabilization of the LRRC8D LRR pair (Fig. 1f). As in LRRC8A:C12,13, incorporation of non-LRRC8A (i.e. LRRC8D) subunits prevents the LRRs from adopting the stable pseudo-symmetric trimer of dimers arrangement seen in structures of LRRC8A homomers19,22,24. Here, only four LRRs can adopt a stable position at a time.

Destabilization of the LRR arrangement by LRRC8D subunits propagates up the channel to alter the positions of linker regions and transmembrane domains. These changes are stark in conformation 1, where ordering of the LRRC8D LRR pair causes one LRRC8D subunit to peel away from the conduction axis (Fig. 1d). This creates a large lateral intersubunit gap of ~6 Å or wider at the inner leaflet of the membrane; wide enough to permit passage of lipids into and out of the channel pore. Notably, our recent structures of a LRRC8A:C VRAC similarly show that incorporation of a LRRC8C subunit increases LRR dynamics and opens a lateral intersubunit gap capable of passing lipids, suggesting that these properties may be necessary for channel function12. One possibility is that lateral gaps stabilized by heterotypic LRR interactions may allow for the removal of pore-occluding lipids (discussed below), which would be impeded in a rigid channel closed off from the membrane. This may help explain why incorporation of non-LRRC8A subunits is necessary for full channel activation3,5 as LRRC8A homomeric channels show reduced LRR dynamics, maintain small intersubunit gaps, and generate only small currents in cells.

We observe density inside the LRRC8A:D channel pore consistent with lipids that occlude the conduction axis, reminiscent of those found in LRRC8A:C and chimeric LRRC8A-C structures (Fig. 3 and Supplementary Fig. 4d)12,25. We modeled this density as three 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) molecules, which bind in a ring-like conformation. The three lipids bind in an upright conformation and are pinched together by the C-terminal ends of each subunit’s transmembrane helix 1 (TM1). The phospholipid head groups are exposed to an extracellular-facing vestibule between the C-terminus of TM1 and the selectivity filter, while the hydrocarbon tails point intracellularly into the channel pore, often packing in hydrophobic grooves formed between subunits. The density for pore lipids is varied and weaker than adjacent regions for the protein, indicating that pore lipids are likely flexible and conformationally variable within the pore. Our structures suggest that most phospholipids could be accommodated, perhaps with the exception of those with larger headgroups, such as phosphatidylinositols and glycolipids. The presence of pore lipids, as modeled, constricts the pore to a radius of 1.5–1.6 Å and is expected to result in channel closure (Fig. 3c).

To investigate the stability of lipids inside the channel pore, we carried out all-atom molecular dynamics simulations initiated from the LRRC8A:D conformation 1 structure (Fig. 4 and Supplementary Figs. 5 and 6). This fully atomistic representation is required to accurately model non-covalent interactions between the bound lipids, the protein, and water in the pore. We elected not to include the LRRs in our simulations, as they were not fully resolved in the structure (Supplementary Fig. 5a). We retained the three modeled pore lipids as DOPE and embedded the protein in a 9:1 ratio of 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) and cholesterol (see Methods). We initiated six independent simulations, resulting in 6 µs of unbiased simulations. In one simulation, the three pore lipids remained close to their initial positions over the course of the 1-µs simulation, constricting the pore to a radius smaller than that of a bare Cl– ion (<1.8 Å), consistent with channel closure (Fig. 4, Supplementary Fig. 5c and Supplementary Movie 1). In the five remaining simulations, lipids dissociated from their starting positions in tens to hundreds of nanoseconds, accompanied by an increase in pore radius and pore water occupancy (Fig. 4, Supplementary Fig. 5c, d and Supplementary Movie 2). Pore radius measurements indicate that retaining at least two lipids close to their initial positions is sufficient for channel closure. To test if the initial configuration of bulk lipids may have impacted stability of the protein or the pore lipids, we ran six additional independent replicas with the protein embedded in a pure POPC bilayer for 1 µs each. Pore lipid stability was similar to that seen in the mixed bilayer, and we also observed one simulation where pore lipids did not dissociate from their initial binding positions (Supplementary Fig. 5b, c).

In all simulations, we observed that the pore lipid tails either packed against the pore-lining helices or hydrophobic intersubunit gaps, while the pore lipid head groups remained near the center of the pore where they were well hydrated (Fig. 4b and Supplementary Movies 1 and 2). When pore lipids dissociated from their starting positions, the lipid tails retained these favorable hydrophobic interactions, while lipid head groups retreated intracellularly within the pore, sometimes even flipping their orientation (Supplementary Movie 2). Bulk lipid tails could also access the intersubunit gaps, suggesting a potential pathway for lipids to evacuate from the channel pore, although we did not observe lipids exchanging in and out the pore on the timescales of our simulations.

We asked whether conformational changes in the protein relative to its starting structure could be leading to lipid dissociation events in the simulations. To test this, we initiated a third set of simulations with gentle harmonic restraints applied to the protein backbone in order to restrict deviation from the initial cryo-EM structure (Fig. 4, Supplementary Fig. 5c, d and Supplementary Movie 3). All three pore lipids remained close to their starting positions within the pore over the course of these six 1-µs simulations, in good agreement with the starting cryo-EM-derived structure.

We found that in simulations without backbone restraints, LRRC8D subunits consistently rotated such that the large intersubunit gap between LRRC8A and LRRC8D subunits compacted, while the interfaces between LRRC8A subunits remained similar to the starting configuration (Supplementary Fig. 6a, b). These conformational changes did not obviously alter the radius of the channel pore (Supplementary Fig. 5c). Thus, lipids may disengage from their initial positions in the majority of our unrestrained simulations, not because of large-scale protein movements, but because of stochastic thermal motions in the protein altering the interactions between lipids, water, and protein within the pore. This hypothesis is consistent with the observation that mutations in the LRRs and linker regions, which increase channel dynamics, including recently described patient mutations, can result in constitutively active channels11,12,26.

To experimentally test whether the observed pore lipids contribute to channel gating in LRRC8A:D VRACs, we used whole-cell patch clamp electrophysiology to characterize hypotonicity-induced currents from wild-type and mutant LRRC8A:D channels expressed in LRRC8A-E–/– HeLa cells (Fig. 5 and Supplementary Fig. 7). We compared wild-type channels to those with a T48D mutation in LRRC8A and/or LRRC8D subunits, which is predicted to electrostatically impede lipid occupancy in the channel pore (Fig. 5a). We previously characterized a T48D mutation in LRRC8A:C VRACs and found that it results in constitutive channel activity12. Cryo-EM structures of a LRRC8A(T48D):C channel showed that the mutation resulted in a loss of pore lipids without altering the channel’s overall structure, stoichiometry, or conformational states. This is consistent with the T48D mutation increasing channel activity directly by disrupting pore lipid occupancy. In cells transfected with wild-type LRRC8A and LRRC8D subunits, we observed robust channel activation (mean fold activation ± s.e.m. = 20.9 ± 3.9) upon hypotonicity-induced cell swelling, consistent with wild-type LRRC8A:D currents (Fig. 5b, c and Supplementary Fig. 7). Introduction of T48D into LRRC8D alone was not sufficient to induce significant constitutive channel activity (28.8 ± 4.6, n.s.). However, introduction of T48D into LRRC8A resulted in constitutively active channels with reduced fold activation upon swelling (2.8 ± 0.4, P = 0.0050 compared to WT). Incorporation of the T48D mutation in both LRRC8A and LRRC8D subunits further reduced fold activation (1.6 ± 0.2, P = 0.0032 compared to WT, P = 0.0326 compared to LRRC8A(T48D):D). The increased penetrance of the T48D mutation in LRRC8A compared to LRRC8D is consistent with LRRC8A:D VRACs containing more LRRC8A than LRRC8D subunits under these expression conditions. Altogether, from these data, we conclude that LRRC8A:D VRACs are constricted in the closed state by pore lipids.

In summary, we resolved two conformations of a LRRC8A:D VRAC, revealing a hexameric assembly with high structural variability in the cytoplasmic LRR domains and linker regions. These structures show how incorporation of LRRC8D subunits increases hydrophobicity and expands the selectivity filter, which, in conjunction with N-terminal sequence differences, may contribute to an increased selectivity of LRRC8D-containing VRACs for larger and non-anionic substrates. However, mutagenesis experiments, especially using uncharged substrates, are required to definitively test the contribution of these residues to any selectivity differences. On the other hand, the structural determinants of other channel properties, including single-channel conductance and open probability, could not be inferred from these structures. It is also unclear whether the two observed conformations represent different states along a gating trajectory, as we do not yet understand how different LRR arrangements can affect channel properties. Since both conformations are occluded by lipids, we interpret both structures to represent closed states. Whether and how these conformations interconvert during channel gating remains to be determined.

Lastly, we observed lipids bound within the channel pore and validated their role in channel closure using molecular dynamics simulations and electrophysiology recordings. What forces drive the removal of pore lipids and what paths lipids take to exit the channel during opening remain unclear. Resolving this will ultimately require an understanding of what signals or forces are sensed by the channel for activation and/or capturing an open state of the channel for structural studies, but our simulations identify one potential mechanism and route for lipid movement through the channel pore.

Methods
Construct design and protein expression
The protein expression construct for LRRC8A was described previously12. Briefly, the Mus musculus LRRC8A sequence (UniProt: Q80WG5) was codon optimized for Spodoptera frugiperda and cloned into a custom vector based on the pACEBAC1 backbone (MultiBac; Geneva Biotech, Geneva, Switzerland) with an added C-terminal PreScission protease cleavage site, linker sequence, superfolder GFP (sfGFP), and 7xHis tag. The sequence for apocytochrome b562RIL (BRIL)14,28 was then codon optimized for Spodoptera frugiperda and inserted into the LRRC8A extracellular loop, between residues 76 and 91, generating a construct for expression of mmLRRC8A(76-BRIL-91)-SNS-LEVLFQGP-SRGGSGAAAGSGSGS-sfGFP-GSS-7xHis (LRRC8A BRIL). The coding sequence for Mus musculus LRRC8D (UniProt: Q8BGR2) was codon optimized for Spodoptera frugiperda, synthesized (Gen9, Cambridge, MA), and cloned into a custom vector based on the pACEBAC1 backbone (MultiBac) with an added C-terminal TEV protease cleavage site, linker sequence, and mCherry tag, generating a construct for expression of mmLRRC8D-SNS-ENLYFQG-SRGSGSGS-mCherry. The LRRC8A and LRRC8D cassettes, each with a polyhedrin promoter and SV40 terminator, were iteratively cloned using the I-CeuI and BstXI sites into the pACEBAC1 backbone to generate the dual LRRC8A BRIL:D expression plasmid. MultiBac cells were then used to generate a LRRC8A BRIL:D bacmid according to the manufacturer’s instructions.

For electrophysiology, untagged versions of Mus musculus LRRC8A and LRRC8D were each cloned into mammalian expression vectors containing a cytomegalovirus (CMV) promoter and IRES eGFP or mCherry expression reporters, respectively. The LRRC8A(T48D) mutant construct was described previously12. The T48D mutation was introduced into LRRC8D by PCR using the following primers: GATGCAACTGGATAAGGACCAGG (forward) and GTACCAGCGAATATAGC (reverse).

Channel expression and purification
For protein production, Sf9 cells (Expression Systems, Davis, CA) were cultured in ESF 921 medium (Expression Systems) supplemented with 100 units/mL penicillin, 100 μg/mL streptomycin, and 0.25 μg/mL Amphotericin B (Gibco) and grown at 27°C, shaking at 130 rpm. Baculovirus generation and amplification were conducted as described previously12,29. Briefly, P1 baculovirus was generated from cells transfected with LRRC8A BRIL:D bacmid using Escort IV reagent (Millipore Sigma, Burlington, MA) according to the manufacturer’s instructions. P2 virus was generated by infecting cells at 2 million cells/mL with P1 virus at a multiplicity of infection (MOI) of ~0.1. Infection was monitored by fluorescence, and cells were harvested at 72 h. P3 virus was generated in a similar manner to expand the viral stock. P3 virus was used to infect Sf9 cells at 4 million cells/mL at a MOI of ~2–5. At 72 h, infected cells expressing LRRC8A BRIL:D were harvested by centrifugation at 2500 x g for 10 min and frozen at −80°C. Cell pellets for purification came from two batches of 1 L cultures (total: 2 L culture, ~28 mL cell pellet).

For protein purification (summarized in Supplementary Fig. 1), cell pellets were thawed and resuspended in 100 mL of lysis buffer (50 mM HEPES, 150 mM KCl, 1 mM EDTA, pH 7.4). Protease inhibitors (final concentrations: E64 (1 μM), pepstatin A (1 μg/mL), soy trypsin inhibitor (10 μg/mL), benzamidine (1 mM), aprotinin (1 μg/mL), leupeptin (1 μg/mL), AEBSF (1 mM), and PMSF (1 mM)) were added to the lysis buffer immediately before use. Benzonase (4 μl) was added after cell thaw. Cells were lysed by sonication and centrifuged at 150,000 x g for 1 h at 4°C. The supernatant was discarded, and residual nucleic acid was removed from the top of the membrane pellet using D-PBS. Membrane pellets were scooped into a Dounce homogenizer and homogenized in 150 mL of extraction buffer (50 mM HEPES, 150 mM KCl, 1 mM EDTA, 1% (m/v) glyco-diosgenin (GDN: Anatrace, Maumee, OH), all protease inhibitors present in lysis buffer except PMSF, pH 7.4). The resulting membrane homogenate was gently stirred for 2 h at 4°C to extract membrane proteins, followed by centrifugation at 30,000 x g for 45 min at 4°C. The supernatant, containing solubilized membrane proteins, was bound to 5 mL of pre-washed sepharose resin coupled to α-mCherry nanobody with gentle stirring for 1 h at 4°C. The resin was then collected in a column and washed with 10 mL of buffer 1 (20 mM HEPES, 150 mM KCl, 1 mM EDTA, 0.02% GDN, pH 7.4), 40 mL of buffer 2 (20 mM HEPES, 500 mM KCl, 1 mM EDTA, 0.02% GDN, pH 7.4), and 10 mL of buffer 1. The resin was then resuspended with ~6 mL of buffer 1 containing 1.5 mM dithiothreitol (DTT) and 1 mg of TEV protease and nutated gently in the capped column overnight at 4°C. Cleaved LRRC8 complexes were eluted with an additional ~7 mL of buffer 1. Eluate was then applied to a column containing 5 mL of sepharose resin coupled to α-GFP nanobody for a total of five passes through the resin. The resin was then washed with 20 mL of buffer 1, 10 mL of buffer 2, and another 10 mL of buffer 1. The resin was then resuspended with 6 mL of buffer 1 containing 0.5 mg of PreScission protease and nutated gently in the capped column for 2 h at 4°C. Cleaved LRRC8A BRIL:D heteromeric complexes were then eluted with an additional ~7 mL of buffer 1, spin concentrated to ~500 μl with a 100 kDa molecular weight cutoff Amicon Ultra spin concentrator (Millipore), and then loaded onto a Superose 6 Increase column (GE Healthcare, Chicago, IL) equilibrated in buffer 1 on an NGC system (Bio-Rad, Hercules, CA) running Chromlab 6.0. Peak fractions containing LRRC8A BRIL:D complexes were pooled and spin concentrated to <500 μL. Purified α-BRIL Fab BAG2 and α-Fab Nb12,15,16 were thawed, diluted with buffer 1, and added in molar excess to LRRC8A BRIL:D complexes at a ratio of ~1:1.5:3 (LRRC8:BAG2:Nb) and gently nutated at 4°C for 30 min. The mixture was spin concentrated, centrifuged at 21,000 x g for 5 min at 4°C, and the supernatant (~8 μL at 1.9 mg/mL) was used immediately for cryo-EM sample preparation.

Cryo-EM sample preparation
To prepare cryo-EM grids, 2 μL of sample was applied to a freshly glow-discharged (PELCO easiGlow, settings: 0.39 mBar, 25 mA, 25 s glow, 10 s hold) Holey Carbon 300 mesh R 1.2/1.3 gold grid (Quantifoil, Großlöbichau, Germany). After a ~5 s manual wait time, the grid was blotted (Whatman #1 filter paper) for 3 s at blot force 1 and immediately plunge frozen in liquid nitrogen-cooled liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific) operated at 4°C and 100% humidity. Grids were clipped after freezing. For grid preparation, the operator was wearing a mask.

Cryo-EM data collection
All datasets were collected on a Titan Krios G3i electron microscope (Thermo Fisher) operated at 300 kV and equipped with a Gatan BioQuantum Imaging Filter with a slit width of 20 eV. Dose-fractionated images (~50 electrons per Å2 applied over 50 frames) were recorded on a K3 direct electron detector (Gatan) with a pixel size of 1.048 Å. 242 movies were collected around a central hole position using image shift with an 11x11 hole pattern and two positions were targeted per hole. The defocus target was varied from −0.6 to −1.6 μm using SerialEM30.

Cryo-EM data processing
9730 movie stacks were collected, motion-corrected using MotionCor231 in RELION 3.132, and CTF-corrected using CTFFIND 4.1.1433 (Supplementary Figs. 2 and 3 and Supplementary Table 1 provide additional processing details). Micrographs with a CTFFIND reported resolution estimate greater than 4 Å were discarded, leaving 8,695 micrographs for further processing. An initial particle set of 579 particles was generated by manual picking in RELION and used to train Topaz34 in RELION, which in turn was used to pick a set of 20,614 particles from 500 micrographs. These particles were cleaned to 6531 particles using 2D classification and ab-initio jobs in cryoSPARC35. Particles were transferred back to RELION using UCSF pyem tools36 and used to train Topaz to pick particles from all 8695 micrographs, which were then cleaned in cryoSPARC using 2D classification, ab initio, and heterogenous refinement. The process of all-micrograph Topaz particle picking followed by particle cleaning in cryoSPARC was repeated iteratively for three total times, after which the three particle stacks were merged and de-duplicated in RELION with a 100 Å minimum inter-particle distance to generate a stack of 220,293 particles. These particles were non-uniform refined in cryoSPARC 4.2.137, then transferred to RELION for post-processing and Bayesian polishing38. The polished particle stack was then further cleaned using 2D classification and heterogeneous refinement jobs in cryoSPARC to generate a final “consensus” particle stack of 120,274 particles. The consensus particle stack was non-uniform refined, then iteratively CTF refined (local, then global), followed by a final non-uniform refinement to give a 3.12 Å consensus map, which displayed marked heterogeneity in some subunits’ linker regions and LRR domains.

To resolve this heterogeneity, we used 3D variability analysis and display jobs combined with heterogeneous refinement in cryoSPARC18. First, using the consensus map, we generated a mask to exclude micellar density. We next applied this mask and the consensus particle stack to a four-component 3D variability analysis job with a 5 Å filter resolution. Next, using the 3D variability display job on simple mode (20 frames, 5 Å filter resolution), we identified two 3D variability components with pronounced conformational changes in subunit linker regions and LRR domains. We used the end volumes (i.e., frames 0 and 19) of these two components produced from the 3D variability display output, alongside a 5 Å low pass-filtered consensus map, as volume references for heterogeneous refinement of the consensus particle stack. Each class was then non-uniform refined, resulting in two well-resolved LRRC8A BRIL:D classes (class 0: 3.47 Å, 27,322 particles; class 1: 3.50 Å, 29,827 particles), one class with less resolved LRRs (class 2, 3.48 Å, 26,507 particles), and two classes of significantly lower resolution (class 3: 8.57 Å, 19,744 particles; class 4: 8.44 Å, 16,874 particles). The particles from classes 2–4 were pooled together and applied to a second round of heterogeneous refinement, with the non-uniformed volumes from classes 0, 1, and 2 as reference volumes. Particles which sorted into classes 0 and 1 in the second round of heterogenous refinement were combined with the initial class 0 and 1 particle stacks and non-uniformed, giving rise to maps for two conformations of LRRC8A BRIL:D: conformation 1 (3.29 Å, 43,907 particles, from class 0 particle stacks) and conformation 2 (3.35 Å, 46,893 particles, from class 1 particle stacks). A particle box size of 416 pixels was used throughout, and C1 symmetry was applied for all 3D jobs.

Structure modeling, refinement, and analysis
LRRC8A and LRRC8D subunit identity was inferred from the positions of globular densities corresponding to inserted BRIL domains and confirmed with high-resolution features during modeling (Supplementary Fig. 4). Sharpened and unsharpened maps from cryoSPARC non-uniform refinement were used to build models in Coot39. As a starting point for LRRC8A:D conformation 1, one LRRC8A subunit from a structural model of LRRC8A BRIL:C (PDB: 8DS3, chain B)12 was trimmed (removing residues 412–808) and docked into the LRRC8A:D map for each of the six subunits. For the two LRRC8D subunits, the sequence was then mutated. For LRRC8A:D conformation 2, the LRRC8A:D conformation 1 model was used as a starting point. Models were real-space refined in Phenix40 using sharpened maps and assessed for proper stereochemistry and geometry using MolProbity41 (Supplementary Table 1).

The final LRRC8A BRIL:D models consist of 1873 and 1880 amino acid residues for conformations 1 and 2, respectively. Unmodeled regions consist of each subunit’s N-terminus (residues 1–14), extracellular loop 1 (residues 61–92, 69–92, or 69–93 for LRRC8A subunits, along with their inserted BRIL:α-BRIL Fab:α-Fab Nb complexes, residues 61–133 for LRRC8D subunits), intracellular loop (residues 175–231 for LRRC8A subunits, residues 217–276 for LRRC8D subunits), and LRR domain (residues 412–810 for LRRC8A subunits, residues 457–859 for LRRC8D subunits). In both conformations, 18 lipid molecules were modeled as DOPE (3/18 modeled completely), with ligand restraints obtained from the REFMAC monomer library using Coot.

For display purposes, we docked models for the best resolved LRRs in unsharpened maps using ChimeraX42. For LRRC8A subunits, we docked a model of a tightly interacting pair of LRRC8A LRRs obtained from a structural model of LRRC8A BRIL:C (PDB: 8DS3, Chains B and C, residues 412–808)12. For LRRC8D subunits, we used the AlphaFold3 Server43 to predict a model of a LRRC8D monomer, which was trimmed to include residues 457–848 and docked. Two LRRC8A and two LRRC8D LRRs were docked for conformation 1; four LRRC8A LRRs were docked for conformation 2.

Measurements of pore constriction at the selectivity filter (Fig. 2) and pore lipid gate (Fig. 3) were made with HOLE44 using protonated models. Figures were prepared using ChimeraX, Prism, and Adobe Illustrator software.

Molecular dynamics simulations
We initiated simulations from the atomic model for LRRC8A:D conformation 1 (PDB: 9DX7). We retained the three pore-bound lipids and modeled them as DOPE; we removed the remaining lipid molecules from the peripheries of the protein structure. Prime (Schrödinger) was used to model hydrogen atoms and to add neutral acetyl and methylamide groups to cap protein termini. We retained titratable residues in their dominant protonation state at pH 7.4, resulting in protonation of D110 in chain D. To hydrate the vestibule immediately beneath the constriction point, we carried out an initial molecular dynamics simulation (using the protocols described below but with a shorter, 20-ns equilibration) to allow waters to flood into the vestibule while the protein remained restrained. We then used post-equilibration snapshots to extract these waters and add them back into the initial structure. We also used Dowser45 to hydrate pockets near protein chains.

We used two different software protocols to place the prepared LRRC8A:D structure into a lipid bilayer and to create a simulation box. We reasoned that these different approaches could result in an increased diversity of initial configurations of bulk lipid interactions with the protein.

In the first strategy, we used PACKMOL-Memgen46 within AmberTools (2025)47 to generate a simulation box. Bilayers were composed of POPC in a 9:1 ratio with cholesterol. Sodium and chloride ions were added to neutralize the system to a concentration of 150 mM. Box dimensions were selected to maintain a 20 Å buffer between the protein image and the edge of the box, resulting in a simulation box of 145 Å × 146 Å × 164 Å and 391,424 atoms, including ‘dummy’ atoms employed in the four-point OPC water model. We used tLeap47 to apply force-field parameters. We employed the ff19SB protein force field48, the Lipid21 lipid force field49, the four-point OPC water model, and corresponding 12-6-4 ion force field50.

In the second strategy, we employed the CHARMM-GUI Membrane Builder tool51,52 to prepare the simulation box and apply the same force-field parameters as those listed above. We also used CHARMM-GUI to protonate the cryo-EM structure with identical protonation of D110 in chain D and disulfide bond specification as used to prepare the system with Prime. Bilayers were composed only of POPC lipids and lacked cholesterol. Sodium and chloride ions were added to neutralize the system to a concentration of 150 mM. The simulation box was 140 Å × 140 Å × 166 Å and composed of 377,384–379,368 atoms.

For both initial configurations, we initiated simulations using the Compute Unified Device Architecture version of Particle Mesh Ewald MD using the AMBER24 software on single graphics processing units (GPUs)53,54. Systems were minimized in two stages, each composed of steepest descent minimization followed by conjugate gradient minimization. Systems were then heated from 0 K to 100 K in the NVT ensemble over 12.5 ps and then from 100 K to 310 K in the NPT ensemble over 125 ps at 1 bar, with harmonic restraints of 5.0 kcal mol−1 Å−2 placed on all atoms (including lipids), except for waters and ions. Systems were equilibrated at 310 K in the NPT ensemble at 1 bar using either of two restraint protocols over 60 ns: in the first protocol, restraints on protein and lipid atoms were tapered from 5.0 kcal mol−1 Å−2 by 1.0 kcal mol−1 Å−2 over 20 ns and then from 0.5 kcal mol−1 Å−2 by 0.1 kcal mol−1 Å−2 over 20 additional ns. An additional 20 ns of restrained equilibration were performed with only protein and pore lipids restrained with a force of 0.1 kcal mol−1 Å−2. In the second protocol, restraints on protein and lipid atoms were tapered from 5.0 kcal mol−1 Å−2 by 1.0 kcal mol−1 Å−2 over 25 ns; held at 0.5 kcal mol−1 Å−2 for 5 additional ns; then tapered again from 0.5 kcal mol−1 Å−2 by 0.1 kcal mol−1 Å−2 with protein and pore lipids restrained over 25 ns; and then held once more at 0.05 kcal mol−1 Å−2 with protein and pore lipids restrained for 5 additional ns. Production simulations were carried out in the NPT ensemble at 310 K and 1 bar, using a Langevin thermostat for temperature coupling and a Berendsen barostat with semi-isotropic control for pressure coupling. For production simulations performed with restraints on the protein backbone, during equilibration, backbone restraints were held to 0.5 kcal mol−1 Å−2 while restraints on protein side chains and pore lipids continued to decrease per the protocols described above. Nonbonded interactions were cut off at 10.0 Å; long-range electrostatic interactions were calculated using Particle Mesh Ewald with an Ewald coefficient of 0.27511 and a B-spline interpolation order of 4. The FFT grid size was chosen such that the width of each grid cell was ~1 Å. We applied hydrogen mass repartitioning to use a 4-fs time step and constrained bond lengths to hydrogen atoms using SHAKE55,56. Trajectory snapshots were saved every 200 ps.

Simulation analysis was performed using Visual Molecular Dynamics (VMD)57 and the MDAnalysis python package58. We carried out three separate analyses. (1) To quantify the number of lipids present in the pore, we considered a lipid bound if the lipid tilt angle with respect to the z-axis was >100° and the head group center of geometry z coordinate was above the T48 Cα atoms. (2) To measure constriction within the pore, we used CAVER 3.0159 to identify permeation pathways from select simulation snapshots, with parameters of probe radius=0.05, shell radius=10.0, shell depth=4.0, and maximum number of clusters=999. (3) To measure compaction of the LRRC8D–LRRC8A and LRRC8A–LRRC8A subunit interfaces, we calculated the centers of mass of the set of helices composing the transmembrane regions of chains F and A (LRRC8D–LRRC8A distance) and of chains B and C (LRRC8A–LRRC8A distance). All production simulation data, downsampled every 1 ns, were included in our analysis of the number of bound lipids in the pore and water occupancy (prior to segmentation by lipid-bound state). Production data was downsampled every 10 ns for pore radii analysis with CAVER. For a small number of simulation frames, CAVER failed to identify contiguous pores, and we excluded these data from our traces and pore profiles. A table summarizing molecular dynamics simulation details is provided as Supplementary Table 2, and a molecular dynamics simulation checklist is provided as Supplementary Table 3.

Electrophysiology
HeLa LRRC8A-E–/– cells22 were cultured in DMEM (Gibco) supplemented with 10% FBS (Gibco), 100 units/mL penicillin, and 100 μg/mL streptomycin (Gibco) and grown at 37°C in the presence of 5% CO2. Trypsinized cells were deposited on 5 mm glass coverslips in a 6-well dish 6 h to 2 days prior to transfection. At least 30 min prior to transfection, wells were replaced with antibiotic-free growth media. Consistent with a prior report60, we were unable to reproducibly obtain large wild-type VRAC currents with a 1:1 transfection ratio of LRRC8A and LRRC8D plasmids. Thus, for all transfections, a 1:5 mass ratio of LRRC8A and LRRC8D plasmids was used and mixed with FuGENE 6 at a 1:3 volume ratio diluted in serum- and antibiotic-free DMEM (typically, 0.5 μg LRRC8A and 2.5 μg LRRC8D mixed with 9 μL FuGENE 6 in 100 μL DMEM). Media was replaced 12–24 h post-transfection with growth media containing antibiotics. Patch clamp experiments were conducted 36–48 h post-transfection.

For patch clamp recording, coverslips were placed in a perfusion chamber at room temperature in isotonic bath solution (90 mM NaCl, 2 mM KCl, 1 mM CaCl2, 1 mM MgCl2, 10 mM HEPES, 10 mM glucose, adjusted to pH 7.4 with NaOH). Mannitol was used to adjust the solution osmolarity to ~330 mOsm (the approximate osmolarity of the cell culture media) as measured by a vapor pressure osmometer (VAPRO, Model 5600, ELITechGroup) or a freezing-point depression osmometer (Model 3320, Advanced Instruments). Borosilicate glass pipettes were pulled to a resistance of ~1.5–4 MΩ and filled with pipet solution (133 mM CsCl, 5 mM EGTA, 2 mM CaCl2, 1 mM MgCl2, 10 mM HEPES, ~4 mM MgATP, adjusted to pH 7.4 with CsOH and ~330 mOsm with mannitol). An Axopatch 200B amplifier connected to a Digidata 1550B digitizer (Molecular Devices) was used for data acquisition with pClamp 10.7 software. Analog signals were filtered at 1 kHz and sampled at 10 kHz. Pressure application from the patch pipette was accomplished with a high-speed pressure clamp (HSPC, ALA Scientific). Once a patch was achieved in the whole-cell mode and a stable whole-cell capacitance was measured, voltage families were recorded to monitor pre-swelling currents with the following voltage protocol applied every 46 s: Vhold = 0 mV; Vtest = −100 to +100 mV, Δ20 mV, 400 ms. When initial currents stabilized, hypotonic bath solution (same components as isotonic, but adjusted to ~250 mOsm with mannitol) was exchanged into the chamber, and currents were monitored over the course of cell swelling until currents stopped increasing for multiple records or the patch broke. If a patch exhibited signs of leaking or sealing, a membrane test protocol was examined to either reseal, re-break-in, or discard the patch. Care was taken to make sure that the measured pressure was slightly negative (i.e., −1 mmHg) after the whole-cell mode was achieved. Cells were monitored using a brightfield microscope and were selected for patching based on cell morphology (aiming for healthy interphase cells) and the presence of fluorescent reporters (GFP and mCherry). Recordings were not corrected for liquid junction potentials (measured as <3 mV).

Data was analyzed using Clampfit 10.7, Excel, and Graphpad Prism 10 software. To quantify current densities, the mean current density from the last 50 ms of each trace prior to the onset of capacitive currents was measured using Clampfit and normalized by the cell’s measured capacitance. The displayed data are current densities at 60 mV (where VRAC current inactivation is minimal) averaged from three consecutive records after achieving stable currents in isotonic or hypotonic solution, and the ratio of these current densities was used to measure fold activation. For each cell, the reversal potential was measured in Prism as the x-intercept (voltage) of a linear fit of the current amplitudes at 0 mV and −20 mV from three averaged consecutive hypotonic records. For plotting representative current traces, a single isotonic and hypotonic record was chosen, the data were decimated 10-fold (to 1 ms sampling interval), and plotted in Prism. Prism was used for statistical analyses of fold activation. A Brown-Forsythe and Welch one-way ANOVA test followed by a Dunnett’s T3 multiple comparison test was used to compare all mutant channels to wild-type LRRC8A:D. An unpaired t-test was used to compare LRRC8A(T48D):D and LRRC8A(T48D):D(T48D).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "LRRC8/VRAC channels have important roles in cell volume regulation and in the transport of small organic molecules, including neurotransmitters. The study by Lurie et al. for the first time presents cryo-EM structures of mouse LRRC8A/D heteromeric channels. Several cryo-EM structures of LRRC8 channels have already been published previously. Initially, these studies focused on non-physiological homomeric channels, but more recently also structures of heteromeric VRAC channels were reported. Many aspects of VRACs remain poorly understood, including the important question of how a channel can be highly selective for Cl over Na, but also conduct organic substrates with different electric charges, an issue indirectly addressed in the present work.\nThe work is well-conducted, clearly presented and largely agrees both with the previously published structures of LRRC8A and D homomers, and with LRRC8A/C heteromeric channels. It provides limited new insights, but is a useful and necessary resource for future research and especially for isoform-specific drug discovery.\nSpecific comments:\nWhile increased amplitudes of the T48D mutation are consistent with the concept of lipid gating, this model is difficult to unequivocally prove. It is possible that the lipids in the pore are an artifact of protein purification and simply coincide with the functional effects of a mutation in a pore-lining residue. MD simulations may strengthen this conclusion. Lipid gating model would suggest state-dependent accessibility of the pore for e.g. thiol reagents which may be small enough to enter the selectivity filter of the ‘closed’ state.\nThe present structure contains no resolved N-termini which are known to participate in the pore lining. While the present structure cannot allow new conclusions on the role of the N-terminus, the potential contribution of LRRC8D N-termini to substrate selectivity should be discussed. N-terminal sequences are not conserved among the paralogues, and LRRC8D differs from other LRRC8s in least one position, previously implicated in affecting ion selectivity, namely Ala8 (10.1016/j.celrep.2023.112926). Such a discussion is also relevant in view of the role of LRRC8D in allowing the transport of small molecules.", "reviewer_2": "Heteromeric assembly allows VRACs to diversify their channel properties. While cryo-EM structures of LRRC8A:C heteromers have revealed important features, including lipid binding along the permeation pathway, the assembly mechanisms of other heteromeric channels remain unclear. In this study, Lurie et al. present the cryo-EM structures of LRRC8A:D heteromers in two distinct conformations. In each conformation, two LRRC8D protomers are aligned next to each other, destabilizing the LRR domains and widening the lateral fenestrations. The authors also model lipids within the pore, consistent with previous observations in LRRC8A:C VRACs. Based on the constitutive activity of the T48D mutant, which prevents lipids from entering the pore, they propose that LRRC8A:D VRACs are also gated by lipids.\nOverall, the structures are of high quality and provide valuable insights into LRRC8A:D heteromers. However, the proposed lipid-gating and ion selectivity mechanisms are largely based on visual inspection of the structures, making these potentially interesting ideas speculative. A few additional experiments could provide more compelling mechanistic insights, which are currently lacking in the field. Below are my specific comments:\n1. Lack of functional validation for the proposed ion selectivity mechanism: LRRC8A:D VRACs exhibit distinct properties, including reduced Cl⁻ conductance and increased permeability to large, non-anionic substrates. While the structural data suggest that F144 plays a key role in these properties, no experimental validation is provided. If the proposed mechanism is correct, mutating the corresponding residue in other heteromers, such as LRRC8A:C, should make them behave like LRRC8A:D. Without such experiments, the proposed ion selectivity mechanism (Fig. 2) remains speculative.\n2. Potential role of LRR domain destabilization in ion permeation: One of the notable features of LRRC8A:D VRACs is the destabilized LRR domains, which appear to influence the way protomers interact within the transmembrane (TM) region. This rearrangement could also affect permeant selectivity. To distinguish this effect from the proposed ion selection mechanism at the selectivity filter, it would be beneficial to compare ion selectivity and conductance among wild-type channels, LRRC8A homomers carrying the F144 mutation, LRRC8A:D heteromers with F144 mutations in all six subunits, and those with additional mutations at key residues such as R103 and L105.\n3. Unclear mechanism of lipid gating: The lipid-gating mechanism is intriguing but remains speculative. While the pore-lining residues appear to accommodate lipid molecules, it is unclear how these lipids are displaced when the channel opens. What forces drive the removal of lipids from these binding sites? Further functional assays or molecular dynamics simulations could provide more insights into the gating mechanism.\n4. Limited background on the T48D mutation: The T48D mutation is an important tool in this study, but insufficient background is provided. It would be helpful to clarify that the overall VRAC structure is not affected by this mutation and that the structure of the mutant channel lacks bound lipids inside the pore.\n5. Unclear justification for lipid assignments in the pore: Although certain lipids are modeled within the pore, it is unclear how well these assignments fit the density map. Figure 3B does not provide sufficient detail to fully evaluate the placement of lipids. It would be beneficial to display each lipid density in a manner similar to Extended Data Figure 4.\n6. Structural differences between Conformation 1 and Conformation 2: The two observed conformations appear to differ in their TM regions. Do these conformations represent different functional states of the channel? Additionally, what are the consequences of the differing stability in the LRR domains? Addressing these questions would enhance our understanding of how these structural changes relate to channel function."}, "author_response": "We thank the reviewer for this suggestion and agree that MD simulations could provide important support for the model and strengthen the paper’s conclusions. We carried out all-atom molecular dynamics simulations of LRRC8A:D under three conditions, each with six 1-µs replicates. We note that our simulations create conditions that challenge pore lipid stability in two respects: (i) the low-resolution intracellular LRRs were not included in the simulated structures, an effect we expect to increase channel dynamics and promote channel opening, and (ii) the channel vestibule underneath the pore lipids was filled with water, though large intersubunit openings observed in our structures of LRRC8A:C and LRRC8A:D suggest that lipids could also partially occupy this space. The simulated structure also lacks N-termini, which could otherwise reduce the dielectric of the vestibule and further increase the favorability of lipids residing within the pore, as noted by the reviewer in the subsequent comment. We hypothesized that lipids retained as an artifact would have low affinity for the pore and consistently dissociate in simulation, whereas lipids that gate the pore should remain bound over hundreds of nanoseconds.\nTwo of the three simulation conditions, which had no backbone restraints applied but differed in their bulk lipid compositions, showed both behaviors: pore lipids dissociated away from their initial positions after tens to hundreds of nanoseconds in some replicates and remained bound in the pore for the duration of the 1-μs simulation in other replicates. We wondered whether the observed dissociation events could occur because the simulated structure lacks the LRR domains, which may impose physical constraints on inter-subunit packing. In a third simulation condition performed with gentle restraints applied to protein backbone atoms, all three lipids remain bound within the pore for the duration of each replicate. Unrestrained simulations consistently show movement of channel subunits to seal the large LRRC8A-LRRC8D intersubunit gap, but these movements do not obviously disrupt pore lipid binding. Since we do not observe major differences between lipid-bound and lipid-dissociated conformations, we propose that other thermal motions in the protein in the absence of intracellular restraints can alter interactions between pore lipids, water and protein, leading to their stochastic dissociation.\nAltogether, these simulations show how lipids can reside within the pore on long timescales and support the model for lipid block.\nWe also agree that the lipid gating model suggests state-dependent accessibility and inhibition by small molecules. We have added a citation to a recent preprint (Yamada T., et al. bioRxiv (2025). https://doi.org/10.1101/2025.02.24.639894) that shows, as predicted by a lipid-gating model, that when applied extracellularly, a membrane-impermeable thiol-reactive agent, MTSET, inhibits a mutant LRRC8 channel incorporating an N-terminal cysteine residue only when applied during cell swelling when channels are open.\nWe have added additional text and citations to emphasize this important point.\nWe agree with the reviewer that functional validation is necessary to understand how and to what extent F144 contributes to creating LRRC8A:D VRACs’ unique substrate selectivity profile. We attempted to establish an experimental approach for measuring differences in substrate selectivity of defined VRAC heteromers and their mutants; we detail these attempts below. However, we were unable to convincingly show substantial selectivity differences for two charged substrates (taurine and glutamate) between LRRC8A:C and LRRC8A:D VRACs, a prerequisite for evaluating changes from selectivity filter mutations. We believe further investigation is warranted, but requires other approaches and is better left to future dedicated studies. We have therefore adjusted the text throughout to reflect the limited evidence describing F144’s role in channel selectivity, cite and discuss work on other determinants of selectivity, and highlight the importance of additional follow-up studies for functionally testing F144’s role in LRRC8A:D substrate selectivity.\nWe measured substrate selectivity using patch clamp electrophysiology. While most substrates unique to transport by LRRC8A:D VRACs are electrically neutral, we selected two which we reasoned may show some difference in selectivity between LRRC8A:C and LRRC8A:D VRACs: glutamate and taurine. Glutamate was selected as it is a known VRAC substrate, it is of large size, and a previous study has shown a modest increased selectivity for aspartate by LRRC8A:D VRACs when compared to LRRC8A:C VRACs. On the other hand, taurine has been shown by multiple studies to be primarily transported by LRRC8D-containing VRACs and thus should show selectivity differences between LRRC8A:D and LRRC8A:C VRACs.\nWe transfected LRRC8–/– HeLa cells to express LRRC8A:C and LRRC8A:D VRACs and recorded currents under bi-ionic conditions, replacing the majority of the anionic component in our conventional chloride-based recording solutions with glutamate or taurine on one side of the membrane. We tested both configurations for each anion in four separate conditions: i) chloride in and glutamate out, ii) glutamate in and chloride out, iii) taurine in and chloride out, and iv) chloride in and taurine out. To record currents carried by taurine, which is electroneutral at neutral pH, we adjusted taurine-containing solutions to pH 8.2.\nNo consistent differences in substrate selectivity were observed (Response Figure 1). Glutamate showed small differences between LRRC8A:C and LRRC8A:D that depended on the ionic configuration (i.e. when glutamate was applied extracellularly, LRRC8A:D was more selective for glutamate than LRRC8A:C, but the opposite was observed when glutamate was applied intracellularly). Taurine showed no significant selectivity differences between LRRC8A:C and LRRC8A:D with high cell-to-cell variability and ionic configuration-dependent results. Large shifts in the reversal potential over the course of recording a cell were frequently observed, an unanticipated confounding factor. It is unclear whether this is due to changes in channel selectivity, other channel currents, or effects from ion depletion and accumulation – an inherent problem with long bi-ionic recordings of channel currents (Li M., et al. Nat Neurosci (2015). https://doi.org/10.1101/2025.02.24.639894).\nSubstrate selectivity differences between LRRC8A:D and LRRC8A:C/E may be most different for uncharged or zwitterionic organic substrates including cisplatin, myo-inositol, GABA, and taurine (in the neutral form, see: Schober A.L., et al. J Physiol (2017). https://doi.org/10.1113/JP275053). Due to these substrates’ lack of overall charge, scintillation counting would be the preferred approach to assess their permeability. This experiment is challenging with our current setup because LRRC8–/– cells show low transfection efficiency (often much lower than 10%), making effects from expression level and intrinsic channel properties difficult to distinguish. In future work, specific VRAC heteromers and mutants could be expressed by generating transgenic stable cell lines, but this falls outside the scope of this study.\nWe were unable to establish a reliable approach for characterizing substrate selectivity differences between LRRC8A:D VRACs and other heteromers and think further efforts are better suited for future studies. Please see our response to Comment 1 above.\nWe addressed this important issue with new molecular dynamics simulations, which provide several insights into lipid dissociation.\nFirst, we observe dissociation only in simulations where the protein backbone is not restrained (i.e. can relax away from the starting structure). 10 of 12 unrestrained simulations show dissociation of one, two, or all three lipids from their starting position in the pore. Analyses of pore diameter during the simulations show that movement of two lipids is usually required to create a pore large enough for Cl– conduction. In simulations where the protein backbone is gently restrained to the conformation seen in cryo-EM, lipids remain near their original positions and sterically seal the pore to permeable ions for the entire µs, suggesting that rearrangements in protein conformation relative to the starting conformation lead to lipid destabilization and their stochastic dissociation. These motions may arise in our simulations due to the absence of the LRR domains and/or the absence of N-termini within the pore.\nSecond, during dissociation, hydrocarbon tails pack into hydrophobic grooves along the vestibule and slide down the helices while the polar headgroups flip down into the aqueous cavity (Response Fig. 2). We speculate that full dissociation may occur via lateral intersubunit gaps, since the tails of bulk lipids can also access these same gaps. Flipping of the headgroups may allow pore lipids to partition into the inner leaflet via this pathway. We raise these points in the text while noting the limitations of our simulation setup. We suggest additional experiments in the future will be important to better understand forces and molecular mechanisms involved in delipidation and channel opening.\nWe have edited the text to better explain our previous work with the T48D mutant and its effects on channel structure.\nWe have added images of the modeled pore lipids within map density for both conformations (Supplementary Figure 4D). In the text we also describe in more detail how lipids were modeled within the pore.\nWe agree that the different conformations observed in cryo-EM studies of LRRC8A:D and LRRC8A:C (Kern D.M. et al., Nat Struct Mol Biol (2023). https://doi.org/10.1038/s41594-023-00944-6) channels, which include differences in TM regions, are intriguing. However, it is not obvious from the structures that they represent different functional states because all resolved structures are closed by pore lipids. For the same reason, we do not yet understand the consequences of differing LRR interactions and arrangements on channel function. We expect future work, including MD simulations initiated from different starting structures, could provide insight into potential functional differences between captured conformations. We have added additional text to detail this important point."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Lurie et al addressed all of my concerns in their revised manuscript and I can recommend it for publication.", "reviewer_2": "The authors have adequately addressed the major concerns raised by the reviewers. The newly added MD simulation studies, in particular, provide much needed mechanistic insights into lipid-gating. This is an interesting paper, and I have no further comments.", "reviewer_3": "The manuscript by Lurie et al. presents the first cryo-EM structures of the heteromeric LRRC8A:D volume-regulated anion channel (VRAC), complemented by molecular dynamics (MD) simulations and electrophysiological characterization. The study provides important structural insights into the stoichiometry, assembly, and a conserved lipid-gating mechanism of this channel. The authors have adequately addressed several concerns raised in the initial review, particularly through the addition of MD simulations that bolster the lipid-gating hypothesis. The structural data are of high quality, and the manuscript is generally well-written. However, a few key issues regarding functional correlation and mechanistic depth remain and should be addressed before publication.\nMajor Comments:\n1. Lipid-Gating Mechanism and MD Simulations. The addition of all-atom molecular dynamics simulations significantly strengthens the proposed lipid-gating model. The observation that pore lipids remain stably bound in backbone-restrained simulations but can dissociate in unrestrained ones is compelling. However, the mechanistic link between lipid evacuation and channel activation remains somewhat speculative. The authors propose that lateral gaps facilitate lipid exit, but no full lipid exchange was observed in the simulations. It would be beneficial to more explicitly discuss the limitations of the current simulation timescales and the potential energetic or structural triggers (e.g., membrane tension, subunit rearrangement) required for complete lipid removal and channel opening. Speculating on how the observed conformational dynamics might couple to this process in the Discussion would add valuable insight.\n2. Functional Correlation of Selectivity Filter. The structural analysis of the selectivity filter, particularly the role of LRRC8D-F144 in increasing hydrophobicity and pore size, is well-described and logically presented. However, as noted by the authors in their response, direct functional validation of F144's role in substrate selectivity is lacking. The attempts to measure differences in glutamate and taurine permeability between LRRC8A:C and LRRC8A:D are honestly reported but inconclusive. The manuscript should therefore more explicitly state in the main text that the role of F144, while structurally plausible, remains a hypothesis requiring future functional validation, ideally with uncharged substrates and more robust assay systems. The added discussion on the potential contribution of N-terminal residues is appropriate, but the central claim regarding the selectivity filter would benefit from a clearer framing as a structural model awaiting functional confirmation.\n3. Biological Significance of the Two Conformations. The two resolved conformations reveal interesting dynamics in the LRR and transmembrane domains. The authors rightly note that both appear to be closed states. The question of whether these represent distinct functional intermediates or simply conformational heterogeneity in the closed state remains open. While resolving this may be beyond the scope of the current study, the Discussion should briefly explore the potential functional implications of this observed flexibility. For instance, could one conformation be more primed for activation or lipid evacuation than the other? Linking this to the known requirement of non-LRRC8A subunits for full channel activation could be a fruitful avenue for speculation."}, "author_response": "We thank the reviewer for their careful reading of the manuscript and have addressed the points raised with changes to the text.\nWe have more fully addressed this in the final paragraph. We note that additional forces that drive lipid removal remain unclear, that we did not observe full lipid evacuation, that longer simulations or additional forces may be required for full lipid exit from the pore, and that full lipid dissociation from the pore is not required for opening a pore large enough to conduct ions based on our simulations.\nWe more explicitly state that mutagenesis experiments, especially using uncharged substrates, are required to definitively test the hypothesis that these differences in the selectivity filter contribute to differences in substrate selectivity between channel types.\nWe have expanded discussion of the two conformations and state that whether they represent different states along a gating trajectory or interconvert during the gating cycle remain open questions to be addressed in future work."}]

Decision letter