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.     

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.     

Rapid reverse phase-HPLC assay of HMG-CoA reductase activity

Radioisotope-based and mass spectrometry coupled to chromatographic techniques are the conventional methods for monitoring HMG-CoA reductase (HMGR) activity. Irrespective of offering adequate sensitivity, these methods are often cumbersome and time-consuming, requiring the handling of radiolabeled chemicals or elaborate ad-hoc derivatizing procedures. We propose a rapid and versatile reverse phase-HPLC method for assaying HMGR activity capable of monitoring the levels of both substrates (HMG-CoA and NADPH) and products (CoA, mevalonate, and NADP⁺) in a single 20 min run with no pretreatment required. The linear dynamic range was 10–26 pmol for HMG-CoA, 7–27 nmol for NADPH, 0.5–40 pmol for CoA and mevalonate, and 2–27 nmol for NADP⁺, and limit of detection values were 2.67 pmol, 2.77 nmol, 0.27 pmol, and 1.3 nmol, respectively.HMG-CoA reductase (HMGR) is the enzyme that catalyze the four-electron reductive deacylation of HMG-CoA to CoA and mevalonate (Fig. 1) (1). This reaction is the controlling step in the biosynthesis of sterols and isoprenoids (2, 3); hence, a large number of studies on the modulation of HMGR activity are continuously performed in the effort of developing new drugs in the treatment of hypercholesterolemic disorders (1).Open in a separate windowFig. 1.Schematic representation of HMGR enzymatic reaction.HMGR activity is conventionally assayed using elaborate radiochemical assay (4–9), chromatographic techniques coupled with mass spectrometry (10–15), or spectrophotometrically by monitoring the decrease in the absorbance of cofactor NADPH at 340 nm (16).Herein, as an alternative for laboratories with no access to the expensive LC/MS equipment, we propose a rapid and adequately sensitive HPLC-based method capable of monitoring both the levels of all the species involved in the equilibrium in a single analysis and the kinetics of HMGR-catalyzed reactions.     

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.     

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

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

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

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

A Neo-Sex Chromosome That Drives Postzygotic Sex Determination in the Hessian Fly (Mayetiola destructor)

Two nonoverlapping autosomal inversions defined unusual neo-sex chromosomes in the Hessian fly (Mayetiola destructor). Like other neo-sex chromosomes, these were normally heterozygous, present only in one sex, and suppressed recombination around a sex-determining master switch. Their unusual properties originated from the anomalous Hessian fly sex determination system in which postzygotic chromosome elimination is used to establish the sex-determining karyotypes. This system permitted the evolution of a master switch (Chromosome maintenance, Cm) that acts maternally. All of the offspring of females that carry Cm-associated neo-sex chromosomes attain a female-determining somatic karyotype and develop as females. Thus, the chromosomes act as maternal effect neo-W''s, or W-prime (W′) chromosomes, where ZW′ females mate with ZZ males to engender female-producing (ZW′) and male-producing (ZZ) females in equal numbers. Genetic mapping and physical mapping identified the inversions. Their distribution was determined in nine populations. Experimental matings established the association of the inversions with Cm and measured their recombination suppression. The inversions are the functional equivalent of the sciarid X-prime chromosomes. We speculate that W′ chromosomes exist in a variety of species that produce unisexual broods.SEX chromosomes are usually classified as X, Y, Z, or W on the basis of their pattern of segregation and the gender of the heterogametic sex (Ohno 1967). However, when chromosome-based sex determination occurs postzygotically, the same nomenclature confounds important distinctions and may hide interesting evolutionary phenomena. The Hessian fly (Mayetiola destructor), a gall midge (Diptera: Cecidomyiidae) and an important insect pest of wheat, presents an excellent example (Stuart and Hatchett 1988, 1991). In this insect, all of the female gametes and all of the male gametes have the same number of X chromosomes (Figure 1A); no heterogametic sex exists. Nevertheless, Hessian fly sex determination is chromosome based; postzygotic chromosome elimination produces different X chromosome to autosome ratios in somatic cells (male A1A2X1X2/A1A2OO and female A1A2X1X2/A1A2X1X2, where A1 and A2 are the autosomes, X1 and X2 are the X chromosomes, and the paternally derived chromosomes follow the slash) (Stuart and Hatchett 1991; Marin and Baker 1998). Thus, Hessian fly “X” chromosomes are defined by their haploid condition in males, rather than by their segregation in the gametes.Open in a separate windowFigure 1.—Chromosome behavior and sex determination in the Hessian fly. (A) Syngamy (1) establishes the germ-line chromosome constitution: ∼32 maternally derived E chromosomes (represented as a single white chromosome) and both maternally derived (black) and paternally derived (gray) autosomes and X chromosomes. During embryogenesis, while the E chromosomes are eliminated, the paternally derived X chromosomes are either retained (2) or excluded (3) from the presumptive somatic cells. When the paternally derived X chromosomes are retained (2), a female-determining karyotype is established. When they are eliminated (3), a male-determining karyotype is established. Thelygenic mothers carry Cm (white arrow), which conditions all of their offspring to retain the X chromosomes. Recombination occurs during oogenesis (4). All ova contain a full complement of E chromosomes and a haploid complement of autosomes and X chromosomes. Chromosome elimination occurs during spermatogenesis (5). Sperm contain only the maternally derived autosomes and X chromosomes. (B) The segregation of Cm (white dot) on a Hessian fly autosome among monogenic families. Thelygenic females produce broods composed of equal numbers of thelygenic (Cm/−) and arrhenogenic (−/−) females (box 1). Arrhenogenic females produce males (box 2). (C) Matings between monogenic and amphigenic families. Cm (white dot) is dominant to the amphigenic-derived chromosomes (gray dot) and generates all-female offspring (box 3). Amphigenic-derived chromosomes are dominant to the arrhenogenic-derived chromosomes (no dot) and generate offspring of both sexes (box 4).An autosomal, dominant, genetic factor called Chromosome maintenance (Cm) complicates Hessian fly sex determination further (Stuart and Hatchett 1991). Cm has a maternal effect that acts upstream of X chromosome elimination during embryogenesis (Figure 1A). It prevents X chromosome elimination so that all of the offspring of Cm-bearing mothers obtain a female-determining karyotype. Cm-bearing females produce only female offspring and are therefore thelygenic. The absence of Cm usually has the opposite effect; all of the offspring of most Cm-lacking females obtain a male-determining karyotype. These Cm-lacking females produce only male offspring and are therefore arrhenogenic. Like a sex-determining master switch, Cm is usually heterozygous and present in only one sex (Figure 1B). Thus, thelygenic females (Cm/−) are “heterogametic,” as their Cm-containing gametes and Cm-lacking gametes produce thelygenic (Cm/−) and arrhenogenic (−/−) females in a 1:1 ratio. Collectively, thelygenic and arrhenogenic females are called monogenic because they produce unisexual families. However, some Hessian fly females produce broods of both sexes and are called amphigenic. No mating barrier between monogenic and amphigenic families exists (Figure 1C), but amphigenic females have always been found in lower abundance (Painter 1930; Gallun et al. 1961; Stuart and Hatchett 1991). In experimental matings, the inheritance of maternal phenotype was consistent with the segregation of three Cm alleles (Figure 1C): a dominant thelygenic allele, a hypomorphic amphigenic allele, and a null arrhenogenic allele (Stuart and Hatchett 1991).Here we report the genetic and physical mapping of Cm on Hessian fly autosome 1 (A1). Two nonoverlapping inversions were identified that segregated perfectly with Cm. The most distal inversion was present in all thelygenic females examined. The more proximal inversion extended recombination suppression. These observations suggested that successive inversions evolved to suppress recombination around Cm after it arose. The inversions therefore appear to have evolved in response to the forces that shaped vertebrate Y and W chromosomes (Charlesworth 1996; Graves and Shetty 2001; Rice and Chippindale 2001; Carvalho and Clark 2005). We therefore believe the inversion-bearing chromosomes may be classified as maternal effect neo-W''s.     

Big steps toward understanding dynein

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

Anatomy of dynein

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

The chemomechanical cycle of dynein

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

The MTBD of dynein

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

Structures around the first ATP binding site

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

Processivity of dynein

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

Dyneins in the axoneme

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

Closing remarks

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

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.     

The quest for spatio-temporal control of CAR T cells

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

Genetics of Sexual Development: An Evolutionary Playground for Fish

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

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.     

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.     

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.     

Mechanisms of spindle positioning

Accurate positioning of spindles is essential for asymmetric mitotic and meiotic cell divisions that are crucial for animal development and oocyte maturation, respectively. The predominant model for spindle positioning, termed “cortical pulling,” involves attachment of the microtubule-based motor cytoplasmic dynein to the cortex, where it exerts a pulling force on microtubules that extend from the spindle poles to the cell cortex, thereby displacing the spindle. Recent studies have addressed important details of the cortical pulling mechanism and have revealed alternative mechanisms that may be used when microtubules do not extend from the spindle to the cortex.Mitotic and meiotic spindles are precisely positioned within eukaryotic cells for several reasons. In animal cells, spindle position determines the location of contractile ring assembly (Green et al., 2012). Thus, placing a spindle in the center of the cell will result in daughter cells of equal size, whereas positioning the spindle asymmetrically results in daughter cells of different sizes. In oocytes, the extreme asymmetrical positioning of the meiotic spindle allows expulsion of three fourths of the chromosomes into two tiny polar bodies while preserving most of the cytoplasm in the egg for the developing zygote. In polarized cells, where proteins and RNAs are asymmetrically distributed before division, the orientation of the spindle relative to the polarity axis determines whether the daughter cells will have the same or different developmental fates. An excellent review of the developmental context of spindle positioning is provided by Morin and Bellaïche (2011). In budding yeast (Slaughter et al., 2009) and plants (Rasmussen et al., 2011), the site of cytokinesis is determined before the spindle forms. In these organisms, the spindle must be oriented relative to the predetermined division plane to ensure that both daughter cells receive a complete chromosome complement.The majority of research on the mechanisms of spindle positioning has focused on cell types that have “astral” microtubules. Astral microtubules have minus ends embedded in the spindle poles and plus ends extending outward, away from the spindle toward the cell cortex (Fig. 1, A and B). Astral microtubules have been proposed to mediate spindle positioning by generating pulling forces at the cortex or pulling forces against the cytoplasm. The minus ends of astral microtubules are embedded in the pericentriolar material that surrounds the centrioles of animal cells or in the spindle pole bodies of fungi. This attachment is essential for pulling forces on the astral microtubules to move the spindle. However, late stage oocytes of several animal phyla and all cells of flowering plants lack centrioles and lack obvious astral microtubules. Thus, these cell types have evolved alternative spindle positioning mechanisms. Here we review recent advances in both astral and nonastral spindle positioning mechanisms.Open in a separate windowFigure 1.Mitotic spindle movements in the C. elegans zygote. (A) Schematic diagram of a single-celled C. elegans embryo showing cortical pulling by cytoplasmic dynein during pronuclear centration and rotation. The nuclei move toward the anterior (left) so that the spindle assembles in the center of the embryo. (B) Schematic diagram of a single-celled C. elegans embryo showing cortical pulling by dynein during anaphase. The spindle moves to the posterior (right) so that cytokinesis generates two cells of different sizes. The squares highlight a dynein molecule that pulls toward the posterior before spindle displacement, then pulls toward the anterior after spindle displacement. (C) Schematic drawing of cytoplasmic pulling that contributes to centering the pronuclei. (D) Illustration of a spindle that was centered at metaphase but in which both poles moved all the way to the posterior end of the embryo. This occurs in zyg-8 mutants (Gönczy et al., 2001), cls-1,2 (RNAi) embryos (Espiritu et al., 2012), and zyg-9(ts) mutants shifted to a nonpermissive temperature at metaphase (Bellanger et al., 2007), possibly because astral microtubules are too short to reach force generators that would pull toward the anterior.

Cortical versus cytoplasmic pulling

The most prominent model of spindle positioning involves a cortical pulling mechanism. In this model, the minus end–directed microtubule motor protein, cytoplasmic dynein, is attached to the cell cortex and exerts pulling forces on the plus ends of astral microtubules that reach the cortex. In the single-celled Caenorhabditis elegans embryo at early prophase, complexes of GPR-1,2 (G protein regulator) and LIN-5 (abnormal cell lineage), positive regulators of cytoplasmic dynein, are more concentrated at the anterior cortex of the embryo (Fig. 1 A; Park and Rose, 2008), resulting in greater net pulling force toward the anterior. This results in net movement of the pronuclei to the center of the embryo and rotation of the centrosome–pronuclear complex so that the metaphase spindle forms in the center of the embryo with its poles oriented along the anterior-posterior axis of the embryo (Fig. 1 B). During metaphase and early anaphase, GPR-1,2 and LIN-5 become more concentrated at the posterior end of the embryo, resulting in movement of the spindle toward the posterior so that cytokinesis generates daughter cells of different sizes (Fig. 1 B; Grill et al., 2001, 2003). Depending on the relative distribution of active force generators at the cortex, this mechanism can also lead to centering the spindle within the cell to allow symmetric cytokinesis as occurs in HeLa cells (Kiyomitsu and Cheeseman, 2012) and LLC-Pk1 cells (Collins et al., 2012). Cortical pulling might involve pulling on the sides of microtubules that bend as they approach the cortex (Fig. 1 A, 1) or end-on interactions that require coupling of microtubule depolymerization with pulling (Fig. 1 A, 2), as occurs at kinetochores during anaphase A (McIntosh et al., 2010).Cortical pulling differs from a cytoplasmic pulling mechanism most clearly proposed by Kimura and Kimura (2011) and diagrammed in Fig. 1 C. In this cytoplasmic pulling mechanism, the viscous drag on membranous organelles transported toward the minus ends of astral microtubules by cytoplasmic dynein generates a force in the opposite direction, toward the cortex. Depletion of RAB-5, RAB-7, or RILP-1 (RAB-7 interacting lysosomal protein homologue) blocks dynein-dependent organelle transport and slows the velocity of pronuclear centration in C. elegans without affecting other dynein-dependent movements (Kimura and Kimura, 2011). Elimination of cortical pulling by depleting GPR-1,2 also slows pronuclear centration (Park and Rose, 2008), which suggests that cortical pulling and cytoplasmic pulling each contribute 50% of the velocity of pronuclear centration. Unlike end-on cortical pulling, cytoplasmic pulling force is proportional to the length of the astral microtubules because more organelles will be transported on a long microtubule than a short microtubule (Fig. 1 C). This generates a self-centering mechanism as the length of the astral microtubules equalize when the pronuclei reach the center of the zygote (Fig. 1 A; Hamaguchi and Hiramoto, 1986). Cytoplasmic pulling may predominate in very large zygotes where astral microtubules clearly do not reach the cortex but where both pronuclei and the mitotic spindle are centered (Mitchison et al., 2012).

Evidence for cortical pulling.

The cortical pulling mechanism requires that microtubules extend from the spindle to the cortex and form a contiguous structure that is mechanically robust enough that the force generators do not pull the minus ends of the microtubules out of the spindle pole or cause the plasma membrane to buckle inward. Experimental evidence for this contiguous mechanical linkage comes from experiments in oocytes of the marine annelid, Chaetopterus. Insertion of a glass needle into the meiotic spindle allowed pulling the spindle away from the cortex, which caused inward buckling of the cortex. Further pulling resulted in sudden release of the spindle from the cortex, restoration of cortical shape, and concomitant disappearance of a birefringent aster extending between the spindle and cortex (Lutz et al., 1988). The second requirement for a cortical pulling mechanism is that force is generated at the cortex. Using a laser to cut the central spindle of an early anaphase C. elegans embryo, Grill et al. (2001) showed that spindle poles are pulled from outside the spindle rather than pushed from inside the spindle during posterior spindle displacement. Fragmentation of centrosomes with a laser (Grill et al., 2003) revealed that astral microtubules freed from the spindle move outward toward the cortex. Either cortical pulling or cytoplasmic pulling could explain this result; however, the asymmetric distribution of fragment velocities is controlled by proteins that are localized at the cortex, GPR-1,2 (Grill et al., 2003) and LET-99 (lethal-99; Krueger et al., 2010). In a key experiment, Redemann et al. (2010) showed that microtubule plus ends pull tubular invaginations of the plasma membrane inward when cortical stiffness is partially reduced. This experiment showed that astral microtubules are pulling on the cortex during spindle displacement, which would not occur if forces were generated by movement of cytoplasmic organelles along the sides of microtubules.

End-on versus side-on microtubule–cortex interactions.

How do microtubules interact with force generators at the cortex? Astral microtubules might polymerize to the cortex then bend along it so that cortical motors interact with the side of the microtubule to generate force (Fig. 1 A, 1). This type of interaction would be consistent with the in vitro gliding motility of microtubules generated by cytoplasmic dynein immobilized on glass coverslips (Paschal et al., 1987; Vallee et al., 1988) and is the only type of dynein-dependent cortical microtubule interaction observed in budding yeast (Adames and Cooper, 2000). Studies of C. elegans embryos, however, support end-on microtubule contacts (Fig. 1 A, 2) as the functional contact with cortical force generators for posterior displacement of the early anaphase spindle. Live imaging of YFP-tubulin in optical sections at the surface of the embryo during anaphase revealed dots rather than lines, indicating that the microtubules contacting the cortex are <200 nm in length (Labbé et al., 2003; Kozlowski et al., 2007). When short microtubule fragments are generated by ectopic katanin activity, dynein-dependent gliding of microtubule “lines” on the cortex is frequently observed (Gusnowski and Srayko, 2011), indicating that cortical dynein is capable of moving microtubules along the cortex via side-on interactions in wild-type embryos but that it does not during posterior displacement of the anaphase spindle. A likely explanation comes from the finding that astral microtubule plus ends undergo catastrophe (switch to depolymerization) on average 1.4 s after polymerizing to the cortex (Kozlowski et al., 2007). Thus astral microtubule plus ends do not have time to polymerize along the cortex to establish extensive side-on contacts. Support for this idea comes from depletion of the conserved plasma membrane protein EFA-6 (exchange factor for Arf) from the C. elegans embryo. In the absence of EFA-6, the residence time of microtubule plus ends at the cortex increases fivefold, astral microtubules form extensive lateral contacts with the cortex, and centrosomes exhibit movements consistent with excessive dynein-dependent cortical pulling (O’Rourke et al., 2010). Side-on contacts of astral microtubules with the cortex occur later during telophase in the wild-type C. elegans embryo (Kozlowski et al., 2007), but the nature of this switch has not been addressed.The two distinct activities of cytoplasmic dynein, end-on pulling and walking along the side of a microtubule, have been genetically separated in C. elegans. Cortical pulling forces during early anaphase require the redundant cortical dynein activators GPR-1 and -2 (Grill et al., 2003), which bind to LIN-5 (Gotta et al., 2003). GPR-1,2/LIN-5 is anchored in the plasma membrane via the myristoyl and palmitoyl lipid modifications of the redundant Gα proteins GOA-1 and GPA-16 (Gotta et al., 2003; Park and Rose, 2008; Kotak et al., 2012). The complex of GPR-1 and LIN-5 interacts with the dynein light chain DYRB-1 (Couwenbergs et al., 2007) and the dynein regulator LIS-1 (human lissencephaly gene related; Nguyen-Ngoc et al., 2007). GPR-1 and -2, however, are not required for dynein-dependent gliding of severed microtubule fragments along the cortex (Gusnowski and Srayko, 2011), dynein-dependent transport of membranous organelles along the sides of microtubules (Kimura and Kimura, 2011), dynein-dependent positioning of the acentriolar C. elegans meiotic spindle (van der Voet et al., 2009), or dynein-dependent centration of the male pronucleus (Kimura and Kimura, 2011). These GPR-independent activities of cytoplasmic dynein likely do not require end-on pulling.Recently, end-on pulling by cytoplasmic dynein has been reconstituted in vitro with a purified preparation of artificially dimerized budding yeast cytoplasmic dynein. Laan et al. (2012) immobilized purified cytoplasmic dynein on microfabricated barriers and observed the interaction of centrosome-nucleated microtubules as they approached these dynein-coated barriers. Microtubule plus ends hitting a dynein-coated barrier switched to catastrophe with high frequency but the microtubule depolymerization rate after the catastrophe was reduced. The result was an extended period of interaction between the depolymerizing plus end and the dynein-coated barrier. Plus-end depolymerization pulled the centrosome toward the barrier, and in similar reactions the pulling force was measured as high as 5 pN. Side-on interactions with the barrier were not observed. Whereas ATP was required for this end-on pulling, it is not clear if the energy source is ATP hydrolysis–driven stepping by dynein or if the energy source is GTP hydrolysis–driven depolymerization of the microtubule. In the latter case, ATP might only be required to prevent rigor binding of dynein to the microtubule. Indeed, an artificial rigor binding of a streptavidin-coated bead to a depolymerizing biotinylated microtubule plus end resulted in a pulling force that is restricted to an extremely short distance (Grishchuk et al., 2005). In vitro reconstitution of pulling force coupled to depolymerizing microtubule plus ends was first demonstrated with beads coated with kinesin-1 or a nonmotile kinesin chimera (NK350; Lombillo et al., 1995). Like barrier-bound cytoplasmic dynein, kinesin-coated beads slowed the depolymerization rate of microtubule plus ends, whereas NK350-coated beads enhanced the depolymerization rate of bound plus ends. ATP enhanced depolymerization-coupled pulling for both kinesin-1 and NK350, just as it did for barrier-bound cytoplasmic dynein. The in vitro pulling reaction reconstituted by Laan et al. (2012) seems unlikely to be the same reaction that pulls on plus ends in the anaphase budding yeast cell, as these are through side-on interactions (Adames and Cooper, 2000). In vitro reconstitution of a GPR/LIN-5–dependent, end-on pulling reaction with purified metazoan cytoplasmic dynein may reveal mechanisms acting on spindles in vivo.Another interesting contrast between end-on versus side-on cortical pulling reactions is suggested by differences in the dependence on cortical F-actin. F-actin is required for cortical rigidity to prevent end-on microtubule contacts from pulling membrane tubules inward instead of moving the spindle pole outward (Redemann et al., 2010). Side-on pulling by cortical dynein in budding yeast, however, does not require F-actin (Theesfeld et al., 1999; Heil-Chapdelaine et al., 2000a). The curvature of microtubules gliding on the bud cortex indicates that the microtubule is engaged with multiple dynein molecules distributed over several microns of cortex, and this distribution of force might allow effective pulling against a less rigid cortex. Alternatively, rigidity of the yeast plasma membrane might be mediated by oligomers of BAR domain proteins like Num1 (nuclear migration; Tang et al., 2012) or eisosomes (Walther et al., 2006; Olivera-Couto et al., 2011), by osmotic pressure, or by attachment of the plasma membrane to the cell wall.

Why the spindle is not pulled all the way to the cortex with more active force generators.

What prevents the C. elegans centrosome–pronuclear complex from moving all the way to the anterior cortex where the concentration of cortical force generators is highest during prophase (Fig. 1 A), and what prevents the spindle from moving all the way to the posterior cortex, which has the highest concentration of active force generators during anaphase (Fig. 1, B and D)? Increasing the concentration of GPR-1,2/LIN-5 at the anterior cortex causes pronuclei to move further toward the anterior, but they still do not crash into the anterior cortex (Panbianco et al., 2008). During metaphase/anaphase (Fig. 1 B), weak cortical pulling on the anterior aster might oppose strong pulling on the posterior aster. Supporting this idea, spindle severing results in the posterior aster moving further posterior than when the spindle is intact (Grill et al., 2001), but the posterior pole still does not move all the way to the posterior cortex. Monopolar spindles move toward the posterior in a GPR1,2-dependent manner but then reverse direction and oscillate along the anterior-posterior axis (Krueger et al., 2010). Laan et al. (2012) found that centrosome-nucleated microtubule asters could accurately self-center within microchambers whose walls are coated with dynein. Their mathematical modeling suggested that self-centering was achieved because of a balance between cortical pulling by dynein and pushing by polymerizing microtubules (Dogterom and Yurke, 1997) that have not yet engaged a dynein molecule. Thus, cortical pushing by astral microtubules might buffer cortical pulling in vivo. Grill and Hyman (2005) suggested another simple solution. When a spindle pole moves to the posterior, it passes a subset of cortical force generators that were initially pulling toward the posterior (Fig. 1 B, box). After passage of the spindle pole, these force generators pull toward the anterior. Failure in any of these buffering mechanisms might explain why both spindle poles move to the posterior cortex (Fig. 1 D) in mutants that have either short astral microtubules or fewer astral microtubules reaching the cortex (Gönczy et al., 2001; Bellanger et al., 2007; Espiritu et al., 2012).Recent experiments in HeLa and LLC-Pk1 cells have revealed a more sophisticated feedback mechanism that effectively centers the mitotic spindle. Kiyomitsu and Cheeseman (2012) found that HeLa cell mitotic spindles oscillate back and forth along their pole-to-pole axis. They found that dynein/dynactin formed a lateral crescent on the cortex when the spindle was far from that lateral cortex (Fig. 2 A). As the spindle moved toward the dynein crescent, the dynein crescent disappeared as the spindle approached and a new crescent appeared on the opposite lateral cortex (Fig. 2 C). They found that polo kinase 1 (Plk1), which is concentrated on spindle poles, causes dynein/dynactin to dissociate from the LGN–NuMA–Gαi complex (Leu-Gly-Asn repeat enriched protein–nuclear mitotic apparatus protein–Gα; homologues of GPR-1,2–LIN-5–Gα), which explains why the dynein crescent disappears once the spindle pole gets close to the cortex. They also found that the GTP-Ran gradient produced by chromosome-bound RCC1 (regulator of chromosome condensation) was responsible for inhibiting LGN–NuMA association with the cortex, and hence dynein, above the central spindle (Fig. 2). The RCC1 pathway explains why the spindles move only along their pole–pole axis. The two pathways combine to center the spindle in two different axes. Similar spindle oscillations with dynein/dynactin crescents forming only when the spindle pole is far from the cortex were reported in LLC-Pk1 epithelial cells (Collins et al., 2012). Because spindles are small relative to the two-dimensional flattened area of these cells, the role of this pathway in centering spindles to allow symmetric cytokinesis is much more obvious than in HeLa cells.Open in a separate windowFigure 2.How to center a spindle. Schematic diagram of a metaphase HeLa cell where the spindle oscillates along its pole–pole axis to maintain a centered position to allow symmetric cytokinesis. (A) When the left spindle pole is close to the cortex, Plk1 on the pole (red) causes dynein (green) to dissociate from LGN–NuMA complexes (purple; human homologues of GPR-1,2/LIN-5). (B and C) The spindle moves to the right because of the higher concentration of LGN–NuMA–dynein complexes on the right cortex. When chromosomes are close to the cortex as in A, the GTP-Ran gradient from the chromosomes causes dissociation of LGN/NuMA from the cortex. This second system centers the spindle in the axis perpendicular to the pole–pole axis.There are normal situations where movement of one spindle pole all the way to the cortex occurs. This exaggerated movement that results in one set of astral microtubules being splayed onto the cortex is observed in fourth cleavage sea urchin embryos (Holy and Schatten, 1991) and for the female meiotic spindles of Chaetopterus (Lutz et al., 1988) and Spisula solidissima (Pielak et al., 2004).

The budding yeast model.

The S. cerevisiae mitotic spindle is positioned relative to a preformed bud neck by two sequential and partially redundant cortical pulling pathways to ensure that chromosomes are deposited into both mother and daughter cells. Spindle positioning is also monitored by a budding yeast–specific checkpoint (Caydasi et al., 2010). Deletion of genes in any one positioning pathway results in a viable yeast strain, whereas double mutants bearing deletions of genes in both positioning pathways or of genes in one positioning pathway plus the checkpoint results in lethality (Miller et al., 1998). In the early pathway, the microtubule plus-end tip tracking protein, Bim1 (binding to microtubules; EB1 homologue), binds to the yeast-specific adaptor protein Kar9 (karyogamy; Korinek et al., 2000; Lee et al., 2000; Miller et al., 2000), which binds to the yeast myosin V (Myo2; Yin et al., 2000). Myosin V transports the growing microtubule plus end toward the bud tip on polarized actin cables (Hwang et al., 2003). This results in a unique “sweeping” or “pivoting” of the growing astral microtubule toward the bud neck (Fig. 3 A; Adames and Cooper, 2000). Because Bim1 only binds to growing microtubule plus ends (Zimniak et al., 2009), this mechanism alone cannot bring the spindle to the bud neck because the polymerizing astral microtubule would push the spindle back into the mother cell. To prevent the astral microtubules from becoming too long, the kinesin-8 family member, Kip3, passively tracks the plus end until the plus end reaches the bud cortex (Fig. 3 A). Kip3 then switches to a plus-end depolymerase and shortens the astral microtubule to pull the spindle to the bud neck (Fig. 3, B and C; Gupta et al., 2006). Purified Kip3 has unique biochemical properties that contribute to its in vivo function. In vitro, Kip3 accumulates at plus-end tips via its plus end–directed motor activity. Longer microtubules accumulated more Kip3 at their plus ends than short microtubules simply because there are more lateral binding sites on a long microtubule and Kip3 switches to a plus-end depolymerase only when the microtubule has reached a threshold length (Varga et al., 2009). Plus-end pulling by the fission yeast homologue of Kip3 has also been reconstituted in vitro (Grissom et al., 2009). More recent work suggests that Kip3-dependent cortical pulling requires the concerted action of the cortical protein, Bud6 (BUD site selection), and the plus end tracker, Bim1, as well as cytoplasmic dynein (ten Hoopen et al., 2012).Open in a separate windowFigure 3.Spindle positioning in budding yeast. Schematic diagram of the two sequential spindle positioning pathways of budding yeast. In the early pathway (A–C), myosin V transports the plus end of an astral microtubule toward the bud tip on a polarized actin cable. Once the plus end has reached the bud cortex, the plus-end depolymerase, KIP3, is activated to allow pulling of the spindle pole toward the bud neck. In the late pathway (D–F), the plus end–directed microtubule motor Kip2 transports dynein to the plus ends of microtubules via the adaptor protein Bik1 (D). Dynein can be targeted to plus ends by two additional Bik1-dependent mechanisms (see text). When dynein reaches the bud cortex on a polymerizing microtubule plus end (E), contact with the cortical protein Num1 allows dynein to pull the spindle toward the bud (F). During early anaphase (D and E), dynein is not loaded onto microtubules in the mother cell. During late anaphase (F), dynein is loaded on microtubules in the mother cell to prevent movement of the spindle all the way into the bud.The late pathway is initiated when the yeast-specific dynein inhibitor She1 (sensitivity to high expression) is removed from astral microtubules at the metaphase–anaphase transition (Woodruff et al., 2009). She1 appears to act specifically by preventing recruitment of dynactin to microtubules (Bergman et al., 2012) and by inhibiting dynein motility (Markus et al., 2012). In the late pathway, cytoplasmic dynein is targeted to growing microtubule plus ends by the plus-end tracking protein Bik1 (bilateral karyogamy defect; Sheeman et al., 2003), which itself is targeted to plus ends by three partially redundant mechanisms. In the first mechanism, Bik1 is transported as cargo by the plus end–directed kinesin Kip2 (Carvalho et al., 2004). Cytoplasmic dynein is thus transported as cargo to the bud cortex by Bik1–Kip2 complexes (Fig. 3 D). In the absence of Kip2, Bik1 (and therefore dynein) can still track growing microtubule plus ends either through a second mechanism that requires the C-terminal tyrosine residue of α-tubulin or a third mechanism that requires the plus end–tracking protein Bim1 (Caudron et al., 2008). This is partially consistent with results of reconstitution experiments with purified proteins showing that the Bik1 homologue, CLIP170, tracks growing plus ends through a mechanism that involves binding to both the Bim1 homologue, EB1, and the C-terminal tyrosine-containing motif of α-tubulin (Bieling et al., 2008). When a microtubule plus end carrying dynein contacts the yeast-specific cortical protein Num1, Num1 apparently stimulates off-loading of the dynein tail onto the cortex so that the dynein motor domains engage the microtubule in a cortical pulling reaction (Fig. 3, E and F). Deletion of Num1 results in accumulation of inactive dynein at plus ends (Lee et al., 2003; Sheeman et al., 2003; Markus and Lee, 2011). In contrast with the GPR-dependent end-on cortical interactions observed in C. elegans, dynein-dependent cortical pulling is mediated by lateral sliding of astral microtubules along the yeast bud cortex (Fig. 3, E and F; Adames and Cooper, 2000). Also, unlike cortical GPR/LIN-5 in animal cells, cortical Num1 is distributed in patches throughout both mother and daughter cells (Heil-Chapdelaine et al., 2000b) and even participates in mitochondrial positioning and fission throughout the cell (Cerveny et al., 2007; Hammermeister et al., 2010). If there is no asymmetrically distributed cortical activator of dynein, how is dynein-mediated pulling directed specifically toward the bud cortex?

Why both spindle poles are not pulled to the same cortex in budding yeast.

The budding yeast spindle, rather than the cortex, is asymmetrical. At metaphase, Kar9 is asymmetrically localized on the spindle pole oriented toward the bud and on the plus ends of astral microtubules emanating from that bud-proximal spindle pole (Liakopoulos et al., 2003). This alone would explain why only one spindle pole moves toward the bud but then leaves the question of how Kar9 asymmetry is established. Cepeda-García et al. (2010) found that Kar9 at spindle poles became symmetrical and reduced in concentration after depolymerization of actin cables, depolymerization of microtubules, or disruption of the myosin V–Kar9 interaction. When microtubules were repolymerized, Kar9 was initially symmetrical on both spindle poles and quickly repolarized onto the first microtubule to make a functional cortical contact. These results suggested a positive feedback loop in which functional cortical pulling by Bim1–Kar9–Myo2 complexes causes loading of additional Kar9. Cytoplasmic dynein also accumulates preferentially on the plus-end tips that reach the bud neck first (Fig. 3, D and E) and on the bud-proximal spindle pole during metaphase. The asymmetrical accumulation of dynein on the bud-proximal microtubules and bud-proximal spindle pole requires kinases that are found at the bud neck. Thus, the asymmetry of dynein localization may be generated by a positive feedback loop, as suggested for Kar9 asymmetry. After the spindle is pulled into the bud neck, during anaphase, dynein becomes symmetrical on the plus ends emanating from both poles (Fig. 3 F; Grava et al., 2006). This regulation prevents both poles from moving toward the bud during metaphase and then prevents the spindle from being pulled all the way into the bud during anaphase. Asymmetric localization of dynein on spindle poles or microtubule plus ends has not yet been reported in animal cells.

Other spindle positioning mechanisms

In the examples of the C. elegans and budding yeast mitotic spindles, long astral microtubules are in contact with the cell cortex. In cells where spindles have no astral microtubules, other mechanisms must be at work. Female meiotic spindles are universally positioned with one spindle pole contacting the oocyte cortex so that one set of chromosomes can be eliminated in a polar body through an extremely asymmetrical division (Fabritius et al., 2011). Female meiotic spindles of at least three animal phyla (Chordata, Nematoda, and Arthropoda), however, have no centrioles in their spindle poles and no apparent astral microtubules. Work in C. elegans and mice suggests that different species have evolved different mechanisms for acentriolar meiotic spindle positioning.

Parallel metaphase meiotic spindles.

The metaphase I and metaphase II spindles of C. elegans (Fig. 4 A) and the metaphase II spindle of mouse (Fig. 4 B) are positioned at the cortex in a parallel orientation, with both poles equidistant from the cortex. In C. elegans, this parallel cortical positioning requires microtubules, kinesin-1, and a worm-specific kinesin-1–binding partner, KCA-1 (Yang et al., 2003, 2005), but is independent of F-actin (Yang et al., 2003). Kinesin-1 and microtubules are also required to move the nucleus to the cortex before germinal vesicle breakdown and to drive transport of yolk granules inward from the cortex, which results in a circular streaming pattern. It has been suggested that kinesin-1 may only move the germinal vesicle to the cortex and that an additional, unidentified pathway moves the spindle over the remaining distance to the cortex and establishes the parallel orientation (McNally et al., 2010). The mouse metaphase II spindle is maintained in a similar parallel orientation at the cortex, but this positioning requires the actin nucleator ARP2/3. ARP2/3 also drives streaming of actin filaments and cytoplasm in a pattern that has been proposed to push the spindle into the cortex to maintain parallel cortical position (Fig. 4 B; Yi et al., 2011).Open in a separate windowFigure 4.A plethora of nonastral spindle positioning mechanisms. (A) Metaphase C. elegans meiotic spindles are positioned in a parallel orientation at the cortex by microtubules and kinesin-1. (B) The mouse metaphase II spindle may be positioned by actin-dependent cytoplasmic streaming. Pole-first migration of the mouse meiosis I spindle to the cortex may be mediated by cargo transport on parallel actin filaments by spindle pole–bound myosin II (C), myosin II–based contraction of anti-parallel actin filaments (D), or pushing forces generated by polymerizing actin filaments nucleated by formin molecules on the spindle (E) or nucleated by formin molecules on the cortex (F). Red arrows indicate the pointed ends of actin filaments. (G) One spindle pole of the early anaphase C. elegans meiotic spindle may be transported to the cortex as cargo by dynein on polarized cytoplasmic microtubules.

Pole first migration of the mouse meiosis I spindle.

Unlike the C. elegans germinal vesicle, the mouse germinal vesicle is centered in the egg at germinal vesicle breakdown. Thus, the meiosis I spindle assembles near the center of the egg then migrates in a pole-first orientation to the nearest cortex so that it never adopts a parallel orientation (Fig. 4 C). This migration requires F-actin (Verlhac et al., 2000) and the actin nucleators Formin 2 (Dumont et al., 2007), Spire 1 and Spire 2 (Pfender et al., 2011), and ARP2/3 (Sun et al., 2011), but the mechanism of movement remains unclear. Schuh and Ellenberg (2008) demonstrated the existence of actin bundles extending between the spindle and an invagination of the cortex during spindle migration. The invagination indicated a pulling mechanism, and Schuh and Ellenberg (2008) suggested that myosin II on the spindle poles might walk on a discontinuous actin network with barbed ends oriented toward the cortex (Fig. 4 C). In support of this model, the Rho kinase inhibitor ML7 eliminated spindle pole staining by an antibody specific for phosphorylated myosin regulatory light chain and blocked spindle migration (Schuh and Ellenberg, 2008). However, Li et al. (2008) found that the myosin ATPase inhibitor, blebbistatin, had no effect on spindle migration even though it completely blocked polar body extrusion (cytokinesis). Because myosin II typically acts by forming bipolar thick filaments that exert contractile force on antiparallel actin filaments, a myosin II–based model would make more sense if myosin II was concentrated on antiparallel actin bundles extending between the spindle and cortex (Fig. 4 D). Myosin V is more typically associated with the transport of cargo on uniformly oriented actin filaments, and a recent study has shown that myosin V drives transport of secretory vesicles outward toward the cortex in germinal vesicle stage oocytes (Schuh, 2011). This study at least suggests that the cytoplasmic actin meshwork has a net polarity with barbed ends toward the cortex, a prerequisite for a myosin cargo transport model (Fig. 4 C).Li et al. (2008) proposed a completely different mechanism in which F-actin nucleated near the chromosomes generates a cloud of F-actin that pushes the spindle toward the cortex (Fig. 4 E) in a manner analogous to Listeria monocytogenes motility (Lambrechts et al., 2008). Support for this pushing model comes from imaging of an actin cloud behind the migrating spindle and localization of Formin 2 around the spindle (Li et al., 2008). In addition, Formin 2 overexpression causes invaginations in the nuclear envelope, which is consistent with inward pushing from the cortex, rather than protrusions that would be consistent with pulling forces from the cortex (Azoury et al., 2011). Formin 2 is symmetrical around the cortex during prophase but clears from the cortex in front of the migrating spindle, which is consistent with nucleation-based pushing from behind (Fig. 4 F; Azoury et al., 2011). As previously mentioned, cortical stiffness is a prerequisite for cortical pulling mechanisms because pulling on an unsupported plasma membrane should cause the membrane to invaginate inward instead of the spindle moving outward. Strikingly, cortical stiffness of the mouse oocyte decreases sixfold during spindle migration (Larson et al., 2010). Clearly, more work is required to resolve the mechanism of pole-first spindle migration in the mouse oocyte.

Rotation of the parallel metaphase spindle to a perpendicular anaphase spindle.

Activation of the anaphase-promoting complex results in rotation of the parallel metaphase meiotic spindle to a perpendicular orientation in both meiotic divisions of C. elegans, whereas fertilization induces rotation during meiosis II in mouse. In kinesin-1–depleted C. elegans embryos, the metaphase I spindle is far from the cortex and initiates pole-first migration to the cortex at the same time that wild-type rotation initiates (Yang et al., 2005). Both spindle rotation and late spindle migration in a kinesin mutant require cytoplasmic dynein (Ellefson and McNally, 2009, 2011). These results indicate that spindle rotation is simply migration of one spindle pole toward the cortex (analogous to spindle migration during mouse meiosis I). However, unlike dynein-dependent migration of one spindle pole toward the cortex during C. elegans mitosis or HeLa cell mitosis, C. elegans meiotic spindle rotation does not require GPR-1,2 (van der Voet et al., 2009). One possible model for C. elegans meiotic spindle rotation is that cytoplasmic dynein, which accumulates on both spindle poles just before and during rotation, transports one spindle pole as cargo on cytoplasmic microtubules with minus ends anchored at the cortex (Fig. 4 G). The orientation of these microtubules is inferred from the direction of kinesin-dependent yolk granule movement (McNally et al., 2010) and hook decoration in Xenopus laevis oocytes (Pfeiffer and Gard, 1999). This model is essentially the same as the myosin cargo transport model proposed for mouse meiosis I (Fig. 4 C). Rotation of the mouse meiosis II spindle is actin dependent (Maro et al., 1984) and myosin II dependent (Matson et al., 2006; Wang et al., 2011), but the mechanism is unknown.

Unifying themes and future directions

Work in HeLa cells and budding yeast suggests that negative feedback loops might generally lead to spindle centering and that positive feedback loops might generally lead to asymmetrical spindle positioning. Whereas the recent x-ray crystal structures of cytoplasmic dynein (Carter et al., 2011; Kon et al., 2012) have revealed great insights into how the motor walks, they have revealed little about how the motor is locally activated and anchored at the cortex or how GPR-1/LIN-5 switches the motor from a side-on motor to an end-on motor. More attention needs to be focused on the distinction between cortical stiffness and cortical anchoring required in any cortical pulling mechanism. The nonastral spindle positioning mechanisms acting in oocytes of mouse and C. elegans will require a much more detailed understanding of the polarity of cytoplasmic actin filaments and cytoplasmic microtubules.     

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.     

Deciphering the Physalis floridana Double-Layered-Lantern1 Mutant Provides Insights into Functional Divergence of the GLOBOSA Duplicates within the Solanaceae

Glycosylphosphatidylinositol-anchor biosynthesis and glycosylphosphatidylinositol modification of proteins are central to coordinated plant development.Since their discovery (Low and Saltiel, 1988), glycosylphosphatidylinositol-anchored proteins (GPI-APs) have provoked intense interest as crucial regulators for growth, morphogenesis, reproduction, and disease pathogenesis in organisms ranging from yeast and trypanosomes to animals and plants. The lipid moiety, the glycosylphosphatidylinositol (GPI) anchor, is synthesized in the endoplasmic reticulum (ER); the protein component is cotranslationally inserted into the ER and posttranslationally modified by the addition of a GPI anchor (Kinoshita et al., 2013; Fig. 1). GPI-APs are then transported via the Golgi to the outer surface of the plasma membrane. The lipid anchor mediates stable attachment of these proteins to the cell surface, where some play important roles as signaling regulators from sphingolipid- and sterol-enriched membrane microdomains (Simons and Gerl, 2010). Some GPI-APs are released from the cell membrane by phosphatidylinositol-specific phospholipases to the extracellular matrix, where they might engage in processes such as cell adhesion and cell-cell communication. In Arabidopsis (Arabidopsis thaliana), there are about 250 predicted GPI-APs (Borner et al., 2003), a relatively large number compared with about 150 in mammals and 50 in the budding yeast (Saccharomyces cerevisiae). Important functions for plant GPI-APs have been elucidated through the study of individual proteins, such as the COBRA family in cell expansion and cell wall biosynthesis (Brady et al., 2007), ARABINOGALACTAN PROTEIN18 in megagametogenesis (Demesa-Arévalo and Vielle-Calzada, 2013), and LORELEI in the pollen tube-female gametophyte interaction (Capron et al., 2008; Tsukamoto et al., 2010; Duan et al., 2014). However, it is the studies of mutants defective in GPI biosynthesis that underscore the general importance of GPI-APs as a class: lacking the capacity to assemble the anchor is lethal.Open in a separate windowFigure 1.GPI anchor biosynthesis pathway. Ten or 11 stepwise modifications of phosphoinositide occur, starting from the synthesis of N-glucosamine-phosphoinositide on the cytoplasmic surface of the ER, followed by its flipping to the ER lumenal side for additional modifications, ending with the addition of the terminal ethanolamine phosphate. Proteins destined for GPI modification are synthesized with a C-terminal signature sequence recognized by the GPI transamidase (a five-protein-enzyme complex) that concomitantly cleaves the peptide at what is designated as the ω and ω+1 amino acids and attaches the GPI anchor in a transamination reaction (red arrows). The GPI-modified proteins are then sorted and transported via the Golgi apparatus to the cell membrane. The established biosynthetic proteins from Arabidopsis and their mammalian homologs are indicated; the galactosylation step appears to be plant specific. The diagram is modeled after figure 3 in Ellis et al. (2010), which also provided a complete list of potential plant orthologs of the human and yeast proteins in the pathway.The GPI anchor is synthesized by an elaborate biosynthetic pathway, starting on the cytoplasmic side of the ER and ending with a completely assembled core anchor on the lumenal surface of the ER (Fig. 1). Prior to their transport out of the ER, proteins destined for GPI modification are cleaved at a C-terminal signature sequence by a GPI transamidase complex that in two enzymatic steps concomitantly attaches a GPI anchor to the C terminus of processed proteins (Kinoshita, 2014). Most of the knowledge on GPI biosynthesis and GPI-AP modification is derived from studies in mammals and yeast, but the pathway is likely to be conserved in plants (Ellis et al., 2010). In a recent article in Plant Physiology, Dai et al. (2014) reported that a GPI anchor biosynthesis mutant, abnormal pollen tube guidance1 (atpg1), displays both embryo lethality and severely depressed male fertility. They determined that APTG1 is homologous to the yeast GPI10 and human PIG-B (for phosphatidylinositol glycan anchor biosynthesis) proteins, the last of three distinct mannosyltransferases that modify the precursor anchor (Fig. 1), and showed that APTG1 can functionally substitute for GPI10 in a conditionally lethal gpi10 yeast mutant. Previous studies have demonstrated that Arabidopsis SETH1 (a male fertility god in Egyptian mythology), SETH2, and PEANUT1 (PNT1), encoding homologs of mammalian PIG-C, PIG-A, and PIG-M (Fig. 1) and their corresponding yeast counterparts, respectively, are important for male fertility (Lalanne et al., 2004; Gillmor et al., 2005). In addition, loss of the first mannosyltransferase in the pathway in pnt1 results in early seedling lethality. pnt1 embryos are severely defective, displaying various cell division anomalies and exhibiting altered levels and ectopic deposition of cell wall polymers. The results reported by Dai et al. (2014), therefore, further demonstrate the conservation of the GPI biosynthesis pathway and the importance of GPI anchoring in plant development and reproduction.The aptg1 mutant was isolated in a search for mutants defective in pollen tube targeting of ovules (Fig. 2), an intriguing and crucial step in plant reproduction. A pollen tube is guided to an ovule by attractants, and upon reaching the target, the female gametophyte, the pollen tube ruptures, ejecting its cytoplasm and releasing sperm for fertilization (Dresselhaus and Franklin-Tong, 2013). aptg1 pollen tubes either fail to target ovules or undertake a more twisted pathway before entering an ovule. In an earlier study, Li et al. (2013) showed that a GPI-AP, COBRA-LIKE10 (COBL10), is required to maintain normal pollen tube growth rates and ovule targeting efficiency. In aptg1 pollen tubes, citrine fluorescent protein-COBL10 was absent from its normal apical membrane location while the citrine fluorescent signal in the cytoplasm was more intense, implying that the diminished presence of COBL10 on the apical membrane could be an underlying cause for the ovule-targeting phenotype. This observation also demonstrates that GPI anchoring is important for the subsequent sorting and transport of these proteins to their destined locations (Kinoshita et al., 2013) and consistent with a wholesale failure of GPI-APs to reach their functional locations as underlying lethality in GPI biosynthesis mutants.Open in a separate windowFigure 2.Pollen tube targeting of ovules in an Arabidopsis pistil. GUS-expressing pollen grains pollinated the pistil. Each blue dot represents discharged cytoplasm from a pollen tube that, in response to attractants, has successfully targeted the ovule and penetrated the female gametophyte and was induced to burst. The cytoplasmic discharge releases sperm for fertilization.While it is clear that major biological roles are played by GPI-APs, many questions remain. Most constituents of the plant GPI anchor biosynthetic pathway remain to be functionally established (Fig. 1). Much has been said about the membrane environments where GPI-APs are localized, but we are far from understanding the precise roles they play in assembling these domains and regulating their functional dynamics. Advances in high-resolution imaging at the cell surface and biochemical approaches to determine the constituents in these membrane microdomains (Simons and Gerl, 2010) should accelerate our understanding of the importance of GPI anchoring as a conserved strategy among eukaryotes to control a wide range of processes.