3D-SIM Super-resolution of FtsZ and Its Membrane Tethers in Escherichia coli Cells

FtsZ, a bacterial homolog of eukaryotic tubulin, assembles into the Z ring required for cytokinesis. In Escherichia coli, FtsZ interacts directly with FtsA and ZipA, which tether the Z ring to the membrane. We used three-dimensional structured illumination microscopy to compare the localization patterns of FtsZ, FtsA, and ZipA at high resolution in Escherichia coli cells. We found that FtsZ localizes in patches within a ring structure, similar to the pattern observed in other species, and discovered that FtsA and ZipA mostly colocalize in similar patches. Finally, we observed similar punctate and short polymeric structures of FtsZ distributed throughout the cell after Z rings were disassembled, either as a consequence of normal cytokinesis or upon induction of an endogenous cell division inhibitor.The assembly of the bacterial tubulin FtsZ has been well studied in vitro, but the fine structure of the cytokinetic Z ring it forms in vivo is not well defined. Super-resolution microscopy methods including photoactivated localization microscopy (PALM) and three-dimensional-structured illumination microscopy (3D-SIM) have recently provided a more detailed view of Z-ring structures. Two-dimensional PALM showed that Z rings in Escherichia coli are likely composed of loosely-bundled dynamic protofilaments (1,2). Three-dimensional PALM studies of Caulobacter crescentus initially showed that Z rings were comprised of loosely bundled protofilaments forming a continuous but dynamic ring (1–3). However, a more recent high-throughput study showed that the Z rings of this bacterium are patchy or discontinuous (4), similar to Z rings of Bacillus subtilis and Staphylococcus aureus using 3D-SIM (5). Strauss et al. (5) also demonstrated that the patches in B. subtilis Z rings are highly dynamic.Assembly of the Z ring is modulated by several proteins that interact directly with FtsZ and enhance assembly or disassembly (6). For example, FtsA and ZipA promote ring assembly in E. coli by tethering it to the cytoplasmic membrane (7,8). SulA is an inhibitor of FtsZ assembly, induced only after DNA damage, which sequesters monomers of FtsZ to prevent its assembly into a Z ring (9). Our initial goals were to visualize Z rings in E. coli using 3D-SIM, and then examine whether any FtsZ polymeric structures remain after SulA induction. We also asked whether FtsA and ZipA localized in patchy patterns similar to those of FtsZ.We used a DeltaVision OMX V4 Blaze microscope (Applied Precision, GE Healthcare, Issaquah, WA) to view the high-resolution localization patterns of FtsZ in E. coli cells producing FtsZ-GFP (Fig. 1). Three-dimensional views were reconstructed using softWoRx software (Applied Precision). To rule out GFP artifacts, we also visualized native FtsZ from a wild-type strain (WM1074) by immunofluorescence (IF).Open in a separate windowFigure 1Localization of FtsZ in E. coli. (A) Cell with a Z ring labeled with FtsZ-GFP. (B) Rotated view of Z ring in panel A. (C) Cell with a Z ring labeled with DyLight 550 (Thermo Fisher Scientific, Waltham, MA). (D) Rotated view of Z ring in panel C. (B1 and D1) Three-dimensional surface intensity plots of Z rings in panels B and D, respectively. (E) A dividing cell producing FtsZ-GFP. The cell outline is shown in the schematic. (Asterisk) Focus of FtsZ localization; (open dashed ovals) filamentous structures of FtsZ. Three-dimensional surface intensity plots were created using the software ImageJ (19). Scale bars, 1 μm.Both FtsZ-GFP (Fig. 1, A, B, and B1) and IF staining for FtsZ (Fig. 1, C, D, and D1) consistently localized to patches around the ring circumference, similar to the B. subtilis and C. crescentus FtsZ patterns (4,5). Analysis of fluorescence intensities (see Fig. S1, A and B, in the Supporting Material) revealed that the majority of Z rings contain one or more gaps in which intensity decreases to background levels (82% for FtsZ-GFP and 69% for IF). Most rings had 3–5 areas of lower intensity, but only a small percentage of these areas had fluorescence below background intensity (34% for FtsZ-GFP and 21% for IF), indicating that the majority of areas with lower intensity contain at least some FtsZ.To elucidate how FtsZ transitions from a disassembled ring to a new ring, we imaged a few dividing daughter cells before they were able to form new Z rings (Fig. 1 E). Previous conventional microscopy had revealed dynamic FtsZ helical structures (10), but the resolution had been insufficient to see further details. Here, FtsZ visualized in dividing cells by 3D-SIM localized throughout as a mixture of patches and randomly-oriented short filaments (asterisk and dashed oval in Fig. 1, respectively). These structures may represent oligomeric precursors of Z ring assembly.To visualize FtsZ after Z-ring disassembly another way, we overproduced SulA, a protein that blocks FtsZ assembly. We examined E. coli cells producing FtsZ-GFP after induction of sulA expression from a pBAD33-sulA plasmid (pWM1736) with 0.2% arabinose. After 30 min of sulA induction, Z rings remained intact in most cells (Fig. 2 A and data not shown). The proportion of cellular FtsZ-GFP in the ring before and after induction of sulA was consistent with previous data (data not shown) (1,11).Open in a separate windowFigure 2Localization of FtsZ after overproduction of SulA. (A) Cell producing FtsZ-GFP after 0.2% arabinose induction of SulA for 30 min. (B) After 45 min. (B1) Magnified cell shown in panel B. (C) Cell producing native FtsZ labeled with AlexaFluor 488 (Life Technologies, Carlsbad, CA) 30 min after induction; (D) 45 min after induction. (D1) Magnified cell shown in panel D. Scale bars, 1 μm. (Asterisk) Focus of FtsZ localization; (open dashed ovals) filamentous structures of FtsZ.Notably, after 45 min of sulA induction, Z rings were gone (Fig. 2, B and B1), replaced by numerous patches and randomly-oriented short filaments (asterisk and dashed ovals in Fig. 2), similar to those observed in a dividing cell. FtsZ normally rapidly recycles from free monomers to ring-bound polymers (11), but a critical concentration of SulA reduces the pool of available FtsZ monomers, resulting in breakdown of the Z ring (9). The observed FtsZ-GFP patches and filaments are likely FtsZ polymers that disassemble before they can organize into a ring.We confirmed this result by overproducing SulA in wild-type cells and detecting FtsZ localization by IF (Fig. 2, C, D, and D1). The overall fluorescence patterns in cells producing FtsZ-GFP versus cells producing only native FtsZ were similar (Fig. 2, B1 and D1), although we observed fewer filaments with IF, perhaps because FtsZ-GFP confers slight resistance to SulA, or because the increased amount of FtsZ in FtsZ-GFP producing cells might titrate the SulA more effectively.Additionally, we wanted to observe the localization patterns of the membrane tethers FtsA and ZipA. Inasmuch as both proteins bind to the same C-terminal conserved tail of FtsZ (12–14), they would be expected to colocalize with the circumferential FtsZ patches in the Z ring. We visualized FtsA using protein fusions to mCherry and GFP (data not shown) as well as IF using a wild-type strain (WM1074) (Fig. 3 A). We found that the patchy ring pattern of FtsA localization was similar to the FtsZ pattern. ZipA also displayed a similar patchy localization in WM1074 by IF (Fig. 3 B).Open in a separate windowFigure 3Localization of FtsA (A) and ZipA (B) by IF using AlexaFluor 488. (C) FtsA-GFP ring. (D) Same cell shown in panel C with ZipA labeled with DyLight 550. (C1 and D1) Three-dimensional surface intensity plots of FtsA ring from panel C or ZipA ring from panel D, respectively. (E) Merged image of FtsA (green) and ZipA (red) from the ring shown in panels C and D. (F) Intensity plot of FtsA (green) and ZipA (red) of ring shown in panel E. The plot represents intensity across a line drawn counterclockwise from the top of the ring around the circumference, then into its lumen. Red/green intensity plot and three-dimensional surface intensity plots were created using the software ImageJ (19). Scale bar, 1 μm.To determine whether FtsA and ZipA colocalized to these patches, we used a strain producing FtsA-GFP (WM4679) for IF staining of ZipA using a red secondary antibody. FtsA-GFP (Fig. 3 C) and ZipA (Fig. 3 D) had similar patterns of fluorescence, although the three-dimensional intensity profiles (Fig. 3, C1 and D1) reveal slight differences in intensity that are also visible in a merged image (Fig. 3 E). Quantitation of fluorescence intensities around the circumference of the rings revealed that FtsA and ZipA colocalized almost completely in approximately half of the rings analyzed (Fig. 3 F, and see Fig. S2 A), whereas in the other rings there were significant differences in localization in one or more areas (see Fig. S2 B). FtsA and ZipA bind to the same C-terminal peptide of FtsZ and may compete for binding. Cooperative self-assembly of FtsA or ZipA might result in large-scale differential localization visible by 3D-SIM.In conclusion, our 3D-SIM analysis shows that the patchy localization of FtsZ is conserved in E. coli and suggests that it may be widespread among bacteria. After disassembly of the Z ring either in dividing cells or by excess levels of the cell division inhibitor SulA, FtsZ persisted as patches and short filamentous structures. This is consistent with a highly dynamic population of FtsZ monomers and oligomers outside the ring, originally observed as mobile helices in E. coli by conventional fluorescence microscopy (10) and by photoactivation single-molecule tracking (15). FtsA and ZipA, which bind to the same segment of FtsZ and tether it to the cytoplasmic membrane, usually display a similar localization pattern to FtsZ and each other, although in addition to the differences we detect by 3D-SIM, there are also likely differences that are beyond its ∼100-nm resolution limit in the X,Y plane.As proposed previously (16), gaps between FtsZ patches may be needed to accommodate a switch from a sparse Z ring to a more condensed ring, which would provide force to drive ring constriction (17). If this model is correct, the gaps should close upon ring constriction, although this may be beyond the resolution of 3D-SIM in constricted rings. Another role for patches could be to force molecular crowding of low-abundance septum synthesis proteins such as FtsI, which depend on FtsZ/FtsA/ZipA for their recruitment, into a few mobile supercomplexes.How are FtsZ polymers organized within the Z-ring patches? Recent polarized fluorescence data suggest that FtsZ polymers are oriented both axially and circumferentially within the Z ring in E. coli (18). The seemingly random orientation of the non-ring FtsZ polymeric structures we observe here supports the idea that there is no strong constraint requiring FtsZ oligomers to follow a circumferential path around the cell cylinder. The patches of FtsZ in the unperturbed E. coli Z ring likely represent randomly oriented clusters of FtsZ filaments that are associated with ZipA, FtsA, and essential septum synthesis proteins. New super-resolution microscopy methods should continue to shed light on the in vivo organization of these protein assemblies.     

Conformational States of Melittin at a Bilayer Interface

The distribution of peptide conformations in the membrane interface is central to partitioning energetics. Molecular-dynamics simulations enable characterization of in-membrane structural dynamics. Here, we describe melittin partitioning into dioleoylphosphatidylcholine lipids using CHARMM and OPLS force fields. Although the OPLS simulation failed to reproduce experimental results, the CHARMM simulation reported was consistent with experiments. The CHARMM simulation showed melittin to be represented by a narrow distribution of folding states in the membrane interface.Unstructured peptides fold into the membrane interface because partitioned hydrogen-bonded peptide bonds are energetically favorable compared to free peptide bonds (1–3). This folding process is central to the mechanisms of antimicrobial and cell-penetrating peptides, as well as to lipid interactions and stabilities of larger membrane proteins (4). The energetics of peptide partitioning into membrane interfaces can be described by a thermodynamic cycle (Fig. 1). State A is a theoretical state representing the fully unfolded peptide in water, B is the unfolded peptide in the membrane interface, C is the peptide in water, and D is the folded peptide in the membrane. The population of peptides in solution (State C) is best described as an ensemble of folded and unfolded conformations, whereas the population of peptides in State D generally is assumed to have a single, well-defined helicity, as shown in Fig. 1 A (5). Given that, in principle, folding in solution and in the membrane interface should follow the same basic rules, peptides in state D could reasonably be assumed to also be an ensemble. A fundamental question (5) is therefore whether peptides in state D can be correctly described as having a single helicity. Because differentiating an ensemble of conformations and a single conformation may be an impossible experimental task (5), molecular-dynamics (MD) simulations provide a unique high-resolution view of the phenomenon.Open in a separate windowFigure 1Thermodynamic cycles for peptide partitioning into a membrane interface. States A and B correspond to the fully unfolded peptide in solution and membrane interface, respectively. The folded peptide in solution is best described as an ensemble of unfolded and folded conformations (State C). State D is generally assumed to be one of peptides with a narrow range of conformations, but the state could actually be an ensemble of states as in the case of State C.Melittin is a 26-residue, amphipathic peptide that partitions strongly into membrane interfaces and therefore has become a model system for describing folding energetics (3,6–8). Here, we describe the structural dynamics of melittin in a dioleoylphosphatidylcholine (DOPC) bilayer by means of two extensive MD simulations using two different force fields.We extended a 12-ns equilibrated melittin-DOPC system (9) by 17 μs using the Anton specialized hardware (10) with the CHARMM22/36 protein/lipid force field and CMAP correction (11,12) (see Fig. S1 and Fig. S2 in the Supporting Material). To explore force-field effects, a similar system was simulated for 2 μs using the OPLS force field (13) (see Methods in the Supporting Material). In agreement with x-ray diffraction measurements on melittin in DOPC multilayers (14), melittin partitioned spontaneously into the lipid headgroups at a position below the phosphate groups at similar depth as glycerol/carbonyl groups (Fig. 2).Open in a separate windowFigure 2Melittin partitioned into the polar headgroup region of the lipid bilayer. (A) Snapshot of the simulation cell showing two melittin molecules (MLT1 and MLT2, in yellow) at the lipid-water interface. (B) Density cross-section of the simulation cell extracted from the 17-μs simulation. The peptides are typically located below the lipid phosphate (PO4) groups, in a similar depth as the glycerol/carbonyl (G/C) groups.To describe the secondary structure for each residue, we defined helicity by backbone dihedral angles (φ, ψ) within 30° from the ideal α-helical values (–57°, –47°). The per-residue helicity in the CHARMM simulation displays excellent agreement with amide exchange rates from NMR measurements that show a proline residue to separate two helical segments, which are unfolded below Ala⁵ and above Arg²² (15) (Fig. 3 A). In contrast, the OPLS simulation failed to reproduce the per-residue helicity except for a short central segment (see Fig. S3).Open in a separate windowFigure 3Helicity and conformational distribution of melittin as determined via MD simulation. (A) Helicity per residue for MLT1 and MLT2. (B) Corresponding evolution of the helicity. (C) Conformational distributions over the entire 17-μs simulation.Circular dichroism experiments typically report an average helicity of ∼70% for melittin at membrane interfaces (3,6,16,17), but other methods yield average helicities as high as 85% (15,18). Our CHARMM simulations are generally consistent with the experimental results, especially amide-exchange measurements (15); melittin helicity averaged to 78% for MLT1, whereas MLT2 transitioned from 75% to 89% helicity at t ≈ 8 μs, with an overall average helicity of 82% (Fig. 3 B). However, in the OPLS simulation, melittin steadily unfolds over the first 1.3 μs, after which the peptide remains only partly folded, with an average helicity of 33% (see Fig. S3). Similar force-field-related differences in peptide helicity were recently reported, albeit at shorter timescales (19). Although suitable NMR data are not presently available, we have computed NMR quadrupolar splittings for future reference (see Fig. S4).To answer the question asked in this article—whether the conformational space of folded melittin in the membrane interface can be described by a narrow distribution—the helicity distributions for the equilibrated trajectories are shown in Fig. 3 C. Whereas MLT1 in the CHARMM simulation produces a single, narrow distribution of the helicity, MLT2 has a bimodal distribution as a consequence of the folding event at t ≈ 8 μs (Fig. 3 C). We note that CHARMM force fields have a propensity for helix-formation and this transition might therefore be an artifact. We performed a cluster analysis to describe the structure of the peptide in the membrane interface. The four most populated conformations in the CHARMM simulation are shown in Fig. 4. The dominant conformation for both peptides was a helix kinked at G12 and unfolded at the last 5–6 residues of the C-terminus. The folding transition of MLT2 into a complete helix is visible by the 48% occupancy of a fully folded helix.Open in a separate windowFigure 4Conformational clusters of the two melittin peptides (MLT1 and MLT2) from the 17-μs CHARMM simulation in DOPC. Clustering is based on Cα-RMSD with a cutoff criterion of 2 Å.We conclude that the general assumption when calculating folding energetics holds: Folded melittin partitioned into membrane interfaces can be described by a narrow distribution of conformations. Furthermore, extended (several microsecond) simulations are needed to differentiate force-field effects. Although the CHARMM and OPLS simulations would seem to agree for the first few hundred nanoseconds, the structural conclusions differ drastically with longer trajectories, with CHARMM parameters being more consistent with experiments. However, as implied by the difference in substate distributions between MLT1 and MLT2, 17 μs might not be sufficient to observe the fully equilibrated partitioning process. The abrupt change in MLT2 might indicate that the helicity will increase to greater than experimentally observed in a sufficiently long simulation. On the other hand, it could be nothing more than a transient fluctuation. Increased sampling will provide further indicators of convergence of the helix partitioning process.     

Voltage Sensor Gating Charge Transfer in a hERG Potassium Channel?Model

Relaxation of a hERG K⁺ channel model during molecular-dynamics simulation in a hydrated POPC bilayer was accompanied by transitions of an arginine gating charge across a charge transfer center in two voltage sensor domains. Inspection of the passage of arginine side chains across the charge transfer center suggests that the unique hydration properties of the arginine guanidine cation facilitates charge transfer during voltage sensor responses to changes in membrane potential, and underlies the preference of Arg over Lys as a mobile charge carrier in voltage-sensitive ion channels.The response of voltage-sensitive ion channels to changes in membrane potential is mediated by voltage sensor domains (VSD) containing a transmembrane helical segment (S4) with a repeating motif of positively charged and hydrophobic amino acids (Fig. 1) (1,2). Changes in membrane potential drive the S4 helix through the membrane plane with the charged side chains (largely arginine) on S4 swapping Glu/Asp carboxylate partners that lie on less mobile elements of the VSD (2). Movement of S4 is coupled to the ion-conducting pore to transmit changes in membrane potential to channel gating (3).Open in a separate windowFigure 1Structures of the VSD of membrane domains before MD in a POPC bilayer. The S2 (pink) and S4 (blue) helices of the VSD of the hERG model (A) and Kv1.2/2.1 chimera structure (B) are highlighted. (C) Sequence alignment of S2 and S4 among homologous voltage-sensitive K⁺ channels.The VSD charge-pairing motif of K⁺ and Na⁺ channels is best represented in VSD states at zero membrane potential (S4 helix up) for which crystal structures exist for Kv1.2 (4), Kv1.2/2.1 chimera (5), and Na_v channels (6,7). In these states, positively charged residues on the intra- and extracellular sections of the S4 helix are separated by a hydrophobic charge-transfer center (CTC) (1) or plug (8) containing a highly conserved Phe residue (Fig. 1). This plug restricts water incursion across the VSD, focusing the electric field across a narrow region near the bilayer center. In voltage-driven transitions between S4 down- and up-states, positively charged S4 side chains move across the CTC.The ether-à-go-go (eag) and eag-related family of voltage-sensitive K⁺ channels likely share similar charge pairing interactions with VSDs in other channels (9,10). However, eag VSDs contain an extra negative charge on S2 (underlined in Fig. 1 C) so that in hERG, Asp residues (D460 and D466) lie approximately one helical turn above and below the conserved charge-transfer center Phe (F463) (Fig. 1). This eag-specific motif might be expected to facilitate transfer of Arg side chains through the CTC and to stabilize the voltage sensor (VS) in the up state. We recently described an open state (VS-up) hERG model built on the crystal structure template of the Kv1.2/2.1 chimera and molecular-dynamics (MD) simulation of this model in a hydrated POPC bilayer (11). We have inspected an extended version of this simulation and identified transitions of a gating charge into the CTC despite the absence of a membrane potential change. These transitions are absent in equivalent MD simulations of the chimera structure in a POPC bilayer.Fig. 1 shows a single VS from starting structures of the hERG model and the chimera structure in a hydrated POPC bilayer, after restrained MD to anneal the protein-lipid interface (see Methods in the Supporting Material). Because the hERG model is constructed on the chimera structure according to the alignment in Fig. 1 the pattern of pairing between S4 charges and acidic VS side chains is equivalent in the hERG model and chimera structure.The arrangement of charge-paired side chains remains constant during MD in all subunits of the chimera (e.g., Fig. 2 E and see Fig. S2 in the Supporting Material). However, in two subunits of the hERG model the R534 side chain moves toward the extracellular side of the bilayer, sliding into the CTC to form a charge interaction with the extra Asp residue (D460 in hERG) that lies just above F463 (Fig. 2, A–C). This transition is facilitated by changes in side-chain rotamers of R534 and F463 as the planar Arg guanidine group rotates past the F463 ring, and the availability of D460 as a counterion for the R534 guanidine (Fig. 2). Movement of an Arg guanidine past the Phe side chain of the CTC is similar to that described in steered MD of an isolated VS domain (12).Open in a separate windowFigure 2Movement of the R534 side chain across the CTC in chain a of the hERG model simulation (A). Similar transitions are observed in chains a and b (panels B and C), but not chains c (D) or d (not shown), where the R534 side chain remains close to D466. In all subunits of the Kv1.2/2.1 chimera simulation, charge pairing of the starting structure (Fig. 1B) was maintained throughout (e.g., panel E and see Fig. S2 in the Supporting Material). (Black and blue lines) Distances from the Arg CZ or Lys ε atom to the two O atoms, respectively, of Asp or Glu.Mason et al. (13) have shown, using neutron scattering, that the low charge density guanidine cation (Gdm⁺) corresponding to the Arg side chain is poorly hydrated above and below the molecular plane. This property may underlie the universal preference for Arg (over Lys) in voltage sensor charge transfer. Although the poorly-hydrated surfaces of Gdm⁺ interact favorably with nonpolar (especially planar) surfaces (14,15), Gdm⁺ retains in-plane hydrogen bonding (13). In the transition of R534 across the CTC, in-plane solvation of the guanidine side chain is provided initially by D466, D501, and water molecules below the CTC, and during and after the transition by D501 and D460 side chains and waters above the CTC (Fig. 3, A and B). Complete transfer of the R534 side chain across the CTC was not observed, but would be expected to involve movement of the guanidine group away from H-bonding distance with D501.Open in a separate windowFigure 3In-plane solvation of R534 guanidine in the charge transfer center during the hERG model MD (A). (Dotted lines) H-bond distances of <2.5 Å. The right-hand group consists of top-down (B) and end-on (C) views of the distribution of oxygen atoms around the side chain of hERG R534 at 20-ns intervals during MD (subunit a). (D) End-on view of equivalent atom distributions around the K302 side chain during the Kv1.2/2.1 chimera MD (subunit c). (Red spheres, water O; pink, Asp OD1 and OD2; purple:, Glu OE1 and OE2.)The atom distribution around the R534 side chain during MD (Fig. 3, B and C) conforms to the experimental Gdm⁺ hydration structure (13), with H-bonding to waters and side-chain Asp O atoms exclusively in the guanidine plane. The passage of Gdm⁺ through the CTC is facilitated by the hydrophobic nature of Gdm⁺ above and below the molecular plane (13), which allows interaction with the nonpolar groups (especially F463) in the CTC (Fig. 3 A and see Fig. S3). This contrasts with the solvation properties of the Lys amino group (e.g., K302 of the Kv1.2/2.1 chimera (Fig. 1), which has a spherical distribution of H-bonding and charge-neutralizing oxygen atoms (Fig. 3 D and see Fig. S4).To further test these interpretations, we ran additional MD simulations of the isolated hERG VS domain model and an R534K mutant in a hydrated POPC bilayer. Again, the R534 side chain entered the CTC in the wild-type model simulation whereas the K534 side chain did not (see Fig. S5). Inspection of the atom distributions in Fig. 3 D (and see Fig. S4) indicates that the pocket below the conserved Phe of the CTC is particularly favorable for a Lys side chain, with waters and acidic side chains that satisfy the spherical solvation requirements of the terminal amino group, and nonpolar side chains that interact with the aliphatic part of the side chain.The occurrence of transitions of the R534 side chain through the CTC in the hERG model, in the absence of a change in membrane potential, indicates a relaxation from a less-stable starting structure. However, the path of the R534 side chain provides useful molecular-level insight into the nature of charge transfer in voltage sensors. How do these observations accord with broader evidence of charge transfer in voltage-sensitive channels in general, and hERG in particular? Studies with fluorinated analogs of aromatic side chains equivalent to F463 of hERG or F233 of the chimera indicate the absence of a significant role for cation-π interactions involving the CTC aromatic group in K⁺ and Na_v channels, although a planar side chain is preferred in some cases (1,16). In hERG, F463 can be replaced by M, L, or V with small effects on channel gating (17), indicating that the hERG CTC requires only a bulky nonpolar side chain to seal the hydrophobic center of the VS and allow passage of the Arg side chain through the CTC. Both absence of requirement for cation-π interactions, and accommodation of nonplanar hydrophobic side chains in a functional hERG CTC, are broadly consistent with the interpretation that it is the poorly-hydrated nature of the Arg guanidine group above and below the molecular plane (together with its tenacious proton affinity (18)) that governs its role in carrying gating charge in voltage sensors.While the simulations suggest that R534 may interact with D460 in the open channel state, the possibility that the extra carboxylate side chain above the CTC might facilitate gating charge transfer is seemingly inconsistent with the slow activation of hERG, although hERG D460C does activate even more slowly than the WT channel (9). However, S4 movement in hERG occurs in advance of channel opening (19), and slow gating is partly mediated by interactions involving hERG cytoplasmic domains (20); thus, slow S4 movement may not be an inherent property of the hERG voltage sensor. Recent studies show that when hERG gating is studied at very low [Ca²⁺] (50 μM) and low [H⁺] (pH 8.0), the channel is strongly sensitized in the direction of the open state; this effect is reduced in hERG D460C (and hERG D509C) (10). These observations support a role for the extra hERG Asp residues in binding Ca²⁺ (and H⁺) (10), allowing the channel to be allosterically responsive to changes in pH and [Ca²⁺]. A true comparison of a hERG model with experimental channel gating might involve studies on a channel lacking cytoplasmic domains that modulate gating, and using conditions (high pH and low [Ca²⁺]) that leave the eag-specific Asp residues unoccupied. This could reveal the inherent current-voltage relationships and kinetics of the hERG voltage sensor.     

Interplay between Velocity and Travel Distance of Kinesin-based Transport in the Presence of Tau

Although the disease-relevant microtubule-associated protein tau is known to severely inhibit kinesin-based transport in vitro, the potential mechanisms for reversing this detrimental effect to maintain healthy transport in cells remain unknown. Here we report the unambiguous upregulation of multiple-kinesin travel distance despite the presence of tau, via decreased single-kinesin velocity. Interestingly, the presence of tau also modestly reduced cargo velocity in multiple-kinesin transport, and our stochastic simulations indicate that the tau-mediated reduction in single-kinesin travel underlies this observation. Taken together, our observations highlight a nontrivial interplay between velocity and travel distance for kinesin transport, and suggest that single-kinesin velocity is a promising experimental handle for tuning the effect of tau on multiple-kinesin travel distance.Conventional kinesin is a major microtubule-based molecular motor that enables long-range transport in living cells. Although traditionally investigated in the context of single-motor experiments, two or more kinesin motors are often linked together to transport the same cargo in vivo (1–4). Understanding the control and regulation of the group function of multiple kinesins has important implications for reversing failure modes of transport in a variety of human diseases, particularly neurodegenerative diseases. Tau is a disease-relevant protein enriched in neurons (5,6). The decoration of microtubules with tau is known to strongly inhibit kinesin transport in vitro (7–9), but how kinesin-based transport is maintained in the presence of high levels of tau, particularly in healthy neurons, remains an important open question. To date, no mechanism has been directly demonstrated to reverse the inhibitory effect of tau on kinesin-based transport. Here we present a simple in vitro study that demonstrates the significant upregulation of multiple-kinesin travel distance with decreasing ATP concentration, despite the presence of tau.This investigation was motivated by our recent finding that single-kinesin velocity is a key controller for multiple-kinesin travel distance along bare microtubules (10). The active stepping of each kinesin motor is stimulated by ATP (11), and each kinesin motor remains strongly bound to the microtubule between successive steps (10,11). As demonstrated for bare microtubules (10), with decreasing ATP concentrations, each microtubule-bound kinesin experiences a decreased stepping rate per unit time and spends an increased fraction of time in the strongly bound state; additional unbound kinesins on the same cargo have more time to bind to the microtubule before cargo travel terminates. Thus, reductions in single-kinesin velocity increase the probability that at least one kinesin motor will remain bound to the microtubule per unit time, thereby increasing the travel distance of each cargo (10). Because this effect only pertains to the stepping rate of each individual kinesin and does not address the potential presence of roadblocks such as tau on the microtubules, we hypothesized in this study that single-kinesin velocity may be exploited to relieve the impact of tau on multiple-kinesin travel distance.We focused our in vitro investigation on human tau 23 (htau23, or 3RS tau), an isoform of tau that exhibits the strongest inhibitory effect on kinesin-based transport (7–9). Importantly, htau23 does not alter the stepping rate of individual kinesins (7,9), supporting our hypothesis and enabling us to decouple single-kinesin velocity from the potential effects of tau. We carried out multiple-kinesin motility experiments using polystyrene beads as in vitro cargos (8,10), ATP concentration as an in vitro handle to controllably tune single-kinesin velocity (10,11), and three input kinesin concentrations to test the generality of potential findings for multiple-kinesin transport. Combined with previous two-kinesin studies (10,12), our measurements of travel distance (Fig. 1 A) indicate that the lowest kinesin concentration employed (0.8 nM) corresponds to an average of ∼2–3 kinesins per cargo. Note that in the absence of tau, the observed decrease in bead velocity at the higher kinesin concentrations (Fig. 1 A) is consistent with a recent in vitro finding (13). At 1 mM ATP, htau23 reduced kinesin-based travel distance by a factor of two or more (Fig. 1, A and B). This observation is in good agreement with previous reports (7,8).Open in a separate windowFigure 1Distributions of multiple-kinesin travel distances measured at three experimental conditions, to verify the effect of tau (A and B) and to investigate the impact of single-kinesin velocity on the tau effect (B and C). Shaded bars at 8.7 μm indicate counts of travel exceeding the field of view. The mean travel distance (d; ± standard error of mean, SEM), sample size (n), and corresponding mean velocity (v; ± SEM) are indicated. MT denotes microtubule. Mean travel distance increased substantially at 20 μM ATP (C), despite the presence of htau23. This effect persisted across all three kinesin concentrations tested (left to right).Consistent with our hypothesis, reducing the available ATP concentration to 20 μM increased the multiple-kinesin travel distance by >1.4-fold for all three input kinesin concentrations (Fig. 1, B and C), despite the presence of htau23. The corresponding reduction in single-kinesin velocity with decreasing ATP concentration (10,11) is reflected in the ∼3.4-fold reduction in the measured bead velocities (Fig. 1, B and C). Therefore, the strong negative relationship between single-kinesin velocity and multiple-kinesin travel distance occurs not only for bare microtubules (10), but also for tau-decorated microtubules.What causes the observed increase in travel distance at the lower ATP concentration (Fig. 1, B and C)? In addition to the mechanism discussed above for the case of bare microtubules (10), an intriguing mechanism was suggested by recent studies of tau-microtubule interactions in which htau23 was observed to dynamically diffuse along microtubule lattices (14,15): reducing the stepping rate of a microtubule-bound kinesin may effectively increase the probability that a tau roadblock can diffuse away before the kinesin takes its next step.Perhaps surprisingly, although htau23 does not impact single-kinesin velocity (7,9), we observed a modest reduction in the average velocity of multiple-kinesin transport in experiments using tau-decorated microtubules (Fig. 1, A and B). This decreased velocity reflects a substantially larger variance in the instantaneous velocity for bead trajectories in the presence of htau23 (see Fig. S1 in the Supporting Material), as quantified by parsing each bead trajectory into a series of constant-velocity segments using a previously developed automatic software incorporating Bayesian statistics (16).To test the possibility that single-kinesin travel distance impacts multiple-kinesin velocity, we performed stochastic simulations (see the Supporting Material) that assumed N identical kinesin motors available for transport and included kinesin’s detachment kinetics (17). Previously, this model successfully captured multiple-dynein travel distances in vivo using single-dynein characteristics measured in vitro (18). In this study, we introduced one (and only one) free parameter to reflect the probability of each bound kinesin encountering tau at each step. When encountering tau, each kinesin has a 54% probability of detaching from the microtubule (interpolated from Fig. 2A of Dixit et al. (7)); the undetached kinesin is assumed to remain engaged in transport and completes its step along the microtubule despite the presence of tau.Remarkably, our simple simulation suggested that the tau-mediated reduction in single-kinesin travel is sufficient to reduce multiple-kinesin velocity (Fig. 2 A). The majority of the velocity decrease is predicted to occur at the transition from single-kinesin to two-kinesin transport (Fig. 2). Further decreases in cargo velocity with increasing motor number are predicted to be modest and largely independent of tau (Fig. 2 B). The results of our simulation remain qualitatively the same when evaluated at two bounds (40 and 65%) encompassing the interpolated 54% probability of kinesin detaching at tau (see Fig. S2).Open in a separate windowFigure 2Stochastic simulations predict a tau-dependent reduction in multiple-kinesin velocity, assuming that the only effect of tau protein is to prematurely detach kinesin from the microtubule (or, to reduce single-kinesin travel distance). (A) Average velocity of cargos carried by the indicated number of kinesins was evaluated at 1 mM ATP, and for four probabilities that a kinesin may encounter tau at each step. Mean velocity was evaluated using 600 simulated trajectories for all simulation conditions. Error bars indicate SEM. (B) Change in cargo velocity with each additional kinesin (ΔVel/kinesin) as a function of tau-encounter probability. These values were calculated from cargo velocities shown in panel A. Error bars indicate SEM.We note that our simple simulations do not consider the possibility that kinesin may pause in front of a tau roadblock, as previously reported in Dixit et al. (7). We omitted this consideration because the interaction strength between kinesin and the microtubule in such a paused state is unknown. In a multiple-motor geometry, could a paused kinesin be dragged along by the other motors bound to the same cargo? Could a tau roadblock be forcefully swept off the microtubule surface by the collective motion of the cargo-motor complex? Significant experimental innovations are necessary to specifically address these questions in future multiple-motor assays and to guide modeling efforts. Nonetheless, our simple simulation demonstrates that reducing single-kinesin travel distance is sufficient to decrease multiple-kinesin travel distance.Taken together, our observations highlight a nontrivial interplay between velocity and travel distance for kinesin-based transport in the presence of tau. We uncover a previously unexplored dual inhibition of tau on kinesin-transport: in addition to limiting cargo travel distance, the tau-mediated reduction in single-kinesin travel distance also leads to a modest reduction in multiple-kinesin velocity. We provide what we believe to be the first demonstration of the unambiguous upregulation of multiple-kinesin travel distance despite the presence of tau, via reducing single-kinesin velocity, suggesting a mechanism that could be harnessed for future therapeutic interventions in diseases that result from aberrant kinesin-based transport.     

Ratiometric Imaging of the T-Cell Actin Cytoskeleton Reveals the Nature of Receptor-Induced Cytoskeletal Enrichment

The T-cell actin cytoskeleton mediates adaptive immune system responses to peptide antigens by physically directing the motion and clustering of T-cell receptors (TCRs) on the cell surface. When TCR movement is impeded by externally applied physical barriers, the actin network exhibits transient enrichment near the trapped receptors. The coordinated nature of the actin density fluctuations suggests that they are composed of filamentous actin, but it has not been possible to eliminate de novo polymerization at TCR-associated actin polymerizing factors as an alternative cause. Here, we use a dual-probe cytoskeleton labeling strategy to distinguish between stable and polymerizing pools of actin. Our results suggest that TCR-associated actin consists of a relatively high proportion of the stable cytoskeletal fraction and extends away from the cell membrane into the cell. This implies that actin enrichment at mechanically trapped TCRs results from three-dimensional bunching of the existing filamentous actin network.The T-cell actin cytoskeleton is critical for proper antigen recognition by the mammalian adaptive immune system. During T-cell receptor (TCR) triggering by antigen peptides presented on major histocompatibility proteins (pMHCs) on the surfaces of antigen-presenting cells (APCs), the T-cell actin cytoskeleton adopts a pattern of centrosymmetric retrograde flow (1–3). This simultaneously promotes further TCR triggering (4) and rearranges various T-cell membrane proteins and their APC counterparts into an organized cell-cell interface termed the immunological synapse (IS) (5–7). During this process, TCRs form microclusters that move to the center of the IS in an actin-dependent manner (8,9). When engineered physical barriers interrupt the centripetal motion of TCR clusters, actin flow slows near the pinned microclusters, and the cytoskeletal network transiently accumulates and dissipates at the sites (10,11). The amplitude and duration of the induced cytoskeletal fluctuations are much greater than would be expected for a random distribution of independent objects, indicating that the actin in the local environment is coordinated. Whether this coordination arises from a rearrangement in the existing F-actin network or represents de novo polymerization of the cytoskeleton, as predicted by the association of TCRs with actin polymerizing factors (12), remains unclear. Here, we use a dual-probe cytoskeleton labeling approach that has previously been applied to distinguish between stable and dynamic populations of actin by exploiting the different relative affinities of monomeric actin and actin-binding proteins toward each population (13). This strategy reveals that TCR-associated actin is composed primarily of the stable cytoskeletal fraction and that local enrichment results from three-dimensional bunching of the existing filamentous actin network.Primary T cells from mice transgenic for the AND TCR were triggered using synthetic APCs consisting of supported lipid bilayers functionalized with pMHC and the integrin ligand intercellular adhesion molecule 1. Nanopatterned metal grids on the bilayer substrate acted as diffusion barriers that prevented lateral transport of TCR-pMHC complexes (14,15). Transient enrichment of actin at TCR clusters trapped at these barriers was visualized using fluorescent fusions of actin itself (mKate2-β-actin) and the F-actin binding domain of utrophin (EGFP-UtrCH). Such a dual-probe strategy theoretically allows for discrimination between different pools of actin: dynamic populations characterized by high polymerization and/or short filament fragments tend to be relatively better labeled by direct actin fusions whereas stable populations composed of longer filaments can support higher labeling by fluorescent fusions of F-actin binding proteins. This visualization method has been validated in Xenopus oocytes, where it distinguishes actin populations during wound healing (13). It has not been explicitly applied to T cells; however, simultaneous labeling of the Jurkat cell cytoskeleton using EGFP-actin and Alexa 568-phalloidin reveals distinct populations of actin consistent with the results expected from Xenopus (13,16).Our results show that the T-cell periphery is relatively enriched in mKate2-β-actin (Fig. 1 C, box 1), while EGFP-UtrCH dominates toward the center of the IS (Fig. 1 C, box 2). We infer from this probe distribution that the cytoskeleton at the T-cell periphery is composed of short fragments and is a site of active polymerization, whereas at the center of the IS, actin filaments are longer and predominantly stable. This is consistent with previous models of the T-cell actin network (3,16). An effective way to highlight each of these cytoskeletal regions is to consider the relative ratios of the two probes at each location. In this case, a high UtrCH/actin ratio corresponds to stable actin, and a high actin/UtrCH ratio corresponds to dynamic actin (Fig. 1 D). When T cells are treated with cytochalasin D, an inhibitor of actin polymerization, the overall UtrCH/actin ratio of the cell decreases as would be expected from a general decrease in polymerized actin (see Movie S7 and Movie S8 in the Supporting Material). However, it should be noted that photobleaching can also shift the UtrCH/actin ratio over time. We limit quantitative analysis of the ratio to its spatial gradients at a single time point, but such analysis is possible in systems that permit rigorous calibration for probe expression and photobleaching.Open in a separate windowFigure 1Ratiometric imaging of the cytoskeleton in live T cells distinguishes between dynamic and stable actin populations. (A) mKate2-β-actin, (B) EGFP-UtrCH, and (C) merged images of a triggered T cell show different actin pools. The cutouts in panel C correspond to (1) a region high in dynamic actin featuring short, polymerizing filaments and/or actin monomers and (2) a region with a stable actin population featuring longer filaments to which UtrCH can bind. (D) The UtrCH/actin ratio image highlights pools of relatively high UtrCH (red) or actin (blue). (Scale bars: 5 μm.)Actin enrichment at trapped TCR clusters incorporates both mKate2-β-actin (Fig. 2, A and C) and EGFP-UtrCH (Fig. 2, B and C). The relative UtrCH/actin ratio at these sites (Fig. 2 D, box 2) is quite high relative to nearby background areas (Fig. 2 D, box 1), indicating that the actin is derived primarily from the stable actin population.Open in a separate windowFigure 2Receptor-induced cytoskeletal enrichment at sites of pinned TCRs corresponds to a primarily stable actin fraction. (A) mKate2-β-actin, (B) EGFP-UtrCH, and (C) merged images of a triggered T cell interacting with a nanopatterned supported lipid bilayer show actin enrichment corresponding to putative sites of pinned TCRs. (D) The UtrCH/actin ratio is high at sites displaying actin enrichment, indicating a primarily stable actin fraction in (1) these regions compared to (2) nearby background areas. (Scale bars: 5 μm.)The three-dimensional distribution of TCR-associated actin was analyzed in dual-labeled live T cells using a spinning disk confocal microscope. The recordings show actin extending away from the cell membrane in the vicinity of trapped TCRs, while the rest of the actin cytoskeleton remains relatively flat (Fig. 3 and see Fig. S1 in the Supporting Material). These protrusions of actin away from the membrane surface are predominantly composed of stable, filamentous actin, as indicated by their relatively high UtrCH/actin ratio (Fig. 3 B).Open in a separate windowFigure 3Three-dimensional ratiometric imaging shows that actin enrichment extends away from the cell membrane. Single planes from (A) merged mKate2-β-actin and EGFP-UtrCH and (B) UtrCH/actin ratio three-dimensional stacks show actin enrichment at the cell membrane. Cutouts represent Z projections passing through sites of (1) enrichment and (2) nearby background regions. The color distribution in panel B is analogous to that in Figs. 1D and ​and22D, and is omitted for clarity. (Scale bar: 5 μm in the x axis only. Scale box: 1 μm.)Our interpretation of these results is that the filamentous actin network is relatively dense at sites of pinned TCRs. This is the simplest explanation out of several possibilities, one of which is formin-mediated mKate2-β-actin-deficient actin nucleation (17). Filament bunching at pinned TCRs can arise from consistent biophysical properties without assuming heterogeneity between the biochemistry of these receptors and other actin-associated proteins such as those at the cell edge, where locally high probe ratios are absent.Although TCRs are intentionally trapped as part of this experimental strategy, it is likely APCs can naturally impede TCR ligand mobilities under certain circumstances, and this has been shown to impact T-cell signaling (18,19). Actin architecture near cell surface proteins has been extensively studied in focal adhesions of fibroblasts (20), but the lack of stress fibers in T cells makes it unlikely that the two structures are similar. Thus, receptor-induced cytoskeletal enrichment at TCR clusters adds to the catalog of actin behaviors in situ, which is conveniently probed by techniques such as ratiometric dual-probe imaging in live cells. These techniques can be coupled to various spatial analysis algorithms to further extend their utility.     

Big steps toward understanding dynein

Dynein is a microtubule-based molecular motor that is involved in various biological functions, such as axonal transport, mitosis, and cilia/flagella movement. Although dynein was discovered 50 years ago, the progress of dynein research has been slow due to its large size and flexible structure. Recent progress in understanding the force-generating mechanism of dynein using x-ray crystallography, cryo-electron microscopy, and single molecule studies has provided key insight into the structure and mechanism of action of this complex motor protein.It has been 50 years since dynein was discovered and named by Ian Gibbons as a motor protein that drives cilia/flagella bending (Gibbons, 1963; Gibbons and Rowe, 1965). In the mid-1980s, dynein was also found to power retrograde transport in neurons (Paschal and Vallee, 1987). Subsequently, the primary amino acid sequence of the cytoplasmic dynein heavy chain, which contains the motor domain, was determined from the cDNA sequence (Mikami et al., 1993; Zhang et al., 1993). Like other biological motors, such as kinesins and myosins, the amino acid sequence of the dynein motor domain is well conserved. There are 16 putative genes that encode dynein heavy chains in the human genome (Yagi, 2009). Among these is one gene encoding cytoplasmic dynein heavy chain and one encoding retrograde intraflagellar transport dynein heavy chain, while the rest encode for heavy chains of axonemal dyneins. Most of the genes encoding the human dynein heavy chain have a counterpart in Chlamydomonas reinhardtii, which suggests that their functions are conserved from algae to humans.Dynein is unique compared with kinesin and myosin because dynein molecules form large molecular complexes. For example, one axonemal outer arm dynein molecule of C. reinhardtii is composed of three dynein heavy chains, two intermediate chains, and more than ten light chains (King, 2012). Mammalian cytoplasmic dynein consists of two heavy chains and several smaller subunits (Fig. 1 A; Vallee et al., 1988; Allan, 2011). The cargoes of cytoplasmic dynein are various membranous organelles, including lysosomes, endosomes, phagosomes, and the Golgi complex (Hirokawa, 1998). It is likely that one cytoplasmic dynein heavy chain can adapt to diverse cargos and functions by changing its composition.Open in a separate windowFigure 1.Atomic structures of cytoplasmic dynein. (A) Schematic structure of cytoplasmic dynein complex, adapted from Allan (2011). (B) The primary structure of cytoplasmic dynein. (C and D) The atomic model of D. discoideum cytoplasmic dynein motor domain (PDB accession no. 3VKG) overlaid on a microtubule (EMDB-5193; Sui and Downing, 2010) according to the orientation determined by Mizuno et al. (2007) (C) Side view. (D) View from the plus end of microtubule. (E) Schematic domain structure of dynein.Dynein must have a distinct motor mechanism from kinesin and myosin, because it belongs to the AAA+ family of proteins and does not have the conserved amino acid motifs, called the switch regions, present in kinesins, myosins, and guanine nucleotide-binding proteins (Vale, 1996). Therefore, studying dynein is of great interest because it will reveal new design principles of motor proteins. This review will focus on the mechanism of force generation by cytoplasmic and axonemal dynein heavy chains revealed by recent structural and biophysical studies.

Anatomy of dynein

To understand the chemomechanical cycle of dynein based on its molecular structure, it is important to obtain well-diffracting crystals and build accurate atomic models. Recently, Kon and colleagues determined the crystal structures of Dictyostelium discoideum cytoplasmic dynein motor domain, first at 4.5-Å resolution (Kon et al., 2011), and subsequently at 2.8 Å (without the microtubule binding domain) and 3.8-Å (wild type) resolution (Kon et al., 2012). Carter and colleagues also determined the crystal structures of the Saccharomyces cerevisiae (yeast) cytoplasmic dynein motor domain, first at 6-Å resolution (Carter et al., 2011), and later at 3.3–3.7-Å resolution (Schmidt et al., 2012). According to these crystal structures as well as previous EM studies, the overall structure of the dynein heavy chain is divided into four domains: tail, linker, head, and stalk (Fig. 1, B–E). Simply put, each domain carries out one essential function of a motor protein: the tail is the cargo binding domain, the head is the site of ATP hydrolysis, the linker is the mechanical amplifier, and the stalk is the track-binding domain.The tail, which is not part of the motor domain and is absent from crystal structures, is located at the N-terminal ∼1,400 amino acid residues and involved in cargo binding (gray in Fig. 1, B and E). The next ∼550 residues comprise the “linker” (pink in Fig. 1, B–E), which changes its conformation depending on the nucleotide state (Burgess et al., 2003; Kon et al., 2005). This linker domain was first observed by negative staining EM in combination with single particle analysis of dynein c, an isoform of inner arm dynein from C. reinhardtii flagella (Burgess et al., 2003). According to the crystal structures, the linker is made of bundles of α-helices and lies across the AAA+ head domain, forming a 10-nm-long rod-like structure (Fig. 1, C and D). Recent class averaged images of D. discoideum cytoplasmic dynein show that the linker domain is stiff along its entire length when undocked from the head (Roberts et al., 2012). The head (motor) domain of dynein is composed of six AAA+ (ATPase associated with diverse cellular activities) modules (Neuwald et al., 1999; color-coded in Fig. 1, B–E). Although many AAA+ family proteins are a symmetric homohexamer (Ammelburg et al., 2006), the AAA+ domains of dynein are encoded by a single heavy chain gene and form an asymmetric heterohexamer. Among the six AAA+ domains, hydrolysis at the first AAA domain mainly provides the energy for dynein motility (Imamula et al., 2007; Kon et al., 2012). The hexameric ring has two distinct faces: the linker face and the C-terminal face. The linker face is slightly convex and the linker domain lies across this side (Fig. 1 D, left side). The other side of the ring has the C-terminal domain (Fig. 1 D, right side).The stalk domain of dynein was identified as the microtubule-binding domain (MTBD; Gee et al., 1997). It emanates from the C-terminal face of AAA4 and is composed of antiparallel α-helical coiled-coil domain (yellow in Fig. 1, B–E). The tip of the stalk is the actual MTBD. Interestingly, the crystal structures revealed another antiparallel α-helical coiled coil that emerges from AAA5 (orange in Fig. 1, B–E), and this region is called the buttress (Carter et al., 2011) or strut (Kon et al., 2011), which was also observed as the bifurcation of the stalk by negative-staining EM (Burgess et al., 2003; Roberts et al., 2009). The tip of the buttress/strut is in contact with the middle of the stalk and probably works as a mechanical reinforcement of the stalk.

The chemomechanical cycle of dynein

Based on structural and biochemical data, a putative chemomechanical cycle of dynein is outlined in Fig. 2 (A–E). In the no-nucleotide state, dynein is bound to a microtubule through its stalk domain, and its tail region is bound to cargoes (Fig. 2 A). The crystal structures of yeast dynein are considered to be in this no-nucleotide state. When ATP is bound to the AAA+ head, the MTBD quickly detaches from the microtubule (Fig. 2 B; Porter and Johnson, 1983). The ATP binding also induces “hinging” of the linker from the head (Fig. 2 C). According to the biochemical analysis of recombinant D. discoideum dynein (Imamula et al., 2007), the detachment from the microtubule (Fig. 2, A and B) is faster than the later hinging (Fig. 2, B and C). As a result of these two reactions, the head rotates or shifts toward the minus end of the microtubule (for more discussion about “rotate” versus “shift” see the “Dyneins in the axoneme” section) and the MTBD steps forward. The directionality of stepping seems to be mainly determined by the MTBD, because the direction of dynein movement does not change even if the head domain is rotated relative to the microtubule by insertion or deletion of the stalk (Carter et al., 2008). In the presence of ADP and vanadate, dynein is considered to be in this state (Fig. 2 C).Open in a separate windowFigure 2.Presumed chemomechanical cycle and stepping of dynein. (A–E) Chemomechanical cycle of dynein. The pre- and post-power stroke states are also called the primed and unprimed states, respectively. The registries of the stalk coiled coil are denoted as α and β according to Gibbons et al. (2005). (F and G) Processive movement of kinesin (F) and dynein (G). (F) Hand-over-hand movement of kinesin. A step by one head (red) is always followed by the step of another head (green). The stepping of kinesin is on one protofilament of microtubule. (G) Presumed stepping of dynein. The step size varies and the interhead separation can be large. A step by one head (red) is not always flowed by the step of another head (green). (H) A model of strain-based dynein ATPase activation. (G, top) Without strain, the gap between the AAA1 and AAA2 is open and the motor domain cannot hydrolyze ATP. (G, bottom) Under a strain imposed between MTBD and tail (thin black arrows), the gap becomes smaller (thick black arrows) and turns on ATP hydrolysis by dynein.After the MTBD rebinds to the microtubule at the forward site (Fig. 2 D), release of hydrolysis products from the AAA+ head is activated (Holzbaur and Johnson, 1989) and the hinged linker goes back to the straight conformation (Fig. 2 E; Kon et al., 2005). The crystal structure of D. discoideum dynein is considered to be in the state after phosphate release and before ADP release. This straightening of the linker is considered to be the power-generating step and brings the cargo forward relative to the microtubule.

The MTBD of dynein

As outlined in Fig. 2, the nucleotide state of the head domain may control the affinity of the MTBD to the microtubule. Conversely, the binding of the MTBD to the microtubule should activate the ATPase activity of the head domain. This two-way communication is transmitted through the simple ∼17-nm-long α-helical coiled-coil stalk and the buttress/strut, and its structural basis has been a puzzling question.Currently there are three independent MTBD atomic structures in the Protein Data Bank (PDB): One of the crystal structures of the D. discoideum dynein motor domain contains the MTBD (Fig. 3 A), and Carter et al. (2008) crystallized the MTBD of mouse cytoplasmic dynein fused with a seryl tRNA-synthetase domain (Fig. 3 C). The MTBD structure of C. reinhardtii axonemal dynein was solved using nuclear magnetic resonance (PDB accession no. 2RR7; Fig. 3 B). The MTBD is mostly composed of α-helices and the three structures are quite similar to each other within the globular MTBD (Fig. 3). Note that dynein c has an additional insert at the MTBD–microtubule interface (Fig. 3 B, inset), whose function is not yet clear. The three structures start to deviate from the junction between the MTBD and the coiled-coil region of the stalk (Fig. 3, A–C, blue arrowheads). Particularly, one of the stalk α-helix (CC2) in D. discoideum dynein motor domain appears to melt at the junction with the MTBD (Fig. 3 A, red arrowhead). This structural deviation suggests that the stalk coiled coil at the junction is flexible, which is consistent with the observation by EM (Roberts et al., 2009).Open in a separate windowFigure 3.Atomic models of the MTBD of dynein. (A) D. discoideum cytoplasmic dynein (PDB accession no. 3VKH). (B) C. reinhardtii dynein c (PDB accession no. 2RR7). The inset shows the side view, highlighting the dynein c–specific insert. (C) Mouse cytoplasmic dynein (PDB accession no. 3ERR). (D) Mouse cytoplasmic dynein fit to the MTBD–microtubule complex derived from cryo-EM (PDB accession no. 3J1T). All the MTBD structures were aligned using least square fits and color-coded with a gradient from the N to C terminus. CC1, coiled coil helix 1; CC2, coiled coil helix 2. The blue arrowheads points to the junction between MTBD and the stalk, where a well-conserved proline residue (colored pink) is located. In C and D, two residues (isoleucine 3269 and leucine 3417) are shown as spheres. The two residues form hydrophobic contacts in the β-registry (C), whereas they are separated in the α-registry (D) because of the sliding between the two α-helices (blue and red arrows). Conformational changes observed in the mouse dynein MTBD in complex with a microtubule by cryo-EM are shown by black arrows. Note that the cryo-EM density map does not have enough resolution to observe sliding between CC1 and CC2. The sliding was modeled based on targeted molecular dynamics (Redwine et al., 2012).Various mechanisms have been proposed to explain how the affinity between the MTBD and a microtubule is controlled. Gibbons et al. (2005) proposed “the helix-sliding hypothesis” (for review see Cho and Vale, 2012). In brief, this hypothesis proposes that the sliding between two α-helices CC1 and CC2 (Fig. 3, C and D; blue and red arrows) may control the affinity of this domain to a microtubule. When Gibbons’s classification (Gibbons et al., 2005) of the sliding state is applied to the three MTBD structures, the stalk in the D. discoideum dynein motor domain is in the “α-registry” state (not visible in Fig. 3 A because of the melting of CC2), which corresponds to the strong binding state. However, the mouse cytoplasmic and C. reinhardtii axonemal MTBDs have the “β-registry” stalk (Fig. 3 C), which corresponds to the weak binding state.To observe conformational changes induced by the α-registry and/or microtubule binding, Redwine et al. (2012) solved the structure of mouse dynein MTBD in complex with a microtubule at 9.7-Å resolution using cryo-EM and single particle analysis. The MTBD was coupled with seryl tRNA-synthetase to fix the stalk helix in the α-registry. At this resolution, α-helices are visible, and they used molecular dynamics to fit the crystal structure of mouse MTBD (β-registry) to the cryo-EM density map. According to this result, the first helix H1 moves ∼10 Å to a position that avoids a clash with the microtubule (Fig. 3 D, black arrows). This also induces opening of the stalk helix (CC1). Together with mutagenesis and single-molecule motility assays, Redwine et al. (2012) proposed that this new structure represents the strong binding state. Currently, it is not clear why the MTBD structure of D. discoideum dynein motor domain (α-registry, Fig. 3 A) is not similar to the new α-registry mouse dynein MTBD, and this problem needs to be addressed by further studies.

Structures around the first ATP binding site

Another central question about motor proteins is how Ångstrom-scale changes around the nucleotide are amplified to generate steps >8 nm. For dynein, the interface between the first nucleotide-binding pocket and the linker seem to be the key force-generating element (Fig. 4). The crystal structures of dynein give us clues about how nucleotide-induced conformational changes may be transmitted to and amplified by the linker domain.Open in a separate windowFigure 4.Structures around the first ATP binding site. (A) Schematic domain structure of the head domain. Regions contacting the linker domain are colored purple. (B) AAA submodules surrounding the first nucleotide-binding pocket (PDB accession no. 3VKG, chain A). The linker is connected to AAA1 domain by the “N-loop.” To highlight that the two finger-like structures are protruding, the shadow of the atomic structure has been cast on the plane parallel to the head domain. (C) Interaction between the linker and the two finger-like structures. The pink arrowhead points to the hinge-like structure of the linker. The pink numbers indicates the subdomain of the linker.The main ATP catalytic site is located between AAA1 and AAA2 (Fig. 4, A and B). There are four ADP molecules in the D. discoideum dynein crystal structures, but the first ATP binding site alone drives the microtubule-activated ATPase activity, based on biochemical experiments on dyneins whose ATP binding sites were mutated (Kon et al., 2012).One AAA+ module is composed of a large submodule and a small α submodule (Fig. 4 B). The large α/β submodule is located inside of the ring and the small α submodule is located outside. The large submodule bulges toward the linker face, and the overall ring forms a dome-like shape (Fig. 1 D).The main ATP catalytic site is surrounded by three submodules: AAA1 large α/β, AAA1 small α, and AAA2 large α/β (Fig. 4, A and B). Based on the structural changes of other AAA+ proteins (Gai et al., 2004; Suno et al., 2006; Wendler et al., 2012), the gap between AAA1 and AAA2 modules is expected to open and close during the ATPase cycle.In fact, the size of the gap varies among the dynein crystal structures. The crystal structures of yeast dyneins show a larger gap between AAA1 and AAA2, which might be the reason why no nucleotide was found in the binding pocket. Although Schmidt et al. (2012) soaked the crystals in a high concentration of various nucleotides (up to 25 mM of ATP), no electron densities corresponding to the nucleotide were observed at the first ATP binding site. Among dynein crystal structures, one of D. discoideum dynein (PDB accession no. 3VKH, chain A) has the smallest gap, but it is still considered to be in an “open state” because the arginine finger in the AAA2 module (Fig. 4 B, red) is far from the phosphates of ADP. Because the arginine finger is essential for ATP hydrolysis in other AAA+ proteins (Ogura et al., 2004), the gap is expected to close and the arginine finger would stabilize the negative charge during the transition state of ATP hydrolysis.The presumed open/close conformational change between AAA1 and AAA2 would result in the movement of two “finger-like” structures protruding from the AAA2 large α/β submodule (Fig. 4 B). The two finger-like structures are composed of the H2 insert β-hairpin and preSensor I (PS-I) insert. In D. discoideum dynein crystal structure, the two finger-like structures are in contact with the “hinge-like cleft” of the linker (Fig. 4 C, pink arrowhead). The hinge-like cleft is one of the thinnest parts of the linker, where only one α-helix is connecting between the linker subdomains 2 and 3.In the yeast crystal structures, which have wider gaps between AAA1 and AAA2, the two finger-like structures are not in direct contact with the linker and separated by 18 Å. Instead, the N-terminal region of the linker is in contact with the AAA5 domain (Fig. 4 A). To test the functional role of the linker–AAA5 interaction, Schmidt et al. (2012) mutated a residue involved in the interaction (Phe3446) and found that the mutation resulted in severe motility defects, showing strong microtubule binding and impaired ATPase activities. In D. discoideum dynein crystals, there is no direct interaction between AAA5 and the linker, which suggests that the gap between AAA1 and AAA2 may influence the interaction between the head and linker domain. The contact between the linker and AAA5 may also influence the gap around AAA5, because the gap between AAA5 and AAA6 is large in yeast dynein crystal, whereas the one between AAA4 and AAA5 is large in D. discoideum dynein.The movement of two finger-like structures would induce remodeling of the linker. According to the recent cryo-EM 3D reconstructions of cytoplasmic dynein and axonemal dynein c (Roberts et al., 2012), the linker is visible across the head and there is a large gap between AAA1 and AAA2 in the no-nucleotide state. This linker structure is considered to be the “straight” state (Fig. 2, A and E). In the presence of ADP vanadate, the gap between AAA1 and AAA2 is closed and the N-terminal region of linker is near AAA3, which corresponds to the pre-power stroke “hinged” state (Fig. 2, C and D). The transition from the hinged state to the straight state of the linker is considered to be the force-generating step of dynein.

Processivity of dynein

As the structure of dynein is different from other motor proteins, dynein’s stepping mechanism is also distinct. Both dynein and kinesin are microtubule-based motors and move processively. Based on the single molecule tracking experiment with nanometer accuracy (Yildiz et al., 2004), it is widely accepted that kinesin moves processively by using its two motor domain alternately, called the “hand-over-hand” mechanism. To test whether dynein uses a similar mechanism to kinesin or not, recently Qiu et al. (2012) and DeWitt et al. (2012) applied similar single-molecule approaches to dynein.To observe the stepping, the two head domains of yeast recombinant cytoplasmic dynein were labeled with different colors and the movement of two head domains was tracked simultaneously. If dynein walks by the hand-over-hand mechanism, the step size would be 16 nm and the stepping of one head domain would always be followed by the stepping of another head domain (alternating pattern), and the trailing head would always take a step (Fig. 2 F). Contrary to this prediction, both groups found that the stepping of the head domains is not coordinated when the two head domains are close together. These observations indicated that the chances of a leading or trailing head domain stepping are not significantly different (Fig. 2 G; DeWitt et al., 2012; Qiu et al., 2012).This stepping pattern predicts that the distance between the head domains can be long. In fact, the distance between the two head domains is on average ∼18 ± 11 nm (Qiu et al., 2012) or 28.4 ± 10.7 nm (DeWitt et al., 2012), and as large as ∼50 nm (DeWitt et al., 2012). When the two head domains are separated, there is a tendency where stepping of the trailing head is preferred over that of the forward head.In addition, even though the recombinant cytoplasmic dynein is a homodimer, the two heavy chains do not function equally. While walking along the microtubule, the leading head tends to walk on the right side, whereas the trailing head walks on the left side (DeWitt et al., 2012; Qiu et al., 2012). This arrangement suggests that the stepping mechanism is different between the two heads. In fact, when one of the two dynein heavy chains is mutated to abolish the ATPase activity at AAA1, the heterodimeric dynein still moves processively (DeWitt et al., 2012), with the AAA1-mutated dynein heavy chain remaining mostly in the trailing position. This result clearly demonstrates that allosteric communication between the two AAA1 domains is not required for processivity of dynein. It is likely that the mutated head acts as a tether to the microtubule, as it is known that wild-type dynein can step processively along microtubules under external load even in the absence of ATP (Gennerich et al., 2007).These results collectively show that dynein moves by a different mechanism from kinesin. It is likely that the long stalk and tail allow dynein to move in a more flexible manner.

Dyneins in the axoneme

As mentioned in the introduction, >10 dyneins work in motile flagella and cilia. The core of flagella and cilia is the axoneme, which is typically made of nine outer doublet microtubules and two central pair microtubules (“9 + 2,” Fig. 5 A). The axonemes are found in various eukaryotic cells ranging from the single-cell algae C. reinhardtii to human. Recent extensive cryo-electron tomography (cryo-ET) in combination with genetics revealed the highly organized and complex structures of axonemes that are potentially important for regulating dynein activities (Fig. 5, C and D; Nicastro et al., 2006; Bui et al., 2008, 2009, 2012; Heuser et al., 2009, 2012; Movassagh et al., 2010; Lin et al., 2012; Carbajal-González et al., 2013; Yamamoto et al., 2013).Open in a separate windowFigure 5.Arrangement of axonemal dyneins. (A) The schematic structure of the motile 9 + 2 axoneme, viewed from the base of flagella. (B) Quasi-planar asymmetric movement of the 9 + 2 axoneme typically observed in trachea cilia or in C. reinhardtii flagella. (C and D) 3D structure of a 96-nm repeat of doublet microtubules in the distal/central region of C. reinhardtii flagella (EMDB-2132; Bui et al., 2012). N-DRC, the nexin-dynein regulatory complex; ICLC, intermediate chain/light chain complex. Inner arm dynein subspecies are labeled according to Bui et al. (2012) and Lin et al. (2012). To avoid the confusion with the linker domain of dynein, the structures connecting between outer and inner arm dyneins are labeled as “connecters,” which are normally called “linkers.” Putative ATP binding sites of outer arm dynein determined by biotin-ADP (Oda et al., 2013) are indicated by orange circles. The atomic structure of cytoplasmic dynein is placed into the β-heavy chain of outer arm dynein and its enlarged view is shown in the inset. (D) Two doublet microtubules, viewed from the base of flagella.The basic mechanochemical cycles of axonemal dyneins are believed to be shared with cytoplasmic dynein. Dynein c is an inner arm dynein of C. reinhardtii and used extensively to investigate the conformational changes of dynein, as shown in Fig. 2 (A–E), by combining EM and single-particle analysis (Burgess et al., 2003; Roberts et al., 2012). Structural changes of axonemal dyneins complexed with microtubules are also observed by quick-freeze and deep-etch EM (Goodenough and Heuser, 1982; Burgess, 1995), cryo-EM (Oda et al., 2007), negative-staining EM (Ueno et al., 2008), and cryo-ET (Movassagh et al., 2010). According to these studies, the AAA+ head domains are constrained near the A-tubule in the no-nucleotide state. In the presence of nucleotide, the head domains move closer to the B-tubule and/or the minus end of microtubule, and their appearance becomes heterogeneous, which is consistent with the observation of isolated dynein c that shows greater flexibility between tail and stalk in the ADP/vanadate state (Burgess et al., 2003).One of the controversies about the structural changes of axonemal dyneins is whether their stepping involves “rotation” or “shift” of the head (Fig. 2, B to D). The stalk angle relative to the microtubule seems to be a constant ∼60° irrespective of the nucleotide state (Ueno et al., 2008; Movassagh et al., 2010). This angle is similar to the angle obtained from cryo-EM study of the MTBD–microtubule complex (Redwine et al., 2012). Based on these observations, Ueno et al. (2008) and Movassagh et al. (2010) hypothesize that the “shift” of the head pulls the B-microtubule toward the distal end. However, Roberts et al. (2012) propose that the “rotation” of head and stalk is involved in the stepping based on the docking of dynein c head into an averaged flagella tomogram obtained by Movassagh et al. (2010). This issue needs to be resolved by more reliable and high-resolution data, but these two models may not be mutually exclusive. For example, averaged tomograms may be biased toward the microtubule-bound stalk because tomograms are aligned using microtubules.To interpret these structural changes of axonemal dyneins, docking atomic models of dynein is necessary. According to Roberts et al. (2012), the linker face of inner arm dynein c is oriented outside of axoneme (Fig. 5 D). For outer arm dyneins, we used cryo-EM in combination with biotin-ADP-streptavidin labeling and showed that the ATP binding site, most likely AAA1, is on the left side of the AAA+ head (Fig. 5 C; Oda et al. (2013)). Assuming that the stalks extend out of the plane toward the viewer, the linker face of outer arm dynein is oriented outside of axoneme (Fig. 5 C, inset; and Fig. 5 D). If it were the opposite, the AAA1 would be located on the right side of the AAA+ head. In summary, both inner and outer arm dynein seem to have the same arrangement, with their linker face oriented outside of the axoneme (Fig. 5 D).A unique characteristic of axonemal dyneins is that these dyneins are under precise temporal and spatial control. To generate a planer beating motion (Fig. 5 B), dyneins should be asymmetrically controlled, because the dyneins located on doublets 2–4 drive the effective stroke, whereas the ones on doublets 6–8 drive the recovery stroke (Fig. 5 A). Based on the cryo-ET observation of axonemes, Nicastro et al. (2006) proposed that “linkers” between dyneins provide hard-wiring to coordinate motor activities. Because the linkers in axonemes are distinct structures from the linker domain of dynein, for clarity, here we call them “connecters.” According to the recent cryo-ET of proximal region of C. reinhardtii flagella (Bui et al., 2012), there are in fact asymmetries among nine doublets that are localized to the connecters between outer and inner arm dynein, called the outer-inner dynein (OID) connecters (Fig. 5, A and C). Recently we identified that the intermediate chain 2 (IC2) of outer arm dynein is a part of the OID connecters, and a mutation of the N-terminal region of IC2 affects functions of both outer and inner arm dyneins (Oda et al., 2013), which supports the idea that the connecters between dyneins are involved in axonemal dynein regulation.

Closing remarks

Thanks to the crystal structures, we can now design and interpret experiments such as single molecule assays and EM based on the atomic models of dynein. Our understanding of the molecular mechanism and cellular functions of dyneins will be significantly advanced by these experiments in the near future.One important direction of dynein research is to understand the motor mechanisms closer to the in vivo state. For example, the step sizes of cytoplasmic dynein purified from porcine brain is ∼8 nm independent of load (Toba et al., 2006). This result suggests that intermediate and light chain bound to the dynein heavy chain may modulate the motor activity of dynein. To address such questions, Trokter et al. (2012) reconstituted human cytoplasmic dynein complex from recombinant proteins, although the reconstituted dynein did not show robust processive movement. Further studies are required to understand the movement of cytoplasmic dynein. Similarly, axonemal dyneins should also be studied using mutations in a specific gene that does not affect the overall flagella structure, rather than depending on null mutants that cause the loss of large protein complexes.Detailed full chemomechanical cycle of dynein and its regulation are of great importance. Currently, open/closed states of the gap between AAA1 and AAA2 are not clearly correlated with the chemomechanical cycle of dynein. Soaking dynein crystal with nucleotides showed that the presence of ATP alone is not sufficient to close the gap, at least in the crystal (Schmidt et al., 2012). This result suggests that other factors such as a conformational change of the linker are required. For other motors, ATP hydrolysis is an irreversible chemical step, which is often “gated” by strain. In the case of kinesin, ATP is hydrolyzed by a motor domain only when a forward strain is applied by the other motor domain through the neck linker (Cross, 2004; Kikkawa, 2008). A similar strain-based gating mechanism may play important roles in controlling the dynein ATPase. Upon MTBD binding to the forward binding site, a strain between MTBD and tail would be applied to the dynein molecule. The Y-shaped stalk and strut/buttress under the strain would force the head domain to close the gap between AAA4 and AAA5 (Fig. 2 H). Similarly, the linker under the strain would be hooked onto the two finger-like structures and close the gap between AAA1 and AAA2 (Fig. 2 H). The gap closure then triggers ATP hydrolysis by dynein. This strain-based gating of dynein is consistent with the observation that the rate of nonadvancing backward steps, which would depend on ATP hydrolysis, is increased by load applied to dynein (Gennerich et al., 2007). To explain cilia and flagella movement, the geometric clutch hypothesis has been proposed (Lindemann, 2007), which contends that the forces transverse (t-force) to the axonemal axis act on the dynein to regulate dynein activities. In the axoneme, dynein itself can be the sensor of the t-force by the strain-based gating mechanism. Further experiments are required to test this idea, but the strain-based gating could be a shared property of biological motors.     

Real-Time Imaging of Electrical Signals with an Infrared FDA-Approved Dye

Clinical methods used to assess the electrical activity of excitable cells are often limited by their poor spatial resolution or their invasiveness. One promising solution to this problem is to optically measure membrane potential using a voltage-sensitive dye, but thus far, none of these dyes have been available for human use. Here we report that indocyanine green (ICG), an infrared fluorescent dye with FDA approval as an intravenously administered contrast agent, is voltage-sensitive. The fluorescence of ICG can follow action potentials in artificial neurons and cultured rat neurons and cardiomyocytes. ICG also visualized electrical activity induced in living explants of rat brain. In humans, ICG labels excitable cells and is routinely visualized transdermally with high spatial resolution. As an infrared voltage-sensitive dye with a low toxicity profile that can be readily imaged in deep tissues, ICG may have significant utility for clinical and basic research applications previously intractable for potentiometric dyes.Voltage-sensitive dyes provide a way to observe cellular electrical activity without the physical limitations imposed by electrodes. Although these dyes can monitor membrane potential with a resolution of a few microns from large populations of cells (1), there are three obstacles that prevent the use of these dyes in many research settings, including clinical research:

• 1.Most voltage-sensitive dyes use visible wavelengths of light that prevent imaging of tissues beneath the skin.
• 2.Many of these dyes produce significant toxicity or off-target effects (2).
• 3.Before this report, to our knowledge, no voltage-sensitive dyes have ever been available for administration in humans, which has limited their value in biomedically focused research.

Here, we show that indocyanine green (ICG), an FDA-approved fluorescent dye routinely used in many clinical tests, is voltage-sensitive. Our initial experimental system used Xenopus laevis oocytes. Changes in the membrane potential of the cell induced by two-electrode voltage-clamp resulted in robust, consistent changes in the fluorescence of ICG (Fig. 1, inset). All data in this work was obtained from single acquisitions with no averaging of multiple images. The voltage-dependent fluorescence changes were roughly linear with respect to membrane potential and had a magnitude of ∼1.9% of the baseline fluorescence per 100 mV of membrane potential change (Fig. 1). Additionally, ICG displayed a rapid response with a primary time constant of 4 ms (see Fig. S1 in the Supporting Material), suggesting that this dye could successfully monitor action potentials.Open in a separate windowFigure 1ICG-labeled oocytes showed that ICG’s fluorescence (blue points) is roughly linearly dependent (red line, fit to data) with voltage. (Inset) Oocyte membrane potential was held at −60 mV and then pulsed to potentials ranging from −120 mV (blue) to +120 mV (red). Ex: 780 nm, Em: 818–873 nm.To test this hypothesis, we transformed our oocytes into synthetic neurons, previously dubbed “excitocytes”, by coinjecting them with cRNA of voltage-gated sodium (Na_v) and potassium channel components (3). Under suitable current-clamp conditions, excitocytes fire trains of action potentials similar to those in naturally excitable cells. ICG’s fluorescence clearly recapitulated action potentials firing at speeds above 100 Hz (Fig. 2 A), faster than the physiological firing rates of most neurons (4).Open in a separate windowFigure 2ICG can monitor action potentials. (A) Oocytes coinjected with voltage-gated sodium and potassium channel cRNA fired action potentials (bottom, green) when held under current clamp. ICG fluorescence changes (top, blue) detected these action potentials at a rate of 107 Hz. Stimulus start (black arrow) and end (red arrow) are shown. (B and C) ICG fluorescence (blue, inverted) distinguished between healthy action potentials from wild-type sodium channels (B, green) and diseased action potentials from sodium channels with a myotonic substitution (C, green). Cells are stimulated for the entire time course of these panels. The delay between action potentials and the ICG signal is due to a low-pass filtering effect caused by the dye response time and the camera integration time. (D) In cells with myotonic sodium channels, a brief stimulus (top, black) was sufficient to elicit a train of action potentials (bottom, green) that only ceased upon significant hyperpolarization, as expected in a myotonia. ICG fluorescence (middle, blue) successfully followed each one of these action potentials.We extended the excitocyte technique from wild-type channels to evaluation of channelopathies and their effects on excitability to determine whether ICG could discriminate between normal and diseased action potentials based on shape. We compared excitocytes injected with wild-type Na_v channel cRNA to those injected with cRNA coding for a version of Na_v channel containing a point mutation, G1306E, which produces episodic myotonia (5). This disease is characterized by continued action potential firing in skeletal muscles after cessation of voluntary stimuli; the resulting prolonged muscle contractions are the hallmark of myotonia. Compared to the wild-type Na_v channel, the G1306E mutation causes a slowing of the fast inactivation of the Na_v channels, which in turn results in broadened action potentials (5). The electrical recordings and the ICG fluorescence response clearly distinguished the sharp action potentials produced by the healthy sodium channel (Fig. 2 B, and see Fig. S2) from the wider peaks produced by the myotonic sodium channel (Fig. 2 C, and see Fig. S2). Furthermore, a brief injection of current led to repetitive firing and hyperexcitability that persisted after the stimulus was stopped. ICG fluorescence clearly resolved every action potential of this myotonia-like behavior (Fig. 2 D). The successful recreation of disease-like action potentials validates the excitocyte system as a convenient method for investigating the electrophysiological effects of channelopathies.We next investigated whether ICG’s voltage sensitivity extended to excitable mammalian tissue. This validation was critical, inasmuch as other voltage-sensitive dyes have shown promise in invertebrate preparations but had much smaller signals in mammalian cells (6). We first measured ICG fluorescence from cultured rat dorsal root ganglion neurons. Under whole-cell current clamp, we observed neurons firing in the stereotypical fashion of the nociceptive C-type fiber, and these action potentials were clearly visible in the ICG fluorescence (Fig. 3 A, and see Fig. S3). We also examined syncytia of cultured cardiomyocytes from neonatal rats (7) to further validate ICG’s utility; these cells beat spontaneously and showed changes in ICG fluorescence indicative of changes in membrane potential (Fig. 3 B). Although we cannot formally exclude the possibility that the cardiomyocytes’ physical motion produced fluorescence changes, several observations suggested that these effects were minimal (see Fig. S4). Taken together, our results in frog and rat cells confirmed that ICG voltage sensitivity was broadly applicable across a range of tissues and not confined to a particular animal or cell lineage.Open in a separate windowFigure 3ICG follows electrical activity in living mammalian tissue. (A) Rat cultured dorsal root ganglion cells under current-clamp (black arrow, pulse start; red arrow, pulse end) fired action potentials (green), that ICG fluorescence tracked (blue, inverted, low-pass-filtered at 225 Hz; blue arrow, relative fluorescence change). (B) ICG fluorescence sensed spontaneous membrane potential changes in cardiomyocyte syncytia. (C) In rat brain slices, ICG responds differently to no stimulus (black) and stimuli of increasing intensity (magenta, cyan, green, and blue, increasing amplitude; scale bar shows relative fluorescence change). Weaker stimuli traces (e.g., magenta) show complete fluorescence recovery whereas larger stimuli (e.g., blue) do not fully recover within this time course; traces are vertically offset for clarity. (D) Tetrodotoxin (TTX) reduced the ICG response to a stimulus over 12 min (green, pre-TTX; cyan, magenta, and black, increasing time post-TTX; low-pass-filtered at 40 Hz; black arrow, stimulus).Finally, we tested whether ICG voltage sensitivity could be detected in a complex tissue. Rat hippocampal slice cultures comprise a well-described organotypic preparation in which the three-dimensional architecture, neuronal connections, and glial interactions are maintained (8,9). Using these rat brain explants, we found that brain excitation produced by field electrode stimulation was clearly accompanied by ICG fluorescence changes (see Fig. S5). Additionally, ICG discriminated between electrical responses caused by differing excitation intensities and durations (Fig. 3 C, and see Fig. S5). To confirm that the fluorescence changes originated from changes in excitable cell activity, we used the Na_v channel blocker tetrodotoxin (TTX). When applied to brain slices, electrical excitability was clearly inhibited (Fig. 3 D, and see Fig. S5) and partial recovery was observed upon subsequent TTX removal (see Fig. S5). These signals measuring brain slice activity were similar in shape and magnitude to those reported using other voltage-sensitive dyes (10,11). This demonstrates that ICG can report on electrical activity even in a physiological architecture with many nonexcitable cells.To our knowledge, this is the first report that a clinically approved fluorescent dye is voltage-sensitive. Our results demonstrate that indocyanine green can accurately detect action potentials at firing rates common in mammalian neurons, and that it is sensitive enough to distinguish between healthy and diseased action potentials in a model system. ICG can measure electrical activity in mammalian neurons, cardiomyocytes, and explanted brain tissue. This voltage sensitivity was observed with both monochromatic and broad-band illumination sources (data not shown), under labeling conditions that differed in solution composition, duration, and dye concentration (see Methods in the Supporting Material), and at temperatures ranging from 19°C to 30°C. ICG’s water solubility further extends its potential utility. This robustness suggests that ICG can be used to measure voltage in many environments and tissues.ICG has been FDA-approved for use in ophthalmic angiography, as well as in tests of cardiac output and hepatic function (12) and is additionally used off-label in a number of surgical applications (13). Interestingly, ICG has been shown to clearly label retinal ganglion cells in human patients (see Fig. S6) (14). This provides immediate motivation for biomedical investigations, because laboratory findings with ICG can potentially be translated to humans. Although many other voltage-sensitive dyes have been described, some with similar structures to ICG (15) and others with faster or larger signals (15,16), as of this writing none of these are FDA-approved. Additionally, ICG utilizes wavelengths further into the infrared spectrum than other available fast potentiometric dyes (17) and can thus be imaged in tissues up to 2 cm deep (18). This presents the possibility of optically imaging electrical activity deeper inside tissues than is feasible today. Although two-photon excitation with voltage-sensitive dyes can improve imaging depth, it remains intrinsically limited by the unaffected emission wavelength (19). Finally, ICG has been used in patients for more than 50 years and is known to have low toxicity (18,20). These properties suggest that ICG voltage sensitivity could extend the capabilities of modern electrophysiological techniques for disease diagnosis and monitoring in the clinic, and allow for the investigation of previously inaccessible experimental systems in basic research.     

Focus Issue on Plant Cell Walls: Sequencing around 5-Hydroxyconiferyl Alcohol-Derived Units in Caffeic Acid O-Methyltransferase-Deficient Poplar Lignins

Caffeic acid O-methyltransferase (COMT) is a bifunctional enzyme that methylates the 5- and 3-hydroxyl positions on the aromatic ring of monolignol precursors, with a preference for 5-hydroxyconiferaldehyde, on the way to producing sinapyl alcohol. Lignins in COMT-deficient plants contain benzodioxane substructures due to the incorporation of 5-hydroxyconiferyl alcohol (5-OH-CA), as a monomer, into the lignin polymer. The derivatization followed by reductive cleavage method can be used to detect and determine benzodioxane structures because of their total survival under this degradation method. Moreover, partial sequencing information for 5-OH-CA incorporation into lignin can be derived from detection or isolation and structural analysis of the resulting benzodioxane products. Results from a modified derivatization followed by reductive cleavage analysis of COMT-deficient lignins provide evidence that 5-OH-CA cross couples (at its β-position) with syringyl and guaiacyl units (at their O-4-positions) in the growing lignin polymer and then either coniferyl or sinapyl alcohol, or another 5-hydroxyconiferyl monomer, adds to the resulting 5-hydroxyguaiacyl terminus, producing the benzodioxane. This new terminus may also become etherified by coupling with further monolignols, incorporating the 5-OH-CA integrally into the lignin structure.Lignins are polymeric aromatic constituents of plant cell walls, constituting about 15% to 35% of the dry mass (Freudenberg and Neish, 1968; Adler, 1977). Unlike other natural polymers such as cellulose or proteins, which have labile linkages (glycosides and peptides) between their building units, lignins’ building units are combinatorially linked with strong ether and carbon-carbon bonds (Sarkanen and Ludwig, 1971; Harkin, 1973). It is difficult to completely degrade lignins. Lignins are traditionally considered to be dehydrogenative polymers derived from three monolignols, p-coumaryl alcohol 1h (which is typically minor), coniferyl alcohol 1g, and sinapyl alcohol 1s (Fig. 1; Sarkanen, 1971). They can vary greatly in their composition in terms of their plant and tissue origins (Campbell and Sederoff, 1996). This variability is probably determined and regulated by different activities and substrate specificities of the monolignol biosynthetic enzymes from different sources, and by the carefully controlled supply of monomers to the lignifying zone (Sederoff and Chang, 1991).Open in a separate windowFigure 1.The monolignols 1, and marker compounds 2 to 4 resulting from incorporation of novel monomer 15h into lignins: thioacidolysis monomeric marker 2, dimers 3, and DFRC dimeric markers 4.Recently there has been considerable interest in genetic modification of lignins with the goal of improving the utilization of lignocellulosics in various agricultural and industrial processes (Baucher et al., 2003; Boerjan et al., 2003a, 2003b). Studies on mutant and transgenic plants with altered monolignol biosynthesis have suggested that plants have a high level of metabolic plasticity in the formation of their lignins (Sederoff et al., 1999; Ralph et al., 2004). Lignins in angiosperm plants with depressed caffeic acid O-methyltransferase (COMT) were found to derive from significant amounts of 5-hydroxyconiferyl alcohol (5-OH-CA) monomers 15h (Fig. 1) substituting for the traditional monomer, sinapyl alcohol 1s (Marita et al., 2001; Ralph et al., 2001a, 2001b; Jouanin et al., 2004; Morreel et al., 2004b). NMR analysis of a ligqnin from COMT-deficient poplar (Populus spp.) has revealed that novel benzodioxane structures are formed through β-O-4 coupling of a monolignol with 5-hydroxyguaiacyl units (resulting from coupling of 5-OH-CA), followed by internal trapping of the resultant quinone methide by the phenolic 5-hydroxyl (Ralph et al., 2001a). When the lignin was subjected to thioacidolysis, a novel 5-hydroxyguaiacyl monomer 2 (Fig. 1) was found in addition to the normal guaiacyl and syringyl thioacidolysis monomers (Jouanin et al., 2000). Also, a new compound 3g (Fig. 1) was found in the dimeric products from thioacidolysis followed by Raney nickel desulfurization (Lapierre et al., 2001; Goujon et al., 2003).Further study with the lignin using the derivatization followed by reductive cleavage (DFRC) method also confirmed the existence of benzodioxane structures, with compounds 4 (Fig. 1) being identified following synthesis of the authentic parent compounds 9 (Fig. 2). However, no 5-hydroxyguaiacyl monomer could be detected in the DFRC products. These facts imply that the DFRC method leaves the benzodioxane structures fully intact, suggesting that the method might therefore be useful as an analytical tool for determining benzodioxane structures that are linked by β-O-4 ethers. Using a modified DFRC procedure, we report here on results that provide further evidence for the existence of benzodioxane structures in lignins from COMT-deficient plants, that 5-OH-CA is behaving as a rather ideal monolignol that can be integrated into plant lignins, and demonstrate the usefulness of the DFRC method for determining these benzodioxane structures.Open in a separate windowFigure 2.Synthesis of benzodioxane DFRC products 12 (see later in Fig. 6 for their structures). i, NaH, THF. ii, Pyrrolidine. iii, 1g or 1s, benzene/acetone (4/1, v/v). iv, DIBAL-H, toluene. v, Iodomethane-K₂CO₃, acetone. vi, Ac₂O pyridine.     

Single-Particle Tracking Demonstrates that Actin Coordinates the Movement of the Ebola Virus Matrix Protein

The Ebola virus causes severe hemorrhagic fever and has a mortality rate that can be as high as 90%, yet no vaccines or approved therapeutics, to our knowledge, are available. To replicate and egress the infected host cell the Ebola virus uses VP40, its major matrix protein to assemble at the inner leaflet of the plasma membrane. The assembly and budding of VP40 from the plasma membrane of host cells seem still poorly understood. We investigated the assembly and egress of VP40 at the plasma membrane of human cells using single-particle tracking. Our results demonstrate that actin coordinates the movement and assembly of VP40, a critical step in viral egress. These findings underscore the ability of single-molecule techniques to investigate the interplay of VP40 and host proteins in viral replication.The actin cortex below the plasma membrane of mammalian cells is essential for maintenance of cell shape and for cell movement. This cortex has also been found to play an essential role in the replication process of a number of viruses including West Nile virus (1), respiratory syncytial virus (2), influenza (3), and vaccinia virus (4). Additionally, actin has been found to play a central role in the assembly and budding of HIV-1 (5) whereas Marburg virus has been shown to use actin-enriched filopodia to exit the host cell (6). Actin has also been found to be packaged into Ebola-virus-like particles (VLPs) (7). Ebola virus, which causes severe hemorrhagic fever, harbors a single-stranded negative-sense RNA genome encoding seven proteins. Of these seven proteins, VP40 is the most abundantly expressed and has been found to play a central role in the budding of the virus from the plasma membrane (8). Whereas actin has been found in Ebola VLPs (7), the role of actin in Ebola VP40 assembly is still seemingly unknown. Here, we have used Raster image correlation spectroscopy (RICS) (9) and three-dimensional single-particle tracking (see Fig. S1 in the Supporting Material) (10) to investigate the dynamics of Ebola VP40 and actin. We report that preassembled VLPs (pVLPs) of Ebola VP40 require actin for directed movement and assembly.Ebola VP40 has been demonstrated to colocalize with actin and actin is found in VP40 VLPs (7), suggesting an important role for actin in the replication cycle of the virus. To confirm the colocalization between VP40 and actin in HEK293 and CHO-K1 cells, we used confocal microscopy to examine the distribution of EGFP-VP40 and mCherry-actin. EGFP-VP40 and mCherry-actin displayed colocalization at the plasma membrane of HEK293 and CHO-K1 cells (see Fig. S2 A), which was markedly reduced in response to treatment with LAT-A (see Fig. S2 B and Fig. S3 A), an actin polymerization inhibitor. VP40 plasma membrane localization was not disrupted by LAT-A treatment (not unexpected, as VP40 is a lipid-binding protein (11) where high affinity for the PM drives its cellular localization (E. Adu-Gyamfi and R. V. Stahelin, unpublished)). To test whether this VP40-actin interaction is important to viral egress, we detected EGFP-VP40 with an anti-EGFP antibody used to measure VLPs formed from cells expressing EGFP-VP40. This was also performed to assess the effect of pharmacological treatment on EGFP-VP40-expressing cells with LAT-A or with the microtubule polymerization inhibitor nocodazole (see Fig. S3 B). LAT-A treatment led to a significant reduction in VLP formation whereas nocodazole did not display detectable effects.To test whether the VP40 and actin are engaged in synchronized movement, we performed time-lapse imaging in both the green and red channels. We observed that the pVLPs move with actin fibers extending from the plasma membrane (see Movie S1 in the Supporting Material). The movement was rapid, and caused smaller particles to merge into larger filamentous forms. To further demonstrate that the motion of actin and VP40 spatially overlapped, we used RICS to obtain correlation maps of EGFP-VP40 and mCherry-actin (Fig. 1). The spatial cross-correlation map indicated significant overlap of VP40 and actin movement (Fig. 2, A–C) at the plasma membrane (Fig. 1 and see Fig. S6), but not in the cytosol (see Fig. S5 and Fig. S7). In contrast, EGFP-VP40 and mCherry-α-tubulin (see Fig. S8, Fig. S9, and Fig. S10) displayed no significant spatial cross-correlation at the plasma membrane (Fig. S11) or other regions of the cell (see Fig. S12), supporting the VLP egress data where inhibition of microtubule polymerization did not influence viral egress.Open in a separate windowFigure 1EGFP-VP40 and mCherry-actin RICS analysis at the membrane. (A) HEK293 cells expressing EGFP-VP40 and mCherry-actin were imaged for 100 frames at 256 × 256 pixels. (White scale bar = 2 μm.) (B) Average intensity image of EGFP-VP40 across the 100 collected frames. (Pink box) Used to select a region of interest to yield the (C) average EGFP-VP40 intensity image. (D) Average intensity image of mCherry-actin taken for 100 frames at 256 × 256 pixels was used to select the same region of interest as in panel B (pink box) to yield the (E) average intensity image of the mCherry-actin signal in this region. (F) The two-dimensional spatial cross-correlation analysis of panels C and E demonstrates significant cross-correlation of VP40 and actin signals.Open in a separate windowFigure 2Three-dimensional RICS correlation maps of VP40 and actin cross-correlate at the plasma membrane. (A) EGFP-VP40 and (B) mCherry-actin (Fig. 1 and see Fig. S6 in the Supporting Material) RICS autocorrelation functions. (C) Appreciable cross-correlation is observed for EGFP-VP40 and mCherry-actin at the plasma membrane.To test whether the motion of the pVLPs is directed by actin, we applied the three-dimensional orbital tracking method first introduced by Levi et. al. (10). Tracking of isolated particles (Fig. 3 A) in five different cells allowed determination of the pVLPs trajectories (Fig. 3 D), which suggested that the VP40 particles undergo a directed motion. To verify this, we plotted the mean-square displacement (MSD) curves for the pVLPs (Fig. 3 C), which confirmed the trajectory was characteristic of directed motion. Analysis of the intensity profile of the dynamic VP40 particles suggested that the intensity of the particle changes with respect to time. Bleaching is expected if the molecule is exposed to the laser beam for an extended period of time; however, an increase in intensities was observed along the trajectory of the green channel due to addition of VP40 molecules. This suggests that the movement of the particles along actin fibers promote multimerization and maturation of the pVLPs. When actin polymerization was inhibited in four different cells with LAT-B, the rapid movement (see Fig. S13) and the directed trajectories of the pVLPs were lost (Fig. 3, E and F). This was reflected in a change from directed motion to movement indicative of random then constrained diffusion (Fig. 3, E and F).Open in a separate windowFigure 3Actin directs the movement of VP40 particles. HEK293 cells transfected with EGFP-VP40 were imaged with an electronic zoom of 2000 mV, corresponding to 72 nm/pixel in both X and Y. (A) An isolated and representative VP40 particle (highlighted by white box, inset) was tracked as described in the Supporting Material. (B) Intensity profile of the pVLP in A demonstrates increases in EGFP-VP40 intensity along the trajectory. (C) MSD of the pVLP, which follows a ballistic motion with a velocity of 0.067 ± 0.01 μm² s⁻¹. (D) The three-dimensional trajectory of the particle shown in panels A–C. (E) MSD curve of VP40 particles yields random then constrained diffusion after LAT-B treatment with a mean velocity of 0.017 ± 0.006 μm² s⁻¹ (p < 0.001). (F) Three-dimensional trajectory of the same particle shown in panel E displays a random then constrained diffusion.Taken together, our findings demonstrate that the movement of the pVLPs is driven by actin. Analysis of the pVLPs trajectories also suggests that the motion of pVLPs on actin enables further addition of VP40 molecules. These findings raise important questions regarding contemporary understanding of Ebola assembly and egress. VP40 lacks a consensus actin-binding motif, suggesting an adaptor protein such as an actin motor protein may function in this process. For instance, Myo10 has been found to be essential to Marburg virus release (6); however, Marburg VP40-Myo10 direct interactions were not observed, suggesting other cellular adaptor proteins may function in this process. Given the pathogenic nature of the Ebola virus and the necessity of VP40 to the assembly and egress of the virus (8), the VP40-actin coordination represents, to us, a novel target for therapeutic development.     

Simultaneous Raman Microspectroscopy and Fluorescence Imaging of Bone Mineralization in Living Zebrafish Larvae

Confocal Raman microspectroscopy and fluorescence imaging are two well-established methods providing functional insight into the extracellular matrix and into living cells and tissues, respectively, down to single molecule detection. In living tissues, however, cells and extracellular matrix coexist and interact. To acquire information on this cell-matrix interaction, we developed a technique for colocalized, correlative multispectral tissue analysis by implementing high-sensitivity, wide-field fluorescence imaging on a confocal Raman microscope. As a proof of principle, we study early stages of bone formation in the zebrafish (Danio rerio) larvae because the zebrafish has emerged as a model organism to study vertebrate development. The newly formed bones were stained using a calcium fluorescent marker and the maturation process was imaged and chemically characterized in vivo. Results obtained from early stages of mineral deposition in the zebrafish fin bone unequivocally show the presence of hydrogen phosphate containing mineral phases in addition to the carbonated apatite mineral. The approach developed here opens significant opportunities in molecular imaging of metabolic activities, intracellular sensing, and trafficking as well as in vivo exploration of cell-tissue interfaces under (patho-)physiological conditions.Understanding fundamental biological processes relies on probing intra- and extracellular environments, targeted delivery inside living cells and tissues, and real-time detection and imaging of chemical markers and biomolecules (1,2). Typically, information about molecules in cellular environments is obtained by fluorescence microscopy (3). This is a powerful imaging tool for localizing and imaging samples but requires fluorescent labels and markers and lacks capabilities for quantitative mapping of the chemical composition in complex systems. In this regard, confocal Raman spectroscopic imaging is becoming increasingly popular for label-free chemical detection, due to the inherent scattering nature of all biomolecules (4,5). However, confocal Raman imaging alone does not allow live, high-resolution imaging of larger regions of interest in complex biological tissues. Transcutaneous Raman spectroscopy has the potential as a tool for in vivo bone quality assessment (6), whereas the time- and space-resolved Raman spectroscopy allows the visualization in vivo of the distributions of molecular species in human and yeast cells (4,5,7). Here we developed a correlative Raman and fluorescence imaging method that combines the strengths and compensates for the shortcomings of each of these imaging modalities and allows studying in vivo processes in complex animal models such as zebrafish larvae. There are two main advantages of this approach over previous studies (8,9): low light intensity and high acquisition rate, making it well suited for real-time investigation of live samples.Fig. 1, a and b, shows a schematic representation of the experimental setup and of the optical path, respectively. The two techniques are implemented on a commercially available Raman microscope body to perform simultaneously confocal Raman spectroscopy and wide-field fluorescence imaging (see the Supporting Material for details of components). Briefly, the multimodality of the setup is provided by a combination of dichroic mirrors (DM 1–3) and filters that at turns reflect or transmit the excitation and emission signals. This combination of optics allows simultaneous collection of fluorescence images (2560 × 2160 pixels at 30 fps) with excitation at 400 and 490 nm and spatially resolved Raman spectra with excitation at 633 nm.Open in a separate windowFigure 1Fluorescence imaging of zebrafish larvae. (a) Cartoon of the experimental setup showing how the different modules are assembled onto the microscope for the simultaneous use of confocal Raman spectroscopy and fluorescence imaging. (b) Schematic representation of the optical path. (c) Fluorescence image of calcium-containing tissues, and fluids stained with calcein blue and excited at 400 nm (top). Endothelial cells of transgenic tg(fli1:EGFP)y1 zebrafish excited at 490 nm (bottom).As a proof of principle, we have studied the different mineral phases involved in bone formation of the zebrafish larvae. The bone development process involves the transport of ions to specific cells (osteoblasts) that are responsible for the subsequent mineral formation and deposition. The mineral phase in these cells is a poorly characterized disordered calcium phosphate (10–12). The mineral-bearing intracellular vesicles release their content into the extracellular collagen fibrils, where the mineral subsequently crystallizes as carbonated hydroxyapatite (13). Very little is known about the phase transformations the mineral undergoes after the deposition into the collagen matrix in vivo. Raman spectroscopy studies of bone tissue in organ cultures evidenced that the inorganic mineral deposition proceeds through transient intermediates including octacalcium phosphate-like (OCP) minerals (14).To assess the feasibility of imaging a vertebrate organism, fluorescence images of an entire zebrafish larva (Fig. 1 c) were acquired with the correlative fluorescence-Raman setup. The two images in Fig. 1 c were composed by merging several low-magnification (10×) fluorescence images. Larvae of transgenic zebrafish Tg(fli:EGFP); nac mutants (albino fish) expressing EGFP in the cytoplasm of endothelial cells was used. The newly formed bones were stained by soaking the live embryo noninvasively in the calcium markers calcein blue 0.2% wt or calcein green 0.2% wt.The calcein blue marker is excited at 400 nm. It is labeling bones and can be also detected as a fluorescent marker not associated with formed bones (e.g., stomach) (Fig. 1 c, top). At 490 nm, calcein green and endothelial cells within blood vessels expressing EGFP are excited (Fig. 1 c, bottom). Because EGFP and calcein blue have significantly different excitation and emissions spectra, dual staining with calcein blue (as a mineral marker) and EGFP allows fast-switching dual-wavelength fluorescence imaging. Furthermore, because the spectra of the calcium markers and EGFP do not extend beyond the Raman laser, these fluorophores are appropriate candidates for experiments requiring Raman and fluorescence imaging. The dual-excitation offers the capability of mapping several tissues in a single experiment at the video rate. This, in principle, could be used to probe different parameters of the microenvironment (e.g., pH (15), temperature (16), viscosity (17), and calcium concentration (18)) using wavelength-ratiometric fluorescence imaging which, in correlation with confocal Raman spectroscopy, could open new strategies in studies of the microenvironmental properties in living tissues.The fin rays of zebrafish are a simple, growing bone-model system, in which the fins are gradually mineralized within spatially resolved regions (19). Raman spectroscopy revealed details of the calcein green-stained fin where new bone is deposited (Fig. 2). In Fig. 2 a, a fluorescence image of a zebrafish larva analogous to the top image in Fig. 1 c is shown. The right inset in Fig. 3 b shows higher-magnification (60× water-immersion objective) details of the calcein green-stained fin typical of newly deposited bone. Raman spectra of progressively mineralized bone tissue were acquired within representative regions (Fig. 2 b; numbered 1–4). The spectra exhibit characteristic bands that can be assigned to the organic protein extracellular matrix (amide I, amide III, Phe, C-H, etc.) and the inorganic mineral content (v₁, v₂, v₄ of PO₄³⁻).Open in a separate windowFigure 2Correlative fluorescence-Raman imaging of zebrafish fin bone maturation. (a) Low-resolution (10×) fluorescence image of zebrafish stained with calcein green, with high-resolution (60×) details (right inset in panel b) of a representative fin ray region where Raman spectra (b) of progressively mineralized bone tissue were acquired (numbered 1–4). (Left inset in panel b) Integral of the orientation independent mineral band (v₂) where a clear drop of the mineral content can be observed.The analyses of the orientation-independent v₂ phosphate band revealed a clear drop in the mineral content based on the intensity integral (left inset in Fig. 2 b). Assuming that the spectrum collected in region 4 contains only organic matrix (very small phosphate-related peaks) and by subtracting it from the spectrum of mineral-rich bone region (spectrum 1, proximal part of the tail bone), spectral features of only the mineral phase can be plotted (black line). In addition to the phosphate (PO₄³⁻) and carbonate (CO₃²⁻) bands assignable to the carbonated apatite phase characteristic of the more mature bone mineral, several peaks related to the hydrogen phosphate (HPO₄²⁻) species can be clearly distinguished.The HPO₄²⁻ peaks are characteristic of the OCP mineral phase that has been postulated, together with amorphous calcium phosphate, as an intermediate mineral phase in the process of bone maturation (10,13,14,20), but never observed directly in living animals. Our findings show in vivo potential of the correlative setup envisioned by Crane et al. (14) and confirm that the mineral maturation indeed proceeds through an OCP-like mineral phase. Further analysis of the mineral spectrum in Fig. 2 b reveals an extremely broad band in the region 800–1100 cm⁻¹. This envelope can be related to hydrogenated phosphate species typical of amorphous calcium phosphate precipitated in an acidic environment (see Fig. S1 in the Supporting Material), suggesting that this phase is also contributing to the maturation process.In conclusion, the methodology developed here allows for unprecedented chemical characterization of fluorescently-labeled biological tissues in vivo. The approach is suitable for long-term in vivo characterization of zebrafish bone mineralization under (patho-)physiological conditions. Furthermore, the setup can be upgraded to host other advance fluorescence imaging techniques such as super-resolution microscopy (e.g., photoactivated localization microscopy), two-photon excitation, and Forster resonance energy transfer or fluorescence lifetime imaging microscopy, and be applied on both in vivo and in vitro specimens. This opens significant opportunities in molecular imaging of metabolic activities, intracellular sensing, and trafficking as well as in vivo exploration of cell-tissue interfaces.     

Novel Growth Regime of MDCK II Model Tissues on Soft Substrates

It is well established that MDCK II cells grow in circular colonies that densify until contact inhibition takes place. Here, we show that this behavior is only typical for colonies developing on hard substrates and report a new growth phase of MDCK II cells on soft gels. At the onset, the new phase is characterized by small, three-dimensional droplets of cells attached to the substrate. When the contact area between the agglomerate and the substrate becomes sufficiently large, a very dense monolayer nucleates in the center of the colony. This monolayer, surrounded by a belt of three-dimensionally packed cells, has a well-defined structure, independent of time and cluster size, as well as a density that is twice the steady-state density found on hard substrates. To release stress in such dense packing, extrusions of viable cells take place several days after seeding. The extruded cells create second-generation clusters, as evidenced by an archipelago of aggregates found in a vicinity of mother colonies, which points to a mechanically regulated migratory behavior.Studying the growth of cell colonies is an important step in the understanding of processes involving coordinated cell behavior such as tissue development, wound healing, and cancer progression. Apart from extremely challenging in vivo studies, artificial tissue models are proven to be very useful in determining the main physical factors that affect the cooperativity of cells, simply because the conditions of growth can be very well controlled. One of the most established cell types in this field of research is the Madin-Darby canine kidney epithelial cell (MDCK), originating from the kidney distal tube (1). A great advantage of this polarized epithelial cell line is that it retained the ability for contact inhibition (2), which makes it a perfect model system for studies of epithelial morphogenesis.Organization of MDCK cells in colonies have been studied in a number of circumstances. For example, it was shown that in three-dimensional soft Matrigel, MDCK cells form a spherical enclosure of a lumen that is enfolded by one layer of polarized cells with an apical membrane exposed to the lumen side (3). These structures can be altered by introducing the hepatocyte growth factor, which induces the formation of linear tubes (4). However, the best-studied regime of growth is performed on two-dimensional surfaces where MDCK II cells form sheets and exhibit contact inhibition. Consequently, the obtained monolayers are well characterized in context of development (5), mechanical properties (6), and obstructed cell migration (7–9).Surprisingly, in the context of mechanics, several studies of monolayer formation showed that different rigidities of polydimethylsiloxane gels (5) and polyacrylamide (PA) gels (9) do not influence the nature of monolayer formation nor the attainable steady-state density. This is supposedly due to long-range forces between cells transmitted by the underlying elastic substrate (9). These results were found to agree well with earlier works on bovine aortic endothelial cells (10) and vascular smooth muscle cells (11), both reporting a lack of sensitivity of monolayers to substrate elasticity. Yet, these results are in stark contrast with single-cell experiments (12–15) that show a clear response of cell morphology, focal adhesions, and cytoskeleton organization to substrate elasticity. Furthermore, sensitivity to the presence of growth factors that are dependent on the elasticity of the substrate in two (16) and three dimensions (4) makes this result even more astonishing. Therefore, we readdress the issue of sensitivity of tissues to the elasticity of the underlying substrate and show that sufficiently soft gels induce a clearly different tissue organization.We plated MDCK II cells on soft PA gels (Young’s modulus E = 0.6 ± 0.2 kPa), harder PA gels (E = 5, 11, 20, 34 kPa), and glass, all coated with Collagen-I. Gels were prepared following the procedure described in Rehfeldt et al. (17); rigidity and homogeneity of the gels was confirmed by bulk and microrheology (see the Supporting Material for comparison). Seeding of MDCK II cells involved a highly concentrated solution dropped in the middle of a hydrated gel or glass sample. For single-cell experiments, cells were dispersed over the entire dish. Samples were periodically fixed up to Day 12, stained for nuclei and actin, and imaged with an epifluorescence microscope. Details are described in the Supporting Material.On hard substrates and glass it was found previously that the area of small clusters expands exponentially until the movement of the edge cannot keep up with the proliferation in the bulk (5). Consequently, the bulk density increases toward the steady state, whereas the density of the edge remains low. At the same time, the colony size grows subexponentially (5). This is what we denote “the classical regime of growth”. Our experiments support these observations for substrates with E ≥ 5 kPa. Specifically, on glass, colonies start as small clusters of very low density of 700 ± 200 cells/mm² (Fig. 1, A and B), typically surrounded by a strong actin cable (Fig. 1, B and C). Interestingly, the spreading area of single cells (Fig. 1 A) on glass was found to be significantly larger, i.e., (2.0 ± 0.9) × 10⁻³ mm². After Day 4 (corresponding cluster area of 600 ± 100 mm²), the density in the center of the colony reached the steady state with 6,800 ± 500 cells/mm², whereas the mean density of the edge profile grew to 4,000 ± 500 cells/mm². This density was retained until Day 12 (cluster area 1800 ± 100 mm²), which is in agreement with previous work (9).Open in a separate windowFigure 1Early phase of cluster growth on hard substrates. (A) Well-spread single cells, and small clusters with a visible actin cable 6 h after seeding. (B) Within one day, clusters densify and merge, making small colonies. (C) Edge of clusters from panel B.In colonies grown on 0.6 kPa gels, however, we encounter a very different growth scenario. The average spreading area of single cells is (0.34 ± 0.3) × 10⁻³ mm², which is six times smaller than on glass substrates (Fig. 2 A). Clusters of only few cells show that cells have a preference for cell-cell contacts (a well-established flat contact zone can be seen at the cell-cell interface in Fig. 2 A) rather than for cell-substrate contacts (contact zone is diffusive and the shape of the cells appears curved). The same conclusion emerges from the fact that dropletlike agglomerates, resting on the substrate, form spontaneously (Fig. 2 A), and that attempts to seed one single cluster of 90,000 cells fail, resulting in a number of three-dimensional colonies (Fig. 2 A). When the contact area with the substrate exceeds 4.7 × 10⁻³ mm², a monolayer appears in the center of such colonies (Fig. 2 B). The colonies can merge, and if individual colonies are small, the collapse into a single domain is associated with the formation of transient irregular structures (Fig. 2 B). Ultimately, large elliptical colonies (average major/minor axis of e = 1.8 ± 0.6) with a smooth edge are formed (Fig. 2 C), unlike on hard substrates where circular clusters (e = 1.06 ± 0.06) with a ragged edge comprise the characteristic phenotype.Open in a separate windowFigure 2Early phase of cluster growth on soft substrates. (A) Twelve hours after seeding, single cells remain mostly round and small. They are found as individual, or within small, three-dimensional structures (top). The latter nucleate a monolayer in their center (bottom), if the contact area with the substrate exceeds ∼5 × 10⁻³ mm². (B) Irregularly-shaped clusters appear due to merging of smaller droplets. A stable monolayer surrounded by a three-dimensional belt of densely packed cells is clearly visible, even in larger structures. (C) All colonies are recorded on Day 4.Irrespective of cluster size, in the new regime of growth, the internal structure is built of two compartments (Fig. 2 B):

• 1.The first is the edge (0.019 ± 0.05-mm wide), a three-dimensional structure of densely packed cells. This belt is a signature of the new regime because on hard substrates the edge is strictly two-dimensional (Fig. 1 C).
• 2.The other is the centrally placed monolayer with a spatially constant density that is very weakly dependent on cluster size and age (Fig. 3). The mean monolayer density is 13,000 ± 2,000 cells/mm², which is an average over 130 clusters that are up to 12 days old and have a size in the range of 10⁻³ to 10 mm², each shown by a data point in Fig. 3. This density is twice the steady-state density of the bulk tissue in the classical regime of growth.Open in a separate windowFigure 3Monolayer densities in colonies grown on 0.6 kPa substrates, as a function of the cluster size and age. Each cluster is represented by a single data point signifying its mean monolayer density. (Black lines) Bulk and (red dashed lines) edge of steady-state densities from monolayers grown on glass substrates. Error bars are omitted for clarity, but are discussed in the Supporting Material.

Until Day 4, the monolayer is very homogeneous, showing a nearly hexagonal arrangement of cells. From Day 4, however, defects start to appear in the form of small holes (typical size of (0.3 ± 0.1) × 10⁻³ mm²). These could be attributed to the extrusions of viable cells, from either the belt or areas of increased local density in the monolayer (inset in Fig. 4). This suggests that extrusions serve to release stress built in the tissue, and, as a consequence, the overall density is decreased.Open in a separate windowFigure 4Cell nuclei within the mother colony and in the neighboring archipelago of second-generation clusters grown on 0.6 kPa gels at Day 12. (Inset; scale bar = 10 μm) Scar in the tissue, a result of a cell-extrusion event. (Main image; scale bar = 100 μm) From the image of cell nuclei (left), it is clear that there are no cells within the scar, whereas the image of actin (right) shows that the cytoplasm of the cells at the edge has closed the hole.Previous reports suggest that isolated MDCK cells undergo anoikis 8 h after losing contact with their neighbors (18). However, in this case, it appears that instead of dying, the extruded cells create new colonies, which can be seen as an archipelago surrounding the mother cluster (Fig. 4). The viability of off-cast cells is further evidenced by the appearance of single cells and second-generation colonies with sizes varying over five orders of magnitude, from Day 4 until the end of the experiment, Day 12. Importantly, no morphological differences were found in the first- and second-generation colonies.In conclusion, we show what we believe to be a novel phase of growth of MDCK model tissue on soft PA gels (E = 0.6 kPa) that, to our knowledge, despite previous similar efforts (9), has not been observed before. This finding is especially interesting in the context of elasticity of real kidneys, for which a Young’s modulus has been found to be between 0.05 and 5 kPa (19,20). This coincides with the elasticity of substrates studied herein, and opens the possibility that the newly found phase of growth has a particular biological relevance. Likewise, the ability to extrude viable cells may point to a new migratory pathway regulated mechanically by the stresses in the tissue, the implication of which we hope to investigate in the future.     

Coaligned Dual-Channel STED Nanoscopy and Molecular Diffusion Analysis at 20 nm Resolution

We report on a fiber laser-based stimulated emission-depletion microscope providing down to ∼20 nm resolution in raw data images as well as 15–19 nm diameter probing areas in fluorescence correlation spectroscopy. Stimulated emission depletion pulses of nanosecond duration and 775 nm wavelength are used to silence two fluorophores simultaneously, ensuring offset-free colocalization analysis. The versatility of this superresolution method is exemplified by revealing the octameric arrangement of Xenopus nuclear pore complexes and by quantifying the diffusion of labeled lipid molecules in artificial and living cell membranes.Since its first demonstration in (live) cell imaging (1), stimulated emission depletion (STED) fluorescence microscopy has been realized in many variants. Particularly, the key phenomenon employed in this method, namely switching fluorophores transiently off by stimulated emission, has been accomplished with laser pulses varying from picoseconds to nanoseconds in duration, and from kHz to MHz in repetition rate. Because continuous-wave beams are suitable as well (2), STED microscopy has been implemented with rather different laser systems, ranging from model-locked femtosecond to continuous-wave laser diodes (3,4). Although it underscores the versatility of STED to modulate the fluorescence capability of a fluorophore, this wide range of options may confuse adopters when balancing simplicity, applicability, and resolution gain. The situation is exacerbated when implementing pairs of excitation and STED beams for dual-color colocalization studies (5,6).Here we report on a simple arrangement providing dual-color STED nanoscopy (Fig. 1) and molecular diffusion quantification down to ∼20 nm in (living) cells. The presented dual-channel STED microscope utilizes a single fiber laser providing a 20-MHz train of 775 nm wavelength pulses of 1.2-ns duration. This compact laser source enables STED on fluorophores emitting in the orange to red range. Specifically, we applied this laser on the orange dyes Atto590 and Atto594 (excitation: 595 nm; detection: 620 ± 20 nm), and the red dyes KK114 and Abberior Star635P (excitation: 640 nm; detection: 670 ± 20 nm). Although the spectra of the dyes are partially overlapping, the individual color channels can be separated without data processing (see Fig. S1 and Fig. S2 in the Supporting Material). Both channels are recorded simultaneously within 50 ns, using temporally interleaved pulsed excitation in combination with time-gated detection (5,7,8).Open in a separate windowFigure 1Fluorescence nanoscopy of protein complexes with a compact near-infrared nanosecond-pulsed STED microscope. (A) STED reveals immunolabeled subunits in amphibian NPC; raw data smoothed with a Gaussian filter extending over 14 nm in FWHM. The diameter of the octameric gp210 ring is established as ∼160 nm. Scale bar, 500 nm. (B) Individual NPC image showing eight antibody-labeled gp210 homodimers as 20–40 nm sized units and a 80 nm-sized localization of the subunits in the central channel.Because in STED microscopy, the STED doughnuts firmly determine the position of the fluorescently active molecules, the use of a single doughnut for both fluorophores guarantees that the two color channels are almost perfectly coaligned. The use of the doughnut even counteracts misalignments of the confocal excitation and detection channels (Fig. 2, and see Fig. S3), making STED microscopy particularly powerful for colocalization measurements.Open in a separate windowFigure 2Determination of the colocalization accuracy. Xenopus A6 cells, labeled with an antiserum against multiple NUP subunits in the central NPC channel and two secondary antibodies decorated with the fluorophores Abberior STAR635P and Atto594 were imaged by STED microscopy. (A) Upon overlaying both channels, a high degree of colocalization is directly visible. Scale bar, 200 nm. (B) Quantification of the colocalization by cross correlation of much larger images (see Fig. S3). The correlation is maximal for zero displacement of the images, proving colocalization. (C) Confocal image of monocolored fluorescent beads taken with improperly coaligned excitation beams (left). Improper coalignment spoils the colocalization accuracy in confocal imaging; the two channels should be perfectly coaligned, but they show a false offset as indicated by the color difference. The offset is quantified by the cross correlation of the two channels (right). (D) The STED image of the same beads (left) not only shows 10-fold improved resolution over the confocal image in panel C, but also improved colocalization, again quantified by cross correlation (right). Thus, by predetermining the position of emission, the STED doughnut counteracts errors induced by imperfect coalignment of the two confocal color channels (for details, see Fig. S3). Scale bars = 100 nm.The cross section for stimulated emission is lower at 775 nm as compared to that found at somewhat shorter wavelengths (5), yet STED pulse energies of ∼7 nJ in the focus are sufficient to yield a resolution of ∼30 nm and ∼20 nm in the orange and red channels, respectively (see Fig. S4). In addition, due to the lower peak intensity, the 1.2 ns pulses are likely to induce less nonlinear absorption and hence less photostress as compared to their more commonly used <0.2 ns counterparts (8,9). On the other hand, the pulses are only 2–4 times shorter than the typical lifetime of the excited state, which lessens their STED efficiency. This slight reduction is neutralized here by detecting photons emitted ∼1 ns after excitation (5,7,8).The potential of this straightforward implementation of STED microscopy is evident when imaging immunolabeled nuclear pore complexes (NPCs) of cultured Xenopus cells. Contrary to the confocal recording, STED microscopy reveals subunits of this protein complex, specifically the typical eightfold symmetry of its peripheral transmembrane protein gp210, along with a set of proteins in the central pore channel (Fig. 1, and see Fig. S5 and Fig. S6). Unlike in stochastic superresolution imaging of gp210 (10), the color channels are inherently coaligned and simultaneously recorded simply by executing a single scan. Apart from a weak smoothing and background subtraction applied to enhance image contrast, the images are raw.Because fluorescence off-switching by STED is an instant process, STED microscopy can be employed to study fast spatial translocations, such as the diffusion of molecules on the nanoscale (3). To benchmark the performance of our setup, we analyzed the diffusion of a fluorescent glycerophospholipid analog (11) by fluorescence correlation spectroscopy (FCS) in membranes of living mammalian PtK2-cells (Fig. 3). STED allowed us to reduce the diameter of the probed area from the 250 nm-sized diffraction limit down to 19 nm (FWHM), representing σ = 8 nm in standard deviation of a Gaussian fit. The attained subdiffraction area is 2.5 times smaller as compared to what has been reported in living cells to date (4). In model membranes, the smallest diameter was 15 nm (σ = 6.4 nm).Open in a separate windowFigure 3Nanoscale molecular diffusion analyzed by STED FCS. (A) For moderate and larger STED beam power P_STED, the resolution scales inversely with its square-root, attaining 15 nm in FWHM of the distribution of fluorescence emission in space, describing the measurement area. Note the relatively small threshold power P_S = 1.4 mW, which implies that a large resolution gain is already attained for P_STED < 100 mW. (Inset) The resolution was determined by measuring the transit time of a fluorescent phospholipid-analog (DSPE-PEG-KK114) in a lipid model membrane through the detection area by FCS. (B) In living mammalian Ptk2-cells, the transit time of the lipid analog scales linearly with the detection area, revealing a diffusion constant D_lat = 0.33 μm²/s, and showing that this lipid analog diffuses largely freely in the plasma membrane down to <20 nm scales.In both measurements, the molecular transit time depends linearly on the probed area, indicating that the labeled lipid molecules diffuse essentially freely down to spatial scales of 20 nm. Accordingly, the anomaly exponent α was close to 1 with values of α > 0.85, showing only minor deviations from free diffusion (see Fig. S7). Because the diameter is inversely proportional to the square-root of the STED beam power, the resolution can be adapted to a particular application need (Fig. 3, A and B).In summary, our arrangement provides up-to-date STED microscopy resolution in offset-free colocalization recordings. The ready-to-use near-infrared laser pulses keep undesired single and multiphoton absorption low and leave the visible spectrum amenable for further studies.     

Tracking Single Serotonin Transporter Molecules at the Endoplasmic Reticulum and Plasma Membrane

Transmembrane proteins are synthesized and folded in the endoplasmic reticulum (ER), an interconnected network of flattened sacs or tubes. Up to now, this organelle has eluded a detailed analysis of the dynamics of its constituents, mainly due to the complex three-dimensional morphology within the cellular cytosol, which precluded high-resolution, single-molecule microscopy approaches. Recent evidences, however, pointed out that there are multiple interaction sites between ER and the plasma membrane, rendering total internal reflection microscopy of plasma membrane proximal ER regions feasible. Here we used single-molecule fluorescence microscopy to study the diffusion of the human serotonin transporter at the ER and the plasma membrane. We exploited the single-molecule trajectories to map out the structure of the ER close to the plasma membrane at subdiffractive resolution. Furthermore, our study provides a comparative picture of the diffusional behavior in both environments. Under unperturbed conditions, the majority of proteins showed similar mobility in the two compartments; at the ER, however, we found an additional 15% fraction of molecules moving with 25-fold faster mobility. Upon degradation of the actin skeleton, the diffusional behavior in the plasma membrane was strongly influenced, whereas it remained unchanged in the ER.Live-cell microscopy and three-dimensional electron tomography has boosted our understanding of endoplasmic reticulum (ER) dynamics and morphology. Proteins have been identified which regulate the formation of cisternae versus tubelike membranes, and the contacts between ER and the various cellular organelles have been studied in detail (1). Little information, however, is available when it comes to protein dynamics and organization within the ER membrane. Its complex three-dimensional topology hampers standard diffraction-limited fluorescence microscopy approaches: in fluorescence recovery after photobleaching, for example, the obtained diffusion coefficients can be several-folds off, if the ER morphology is not correctly taken into account (2). A method is therefore needed which allows for resolving molecular movements on length scales below the typical dimensions of the ER structures.In principle, single-molecule tracking would provide the required spatial resolution due to the high precision in localizing the moving point emitters: localization errors of <40 nm can be easily achieved (3). This technique has given rise to multiple studies, in which the paths of the diffusing objects were used to make conclusions on the properties of the environment; particularly, the plasma membrane has become a favorite target for such investigations, yielding precise determinations of the diffusion coefficients of a variety of membrane proteins or lipids (4).Here, we report what is, to our knowledge, the first application of single-molecule tracking for a comparative study of the diffusion dynamics of a membrane protein at the ER versus the plasma membrane. As the protein of interest, we chose the human serotonin transporter (SERT): it is a polytopic membrane protein containing 12 transmembrane domains, with both C- and N-termini residing in the cytoplasm. Stable SERT oligomers of various degrees were observed to coexist in the plasma membrane (5). Functionally, SERT (6) is a pivotal element in shaping serotonergic neurotransmission: SERT-mediated high-affinity uptake of released serotonin clears the synaptic cleft and supports refilling of vesicular stores (7). Wild-type SERT (SERT-wt) is efficiently targeted to the presynaptic plasma membrane, whereas the truncation of its C-terminus (SERT-ΔC30) retains the mutant protein in the ER (8). The N-terminal mGFP- and eYFP-fusion constructs of the two versions of SERT thus allowed us to specifically address SERT located at the ER (eYFP-SERT-ΔC30) or at the plasma membrane (mGFP-SERT-wt (7)).Our experiments were performed at 37°C on proteins heterologously expressed in CHO cells. Total internal reflection (TIR) illumination afforded a reduction in background fluorescence and allowed for selective imaging of single mGFP-SERT-wt molecules at the cells’ plasma membrane or single eYFP-SERT-ΔC30 molecules at plasma membrane-proximal ER (Fig. 1 and see the Supporting Material). TIR was particularly crucial for single-molecule imaging of the ER-retained mutant, where out-of-focus background would surpass the weak single-molecule signals in epi-illumination.Open in a separate windowFigure 1Schematics of the plasma membrane (PM) and a part of the ER containing mGFP-SERT-wt or the ER-retained eYFP-SERT-ΔC30 mutant, respectively. Both can be excited by total internal reflection fluorescence (TIRF) excitation. Experiments were carried out either on cells expressing mGFP-SERT-wt or eYFP-SERT-ΔC30.For both mutants, the majority of molecules were mobile: in fluorescence-recovery-after-photobleaching experiments we observed a mobile fraction of 82 ± 8% for mGFP-SERT-wt and 91 ± 4% for eYFP-SERT-ΔC30. For single-molecule tracking, the high surface density of signals was reduced by completely photobleaching a rectangular part of the cell in epi-illumination; after a brief recovery period, a few single-molecule signals had entered the bleached area and could be monitored and tracked at high signal/noise using TIR excitation. Samples were illuminated stroboscopically for t_ill = 2 ms, and movies of 500 frames were recorded with a delay of t_del = 6 ms; the short delay times ensured that even rapidly diffusing molecules hardly reached the borders of the ER tubes between two consecutive frames. This illumination protocol was run for 20 times per cell, yielding ∼2500 trajectories per cell.The single-molecule localizations were first used to map those areas that are accessible to the diffusing proteins. eYFP-SERT-ΔC30 showed distinct hotspots, representing plasma membrane-proximal ER, excitable by the evanescent field (Fig. 2 A). These hotspots hardly moved within the timescale of an experiment (tens of minutes, see Fig. S1 in the Supporting Material); indeed, remarkable ER stability was previously observed using superresolution microscopy (9). In contrast, a rather homogeneous distribution was observed for mGFP-SERT-wt in the plasma membrane (Fig. 2 B).Open in a separate windowFigure 2Superresolution and tracking data at the ER and the plasma membrane. Superresolution images are shown for the ER-retained SERT mutant eYFP-SERT-ΔC30 (A) and for mGFP-SERT-wt in the plasma membrane (B). (C and D) Diffusion coefficients of eYFP-SERT-ΔC30 (C) and mGFP-SERT-wt (D) are shown as normalized histograms before (blue) and after (red) Cytochalasin D treatment. Data were fitted by Gaussian mobility distributions (see Table S1 in the Supporting Material for the fit results).Next, we compared the mobility of the observed proteins. Single-molecule localizations were linked to trajectories as described in Gao and Kilfoil (10), and the apparent diffusion coefficient, D, of each molecule was estimated from the first two points of the mean-square displacement membrane. The distribution of log₁₀ D showed a pronounced single peak (Fig. 2 D). It could be well fitted by a linear combination of two Gaussian functions, with the major fraction (85%) characterized by D_wt = 0.30 μm²/s; a broad shoulder to the left indicates the presence of proteins that are immobilized during the observation period. In contrast, the mobility of the ER-retained mutant showed a substantially different distribution, containing two clearly visible peaks (Fig. 2 C). We fitted the data with a three-component Gaussian model: the main fraction (82%) behaved similar to SERT at the plasma membrane, with D_ΔC30 = 0.32 μm²/s. In addition, a large fraction (15%) with high mobility of D_ΔC30 = 7.8 μm²/s and a minor fraction (3%) with low mobility was observed. The proteins responded as expected to degradation of the actin membrane skeleton (red bars in Fig. 2, C and D): at the plasma membrane, the mobility of mGFP-SERT-wt increased 4.6-fold (mean values), whereas at the ER membrane there was only a minor change for eYFP-SERT-ΔC30 mobility (1.06-fold increase; note that the ER is not connected to actin filaments (11)).The observation of a high mobility subfraction at the ER membrane is surprising. In general, the presence of obstacles—irrespective of whether randomly distributed or clustered, mobile or immobile—reduces the diffusivity of mobile tracers in a membrane (12). It is generally assumed that the high protein density in cell membranes is responsible for the rather low fluidity when compared to synthetic membranes (compare, e.g., Saxton and Jacobson (13) with Weiss et al. (14)). Interestingly, the observed diffusion constant of 7.8 μm²/s is of similar order as the mobility determined for various proteins in synthetic lipid membranes (14). It is thus tempting to hypothesize the presence of extended protein-depleted regions of higher fluidity within the ER membrane; such membrane domains were indeed observed already at the plasma membrane (15). We were also concerned, however, that protein degradation fragments could have contributed to our data: the three-dimensional mobility of an 85-kDa protein is ∼10 μm²/s (16), similar to the high mobility diffusion constant of eYFP-SERT-ΔC30.We tested the two explanations by analyzing the spatial distribution of fast (D_ΔC30 > 1 μm²/s) versus slow trajectories (D_ΔC30 < 1 μm²/s) of eYFP-SERT-ΔC30 (Fig. 3). Both types of trajectories clustered in the same regions, and no segregation into ER subdomains was observable at the resolved length scales. This finding—on the one hand—disfavors freely diffusing protein fragments as the origin of the high mobility fraction. On the other hand, it calls for further experiments to identify the origin of the fast and the slow mobility subfraction. Interestingly, when analyzing all eYFP-SERT-ΔC30 trajectories we found that 80% of the molecules showed diffusion confined to domains of 230-nm radius (see Fig. S2). This size is clearly smaller than the lateral extensions of the visible ER regions observed in Fig. 3. The finding indicates domain formation at the ER membrane; domains are averaged out in Fig. 3 due to the long recording times. Note that free diffusion was observed for mGFP-SERT-wt at the plasma membrane (5).Open in a separate windowFigure 3Ripley’s K function analysis of the different mobility fractions in the ER. For the cell presented in Fig. 2, the first position of every slow (D < 1 μm²/s; red) and fast (D > 1 μm²/s; blue) trajectory was plotted in panel A. Contour lines indicate regions of ER attachment to the plasma membrane. In panel B, the point-correlation function L(r)−r is plotted for the slow (red) and fast (blue) fraction. Furthermore, the correlation between fast versus slow is plotted (green). All three curves show a peak at ∼450 nm, which agrees with the extensions of the ER attachment zones.In conclusion, we have shown that single-molecule tracking is feasible for constituents of the ER membrane. We found a surprising diffusion behavior of SERT resulting in the following:

• 1.A slow fraction showing mobility reminiscent of protein diffusion in the plasma membrane, likely reflecting SERT diffusing in protein-crowded regions of the ER membrane; and
• 2.A fast fraction showing 25-fold faster diffusion kinetics.

This likely represents diffusion in altered ER membrane environments, possibly of different lipid or protein composition. Given the fact that synthesis of virtually all membrane proteins and most lipids proceeds at the ER membrane, ER heterogeneity at the nanoscale due to the continuous synthesis activity and selection for correct folding appears highly plausible.     

Genetics of Sexual Development: An Evolutionary Playground for Fish

Teleost fishes are the most species-rich clade of vertebrates and feature an overwhelming diversity of sex-determining mechanisms, classically grouped into environmental and genetic systems. Here, we review the recent findings in the field of sex determination in fish. In the past few years, several new master regulators of sex determination and other factors involved in sexual development have been discovered in teleosts. These data point toward a greater genetic plasticity in generating the male and female sex than previously appreciated and implicate novel gene pathways in the initial regulation of the sexual fate. Overall, it seems that sex determination in fish does not resort to a single genetic cascade but is rather regulated along a continuum of environmental and heritable factors.IN contrast to mammals and birds, cold-blooded vertebrates, and among them teleost fishes in particular, show a variety of strategies for sexual reproduction (Figure 1), ranging from unisexuality (all-female species) to hermaphroditism (sequential, serial, and simultaneous, including outcrossing and selfing species) to gonochorism (two separate sexes at all life stages). The underlying phenotypes are regulated by a variety of sex determination (SD) mechanisms that have classically been divided into two main categories: genetic sex determination (GSD) and environmental sex determination (ESD) (Figure 2).Open in a separate windowFigure 1Reproductive strategies in fish. Fish can be grouped according to their reproductive strategy into unisexuals, hermaphrodites, and gonochorists. Further subdivisions of these three categories are shown with pictures of species exemplifying the strategies. Fish images: Amphiprion clarkii courtesy of Sara Mae Stieb; Hypoplectrus nigricans courtesy of Oscar Puebla; Scarus ferrugineus courtesy of Moritz Muschick; Astatotilapia burtoni courtesy of Anya Theis; Poecilia formosa and Kryptolebias marmoratus courtesy of Manfred Schartl; Trimma sp. courtesy of Rick Winterbottom [serial hermaphroditism has been described in several species of the genus Trimma (Kuwamura and Nakashima 1998; Sakurai et al. 2009; and references therein)].Open in a separate windowFigure 2Sex-determining mechanisms in fish. Sex-determining systems in fish have been broadly classified into environmental and genetic sex determination. For both classes, the currently described subsystems are shown.Environmental factors impacting sex determination in fish are water pH, oxygen concentration, growth rate, density, social state, and, most commonly, temperature (for a detailed review on ESD see, e.g., Baroiller et al. 2009b and Stelkens and Wedekind 2010). As indicated in Figure 2, GSD systems in fish compose a variety of different mechanisms and have been reviewed in detail elsewhere (e.g., Devlin and Nagahama 2002; Volff et al. 2007).The GSD systems that have received the most scientific attention so far are those involving sex chromosomes, which either may be distinguishable cytologically (heteromorphic) or appear identical (homomorphic). In both cases, one sex is heterogametic (possessing two different sex chromosomes and hence producing two types of gametes) and the other one homogametic (a genotype with two copies of the same sex chromosome, producing only one type of gamete). A male-heterogametic system is called an XX-XY system, and female-heterogametic systems are denoted as ZZ-ZW. Both types of heterogamety exist in teleosts and are even found side by side in closely related species [e.g., tilapias (Cnaani et al. 2008), ricefishes (Takehana et al. 2008), or sticklebacks (Ross et al. 2009)]; for more details on the phylogenetic distribution of GSD mechanisms in teleost fish, see Mank et al. (2006). Note that sex chromosomes in fish are mostly homomorphic and not differentiated (Ohno 1974), which is in contrast to the degenerated Y and W chromosomes in mammals (Graves 2006) and birds (Takagi and Sasaki 1974), respectively. This is one possible explanation for the viable combination of different sex chromosomal systems within a single species or population of fish (Parnell and Streelman 2013) and could be a mechanistic reason why sex chromosome turnovers occur easily and frequently in this group (Mank and Avise 2009). Additionally, fish can have more complex sex chromosomal systems involving more than one chromosome pair (see Figure 2). Even within a single fish species, more than two sex chromosomes may occur at the same time, or more than two types of sex chromosomes may co-exist in the same species (Schultheis et al. 2006; Cioffi et al. 2013), which can sometimes be due to chromosome fusions (Kitano and Peichel 2012).Detailed insights on the gene level for GSD/sex chromosomal systems are currently available for only a limited number of fish species, and all but one of these cases involve a rather simple genetic system with male heterogamety and one major sex determiner (see below). The only exception is the widely used model species zebrafish (Danio rerio), which has a polyfactorial SD system implicating four different chromosomes (chromosomes 3, 4, 5, and 16) (Bradley et al. 2011; Anderson et al. 2012) and also environmental cues (Shang et al. 2006).In this review, we focus on newly described genetic sex-determining systems and possible mechanisms allowing their emergence in fishes, which are the most successful group of vertebrates with ∼30,000 species.     

Indolicidin Binding Induces Thinning of a Lipid Bilayer

We use all-atom molecular dynamics simulations on a massive scale to compute the standard binding free energy of the 13-residue antimicrobial peptide indolicidin to a lipid bilayer. The analysis of statistical convergence reveals systematic sampling errors that correlate with reorganization of the bilayer on the microsecond timescale and persist throughout a total of 1.4 ms of sampling. Consistent with experimental observations, indolicidin induces membrane thinning, although the simulations significantly overestimate the lipophilicity of the peptide.Antimicrobial peptides are a component of the innate immune system of eukaryotes (1). As such, they must interact with pathogenic membranes, either during translocation or by disrupting their structural integrity (2). Here we examine the binding of the 13-residue cationic antimicrobial peptide indolicidin (3) (ILPWKWPWWPWRR-NH₂) to a lipid membrane as a first step towards elucidating its mechanism of action.Molecular solutes interact with lipid membranes in many cellular processes (4). Computational approaches such as molecular dynamics simulations have been widely used to characterize these interactions (5). However, molecular dynamics simulations can require unfeasibly long times to reach equilibrium (6). Therefore, it is common to compute equilibrium properties of solute insertion into lipid bilayers using umbrella sampling (7) simulations in which the solute is restrained along the bilayer normal using harmonic restraining potentials, or umbrellas, centered at zi0 values distributed between bulk water and the bilayer center.It is often assumed that equilibrium properties rapidly attain convergence in umbrella sampling simulations; accordingly, convergence measures are rarely published (8). However, we have recently shown that umbrella sampling simulations require up to 100 ns per umbrella (3 μs in total) to eliminate systematic sampling errors in the standard free energy of binding, ΔGbind0, of an arginine side-chain analog from bulk water to a lipid bilayer (8). The fact that umbrella sampling has been used to investigate the bilayer insertion of substantially larger solutes (9) motivates a systematic evaluation of statistical sampling convergence of ΔGbind0 for indolicidin in a lipid bilayer.To estimate ΔGbind0 of indolicidin to a lipid bilayer, we conducted 60 sets of umbrella-sampling simulations while systematically varying the initial conformation. In each umbrella sampling simulation, each umbrella was simulated for 1.5 μs, yielding a total simulation time of 1.4 ms and 60 independent free energy or potential of mean force (PMF) profiles from bulk water to the center of a POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine) lipid bilayer.The PMF profiles indicate that indolicidin strongly binds to the bilayer, partitioning inside the lipid headgroups (Fig. 1, A and E). Importantly, the mean estimate of ΔGbind0 decays exponentially with equilibration time t_eq, indicating that systematic sampling errors in individual simulations continued to decrease throughout the 1.5-μs interval as rare events led to more favorable states (Fig. 1 B). The low frequency of transitions to more favorable states exacerbates the requirement for massive sampling using multiple independent simulations.Open in a separate windowFigure 1PMF for indolicidin partitioning into a POPC bilayer. (A) Average PMF from 60 independent umbrella-sampling simulations based on 1 < t ≤ 1.5 μs/umbrella. (B) Average ΔGbind0 from 50-ns time intervals per umbrella (t_eq < t ≤ t_eq+50 ns) as a function of equilibration time, t_eq. (Solid line) Single exponential fit to the mean over 0.5 < t_eq ≤ 1.5 μs. (C) Mean values of ΔGbind0 from the 10-μs/umbrella simulations (crosses) together with the mean values of ΔGbind0 (triangles) and exponential fit from panel B. PMF and ΔGbind0 profiles obtained from each of the 60 independent simulations are shown in Fig. S1 in the Supporting Material. (D–F) Representative conformations after 1.5 μs of simulation at zi0 = (D) 3 nm, (E) 1.2 nm, and (F) 0.0 nm. To see this figure in color, go online.Computational limitations precluded extending all 60 sets of umbrella-sampling simulations to even longer times. Instead, we identified the two simulations at each umbrella that appeared to be most representative of equilibrium and extended each to 10 μs per umbrella (see Methods in the Supporting Material). The resulting estimates of ΔGbind0 continued to decrease until t_eq = 4 μs (68 μs in total), after which they stabilized at the asymptotic limit of the exponential fit of the shorter simulation data (ΔGbind0 = −26 ± 5 kcal/mol; Fig. 1 C).As indolicidin approaches the bilayer, it is drawn closer (Fig. 2 A) as salt bridges form between the peptide and the phospholipid headgroups (Fig. 2 B), inducing their protrusion (Figs. 1 D and ​and22 C). At large separation distances, this state is attained only when the peptide becomes highly extended (Fig. 2, D and E). As indolicidin is inserted more deeply, the surface of the lipid bilayer invaginates (Figs. 1 E and ​and22 C), maintaining peptide-lipid salt bridges (Fig. 2 B) and leading to the formation of a pore when the solute is near the bilayer center (Figs. 1 F and ​and22 C, and see Fig. S2, Fig. S3, Fig. S4, and Fig. S5 in the Supporting Material). These Boltzmann-weighted ensemble averages may not be mechanistically representative of nonequilibrium binding events (8,10).Open in a separate windowFigure 2Slow equilibration of bilayer and peptide. (A–D) Color quantifies conformational reorganization for t_eq < t ≤ t_eq + 100 ns as a function of t_eq and |zi0|. (A) Deviation of insertion depth, z, from zi0, Δz ≡ z − zi0; (B) number of peptide-lipid salt bridges, N_SB; (C) volume change of the bilayer’s proximal leaflet in the radial vicinity of the solute, V_ε; and (D) peptide end-to-end distance (EED). There is no sampling for t > 0.5 μs at |zi0| ≥ 4.5 nm. (E) Representative time-series of a trajectory at zi0 = 3.9 nm. (F) Representative conformation at 10 μs for |zi0| = 1.2 nm. To see this figure in color, go online.The reorganization of the peptide, the bilayer, and the ionic interactions between them became more pronounced with increasing simulation time at peptide insertion depths shallower than the global free energy minimum (|zi0| >1.4 nm; Fig. 1 A and Fig. 2, B–D). These conformational transitions are likely the source of the systematic drift of ΔGbind0. Reorganization of the bilayer also controls the rate of equilibration during membrane insertion of an arginine side chain (8,9) and a cyclic arginine nonamer (11), suggesting that slow reorganization of lipids around cationic solutes presents a general impediment to simulation convergence.Consistent with the perturbation of membrane thickness observed by in situ atomic force microscopy (12), our results suggest that indolicidin insertion induces local thinning of the bilayer (Fig. 1, E and F, and Fig. 2, C and F). The different conformational ensembles sampled by the peptide in water and in the lipid bilayer (Fig. 2 D) are consistent with the observations that indolicidin is disordered in solution (13) and adopts stable conformations in the presence of detergent (14). Although the peptide’s conformation continued to change when it was deeply inserted (Fig. 2 D), the amount of water in the bilayer’s hydrophobic core converged relatively rapidly (see Fig. S2). Indolicidin can induce the formation of hydrated, porelike defects (see Fig. S2, Fig. S3, Fig. S4, and Fig. S5) but does not act as a chloride carrier (see Fig. S6 and Fig. S7). Future studies of the mechanism of indolicidin action will examine the effect of multiple peptide binding.The PMF profile presented in this Letter is strikingly different from that computed by Yeh et al. (15) using different force field parameters for indolicidin partitioning into a DMPC (1,2-dimyristoyl-sn-glycero-3-phosphatidylcholine) bilayer, from which the binding free energy was estimated to be 0 kcal/mol (15). However, that study comprised only 25 ns per umbrella and likely suffers from systematic sampling errors induced by initial conditions (see Fig. S8).Our estimate of the binding affinity is much larger than the values obtained for indolicidin and large unilamellar POPC vesicles using isothermal titration calorimetry, −7.4 kcal/mol (16), and equilibrium dialysis, −8.8 kcal/mol (13). Such a discrepancy suggests that the relative accuracy of binding free energies for amino-acid side-chain analogs (8,9,17) does not necessarily extend to polypeptides. Although more work is needed to elucidate the source of this discrepancy, this study underlines the importance of attaining convergence before evaluating force-field accuracy.Importantly, this work also highlights the extensive sampling required to remove systematic errors induced by initial conditions in atomistic simulations of peptides in membranes. Slow equilibration of the system is due to rare transitions across hidden free energy barriers involving reorganization of the membrane. Two simple recommendations are 1), evaluating the time-dependence of ensemble averages, and 2), conducting multiple simulations with different initial conditions. We have recently shown that by using enhanced sampling techniques it is possible to identify the locations of hidden free energy barriers without a priori knowledge (9). Future research will examine strategies for speeding up the crossing of these barriers, such as optimized order parameters including bilayer reorganization and enhanced sampling techniques including a random walk along the order parameter (9).