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.     

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).     

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.     

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

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

Conformational States of Melittin at a Bilayer Interface

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

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

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

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

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

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.     

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.     

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

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

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

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

To avoid a mating mishap,yeast focus and communicate

During mating, yeast cells must perforate their rigid cell walls at the right place to allow cell–cell fusion. In this issue, Dudin et al. (2015; J. Cell Biol. http://dx.doi.org/jcb.201411124) image mating fission yeast cells with unprecedented spatiotemporal resolution. The authors find that when mating cells come into contact, they form aster-like actin structures that direct cell wall remodeling precisely to the point of contact.At its core, sex is about the fusion of two haploid cells to form a diploid. For nonmotile cells like yeasts, that requires growth of mating projections to bridge the distance between the mating partners (Fig. 1 A). Yeast cells are protected from osmotic lysis by rigid cell walls, and growth of the mating projection involves local secretion of hydrolases that make the cell wall more elastic at the growing tip (Klis et al., 2006). As the wall expands, new components are added by synthases to maintain a continuous, unbroken wall. The process is orchestrated by a “cell wall integrity” signaling pathway, which monitors cell wall stress and delicately balances hydrolysis and synthesis to guarantee that no holes develop (Levin, 2011). But when it comes to mating, a hole must be made in both partners’ walls at the point of contact to allow cell–cell fusion. Precise positioning is key, as an off-center hole would lead to lysis. How is such precision achieved?Open in a separate windowFigure 1.Cell fusion during yeast mating: focus and communication. (A) Mating fission yeast cells grow projections toward each other and fuse at the point of contact. (B, left) Secreted hydrolases weaken the rigid cell wall to enable expansion, and rapidly diffuse away. (B, right) At a point of cell–cell contact, diffusional escape paths are longer, so hydrolases build up. (C) Focused delivery of secretory vesicles (ves) in mating budding yeast after contact. The image is adapted from Gammie et al. (1998), © The American Society for Cell Biology. (D) Actin cables during growth of the projection (left) and in the fusion focus (right). (E) Distribution of hydrolases and synthases in fusing cells. (F) The fusion focus forms first in the h⁻ mating partner and then in the h⁺ mating partner. CW, cell wall; PM, plasma membrane; N, nucleus; V, vacuole.An appealingly simple hypothesis—based on the observation that many hydrolases are secreted enzymes that can only transiently degrade the wall before diffusing away (Fig. 1 B)—is that when the mating projections come into contact, hydrolases from one partner would diffuse into the local wall of the other. Because diffusional escape paths are longer when cells are juxtaposed, hydrolases would be concentrated and make a hole only at the point of contact (Huberman and Murray, 2014). However, this purely geometrical effect cannot be the whole story, as classic genetic studies identified mutants of Saccharomyces cerevisiae that grew mating projections and achieved cell wall contact but failed to degrade the cell wall between mating partners (Kurihara et al., 1994). One set of mutants revealed that fusion requires especially high levels of pheromone secretion, which suggests that mating partners signal to each other to coordinate local wall remodeling (Brizzio et al., 1996). Elegant cytological analyses of another set of mutants have also suggested that vesicles delivering hydrolases are targeted precisely to the site of cell–cell contact (Fig. 1 C; Gammie et al., 1998). These inferences are strongly supported and expanded by a study in this issue (Dudin et al.), which provides a beautifully detailed characterization of mating in the distantly related fission yeast Schizosaccharomyces pombe.Using time-lapse microscopy and super-resolution imaging to monitor components of the actin cytoskeleton, Dudin et al. (2015) found that actin cables directed myosin V traffic to a broad zone at the tip of the growing mating projection. However, after cell–cell contact, actin cables were tightly focused toward a central “fusion focus” (Fig. 1 D). After focus formation, hydrolases were concentrated in a narrow region, whereas synthases were still distributed broadly (Fig. 1 E). The authors suggest that tightly focused myosin V–mediated delivery of hydrolases overwhelms the local synthases to make a hole in the central cell wall. In the surrounding wall, synthases counteract hydrolases to maintain cell wall integrity.How does the fusion focus form? A mating-specific formin, Fus1, became tightly localized to a small spot, where it presumably promoted focused actin polymerization and barbed-end anchoring (Dudin et al., 2015). Focus formation could arise from highly focused upstream signaling by formin regulators like Cdc42. Another possibility is suggested by the observation that, as also seen in budding yeast (Sheltzer and Rose, 2009), myosin V was required for focus formation. Thus, one could envision a positive feedback focusing mechanism in which formin-nucleated actin cables enable myosin V–mediated delivery of formins or their activators. Cells in which fusion focus formation was blocked by mutation of Fus1 or myosin V were unable to degrade juxtaposed cell walls and kept growing longer projections, attesting to the importance of the focus in enabling cell wall degradation.Why does the fusion focus only form upon cell–cell contact? The walls of the mating projections display mating type–specific agglutinins, which help mating partners stick to each other and might conceivably signal that contact has been established. Alternatively, focus formation might be triggered upon perception of a high-threshold pheromone concentration (Brizzio et al., 1996): pheromone levels would rise as the projections approach each other, and might be further increased after contact due to the same geometrical considerations discussed earlier for hydrolases.Intriguingly, Dudin et al. (2015) found that one of the mating partners, the h⁻ cell, always developed an actin fusion focus before the other, the h⁺ cell (Fig. 1 F). The basis for this asynchrony is unknown, but if the focus is indeed triggered by a threshold pheromone level, it could be that one pheromone crosses the threshold before the other. The h⁻ cells produce M-factor, whereas h+ cells produce P-factor. If P-factor were to accumulate more rapidly at the contact site, it might reach critical levels and trigger h⁻ cells to make their focus first. The ensuing more focused secretion of M-factor by the h⁻ cell might then trigger and correctly position focus formation by the h⁺ cell. Whatever the mechanism, the finding that one partner always focuses first makes it attractive to speculate that this asynchrony enables communication between mating partners that allows them to coordinate focus formation directly across from each other.     

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.     

Concerted actions of DnaA complexes with DNA-unwinding sequences within and flanking replication origin oriC promote DnaB helicase loading

Unwinding of the replication origin and loading of DNA helicases underlie the initiation of chromosomal replication. In Escherichia coli, the minimal origin oriC contains a duplex unwinding element (DUE) region and three (Left, Middle, and Right) regions that bind the initiator protein DnaA. The Left/Right regions bear a set of DnaA-binding sequences, constituting the Left/Right-DnaA subcomplexes, while the Middle region has a single DnaA-binding site, which stimulates formation of the Left/Right-DnaA subcomplexes. In addition, a DUE-flanking AT-cluster element (TATTAAAAAGAA) is located just outside of the minimal oriC region. The Left-DnaA subcomplex promotes unwinding of the flanking DUE exposing TT[A/G]T(T) sequences that then bind to the Left-DnaA subcomplex, stabilizing the unwound state required for DnaB helicase loading. However, the role of the Right-DnaA subcomplex is largely unclear. Here, we show that DUE unwinding by both the Left/Right-DnaA subcomplexes, but not the Left-DnaA subcomplex only, was stimulated by a DUE-terminal subregion flanking the AT-cluster. Consistently, we found the Right-DnaA subcomplex–bound single-stranded DUE and AT-cluster regions. In addition, the Left/Right-DnaA subcomplexes bound DnaB helicase independently. For only the Left-DnaA subcomplex, we show the AT-cluster was crucial for DnaB loading. The role of unwound DNA binding of the Right-DnaA subcomplex was further supported by in vivo data. Taken together, we propose a model in which the Right-DnaA subcomplex dynamically interacts with the unwound DUE, assisting in DUE unwinding and efficient loading of DnaB helicases, while in the absence of the Right-DnaA subcomplex, the AT-cluster assists in those processes, supporting robustness of replication initiation.

The initiation of bacterial DNA replication requires local duplex unwinding of the chromosomal replication origin oriC, which is regulated by highly ordered initiation complexes. In Escherichia coli, the initiation complex contains oriC, the ATP-bound form of the DnaA initiator protein (ATP–DnaA), and the DNA-bending protein IHF (Fig. 1, A and B), which promotes local unwinding of oriC (1, 2, 3, 4). Upon this oriC unwinding, two hexamers of DnaB helicases are bidirectionally loaded onto the resultant single-stranded (ss) region with the help of the DnaC helicase loader (Fig. 1B), leading to bidirectional chromosomal replication (5, 6, 7, 8). However, the fundamental mechanism underlying oriC-dependent bidirectional DnaB loading remains elusive.Open in a separate windowFigure 1Schematic structures of oriC, DnaA, and the initiation complexes. A, the overall structure of oriC. The minimal oriC region and the AT-cluster region are indicated. The sequence of the AT-cluster−DUE (duplex-unwinding element) region is also shown below. The DUE region (DUE; pale orange bars) contains three 13-mer repeats: L-DUE, M-DUE, and R-DUE. DnaA-binding motifs in M/R-DUE, TT(A/G)T(T), are indicated by red characters. The AT-cluster region (AT cluster; brown bars) is flanked by DUE outside of the minimal oriC. The DnaA-oligomerization region (DOR) consists of three subregions called Left-, Middle-, and Right-DOR. B, model for replication initiation. DnaA is shown as light brown (for domain I–III) and darkbrown (for domain IV) polygons (right panel). ATP–DnaA forms head-to-tail oligomers on the Left- and Right-DORs (left panel). The Middle-DOR (R2 box)-bound DnaA interacts with DnaA bound to the Left/Right-DORs using domain I, but not domain III, stimulating DnaA assembly. IHF, shown as purple hexagons, bends DNA >160° and supports DUE unwinding by the DnaA complexes. M/R-DUE regions are efficiently unwound. Unwound DUE is recruited to the Left-DnaA subcomplex and mainly binds to R1/R5M-bound DnaA molecules. The sites of ssDUE-binding B/H-motifs V211 and R245 of R1/R5M-bound DnaA molecules are indicated (pink). Two DnaB homohexamer helicases (light green) are recruited and loaded onto the ssDUE regions with the help of the DnaC helicase loader (cyan). ss, single stranded.The minimal oriC region consists of the duplex unwinding element (DUE) and the DnaA oligomerization region (DOR), which contains specific arrays of 9-mer DnaA-binding sites (DnaA boxes) with the consensus sequence TTA[T/A]NCACA (Fig. 1A) (3, 4). The DUE underlies the local unwinding and contains 13-mer AT-rich sequence repeats named L-, M-, and R-DUE (9). The M/R-DUE region includes TT[A/G]T(A) sequences with specific affinity for DnaA (10). In addition, a DUE-flanking AT-cluster (TATTAAAAAGAA) region resides just outside of the minimal oriC (Fig. 1A) (11). The DOR is divided into three subregions, the Left-, Middle-, and Right-DORs, where DnaA forms structurally distinct subcomplexes (Fig. 1A) (8, 12, 13, 14, 15, 16, 17). The Left-DOR contains high-affinity DnaA box R1, low-affinity boxes R5M, τ1−2, and I1-2, and an IHF-binding region (17, 18, 19, 20). The τ1 and IHF-binding regions partly overlap (17).In the presence of IHF, ATP–DnaA molecules cooperatively bind to R1, R5M, τ2, and I1-2 boxes in the Left-DOR, generating the Left-DnaA subcomplex (Fig. 1B) (8, 17). Along with IHF causing sharp DNA bending, the Left-DnaA subcomplex plays a leading role in DUE unwinding and subsequent DnaB loading. The Middle-DOR contains moderate-affinity DnaA box R2. Binding of DnaA to this box stimulates DnaA assembly in the Left- and Right-DORs using interaction by DnaA N-terminal domain (Fig. 1B; also see below) (8, 12, 14, 16, 21). The Right-DOR contains five boxes (C3-R4 boxes) and cooperative binding of ATP–DnaA molecules to these generates the Right-DnaA subcomplex (Fig. 1B) (12, 18). This subcomplex is not essential for DUE unwinding and plays a supportive role in DnaB loading (8, 15, 17). The Left-DnaA subcomplex interacts with DnaB helicase, and the Right-DnaA subcomplex has been suggested to play a similar role (Fig. 1B) (8, 13, 16).In the presence of ATP–DnaA, M- and R-DUE adjacent to the Left-DOR are predominant sites for in vitro DUE unwinding: unwinding of L-DUE is less efficient than unwinding of the other two (Fig. 1B) (9, 22, 23). Deletion of L-DUE or the whole DUE inhibits replication of oriC in vitro moderately or completely, respectively (23). A chromosomal oriC Δ(AT-cluster−L-DUE) mutant with an intact DOR, as well as deletion of Right-DOR, exhibits limited inhibition of replication initiation, whereas the synthetic mutant combining the two deletions exhibits severe inhibition of cell growth (24). These studies suggest that AT-cluster−L-DUE regions stimulate replication initiation in a manner concerted with Right-DOR, although the underlying mechanisms remain elusive.DnaA consists of four functional domains (Fig. 1B) (4, 25). Domain I supports weak domain I–domain I interaction and serves as a hub for interaction with various proteins such as DnaB helicase and DiaA, which stimulates ATP–DnaA assembly at oriC (26, 27, 28, 29, 30). Two or three domain I molecules of the oriC–DnaA subcomplex bind a single DnaB hexamer, forming a stable higher-order complex (7). Domain II is a flexible linker (28, 31). Domain III contains AAA+ (ATPase associated with various cellular activities) motifs essential for ATP/ADP binding, ATP hydrolysis, and DnaA–DnaA interactions in addition to specific sites for ssDUE binding and a second, weak interaction with DnaB helicase (1, 4, 8, 10, 19, 25, 32, 33, 34, 35). Domain IV bears a helix-turn-helix motif with specific affinity for the DnaA box (36).As in typical AAA+ proteins, a head-to-tail interaction underlies formation of ATP–DnaA pentamers on the DOR, where the AAA+ arginine-finger motif Arg285 recognizes ATP bound to the adjacent DnaA protomer, promoting cooperative ATP–DnaA binding (Fig. 1B) (19, 32). DnaA ssDUE-binding H/B-motifs (Val211 and Arg245) in domain III sustain stable unwinding by directly binding to the T-rich (upper) strand sequences TT[A/G]T(A) within the unwound M/R-DUE (Fig. 1B) (8, 10). Val211 residue is included in the initiator-specific motif of the AAA+ protein family (10). For DUE unwinding, ssDUE is recruited to the Left-DnaA subcomplex via DNA bending by IHF and directly interacts with H/B-motifs of DnaA assembled on Left-DOR, resulting in stable DUE unwinding competent for DnaB helicase loading; in particular, DnaA protomers bound to R1 and R5M boxes play a crucial role in the interaction with M/R-ssDUE (Fig. 1B) (8, 10, 17). Collectively, these mechanisms are termed ssDUE recruitment (4, 17, 37).Two DnaB helicases are thought to be loaded onto the upper and lower strands of the region including the AT-cluster and DUE, with the aid of interactions with DnaC and DnaA (Fig. 1B) (25, 38, 39). DnaC binding modulates the closed ring structure of DnaB hexamer into an open spiral form for entry of ssDNA (40, 41, 42, 43). Upon ssDUE loading of DnaB, DnaC is released from DnaB in a manner stimulated by interactions with ssDNA and DnaG primase (44, 45). Also, the Left- and Right-DnaA subcomplexes, which are oriented opposite to each other, could regulate bidirectional loading of DnaB helicases onto the ssDUE (Fig. 1B) (7, 8, 35). Similarly, recent works suggest that the origin complex structure is bidirectionally organized in both archaea and eukaryotes (1, 46). In Saccharomyces cerevisiae, two origin recognition complexes containing AAA+ proteins bind to the replication origin region in opposite orientations; this, in turn, results in efficient loading of two replicative helicases, leading to head-to-head interactions in vitro (46). Consistent with this, origin recognition complex dimerization occurs in the origin region during the late M-G1 phase (47). The fundamental mechanism of bidirectional origin complexes might be widely conserved among species.In this study, we analyzed various mutants of oriC and DnaA in reconstituted systems to reveal the regulatory mechanisms underlying DUE unwinding and DnaB loading. The Right-DnaA subcomplex assisted in the unwinding of oriC, dependent upon an interaction with L-DUE, which is important for efficient loading of DnaB helicases. The AT-cluster region adjacent to the DUE promoted loading of DnaB helicase in the absence of the Right-DnaA subcomplex. Consistently, the ssDNA-binding activity of the Right-DnaA subcomplex sustained timely initiation of growing cells. These results indicate that DUE unwinding and efficient loading of DnaB helicases are sustained by concerted actions of the Left- and Right-DnaA subcomplexes. In addition, loading of DnaB helicases are sustained by multiple mechanisms that ensure robust replication initiation, although the complete mechanisms are required for precise timing of initiation during the cell cycle.     

The Total Branch Length of Sample Genealogies in Populations of Variable Size

We consider neutral evolution of a large population subject to changes in its population size. For a population with a time-variable carrying capacity we study the distribution of the total branch lengths of its sample genealogies. Within the coalescent approximation we have obtained a general expression—Equation 20—for the moments of this distribution with a given arbitrary dependence of the population size on time. We investigate how the frequency of population-size variations alters the total branch length.MODELS for gene genealogies of biological populations often assume a constant, time-independent population size N. This is the case for the Wright–Fisher model (Fisher 1930; Wright 1931), for the Moran model (Moran 1958), and for their representation in terms of the coalescent (Kingman 1982). In real biological populations, by contrast, the population size changes over time. Such fluctuations may be due to catastrophic events (bottlenecks) and subsequent population expansions or just reflect the randomness in the factors determining the population dynamics. Many authors have argued that genetic variation in a population subject to size fluctuations may nevertheless be described by the Wright–Fisher model, if one replaces the constant population size in this model by an effective population size of the form(1)where N_l stands for the population size in generation l. The harmonic average in Equation 1 is argued to capture the significant effect of catastrophic events on patterns of genetic variation in a population: if, for example, a population went through a recent bottleneck, a large fraction of individuals in a given sample would originate from few parents. This in turn would lead to significantly reduced genetic variation, parameterized by a small value of N_eff. (See, e.g., Ewens 1982 for a review of different measures of the effective population size and Sjödin et al. 2005 and Wakeley and Sargsyan 2009 for recent developments of this concept.)The concept of an effective population size has been frequently used in the literature, implicitly assuming that the distribution of neutral mutations in a large population of fluctuating size is identical to the distribution in a Wright–Fisher model with the corresponding constant effective population size given by Equation 1. However, recently it was shown that this is true only under certain circumstances (Kaj and Krone 2003; Nordborg and Krone 2003; Jagers and Sagitov 2004). It is argued by Sjödin et al. (2005) that the concept of an effective population size is appropriate when the timescale of fluctuations of N_l is either much smaller or much larger than the typical time between coalescent events in the sample genealogy. In these limits it can be proved that the distribution of the sample genealogies is exactly given by that of the coalescent with a constant, effective population size.More importantly, it follows from these results that, in populations with variable size, the coalescent with a constant effective population size is not always a valid approximation for the sample genealogies. Deviations between the predictions of the standard coalescent model and empirical data are frequently observed, and there are a number of different statistical tests quantifying the corresponding discrepancies (see, for example, Tajima 1989, Fu and Li 1993, and Zeng et al. 2006). The analysis of such deviations is of crucial importance in understanding, for example, human genetic history (Garrigan and Hammer 2006). But while there is a substantial amount of work numerically quantifying deviations, often in terms of a single number, little is known about their qualitative origins and their effect upon summary statistics in the population in question.The question is thus to understand the effect of population-size fluctuations on the patterns of genetic variation, in particular for the case where the scale of the population-size fluctuations is comparable to the time between coalescent events in the ancestral tree. As is well known, many empirical measures of genetic variation can be computed from the total branch length of the sample genealogy (the expected number of single-nucleotide polymorphisms, for example, is proportional to the average total branch length).The aim of this article is to analyze the distribution of the scaled total branch length T_n for a sample genealogy in a population of fluctuating size, as illustrated in Figure 1. For the genealogy of n ≥ 2 lineages sampled at the present time, the expression ⌊NT_n⌋ gives the total branch length in terms of generations. Here ⌊Nt⌋ is the largest integer ≤Nt, and the scaling factor N is a suitable measure of the number of genes in the population and serves as a counterpart of the constant generation size of the standard Wright–Fisher model.Open in a separate windowFigure 1.—The effect of population-size oscillations on the genealogy of a sample of size n = 17 (schematic). Left, genealogy described by Kingman''s coalescent for a large population of constant size, illustrated by the light blue rectangle; right, sinusoidally varying population size. Coalescence is accelerated in regions of small population sizes and vice versa. This significantly alters the tree and gives rise to changes in the distribution of the number of mutations and of the population homozygosity.A motivating example is given in Figure 2, which shows numerically computed distributions ρ(T_n) of the total branch lengths T_n for a particular population model with a time-dependent carrying capacity. The model is described briefly in the Figure 2 legend and in detail in a model for a population with time-dependent carrying capacity. As Figure 2 shows, the distributions depend in a complex manner on the form of the size changes. We observe that when the frequency of the population-size fluctuations is very small (Figure 2a), the distribution is well described by the standard coalescent result(2)(Hein et al. 2005). When the frequency is very large (Figure 2e), Equation 2 also applies, but with a different time scaling reflecting an effective population size: t on the right-hand side (rhs) in Equation 2 is replaced by t/c with c = N/N_eff. Apart from these special limits, however, the form of the distributions appears to depend in a complicated manner upon the frequency of the population-size variation. The observed behavior is caused by the fact that coalescence proceeds faster for smaller population sizes and more slowly for larger population sizes, as illustrated in Figure 1. But the question is how to quantitatively account for the changes shown in Figure 2.Open in a separate windowFigure 2.—Numerically computed distributions of the scaled total branch lengths T_n in genealogies of samples of size n = 10. The model employed in the simulations is outlined in a model for a population with time-dependent carrying capacity. It describes a population subject to a time-varying carrying capacity, K_l = K₀(1 + ɛ sin(2πνl)). The frequency of the time changes is determined by ν, and l = 1, 2, 3, … labels discrete generations forward in time. The parameter N = K₀ describes the typical population size, which is taken here to be equal to the time-averaged carrying capacity. a–e show for populations with increasingly rapidly oscillating carrying capacity. The dashed red line in a shows that in the limit of low frequencies the standard coalescent result, Equation 2, is obtained. The dashed red line in e shows that also in the limit of large frequencies the standard coalescent result is obtained, but now with an effective population size. The dashed red line in d is a two-parameter distribution, Equation 41, derived in comparison between numerical simulations and coalescent predictions. Further numerical and analytical results on the frequency dependence of the moments of these distributions are shown in Figure 4. Parameter values used: K₀ = 10,000, ɛ = 0.9, and r = 1 (see a model for a population with time-dependent carrying capacity for the exact meaning of the intrinsic growth rate r) and (a) νN = 0.001, (b) νN = 0.1, (c) νN = 0.316, (d) νN = 1, and (e) νN = 100.We show in this article that the results of the simulations displayed in Figure 2 are explained by a general expression—Equation 20—for the moments of the distributions shown in Figure 2. Our general result is obtained within the coalescent approximation valid in the limit of large population size. But we find that in most cases, the coalescent approximation works very well down to small population sizes (a few hundred individuals). Our result enables us to understand and quantitatively describe how the distributions shown in Figure 2 depend upon the frequency of the population-size oscillations. It makes possible to determine, for example, how the variance, skewness, and the kurtosis of these distributions depend upon the frequency of demographic fluctuations. This in turn allows us to compute the population homozygosity and to characterize genetic variation in populations with size fluctuations.The remainder of this article is organized as follows. The next section summarizes our analytical results for the moments of the total branch length. Following that, we describe the model employed in the computer simulations. Then, corresponding numerical results are compared to the analytical predictions. And finally, we summarize how population-size fluctuations influence the distribution of total branch lengths and conclude with an outlook.     

Discovery and Characterization of a Novel Lachrymatory Factor Synthase in Petiveria alliacea and Its Influence on Alliinase-Mediated Formation of Biologically Active Organosulfur Compounds

A novel lachrymatory factor synthase (LFS) was isolated and purified from the roots of the Amazonian medicinal plant Petiveria alliacea. The enzyme is a heterotetrameric glycoprotein comprised of two α-subunits (68.8 kD each), one γ-subunit (22.5 kD), and one δ-subunit (11.9 kD). The two α-subunits are glycosylated and connected by a disulfide bridge. The LFS has an isoelectric point of 5.2. It catalyzes the formation of a sulfine lachrymator, (Z)-phenylmethanethial S-oxide, only in the presence of P. alliacea alliinase and its natural substrate, S-benzyl-l-cysteine sulfoxide (petiveriin). Depending on its concentration relative to that of P. alliacea alliinase, the LFS sequesters, to varying degrees, the sulfenic acid intermediate formed by alliinase-mediated breakdown of petiveriin. At LFS:alliinase of 5:1, LFS sequesters all of the sulfenic acid formed by alliinase action on petiveriin, and converts it entirely to (Z)-phenylmethanethial S-oxide. However, starting at LFS:alliinase of 5:2, the LFS is unable to sequester all of the sulfenic acid produced by the alliinase, with the result that sulfenic acid that escapes the action of the LFS condenses with loss of water to form S-benzyl phenylmethanethiosulfinate (petivericin). The results show that the LFS and alliinase function in tandem, with the alliinase furnishing the sulfenic acid substrate on which the LFS acts. The results also show that the LFS modulates the formation of biologically active thiosulfinates that are downstream of the alliinase in a manner dependent upon the relative concentrations of the LFS and the alliinase. These observations suggest that manipulation of LFS-to-alliinase ratios in plants displaying this system may provide a means by which to rationally modify organosulfur small molecule profiles to obtain desired flavor and/or odor signatures, or increase the presence of desirable biologically active small molecules.Lachrymatory factor synthase (LFS) is the term coined to refer to the recently discovered enzyme shown to catalyze the formation of the sulfine responsible for the lachrymatory effect of onion (Allium cepa), (Z)-propanethial S-oxide (PTSO; Imai et al., 2002). Until the discovery of the onion LFS, the formation of the onion lachrymatory factor (LF) was thought to be mediated by only a single enzyme, onion alliinase. Alliinases, which are pyridoxal 5′-P (PLP)-dependent Cys sulfoxide lyases most often found in members of the Allium genus, catalyze the breakdown of Cys sulfoxide derivatives to yield fleeting sulfenic acid intermediates and α-aminoacrylic acid (Scheme 1; Block, 1992; Shimon et al., 2007). Once formed, the sulfenic acids are most often observed to spontaneously condense with loss of water to form thiosulfinates, whereas the α-aminoacrylic acid is further hydrolyzed with loss of ammonia to form pyruvate. The S-substituted Cys sulfoxides that are acted upon by alliinases differ from one another by the identity of the sulfur-bound R group. In Allium plants, the R groups are alk(en)yl, with R = methyl and 2-propenyl appearing in large quantities in garlic (Allium sativum) and R = methyl and (E)-1-propenyl preponderating in onion (Scheme 1). The Cys sulfoxide that serves as the precursor of the onion lachrymator is (E)-S-(1-propenyl)-l-Cys sulfoxide (isoalliin). It is structurally distinct from other naturally occurring S-substituted Cys sulfoxides so far reported in that it is α,β-unsaturated. This structural feature affords its corresponding 1-propenylsulfenic acid (PSA) the possibility of undergoing a [1,4]-sigmatropic rearrangement that, in principle, would furnish the onion lachrymator, PTSO. Indeed, the formation of the onion lachrymator was proposed to occur by such a mechanism (Scheme 2; Block, 1992). Thus, it was surmised that were the α,β-unsaturation to be absent in the precursor S-substituted Cys sulfoxide, the [1,4]-sigmatropic rearrangement that would lead to sulfine formation could not occur. Consequently, it was not surprising that other S-substituted Cys sulfoxides constitutively present in garlic, onion, and other alliinase-containing plants, but devoid of this α,β-unsaturation in the sulfur-bound R group, did not themselves yield lachrymators on plant tissue wounding. It has since been discovered, however, that formation of the onion lachrymator is not catalyzed by onion alliinase, but instead by a novel class of enzyme—LFS. Imai et al. (2002) observed that although a crude preparation of onion alliinase yielded both the LF and the corresponding thiosulfinate, the protein fraction with lachrymator-forming ability could be completely separated from that with alliinase activity by passing the crude onion protein preparation through a hydroxyapatite column. The LFS was subsequently purified and shown to be highly substrate specific, producing the LF from only (E)-S-(1-propenyl)-l-Cys sulfoxide (isoalliin), which occurs constitutively in onion. Interestingly, the LF was detected only when three components, namely, the purified onion alliinase, isoalliin, and the onion LFS, were present in the reaction mixture simultaneously (Imai et al., 2002). Omission of the LFS from the reaction mixture resulted in an increased yield of thiosulfinates, but no LF. Although the complete cDNA sequence of the onion LFS has been determined (Imai et al., 2002), to our knowledge, full biochemical characterization of the enzyme has yet to be reported.Open in a separate windowScheme 1.Alliinase-mediated formation of thiosulfinates from Cys sulfoxide precursors (Block, 1992; Shimon et al., 2007). Alliin is S-allyl-l-Cys sulfoxide, isoalliin is (E)-S-(1-propenyl)-l-Cys sulfoxide, methiin is S-methyl-l-Cys sulfoxide, and propiin is S-propyl-l-Cys sulfoxide.Open in a separate windowScheme 2.Mechanism advanced by Block (1992) to account for formation of the onion lachrymator, PTSO. Alliinase-bound PLP forms a Schiff base with bound isoalliin. General base catalysis at the active site yields an α,β-unsaturated sulfenic acid that can undergo a [1,4]-sigmatropic rearrangement to furnish the sulfine.In the course of our studies on the organosulfur chemistry of non-Allium plants, we isolated and characterized the S-benzyl-l-Cys sulfoxides (petiveriins) and S-(2-hydroxyethyl)-l-Cys sulfoxides (2-hydroxyethiins) from the Amazonian medicinal plant Petiveria alliacea (Fig. 1; Kubec and Musah, 2001; Kubec et al., 2002). These compounds are S-substituted Cys sulfoxide derivatives with R = benzyl and 2-hydroxyethyl, respectively, that, to our knowledge, had never before been isolated from plants. We showed that, as has been observed in garlic and onion, symmetrical and mixed thiosulfinate derivatives of the corresponding petiveriin and 2-hydroxyethiin precursors could be extracted with ether solvent (Fig. 1; Kubec et al., 2002) upon root tissue disruption. We have also shown that an alliinase that mediates the transformation of the petiveriins and 2-hydroxyethiins to their corresponding thiosulfinates is present in P. alliacea (Musah et al., 2009). Interestingly, while working with P. alliacea root extracts, we noted the presence of a potent lachrymator that we subsequently determined to be a sulfine—(Z)-phenylmethanethial S-oxide (PMTSO; Fig. 2; Kubec et al., 2003). However, the biochemical precursor of PMTSO and the pathway(s) leading to its formation upon disruption of P. alliacea tissue remain to be determined. Given that the onion LF (PTSO), whose formation is mediated by an LFS, is also a sulfine, we were prompted to investigate the possibility of the presence of a LFS in P. alliacea. In this report, we describe our confirmation of the existence of a LFS in P. alliacea, and detail biochemical characterization of this novel class of enzymes.Open in a separate windowFigure 1.Cys sulfoxides and their corresponding thiosulfinate derivatives isolated from the Amazonian medicinal plant P. alliacea. The breakdown of the Cys sulfoxides is mediated by P. alliacea alliinase.Open in a separate windowFigure 2.Lachrymatory sulfine isolated from P. alliacea.     

Bringing Rad52 foci into focus

In 2007, we published the results of a genome-wide screen for ORFs that affect the frequency of Rad52 foci in yeast. That paper was published within the constraints of conventional online publishing tools, and it provided only a glimpse into the actual screen data. New tools in the JCB DataViewer now show how these data can—and should—be shared.

Complete screen data

https://doi.org/10.1083/jcb.201108095.dv The Rad52 protein has pivotal functions in double strand break repair and homologous recombination. The activity of Rad52 is often monitored by the subnuclear foci that it forms spontaneously in S phase or after DNA damage (Lisby et al., 2001). In mammals, the functions of yeast Rad52 may be divided between human RAD52 and the tumor suppressor BRCA2 (Feng et al., 2011). The full host of molecular players that govern Rad52 focus formation and maintenance was not well known when we initiated our screen. Using a high-content, image-based assay, we assessed the proportion of cells containing spontaneous Rad52-YFP foci in 4,805 viable Saccharomyces cerevisiae deletion strains (Alvaro et al., 2007). Starting with 96-well arrays of a deletion strain library, we created hybrid diploid strains (homozygous for the deletions) using systematic hybrid loss of heterozygosity (SHyLOH; Alvaro et al., 2006). We then manually and sequentially examined each strain using epifluorescence microscopy for the presence of Rad52-YFP foci. All of our image analysis was performed manually.As is often the case, our screen was published showing only a couple of representative images and providing data tables to summarize the findings. Tomes of data that could not be included in the published paper were relegated to supplemental Excel tables, typical of genome-wide screens. Also, the raw image data were sequestered in the laboratory on DVDs. With considerable help from JCB and Glencoe Software, we are delighted that the raw data from our Rad52 screen are now freely available online through the JCB DataViewer. A new interface within the JCB DataViewer brings presentation and preservation of high-content, multidimensional image-based screening data to a whole new level. To facilitate the development of this new interface, JCB required a dataset that was not time sensitive, and we were happy to provide our previously published Rad52 data. In the future, this new interface will be used to present high-content screening (HCS) datasets linked to published JCB papers. Indeed, the first publication of this sort appears in this issue of JCB (Rohn et al., 2011).The presentation of our data in the JCB DataViewer clearly shows the many benefits of this new publishing resource for the scientific community. Users now can view the complete collection of 3D image data across the entire screen, not just the two images in our original publication (Alvaro et al., 2007). Additionally, detailed information on image acquisition parameters, locus identities, and more is easily accessible (Fig. 1). Phenotypic scoring results can be visualized in interactive chart formats (Fig. 1), and search (Fig. 2) and database-linking tools (Fig. 1) allow extensive mining of the data for genes and phenotypes of interest. These tools provide an unprecedented view into HCS data in their entirety, as well as a means for authors to share and archive their data. This kind of accessibility to the direct visualization of the entire set of original screening data, on a scale previously only available to the scientists performing the screen, allows users to understand the full context of the image data analyzed in a screen. Furthermore, it is only through full access to the raw images and associated metadata that this information can be of maximum use to the community for large-scale data mining.Open in a separate windowFigure 1.The HCS interface of the JCB DataViewer provides interactive tools for the analysis of complete datasets from image-based screens. The miniviewer (top left) provides information for each gene in the screen through a zoomable and scrollable display of original multidimensional image data. It contains detailed metadata and a gene ontology (GO) summary, a link to a relevant external database (e.g., the Saccharomyces Genome Database [SGD]; top right), and a link to phenotypic scoring data for the complete screen in the chart view (bottom right). Within the chart view, hits designated by the screen authors are shown in blue, and the strain currently on display in the miniviewer is shown in red. The plate view (bottom left) shows the position of the strain of interest (red box) relative to other strains screened.Open in a separate windowFigure 2.The HCS interface of the JCB DataViewer provides search tools for the mining of complete datasets from image-based screens. (A) Users can search screen data by gene name or keywords (e.g., DNA repair). (B) Users can pick candidates for further analysis from the phenotypic scoring information in the chart view.As in all large-scale screens, the real data are variable; e.g., some strains provide a clear Rad52 focus phenotype, whereas others are more ambiguous. For our particular screen, images were not collected using automated technology but were acquired manually, strain by strain, over a period of months, leading to different levels of fluorescence intensity of Rad52-YFP as a result of, for example, changes in the intensity of our mercury arc lamp. Differences also exist in the number of fields and z stacks captured for each strain. In the absence of automated image collection, images from the primary screen in a few cases were not archived with the others and thus for all intents and purposes have been lost. In addition, our Rad52 screen only assayed nonessential genes, and some mutants are refractory to the SHyLOH methodology. Knowing all of this information allows users to view the data in a realistic manner and further highlights the importance of providing a central repository to archive HCS data.When published through conventional publication media, many important imaging details are known only to the original screeners. The new HCS interface of the JCB DataViewer shines a light on screening data as metadata become freely accessible, allowing any user to ask novel questions of the dataset. For example, the plate view for images (Fig. 1) allows users to assess whether neighboring colonies played any role in determining the phenotype and to delve deeper into why that might be. For example, are any “hits” a result of contamination from adjacent strains, resulting in clusters of positives? In the context of an automated screen, how were control and experimental samples arrayed across a plate during data collection? Did the controls on a particular plate behave as expected? Because our screen used a novel chromosome-specific loss of the heterozygosity method, users can ask whether mutations on specific chromosomes share features of Rad52 foci levels. The global resolution of the dataset provided through this new interface puts users of the dataset as close to the seat of the original screening scientist as possible, allowing them to ask, “what did the authors really see?”Presenting HCS data in the JCB DataViewer holds immense potential value to the scientific community. Through this new interface, users can access powerful interactive tools for analyzing scored phenotypes across the entire dataset (Fig. 1). Each gene ID can be charted against the phenotypic parameters scored in the original screen (e.g., the percentage of cells with Rad52 foci) and compared with all other loci (Fig. 1). Users can take our data and create their own list of hits based on their criteria, create a gallery of thumbnails for their selections (Fig. 2), and seamlessly move between their list of hits and the original data in the plate display format (Fig. 1). Users can also compare their candidates with our list (Fig. 2). The ability to visualize these data for comparative analyses creates a whole new perspective. The HCS interface of the JCB DataViewer allows users to look for their favorite gene, compare related genes, and discover new genes they never anticipated were involved in a given process.In summary, these new features of the JCB DataViewer will allow users to access the primary data from large-scale screens and to look at the full dataset to see what all of the images really look like. The ability to mine these data opens up whole new dimensions in data sharing and transparency. In the future, we anticipate that it will be possible to search many genome-wide screens, such as our Rad52 dataset, to identify commonalities in protein localization, concentration, cell morphology, etc. However, this will only occur if image data are archived and made freely available to the scientific community. We wholeheartedly support the efforts of JCB and hope that groups that use image-based HCS will increasingly make their images available using tools such as the JCB DataViewer.     

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.