Focus Issue on Roots: Visualization of Root Water Uptake: Quantification of Deuterated Water Transport in Roots Using Neutron Radiography and Numerical Modeling

Our understanding of soil and plant water relations is limited by the lack of experimental methods to measure water fluxes in soil and plants. Here, we describe a new method to noninvasively quantify water fluxes in roots. To this end, neutron radiography was used to trace the transport of deuterated water (D₂O) into roots. The results showed that (1) the radial transport of D₂O from soil to the roots depended similarly on diffusive and convective transport and (2) the axial transport of D₂O along the root xylem was largely dominated by convection. To quantify the convective fluxes from the radiographs, we introduced a convection-diffusion model to simulate the D₂O transport in roots. The model takes into account different pathways of water across the root tissue, the endodermis as a layer with distinct transport properties, and the axial transport of D₂O in the xylem. The diffusion coefficients of the root tissues were inversely estimated by simulating the experiments at night under the assumption that the convective fluxes were negligible. Inverse modeling of the experiment at day gave the profile of water fluxes into the roots. For a 24-d-old lupine (Lupinus albus) grown in a soil with uniform water content, root water uptake was higher in the proximal parts of lateral roots and decreased toward the distal parts. The method allows the quantification of the root properties and the regions of root water uptake along the root systems.Understanding how and where plant roots extract water from soil remains an open question for both plant and soil scientists. One of the open questions concerns the locations of water uptake along the root system (Frensch and Steudle, 1989; Doussan et al., 1998; Steudle, 2000; Zwieniecki et al., 2003; Javaux et al., 2008). A motivation of these studies is that a better prediction of root water uptake may help to optimize irrigation and identify optimal traits to capture water. Despite its importance, there is little experimental information on the spatiotemporal distribution of the uptake zone along roots growing in soil. The lack of experimental data is largely due to the technical difficulties in measuring water fluxes in soils and roots.Quantitative information on the rate and location of root water uptake along roots growing in soil is needed to better understand the function of roots in extracting water from the soil and tolerating drought events. Such information may show which parts of roots are more involved in water extraction and how root hydraulic properties change during root growth and exposure to water-limiting conditions. For instance, it is not clear how root anatomy and the hydraulic conductivity of roots change as the soil becomes dry or the transpiration demand increases. Quantitative information of the location of root water uptake can be used to estimate the spatial distribution of hydraulic conductivities along roots. This information is needed to parameterize the most recent and advanced models of root water uptake, such as those of Doussan et al. (1998) and Javaux et al. (2008).Most of the experimental information on the spatial distribution of water uptake is limited to roots grown in hydroponic and aeroponic cultures (Frensch and Steudle, 1989; Varney and Canny, 1993; Zwieniecki et al., 2003; Knipfer and Fricke, 2010a). These investigations substantially improved our knowledge of the mechanism of water transport in roots. However, roots grown in hydroponic and aeroponic cultures may have different properties than those of roots grown in soils. As the soil dries, the hydraulic conductivity of roots and of the root-soil interface changes and likely affects the profile of root water uptake (Blizzard and Boyer, 1980; Nobel and Cui, 1992; Huang and Nobel, 1993; McCully, 1995; North and Nobel, 1997; Carminati et al., 2011; Knipfer et al., 2011; McLean et al., 2011; Carminati, 2012).New advances in imaging techniques are opening new avenues for noninvasively studying water uptake by roots in soils (Doussan et al., 1998; Garrigues et al., 2006; Javaux et al., 2008; Pohlmeier et al., 2008; Moradi et al., 2011). Imaging methods such as x-ray computed tomography, light transmission imaging, NMR, and computed neutron radiography allow quantifying the changes of water content in the root zone with different accuracy and spatial resolution. However, due to the concomitant soil water redistribution, the local changes in soil water content are not trivially related to root uptake. Consequently, the estimation of root water uptake requires coupling the imaging methods with the modeling of water flow in the soil, which, in turn, requires accurate information on the hydraulic properties of soil and roots. An additional complexity is represented by the peculiar and only partly understood hydraulic properties of the soil in the vicinity of the roots, the so-called rhizosphere.The hydraulic properties of the rhizosphere are influenced by root and microorganism activity, soil compaction due to root growth, and the formation of air-filled gaps between soil and roots when roots shrink (Nye, 1994; North and Nobel, 1997; Carminati et al., 2010; Aravena et al., 2011; Moradi et al., 2011; Carminati, 2013; Zarebanadkouki and Carminati, 2014). To date, it has been technically difficult to quantify the hydraulic properties of the rhizosphere. Carminati et al. (2011) showed that the hydraulic properties of the first 1 to 2 mm near the root affect the profile of water content and water potential toward the root.Recently, we introduced a novel method to noninvasively trace the flow of water in soil and roots (Zarebanadkouki et al., 2012, 2013). The method combines neutron radiography and the injection of deuterated water (D₂O). Neutron radiography is an imaging technique that allows one to quantify the water distribution in thin soil samples with high accuracy and spatial resolution (Moradi et al., 2008). D₂O is an isotope of normal water. Its chemical and physical properties are similar to those of water, but in contrast to water, it is almost transparent in neutron transmission imaging (Matsushima et al., 2012). This property makes D₂O an excellent tracer for neutron imaging of water flow.In our previous experiments (Zarebanadkouki et al., 2012, 2013), D₂O was injected next to selected roots and its transport was monitored using time-series neutron radiography with a spatial resolution of 150 μm and a temporal resolution of 10 s for a duration of 2 h. We grew lupine (Lupinus albus) in aluminum containers (width of 25 cm, height of 30 cm, and thickness of 1 cm) filled with a sandy soil. The soil was partitioned into different compartments with a 1-cm layer of coarse sand acting as a capillary barrier (three vertical and four horizontal layers placed at regular intervals). The capillary barriers limited the transport of D₂O into a given region of soil and facilitated the quantification of D₂O transport into the roots. Figure 1 shows selected neutron radiographs of D₂O injection during the day and night. This figure is modified from Zarebanadkouki et al. (2013). The radiographs show that (1) the radial transport of D₂O into the roots was faster during the day than during the night and (2) the axial transport of D₂O along the roots was visible only during the day, while it was negligible at night. The differences between nighttime and daytime measurements were caused by the net flow of water induced by transpiration.Open in a separate windowFigure 1.Neutron radiographs of two samples after injection of 4 mL of D₂O during the day (A and B) and during the night (C and D). D₂O was injected in one compartment during the nighttime and in two compartments during the daytime. The images show the differences between the actual radiographs at time t and the radiograph before injection (t = 0). Brighter colors indicate lower neutron attenuation and higher D₂O-water ratio. The images show that (1) the transport of D₂O was faster during the day than during the night and (2) D₂O moved axially beyond the capillary barrier toward the shoot only during the day. Images are closeups of the original field of view of 15.75 × 15.75 cm showing the distribution of D₂O in the soil and root after D₂O injection. Figures are extracted from Zarebanadkouki et al. (2013). (A neutron radiograph of the whole sample used for daytime measurement is given in Figure 9.) [See online article for color version of this figure.]The interpretation of tracing experiments with D₂O in which water and D₂O are mixed is not straightforward (Carminati and Zarebanadkouki, 2013; Warren et al., 2013a, 2013b). To determine the convective fluxes from the radiographs, Zarebanadkouki et al. (2012, 2013) introduced a diffusion-convection model of D₂O transport in roots. The model was solved analytically. The model described the increase of the average D₂O concentration in the root with a double-exponential equation, in which the rate constants of the first and second phases were related to the transport of D₂O into the cortex and the stele of the roots. Although the model included important details of the root structure, such as different pathways of water across the root tissue, the diffusion of D₂O across the root tissue was strongly simplified. In particular, our previous model assumed that as soon as the roots were immersed in D₂O, the apoplastic free space of the root cortex was instantaneously saturated with D₂O. In other words, we assumed that all cortical cells and the root endodermis were simultaneously immersed in an identical concentration of D₂O equal to that of the soil. Additionally, we assumed that D₂O concentration inside the cortical cell and the root stele was uniform (well-stirred compartment).Although the radiographs clearly showed a significant axial transport of D₂O beyond the capillary barrier during the daytime (Fig. 1B), the model of Zarebanadkouki et al. (2013) was not capable of simulating it appropriately. Indeed, our previous model could only simulate the changes in D₂O concentration in the root segments immersed in D₂O. Since the concentration of D₂O in the root segment beyond the capillary barrier carries additional information on the axial and radial fluxes along the roots, we decided to modify our model to include such information.Another approximation of the previous model was the assumption that the radial water flow to the root was uniform along the root segment immersed in D₂O. However, Zarebanadkouki et al. (2013) found significant variations in root water uptake along the roots and suggested that root water uptake should be measured with a better spatial resolution.The objective of this study was to provide an adequate model to interpret tracing experiments with D₂O. We developed two different models to describe the transport of D₂O into roots. (1) In the first model, we described the transport of D₂O into the roots by taking into account the different pathways of water across the root tissue (i.e. the apoplastic and the cell-to-cell pathways). Although this model captures the complexity of the root structure, it requires several parameters, such as the ratio of the water flow in the apoplast over the water flow in the cell-to-cell pathway. We refer to this model as the composite transport model. (2) In the second model, we simplified the root tissue into a homogenous flow domain comprising both pathways. The latter model is a simplification of the complex root anatomy, but it has the advantage of requiring fewer parameters. We refer to this model as the simplified model.In the next sections, we introduce the two modeling approaches and run a sensitivity analysis to test whether the transport of D₂O into roots is sensitive to the parameters of the composite transport model. The question was, do we need the composite transport model to accurately estimate the water flow into the roots based on the experiments with neutron radiography? Or alternatively, can we use the simplified model to estimate the fluxes without the need of introducing several parameters?Our final goal was to develop a numerical procedure to extract quantitative information on the water fluxes and the root hydraulic properties based on the tracing experiments with neutron radiography. Based on the results of the sensitivity analysis, we chose the simplified model to simulate the experiments. By fitting the observed D₂O transport into the roots, we calculated the profiles of water flux across the roots of a 24-d-old lupine as well as the diffusion permeability of its roots.     

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).     

Focus Issue on Roots: Beyond the Barrier: Communication in the Root through the Endodermis

The root endodermis is characterized by the Casparian strip and by the suberin lamellae, two hydrophobic barriers that restrict the free diffusion of molecules between the inner cell layers of the root and the outer environment. The presence of these barriers and the position of the endodermis between the inner and outer parts of the root require that communication between these two domains acts through the endodermis. Recent work on hormone signaling, propagation of calcium waves, and plant-fungal symbiosis has provided evidence in support of the hypothesis that the endodermis acts as a signaling center. The endodermis is also a unique mechanical barrier to organogenesis, which must be overcome through chemical and mechanical cross talk between cell layers to allow for development of new lateral organs while maintaining its barrier functions. In this review, we discuss recent findings regarding these two important aspects of the endodermis.Soil contains water and dissolved nutrients needed for plant growth, but also holds pathogens and toxic compounds that can be detrimental to the plant. The root system, which is directly in contact with soil particles, can integrate environmental cues to adjust its development in order to optimize nutrient (Péret et al., 2011; Lynch, 2013) and water uptake (Cassab et al., 2013; Lynch, 2013; Bao et al., 2014) or avoid regions of high salinity (Galvan-Ampudia et al., 2013). Once anchored in the soil, roots must deal with the constraints of their local environment and develop specific barriers to balance uptake of nutrients, water, and interactions with symbionts with protection against detrimental biotic and abiotic factors.In young roots, these barriers are mainly formed by the deposition of hydrophobic polymers such as lignin and suberin within the primary cell wall of the endodermis, which separates the pericycle from the cortex (Fig. 1), and of the exodermis, which lies between the cortex and the epidermis (Nawrath et al., 2013). Although formation of an exodermis is species dependent, the endodermis is a distinguishing figure of extant vascular plants (Raven and Edwards, 2001). Within this layer, two barriers (i.e. the Casparian strip and the suberin lamellae) are sequentially deposited and regulate water and nutrient movements between the inner and outer parts of the root. In this review, we discuss how the presence of these two major endodermal barriers affects communication between the different cell layers of the root. We focus on recent articles highlighting the importance of the endodermis in this communication during various biological and developmental processes.Open in a separate windowFigure 1.Endodermal barriers affect radial movement of water and solutes through the root. A, At the root tip, to move from the soil to the outer tissues of the root and then into the stele, water and solute molecules can use either the apoplastic (black lines), symplastic (dotted lines), or transcellular (dashed lines) pathways. B, The deposition of the Casparian strip in the endodermis prevents the free apoplastic diffusion of molecules between the outer part and the inner part of the root forcing molecules to pass through the symplast of endodermal cells. C, The deposition of suberin lamellae prevents the uptake of molecules from the apoplast directly into the endodermis forcing molecules to enter the symplast from more outer tissue layers. Suberin deposition is also likely to prevent the backflow of water and ions out of the stele. Passage cells are unsuberized and may facilitate the uptake of water and nutrients in older parts of the root. Cor, Cortex; End, endodermis; Epi, epidermis; Peri, pericycle; Vasc, vasculature. Figure redrawn and modified from Geldner et al. (2013).     

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

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

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

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

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

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

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

Focus on Water: Leaf Shrinkage with Dehydration: Coordination with Hydraulic Vulnerability and Drought Tolerance

Leaf shrinkage with dehydration has attracted attention for over 100 years, especially as it becomes visibly extreme during drought. However, little has been known of its correlation with physiology. Computer simulations of the leaf hydraulic system showed that a reduction of hydraulic conductance of the mesophyll pathways outside the xylem would cause a strong decline of leaf hydraulic conductance (K_leaf). For 14 diverse species, we tested the hypothesis that shrinkage during dehydration (i.e. in whole leaf, cell and airspace thickness, and leaf area) is associated with reduction in K_leaf at declining leaf water potential (Ψ_leaf). We tested hypotheses for the linkage of leaf shrinkage with structural and physiological water relations parameters, including modulus of elasticity, osmotic pressure at full turgor, turgor loss point (TLP), and cuticular conductance. Species originating from moist habitats showed substantial shrinkage during dehydration before reaching TLP, in contrast with species originating from dry habitats. Across species, the decline of K_leaf with mild dehydration (i.e. the initial slope of the K_leaf versus Ψ_leaf curve) correlated with the decline of leaf thickness (the slope of the leaf thickness versus Ψ_leaf curve), as expected based on predictions from computer simulations. Leaf thickness shrinkage before TLP correlated across species with lower modulus of elasticity and with less negative osmotic pressure at full turgor, as did leaf area shrinkage between full turgor and oven desiccation. These findings point to a role for leaf shrinkage in hydraulic decline during mild dehydration, with potential impacts on drought adaptation for cells and leaves, influencing plant ecological distributions.As leaves open their stomata to capture CO₂ for photosynthesis, water is lost to transpiration, which needs to be replaced by flow through the hydraulic system. The leaf hydraulic system has two components, which act essentially in series: the pathways for water movement through the xylem from the petiole to leaf minor veins, and those through the living bundle sheath and mesophyll cells to the sites of evaporation (Tyree and Zimmermann, 2002; Sack et al., 2004; Sack and Holbrook, 2006). The decline in leaf hydraulic conductance (K_leaf) with dehydration may thus depend on both components. The importance of the xylem component is well established. Vein xylem embolism and cell collapse have been observed in dehydrating leaves (Salleo et al., 2001; Cochard et al., 2004a; Johnson et al., 2009), and computer modeling and experimental work showed that species with high major vein length per leaf area (VLA; i.e. for the first three vein-branching orders) were more resistant to hydraulic decline, providing more pathways around embolisms (Scoffoni et al., 2011). However, the physical impacts of dehydration on the extraxylem pathways have not been studied, even though in turgid leaves these pathways account for 26% to 88% of leaf hydraulic resistance (i.e. of 1/K_leaf), depending on species (Sack et al., 2003a; Cochard et al., 2004b). The aim of this study was to determine whether leaf shrinkage during dehydration relates to the decline of K_leaf as well as the structural determinants of leaf shrinkage.The shrinkage of leaves with dehydration has drawn attention for over 100 years. Leaves shrink in their area (Bogue, 1892; Gardner and Ehlig, 1965; Jones, 1973; Tang and Boyer, 2007; Blonder et al., 2012) and, considered in relative terms, even more strongly in their thickness (Fig. 1; Meidner, 1952; Gardner and Ehlig, 1965; Downey and Miller, 1971; Syvertsen and Levy, 1982; Saini and Rathore, 1983; Burquez, 1987; McBurney, 1992; Sancho-Knapik et al., 2010, 2011). Leaves fluctuate in thickness daily and seasonally according to transpiration (Kadoya et al., 1975; Tyree and Cameron, 1977; Fensom and Donald, 1982; Rozema et al., 1987; Ogaya and Peñuelas, 2006; Seelig et al., 2012). Indeed, the relation of leaf thickness to water status is so tight that using leaf thickness to guide irrigation has led to water savings of up to 45% (Seelig et al., 2012).Open in a separate windowFigure 1.Sketches of a fully turgid leaf (A) versus a strongly dehydrated leaf (B; drawings based on leaf cross sections of sunflower in Fellows and Boyer, 1978). Note the strong reduction in leaf thickness, cell thickness, and intercellular airspaces in the dehydrated leaf. Epidermal cells are shrunk in the dehydrated leaf, inducing whole-leaf area shrinkage. Note that this sketch represents shrinkage for a typical drought-sensitive species. Many species such as oaks (Quercus spp.) will experience less thickness shrinkage and an increase in intercellular airspace (see “Discussion”). [See online article for color version of this figure.]Previous studies of leaf shrinkage with progressive dehydration have tended to focus on single or few species. These studies showed that thickness declines with water status in two phases. Before the bulk leaf turgor loss point (TLP; leaf water potential [Ψ_leaf] at TLP) is reached, the slope of leaf thickness versus Ψ_leaf or relative water content (RWC) is shallower than past TLP for most species (Meidner, 1955, Kennedy and Booth, 1958, Burquez, 1987, McBurney, 1992, Sancho-Knapik et al., 2010, 2011). This is because before TLP, declining Ψ_leaf is strongly driven by declines in turgor pressure, which have a relatively low impact on cell and airspace volume, whereas past the TLP, declining Ψ_leaf depends only on solute concentration, which increases in inverse proportion as cell water volume declines while airspaces may shrink or expand (Tyree and Hammel, 1972, Sancho-Knapik et al., 2011). However, the steepness of the slope of leaf thickness versus Ψ_leaf before TLP seems to vary strongly across species (Meidner, 1955; Kennedy and Booth, 1958; Fellows and Boyer, 1978; Burquez, 1987; Colpitts and Coleman, 1997; Sancho-Knapik et al., 2010).A high leaf cell volume and turgor is crucial to physiological processes (Boyer, 1968; Lawlor and Cornic, 2002). Shrinkage may affect cell connectivity and water transport (Sancho-Knapik et al., 2011). However, no studies have tested for a possible relationship of leaf shrinkage with the decline of K_leaf during dehydration. Such an association would arise if, across species, shrinkage occurred simultaneously with vein xylem embolism or if tissue shrinkage led to declines in the extraxylem hydraulic conductance.To refine our hypotheses, we modified a computer model of the leaf hydraulic system (Cochard et al., 2004b; McKown et al., 2010; Scoffoni et al., 2011) to predict the impact of losses of xylem and extraxylem conductance on the response of K_leaf to dehydration. We characterized the degree of leaf shrinkage in thickness, in the thickness of cells and airspaces within the leaf, and in leaf area for 14 species diverse in phylogeny, leaf traits, and drought tolerance. We hypothesized that loss of extraxylem hydraulic conductance should have a greater impact on K_leaf at less negative water potentials when xylem tensions are too weak to trigger embolism and induce dramatic declines in K_leaf. We hypothesized that species with greater degrees of shrinkage before TLP would experience greater loss of K_leaf. Furthermore, we hypothesized that species from moist habitats would have greater degrees of shrinkage.For insight into the mechanisms and consequences of leaf shrinkage, we also investigated the relationships of 18 indices of leaf shrinkage with a wide range of aspects of leaf structure and composition, including gross morphology, leaf venation architecture, parameters of pressure-volume curves, and leaf water storage. We hypothesized that, across species, shrinkage in whole leaf, cell, and intercellular airspace thickness would be lower for species with greater allocation to structural rigidity and osmotic concentration, and thus shrinkage would be positively correlated with a lower modulus of elasticity (ε), less negative osmotic pressure at full turgor (π_o), lower leaf mass per area (LMA), and lower leaf density. Additionally, we tested the longstanding hypothesis that species with higher major VLA and/or minor VLA (i.e. the fourth and higher vein-branching orders) would shrink less in area and/or thickness with dehydration (Gardner and Ehlig, 1965). Finally, we tested the ability of dehydrated leaves to recover in size with rehydration. We hypothesized that recovery would be greater for mildly than for strongly dehydrated leaves and that species with greater leaf shrinkage would be better able to recover from shrinkage.     

Focus Issue on Roots: Image-Based High-Throughput Field Phenotyping of Crop Roots

Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.Crop root systems represent an underexplored target for improvements as part of community efforts to ensure that global crop yields and productivity keep pace with population growth (Godfray et al., 2010; Gregory and George, 2011; Nelson et al., 2012). The challenge in improving crop root systems is that yield and productivity also depend on soil fertility, which is also a major constraint to global food production (Lynch, 2007). Hence, desired improvements to crop root systems include enhanced water use efficiency and water acquisition given the increased likelihood of drought in future climates (Intergovernmental Panel on Climate Change, 2014). Over the long term, the development of crop genotypes with improved root phenotypes requires advances in the characterization of root system architecture (RSA) and in the relationship between RSA and function.The emerging discipline of plant phenomics aims to expand the scope, throughput, and accuracy of plant trait estimates (Furbank, 2009). In the case of plant roots, structural traits may describe RSA as geometric or topological measures of the root shape at various scales (e.g. diameters and width of the whole root system or a single branch; Lynch, 1995; Den Herder et al., 2010). These traits can be used to predict yield under specific conditions such as drought or low fertility. Understanding the diversity and development of root architectural traits is crucial, because spatial and temporal root deployment affects plant fitness, especially water and nutrient acquisition (Rich and Watt, 2013). Thus, improving plant performance may benefit from improvements in the characterization of root architecture, including understanding how trait variation arises as a function of genotype and environmental conditions (Band et al., 2012; Shi et al., 2013).Current efforts to understand the structure of crop root systems have already led to a number of imaging solutions (Lobet et al., 2013) that are able to extract root architecture traits under various conditions (Fiorani et al., 2012), including laboratory conditions (de Dorlodot et al., 2007) in which plants are often grown in pots or glass containers (Zeng et al., 2008; Armengaud et al., 2009; Le Bot et al., 2010; Clark et al., 2011; Lobet et al., 2011; Naeem et al., 2011; Galkovskyi et al., 2012). In the case of pots, expensive magnetic resonance imaging technologies represent one noninvasive approach to capture high-resolution details of root architecture (Schulz et al., 2013), similar to the capabilities of x-ray microcomputed tomography (μCT) systems. X-ray systems allow capturing of the root architecture at a fine scale in containers with a wide variety of soil types (Mairhofer et al., 2012; Mooney et al., 2012). It has been shown that x-ray μCT paired with specifically designed algorithms has sufficient resolution to recover the root structure in many cases (Mairhofer et al., 2013). Nevertheless, x-ray μCT systems are currently unable to image mature root systems because of technical restrictions in container size.As an alternative, root systems can be imaged directly with a digital camera when grown in glass containers with transparent media such as gellan gum or transparent soil replacements (Downie et al., 2012). Such in situ imaging benefits from controlled lightning conditions during image acquisition, even more so when focusing on less complex root structures of younger plants that allow three-dimensional reconstruction (Clark et al., 2011; Topp et al., 2013). Under such controlled conditions, it is expected that imaging would enable the study of growth of roots over time (Spalding and Miller, 2013; Sozzani et al., 2014).However, all of the above-listed solutions have been used primarily to assess root structures in the early seedling stage (French et al., 2009; Brooks et al., 2010; Sozzani et al., 2014) to approximately 10 d after germination (Clark et al., 2011), which makes it all but impossible to directly observe mature root systems. For example, primary and seminal roots make up the major portion of the seedling root system in maize (Zea mays) during the first weeks after germination. Later in development, postembryonic shoot-borne roots become the major component of the maize root system (Hochholdinger, 2009), not yet accessible to laboratory phenotyping platforms. In addition to phenological limitations, current phenotyping approaches for root architecture require specialized growth conditions with aerial and soil environments that differ from field conditions; the effects of such differences on RSA are only sparsely reported in literature (Hargreaves et al., 2009; Wojciechowski et al., 2009).Indeed, high-throughput field phenotyping can be seen as a new frontier for crop improvement (Araus and Cairns, 2014) because imaging a mature root system under realistic field conditions poses unique challenges and opportunities (Gregory et al., 2009; Zhu et al., 2011; Pieruschka and Poorter, 2012). Challenges are intrinsic to roots grown in the field because the in situ belowground imaging systems to date are unable to capture fine root systems. As a consequence, initial attempts to characterize root systems in the field focused on the manual extraction of structural properties. Manual approaches analyzed the root system’s branching hierarchy in relation to root length and rooting depth (Fitter, 1991). In the late 1980s, imaging techniques were first used (Tatsumi et al., 1989) to estimate the space-filling behavior of roots, an estimation process that was recently automated (Zhong et al., 2009). A weakness of such approaches is that exact space-filling properties, such as the fractal dimension, are sensitive to the incompleteness of the excavated root network (Nielsen et al., 1997, 1999). In particular, the box counting method was criticized for above-ground branching networks of tree crowns (Da Silva et al., 2006). The same critiques apply to root systems, because fine secondary or tertiary roots can be lost or cutoff or can adhere to each other during the cleaning process, making it impossible to analyze the entire network.As an alternative, the shovelomics field protocol has been proposed to characterize the root architecture of maize under field conditions (Trachsel et al., 2011). In shovelomics, the researcher excavates the root at a radius of 20 cm around the hypocotyl and 20 cm below the soil surface. This standardized process captures the majority of the root system biomass within the excavation area. After excavation, the shoot is separated from the root 20 cm above the soil level and washed in water containing mild detergent to remove soil. The current procedure places the washed root on a phenotyping board consisting of a large protractor to measure dominant root angles with the soil level at depth intervals and marks to score length and density classes of lateral roots. A digital caliper is used to measure root stem diameters (Fig. 1). Observed traits vary slightly from crop to crop but generally fit into the following categories by depth or root class: angle, number, density, and diameter. In this way, field-based shovelomics allows the researcher to visually quantify the excavated structure of the root crown and compare genotypes via a common set of traits that do not depend on knowledge or observation of the entire root system network. Of note, shovelomics is of particular use in developing countries, which have limited access to molecular breeding platforms (Delannay et al., 2012), and for which direct phenotypic selection is an attractive option.Open in a separate windowFigure 1.A, Classic shovelomics scoring board to score the angle of maize roots with the soil tissue. B, An example to score rooting depth and angle in common bean.Nonetheless, direct phenotypic selection for root traits of field-grown root systems comes with a number of caveats and drawbacks. To date, the quantification of mature root systems is highly dependent on the researcher, reducing the repeatability of measured quantities (Gil et al., 2007). In addition, manual approaches impose limitations on both the number of accessible traits and the number of samples collected. For example, a typical shovelomics phenotyper can gather 10 to 12 traits from a sample in 2 min or approximately 200 samples in a typical workday (Trachsel et al., 2011). This means that evaluating a statistically more significant trial of 1,000 samples may take 5 d or more. Such a time span introduces variation owing to plant growth and phenology. On the other hand, shovelomics is adaptable to both monocot and dicot roots; therefore, its procedure can be scaled over the huge variety of root morphologies in dicots and monocots. For these reasons, we contend that shovelomics represents a valued target for an automated high-throughput phenotyping approach in the field.We introduce an imaging approach for high-throughput phenotyping of mature root systems under realistic soil conditions to address the limitations of manual data collection (Fig. 2, I and II). Our new algorithms directly extract root architecture traits from excavated root images (Fig. 2, III and IV). Rapid imaging of the roots and decoupled analysis of the collected data at a later time improve both the throughput and synchronization of experiments. In addition to traits that are developed from shovelomics, our algorithms extract previously inaccessible traits such as root tip diameter, spatial distribution, and root tissue angle (RTA). Overall, our approach allows imaging directly at field sites with an easily reproducible imaging setup and involves no transportation cost or expensive hardware. We demonstrate the utility of our approach in the field-based analysis of maize and cowpea (Vigna unguiculata) architecture.Open in a separate windowFigure 2.I, Imaging board on the example of a maize root. The experiment tag is used to capture an experiment number, and the scale marker allows the correction of camera tilting and transforming image coordinates into metric units. II, Camera mounted on a tripod placed on top of the imaging board coated with blackboard paint. Note that images were taken with protection against direct sunlight not shown in the image. III, Example of the segmentation of the original image into a binary image and then into a series of image masks that serve as input to estimate traits for monocot and dicot roots. The sample is that of a maize root, 40 d after planting at the URBC. IV, The imaging pipeline for dicot roots and sparse monocot roots: Original image on the imaging board (a), derived distance map where the lighter gray level represents a larger diameter of the imaged object (b), medial axis includes loops (c), and loop RTP with a sample of the root branching structure (d). Colors are randomly assigned to each path. The sample is that of a cowpea root, approximately 30 d after planting at the URBC.     

Modeling the Hydraulics of Root Growth in Three Dimensions with Phloem Water Sources

Primary growth is characterized by cell expansion facilitated by water uptake generating hydrostatic (turgor) pressure to inflate the cell, stretching the rigid cell walls. The multiple source theory of root growth hypothesizes that root growth involves transport of water both from the soil surrounding the growth zone and from the mature tissue higher in the root via phloem and protophloem. Here, protophloem water sources are used as boundary conditions in a classical, three-dimensional model of growth-sustaining water potentials in primary roots. The model predicts small radial gradients in water potential, with a significant longitudinal gradient. The results improve the agreement of theory with empirical studies for water potential in the primary growth zone of roots of maize (Zea mays). A sensitivity analysis quantifies the functional importance of apical phloem differentiation in permitting growth and reveals that the presence of phloem water sources makes the growth-sustaining water relations of the root relatively insensitive to changes in root radius and hydraulic conductivity. Adaptation to drought and other environmental stresses is predicted to involve more apical differentiation of phloem and/or higher phloem delivery rates to the growth zone.Plant growth involves water uptake by the cells and expansion of the cell walls under the resultant turgor (internal hydrostatic pressure). The water uptake and increase in cell volume are accompanied by nutrient and metabolite deposition. Thus, hydraulics of growth (i.e. the energies, conductivities, and fluxes of water in growing tissue) are fundamental to understanding primary plant growth. Quantitatively, the driving force for water movement in the plant, as in other porous media, is considered to be the gradient in water potential (Ψ), an energy per unit volume given in MPa. Thus, primary growth can be modeled by considering plant tissue to be a distributed sink for water, with low Ψ and/or high hydraulic conductivity driving water deposition into rapidly expanding regions. Molz and Boyer (1978) developed the theoretical basis for predicting the radial water flux in one dimension within the intercalary meristem of growing soybean (Glycine max) hypocotyls. In this aerial tissue, water moves from the xylem both outward to the epidermis and inward to the pith. Thus, in the growing hypocotyls, Ψ is predicted to be least negative in the xylem and to decrease toward the epidermis and the pith. These predictions for growth-induced or growth-sustaining Ψ were confirmed when the experimental technology became sensitive enough to detect the gradients in Ψ (Nonami and Boyer, 1993). Passioura and Boyer (2003) expanded the theory to incorporate anatomical detail and corresponding spatial patterns of hydraulic conductivity. Their model explains experimental results on water relations during growth transients for many areas of the plant.The hydraulics of root growth differ from shoot growth because of differences in xylem anatomy. Root xylem becomes functional perhaps 1 cm behind the tip and well behind the growth zone. To enter the growing cells near the maize (Zea mays) root tip, externally supplied metabolites must move several millimeters without phloem (Fig. 1), and any water supplied by functional xylem would need to move more than 1 cm. Silk and Wagner (1980) provided a theoretical framework for a two-dimensional treatment of the growth-sustaining Ψ gradients in maize roots. They assumed that the water source was external (the soil or root-bathing medium) and that the root surface was in equilibrium with the soil or bathing medium, so that the flow path to growing cells in the root was predicted to be primarily inward. As in the shoot model, growing tissue was seen as a distributed sink for water. However, since the publication of that theory, experimental studies have revealed that the root tip is not in equilibrium with the bathing medium (Pritchard et al., 1996, 2000; Gould et al., 2004; Shimazaki et al., 2005). Pressure probes combined with osmotic potential determinations have shown that the Ψ of exterior root cells ranges from −0.17 to −0.6 MPa, depending on environmental conditions. This range is more negative than in the nutrient medium. Furthermore, evidence has accumulated that at least some water for root growth comes from the phloem. The most obvious evidence is perhaps the growth of nodal (adventitious) roots of maize, rice (Oryza sativa), and other gramineous plants (Westgate and Boyer, 1985). This growth is a normal part of crop development. The nodal roots grow through air and then dry layers of surface soil, making it unlikely that the expanding root cells obtain water from the dry media surrounding the root. Empirical and theoretical studies have concluded that the phloem probably provides water for growth of the primary maize root (Bret-Harte and Silk, 1994; Frensch and Hsiao, 1995; Pritchard, 1996; Pritchard et al., 1996, 2000; Hukin et al., 2002; Gould et al., 2004).Open in a separate windowFigure 1.Primary root growth zone. The tip of the seedling root of maize showing the meristem as part of the apical third of the elongation zone. The boundary of this root section was digitized to provide the computational body-fit grid used for the model. [See online article for color version of this figure.]The model described here follows the concepts of Pritchard and colleagues (1996, 2000) in assuming a pressure-driven bulk flow of solution through the phloem to the region where phloem is beginning to be functional (1–4 mm from the apex; Fig. 1). Water movement can occur from both the surrounding soil and the developing phloem. Henceforth, we refer to the “external water source equilibrium” or EE model, for which the boundary condition is solely an exterior medium of fairly high Ψ (−0.005 to −0.05 MPa) and no conditions are placed on the phloem Ψ (Silk and Wagner (1980), that the exterior of the root is in equilibrium with its bathing solution. Empirical studies have shown that this model is not realistic, because the root maintains peripheral cells at more negative Ψ than the bathing medium. Since this is hypothesized to occur by deposition of apoplastic solutes, we will refer to a model with external water source and apoplastic solutes near the exterior as the EASE model.

Table I.

Acronyms for models and definitions of symbols used in mathematical modeling

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.     

Who’s in control? Principles of Rab GTPase activation in endolysosomal membrane trafficking and beyond

The eukaryotic endomembrane system consists of multiple interconnected organelles. Rab GTPases are organelle-specific markers that give identity to these membranes by recruiting transport and trafficking proteins. During transport processes or along organelle maturation, one Rab is replaced by another, a process termed Rab cascade, which requires at its center a Rab-specific guanine nucleotide exchange factor (GEF). The endolysosomal system serves here as a prime example for a Rab cascade. Along with endosomal maturation, the endosomal Rab5 recruits and activates the Rab7-specific GEF Mon1-Ccz1, resulting in Rab7 activation on endosomes and subsequent fusion of endosomes with lysosomes. In this review, we focus on the current idea of Mon1-Ccz1 recruitment and activation in the endolysosomal and autophagic pathway. We compare identified principles to other GTPase cascades on endomembranes, highlight the importance of regulation, and evaluate in this context the strength and relevance of recent developments in in vitro analyses to understand the underlying foundation of organelle biogenesis and maturation.

Membrane identity in the endomembrane systemOne key feature of eukaryotic cells is the presence of membrane-enclosed organelles, which constantly exchange proteins, lipids, or metabolites via vesicular transport or membrane contact sites (MCSs). Along the endomembrane system, vesicular trafficking requires vesicle budding from the donor membrane and directed transport toward and fusion with the acceptor compartment. The resulting trafficking routes form a regulated network that connects not only the internal organelles, but also the interior and exterior of the cell.The specific identity of organelles within the endomembrane system is defined by the lipid and protein composition of their membranes. This includes signaling lipids such as phosphoinositides (PIPs) and small GTPases of the Ras superfamily of small G proteins, namely of the Rab, Arf, and Arl families, which act as binding platforms for accessory proteins involved in multiple membrane trafficking processes (Balla, 2013).Rab GTPases, like other small GTPases, are key regulatory proteins that switch between an inactive GDP-bound (Rab-GDP) and an active GTP-bound (Rab-GTP) state (Barr, 2013; Goody et al., 2017; Hutagalung and Novick, 2011). Rabs are posttranslationally modified by the addition of geranylgeranyl moieties to C-terminal cysteine residues, which allow their reversible membrane association. Within the cytosol, Rab-GDP is kept soluble by binding to the chaperone-like GDP dissociation inhibitor (GDI). At the target membrane, an organelle-specific guanine nucleotide exchange factor (GEF) activates the Rab after its previous release from GDI, a process possibly supported by other factors (Dirac-Svejstrup et al., 1997). GTP binding stabilizes two loops in the Rab GTPase domain, which allows recruitment and binding of various so-called effector proteins to the Rab-GTP on the membrane. Rab GTPases are inefficient enzymes with a low intrinsic GTP hydrolysis rate and thus depend on a GTPase-activating protein (GAP) to hydrolyze bound GTP. GDI then extracts the Rab-GDP and keeps it soluble in the cytosol until the next activation cycle (Barr, 2013; Goody et al., 2017; Hutagalung and Novick, 2011). In addition to their conserved GTPase domain, Rabs contain a hypervariable C-terminal domain (HVD), which supports GEF recognition and therefore correct localization of the Rab (Thomas et al., 2018)Among various other functions, Rab GTPases are critical for the fusion of vesicles with the acceptor membrane by recruiting tethering proteins, which bring the two membranes into close proximity. Tethers, together with Sec1/Munc18 proteins, promote the folding of membrane-bound SNAREs at the vesicle and the target membrane into tetrameric coiled-coil complexes. This process further reduces the distance between the membranes, bypasses the hydration layer on membranes, and results in mixing of lipid bilayers and consequently membrane fusion (Wickner and Rizo, 2017; Ungermann and Kümmel, 2019).Organization and function of the endolysosomal pathwayEndocytosis allows the rapid adaptation of plasma membrane composition in response to changing environmental conditions by the uptake of membrane proteins from the plasma membrane, which are either transported to and finally degraded in the lysosome or sorted back to the plasma membrane, e.g., receptors after releasing their cargo within the endosomal lumen (Sardana and Emr, 2021). A third fate of endocytosed cargo is trafficking to the Golgi (Laidlaw and MacDonald, 2018). In addition, various kinds of endocytosis allow the uptake of very large particles such as bacteria during phagocytosis or fluids during pinocytosis (Huotari and Helenius, 2011; Babst, 2014). The endocytic pathway is also involved in the quality control system of plasma membrane proteins and allows degradation of damaged cell surface proteins as well as the down-regulation of nutrient transporters and receptors (Sardana and Emr, 2021). During endocytosis, membrane proteins marked by ubiquitination are incorporated into endocytic vesicles, which pinch off the plasma membrane and fuse with the tubular-shaped early endosome (EE) in the cell periphery (Fig. 1 A). The EE serves as a sorting station, at which membrane proteins are either sorted into tubular structures and brought to the recycling endosome (RE) or get incorporated into intraluminal vesicles (ILVs) with the help of four endosomal sorting complexes required for transport (ESCRTs; Sardana and Emr, 2021). A prerequisite for the degradation of cargo in the lysosome is the maturation of EEs into late endosomes (LEs) by changing the organelle surface composition, including specific Rab GTPases and PIPs, and organelle shape. The LE is eventually spherically shaped, containing multiple ILVs and a more acidified lumen. Therefore, it is also called Multivesicular Body (MVB). Upon fusion with the lysosome, ILVs and their content are degraded into precursor molecules, which are reused by the cell (Fig. 1 A; Sardana and Emr, 2021; Huotari and Helenius, 2011).Open in a separate windowFigure 1.Rab GTPases in the endolysosomal pathway.(A) Localization of key Rab GTPases along the endolysosomal pathway. Endocytic vesicles containing cargo (blue dot) or receptor proteins (red) are substrates of endocytosis. Endocytic vesicles (EV) fuse with the EE. Rabs are shown by numbers: Rab5 (green) on early EE is replaced by Rab7 (black) on multivesicular bodies (MVBs). GEFs are shown in blue. Positioning of lysosomes (Lys) depends on binding to motor proteins by either Arl8b (orange, 8b) or Rab7. Recycling occurs via REs involving Rab4, Rab11, and Rab14. MTOC, microtubule organizing center; Nuc, nucleus. (B) Spatiotemporal Rab5-to-Rab7 transition during endosomal maturation. Rab5 (green graph) is rapidly recruited to EE and replaced by Rab7. (C) Model of Rab7 GEF recruitment and activation on endosomes. Mon1-Ccz1 (or the trimeric complex additionally containing Rmc1/C18orf8/Bulli, as indicated by the unlabeled hexagon) requires Rab5-GTP for activation to promote Rab7 recruitment. For details, see text.Central functions of Rab5 and Rab7Along the endolysosomal system, several Rabs coordinate sorting and recycling processes at the EE and LE. Early endosomal Rab5 and late endosomal Rab7 are here the key Rabs conserved among species. Their spatiotemporal activation and therefore functions are tightly coordinated on the level of the MVB/LE (Fig. 1 B).In yeast, the Rab5-like GTPases Vps21, Ypt52, Ypt52, and Ypt10 and the Rab7-like Ypt7 structure the endocytic pathway (Singer-Krüger et al., 1994; Wichmann et al., 1992). In mammalian cells, Rab5 (with Rab5a, b, and c isoforms having nonredundant functions in the endocytic network; Chen et al., 2014, 2009) and Rab7 (with Rab7a and b isoforms, of which Rab7a is the main actor in transport processes along the endocytic pathway [Guerra and Bucci, 2016], whereas Rab7b has a role in the transport from endosome to the Golgi [Kjos et al., 2017; Progida et al., 2010]) are present (Wandinger-Ness and Zerial, 2014). While the overall organization of the endocytic pathway into EE and LE is conserved, yeast seems to have a more ancestral minimal endomembrane system, where the trans-Golgi network acts as EE and RE (Day et al., 2018). In mammalian cells, the more complex endolysosomal system depends on additional Rabs. Rab4 is involved in protein sorting at the EE, activation of Rab5, and recycling of cargo back to the plasma membrane (Kälin et al., 2015; Wandinger-Ness and Zerial, 2014; de Renzis et al., 2002), whereas Rab11 and Rab14 function at REs (Fig. 1 A; Linford et al., 2012; Takahashi et al., 2012). Furthermore, Rab9 is required for retrograde transport between LEs and the trans-Golgi network (Lombardi et al., 1993), and Rab32 and Rab38 function in the biogenesis of lysosome-related organelles (Bowman et al., 2019; Gerondopoulos et al., 2012; Wasmeier et al., 2006).During endosomal maturation, Rab5 is exchanged for Rab7 (Rink et al., 2005; Poteryaev et al., 2010). This Rab switch is highly conserved and a prime example of coordinated Rab turnover during organelle maturation. The rapid transition from Rab5 to Rab7 was explained by a so-called cutout switch, where activation of Rab5 fosters at a threshold value activation of Rab7, which in turn suppresses further Rab5 activation (Fig. 1 B; Del Conte-Zerial et al., 2008). Such a principle may apply to most Rab cascades (Barr, 2013).Rab5 has multiple functions on EEs (Wandinger-Ness and Zerial, 2014). It interacts with a number of effectors such as the lipid kinase Vps34, Rabaptin-5, which is found in complex with the Rab5-GEF Rabex5, Rabenosyn-5, and tethers such as the class C core vacuole/endosome tethering (CORVET) complex or EEA1. Therefore, Rab5 is critical for the homotypic fusion of EEs (Gorvel et al., 1991; Ohya et al., 2009; Christoforidis et al., 1999a, b; Perini et al., 2014; Marat and Haucke, 2016). Vps34 was initially identified in yeast (Schu et al., 1993) and exists in two heterotetrametric complexes, which differ by just one subunit (Kihara et al., 2001). Complex I resides on autophagosomes, whereas complex II functions on endosomes (Fig. 2 D). Both complexes generate a local pool of phosphatidylinositol-3-phosphate (PI3P), to which several effectors bind, including the early endosomal tether EEA1 and ESCRTs (Wallroth and Haucke, 2018). Recent structural insights revealed that Rab5 recruits and activates endosomal complex II, whereas Rab1 acts similarly on autophagosomal complex I (Tremel et al., 2021). This explains how Rab5-GTP promotes the formation of a local endosomal PI3P pool (Franke et al., 2019). Interestingly, Caenorhabditis elegans VPS-34 can recruit the Rab5 GAP TBC-2 to endosomal membranes, suggesting a possible link between PI3P generation and Rab5 inactivation (Law et al., 2017).Open in a separate windowFigure 2.Rab7 activation on autophagosomes.(A and B) Atg8-dependent Mon1-Ccz1 recruitment and activation. Atg8 (violet) recruits Mon1-Ccz1 (and likely also the trimeric GEF complex in higher eukaryotes, as indicated by the unlabeled hexagon) and allows fusion with lysosome. (C) Model of spatiotemporal Rab7 activation on autophagosomes. Maturation is prerequisite for successful fusion. (D) Comparison of proteins involved in maturation of LEs and autophagosomes.Rab7 is a key component in the late endocytic pathway (Langemeyer et al., 2018a). It is found on LEs, lysosomes, and autophagosomes and is required for the biogenesis and positioning of LEs and lysosomes, for MCSs of lysosomes with other organelles, and for the fusion of endosomes and autophagosomes with lysosomes (Fig. 1 A; Guerra and Bucci, 2016; McEwan et al., 2015; Ballabio and Bonifacino, 2020; Cabukusta and Neefjes, 2018). Even though both the metazoan Rab7 and yeast Ypt7 are activated by the homologous Mon1-Ccz1 GEF complex and are required for endosomal maturation, their function on LEs and lysosomes is not entirely conserved. In yeast, active Ypt7 directly binds the hexameric homotypic fusion and vacuole protein sorting (HOPS) tethering complex and mediates SNARE-dependent fusion of LEs or autophagosomes with vacuoles as well as homotypic vacuole fusion (Wickner and Rizo, 2017; Gao et al., 2018a, b). In higher eukaryotes, HOPS also promotes fusion between LEs and lysosomes, yet apparently does not directly interact with Rab7, but rather with the GTPases Rab2 and Arl8b (Gillingham et al., 2014; Fujita et al., 2017; Lőrincz et al., 2017; Khatter et al., 2015). How Rab7 contributes to fusion at the lysosome is still unclear. Rab7 interacts with several proteins on lysosomes, including the cholesterol sensor ORPL1 and the dynein-interacting lysosomal RILP (Jordens et al., 2001; Cantalupo et al., 2001; Rocha et al., 2009). Both proteins also bind HOPS (van der Kant et al., 2015, 2013), as does another multivalent adaptor protein, PLEKHM1 (McEwan et al., 2015), which binds both Arl8b and Rab7 (Marwaha et al., 2017). Interestingly, Arl8b in complex with its effector SKIP also binds TBC1D15, a Rab7 GAP, which may displace Rab7 from LEs before their fusion with lysosomes (Jongsma et al., 2020). It is thus possible that fusion of LEs and autophagosomes with lysosomes requires a complex coordination of the three GTPases, Rab7, Arl8b, and Rab2, with the HOPS complex and other effectors. Some of this complexity may be explained by a second function of Rab7 and Arl8b in binding adapters of the kinesin or dynein motor protein family, which connect LEs and lysosomes to the microtubule network. Thereby Rab7 and Arl8b control the positioning of these organelles to the periphery or perinuclear area via the microtubule network, which has functional implications (Fig. 1 A; Cabukusta and Neefjes, 2018; Bonifacino and Neefjes, 2017). Perinuclear lysosomes are the main places for degradation of cargo delivered by endosomes and autophagosomes, whereas peripheral lysosomes are involved in the regulation of mammalian target of rapamycin complex1 (mTORC1), the master regulator switching between cell growth and autophagy (Johnson et al., 2016; Korolchuk et al., 2011). This also may be connected to the role of lysosomes in lipid homeostasis, as Rab7 seems to control cholesterol export via the lysosomal NPC1 (van den Boomen et al., 2020; Shin and Zoncu, 2020; Castellano et al., 2017). How far the acidification state of perinuclear and peripheral lysosomes also affects their Rab7 and Arl8b mediated localization is still under debate (Ponsford et al., 2021). Thus, it is likely that Rab7 coordinates LE and lysosomal transport and fusion activity in coordination with endosomal biogenesis and cellular metabolism.GEF function and regulation in endosomal maturationThe heterodimeric complex Mon1-Ccz1 was identified as the GEF for Ypt7 in yeast and for Rab7 in higher eukaryotes (Nordmann et al., 2010; Gerondopoulos et al., 2012). The Mon1-Ccz1 complex is an effector of Rab5 (Kinchen and Ravichandran, 2010; Langemeyer et al., 2020; Cui et al., 2014; Li et al., 2015; Poteryaev et al., 2010; Singh et al., 2014), suggesting a direct link to endosomal maturation and Rab turnover (Fig. 1 B). Structural analyses uncovered how the two central longin domains in Mon1 and Ccz1 displace the bound nucleotide from Ypt7 (Kiontke et al., 2017). Unlike yeast, the metazoan Mon1-Ccz1 complex contains a third subunit termed RMC1 or C18orf8 in mammals and Bulli in Drosophila (Vaites et al., 2017; Dehnen et al., 2020; van den Boomen et al., 2020). Even though loss of this subunit impairs endosomal and autophagosomal biogenesis, this subunit does not affect GEF activity toward Rab7 in vitro (Dehnen et al., 2020; Langemeyer et al., 2020), indicating that the general GEF mechanism is conserved across species. As Rab7 is required on LEs, autophagosomes, and lysosomes, spatial recruitment and activity of the Rab7 GEF must be tightly regulated.Rab5 activates the Mon1-Ccz1 GEF complexDuring endosomal maturation, the Mon1-Ccz1 complex is recruited to Rab5- and PI3P-positive endosomes and activates Rab7 for subsequent fusion of endosomes with lysosomes (Nordmann et al., 2010; Poteryaev et al., 2010; Cabrera and Ungermann, 2013; Cabrera et al., 2014; Singh et al., 2014; Fig. 1 C). However, it was postulated that (but remained unclear how) Rab5 affects Rab7 GEF activity. The activity of GEFs is in the simplest way determined in solution, where the respective Rab, which has been loaded with a fluorescent- or radioactive-labeled nucleotide, is incubated with the GEF (Schoebel et al., 2009; Bergbrede et al., 2009). GDP or GTP addition then triggers displacement of the bound nucleotide, which results in a decrease of fluorescence or increase of radioactive signal in solution. Such in-solution assays can uncover the Rab specificity of GEFs yet cannot recapitulate the membrane context and potential regulating factors. Recent approaches therefore used liposomes and prenylated Rab:GDI complexes to address the role of membrane lipids and proteins in GEF activation (Thomas and Fromme, 2016; Thomas et al., 2018; Langemeyer et al., 2020, 2018b; Cezanne et al., 2020; Bezeljak et al., 2020). Details of these reconstituted systems are discussed below. In yeast, prenylated, membrane-bound, and GTP-loaded Rab5-like Vps21 was surprisingly inefficient as a single factor to recruit Mon1-Ccz1 to membranes, whereas addition of PIPs together with Vps21 enhanced recruitment (Langemeyer et al., 2020). However, activity of both the yeast and metazoan Rab7 GEF complexes showed a striking dependence on membrane-bound Rab5-GTP in the GEF assay, whereas PIPs alone were not sufficient to drive GEF activation. These observations demonstrate that the Mon1-Ccz1 complex depends on membrane-bound Rab5 for its Rab7 GEF activity, which nicely explains some of the previous in vivo observations on endosomal Rab5-to-Rab7 exchange (Poteryaev et al., 2010; Rink et al., 2005).This Rab exchange, which occurs similarly on phagosomes (Jeschke and Haas, 2016), is in vivo likely regulated in space and time. Time-lapse microscopy studies revealed that levels of fluorescently labeled Rab5 decreased, while fluorescently labeled Rab7 increased on the surface of a tracked endosome (Poteryaev et al., 2010; Yasuda et al., 2016). Analysis of the spatiotemporal Rab5-to-Rab7 transition in mammalian cells revealed that Rab5-positive endosomes can separate from Rab7-positive membranes, suggesting that a stepwise maturation process also occurs in some cells (Skjeldal et al., 2021). However, in all cases, only some insights on Mon1-Ccz1 regulation are presently available. Phosphorylation is one potential regulatory mechanism in GEF regulation (Kulasekaran et al., 2015). Indeed, yeast Mon1-Ccz1 is a substrate of the vacuolar casein kinase 1 Yck3 (Lawrence et al., 2014). When added to the Rab5-dependent GEF assay, Yck3-mediated phosphorylation inhibited Mon1-Ccz1 GEF activity, presumably by blocking the Rab5 interaction (Langemeyer et al., 2020). How the kinase is in turn regulated and whether this is the only mechanism of Mon1-Ccz1 GEF control is currently unknown.Rab7 activation and function in autophagyThe lysosome is also the destination of the autophagic catabolic pathway. During autophagy, portions of the cytosol, specific organelles, aggregates, or pathogens are engulfed into a double-layered membrane, which upon closure fuses with the lysosome for degradation and reuse of its content (Fig. 2 A; Zhao and Zhang, 2019; Nakatogawa, 2020). Autophagy is a versatile pathway required for adaptation of a cell’s organelle repertoire and quality control.Rab7 is found not just on LEs, but also on autophagosomes (Hegedűs et al., 2016; Gao et al., 2018a), although its precise function seems to differ between organisms (Kuchitsu and Fukuda, 2018). In yeast, the Rab7-homologue Ypt7 mediates HOPS-dependent fusion of autophagosomes with vacuoles (Gao et al., 2018a). In metazoan cells, Rab7 and its effectors PLEKHM1 and WDR91 are required for autolysosome/amphisome-lysosome fusion, yet Rab7 does not seem to directly bind HOPS during fusion of autophagosomes with lysosomes (Xing et al., 2021; McEwan et al., 2015; Gutierrez et al., 2004; Kuchitsu and Fukuda, 2018).Given the striking Rab5 dependence on endosomes in Mon1-Ccz1 activation, the question arises, how does Mon1-Ccz1-mediated Rab7 activation happen on autophagosomes? Some data suggest that yeast and metazoan Rab5 is directly involved in the autophagy process such as autophagosome closure (Ravikumar et al., 2008; Bridges et al., 2012; Zhou et al., 2019, 2017), whereas others do not find direct evidence, for instance in Drosophila (Hegedűs et al., 2016). Studies in yeast revealed that the LC3–like Atg8 protein directly binds and recruits Mon1-Ccz1 to the autophagosomal membrane during starvation, which results in Ypt7 activation as a prerequisite of HOPS-dependent fusion with the vacuole (Gao et al., 2018a; Fig. 2 B). Tight regulation of Mon1-Ccz1 GEF-activity is apparently mandatory to avoid fusion of premature autophagosomes with the vacuole (Fig. 2 C). How Mon1-Ccz1 localization to either endosomes or autophagosomes is coordinated (also with regard to similarities in organelle features; Fig. 2 D) and whether Atg8/LC3 also regulates the activity of the GEF complex are not yet known.Of note, an endosomal-like Rab5-to-Rab7 cascade also occurs on the mitochondrial outer membrane during mitophagy in metazoan cells, a selective pathway to degrade damaged mitochondria (Yamano et al., 2018). Here, Rab5 is activated by a mitochondrially localized Rab5 GEF, followed by Mon1-Ccz1 recruitment and Rab7A activation, which then orchestrates the subsequent mitophagy process. How this process is coupled to autophagosome maturation, and whether Rab7 is then again needed on the formed autophagosome, has not been addressed so far.These data nevertheless demonstrate the adjustable recruitment of Mon1-Ccz1 during endosomal maturation and autophagosome formation and even to the mitochondrial surface. Targeting of the Mon1-Ccz1 complex is likely coordinated between all these processes.A role for ER-endosome MCSs in endosome maturationEndosomes form MCSs with the ER. Such contact sites have multiple roles ranging from lipid transport to ion exchange (Scorrano et al., 2019; Reinisch and Prinz, 2021). The endosome-ER contact depends on Rab7 and contributes to transport and positioning of endosomes, supports endosomal fission, and facilitates endocytic cargo transport and cholesterol transfer between LEs and the ER (Rocha et al., 2009; Friedman et al., 2013; Rowland et al., 2014; Raiborg et al., 2015; Jongsma et al., 2016). Rab7 activation via the Mon1-Ccz1 complex is required for cholesterol export from the lysosome, likely in the context of MCSs. Rab7 binds to the NPC1 cholesterol transporter and may thus promote cholesterol export only at MCSs with the ER or other organelles (van den Boomen et al., 2020). The ER is also involved in endosome maturation, which requires an MCS between Reticulon-3L on the ER and endosomal Rab9. In fact, Rab9 is recruited shortly before the Rab5-to-Rab7 transition (Wu and Voeltz, 2021; Kucera et al., 2016). How Rab9 activation and MCS formation are coordinated with endosomal maturation has not yet been revealed. It is likely that the spatial positioning of endosomes (Fig. 1 A), their acidification, and TORC1 activity also contribute to this process (Bonifacino and Neefjes, 2017; Johnson et al., 2016).Retromer opposes Rab7 activationRetromer is a conserved heteropentameric complex that mediates the formation of vesicular carriers at the endosome and thus allows the transport of receptors back to the Golgi or plasma membrane. The complex consists of a trimeric core (Vps35, Vps26, and Vps29), which binds either a SNX1-SNX4 heterodimer or a SNX3 monomer (Simonetti and Cullen, 2018; Leneva et al., 2021; Kovtun et al., 2018). Retromer is an effector of Rab7, but also recruits the Rab7 GAP TBC1D5 in metazoan cells (Rojas et al., 2008; Kvainickas et al., 2019; Jimenez-Orgaz et al., 2018; Distefano et al., 2018; Seaman et al., 2009). This dual function of retromer may facilitate the formation of endosomal tubules after the Rab5-to-Rab7 transition, and these tubules eventually lose Rab7 once scission has occurred (Jongsma et al., 2020).It is not yet clear how conserved the Rab7-retromer-GAP connection is. Yeast retromer is also an effector of the Rab7-like Ypt7 and coordinates protein recycling at the endosome (Liu et al., 2012; Balderhaar et al., 2010), yet a role of a Rab7 GAP has not been described. However, yeast retromer also binds to the Rab5 GEFs Vps9 and Muk1 (Bean et al., 2015), which suggests that both Rab5 and Rab7 function contribute to efficient tubule formation at the endosome. Whether and how the Rab7 GEF Mon1-Ccz1 is functionally coordinated with retromer will be a topic of future studies.GEF regulation along the endomembrane systemIn the previous section, we focused mainly on the role of the Rab7 GEF in the context of endosome and autophagosome maturation. However, the timing of GEF activation and the subsequent recruitment of their target Rabs is critical for all membrane trafficking processes along the endomembrane system to guarantee maintenance of intracellular organelle organization. Rabs in turn interact with effectors, and effectors such as the lysosomal HOPS complex not only bind SNAREs but also catalyze their assembly and thus drive membrane fusion (Fig. 3 A). The spatiotemporal regulation of GEF activation is therefore at the heart of organelle biogenesis and maturation, and thus membrane trafficking. Within this section, we will now broaden our view by comparing different regulatory principles of GEFs.Open in a separate windowFigure 3.Regulatory mechanisms influence the activity of GEFs.(A) Hierarchical cascade of factors controlling membrane fusion. GEFs integrate various signals and initiate a cascade of protein activities, finally leading to membrane fusion. Signaling lipids, the presence of cargo proteins, upstream GTPases, and kinases influence the activity of GEFs and therefore determine Rab GTPase activation. Consequently, effector proteins such as tethering factors are recruited. This ultimately leads to SNARE-mediated lipid bilayer mixing and membrane fusion. (B) A Rab cascade in yeast exocytosis. Active Ypt32 and PI4P (yellow) on late Golgi compartments and secretory vesicles recruit the GEF Sec2, which in turn promotes activation and stable membrane insertion of the Rab Sec4. (C) Mon1-Ccz1 regulation by phosphorylation. Mon1-Ccz1 is recruited to and activated on LEs by coincidence detection of membrane-associated Rab5 and PI3P (red, Fig. 1 C) and promotes stable membrane insertion of Rab7. This process is terminated by Mon1-Ccz1 phosphorylation by the type I casein kinase Yck3 in yeast (orange). (D) A positive feedback loop of GEF activation on endocytic vesicles and EEs. The Rab5 GEF Rabex-5 binds ubiquitinated cargo on endocytic vesicles and is autoinhibited. Rab5 recruits Rabaptin-5, which binds Rabex-5 and releases the GEF from autoinhibition, generating a positive feedback loop. (E) Membrane factors determine GEF activity of TRAPPII at the trans-Golgi. TRAPPII activity for the Rab Ypt32 requires membrane-associated Arf1 and PI4P. (F) The length of the hypervariable domain of Golgi Rabs defines the substrate specificity for TRAPP complexes. The yeast Rab GTPases Ypt1 and Ypt32 differ in the length of their C-terminal HVD (box). TRAPPII and TRAPPIII complexes have the same active site, which is positioned away from the membrane, and thus discriminate Rab accessibility. (G) Phosphorylation as a mechanism to promote GEF activity. DENND1 GEF activity is autoinhibited, which is released by Akt-mediated phosphorylation. For details, see text.A Rab cascade in exocytosisAnother well-characterized Rab cascade is involved in the exocytic transport of secretory vesicles from the trans-Golgi network to the plasma membrane. At the trans-Golgi, the GEF transport protein particle II (TRAPPII) activates the Rab GTPase Ypt32, which then recruits the GEF Sec2 to secretory vesicles. Sec2 in turn activates the Rab Sec4, which binds the Sec15 subunit of the Exocyst tethering complex and allows vesicles to dock and fuse with the plasma membrane (Fig. 3 B; Walch-Solimena et al., 1997; Ortiz et al., 2002; Dong et al., 2007; Itzen et al., 2007). This cascade is conserved in humans. During ciliogenesis at the plasma membrane, the Ypt32 homologue Rab11 recruits the GEF Rabin 8, which in turn activates the human Sec4 homologue Rab8, a process regulated by phosphorylation (Hattula et al., 2002; Wang et al., 2015; Knödler et al., 2010). Interestingly, yeast Sec2 not only is a GEF, but also interacts with the Sec4 effector Sec15 (Medkova et al., 2006), a principle also observed in the endocytic Rab5 activation cycle, where the GEF Rabex5 interacts with the Rab5 effector Rabaptin-5. This dual role may also apply to Mon1-Ccz1, as the Mon1 homologue in C. elegans, SAND1, and yeast Mon1-Ccz1 can bind the HOPS tethering complex (Poteryaev et al., 2010; Nordmann et al., 2010).At the Golgi, phosphatidylinositol-4-phosphate (PI4P) contributes to directionality and spatiotemporal regulation of the exocytic Rab cascade. Sec2 binds both Ypt32 and PI4P on secretory vesicles via two binding sites, a process called coincidence detection. However, PI4P binding inhibits the interaction of Sec2 with Sec15. As vesicles reach the cell periphery, PI4P levels drop by the activity of Osh4, a lipid transporter, which allows Sec2 to bind the Exocyst subunit rather than Ypt32 (Ling et al., 2014; Mizuno-Yamasaki et al., 2010). In addition, Sec2 is phosphorylated by the plasma membrane–localized casein kinases Yck1 and Yck2 (Stalder et al., 2013; Stalder and Novick, 2016), resulting in effector recruitment rather than further Rab activation.Such a regulation may also apply to yeast Mon1-Ccz1. Anionic phospholipids and PI3P support Mon1-Ccz1 recruitment to liposomes and vacuoles (Langemeyer et al., 2020; Cabrera et al., 2014; Lawrence et al., 2014), whereas phosphorylation of the complex by the casein kinase Yck3 inhibits the binding of Mon1-Ccz1 to the Rab5-like Ypt10 and consequently reduces its GEF activity toward Rab7 (Fig. 3 C; Langemeyer et al., 2020). These observations suggest that the phosphorylation of GEFs by kinases may be a general regulatory principle in Rab cascades.Autoinhibition controls the Rab5 GEFAnother widely used regulatory mechanism is the autoinhibition of GEFs to control their activity. This has been analyzed in detail for the early endosomal Rab5-specific GEF Rabex-5, which interacts with the Rab5-effector Rabaptin-5 (Horiuchi et al., 1997). One factor for Rabex-5 recruitment to endocytic vesicles are ubiquitinated cargo proteins at the plasma membrane (Fig. 3 D; Mattera et al., 2006; Lee et al., 2006). Yet, isolated Rabex-5 has only low GEF activity in vitro (Delprato and Lambright, 2007). Structural analysis revealed that binding of Rabaptin-5 to Rabex-5 causes a rearrangement in the Rabex-5 C-terminus, which releases the GEF from autoinhibition and therefore facilitates nucleotide exchange of Rab5 (Delprato and Lambright, 2007; Zhang et al., 2014). On endosomes, increasing amounts of Rab5-GTP further promotes recruitment of the Rabex-5–Rabaptin-5 complex, resulting in a positive feedback loop of Rab5 activation and GEF recruitment (Lippé et al., 2001). Overall, Rabex-5 GEF activity is regulated by autoinhibition, a feedback loop with the Rab5 effector protein Rabaptin-5, and ubiquitinated cargo, which guarantees precise timing in establishing a Rab5-positive endosome. Of note, the Mon1 subunit of the Rab7 GEF can displace Rabex-5 from endosomal membranes (Poteryaev et al., 2010), which suggests a negative feedback loop of the Rab5 activation cascade once the next GEF is present.Regulation of Arf1 GEFs at different Golgi subcompartmentsThese key principles of GEF regulation in GTPase cascades are also found for Arf GTPases. Arf GTPases are soluble in their GDP-bound state by shielding their N-terminal myristate anchor in a hydrophobic pocket. Like Rabs, Arf GTPases are activated by specific GEFs, and their inactivation requires a specific GAP (Sztul et al., 2019). However, this review only highlights some key findings in the regulation of Rab GEFs and does not address regulation of the corresponding GAPs. Once activated, Arfs insert their lipid anchor and an adjacent amphipathic helix into membranes and are then able to bind effector proteins (Sztul et al., 2019). One of the best-studied Arf-GEFs is Sec7, which activates Arf1, an Arf GTPase involved in intra-Golgi trafficking (Achstetter et al., 1988). Studies on yeast Sec7 revealed that the protein is autoinhibited in solution and depends on three small GTPases—Arf1, the Rab Ypt1, and the Arf-like Arl1—for recruitment to the Golgi, a process supported by anionic lipids found in the late Golgi compartment. Importantly, the late Golgi Rabs Ypt31/32 strongly stimulate GEF activity (McDonold and Fromme, 2014; Richardson et al., 2012, 2016), indicating allosteric activation, as observed for Rab5-dependent Mon1-Ccz1 activation (Langemeyer et al., 2020). In this process, Sec7 dimerizes and promotes Arf1 recruitment and thus establishes a positive feedback loop. Interestingly, membrane binding of two additional Arf1 GEFs of the early Golgi, Gea1/2, depends on Rab1/Ypt1 and neutral membranes. Under these conditions, Gea1/2 is released from autoinhibition, although no positive feedback loop was observed (Gustafson and Fromme, 2017). Thus, Arf GEF regulation and Arf activation are tightly linked to multiple small GTPases and the membrane environment to establish Golgi compartments.Regulation and specificity of TRAPP complexes at the GolgiArf1 activation is also linked to the activation of Golgi-specific Rabs. Arf1-GTP binds to the highly conserved TRAPP GEF complexes at the Golgi (Fig. 3 E). Yeast and mammalian cells contain two TRAPP complexes. In yeast, both complexes share seven core components. TRAPPIII in addition contains Trs85, while accessory TRAPPII subunits are instead Trs130, Trs120, Trs65, and Tca17. Metazoan TRAPP complexes contain additional subunits (Lipatova and Segev, 2019).Interestingly, both complexes share the same catalytic site for Rab1/Ypt1 and Rab11/Ypt32. However, TRAPPIII provides GEF activity toward Rab1/Ypt1. Initially, it was proposed that TRAPPII can activate both Rab1/Ypt1 and Rab11/Ypt32 (Thomas et al., 2019, 2018; Thomas and Fromme, 2016; Riedel et al., 2018); however, it was recently shown that the TRAPPII complex is specific for Rab11/Ypt32 (Riedel et al., 2018; Thomas et al., 2019). Reconstitution of GEF activity on liposomes helped here to unravel TRAPP complex substrate specificity, since in solution assays are not adequate to address some of the features important for specific interactions: Rab11/Ypt32 has a longer HVD between the prenyl anchor and the GTPase domain compared with Rab1/Ypt1 (Fig. 3 F, box). The HVD not only binds TRAPPII but also stretches a longer distance from the membrane (Fig. 3 F). Thereby it allows Rab11/Ypt32, but not Rab1/Ypt1, to reach the active site of membrane-bound TRAPPII. Thus, substrate specificity is controlled by the distance of the GTPase domain from the membrane surface, since the active site seems to be located on the opposing site of the complex from the site of membrane interaction (Fig. 3 F; Thomas et al., 2019). The smaller TRAPPIII has its active site closer to the membrane, binds Ypt1 via its shorter HVD, and facilitates its activation, while Ypt32 with its longer HVD may be positioned too far away from the active site. In addition, both complexes require their respective membrane environment for optimal activity, indicating how Arf and Rab GEFs cooperate in Golgi biogenesis.The GEF DENND1 requires Arf5 for Rab35 activationRecently, another example of Arf-mediated Rab activation was reported (Kulasekaran et al., 2021). Rab35, an endocytic Rab found at the plasma membrane and REs (Sato et al., 2008; Kouranti et al., 2006), is involved in cell adhesion and cell migration by controlling the trafficking of β1-integrin and the EGF receptor (Klinkert and Echard, 2016; Allaire et al., 2013). Arf5 binds the Rab35 GEF DENND1 and stimulates its GEF activity, with dysregulation of this cascade linked to glioblastoma growth (Kulasekaran et al., 2021). DENND1 GEF activity is initially autoinhibited and relieved by phosphorylation via the central Akt kinase (Fig. 3 G; Kulasekaran et al., 2015). Similarly, another DENN-domain containing GEF, DENND3, is phosphorylated by the autophagy-specific ULK kinase and then activates Rab12, a small GTPase involved in autophagosome trafficking (Xu et al., 2015). Thus, it seems that Rab GEF activation is more generally linked to other trafficking proteins, such as Arfs, and controlled by kinases and likely also phosphatases.Lessons from reconstitutionOrganelle biogenesis and maintenance in the endomembrane system are tightly linked to the correct spatial and temporal activation of Rab GTPases. A small yeast cell gets by with 11 Rabs, while human cells encode >60 (Hutagalung and Novick, 2011). Rab activation, and therefore membrane identity, of each organelle depends on the cognate GEF. This puts GEFs into the driver’s seat of any Rab-directed function at cellular membranes. It seems that GEFs integrate, by several regulatory loops, incoming signals from various sources such as membrane composition, cargo proteins, upstream GTPases, or kinases/phosphatases (Fig. 3 A). Yet our insights on the specific membrane targeting and regulation of GEFs remain incomplete for want of available experimental approaches. We briefly discuss here how recent advances on the reconstitution of GEF-mediated Rab activation at model membranes have advanced our understanding of organelle maturation and biogenesis.Reconstitution of any reaction to uncover the essential constituents is limited by the available tools. GEFs, Rabs, Sec18/Munc1 proteins, tethering factors, and SNAREs are for instance required for membrane fusion (Fig. 3 A). Initial assays focused on SNAREs and revealed their important but rather inefficient fusogenicity (Weber et al., 1998). Further analyses uncovered critical activation steps for SNAREs (Malsam et al., 2012; Pobbati et al., 2006; Südhof and Rothman, 2009; Jahn and Scheller, 2006), yet fusion at physiological SNARE concentrations in various in vitro systems does not occur, unless assisted by chaperoning Sec1/Munc18 proteins and tethering factors (Bharat et al., 2014; Lai et al., 2017; Mima and Wickner, 2009; Ohya et al., 2009; Wickner and Rizo, 2017). Most tethers again depend on Rabs for their localization, and Rab localization to membranes requires a GEF (Cabrera and Ungermann, 2013), whose activity can be a limiting factor for fusion (Langemeyer et al., 2020, 2018b). The long avenue of understanding the mechanism and regulation of membrane fusion exemplifies the challenges in dissecting the complexity of a cellular reaction, but also demonstrates the powerful insights obtained from reconstitution of these processes.GEFs determine the localization of the corresponding Rab, and consequently, Rabs follow their GEF if they are mistargeted (Gerondopoulos et al., 2012; Blümer et al., 2013; Cabrera and Ungermann, 2013). However, these anchor-away approaches completely bypass the tight cellular regulation of GEF activation by the mistargeting and additional overexpression of the GEF protein and may allow only statements about GEF/substrate specificity. The spatiotemporal activation of each GEF at the right organelle is vital for the timing of all downstream reactions. GEFs are recruited to membranes by coincidence detection, which includes membrane lipids such as PIPs, membrane packaging defects, and peripheral membrane proteins such as upstream Rabs or other small GTPases. This recruitment is often accompanied by the release from autoinhibition, which may be triggered or inhibited by other regulatory processes such as phosphorylation. It comes as no surprise that pathogens such as Legionella and Salmonella take advantage of the central function of GEFs to establish and nourish their intracellular organellar niche by manipulating small GTPase activity (Spanò and Galán, 2018).To understand the specificity of Rab GEFs (and GAPs), mostly very simplified systems were used. Most GEF assays analyze soluble Rabs loaded with fluorescent 2′-O-(N-methylanthraniloyl) (MANT)-nucleotide or radioactively labeled GTP/GDP and soluble GEF in a test tube, where nucleotide exchange activity is observed upon addition of unlabeled nucleotide (Fig. 4 A). This strategy allows the identification of substrate (Rab) specificity of GEFs, but could also lead to misleading results, as pointed out earlier on the example of the TRAPP complexes and Rab1/Ypt1 or Rab11/Ypt32. In addition, GEF-Rab pairs negatively regulated by one of the above principles could easily be missed.Open in a separate windowFigure 4.Approaches to determine GEF activity in vitro. Methods to determine GEF activity for Mon1-Ccz1. In all approaches, Rab7 is preloaded with fluorescent MANT-GDP. Fluorescence decreases upon GEF-mediated nucleotide exchange. (A) GEF assays. (Ai) In-solution Rab GEF assay. Mon1-Ccz1 (blue, Bulli/Rmc1/C18orf8 subunit, indicated by unlabeled hexagon) and Rab7 (gray) are freely diffusible in the test tube, which results in random collision and Rab activation. (Aii) GEF-mediated activation of artificially recruited Rab7 on liposomes. Rab7 with a C-terminal 6xHis-tag is permanently immobilized on membranes containing the cationic lipid DOGS-NTA. Mon1-Ccz1 unspecifically binds to this membrane surface and activates Rab7. Diffusion is limited to the membrane surface, thus increasing chances of interactions. (Aiii) Reconstitution of Rab5-mediated Rab7 activation by Mon1-Ccz1 on liposomes. Chemically activated, prenylated Rab5 (green), delivered to the membrane by the Rab Escort Protein (REP), allows Mon1-Ccz1 recruitment and Rab7 activation from the GDI complex (see text for further details). (B) Summary of Ai–Aiii. pren., prenylation.As Rabs and GEFs function on membranes, we and others adopted strategies for measuring Rab activation by GEFs on membranes (Fig. 4 B). In a first approach, Rab and other small GTPases (Sot et al., 2013; Schmitt et al., 1994) were immobilized with C-terminal hexahistidine tags on liposomes containing the polycationic lipid 1,2-dioleoyl-sn-glycero-3-[(N-(5-amino-1-carboxypentyl)iminodiacetic acid)succinyl] (DOGS-NTA) and observed higher activity of the added GEF (Cabrera et al., 2014; Thomas and Fromme, 2016). A drawback of this technique is the artificial membrane composition. To avoid potential artifacts of unnaturally charged membranes and permanently membrane-bound Rab, recent studies relied on prenylated Rabs in complex with GDI. Reflecting the natural source of the cytoplasmic Rab pool, this complex was used as a GEF substrate in the presence of liposomes mimicking the natural membrane composition (Cezanne et al., 2020; Bezeljak et al., 2020; Langemeyer et al., 2020, 2018b; Thomas et al., 2018, 2019; Thomas and Fromme, 2016).Even though these observations are recent, the outcome and the understanding of GEF regulation is encouraging. For the Rab5 GEF complex consisting of Rabex5 and Rabaptin5, GEF-dependent Rab5 recruitment to membranes revealed a self-organizing system, nonlinear Rab5 patterning, and collective switching of the Rab5 population (Bezeljak et al., 2020; Cezanne et al., 2020). This is in agreement with mathematical modeling and predictions on bistability and ultrasensitivity of Rab networks (Del Conte-Zerial et al., 2008; Barr, 2013). For the Golgi-resident TRAPPII and TRAPPIII complexes, the membrane composition, the length of the Rab HVD, and the presence of membrane-bound Arf1 determined the GEF specificity for their Rabs (Fig. 3 F; Thomas et al., 2019, 2018; Thomas and Fromme, 2016; Riedel et al., 2018), which is nicely supported by recent structural analyses of yeast and metazoan TRAPPIII (Galindo et al., 2021; Joiner et al., 2021)Our own data uncovered that the yeast and metazoan Mon1-Ccz1(-RMC1) complex required membrane-bound Rab5-GTP to activate Rab7 out of the GDI complex (Langemeyer et al., 2020). Surprisingly, Rab5-GTP not only determined membrane binding of Mon1-Ccz1, but also activated the GEF on membranes by a yet-unknown mechanism (Fig. 1 C). Phosphorylation of yeast Mon1-Ccz1 by the casein kinase Yck3 inhibited this activation, demonstrating possible regulation of GEF activity (Fig. 3 C). Importantly, this finding agrees with the observed Rab5-to-Rab7 switch in vivo (Poteryaev et al., 2010; Rink et al., 2005).Taken together, the available tools open exciting avenues for our understanding of organelle maturation. Reconstitution will allow the investigation of an entire Rab cascade and its regulation by kinases or membrane lipids. It will be possible to determine the cross-talk with lipid kinases and observe possible regulatory loops between Rabs and PI kinases (Tremel et al., 2021). We are confident that such analyses, complemented by in vivo analyses of Rabs or other small GTPases and their GEFs, will clarify the underlying mechanism of organelle maturation and biogenesis along the endomembrane system of eukaryotic cells.     

Cytokinetic astralogy

Division plane specification in animal cells has long been presumed to involve direct contact between microtubules of the anaphase mitotic spindle and the cell cortex. In this issue, von Dassow et al. (von Dassow et al. 2009. J. Cell. Biol. doi:10.1083/jcb.200907090) challenge this assumption by showing that spindle microtubules can effectively position the division plane at a distance from the cell cortex.Cell division, or cytokinesis, is accomplished via constriction of an equatorially localized contractile ring composed of filamentous actin and myosin II (Rappaport, 1996). Accurate division plane specification is essential to properly partition the cytoplasm and permit each daughter cell to receive a single copy of the genome. To ensure this accuracy, microtubules of the mitotic spindle signal to the cell cortex upon anaphase onset and promote assembly of the contractile ring between the separating chromosomes. The precise mechanism by which microtubules position the contractile ring, however, remains elusive.Early models on the nature of the spindle-derived signal proposed that astral rays (later found to be microtubules) position the division plane by either locally promoting contractility at the cell equator or inhibiting contractility at the cell poles (Rappaport, 1996). Recent evidence, though, suggests that distinct microtubule populations within a single cell provide multiple signals to promote accurate division (Canman et al., 2003; Bringmann and Hyman, 2005; Chen et al., 2008; Foe and von Dassow, 2008; von Dassow, 2009).The anaphase mitotic spindle contains several subtypes of microtubules, each of which is likely to contribute to division plane specification. Although kinetochore microtubules drive chromosome segregation during anaphase, nonkinetochore microtubules extend and maintain close proximity with the assembling central spindle (Mastronarde et al., 1993). Central spindle microtubules are highly stable (Salmon et al., 1976) and organize into an antiparallel bundled array between the separating chromosomes (Mastronarde et al., 1993). Preventing central spindle assembly usually results in a complete failure in cytokinesis, and prevents division plane specification in many cell types (Glotzer, 2005). Astral microtubules, however, are highly dynamic and grow out circumferentially from the centrosomes toward the cell cortex. Increasing evidence suggests that the astral microtubule signal inhibits contractility (see below; Canman et al., 2000; Kurz et al., 2002; Lewellyn et al., 2009).Regardless of the mechanism of division plane specification via microtubules, nearly all current models depend on direct contact between microtubules of the mitotic spindle and the cell cortex. Most of these models were based on observations that at the time of division plane specification, astral microtubules contact the cell cortex in nearly all systems studied. Nonkinetochore and/or central spindle microtubules have also been proposed to deliver critical contractile signals to the cell equator (Murata-Hori and Wang, 2002; Canman et al., 2003; Somers and Saint, 2003; Verbrugghe and White, 2004; Lewellyn et al., 2009; Vale et al., 2009). Yet in many cell types (especially early embryos), central spindle microtubules are at some distance from the cell cortex during division plane specification. Despite this, signal delivery for both astral and central spindle microtubules was proposed to occur via direct transport along microtubules to the cell cortex. The study of von Dassow et al. in this issue, however, indicates that accurate division plane specification does not require any close microtubule/cortical contact and may occur via a diffusion-based mechanism (see also Salmon and Wolniak, 1990).By treating echinoderm and Xenopus embryos with controlled levels of trichostatin A (TSA), which destabilizes acetylated dynamic microtubules via inhibition of the tubulin deacetylase HDAC6 (Matsuyama et al., 2002), von Dassow et al. (2009) were able to preferentially prevent astral microtubule growth while leaving central spindle microtubules intact. TSA treatment did not block anaphase onset or central spindle assembly, but resulted in the complete disruption of all direct microtubule contact with the cell cortex. Nevertheless, TSA-treated cells were able to undergo cytokinesis successfully (Fig. 1 A). The lack of astral microtubules in TSA-treated cells was also recapitulated by double centrosome ablation, and again the cells were able to undergo cytokinesis (Fig. 1 B). In both experiments, cytokinesis occurred in a timely manner, but the contractile ring was broader than in control cells. Together, these data suggest that spindle microtubules are sufficient to provide a diffusible stimulatory signal capable of defining the cell division plane without any direct contact with the cell cortex (von Dassow et al., 2009).Open in a separate windowFigure 1.Testing models of division plane specification by targeting distinct microtubule populations. By selectively eliminating astral microtubules with either controlled TSA-treatment (A) or by double centrosome ablation (B), von Dassow et al. (2009) provide strong evidence that microtubule contact with the cell cortex is not essential for successful cytokinesis. When a single centrosome was ablated, the division plane was displaced away from the ablated aster (B); this suggests that astral microtubules provide an inhibitory signal. Further, anucleate cells would only complete cytokinesis if the intracentrosomal distance exceeded the distance from the centrosomes to the cell cortex (C).The authors noticed that cytokinesis occurred selectively at a position with reduced microtubule density in control cells; therefore, they explored the role of astral microtubules in division plane positioning. By selectively ablating one centrosome just before anaphase onset, von Dassow et al. (2009) were also able to provide strong support for an inhibitory role of astral microtubules in division plane specification. When a single centrosome was ablated, the division plane was displaced away from the remaining astral microtubules and toward the ablated centrosome (Fig. 1 B). Further evidence for an inhibitory role of astral microtubules in cytokinesis came from close examination of the intracentrosomal distance in anucleate cells that were able to undergo cytokinesis relative to those that were not. Cells were only able to undergo cytokinesis when the intracentrosomal distance exceeded the distance from the centrosomes to the cell cortex (Fig. 1 C), which suggests that cytokinesis requires an aster-free zone. The authors propose a mechanism in these anucleate cells whereby global activation of contractility drives division plane specification refined by a zone of astral separation (von Dassow et al., 2009). However, one possibility is that a central spindle still forms in these anucleate cells and thus provides the same diffusion-based signal that promotes division in cells without asters. Indeed, antiparallel arrays of bundled microtubules that resemble the central spindle are known to form between asters without intervening chromosomes in other systems (Savoian et al., 1999).To summarize, the results described by von Dassow et al. (2009) support a model in which central spindle microtubules provide a diffusible stimulatory signal to promote the assembly of a broad contractile ring, which is then refined by astral microtubules into a tight contractile ring. It is tempting to speculate on the molecular nature of this diffusible signal and mechanism of the astral refinement during cytokinesis. Signaling via the small GTPase Rho is required for cytokinesis and is dependent on spindle microtubules (Bement et al., 2005; Piekny et al., 2005). von Dassow et al. (2009) showed that in TSA-treated cells lacking astral microtubules, the equatorial zone of active Rho GTPase is broader relative to control cells. Rho activation is promoted (at least in part) via the central spindle–localized GTP exchange factor, ECT2 (Glotzer, 2005). In parallel, the GTPase-activating protein (GAP) CYK4/MgcRacGAP also associates with the central spindle, where it acts to both limit the zone of Rho activity (Miller and Bement, 2009) and to promote the inactivation of another small GTPase, Rac (D''Avino et al., 2004; Yoshizaki et al., 2004; Canman et al., 2008). Perhaps in parallel to central spindle mediated activation of Rho signaling, local inactivation of the inhibitory Rac signal via CYK4 GAP activity would further specify the division plane, even at a distance (Fig. 2). When the dynamic asters are present, they could then additionally amplify Rac signaling at the cell poles via a similar mechanism to what occurs during cell motility (Wittmann and Waterman-Storer, 2001). This local feedback loop would reinforce the positive signal coming from the central spindle via Rho activation and could help delimit active Rho at the cell equator (Fig. 2). Certainly, understanding how Rho activation can be propagated to the cell cortex via diffusion in such an accurate manner will be a major future challenge.Open in a separate windowFigure 2.Model for central spindle–mediated signaling via Rho family small GTPases. Central spindle–localized guanine nucleotide exchange factor ECT2 leads to Rho activation at the cell equator. At the same time, central spindle–localized CYK4 (a Rho family GAP) would also locally inactivate the inhibitory Rac signal. Further refinement of the zone of active Rho by astral microtubule–activated Rac could then sharpen the Rho zone into a tight contractile ring.     

Functional Characterization of the Plastidial 3-Phosphoglycerate Dehydrogenase Family in Arabidopsis

This work contributes to unraveling the role of the phosphorylated pathway of serine (Ser) biosynthesis in Arabidopsis (Arabidopsis thaliana) by functionally characterizing genes coding for the first enzyme of this pathway, 3-phosphoglycerate dehydrogenase (PGDH). We identified two Arabidopsis plastid-localized PGDH genes (3-PGDH and EMBRYO SAC DEVELOPMENT ARREST9 [EDA9]) with a high percentage of amino acid identity with a previously identified PGDH. All three genes displayed a different expression pattern indicating that they are not functionally redundant. pgdh and 3-pgdh mutants presented no drastic visual phenotypes, but eda9 displayed delayed embryo development, leading to aborted embryos that could be classified as early curled cotyledons. The embryo-lethal phenotype of eda9 was complemented with an EDA9 complementary DNA under the control of a 35S promoter (Pro-35S:EDA9). However, this construct, which is poorly expressed in the anther tapetum, did not complement mutant fertility. Microspore development in eda9.1eda9.1 Pro-35S:EDA9 was arrested at the polarized stage. Pollen from these lines lacked tryphine in the interstices of the exine layer, displayed shrunken and collapsed forms, and were unable to germinate when cultured in vitro. A metabolomic analysis of PGDH mutant and overexpressing plants revealed that all three PGDH family genes can regulate Ser homeostasis, with PGDH being quantitatively the most important in the process of Ser biosynthesis at the whole-plant level. By contrast, the essential role of EDA9 could be related to its expression in very specific cell types. We demonstrate the crucial role of EDA9 in embryo and pollen development, suggesting that the phosphorylated pathway of Ser biosynthesis is an important link connecting primary metabolism with development.Plant primary metabolism is a complex process where many interacting pathways must be finely coordinated and integrated in order to achieve proper plant development and acclimation to the environment. An example of such complexity is the biosynthesis of the amino acid l-Ser, which takes place in at least two different organelles and by different pathways. This amino acid is essential for the synthesis of proteins and other biomolecules needed for cell proliferation, including nucleotides and Ser-derived lipids, such as phosphatidylserine and sphingolipids. Additionally, d-Ser has been attributed a signaling function in male gametophyte-pistil communication (Michard et al., 2011).Despite the important role played by Ser in plants, the biological significance of the coexistence of several Ser biosynthetic pathways and how they interact to maintain amino acid homeostasis in cells is not yet understood. Three different Ser biosynthesis pathways have been described in plants (Kleczkowski and Givan, 1988; Ros et al., 2013; Fig. 1). One is the glycolate pathway, which takes place in mitochondria and is associated with photorespiration (Tolbert, 1980, 1997; Douce et al., 2001; Bauwe et al., 2010; Maurino and Peterhansel, 2010). In this pathway, two molecules of Gly are converted to one molecule of Ser in a reaction catalyzed by the Gly decarboxylase complex and Ser hydroxymethyltransferase (Fig. 1). Ser synthesis through the glycolate pathway is obtained in green tissues during daylight hours (Tolbert, 1980, 1985; Douce et al., 2001), suggesting that alternative Ser biosynthesis pathways may be required in the dark and/or in nonphotosynthetic organs. In this respect, Ser can be synthesized through two nonphotorespiratory pathways (Kleczkowski and Givan, 1988), the plastidial phosphorylated pathway (Ho et al., 1998, 1999a, 1999b; Ho and Saito, 2001) and the so-called glycerate pathway, which synthesizes Ser by the dephosphorylation of 3-phosphoglycerate (3-PGA; Kleczkowski and Givan, 1988; Fig. 1). This latter pathway includes the reverse sequence of the section of the oxidative photosynthetic carbon cycle linking 3-PGA to Ser (3-PGA-glycerate-hydroxypyruvate-Ser), these reactions being catalyzed by putative enzymes such as 3-PGA phosphatase, glycerate dehydrogenase, Ala-hydroxypyruvate aminotransferase, and Gly hydroxypyruvate aminotransferase. Although the existence of enzymatic activities of this pathway has been demonstrated (Kleczkowski and Givan, 1988), its functional significance is unknown and genes coding for the specific enzymes of the pathway have not been characterized to date.Open in a separate windowFigure 1.Schematic representation of Ser biosynthesis in plants. The enzymes participating in each Ser biosynthetic pathway are listed separately. Photorespiratory pathway (glycolate pathway): GDC, Gly decarboxylase; SHMT, Ser hydroxymethyltransferase. Glycerate pathway: PGAP, 3-phosphoglycerate phosphatase; GDH, glycerate dehydrogenase; AH-AT, Ala-hydroxypyruvate aminotransferase. Phosphorylated pathway: PSAT, 3-phosphoserine aminotransferase; PSP, 3-phosphoserine phosphatase. Abbreviations used for metabolites are as follows: 3-PHP, 3-phosphohydroxypyruvate; 3-PS, 3-phosphoserine; THF, tetrahydrofolate; 5,10-CH₂-THF, 5,10-methylene-tetrahydrofolate. This figure is adapted from Cascales-Miñana et al. (2013).The plastidial phosphorylated pathway of serine biosynthesis (PPSB; Fig. 1), which is conserved in mammals and plants, synthesizes Ser via 3-phosphoserine utilizing 3-PGA as a precursor (Kleczkowski and Givan, 1988). Evidence for the existence of this pathway in plants stems from the isolation and characterization of its enzyme activities (Handford and Davies, 1958; Slaughter and Davies, 1968; Larsson and Albertsson, 1979; Walton and Woolhouse, 1986). The PPSB involves three enzymes catalyzing sequential reactions: 3-phosphoglycerate dehydrogenase (PGDH), 3-phosphoserine aminotransferase, and 3-phosphoserine phosphatase (PSP; Fig. 1). Genes coding for some isoforms of these enzymes have been cloned and biochemically characterized in Arabidopsis (Arabidopsis thaliana; Ho et al., 1998, 1999a, 1999b; Ho and Saito, 2001).In humans, the PPSB plays a crucial role in cell proliferation control and oncogenesis (Bachelor et al., 2011; Locasale et al., 2011; Pollari et al., 2011; Possemato et al., 2011). The functional significance of the PPSB in plants has recently been unraveled by providing evidence for the crucial role of PSP1, the last enzyme of the pathway in embryo, pollen, and root development (Cascales-Miñana et al., 2013). However, the PPSB still requires further characterization. In order to gain a complete understanding of the PPSB function in plants, precise molecular, metabolic, and genetic knowledge of all the enzymes and genes of the pathway is needed. In this work, we follow a gain- and loss-of-function approach in Arabidopsis to characterize a family of genes coding for putative isoforms of PGDH, the first enzyme of the PPSB. Here, we identify the essential gene of this family and provide evidence for its crucial function in embryo and pollen development.