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.     

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.     

Molecular Flow Quantified beyond the Diffraction Limit by Spatiotemporal Image Correlation of Structured Illumination Microscopy Data

We combine total internal reflection fluorescence structured illumination microscopy with spatiotemporal image correlation spectroscopy to quantify the flow velocities and directionality of filamentous-actin at the T cell immunological synapse. These techniques demonstrate it is possible to image retrograde flow of filamentous-actin at superresolution and provide flow quantification in the form of velocity histograms and flow vector maps. The flow was found to be retrograde and radially directed throughout the periphery of T-cells during synapse formation.Many biological processes are now being visualized with the use of superresolution fluorescence microscopy techniques. However, localization-based techniques primarily rely on fixed or slow moving samples to permit the collection of structural information. The 10-fold gains in resolution afforded by these superresolution techniques are usually possible through sacrificing the factors that originally made microscopy such a powerful tool: the ability to image live cells. In the case of stimulated emission depletion imaging, the scanning approach associated with this technique may fail to detect faster molecular events when imaging whole cellular regions.Structured illumination microscopy (SIM) is an alternative to these methods (1). It increases the resolution of conventional fluorescence microscopy twofold; it has the advantage of using a wide-field system, providing fast acquisition speeds of whole cells with relatively low laser powers; and it is compatible with standard fluorophores. By using a physical grating to produce interference patterns from a laser, periodic illumination is created. This patterned illumination causes information from higher spatial frequencies to be downmodulated (i.e., shifted) into the optical transfer function (support region) of the lens, resulting in higher-resolution spatial information being captured than is ordinarily obtainable.To quantify the directional motion of intracellular molecules, spatiotemporal image correlation spectroscopy (STICS (2)) was applied. Using spatial image correlation in time, STICS measures the similarity of pixels with those surrounding in lagging frames via a correlation function. The correlation function provides information on both flow velocities and directionality, while discounting static structures through the immobile object filter, achieved by subtracting a moving average of pixel intensities.The formation of an immunological synapse between T cells and antigen-presenting cells is a process requiring many dynamic (3) and subdiffraction-limited clustering events (4–6) to take place. The polymerization of actin is important for the spreading of cells over their target antigen-presenting cells (7), as well as cell mobility and migration (8). Retrograde flow of densely meshed cortical actin is observed at the basal membrane of synapse-forming T cells, where it may have a role in the corralling and clustering of signaling molecules at the plasma membrane (9), as well as at the leading edge of migrating cells (10). Filamentous actin is an extremely dynamic (7), densely packed, and thin (7-nm) structure (11,12).Here, we perform STICS on SIM data acquired on a total internal reflection fluorescence (TIRF) microscope system, which generated an evanescent field of 75-nm depth for excitation. To our knowledge, this is the first demonstration of an image correlation approach to quantify molecular dynamics on subresolution length scales using wide-field microscopy. To demonstrate the technique, we analyze two-dimensional actin flows in CD4⁺ T cells during immunological synapse formation, performed after cross-linking of antigen T cell receptors on a coverslip coated with specific antibodies.Fig. 1 a shows a schematic of the TIRF SIM setup. Excitation light (488 nm) passes through a polarizing module and then a phase-grating block, producing diffracted beams. These are then passed through a diffraction filter module to isolate the −1 and +1 order laser beams. These first-order laser beams are angled through the objective to produce total internal reflection conditions at the glass-water interface. The two evanescent waves interfere at the sample, producing structured illumination. The setup then produces lateral and rotational shifts through three orientations, producing nine raw images containing higher spatial frequencies than can normally be acquired by an objective using standard light microscopy. Fig. 1 b demonstrates the increased resolution obtained from TIRF SIM. Shown are the collected Fourier frequencies compared to those of a conventional microscope (dotted red line). Resolution was also measured using sparse 100-nm diameter fluorescent beads. Fig. 1 c shows a magnified image of these beads from which a line profile was obtained (yellow arrow). The full width at half-maximum of this profile (Fig. 1 d) gives a lateral resolution for the system of 120 nm.Open in a separate windowFigure 1(a) Schematic of the TIRF SIM setup. (b) Demonstration of the doubling of spatial resolution of collected frequencies through a Fourier transform (superimposed red circle demonstrating regular spatial frequency limits). (c) SIM reconstructed image of 100-nm bead (scale bar 0.5 μm). (d) (Plotted line) Bead showing full width at half-maximum of 120 nm.We then applied STICS analysis to quantify actin flow in T cell synapses acquired using TIRF SIM (Fig. 2). Fig. 2 a shows a schematic of the STICS analysis. From the raw data, immobile objects are first filtered by subtracting a moving average of the pixel values. Vector maps were obtained from correlation analysis of the time-series as previously published in Hebert et al. (2) and Brown et al. (13). Fig. 2 b shows a reconstructed TIRF SIM image of a mature T cell immunological synapse, representative of a time-point derived from the time series acquired at 1.28 fps (see Movie S1 in the Supporting Material). From this reconstructed image, two representative regions have been selected. In these regions, pseudo-colored actin flow vectors are overlaid onto the fluorescence intensity image. These range in magnitude from 0.01 μm/min (blue) to 5.61 μm/min (red). It can be observed that all flow vectors are directed radially toward the synapse center. A histogram of this flow is shown in Fig. 2 c. The histogram shows a peak retrograde flow velocity of 1.91 ± 1.27 μm/min. These data are representative of n = 7 T-cell synapses imaged by TIRF SIM.Open in a separate windowFigure 2(a) STICS analysis, performed by isolating mobile from immobile structures through a moving average filter (i) and binning a subset of pixels into blocks of superpixels (ii); the STICS software correlates spatial fluorescence fluctuations through time (iii). The code then outputs vector maps showing directionality and flow velocities. (b) TIRF SIM image of actin flow in a T cell 5 min after contact with a stimulatory coverslip. (Zoomed regions) Retrograde actin flow at the synapse periphery. (c) Histograms showing flow speed statistics of vectors from T-cell synapses (n = 7).     

Open and Closed Conformations of the Isolated Transmembrane Domain of Death Receptor 5 Support a New Model of Activation

It has long been presumed that activation of the apoptosis-initiating Death Receptor 5, as well as other structurally homologous members of the TNF-receptor superfamily, relies on ligand-stabilized trimerization of noninteracting receptor monomers. We and others have proposed an alternate model in which the TNF-receptor dimer—sitting at the vertices of a large supramolecular receptor network of ligand-bound receptor trimers—undergoes a closed-to-open transition, propagated through a scissorslike conformational change in a tightly bundled transmembrane (TM) domain dimer. Here we have combined electron paramagnetic resonance spectroscopy and potential-of-mean force calculations on the isolated TM domain of the long isoform of DR5. The experiments and calculations both independently validate that the opening transition is intrinsic to the physical character of the TM domain dimer, with a significant energy barrier separating the open and closed states.Death receptor 5 (DR5) is a member of the tumor necrosis factor receptor (TNFR) superfamily that mediates apoptosis when bound by its cognate ligand, TNF-related apoptosis-inducing ligand (1). Upregulated in cancer cells, DR5 is among the most actively pursued anticancer targets (2). TNF-related apoptosis-inducing ligand binds to preassembled DR5 trimers at their extracellular domains, causing the formation of oligomeric ligand-receptor networks that are held together by receptor dimers (3). In the long-isoform of DR5, this dimer is crosslinked via ligand-induced disulfide bond formation between two transmembrane (TM) domain α-helices at Cys-209, and is further stabilized by a GxxxG motif one helix-turn downstream (3).Our recent study of the structurally homologous TNFR1 showed that receptor activation involves a conformational change that propagates from the extracellular domain to the cytosolic domain through a separation (or opening) of the TM domains of the dimer (4). We have therefore hypothesized that the activation of DR5, and indeed all structurally homologous TNF-receptors, involves a scissorslike opening of the TM domain dimer (Fig. 1).Open in a separate windowFigure 1Activation model of the DR5-L TM dimer. The sequence and positions of the disulfide bond and TOAC spin label (top), along with our previously published model (bottom, left) are shown. We propose an activation model (bottom, right) in which the transmembrane dimer pivots at its disulfide bond to reach an active open conformation.Using electron paramagnetic resonance (EPR) spectroscopy, a technique that has been used previously to study TM helix architecture and dynamics (5,6), and potential-of-mean force (PMF) calculations (7,8), this study addresses the question of whether the isolated disulfide-linked DR5-L TM domain dimer occupies distinct open and closed states (Fig. 1), and how its dynamic behavior contributes to the free-energy landscape of the opening transition of the full-length receptor.The DR5-L TM domain was synthesized with TOAC, an amino acid with a nitroxide spin label rigidly fixed to the α-carbon (9), incorporated at position 32 (Fig. 1), with some minor modification to facilitate EPR measurements. Previous work confirmed that this peptide forms disulfide-linked dimers (e.g., via comparison to 2-ME treated sample) and a negligible population of higher-order oligomers (further supported by model fitting of the EPR data below). For peptide work, residues were renumbered such that Thr-204 corresponds to Thr-1, and so on. The cytosolic Cys-29 (which we previously showed does not participate in a disulfide bond in cells) was replaced with serine to prevent the formation of antiparallel disulfide-linked dimers, and Trp-34 was replaced with tyrosine to prevent intrinsic fluorescence in fluorescence studies (not published). Continuous-wave (CW) dipolar EPR (sensitive only to spin-spin distances <25 Å) was used to measure TOAC-TOAC distances within the TM dimers and revealed an ordered Gaussian distribution centered at 16 Å (full width half-maximum (FWHM) = 4 Å), corresponding to a closed state (Fig. 2 A). Double electron-electron resonance (DEER) (sensitive to spin-spin distances from 15 to 60 Å) also detected a short distance consistent with the dipolar EPR data, along with a longer, disordered component (32.9 Å, FWHM = 28 Å) (Fig. 2 B). Together, these measurements indicate the presence of a compact, ordered closed state and a broader, disordered open state. EPR on oriented membranes also indicated two structural states. Global fitting revealed two populations of spin-label tilt angles (orientation of the nitroxide principal axis relative to the membrane normal): a narrow conformation (24°, FWHM = 20°), and a disordered conformation (50°, FWHM = 48°) (Fig. 2 C). This bimodal orientational distribution (Fig. 2 C) is remarkably consistent with the bimodal distance distribution (Fig. 2 B).Open in a separate windowFigure 2EPR spectra (left) of 32-TOAC-DR5 in lipid, and resulting structural distributions (right). (A) CW dipolar EPR spectra (left) of dimer (1 mM diamide) and monomer (1 mM 2-mercaptoethanol). Best-fit spin-spin distance distribution was a single Gaussian centered at 16 ± 2 Å (right). (B) The DEER waveform (left) of 32-TOAC-DR5 dimer was best fit (right) to a two-Gaussian distribution. The short distance was constrained to agree with the CW data, because DEER has poor sensitivity for distances <20 Å. The long-distance distribution is centered at 32.9 Å and is much broader. (C) CW EPR spectra (left) of 32-TOAC-DR5, with the membrane-normal oriented parallel (red) and perpendicular (blue) to the field. Simultaneous (global) fitting of these spectra reveals narrow and broad components (right). (In panels B and C, the overall distribution is plotted as black, while the closed and open components are plotted as green and magenta, respectively.)We subsequently conducted a PMF calculation (10) using the DR5-L TM dimer starting configuration developed by our group previously (3), embedded in a DMPC bilayer, with the Leu-32/Leu-32 C_α distance as the reaction coordinate. Three calculations were run from independent starting configurations, each using 50 windows spaced in 0.5° increments, and run for 20 ns at each window (totaling 3 μs). Each of the calculations yielded a similar result, and the averaged free energy curve (Fig. 3 A) agrees remarkably well with our EPR measurements: a narrow distribution at the closed conformation (∼16 Å, Fig. 3 B) separated by an ∼3 kcal/mol energy barrier from a broad distribution of accessible open conformations at ∼27 Å, (Fig. 3 C). Each of the three individual PMF plots can be found in Fig. S1 in the Supporting Material.Open in a separate windowFigure 3(A) PMF calculation of the DR5 TM domain dimer along the Leu-32/Leu-32 distance reaction coordinate. The PMF calculation reveals a narrow closed state and a broader open state separated by a free energy barrier. Representative snapshots of the (B) closed state and (C) open state.In the closed state, the helices are tightly packed at the GxxxG interfacial motif and all the way down the juxtaposed helix faces at residues Ala-18, Leu-22, Ala-25, and Val-26. The tight packing is aided by kinking and twisting of the two helices around their common axis, increasing the interacting surface area. In the open conformations, the Ala-18, Leu-22, Ala-25, and Val-26 pairs are dissociated and, interestingly, the GxxxG motif at Gly-10 and Gly-14 remains tightly packed. The open state energy well is only slightly less favorable than the closed state (by ∼2 kcal/mol), and its free energy profile is relatively broad and flat. The increased crossing angle in the open state is facilitated by straightening of the helix kink and is not accommodated by a change in bilayer thickness (see Fig. S3, A and B).The observed change in helix-helix distance (11 Å between the two minima in the PMF) is extremely close to that observed previously in live-cell FRET studies of a constitutively active form of TNFR1 (∼8 Å change between states using large fluorescence probes at the cytosolic domains) (4). The change observed in the EPR data (17 Å) may be an overestimate because the measurement is made between TOAC spin labels that likely protrude from the two helices, depending on rotational orientation. These results collectively show that activation of these receptors requires a small, but clearly significant conformational opening of the TM domains. One important note is that our EPR experiments recapitulate the equilibrium distribution of the two states despite there being no driving force to traverse the barrier between them (∼3 kcal/mol in the closed-to-open transition and ∼1 kcal/mol in the open-to-closed transition, Fig. 3). We do not interpret the results to mean that the dimer necessarily traverses these barriers at 4°C. Rather, there likely exist multiple reaction paths for dimerization of the abstracted TM domains. Finally, in the context of the full-length receptor, how the ligand induces a conformational change capable of overcoming the closed-to-open barrier remains an important question.Whether the observed structural transition in the TM domain dimer of the long-isoform of DR5 is a ubiquitous conformational switch that acts over the entire TNFR superfamily remains unknown. Vilar et al. (11) first proposed a similar scissors-model for activation of p75 neurotrophin receptor, which has a cysteine at the center of its TM helix. The short isoform of DR5 lacks a TM domain cysteine, but does form noncovalent dimers in cells, with likely TM domain dimer contacts (3). Among the other closely related and structurally homologous members of the TNFR superfamily, TNFR1 contains a cysteine at the center of the TM domain, but lacks any discernible small residue motifs (e.g., GxxxG). TNFR2 lacks a TM cysteine on the extracellular side, but does have a GxxxG motif positioned similarly to that of DR5. On the other hand, Death Receptor 4, whose functional distinction from DR5 has remained somewhat elusive, lacks both a cysteine and any recognizable small-residue hydrophobic motif.In summary, we have extended recent findings that point to the TM domain of DR5 as an essential structural component in the conformational change associated with activation. Our findings that the DR5-L TM domain occupies distinct open and closed states, separated by a substantial energy barrier, points the way to further studies across the TNF-receptor superfamily.     

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.     

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.     

Nanoscale Distribution of Ryanodine Receptors and Caveolin-3 in Mouse Ventricular Myocytes: Dilation of T-Tubules near Junctions

We conducted super-resolution light microscopy (LM) imaging of the distribution of ryanodine receptors (RyRs) and caveolin-3 (CAV3) in mouse ventricular myocytes. Quantitative analysis of data at the surface sarcolemma showed that 4.8% of RyR labeling colocalized with CAV3 whereas 3.5% of CAV3 was in areas with RyR labeling. These values increased to 9.2 and 9.0%, respectively, in the interior of myocytes where CAV3 was widely expressed in the t-system but reduced in regions associated with junctional couplings. Electron microscopic (EM) tomography independently showed only few couplings with caveolae and little evidence for caveolar shapes on the t-system. Unexpectedly, both super-resolution LM and three-dimensional EM data (including serial block-face scanning EM) revealed significant increases in local t-system diameters in many regions associated with junctions. We suggest that this regional specialization helps reduce ionic accumulation and depletion in t-system lumen during excitation-contraction coupling to ensure effective local Ca²⁺ release. Our data demonstrate that super-resolution LM and volume EM techniques complementarily enhance information on subcellular structure at the nanoscale.The contraction of cardiac ventricular myocytes depends on the rapid cell-wide transient increase in intracellular [Ca²⁺] upon depolarization of the cell-membrane potential. The cardiac ryanodine receptor (RyR) (1), which is the intracellular Ca²⁺ release channel in the sarcoplasmic reticulum (SR), plays a central role in shaping Ca²⁺ transients. RyRs form clusters of various sizes (2,3) with the majority located within junctions between the SR and the surface membrane and its cytoplasmic extension, the transverse tubular (t-) system. It has been suggested that some RyR clusters are associated with caveolae, a specialized signaling microdomain of the surface membrane. Previous studies were complicated by the limited resolution of optical imaging methods of ∼250 nm, much larger than the nanometer scale of RyRs and caveolae. Accordingly, these studies report varying colocalization between RyRs and caveolin-3 (CAV3), a caveolar marker also expressed in the t-system (4,5).In this work, we investigated the relative distribution of CAV3 and RyRs in mouse ventricular myocytes both in the cytosol and near the cell surface with super-resolution fluorescence microscopy that achieves a resolution approaching 30 nm. Our data revealed unexpected local t-system swellings near junctional couplings, which was supported by two different three-dimensional electron microscopy (EM) modalities with <10-nm resolution: EM tomography and serial block-face scanning EM (SBFSEM).Super-resolution images of CAV3 and RyR labeling at the surface sarcolemma of mouse myocytes showed little overlap, suggesting that few RyRs were in couplings with caveolae (Fig. 1 A, for detailed methods, see the Supporting Material). Only ∼4.8% of RyR labeling was associated with CAV3 positive areas and ∼3.5% of CAV3 associated with RyR positive areas (n = 6 cells from three animals, Fig. 1 B, see also Table S1 in the Supporting Material), broadly consistent with previous data in rats (6). To support this finding, EM tomography was applied to mouse ventricular tissue that included a part of the surface sarcolemma, to our knowledge for the first time. Segmentation of peripheral couplings (containing RyR foot structures) and surface caveolae (∼60 nm in diameter and often interconnected) confirmed that the great majority of peripheral couplings were in regions devoid of caveolae (Fig. 1 C). A few junctional couplings containing feet were between caveolae and subsarcolemmal SR (Fig. 1 D, see also Fig. S1 and Movie S1 in the Supporting Material). We conducted a similar analysis in the cytosol where CAV3 expression occurs in the t-system (5) and RyRs are abundant in dyadic junctions between the t-system and SR terminal cisterns.Open in a separate windowFigure 1Colocalization of CAV3 and RyRs at the surface sarcolemma. (A) Super-resolution micrograph of the distribution of CAV3 (green) and RyRs (red) at the surface of a mouse cardiac myocyte. (B) Analysis of the association of CAV3 with RyRs. The fraction of RyR labeling within CAV3 positive areas was ∼4.8% (front data) whereas ∼3.5% of CAV3 was found in RyR-positive membrane areas. (C) Segmented EM tomogram containing a patch of surface sarcolemma (light blue) and associated caveolae (green) as well as peripheral couplings (red). (D) Detailed view of a region with abundant caveolae. (Arrows) Couplings with caveolae.As shown in Fig. 2 A, the spatial distribution of CAV3 and RyR clusters in super-resolution micrographs taken several microns below the surface sarcolemma is consistent with this view. The association of the two labels is slightly increased (as compared to the surface), according to distance analysis with 9% of CAV3 and 9.2% of RyR labeling associating with each other (Fig. 2 B, n = 6 cells from three animals). The similarity of manually traced t-system in EM tomograms (Fig. 2 C) and super-resolved CAV3 labeling suggested that CAV3 is widely distributed in the t-system except for regions where dyadic membrane junctions occur as CAV3 labeling was much weaker in regions with strong RyR labeling. It was notable that the t-system diameter appeared to increase at regions of strong RyR labeling (Fig. 2 D), broadly consistent with the behavior seen in tomograms (Fig. 2 C). This was confirmed by a quantitative analysis of t-tubule diameters in dyadic versus extradyadic regions on the basis of CAV3 and RyR labeling, with full-width at quarter-maximum mean diameters increasing from ∼150 nm distal to dyads, to ∼190 nm (using CAV3 signal only) or ∼280 nm (using CAV3 and RyR signal) near dyads (Fig. 2, G and H, see also Methods in the Supporting Material). The combined RyR and CAV3 signals seemed to be a better representation of the entire t-system lumen near junctions (see Fig. S2).Open in a separate windowFigure 2Distribution of CAV3 and RyRs in the cell interior. (A) Super-resolution micrograph of CAV3 (green) and RyR (red) distribution at t-system. (Arrow) Direction of longitudinal cell axis. (B) Distance analysis of the CAV3 and RyR association (N = 6 cells per group). (C) Segmented EM tomogram of a similar region with three-dimensional mesh models of t-system membrane (green) and dyadic couplings (red). (D) This image illustrates the tracing (white path) of t-tubules. The label distribution was extracted and linearized along the path (E) to calculate a mask that shows the full width at quarter-maximum diameter along tubules, CAV3 (green) and RyR (red) (F). (G) Histograms of local diameters extracted from traced t-tubules. (H) Mean diameters in junctional (dyad) and nonjunctional (ex-dyad) regions. See main text and the Supporting Material for details. ^**p < 0.01.Taken together, super-resolution imaging and EM tomography strongly support the presence of local t-system dilations in regions where the t-system opposes SR at dyads and such t-system bulges are connected by narrower tubule segments. Further support was provided by SBFSEM, another volume EM technique to study larger cell volumes (albeit at the expense of a slightly lower resolution). SBFSEM clearly showed local t-system dilations were regularly involved in the architecture of most (but not all) dyads (Fig. 3, see also Fig. S3 and Movie S2), as also observed in full three-dimensional super-resolution images (see Fig. S3 C).Open in a separate windowFigure 3Segmented SBFSEM data showing t-system dilations near dyadic junctions. (A) The overview shows t-system membranes (green) and jSR (red) in a mouse myocyte. (B, enlarged inset from panel A) Thin connecting tubules (arrows) and regular swellings in junctional regions at z-lines.Our data identify local dilations of the t-system associated with dyads in mouse cardiac myocytes. Frequent tubule distensions had been observed especially at the intersections of transverse and axial tubules (7), and constrictions were seen in rabbit myocytes although their relationship to dyads was unknown (8). The increased local t-system lumen near junctions may help reduce the predicted ionic accumulation/depletion during excitation-contraction coupling (9). Alternatively, it might simply be secondary to increasing local membrane area and allow the formation of large area junctions that harbor many RyRs. In connection with this point, it would be interesting to investigate the t-system near junctions in species that have larger average tubule diameters (e.g., human and rabbit (10)), or if this architecture changes in mouse heart failure models where t-tubule diameters are often increased.Most peripheral couplings were in regions void of surface caveolae, although a small number of RyR clusters were in junctional couplings between subsarcolemmal SR and caveolae as shown both by the low colocalization between CAV3 and RyRs as well as direct evidence from EM tomography. Similarly, a relatively small fraction of CAV3 colocalized with RyR clusters in the t-system although CAV3 was expressed widely in the t-system. A structural role of CAV3 in the t-system is still unclear—t-tubules in tomogram data did not reveal distinct caveolae shapes on the t-system membrane (see Fig. S4), although this might change in pathology (11). In any case, the t-system exhibits high curvature orthogonal to the tubule axis, which may be supported by CAV3 oligomerization. In addition, the presence of CAV3 in the t-system may be important for regulating other signaling systems (e.g., adrenergic signaling).Finally, our data demonstrate that complementary data from optical super-resolution and three-dimensional EM images assists data interpretation and reliability. We suggest that truly correlative optical and EM imaging approaches should provide further information and improve our knowledge of the basis of cardiac excitation-contraction coupling.     

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.     

Splenogonadal Fusion: An Unusual Case of an Acute Scrotum

We highlight a case on a normal left testicle with a fibrovascular cord with three nodules consistent with splenic tissue. The torsed splenule demonstrated hemorrhage with neutrophilic infiltrate and thrombus consistent with chronic infarction and torsion. Splenogonadal fusion (SGF) is a rather rare entity, with approximately 184 cases reported in the literature. The most comprehensive review was that of 123 cases completed by Carragher in 1990. Since then, an additional 61 cases have been reported in the scientific literature. We have studied these 61 cases in detail and have included a summary of that information here.Key words: Splenogonadal fusion, Acute scrotumA 10-year-old boy presented with worsening left-sided scrotal pain of 12 hours’ duration. The patient reported similar previous episodes occurring intermittently over the past several months. His past medical history was significant for left hip dysplasia, requiring multiple hip surgeries. On examination, he was found to have an edematous left hemiscrotum with a left testicle that was rigid, tender, and noted to be in a transverse lie. The ultrasound revealed possible polyorchism, with two testicles on the left and one on the right (Figure 1), and left epididymitis. One of the left testicles demonstrated a loss of blood flow consistent with testicular torsion (Figure 2).Open in a separate windowFigure 1Ultrasound of the left hemiscrotum reveals two spherical structures; the one on the left is heterogeneous and hyperdense in comparison to the right.Open in a separate windowFigure 2Doppler ultrasound of left hemiscrotum. No evidence of blood flow to left spherical structure.The patient was taken to the operating room for immediate scrotal exploration. A normalappearing left testicle with a normal epididymis was noted. However, two accessory structures were noted, one of which was torsed 720°; (Figure 3). An inguinal incision was then made and a third accessory structure was noted. All three structures were connected with fibrous tissue, giving a “rosary bead” appearance. The left accessory structures were removed, a left testicular biopsy was taken, and bilateral scrotal orchipexies were performed.Open in a separate windowFigure 3Torsed accessory spleen with splenogonadal fusion.Pathology revealed a normal left testicle with a fibrovascular cord with three nodules consistent with splenic tissue. The torsed splenule demonstrated hemorrhage with neutrophillic infiltrate and thrombus consistent with chronic infarction and torsion (Figure 4).Open in a separate windowFigure 4Splenogonadal fusion, continuous type with three accessory structures.     

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.     

The quest for spatio-temporal control of CAR T cells

Chimeric antigen receptors (CARs) are synthetic receptors capable of directing potent antigen-specific anti-tumor T cell responses. A recent report by Wu et al. extends a series of strategies aiming to curb excessive T cell activity, utilizing in this instance a chemical dimerizer to aggregate antigen-binding, T cell-activating and costimulatory domains.Chimeric antigen receptor (CAR) therapy relies on T cell engineering to generate tumor-targeted T cells with enhanced anti-tumor functions¹. CAR therapy has so far achieved its most remarkable clinical successes against CD19-positive hematological malignancies and is now on the verge of being developed for solid tumors². Two safety concerns have, however, emerged from the CD19 experience, which should be addressed for CAR therapy to be broadly applicable. One is the eventual on-target/off-tumor effect of CAR T cells on normal tissues. Even though this concern may be mitigated in the case of CD19 CAR T cell-induced B cell aplasia, strategies designed to reduce or prevent its potential occurrence with other targets are needed². The other concern is a severe cytokine release syndrome (CRS), arising from large-scale synchronized T cell activation upon engaging the target antigen in some CAR T cell recipients².Several innovative strategies have been recently proposed to address these safety concerns. These strategies make use of remote or cell autonomous controls (Figure 1), utilizing small molecules, antibodies or synthetic receptors to regulate T cell activity. One approach is to activate a latent suicide switch, such as the inducible caspase-9 (iCasp9) enzyme, through the administration of a small molecule to induce T cell apoptosis³ (Figure 1a). Bifunctional small molecules that mediate the binding between antigen and CAR have also been developed to regulate target engagement⁴ (Figure 1b). A variation on this approach uses antibodies to mediate antigen recognition on target cells and binding of T cells expressing a synthetic Fc receptor⁵ (Figure 1b). These designs enable remote temporal control of T cell activity but do not provide a means to enhance tumor selectivity of the CAR T cells. To this end, combinatorial approaches integrating two autonomous antigen inputs to control CAR T cell functions have been developed to spatially discriminate between normal and tumor cells expressing a common target. One such approach utilizes synthetic inhibitory receptors, termed iCARs, which are derived from the PD-1 or CTLA-4 receptors, to protect normal cells based on the iCAR''s recognition of an antigen present on the normal cells but not the tumor cells⁶ (Figure 1c). Another approach utilizes complementary signals split between two receptors — a CAR for T cell activation and a chimeric costimulatory receptor (CCR) providing costimulation — such that they are both expressed by the tumor cells but found alone on normal cells⁷ (Figure 1d). Acting in cell autonomous fashion, the required co-engagement of the CCR and the CAR upon recognition of two independent antigens reinforces tumor selectivity in vivo⁷.Open in a separate windowFigure 1Building controls into engineered T cells. (a) The small molecule AP1903 can dimerize the suicide switch iCasp9 to induce T cell apoptosis. (b) Bifunctional small molecule bridging the binding between antigen and CAR or antibody mediating the interaction between antigen and synthetic Fc receptor can be remote controls of CAR T cells. (c) iCAR can inhibit CAR function in the presence of an antigen present in normal cells but not tumor cells. (d) CCR binding to a second antigen in tumor cells is required for full T cell activation. (e) The small molecule AP21976 can dimerize two independent signaling entities through an FKBP-FRB module to control T cell activation. (a, b, e) Strategies employing one remote control (antibody or small molecule) in addition to one autonomous control (antigen A). (c, d) Strategies with two autonomous controls (antigen A and antigen B). Negative regulation involves inducing apoptosis (a) or turning off T cell activation (c) by input 2 while positive regulation (b, d, e) results in T cell activation by input 2.In a recent paper published in Science, Wu et al.⁸ showed a novel design incorporating a remote control of CAR T cells, whereby a small molecule is used to dimerize antigen-binding and signaling domains (Figure 1e). At variance with the small molecule-controlled suicide switch, this ON-switch design represents a positive reversible regulation, as it does not eliminate T cells but rather restricts their activities. The remote control takes advantage of well-established chemically induced dimerization (CID) modules developed in the 1990s, where two proteins bind only in the presence of a third chemical, such as a small molecule⁹. One such widely used CID module is the FKBP and FRB_T2098L that heterodimerize in the presence of rapamycin or its less immunosuppressive analog AP21976. The receptor for antigen and a dual-signaling, costimulatory and activating domain analogous to that of a second generation CAR, were independently fused to FKBP and FRB_T2098L so that AP21976-induced FKBP and FRB_T2098L dimerization could aggregate these entities (Figure 1e). This design controls intracellular assembly of a signaling complex without affecting the antigen binding properties as afforded by the bifunctional small molecules or antibodies at the interface of T cells and target cells (Figure 1b). After screening various domain configurations in leukemic Jurkat cells with AP21976-dependent NFAT activation and IL-2 production assays, a design that worked with both the FKBP-FRB_T2098L and the gibberellin-induced GID1-GAI heterodimerization modules was identified. Single molecule imaging of ON-switch CAR assembly in Jurkat cells showed that two molecular parts are equally constrained to immobilized antigens only in the presence of AP21976. Subsequent characterization of the ON-switch CAR in primary human CD4⁺ T cells showed that both AP21976 and antigen are required for the induction of CD69 expression, a biomarker of T cell activation, the secretion of both IL-2 and IFNγ, and the proliferation of CD4⁺ cells. Most gratifyingly, there was a positive correlation between these responses and the AP21976 dosage, suggesting the possibility of achieving titratable control of T cells. Human primary CD8⁺ T cells with ON-switch CAR in three different cytotoxicity assays also demonstrated antigen- and AP21976-dependent killing of tumor cells, which was also titratable by AP21976. The killing ability of ON-switch CAR CD8⁺ T cells was reversible, as removal of AP21976 abrogated tumor cell lysis.Wu et al. proceeded to explore in vivo activity in a mouse xenograft model. Due to the short plasma half-life and the high cost of AP21976, the study is limited to a very short-term protocol of 39 h. Tumor cells were injected into the peritoneal cavity 14 h prior to the injection of the engineered T cells. Four injections of AP21976 in the subsequent 25 h were required to induce anti-tumor activity in this intraperitoneal cytotoxicity assay. Further investigations with a more relevant protocol allowing for tumor engraftment and longer term follow-up of T cell effectiveness will be needed to establish whether AP21976 can remotely control ON-switch CAR T cells to reject a tumor.Wu and coauthors have thus engineered a novel ON-switch CAR design and demonstrated titratable, reversible and antigen-dependent T cell functions controlled by a dimerizing small molecule. Another group is also conducting preclinical studies exploring a variant small molecule-controlled CAR design for solid tumor rejection¹⁰. However, there are still challenges to address before future clinical applications. The authors pointed out the need to develop controller chemicals that have clinically optimized pharmacokinetic properties, as the half-life of AP21976 is short and impractical for clinical application. Thus, how many injections per day, for how many weeks or months, would be required to achieve tumor rejection? Another unresolved question is whether a small molecule with optimal pharmacokinetic properties could effectively curb CRS and off-tumor reactivity. Overall, this elegant study provides valuable insights for further refining spatio-temporal control of cell therapy and applying it to CAR T cell technology.     

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.     

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.     

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.     

The evolution of genomic imprinting: theories,predictions and empirical tests

The epigenetic phenomenon of genomic imprinting has motivated the development of numerous theories for its evolutionary origins and genomic distribution. In this review, we examine the three theories that have best withstood theoretical and empirical scrutiny. These are: Haig and colleagues'' kinship theory; Day and Bonduriansky''s sexual antagonism theory; and Wolf and Hager''s maternal–offspring coadaptation theory. These theories have fundamentally different perspectives on the adaptive significance of imprinting. The kinship theory views imprinting as a mechanism to change gene dosage, with imprinting evolving because of the differential effect that gene dosage has on the fitness of matrilineal and patrilineal relatives. The sexual antagonism and maternal–offspring coadaptation theories view genomic imprinting as a mechanism to modify the resemblance of an individual to its two parents, with imprinting evolving to increase the probability of expressing the fitter of the two alleles at a locus. In an effort to stimulate further empirical work on the topic, we carefully detail the logic and assumptions of all three theories, clarify the specific predictions of each and suggest tests to discriminate between these alternative theories for why particular genes are imprinted.The discovery of genomic imprinting, where the expression of an allele depends on its parental origin, motivated a diversity of theories attempting to explain its existence (Spencer and Clark, 2014). Three main theories have withstood scrutiny and are the focus of this review: Haig and colleagues'' kinship theory (Haig and Westoby, 1989; Haig, 2000a, 2004); Day and Bonduriansky''s (2004) sexual antagonism theory (see also Bonduriansky, 2007); and Wolf and Hager''s (2006) maternal–offspring coadaptation theory (see also Wolf and Hager, 2009; Wolf, 2013). Although these theories rest on different logic and fundamental assumptions, they share a critical common feature: some process creates a selective asymmetry between the maternally and paternally inherited allelic copies at a locus that causes selection to favor differential expression of the alleles (typically silencing of one of the copies) (Figures 1, ​,2,2, ​,33).Open in a separate windowFigure 1The kinship theory of genomic imprinting has two prerequisites: first, epigenetic marks that differentiate matrigenes from patrigenes; second, a difference in the relatedness of matrigenes and patrigenes to the social group. (a) The social group in the example depicted is a single litter of offspring, and multiple mating produces a relatedness asymmetry between half-siblings. The relatedness for matrigenes is ½ and the relatedness for patrigenes is 0. (Other sources of relatedness asymmetry are possible—e.g., sex-biased dispersal or high fitness variance in one sex—and social interactions are not limited to the juvenile period only). (b) The kinship theory envisions kin selection acting independently on genes of maternal and paternal origin and solves for the evolutionarily stable gene expression strategy for matrigenes and patrigenes. (c) For genes where the matrigenic allele''s optimum expression level is higher than that of the patrigene''s (e.g., a fetal growth inhibitor), the kinship theory predicts silencing of the patrigenic allele; for genes with the opposite effect (e.g., a fetal growth enhancer), the prediction is for patrigenic expression.Open in a separate windowFigure 2(a, b) The sexual antagonism theory of genomic imprinting starts with sexually antagonistic selection, which produces different allele frequencies, shown as pie charts, for genes of maternal and paternal origin. (c, d) Natural selection favors individuals that are able to express the fitter of the two alleles at a locus, which for males will be the patrigenic allele and for females will be the matrigenic allele. (In addition, the sexual antagonism theory may predict matrigenic or patrigenic expression in both sexes, such that the expressed allele derives from the parental sex that experiences stronger selection pressure. This scenario is not depicted).Open in a separate windowFigure 3(a) The maternal–offspring coadaptation theory of genomic imprinting relies on the correlation of genes in the mother and genes of maternal origin in the offspring (shown in light blue). (b) Fitness of offspring is determined by the interaction (shown in dark purple) between the phenotypes of mothers and offspring. (c) Imprinted silencing of the patrigenic allele can be favored for either of two reasons, depending on the genetic architecture of the interacting phenotypes. First, when a single gene governs the interaction and phenotypic matching between mothers and their offspring produces high fitness, then silencing of the patrigenic allele is beneficial to offspring because it raises the probability of producing a match. Second, if different loci are involved in the phenotypic interaction, past correlational selection will have produced a covariance between them, generating haplotypes with combinations of alleles that interact well together. (N.B. This multi-locus interaction is not depicted in the figure.) The offspring is more likely to inherit from its mother an allele that interacts well with the alleles in the mother''s genotype. This also favors the imprinted silencing of the patrigenic allele because it raises the probability that the offspring expresses an allele that makes for a good interaction with the maternal phenotype.Here we provide an overview of the fundamental logic and critical assumptions of these models. We then derive predictions that can be used to distinguish between theories. In doing so, we also highlight ambiguities in and overlap between the predictions they make, with a goal of motivating further research. In addition, we suggest some areas for future work that will test some of these predictions.     

Accuracy in Quantitative 3D Image Analysis

Quantitative 3D imaging is becoming an increasingly popular and powerful approach to investigate plant growth and development. With the increased use of 3D image analysis, standards to ensure the accuracy and reproducibility of these data are required. This commentary highlights how image acquisition and postprocessing can introduce artifacts into 3D image data and proposes steps to increase both the accuracy and reproducibility of these analyses. It is intended to aid researchers entering the field of 3D image processing of plant cells and tissues and to help general readers in understanding and evaluating such data.Advances in digital imaging have led to the generation of an increasing number of 3D data sets (Truernit et al., 2008; Fernandez et al., 2010; Kierzkowski et al., 2012; Roeder et al., 2012). Whole-mount and time-lapse imaging enable all cells in an organ to be analyzed in 3D over time, providing a comprehensive analysis of plant growth and development (Roeder et al., 2011).The generation of these 3D image data sets has led to the development of novel computational approaches to facilitate their analysis (Cunha et al., 2010; Kierzkowski et al., 2012; Bassel et al., 2014; Yoshida et al., 2014). With the development of these new methods comes a need for quality control and standard measures to ensure the accurate analysis of data sets. An overall objective of this approach is the accurate capture and quantification of the 3D geometry of biological objects. The inaccurate abstraction of shape data and introduction of artifacts during image acquisition and postprocessing must be kept to a minimum.Following imaging, typically using confocal microscopy, 3D objects can be identified through the process of segmentation (Roeder et al., 2012). This can be achieved using automatic seeding through a watershed approach or by inflating “balloons” with defined seeds in individual cells (Federici et al., 2012). Vertices and meshes that describe cell surfaces may then be generated using an algorithm such as marching cubes (Lorensen and Cline, 1987). Vertices at defined spacings can be placed on the surface of unique segments, and the surfaces describing these geometric shapes are represented by the triangles connecting adjacent vertices constituting a polygonal mesh.The mesh describing segment surfaces is ultimately what defines the shape of an object in question. Rough and irregular features are often represented by meshes owing to the imperfect nature of data collection from biological samples (Desbrun et al., 1999; Taubin, 2000). In the context of plant cells whose surfaces are naturally smooth, the segmentations and meshes describing them are in practice noisy and contain undesirable geometric irregularities. The lower the quality of the original image being segmented, the greater the irregularities in the mesh that describe the shape.In order to improve the quality of irregularly triangulated polygonal meshes, smoothing operations can be performed. In this way, the roughness of the surface can be reduced, improving the texture and representation of a segmented object.A straightforward and easy to implement operation to remove noise in 3D meshes is Laplacian smoothing (Field, 1988). This process repositions vertices to an average position (barycentre) along a mesh surface to create a smoothed effect. However, Laplacian smoothing has the side effect of slightly shrinking the object in question (Taubin, 2000). While this shrinkage effect is well documented among computer scientists who develop algorithms to modify polygonal meshes, it is perhaps less well understood and discussed by the end user biological community.Smoothing of meshes has the positive effect of making surfaces smoother and removing noise, while enhancing the visual aesthetic of segmented objects providing the appearance that their geometry has been captured accurately (Figures 1A and 1B). In cases where objects have been poorly segmented, the need to remove the jagged appearance of meshes is greater, and additional smoothing steps are often used. This repeated Laplacian smoothing leads to additional smoothing-induced shrinkage and greater abstraction of the object being analyzed. Given that the mesh itself is intended to represent the 3D geometry of an object in question, changing its overall size by smoothing represents a perturbation and manipulation of data that inaccurately reflects the quantitative capture of geometry. The need to remove rough edges in meshes due to noise needs to be balanced with the accuracy that the mesh represents an object in question.Open in a separate windowFigure 1.Effect of Laplacian Smoothing on the Cellular Structure of a 3D Segmented Arabidopsis Radicle.(A) Surface rendering of a mesh following generation using marching cubes with a cube size of 2 μm and no smoothing. Bar = 10 μm.(B) Same as (A) following one Laplacian smoothing pass. (C) Same as (A) following six smoothing passes. (D) Same as (A) following nine smoothing passes. (E) An original confocal stack showing cell walls in green and the multicolored segmented stack before generating the mesh using marching cubes. (F) Smoothing of the mesh in (A) using the Taubin λ/μ algorithm with λ = 0.5, μ = −0.53, and nine smoothing steps. An example of an unsmoothed mesh representing the cells of 3D segmented plant organ can in seen in Figure 1A. The rough edges and irregularities of this primary unsmoothed mesh, coming from a suboptimal noisy confocal image stack, do not accurately represent the surface of these plant cells. The mesh in Figure 1B, which has been smoothed once, appears to be a more accurate representation of cell shape than the unsmoothed mesh in Figure 1A and has not been shrunk dramatically.In the context of plant organ cellular segmentations, additional Laplacian smoothing steps create even smoother cells, but also exaggerated gaps between adjacent cells due to cell shrinkage (Figures 1C and 1D). These spaces do not reflect reality as adjacent cells are physically appressed against their cell walls, which are rarely more than several microns thick. This example demonstrates the abstraction of cell shape that can occur following multiple Laplacian smoothing steps, and the gaps between cells are a hallmark of data that have been postprocessed to the point of inaccuracy.Other factors can affect the response of 3D segmented cells to Laplacian smoothing. These include mesh triangle size, with larger triangles being more susceptible to shrinkage (Desbrun et al., 1999), and cell size, with smaller cells being more susceptible to smoothing-based shrinking than larger cells (Figure 1D).If the purpose of 3D segmentation is strictly qualitative, smoothing-based shrinkage may not present a problem. However, if quantitative analyses are applied to shrunken meshes, this will result in inaccurate data.     

Cytoplasmic diffusion: molecular motors mix it up

Random motion within the cytoplasm gives rise to molecular diffusion; this motion is essential to many biological processes. However, in addition to thermal Brownian motion, the cytoplasm also undergoes constant agitation caused by the activity of molecular motors and other nonequilibrium cellular processes. Here, we discuss recent work that suggests this activity can give rise to cytoplasmic motion that has the appearance of diffusion but is significantly enhanced in its magnitude and which can play an important biological role, particularly in cytoskeletal assembly.The cytoplasm of eukaryotic cells is a highly dynamic and out-of-equilibrium material that undergoes continual restructuring. This is largely driven by active processes such as polymerization of cytoskeletal filaments and forces generated by molecular motors. Such activity usually results in directed movement within cells; examples include the slow retrograde flow at the leading edge during cell crawling (Fisher et al., 1988; Waterman-Storer and Salmon, 1997; Cai et al., 2006) and the transport of motor-bound vesicles along cytoskeletal filaments (Vale, 2003). Given the intrinsically small, micrometer scales involved, the cytoplasm is also subject to the thermal agitation of Brownian motion (Brown, 1828; Einstein, 1905). Random though these thermal fluctuations may be, they are implicated in force generation by polymerization (Peskin et al., 1993; Mogilner and Oster, 1996), as well as the elastic response of cytoskeletal networks (MacKintosh et al., 1995; Gardel et al., 2004; Storm et al., 2005). Moreover, thermal fluctuations give rise to the diffusive transport of small molecules throughout the cell, without which molecular signaling would be impossible. However, it is becoming increasingly clear that nonthermal forces, such as those resulting from motor protein activity, can also lead to strongly fluctuating intracellular motion; this motion differs significantly from the directed motion commonly associated with motor activity (Caspi et al., 2000; Lau et al., 2003; Bursac et al., 2005). These active fluctuations remain poorly understood, and the relative contributions of thermal fluctuations compared with active fluctuations in living cells are only now being fully explored.

Particle-based probes of intracellular motion

To elucidate the nature of fluctuating motion in cells, many studies have analyzed the “passive” fluctuating motion of micrometer-sized spherical probe particles. If such particles were embedded in a viscous liquid driven by thermal Brownian fluctuations, they would exhibit random, diffusive motion, as shown schematically in Fig. 1 a (blue particle). Quantitatively, this means that the distance the particle has moved, Δx, after some time interval, τ, is described by , where the angled brackets indicate an average over many particles, and the diffusion coefficient, D, is given by the Stokes-Einstein equation:which depends on the viscosity η and particle radius a (Einstein, 1905). This reflects the fundamentally thermal origin of diffusion in an equilibrium liquid, depending as well on the temperature (T) and Boltzmann''s constant (k_B). This diffusive time dependence is shown schematically by the blue line in Fig. 1 a. Such motion is in stark contrast with steady particle motion in one direction with constant velocity v, illustrated by the black curve in Fig. 1 a; because , this motion is described by . In principle, one can track the motion of inert tracer particles in cells to determine whether their motion is diffusive with the appropriate diffusion coefficient. However, inside cells this picture is complicated by the fact that the cytoplasm is generally not a simple viscous liquid but rather a structured viscoelastic material (Luby-Phelps et al., 1987; Fabry et al., 2001). In a viscoelastic material, thermal fluctuations do not lead to ordinary diffusion, but rather “subdiffusive” motion, characterized by a different time-dependence: , where α < 1, as shown schematically by the red curve in Fig. 1 a. Thus, studies showing diffusive, or even “superdiffusive” motion (α > 1), within the viscoelastic cytoplasm suggested this random motion is not thermally induced (Caspi et al., 2000). However, other studies assume that random intracellular motion is thermally induced, and use this assumption to extract the mechanical properties of the cell from tracer particle motion (Tseng et al., 2002; Panorchan et al., 2006). To quantitatively clarify this, several recent studies have analyzed both measurements of the “passive” random motion of tracer particles, as well as direct, active measurements of the viscoelastic properties of the cytoplasm, for example, using a magnetic tweezer to pull on cells (Lau et al., 2003; Bursac et al., 2005). These studies have concluded that the random motion not only has an unexpected time dependence but is also significantly larger in magnitude than would be expected for purely thermal fluctuations, suggesting that biological activity can indeed give rise to random fluctuating motion that dominates over thermal fluctuations.Open in a separate windowFigure 1.Quantifying fluctuating motion. (a) Schematic showing different types of particle motion, characterized by the mean square displacement . The particle can exhibit directed (“ballistic”) motion, as shown by the black particle; here α = 2, as shown by the black curve. Particles can also exhibit random diffusive-like behavior, as shown by the blue particle, with α = 1 (blue line). Particles constrained by a viscoelastic network (red particle) often exhibit subdiffusive behavior, with α < 1 (red curve). (b) Data showing the bending motion of a fluctuating intracellular microtubule. The amplitude of a bend, ∝a_q, randomly fluctuates in time, as shown in the bottom inset. The mean squared amplitude difference as a function of lag time displays diffusive-like behavior (α = 1). (c) A CHO cell fixed and stained to reveal the nucleus (blue) and microtubules (green). Bottom inset shows schematically the variables used in the Fourier analysis of microtubule bending. Top inset shows fluctuations in a GFP-tubulin–transfected Cos7 cell over a time difference of 1.6 s. Microtubules that have fluctuated to a new position are in red and the earlier position is in green.Considerable new insight into the underlying physical origin of these motor-driven fluctuations was obtained from studies of a reconstituted actin network incorporating myosin II motors (Mizuno et al., 2007), oligomerized into processive motor assemblies similar to those found in the cellular cytoskeleton (Cai et al., 2006). The mechanical resistance of the actin network was precisely characterized using optical tweezers to actively pull on particles within the network. The fluctuating motion of the particles was also measured, both with and without motors present. By comparing these two measurements, the contribution of the thermal motion could be distinguished from that of the nonthermal motion, showing clearly that myosin motors can give rise to strong random fluctuating motion within the network. Interestingly, these random myosin-generated forces can also lead to a pronounced stiffening of the actin network, increasing the rigidity by as much as 100-fold (Mizuno et al., 2007; unpublished data).

Microtubule bending dynamics reflects active fluctuations

The random fluctuating motion arising from the activity of cytoskeletal motor proteins can have important biophysical consequences. In an attempt to directly measure the underlying fluctuating forces, a different but complementary approach has recently been developed. It uses endogenous cytoskeletal microtubules as probes. Microtubules are stiff biopolymer filaments that are present in almost every animal cell and are physically linked to other components of the cytoskeleton (Rodriguez et al., 2003; Rosales-Nieves et al., 2006). Thus, as with spherical probe particles, their motion reflects forces and fluctuations of the network (Waterman-Storer and Salmon, 1997; Odde et al., 1999). However, in contrast to spherical probes, microtubules also exhibit a local bending motion, whose amplitude can, like a simple elastic spring, be used to determine the applied force (Fig. 1 b). Microtubules in cells indeed appear highly bent, as can be seen, for example, in the fluorescence image of the microtubule network within an adherent CHO cell, shown in Fig. 1 c. These bends fluctuate dynamically in time, which can be seen by subtracting sequential images, as shown in the top inset of Fig. 1 c.This motion was studied in cells by tracking individual fluorescent microtubules, using both Cos7 and CHO cells (Brangwynne et al., 2007b). The curves defined by the microtubules are analyzed using a Fourier analysis technique developed to characterize the bending fluctuations of isolated biopolymers in thermal equilibrium (Gittes et al., 1993; Brangwynne et al., 2007a). This analysis is based on the fact that each curve can be represented as a sum of simpler sinusoidal curves, each of a different amplitude (a_q) and wavelength (λ). The wavelength is typically written in terms of its wave vector, q = 2π/λ, as sketched in the bottom inset of Fig. 1 c. Using this approach, intracellular motion was analyzed by calculating the mean-squared difference in amplitude using , as a function of lag time τ.This quantity is analogous to that calculated for fluctuating particles, , but here effectively measures the fluctuating motion of the microtubule at different wavelengths. In thermal equilibrium, the amplitude difference will grow with τ up to a maximum value given bywhere k_BT is the thermal energy scale and κ is the microtubule bending rigidity. The ratio of these defines the persistence length:which represents the length scale at which thermal fluctuations completely change the direction of the filament; for microtubules, l_p is on the order of 1 mm (Gittes et al., 1993). This establishes the maximum amplitude of bending fluctuations that can be induced by thermal agitation and allows thermal and motor-induced fluctuations within the cell to be distinguished.For microtubules in cells, the amplitude of the fluctuations was found to grow roughly linearly in time, the behavior expected for simple Brownian diffusion (Fig. 1 b). However, strikingly, the maximum bending amplitude in cells is much larger than that expected for thermally induced bends for l_p ≈ 1 mm. This is most apparent for small wavelength bends, q > 1 μm⁻¹, as shown by the blue triangles in Fig. 2 a, which are significantly above the maximum thermal amplitude shown by the solid line. Thus, although random intracellular motion can exhibit features similar to random Brownian motion, it appears inconsistent with a purely thermal origin.Open in a separate windowFigure 2.Microtubule bending in vivo and in vitro. (a) Thermal microtubules in an in vitro network of F-actin exhibit a roughly q⁻² spectrum of fluctuations (solid line), although wave vectors smaller than q ∼0.4 μm⁻¹ have not reached their maximum fluctuations on this time scale (τ = 2 s; red squares). In the presence of myosin II motors (blue squares), the bending fluctuations are significantly larger than thermal on short wavelengths (high q). Curves are means of 10 filaments. Intracellular microtubule fluctuations show similar behavior (blue triangles), with amplitudes larger than thermal on short wavelengths, as shown by the mean of 23 filaments from a CHO cell (τ = 2 s). (b) Microtubules embedded in an in vitro actin network in thermal equilibrium exhibit small fluctuations (inset, red squares, q ∼0.3 μm⁻¹), which evolve subdiffusively, i.e., , because of the elasticity of the surrounding actin network (red squares, mean of ∼10 filaments). In myosin-driven networks, the fluctuations are significantly larger and steplike (inset, blue squares, q ∼0.3 μm⁻¹). These large nonthermal fluctuations are diffusive in character, i.e., (blue squares, mean of ∼10 filaments). (c) The bending fluctuations of microtubules in vitro are highly localized and relax rapidly as shown in the top right inset (78 ms between each frame, top to bottom). These localized bends can be well fit to the expected shape resulting from transverse point forces (Landau and Lifshitz, 1986; Brangwynne et al., 2008). A localized bend with the fit to the theoretical form (red line) is shown in the top inset. From these fits, a distribution of localized force pulses with a mean magnitude of ∼10 pN is found (main plot).In these experiments in living cells, there are several unknown variables that could play a role. For example, the persistence length of microtubules may vary within the cell, caused by a possible length dependence (Pampaloni et al., 2006) or arising from the effects of microtubule-associated proteins (Felgner et al., 1997). A simplified in vitro cytoskeleton was therefore developed, incorporating purified microtubules in a model actin network (Brangwynne et al., 2008). In the absence of motor proteins or other sources of nonequilibrium activity, microtubules embedded in the actin network are subject to only thermal forces that result in small bending fluctuations; as with the motion of spherical probe particles, the viscoelasticity of the surrounding network leads to subdiffusive behavior of the bending amplitudes, , as shown in Fig. 2 b. These fluctuations display a small maximum amplitude corresponding to l_p ≈ 1 mm, as shown by the red squares in Fig. 2 a. When myosin II motor assemblies are added to the actin network, the embedded microtubules show dramatically different behavior: they bend significantly more, and the bends are highly localized. However, although this behavior is driven by processive motor activity, the microtubule bends fluctuate randomly in time, with localized bends growing and shrinking rapidly, as illustrated by the typical time series shown in the inset of Fig. 2 b; this behavior is similar to that found using spherical probe particles (Mizuno et al., 2007). These microtubule bends appear to result from localized transverse forces (Landau and Lifshitz, 1986), as sketched in the bottom inset of Fig. 2 c. Analogous to a simple spring (force proportional to displacement, F = kΔx), the maximum force was directly determined from the amplitude of these bends, revealing force pulses on the order of 10 pN, which is consistent with that of a few myosin motors acting together (Finer et al., 1994). Interestingly, short wavelength bends can also arise from compressive forces acting within cells (Brangwynne et al., 2006).The dynamic, localized microtubule bends observed in vitro lead to fluctuations in the bending amplitudes that are large and distinctly nonthermal at short wavelengths (large q), as shown by the comparison of thermal (Fig. 2 a, red squares) and motor-driven (blue squares) fluctuations for q ≥ 0.2 μm⁻¹. At longer wavelengths, however, the fluctuations are indistinguishable from those of thermally excited filaments, as expected for microtubules whose lateral motion is restricted by the surrounding elastic environment. Surprisingly, with added myosin motors, the actively driven bends fluctuate in a diffusive-like manner, , as shown in Fig. 2 b (blue squares). This nonthermal diffusive behavior can be understood in terms of steplike or on–off dynamics in the forces applied by individual motor assemblies as they bind and unbind to cytoskeletal filaments (Mizuno et al., 2007; MacKintosh and Levine, 2008). Thus, even processive motor activity has stochastic features that can give rise to diffusive-like motion of cytoplasmic components. Although it is likely that myosin II is not the only motor contributing to this behavior in cells, these experiments help elucidate the underlying biophysical processes.

Implications of active fluctuations for transport and cytoskeletal assembly

These random nonthermal force fluctuations appear to be a ubiquitous feature of living cells. They can, therefore, play an important role in a variety of cellular processes. For example, as a result of large microtubule bending fluctuations, the tips of growing microtubules also undergo large fluctuations in directional orientation, leading to highly curved microtubule shapes that appear to be “frozen-in” by the surrounding elastic cytoskeleton, as shown in the example in Fig. 3 a. These random fluctuations in tip orientation are analogous to random thermal fluctuations and give rise to microtubule bends with a thermal-like dependence on q, as shown in Fig. 3 b. However, the corresponding nonequilibrium persistence length is reduced to ∼30 μm, ∼100-fold less than the thermal persistence length. Thus, the microtubule network is more bent by ∼100-fold in these cells as compared with isolated microtubules in solution. Although the effects of microtubule binding proteins or defects in the tubulin lattice could also contribute, tip fluctuations alone are sufficient to explain these large bends. Driven fluctuations can thus play a significant role in determining cytoskeletal architecture.Open in a separate windowFigure 3.Nonequilibrium fluctuations affect the growth dynamics and final structure of the microtubule network. (a) A microtubule within a GFP-tubulin–transfected Cos7 cell, highlighted in red, can be seen growing toward the bottom right, in the direction indicated by the yellow line. In the second frame, the filament experiences a naturally occurring bending fluctuation caused by internal forces, indicated by the arrow. As a result, the orientation of the microtubule tip changes and the microtubule grows upward, giving rise to a long wavelength bend. Frame times are 0, 36, 46, 53, and 92 s, top to bottom. (b) Inset schematic shows how lateral bending fluctuations of microtubules will cause fluctuations in the microtubule tip during growth, giving rise to curved polymerization trajectories. The Fourier spectrum of a single representative microtubule in thermal equilibrium is shown by the green circles, calculated from the maximum variance of the fluctuations. This microtubule has a persistence length, l_p = 4 mm. The Fourier amplitude of an ensemble of microtubules in CHO cells, , is shown by the blue squares.The enhanced diffusive dynamics that result from motor activity may also affect the rates of biochemical reactions that take place on the cytoskeletal scaffold (Forgacs et al., 2004), as well as those usually thought to be limited by thermal diffusion. Thus, “active” cytoplasmic diffusion could represent a kind of microscopic mixing that enables rapid diffusion of vesicles and small molecules.Thermal fluctuations have been known to play a fundamental role in the behavior of all nonliving matter since Einstein''s seminal work in 1905 (Einstein, 1905), in a paper explaining how thermal forces give rise to the diffusive motion first observed by Brown in 1828 (Brown, 1828). We propose that active intracellular force fluctuations represent the biological analogue, controlling cell behavior while subject to biochemical regulation. Interestingly, this may actually be closer to Brown''s initial concept of a vital microscopic activity, in contrast to the nonliving, thermal motion that bears his name (Brown, 1828). This active motion is clearly a ubiquitous and important phenomenon in living cells; indeed, it may be that active processes contribute to virtually all randomly fluctuating, diffusive-like motion in cells.