Silent Genes: Antimicrobial Resistance and Antibiotic Production

Silent genes are DNA sequences that are generally not expressed or expressed at a very low level. These genes become active as a result of mutation, recombination, or insertion. Silent genes can also be activated in laboratory conditions using pleiotropic, targeted genome-wide, or biosynthetic gene cluster approaches. Like every other gene, silent genes can spread through horizontal gene transfer. Most studies have focused on strains with phenotypic resistance, which is the most common subject. However, to fully understand the mechanism behind the spreading of antibiotic resistance, it is reasonable to study the whole resistome, including silent genes. Open in a separate window     

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

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

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.     

Contamination Risks in Work with Synthetic Peptides: flg22 as an Example of a Pirate in Commercial Peptide Preparations

The pattern recognition receptor FLAGELLIN SENSING2 (FLS2) renders plant cells responsive to subnanomolar concentrations of flg22, the active epitope of bacterial flagellin. We recently observed that a preparation of the peptide IDL1, a signal known to regulate abscission processes via the receptor kinases HAESA and HAESA-like2, apparently triggered Arabidopsis thaliana cells in an FLS2-dependent manner. However, closer investigation revealed that this activity was due to contamination by a flg22-type peptide, and newly synthesized IDL1 peptide was completely inactive in FLS2 signaling. This raised alert over contamination events occurring in the process of synthesis or handling of peptides. Two recent reports have suggested that FLS2 has further specificities for structurally unrelated peptides derived from CLV3 and from Ax21. We thus scrutinized these peptides for activity in Arabidopsis cells as well. While responding to <1 nM flg22, Arabidopsis cells proved blind even to 100 μM concentrations of CLV3p and axY^s22. Our results confirm the exquisite sensitivity and selectivity of FLS2 for flg22. They also show that inadvertent contaminations with flg22-type peptides do occur and can be detected even in trace amounts by FLS2.During the last years, the pattern recognition receptor FLAGELLIN SENSING2 (FLS2) and its cognate microbe-associated molecular pattern (MAMP), the peptide flg22 (Felix et al., 1999), have widely been used to study plant innate immunity (Boller and Felix, 2009). Typically, in FLS2-expressing Arabidopsis thaliana cells, flg22 stimulates rapid changes of ion fluxes, including extracellular alkalinization and an induction of defense-related genes, such as FRK1, at threshold concentrations of 10 to 100 pM, while fls2 mutants lacking the receptor kinase FLS2 are completely unresponsive to flg22 (Boller and Felix, 2009). These findings demonstrate that FLS2 has an exquisite sensitivity as a flagellin receptor and that FLS2 is the only receptor for the flg22 ligand in Arabidopsis. Our previous results also indicated an exquisite selectivity of FLS2 with regard to its ligand. For example, the flg22 peptide of Agrobacterium tumefaciens (flg22^A.tum.) is completely inactive as a ligand of FLS2 or as a stimulus for FLS2-dependent responses and therefore has often been used as a negative control in assays for flg22-induced responses (Felix et al., 1999; Asai et al., 2002).Recently, we observed that a synthetic preparation of IDL1, an endogenous peptide signal involved in abscission processes (Stenvik et al., 2008), showed considerable activity as an inducer of MAMP responses and stimulated extracellular alkalinization in Arabidopsis cells at a threshold level of <5 nM (Figure 1A). More surprisingly, when examined in a cell culture of the fls2 efr double mutant, no significant medium alkalinization was detectable after treatment with the IDL1 preparation (Figure 1B). To check if FLS2 was involved in the response to the IDL1 preparation, we made use of the inhibitor flg22-Δ2, which acts as a specific antagonist of flg22 in Arabidopsis (Bauer et al., 2001). Indeed, presence of 30 μM flg22-Δ2 completely abolished the response to 50 nM IDL1 (Figure 1C).Open in a separate windowFigure 1.Effects of IDL1 Peptides on Extracellular pH in Suspension-Cultured Arabidopsis Cells.(A) Alkalinization in response to different doses of two independent preparations of the IDL1 peptide (preparations I and II). wt, wild-type.(B) Alkalinization response in cells from the fls2 efr double mutant.(C) Alkalinization response in wild-type cells to preparation I of IDL1 alone, to preparation I in combination with the flg22 antagonist flg22-Δ2 (30 μM), or to preparation I after digestion (overnight, 37°C) with endoproteinase AspN, as indicated.Based on strong genetic evidence, IDL1 is thought to act as a regulator of abscission processes via the receptor kinases HAESA and HAESA-like 2 (Stenvik et al., 2008), but why should FLS2 be involved? The IDL1 preparation was ∼100-fold less effective than authentic flg22 preparations, as indicated by the EC₅₀ values of 0.1 nM for flg22 (Bauer et al., 2001) and 10 nM for IDL1 (Figure 1A), respectively. We hypothesized that the IDL1 preparation might be contaminated by a peptide of the flg22 type. In contrast with IDL1, which has no acidic amino acid residues, flg22 contains two Asp residues that are important for its biological activity on FLS2 (Felix et al., 1999). We used this difference for selective digestion by the endoproteinase AspN, which cuts peptides N-terminal of Asp residues. Indeed, no activity was left in IDL1 after digestion (Figure 1C). This strongly indicated that the activity was not associated with the IDL1 peptide itself but rather with a flg22-type of contamination. Repurification of IDL1 by C18 reverse-phase chromatography could not separate the IDL1 peptide from the flg22 type of activity. Apart from a dominating signal for IDL1, mass spectrometry analysis of this fraction also revealed faint mass signatures characteristic for flg22 and its spontaneous derivative containing pyroglutamate at its N terminus (see Supplemental Figure 1 online). Together, these results clearly pinpointed a contamination as the source of the flg22-like activity in IDL1.Where did this contamination occur? We observed the same activity with a second, unopened tube from the same batch of the synthetic IDL1 peptide, indicating that a putative flg22 contamination had occurred prior to arrival in our lab, most likely in the company providing the peptide. We therefore resynthesized a new batch of IDL1 and found that it did not cause alkalinization in Arabidopsis wild-type cells even at a concentration of 10 μM (preparation II, Figure 1A). A further, independent, batch of an IDL1-derived peptide with a C-terminal extension by two amino acid residues similarly failed to induce alkalinization in the Arabidopsis wild-type cells (data not shown). These results made us acutely aware of a potential contamination problem when working with peptides unrelated to flg22. Indeed, two recent reports have suggested that FLS2 perceives two additional, unrelated, peptidic signals derived from either CLV3 (Lee et al., 2011) or Ax21 (Danna et al., 2011), respectively. What if these unexpected results were due to inadvertent contamination by flg22 as well?In a recent study (Mueller et al., 2012), we compared the FLS2 orthologs from Arabidopsis and tomato (Solanum lycopersicum) and their chimeras, making use of protoplasts from fls2 mutant plants transformed simultaneously with constructs encoding one of the FLS2 orthologs and a pFRK1:luciferase reporter, an assay system originally introduced by Asai et al. (2002). Protoplasts with both versions of FLS2 exhibited exquisite sensitivity to picomolar concentrations of flg22. However, they failed to respond to the hydroxylated CLV3 peptides CLV3-ΔAra₃-ΔH (12 amino acids) and CLV3-ΔAra₃ (13 amino acids) (described in Ohyama et al., 2009), termed CLV3p and CLV3p-H in our article (see Figure 1 in Mueller et al., 2012). Indeed, even when applied at a concentration of 100 μM, the 12–amino acid CLV3p caused no significant response in protoplasts expressing FLS2 from Arabidopsis (Figure 2A). A marginal transient increase in luminescence occurred in the first 2 h of the experiment, but this effect was also seen in the absence of FLS2 (Figure 3B), demonstrating that it had nothing to do with FLS2-dependent activation of the reporter gene. Our preparation of the CLV3p peptide exhibited the expected strong inhibitory effect in root growth assays with wild-type Arabidopsis and with fls2 mutants but not with the mutant clv2-1 (see Supplemental Figure 2 online).Open in a separate windowFigure 2.The CLV3p Peptide (Arg-Thr-Val-Hyp-Ser-Gly-Hyp-Asp-Pro-Leu-His-His, CLV3-ΔAra₃-ΔH in Ohyama et al., 2009) Does Not Induce Expression of the Reporter pFRK1:luciferase via the Receptor FLS2.Mesophyll protoplasts from efr×fls2 mutants were transformed with pFRK1:luciferase (pFRK1, promoter of the flagellin responsive receptor kinase 1) together with p35S:FLS2-GFP (A) or p35S:GFP (B) and tested for responsiveness to CLV3p and flg22 as indicated. GFP, green fluorescent protein; RLU, relative light units.Open in a separate windowFigure 3.Ax21-Derived Peptides axY^s22 and axY22A Show No Activity as Inducers of Oxidative Burst and Medium Alkalinization in Arabidopsis.(A) Oxidative burst in leaf pieces of Arabidopsis treated with axY^s22, axY22A, or flg22 as indicated. Reactive oxygen species (ROS) were determined by light emission (relative light units [RLU] of the luminometer) in a luminol-based assay. Values and error bars represent mean ± se of n = 6 replicates. (Error bars in all samples not treated with flg22 were smaller than 100 relative light units.)(B) Extracellular alkalinization in cell cultures of Arabidopsis treated with axY^s22, axY22A, or a control peptide (SASRSRIQDADFAAETANLSRSQILQQAGTA) in combination with flg22, as indicated.In previous work by Lee et al. (2009), the sulfated peptide axY^s22, derived from the protein Ax21 of the pathogenic bacterium Xanthomonas oryzae, but not its variant form axY22A, in which the sulfotyrosine was replaced by an Ala, have been reported to cause a resistance response in rice (Oryza sativa) expressing the receptor kinase XA21. Recently, preparations of both of these two peptides have been reported to stimulate immune responses in Arabidopsis when applied at concentrations of 1 to 100 μM (Danna et al., 2011; Figures 1 to 3). Surprisingly, this activation was dependent on the presence of a functional FLS2 receptor, suggesting that these peptides are acting as ligands for FLS2 as well. We obtained fresh preparations of both axY^s22 and axY22A. In our hands, both peptides were completely inactive at concentrations up to 100 μM in oxidative burst and alkalinization assays (Figure 3). The cells used for the alkalinization assays strongly responded to 100 pM of authentic flg22, indicating that the Ax21-related peptides were at least a million times less efficient to induce alkalinization via FLS2 (Figure 3B).The peptide flg22-Δ2 functions as a competitive antagonist that specifically inhibits flg22-induced responses in Arabidopsis (Bauer et al., 2001). Since the FLS2-dependent responses to CLV3p and the Ax21 peptides were reported to be inhibited by excess flg22-Δ2 (Danna et al., 2011; Lee et al., 2011), we also checked whether CLV3p or the Ax21 peptides might interfere with the binding of flg22 to FLS2 (Figure 4). The receptor FLS2 binds carrier-free ¹²⁵I-Tyr-flg22 with a high affinity, and this binding can be specifically competed by 10 μM unlabeled flg22 but neither by 30 μM CLV3p nor by 30 μM axY^s22 (Figure 4). Thus, we cannot confirm that CLV3p or axY^s22 can directly interact and activate FLS2. While inadvertent contamination is a possible explanation, we cannot finally explain the obvious discrepancies to the results in the Lee et al. (2011) and Danna et al. (2011) reports. Also, since our analysis focused on direct and immediate effects on FLS2, we cannot comment on effects that high concentrations of peptides like CLV3p or axY^s22 might exert on prolonged treatment. For example, induction of plant resistance is a highly complex process that develops over days and involves two living systems. Rather than on the host cells, peptides applied might act on the pathogenic bacteria and influence their synthesis of flagellin or their assembly/disassembly of flagellin subunits into flagellar structures. At least for the ax21 peptides, described as a quorum sensing type of signals for bacteria, this is an option to be considered.Open in a separate windowFigure 4.The Peptides axY^s22 and CLV3p Do Not Compete for Binding of flg22 to FLS2.Binding of ¹²⁵I-Tyr-flg22 to wild-type Arabidopsis seedlings in the absence of competitor, in the presence of 10 μM unlabeled flg22, or in the presence of 30 μM unlabeled axY^s22 or CLV3p. Bars and error bars represent radioactivity (counts per min [cpm]) bound to plant material as means and sd of n = 3 replicates.Our results confirm the exquisite sensitivity and selectivity of FLS2 for its cognate ligand, flg22. They also show that extreme care must be taken when attempting to assess the effect of peptides on responses that can also be elicited by flg22. Based on our experience, peptide preparations ordered from different commercial suppliers may occasionally be contaminated by flg22-related activity. We observed a contamination corresponding to ∼1% of flg22 equivalents in one of the IDL1 preparations (Figure 1). However, we would like to emphasize that in a peptide preparation applied at 100 μM a contamination by flg22 of only ∼0.0001% (∼1 ppm) can activate FLS2-dependent responses. Using HPLC and mass spectrometry analysis as common checks for purity, suppliers guarantee that a certain percentage, maximally 99%, of the preparation corresponds to the peptide ordered. However, as exemplified for the contaminated IDL1 preparation (see Supplemental Figure 1 online), contaminations at or below 1% can easily go unnoticed. Also, normally, the molecular characteristics of a potential contaminant are not known, so a flg22-type of activity could be present as a partial degradation product or in the form of an unknown flg22 derivative.We cannot estimate a frequency for cross-contaminations in peptide preparations, but it seems to occur surprisingly often. Over the years, we ordered >100 peptides from various commercial suppliers and had at least two further incidents with flg22-type contaminations. In one of these cases, we ordered, and obtained, flg22 and three structurally unrelated peptides. We found residual flg22-type activity in two out of the three preparations of these unrelated peptides, indicating contamination in the course of commercial peptide synthesis (in this case, by a supplier different from the provider of IDL1) or during handling of these peptides in our lab.There are reasons why contaminations by flg22 might pose a particular risk. First, flg22 has a tendency to stick to surfaces and we recommended the use of 0.1 M NaCl and 1 mg/mL BSA to prevent loss of the peptide during serial dilutions (Felix et al., 1999). In turn, flg22 adhering to tubings, columns, or glassware might provide a source of contamination for peptides getting handled subsequently. Second, we noticed that lyophilized flg22 can easily pick up electrostatic charge and is prone to float around with the slightest streams of air. This could be a particular problem also for preparations handled by robots of peptide manufacturers. Third, due to a considerable demand by an increasing number of labs, flg22-related peptides have been ordered from various peptide suppliers numerous times and, picking up this peptide as inadvertent contamination has become a considerable problem.In conclusion, our study complements and extends the commentary by Segonzac et al. (2012) by demonstrating that the receptor FLS2 has an extraordinarily high affinity and selectivity to its ligand, flg22, and that it is completely blind to the peptides IDL1, CLV3p, axY^s22, and axY22A even in our most sensitive bioassays. Our results and arguments do not apodictically exclude that a receptor like FLS2 could have a second, physiologically relevant, ligand. Also, there may be chemical structures that inadvertently act as surrogates or mimetics of the true ligand flg22. However, in view of the high selectivity of the FLS2 for its genuine ligand flg22, we think the probability of alien interactors is rather low. By contrast, contaminations with flg22-related molecules can and do occur.How can contamination of bioactive peptides be recognized and avoided? The first, probably most important, point is a sharpened awareness about in-lab and supplier-dependent sources of contamination. These risks are often ignored, in particular when working with synthetic, purified peptides. A purity of >95 or >99%, as guaranteed by suppliers, is of limited value with respect to highly active contaminants detectable even at the ppm level. Purification offered by suppliers certainly helps to remove chemicals used in the synthesis process and to get rid of many incomplete variants of the peptide ordered. However, at least theoretically, contaminated equipment used during the purification might contribute to the problem rather than to its solution. Dose–response relationships for the peptides under scrutiny are important to consider physiological relevance in general and to compare activities with published data in particular. Thereby, the higher the dose of a peptide applied, the higher the risk to pick up even spurious contaminants. Furthermore, analysis of bioactive peptides should not depend on a single peptide preparation alone. Peptide variants are crucial to elucidate the specificity of an interaction process. The use of several, independently synthesized and handled peptide preparations should help to reliably detect sporadic contamination events and to distinguish contaminants from true ligands. Finally, at least for the particular problem of contamination by flg22, we can offer testing peptide preparations using the sensitive bioassays established in our labs. As long as we have the hands and capacity to handle such requests, we certainly would like to contribute with such a service to detect pirate peptides.     

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.     

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

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

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

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

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

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

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.     

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.     

A GTPase Chimera Illustrates an Uncoupled Nucleotide Affinity and Release Rate,Providing Insight into the Activation Mechanism

The release of GDP from GTPases signals the initiation of a GTPase cycle, where the association of GTP triggers conformational changes promoting binding of downstream effector molecules. Studies have implicated the nucleotide-binding G5 loop to be involved in the GDP release mechanism. For example, biophysical studies on both the eukaryotic Gα proteins and the GTPase domain (NFeoB) of prokaryotic FeoB proteins have revealed conformational changes in the G5 loop that accompany nucleotide binding and release. However, it is unclear whether this conformational change in the G5 loop is a prerequisite for GDP release, or, alternatively, the movement is a consequence of release. To gain additional insight into the sequence of events leading to GDP release, we have created a chimeric protein comprised of Escherichia coli NFeoB and the G5 loop from the human Giα1 protein. The protein chimera retains GTPase activity at a similar level to wild-type NFeoB, and structural analyses of the nucleotide-free and GDP-bound proteins show that the G5 loop adopts conformations analogous to that of the human nucleotide-bound Giα1 protein in both states. Interestingly, isothermal titration calorimetry and stopped-flow kinetic analyses reveal uncoupled nucleotide affinity and release rates, supporting a model where G5 loop movement promotes nucleotide release.The hydrolysis of guanosine triphosphate (GTP) by GTPases, such as the oncoprotein p21 Ras and heterotrimeric Gα proteins, is a critical regulatory activity for cell growth and proliferation (1). Aberrant GTPases are consequently often implicated in tumorigenesis, developmental disorders, and metabolic diseases (2). Critical for the initiation of a GTPase cycle is the release of guanosine diphosphate (GDP), which allows GTP to bind and switch the protein from an inactive to an active conformation. The GTP is subsequently hydrolyzed to GDP and inorganic phosphate, returning the GTPase to an inactive conformation (3).Given that the release of GDP is the fundamental step in the initiation of a GTPase cycle, the detailed mechanism by which it is released has been under intense scrutiny. Studies using double electron-electron resonance, deuterium-exchange, Rosetta energy analysis, and electron paramagnetic resonance, have shown that the mechanism involves conformational changes in the nucleotide-coordinating G5 loop, one of five nucleotide recognition motifs (4, 5, 6, 7, 8, 9, 10, 11). Structural studies of eukaryotic Gα proteins and the intracellular TEES-type GTPase domain of the prokaryotic iron transporter FeoB (NFeoB) have also illustrated distinct conformations of the G5 loop, depending on the nucleotide-bound state (9, 12).Recently, we reported mutational studies of the G5 loop of Escherichia coli NFeoB, which illustrated a correlation between the sequence composition of the loop and the intrinsic GDP release rate (13). However, despite these observations, it is unclear whether the observed conformational changes in the G5 loop are a prerequisite for GDP release, or if the movement is a consequence of GDP release. To address this fundamental question, in this study we have used a combination of protein engineering and biophysical methods.Initially, to assess the relevance of conformational flexibility in the G5 loop, we aimed to create a protein chimera combining sequence and structural characteristics of both fast and slow GDP-releasing GTPases. We thus engineered a protein chimera using E. coli NFeoB as the scaffold (a protein with fast intrinsic GDP release) and substituted the G5 loop with that of a slow GDP-releasing protein (the human Giα1 protein; Gene ID 2770; Fig. 1 A (5)). GTP hydrolysis assays comparing wild-type (wt) NFeoB (wtNFeoB) and the protein chimera (ChiNFeoB) validated the integrity of the GTPase activities of both proteins (k_cat = 0.46 and 0.36 min⁻¹, respectively). To further assess the ChiNFeoB protein, we determined its crystal structure at 2.2 Å resolution (see Table S1 in the Supporting Material). The ChiNFeoB structure contains two molecules in the asymmetric unit, with molecule A bound to GDP. They are essentially identical to the nucleotide-bound wtNFeoB structure (root-mean-square deviation of 1.2 Å over 226 Cα atoms; Fig. 2).Open in a separate windowFigure 1Chimera model and structural comparison. (A) Illustration highlighting the chimera sequence change. (Orange) Sequence of the extended G5 loop from Giα1, which replaced the NFeoB sequence (gray). (B–F) Structural comparison of the G5 loop between (B) WT apo (PDB:3HYR) and nucleotide-bound (PDB:3HYT) NFeoB structures. (C) NFeoB nucleotide-bound and Giα1 (PDB:2ZJZ). (D) Nucleotide-bound NFeoB and chimera (Chi_GDP). (E) Nucleotide-bound chimera and Giα1. (F) Nucleotide-free (Chi_apo) and bound chimera protein. (G) Overview of the nucleotide binding site and structural overlay of chimera and Giα1 structures. To see this figure in color, go online.Open in a separate windowFigure 2Superimposition of nucleotide-bound NFeoB and chimera protein, with thermodynamic parameters. To see this figure in color, go online.However, the ChiNFeoB structure, when compared to the wtNFeoB structure, revealed an alteration in the conformation of the G5 loop, showing an extra turn on the N-terminal end of the α6 helix. This is structurally distinct from the wtFeoB protein, but with a conformation similar to that of the Giα1 protein (PDB:2ZJZ; Fig. 1, B–F). As in the crystal structures of wtNFeoB and Giα1, ChiNFeoB residues implicated in coordination of the nucleotide base maintain their positions in the G5 loop relative to GDP. In particular, residues Ala^∗150 and Thr^∗151 (NFeoB numbering, the asterisk indicates Giα1 chimera residue) are involved in electrostatic interactions with the nucleotide base moiety, analogous to the structures of both wtNFeoB and Giα1 (Fig. 1 G). Serendipitously, the second molecule in the asymmetric unit of ChiNFeoB (molecule B) was present in the nucleotide-free state. The two molecules (GDP-bound and nucleotide-free) are nearly identical (the superposition of molecules A and B yields a root-mean-square deviation of 0.36 Å over 229 Cα atoms), with the G5 loop adopting a nearly indistinguishable conformation compared to that of the GDP-bound molecule A (Fig. 1 F).Importantly, this conformation is independent of the crystallographic packing, inasmuch as the loop is not involved in any crystal contacts. In contrast, the structures of nucleotide-bound and nucleotide-free wtNFeoB illustrated a large conformational change in the G5 loop (Fig. 1 B). Hence, the substitution in the chimera extends the secondary structure of the α6 helix, and as hypothesized, the engineered ChiNFeoB protein has a G5 loop structure that is more conformationally stable than that of wtNFeoB.We subsequently measured the affinity of the ChiNFeoB protein for GDP using isothermal titration calorimetry (ITC). Nonlinear regression was used to attain the thermodynamic parameters (including the GDP binding affinity, K_a; the corresponding dissociation constant (K_d) was calculated from the equation K_d = 1/K_a). Interestingly, these measurements revealed the ChiNFeoB protein to have an almost 10-fold reduced affinity for GDP (82 vs. 9 μM measured for the WT protein; Fig. 2). In contrast, in a recent alanine scanning mutagenesis study of the G5 loop we observed a fivefold increase in affinity for GDP in a Ser¹⁵⁰Ala mutant (2 μM) (14). This mutant protein has a coordination environment for the GDP base analogous to that of the ChiNFeoB protein (Fig. 1 A), indicating that it is not the presence of an alanine at position 150 that causes the reduced GDP affinity observed for the chimera protein. Instead, the analysis by ITC and comparison with previous mutagenesis studies indicates that the GDP binding site is less accessible in the ChiNFeoB protein, likely due to the introduction of conformational rigidity that accompanies the extension of secondary structural elements within the loop (Fig. 1 D).To further evaluate the functional characteristics of the chimera protein, we used stopped-flow fluorescence assays to determine the rate of nucleotide dissociation (k_off) and association (k_on) for the ChiNFeoB protein. The association rate for the GTP analog mant-GMPPNP was determined from the slope of a linear plot of protein concentration versus the observed association constant (k_obs). The k_on for the chimera was determined to be 3.20 μM⁻¹ min⁻¹ (Supporting Material), the dissociation rate (k_off) of GDP for the chimera was determined to be 16.6 s⁻¹ (vs. 144 s⁻¹ for wtNFeoB;

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

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

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

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

Focus Issue on Roots: Branching Out in Roots: Uncovering Form,Function, and Regulation

Root branching is critical for plants to secure anchorage and ensure the supply of water, minerals, and nutrients. To date, research on root branching has focused on lateral root development in young seedlings. However, many other programs of postembryonic root organogenesis exist in angiosperms. In cereal crops, the majority of the mature root system is composed of several classes of adventitious roots that include crown roots and brace roots. In this Update, we initially describe the diversity of postembryonic root forms. Next, we review recent advances in our understanding of the genes, signals, and mechanisms regulating lateral root and adventitious root branching in the plant models Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and rice (Oryza sativa). While many common signals, regulatory components, and mechanisms have been identified that control the initiation, morphogenesis, and emergence of new lateral and adventitious root organs, much more remains to be done. We conclude by discussing the challenges and opportunities facing root branching research.Branching through lateral and adventitious root formation represents an important element of the adaptability of the root system to its environment. Both are regulated by nutrient and hormonal signals (Bellini et al., 2014; Giehl and von Wirén, 2014), which act locally to induce or inhibit root branching. The net effect of these adaptive responses is to increase the surface area of the plant root system in the most important region of the soil matrix for resource capture (e.g. surface layers for phosphorus uptake and deeper layers for nitrate uptake) or to secure anchorage. Different species use different combinations of lateral or adventitious roots to achieve this, with lateral roots dominating the root system of eudicots while adventitious (crown and brace) roots dominate the root system of monocots, including in cereal crops.Our understanding of the mechanisms controlling lateral and adventitious root development has accelerated in recent years, primarily through research on model plants. The simple anatomy and the wealth of genetic resources in Arabidopsis (Arabidopsis thaliana) have greatly aided embryonic and postembryonic root developmental studies (De Smet et al., 2007; Péret et al., 2009a; Fig. 1, A and E). Nevertheless, impressive recent progress has been made studying root branching in other crop species, notably cereals such as maize (Zea mays) and rice (Oryza sativa).Open in a separate windowFigure 1.A to D, Schematics showing diversity in root system architecture at both seedling (left) and mature (right) stages in eudicots (A and C) and monocots (B and D). A, Arabidopsis root system. B, Maize root system. C, Tomato root system (for clarity, stem-derived adventitious roots are only shown in the labeled region). D, Wheat root system. E and F, Cross sections of emerging lateral root primordia in Arabidopsis (E) and rice (F). E and F are adapted from Péret et al. (2009b).In this Update, we initially describe the diversity of postembryonic root forms in eudicots and monocots (Fig. 1). Next, we highlight recent advances in our understanding of the genes, signals, and mechanisms regulating lateral root and adventitious root branching in Arabidopsis, rice, and maize. Due to space limits, we cannot provide an exhaustive review of this subject area, focusing instead on recent research advances. However, we direct readers to several recent in-depth reviews on lateral root (Lavenus et al., 2013; Orman-Ligeza et al., 2013) and adventitious root development (Bellini et al., 2014).     

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

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

Rescue of a Dominant Mutant With RNA Interference

Maize Mucronate1 is a dominant floury mutant based on a misfolded 16-kDa γ-zein protein. To prove its function, we applied RNA interference (RNAi) as a dominant suppressor of the mutant seed phenotype. A γ-zein RNAi transgene was able to rescue the mutation and restore normal seed phenotype. RNA interference prevents gene expression. In most cases, this is used to study gene function by creating a new phenotype. Here, we use it for the opposite purpose. We use it to reverse the creation of a mutant phenotype by restoring the normal phenotype. In the case of the maize Mucronate1 (Mc1) phenotype, interaction of a misfolded protein with other proteins is believed to be the basis for the Mc1 phenotype. If no misfolded protein is present, we can reverse the mutant to the normal phenotype. One can envision using this approach to study complex traits and in gene therapy.TRANSLUCENT or vitreous maize kernels are harder and able to sustain stronger mechanical strength during harvesting, transportation, and storage. There is a direct link between a vitreous seed phenotype and the type of storage proteins in the seed, collectively called zeins in maize. Zeins, encoded by a multigene family, constitute >60% of all maize seed proteins. They are classified into four groups (α-, β-, γ-, and δ-zein) on the basis of their structures (Esen 1987). Zeins are specifically synthesized in the endosperm ∼10 days after pollination (DAP) and deposited into protein bodies (Wolf et al. 1967; Burr and Burr 1976; Lending and Larkins 1992). Irregularly shaped protein bodies are found in floury or opaque kernel phenotypes (Coleman et al. 1997; Kim et al. 2004, 2006; Wu et al. 2010; Wu and Messing 2010). The terms “floury” and “opaque” were originally created on the basis of the genetic behaviors of the mutant allele causing the soft kernel texture. The floury mutants behave as semidominant or dominant mutants, as floury1 and floury2 do, while the opaque mutants are recessive, as opaque1 and opaque2 are (Hayes and East 1915; Lindstrom 1923; Emerson et al. 1935; Maize Genetics Cooperation 1939). Similar to floury2 with a single mutation in the signal peptide of a 22-kDa α-zein resulting in an unprocessed protein (Coleman et al. 1995), De*-B30 produces an unprocessed 19-kDa α-zein (Kim et al. 2004). It was hypothesized that the two mutant proteins with an unprocessed signal peptide are misfolded and docked in the membranes of the rough endoplasmic reticulum (RER), blocking the deposition of other zein proteins (Coleman et al. 1995; Kim et al. 2004). In Mucronate1 (Mc1), a 38-bp deletion in the C terminus of the 16-kDa γ-zein (γ16-zein) gene resulted in a frameshift and a protein with a different amino-acid tail. This modified 16-kDa γ-zein (Δγ16-zein) has altered solubility properties, which would explain the formation of irregular protein bodies. Because De*-B30 and Mc1 are semidominant and dominant, respectively, they belong to the floury mutant class.The γ-zein genes (γ27-zein and γ16-zein) are homologous copies because maize underwent allotetraploidization and both gene copies have been retained during diploidization (Xu and Messing 2008). The two γ-zeins and the 15-kDa β-zein have a redundant function in stabilizing protein-body formation (Wu and Messing 2010). Knockdown of both γ-zeins with a single RNA interference (RNAi) construct conditioned only partial opacity in the crown, the top of the kernel, as opposed to the remainder or gown area of the kernel. Consistent with its light kernel phenotype, protein bodies in such a γ-zein RNAi (γRNAi) mutant exhibited a slight alteration in morphology. This phenotype is clearly distinguishable from the Mc1 phenotype, which is far more severe. Therefore, if Mc1 is caused by a misfolded chimeric 16-kDa γ-zein, preventing its expression should restore normal kernel phenotype. Indeed, a simple cross of Mc1 with a maize line carrying the γRNAi transgene produced a non-floury phenotype, providing an example of RNAi as a dominant suppressor of a dominant phenotype and as a general tool in marker rescue.

Analysis of the progeny from the cross of Mc1 and γRNAi mutants:

Mc1 seeds (Stock ID U840I) were requested from the Maize Genetics Cooperation Stock Center. The γRNAi transgenic lines have been reported in previous work (Wu et al. 2010; Wu and Messing 2010). Twelve progeny kernels from the cross of the Mc1 mutant [homozygous for the dominant-negative mutant 16-kDa γ-zein alleles (Δγ16/Δγ16) and heterozygous for the γRNAi line (γRNAi/+)] were dissected at 18 DAP for segregation and mRNA accumulation analyses. For each kernel, the embryo and endosperm were separated for DNA and RNA extraction, respectively. As shown in Figure 1A, five and seven kernels were positive and negative for the amplification of the γRNAi gene with a specific primer set, exemplifying a 1:1 segregation of the γRNAi gene.Open in a separate windowFigure 1.—Segregation analysis of the accumulations of mRNAs and proteins from the cross of the Mc1 mutant and the γRNAi line by RT–PCR and SDS–PAGE. (A) γRNAi gene segregation from progeny (Δγ16/Δγ16 x γRNAi/+) by PCR amplification with a specific primer set (GFPF, ACAACCACTACCTGAGCAC and T35SHindIII, ATTAAGCTTTGCAGGTCACTGGATTTTGG). Kernels 3, 8, 9, 10, and 12 are positive for the γRNAi gene and the rest of them are negative. M, DNA markers from top to bottom band are 3, 2, 1.5, 1.4, and 1 kb. (B) RT–PCR analysis of mRNA accumulation from the normal γ16 and mutant Δγ16 alleles in the endosperms with the genotypes corresponding to the embryos analyzed above. Total RNA was extracted by using TRIzol reagent (Invitrogen). Two micrograms of RNA was digested with DNase I (Invitrogen) and then reverse-transcribed. Twenty-five nanograms of cDNA from each of the twelve endosperms was applied for PCR (25 cycles of 30 sec, 94 °C; 30 sec, 58 °C; and 1 min, 72 °C). A specific primer set (γ16F, ATGAAGGTGCTGATCGTTGC and γ16R, TCAGTAGTAGACACCGCCG) was designed for amplification of the full-length γ16-zein coding sequence (552 bp). The lower band (514 bp) from the mutant Δγ16 allele is 38 bp shorter than that from the normal allele (552 bp). Kernels 3, 8, 9, 10, and 12 with the γRNAi gene accumulated significantly less mRNA compared to those without the γRNAi gene (kernels 1, 2, 4, 5, 6, 7, and 11). BA, hybrid of B × A lines. M, DNA markers from top to bottom are 1 kb, 750 bp, and 500 bp. (C) Profile of zein accumulations of 20 kernels from the progeny as described in the text. The zein extraction method has been described elsewhere (Wu et al. 2009). The Δγ16-zein from Mc1 was not extracted by traditional total-zein extraction protocol (70% ethanol and 2% 2-mercaptoethanol). The γ27- and γ16-zeins were knocked down to a nondetectable level in kernels 1, 2, 3, 5, 7, 10, 12, 13, 16, and 20. In γRNAi-gene segregating progeny (kernels 4, 6, 8, 9, 11, 14, 15, 17, 18, and 19), the γ16-zein from the normal γ16 allele is marked by arrowheads. Protein loaded in each lane was equal to 500 μg fresh endosperm at 18 DAP. The size for each band is indicated by the numbers in the “kDa” columns. BA, hybrid of B × A lines; 1–20, kernels from the progeny described above; M, protein markers from top to bottom are 50, 25, 20, and 15 kDa.Due to the 38-bp deletion in the C terminus of the coding region, the Δγ16 allele is shorter than the normal one (Figure 1B). Therefore, most of Δγ16-zein was in the non-zein fraction. In progeny endosperms of another 20 kernels from the same cross described above segregating for the γRNAi gene, two types of γ16-zeins were synthesized: the normal γ16-zein in the ethanol-soluble zein fraction and the Δγ16-zein in the non-zein fraction. In progeny inheriting the γRNAi gene, the γ27- and γ16-zeins were reduced to nondetectable levels (Figure 1C). Although the Δγ16-zein is not in the ethanol-soluble zein fraction, the level of normal γ16-zein is a good indicator of the accumulation of the Δγ16-zein.

Rescue of protein-body morphologies in the Mc1 mutant:

Regular protein bodies are round with distinct membrane boundaries (Figure 2A) and 1–2 μm in diameter at maturity. In homozygous and heterozygous Mc1 mutants (Δγ16/Δγ16 and Δγ16/+), protein bodies were irregularly shaped, some without discrete boundaries (Figure 2, C and D), which is quite different from the absence of normal γ27- or γ16-zeins in maize endosperm (Figure 2B). Indeed, protein bodies of the Mc1 mutant, blocked in the accumulation of Δγ16-zein, showed morphologies with no discernible difference from those in the γRNAi/+ line (Figure 2, B and E).Open in a separate windowFigure 2.—Transmission electron micrographs of protein bodies. The method has been described elsewhere (Wu and Messing 2010). (A) Nontransgenic BA. (B) γRNAi transgenic line (γRNAi/+). (C) Mc1 (Δγ16/Δγ16). (D) Cross of Mc1 mutant and nontransgenic hybrid of B × A lines (Δγ16/+). (E) Cross of Mc1 mutant (Δγ16/Δγ16) and heterologous γRNAi transgenic line (γRNAi/+). PB, protein body; RER, rough endoplasmic reticulum; CW, cell wall; Mt, mitochondria; SG, starch granule. Bars, 500 nm.

Recovery of floury phenotype in progeny:

On the basis of these observations, it is reasoned that irregularly shaped protein bodies (Figure 2, C and D) in the Mc1 mutant cause the floury phenotype (Figure 3, A and B). Because knockdown of γ-zeins caused opacity only in the crown area (Figure 3C), one could envision that once the irregular protein bodies are restored, the kernel would become vitreous in the gown area of the kernel. Indeed, the progeny ear from the cross of Δγ16/Δγ16 and γRNAi/+ showed a 1:1 ratio of floury and vitreous kernels (Figure 3, D and F), and all kernels were vitreous when the Mc1 mutant was pollinated by a homozygous γRNAi line (Figure 3E).Open in a separate windowFigure 3.—Segregation of vitreous and floury kernels from a progeny ear. (A) Mc1 mutant with Δγ16/Δγ16 genotype. (B) The cross of the Mc1 mutant and the nontransgenic hybrid of B × A lines, showing floury phenotype as in A. (C) γRNAi transgenic line with partial opacity only in the crown area. (D) The cross of the Mc1 mutant (Δγ16/Δγ16) and the heterologous γRNAi transgenic line (γRNAi/+), showing a 1:1 ratio of vitreous and floury kernels. A row in the ear is marked with arrowheads and crosses to indicate vitreous and floury gowns of kernels. (E) Cross of the Mc1 mutant (Δγ16/Δγ16) and the γRNAi homozygous transgenic line (γRNAi/γRNAi), showing all vitreous kernels. (F) Truncated kernel phenotype. (Top) Mc1, cross of Mc1 × BA, and γRNAi transgenic line. (Bottom) Three vitreous and floury kernels from D.

Conclusions:

RNAi can be used to rescue mutations that are dominant negative with a single cross, providing a useful tool in genetic analysis, plant breeding, and potentially in gene therapy in general.