Mechanotransduction by Membrane Proteins: C. elegans PEZO-1 is a mechanosensitive ion channel involved in food sensation

PIEZO channels are force sensors essential for physiological processes, including baroreception and proprioception. The Caenorhabditis elegans genome encodes an orthologue gene of the Piezo family, pezo-1, which is expressed in several tissues, including the pharynx. This myogenic pump is an essential component of the C. elegans alimentary canal, whose contraction and relaxation are modulated by mechanical stimulation elicited by food content. Whether pezo-1 encodes a mechanosensitive ion channel and contributes to pharyngeal function remains unknown. Here, we leverage genome editing, genetics, microfluidics, and electropharyngeogram recording to establish that pezo-1 is expressed in the pharynx, including in a proprioceptive-like neuron, and regulates pharyngeal function. Knockout (KO) and gain-of-function (GOF) mutants reveal that pezo-1 is involved in fine-tuning pharyngeal pumping frequency, as well as sensing osmolarity and food mechanical properties. Using pressure-clamp experiments in primary C. elegans embryo cultures, we determine that pezo-1 KO cells do not display mechanosensitive currents, whereas cells expressing wild-type or GOF PEZO-1 exhibit mechanosensitivity. Moreover, infecting the Spodoptera frugiperda cell line with a baculovirus containing the G-isoform of pezo-1 (among the longest isoforms) demonstrates that pezo-1 encodes a mechanosensitive channel. Our findings reveal that pezo-1 is a mechanosensitive ion channel that regulates food sensation in worms.     

Piezo1 helps bile on the pressure

JGP study shows that a mechanosensitive complex containing Piezo1 and Pannexin1 couples osmotic pressure to ATP secretion in bile duct cholangiocytes.

Cholangiocytes are epithelial cells that line the bile ducts within the liver and modify the composition of hepatocyte-derived bile. In this issue of JGP, Desplat et al. identify a mechanosensory complex that may help cholangiocytes respond to changes in osmotic pressure (1).Angélique Desplat (left), Patrick Delmas (center), and colleagues identify a mechanosensitive pathway that couples hypotonic stress to calcium influx and ATP release in cholangiocytes. Cell swelling induces calcium influx through the stretch-activated ion channel Piezo, triggering ATP release by Pannexin1 channels. This leads to the activation of P2X4 receptors and further calcium influx. Piezo1 (red) and Pannexin1 (green) colocalize in cells and may interact to form a mechanosensory complex that facilitates the hypotonic stress response.The activity of cholangiocytes can be regulated not only by chemical signals, such as hormones and bile acids, but also by mechanical cues arising from changes in bile composition and flow. “Abnormal mechanical tension is also an aggravating factor in many biliary diseases, including primary sclerosing cholangitis,” explains Patrick Delmas, a Research Director at Centre National de la Recherche Scientifique/Aix-Marseille-Université. “So, identifying the molecular players in cholangiocyte force sensing could provide a step forward for better management of biliary diseases.”Current models suggest that mechanical cues trigger an influx of calcium into cholangiocytes, leading to the release of ATP, which, by stimulating purinergic receptors at the cell surface, promotes further calcium influx and induces the secretion of anions, water, and HCO₃⁻ to modify the tonicity and pH of hepatic bile (2, 3). To identify mechanosensitive proteins that might regulate this pathway, Delmas and colleagues, including first author Angélique Desplat, purified mouse cholangiocytes from intrahepatic bile ducts and subjected them to hypotonic stress (1). The subsequent cell swelling activates calcium influx and ATP release.Desplat et al. found that depleting or inhibiting the stretch-activated ion channel Piezo1 significantly reduced this response to hypotonic stress. This mechanosensitive channel mediates the initial calcium influx into cholangiocytes when activated by cell swelling.The subsequent release of ATP is mediated by a different channel, however. Desplat et al. found that cholangiocytes express high levels of the gap junction family protein Pannexin1, and that pharmacologically inhibiting Pannexin1 channels reduced the amount of ATP released in response to hypotonic stress and Piezo1 activation.Delmas and colleagues suspect that the increase in intracellular calcium mediated by Piezo1 may activate Pannexin1 channels to release ATP, and this activation may be facilitated by a physical association between the two proteins: the researchers found that recombinant versions of the two channel proteins colocalize within the plasma membrane of cholangiocytes and can be coimmunoprecipitated.Finally, the researchers determined that the ATP released through Pannexin1 channels amplifies the signal initiated by hypotonic stress by activating purinergic P2X4 receptors, leading to further increases in intracellular calcium levels. Transfecting Piezo1-deficient HEK293 cells, which usually don’t respond to hypotonic stress, with cDNAs encoding Piezo1, Pannexin1, and P2X4R was sufficient to reconstitute the entire pathway of calcium influx and ATP release.Cholangiocytes express other mechanosensitive channels, including TRPV4, which has previously been implicated in the cells’ response to hypotonic stress (4). The functions of TRPV4 and Piezo1 may therefore be partially redundant, providing some robustness to cholangiocytes mechanical signaling pathways. However, it is also possible that, in vivo, the two channels respond to different stimuli and elicit distinct downstream effects. “Further investigation is warranted to better understand the respective roles of these two molecular players,” says Delmas. “To continue our work, we would like to challenge our model in vivo by testing whether Piezo1 agonists are able to regulate bile acid secretion.”     

Permeation Redux: Thermodynamics and Kinetics of Ion Movement through Potassium Channels

The fundamental biophysics underlying the selective movement of ions through ion channels was launched by George Eisenman in the 1960s, using glass electrodes. This minireview examines the insights from these early studies and the explosive progress made since then.The recent passing of George Eisenman (December 18, 2013) inspired us to revisit the topic most associated with his passionate input, namely how the membrane proteins known as ion channels control passive movements of ions across biological membranes. Ion permeation has captivated biophysicists for more than half a century, and only now, with the combined advent of atomic-level structures and sophisticated computational wizardry, are the secrets of this amazing process beginning to be revealed. Why “amazing”? For example, because K⁺-selective ion channels can discriminate between K⁺ and Na⁺ ions, which differ in radius by a mere 0.38 Ångstrom, and do so with 1000:1 reliability and at lightning speed near the diffusion limit, the dwell time of an ion in the pore of a channel is as fleeting as ∼10⁻⁸ s. Understanding this remarkably-tuned process in K⁺ channels requires attention to two perspectives: the ability of specific channels to discriminate between the ions they might encounter (i.e., selectivity); and the kinetics of ion movement across the channel pore (i.e., conduction).The classical thermodynamic explanation of ion selectivity is that the relative free energy difference of ions in the pore relative to the bulk solution is the critical quantity to consider (1–4). Some of the earliest insights into thermodynamic selectivity derive from studies of ion binding to aluminosilicate glass electrodes (5,6). Depending on the composition of the glass, these electrodes, originally developed for their proton sensitivity, can exhibit a dramatic range of selectivities among the five alkali metal cations. In rank order, one might expect as many as 5 × 4 × 3 × 2 × 1 = 120 different sequences of selectivities among these five cations. Remarkably, however, in the vast literature of selectivity in biological membranes, typically only 11 sequences are observed (with some exceptions). These became known as the “Eisenman sequences”. The exact same selectivity sequences are observed in glass electrodes of various compositions.Why are the free energy differences the way they are for a given system? To answer this question, one needs a physical mechanism. For Eisenman, numerical calculations stood as a critical component of the process of better understanding Nature. In other words, proposing a physical mechanism that is qualitatively reasonable is not enough—one must also test it by constructing atomic models leading to actual quantitative predictions (Fig. 1). In the early days, the concept of the anionic field strength of a binding site was formulated and tested with direct calculations based on exceedingly simple atomic hard-sphere models of ions, water molecules, and coordinating ligands such as shown in Fig. 1 A (2,5). Remarkably, these simple calculations led to the Eisenman selectivity sequences. Eisenman was able to account for the limited class of sequences by considering the equilibrium binding of cations to the glass, and the energetic competition between water and glass for the ions. The critical factor that determines the selectivity sequence of a given glass is the anionic field strength of the binding site on the glass. Briefly, the smallest group Ia cation, Li⁺, holds water most tenaciously, so it will only dehydrate and bind in the presence of a strongly negative electrostatic potential.Open in a separate windowFigure 1Structural models used in theoretical studies of ion selectivity. (A) Simple model used to introduce the concept of field strength leading to 11 cationic selectivity sequences (2,5,6). Ions, water, and ligands are represented by simple hard-spheres with embedded point charges. Selectivity arises from the difference in the interaction energy of the cation with a water molecule (top) and an anionic coordinating ligand (bottom). (B) Ion-selective transfer process is depicted with atomic models incorporating all molecular details in the case of solvation in liquid water (top) and binding to the K⁺-selective ionophore valinomycin (bottom). Such atomic models were used to carry out some of the earliest MD free energy simulations on ion binding selectivity (12,13,15).By contrast, the largest cation, Cs⁺, holds water least tenaciously. It cannot bind readily to a strongly negative site because the site itself greedily clings to water molecules, and thus prevents Cs⁺ binding. However, Cs⁺ is more willing, relative to the smaller cations, to dehydrate and bind in the presence of a weakly negative electrostatic potential. At the extremes, the highest anionic field strength glass shows a selectivity sequence ofLi⁺ > Na⁺ > K⁺ > Rb⁺ > Cs⁺(sequence XI), and the lowest anionic field strength glass shows a selectivity sequence ofCs⁺ > Rb⁺ > K⁺ > Na⁺ > Li⁺(sequence I).A very simple model, based on the relative Gibbs’ free energies of binding and hydration, explains why there are only 11 sequences (5–7). The critical factor underlying the pattern of these selectivity sequences is that the “ion-site interaction energies fall off as a function of cation size as a lower power of the cation radius than do ion-water interaction energies” (5,6). The icing on the cake is that ion selectivity of channels in membranes appears to follow similar principles (7). The thermodynamic principles are evidently analogous. Moreover, Eisenman’s contributions went far beyond the monovalent cation selectivity of potassium channels. His theoretical approach was seminal in understanding both cation and anion selectivity in a diverse range of physical and biological systems (8,9).The advent of molecular dynamics (MD) simulations around this period was of critical importance to the field. This made it possible to construct increasingly realistic models of proteins (10), including ion channels (11), and examine the ion selectivity of carriers using the alchemical free energy perturbation (FEP) technique (12,13). With no experimental structures yet available for the ion-selective regions of biological K⁺ channels, an important step forward was Eisenman’s realization that other ion-selective systems could be used to computationally test the structural basis of his selectivity theory. Both peptidelike small ionophores, such as valinomycin and nonactin, and the ion-coordinating fivefold symmetry sites in icosahedral virus structures, thus caught his attention (13). As it turned out, these types of structures were indeed very relevant for the selectivity problem, because K⁺-channel filters were eventually shown to be lined likewise by carbonyl groups (14). With the crystallographically determined valinomycin structure at hand, its selectivity could be energetically analyzed by atomistic computer simulations, as illustrated in Fig. 1 B (12,15). The anionic field strength (represented by the carbonyl ligand dipole moment) could then be varied artificially, and the successive progression through the different selectivity sequences, as a function of field strength, directly observed. Likewise, Eisenman and Alvarez (13) made computational predictions for the binding energetics and selectivity of the Ca²⁺ binding site at the fivefold symmetry axis of satellite tobacco necrosis virus, and they subsequently showed experimentally that this binding site had a marked rare-earth ion size selectivity (16). To this day, the general computational FEP/MD framework based on equilibrium thermodynamics used in these studies continues to be a critical tool to understand ion channels (17), transporters (18), and pumps (19).Despite these early insights, it was always clear to Eisenman that explanations of selectivity solely based on thermodynamic equilibrium were too simple to account for the detailed properties observed in biological systems. Since the halcyon days of equilibrium binding studies on glass electrodes, the permeation landscape presented by the pores of ion channels has emerged as richer than anticipated. One important realization is that binding and conduction of ions through a channel may act as contradictory processes, because although an ion has to leave the comfort of its hydration shells to selectively enter the mouth of a channel pore, if it binds the channel too tightly, it cannot move rapidly through it. This mini-conundrum is most apparent, perhaps, for K⁺-channels, which attract K⁺ ions much more forcefully than Na⁺ ions, yet conduct K⁺ ions much faster than Na⁺ ions.Another factor evident in early studies of permeation is that ions encounter a series of obstacles (i.e., energy barriers) and binding sites (i.e., energy wells) as they wend their way through the pore. One approach to understanding permeation is to consider that ions hopscotch from one well to the next over a series of barriers. When the number of barriers is rather limited, say <5, one can use so-called “rate theory” (20) to analyze and formulate the free energy profile experienced by an ion crossing the membrane. Hille (21) proposed that selectivity derives largely from the selectivities of the barriers, not the wells. Eisenman and Horn (7) later considered the possibility that binding sites and barriers within a particular channel might have different selectivity sequences. For example, if a channel presents two barriers, one of which has selectivity sequence I and the other has selectivity sequence XI, the channel as a whole will have an intermediate selectivity sequence that is not an Eisenman sequence at all. Rather, it is a so-called “polarizability sequence” (7). Interestingly, contemporary studies indicate that successive binding sites along K⁺-selective channels display different selectivities (22). Another concept based on Eyring barrier models is that the energy levels for wells and barriers may not be static, and may therefore fluctuate on a timescale relevant to ion permeation (23). Finally, the biophysics of ion permeation and later structural studies show that multiple ions may cohabit the same channel simultaneously, and the interactions among these ions have profound consequences for ion conduction and selectivity.Fast forward to the 21st century: atomic-level structures and all-atom simulations seem to have blown the permeation field wide open, as suggested by recent reviews (24–27). Once the KcsA channel structure was solved (14), the structural origin of K⁺-ion permeation could finally be addressed by computer simulations of the “real structure” and a number MD simulation studies provided novel insight (22,28–31). Needless to say, George Eisenman took great interest in these simulations even though he had by then retired. Also, in the case of KcsA, the initial work largely revolved around calculations of equilibrium ion binding and selectivity, barrier heights, and energy landscape mapping (22,31), because direct all-atom simulations of spontaneous permeation were not possible. However, the general type of knock-on mechanism with multiion occupancy of the channel selectivity filter, involving key distinct states (22,31), and a surprisingly flat energy landscape (22), appear to be robust features of these channels.Even with the advent of MD simulations, the concept of field strength has kept its relevance. For example, the selectivity filter in MD simulations of the KcsA channel displayed a range of atomic flexibility that seemed somewhat shocking at the time because a traditional host-guest mechanism of selectivity would require a fairly rigid cavity-size. Yet, free energy computations indicated that this was not strictly necessary to establish the thermodynamic free energy differences needed to support ion selectivity (32). The resilience of Eisenman’s ideas is not entirely surprising because, as foreseen early on by Bertil Hille (21), the concept of field strength remains “useful if the dipoles of the channel are free to move and can be pulled in by small ions and pushed back by large ones”.Nevertheless, despite the exciting progress, the chapter on ion selectivity in K⁺ channels is far from closed. Very recently, a number of studies have revealed some extremely intriguing multiion aspects of selectivity in K⁺ channels that appear to stand squarely outside the realm of equilibrium thermodynamics. By examining the properties of MthK (33) and NaK (34) mutants, Liu and Lockless (35) and Sauer et al. (36) showed that the channel becomes K⁺-selective only if there are four consecutive binding sites along the filter. This has culminated more recently with studies of two engineered mutants of the NaK channel, referred to as “NaK2K” and “NaK2CNG”. According to reversal potential measurements from single-channel electrophysiology, the NaK2K construct is K⁺-selective and the NaK2CNG construct is nonselective. Remarkably, despite being nonselective in ion permeation, the NaK2CNG filter displays an equilibrium preference for binding K⁺ over Na⁺, as indicated by measurements with isothermal titration calorimetry and concentration-dependent ion replacement within the filter observed through crystallographic titration experiments.K⁺-selective channels bind two or more K⁺ ions in the narrow filter, whereas the nonselective channels bind fewer ions. Based on the crystallographic titration experiments, the NaK2K construct has two high-affinity K⁺ sites whereas the NaK2CNG construct has only one K⁺-selective site. These experiments show that both K⁺-selective and nonselective channels select K⁺ over Na⁺ ions at equilibrium, implying that equilibrium selectivity is insufficient to determine the selectivity of ion permeation (35,36). The data indicate that having multiple K⁺ ions bound simultaneously is required for selective K⁺ conduction, and that a reduction in the number of bound K⁺ ions destroys the multiion selectivity mechanism utilized by K⁺ channels. Although these experimental results are intriguing, the underlying microscopic mechanisms remain unclear. The implication is that the multiion character of the permeation process must, somehow, be a critical element for establishing selective ion conduction through K⁺ channels.The progress made, and the challenges that remain, are perhaps best illustrated by returning to computational studies of the simplest membrane spanning structure known, namely the gramicidin A channel. Before detailed studies of selectivity and conductance of K⁺-channels were launched, computational work on ion conduction through membrane channels was largely focused on this simple channel (37–41). In this case the permeation selectivity was monotonically size-dependent (Eisenman sequence I) and, in this respect, less interesting than K⁺-selective channels. However, from an energetic point of view it was puzzling how this single helical structure could yield free energy barriers low enough to permit high conductivity (7,42). Computer simulations of increasing complexity in this case established that the combined effect of several contributions to ion stabilization along the pore (from the protein, membrane, single-file waters, and bulk solution) indeed results in low barriers to permeation (11,39,40). Furthermore, the most realistic model comes in close agreement with experimental measurements (11,43), although it is clear that work is still needed.     

A TREK inhibitor takes multiple tracks

Single-channel recordings reveal that norfluoxetine inhibits the two-pore domain K⁺ channel TREK-2 by a complex array of mechanisms.

The TREK subfamily of two-pore domain K⁺ channels are expressed throughout the central and peripheral nervous systems and are involved in a diverse range of processes such as mechanosensation, thermosensation, and nociception. Accordingly, channel gating—which is thought to involve changes in the selectivity filter of TREKs—can be regulated by a wide variety of factors, including pressure, temperature, and multiple endogenous ligands (1). In this issue of JGP, Proks et al. reveal that this regulatory complexity is reflected in the fact that the TREK inhibitor norfluoxetine impairs channel activity via several different mechanisms (2).Peter Proks (left), Stephen J. Tucker (center), and colleagues use single-channel recordings to investigate how norfluoxetine inhibits the two-pore domain K⁺ channel TREK-2. Norfluoxetine binds exclusively to the “down” conformation of TREK-2 (right) and prevents the channel’s transmembrane domains from transitioning to the “up” configuration. But Proks et al. find that TREK-2 can be fully active in the down conformation and that norfluoxetine works via multiple mechanisms to inhibit both the open and closed states of the channel.Norfluoxetine is a metabolite of fluoxetine (Prozac), and both compounds are among the few known inhibitors of TREK activity (3). “TREK channels are not the principal targets of fluoxetine, which is mainly a selective serotonin reuptake inhibitor,” explains Stephen J. Tucker from the University of Oxford. “But fluoxetine and norfluoxetine are useful tools to study the mechanisms of TREK channel gating.”Tucker and colleagues previously helped solve the crystal structures of TREK-2 in the presence and absence of norfluoxetine (4). The channel can adopt two distinct conformations, named “up” or “down” depending on the orientation of its transmembrane helices, and norfluoxetine was found to bind within the inner cavity of TREK-2 in a gap that is only formed when the transmembrane helices are in the down configuration. Norfluoxetine can therefore block the transition from the down to up conformation, and it was originally suggested that this might inhibit channel activity by locking the selectivity filter in its closed state. But the mechanism of filter gating appears to be more complex. Tucker’s group, for example, has previously shown using macroscopic recordings that TREK-2 can adopt several open states, some of which may occur in the down conformation (5).To learn more about the mechanisms underlying filter gating and norfluoxetine inhibition, Tucker and colleagues, including first author Peter Proks, turned to single-channel recordings of purified TREK-2 channels embedded in lipid bilayers (2). “We found that norfluoxetine affects both the open and closed states of the channel and is therefore a state-independent inhibitor of TREK-2,” Tucker says. “That information is lost in macroscopic recordings.”Moreover, the fact that highly active channels are sensitive to norfluoxetine inhibition confirms that TREK channels can be fully open in the down conformation. It also indicates that, in addition to blocking changes in transmembrane conformation, norfluoxetine must inhibit TREK channels by other mechanisms as well.“We found that there are several mechanisms involved, all of which converge on the selectivity filter gate,” Tucker says. The researchers also observed a mild voltage dependence of norfluoxetine inhibition, suggesting that it can influence voltage-dependent gating as well.“The complexity with which the drug works reflects the many different ways in which the selectivity filter can gate the channel,” Tucker says. “This, in turn, reflects the polymodal regulation of TREK channels and their ability to integrate a wide variety of signals.”     

Mechanisms of apoptotic phosphatidylserine exposure

It has been a long-standing enigma which scramblase causes phosphatidylserine residues to be exposed on the surface of apoptotic cells, thereby facilitating the phagocytic recognition, engulfment and destruction of apoptotic corpses. In a recent paper in Science, Nagata and coworkers reveal that the scramblases Xkr8 and its C. elegans ortholog, CED-8, are activated by caspase cleavage in apoptotic cells.All cells are separated from the extracellular environment by the plasma membrane, a phospholipid bilayer that prevents diffusion of proteins, ions and other essential molecules into the extracellular space and constitutes the structure in which membrane proteins are embedded. In animal cells, the lipid composition of the outer and inner leaflets of the plasma membrane is not symmetrical. Phosphatidylcholine (PC) and sphingomyelin (SM) are mainly present in the outer leaflet of the plasma membrane, whereas phosphatidylserine (PS), phosphatidylinositol (PI) and phosphatidylethanolamine (PE) are restricted to the inner leaflet. This lipid asymmetry is maintained by the combined action of ATP-dependent enzymes called flippases and floppases, which specifically translocate phospholipids and other molecules from the outer to the inner membrane leaflet and from the inner to the outer membrane leaflet, respectively¹. Lipid composition asymmetry not only defines the curvature and electrochemical properties of the plasma membrane, but is also essential for the correct function of determined lipids, as for instance, PI, which only functions as a second messenger if present in the inner leaflet². Nonetheless, several physiologically relevant processes as diverse as platelet activation, neurotransmitter release, sperm capacitation or apoptosis, require dissipation of plasma membrane lipid asymmetry, a process known as scrambling. The enzymes responsible for this activity are called scramblases, and function to randomize the distribution of phospholipids between both membrane leaflets in an ATP-independent manner^2,3,4.Although plasma membrane asymmetry and the existence of flippases, floppases and scramblases have been known for decades, the identity of the specific enzymes involved in these activities has only begun to be revealed during the last few years. Very recently, the group of Shigekazu Nagata identified TMEM16F as the long sought-after calcium-dependent phospholipid scramblase³. However, to date, the identity of the scramblase(s) involved in apoptosis-related (and calcium-independent) PS exposure had remained elusive. Cell surface PS exposure is a classic feature of apoptotic cells and acts as an “eat me” signal allowing phagocytosis of post-apoptotic bodies. In a recent paper in Science, Nagata''s group identified Xk-Related Protein 8 (Xkr8) as the enzyme responsible for this activity and demonstrated an evolutionarily conserved role of this protein in apoptosis-induced lipid scrambling⁵.To identify enzymes involved in membrane lipid scrambling, Nagata''s group took advantage of their previously generated mouse Ba/F3 pro-B cell line³, which presented a high basal level of PS exposure. They then generated a cDNA library from Ba/F3 cells and overexpressed it in the parental cell line. Through sequential enrichment of cells with increased PS exposure, they were able to isolate a cDNA encoding the Xkr8 protein, which enhanced PS scrambling when overexpressed. Xkr8 overexpression (but not that of TMEM16F) was able to increase apoptosis-associated PS exposure. The authors then noticed that both impaired apoptosis-induced PS exposure and deficient post-apoptotic body clearance were correlated with low Xkr8 expression in leukemia and lymphoma cell lines, which was linked to hypermethylation of its promoter. Interestingly, these two alterations were reverted either by overexpressing Xkr8 or by restitution of endogenous Xkr8 expression after treatment with the demethylating agent 5-aza-2′-deoxycytidine (DAC), suggesting that methylation of the Xrk8 promoter may be a mechanism by which tumor cells evade their phagocytosis after apoptotic death, which may result in increased local inflammation, thus favoring tumor progression. Far from being restricted only to PS exposure, Xrk8 overexpression was able to promote scrambling of multiple lipid species during apoptosis, which was demonstrated by incorporation of fluorescent PC and SM analogues. This scrambling activity was restricted to apoptotic events, as Xkr8 overexpression had no effect on Ca²⁺-induced PS exposure. This specificity may be explained by the presence of an evolutionarily conserved caspase recognition site near Xkr8 C-terminal region, whose mutation prevented both Xkr8 cleavage by caspase-3 or -7 and PS exposure during the course of apoptosis (Figure 1). These results from human cell lines were confirmed in Xkr8^−/− mouse embryonic fibroblasts and fetal thymocytes, which were unable to expose PS upon induction of apoptosis, underscoring the broad physiological relevance of Xkr8 in the apoptotic process. Finally, the authors moved to the nematode Caenorhabditis elegans to analyze whether the role of Xpr8 as lipid scramblase is evolutionarily conserved. C. elegans harbors only one ortholog of Xk proteins, CED-8, known to participate in the phagocytic removal of apoptotic corpses⁶. To determine the role of CED-8 in PS exposure, the authors took advantage of the “floater” assay, which is based on the appearance of floating cells (“floaters”) that have detached from developing C. elegans embryos defective for apoptotic cell phagocytosis⁷. Nagata''s group discovered that ced-8 deficiency leads to the accumulation of floaters. Moreover, ced-8 deficiency synergistically enhanced the number of floaters found in other engulfment mutants, which suggests that CED-8 function is not redundant to that developed by previously known engulfment mutants. This enhancing effect of ced-8 deletion was dependent on CED-3, the C. elegans ortholog of caspase-3, confirming the aforementioned results in mammalian cells. The authors then characterized that floaters resulting from ced-8 deletion show a largely deficient PS exposure after developmental apoptosis, confirming the evolutionarily conserved role of Xk-related proteins in apoptosis-induced lipid scrambling. However, they observed that ced-8 deletion does not lead to a total impairment in apoptotic PS presentation, suggesting that additional proteins must be involved in this process. Indeed, apoptosis-inducing factor can induce PS exposure in mammalian cells in a caspase-independent fashion⁸, and the C. elegans AIF ortholog, WAF-1, physically interacts with and activates another scramblase, SCRM-1⁴.Open in a separate windowFigure 1Xrp8 acts as apoptosis-induced lipid scramblase. Under normal conditions, the combined action of multiple mechanisms, including the activity of flippases and floppases, maintains lipid asymmetry between the outer and inner leaflets of the plasma membrane. Once apoptotic program is activated, caspases-3 and -7 are able to cleave and activate Xrp8 protein, which acts as a lipid scramblase and leads to the loss of lipid asymmetry, resulting in PS exposure to the extracellular space. This acts as the “eat-me” signal that will allow phagocytosis of post-apoptotic cell corpses. PC, phosphatidylcholine; SM, sphingomyelin; PE, phosphatidylethanolamine; PS, phosphatidylserine.In summary, through a series of elegant manipulations, Nagata''s group has found the long-sought caspase-activated lipid scramblase that mediates the exposure of “eat-me” signals in post-apoptotic cell corpses. Further studies involving Xkr8 protein, including the mechanisms participating in its epigenetic repression may open new roads for the study of autoimmune diseases, such as lupus erythematosus, which is associated with failure in the post-apoptotic corpse clearance system.     

Flexible open conformation of the AP-3 complex explains its role in cargo recruitment at the Golgi

Vesicle formation at endomembranes requires the selective concentration of cargo by coat proteins. Conserved adapter protein complexes at the Golgi (AP-3), the endosome (AP-1), or the plasma membrane (AP-2) with their conserved core domain and flexible ear domains mediate this function. These complexes also rely on the small GTPase Arf1 and/or specific phosphoinositides for membrane binding. The structural details that influence these processes, however, are still poorly understood. Here we present cryo-EM structures of the full-length stable 300 kDa yeast AP-3 complex. The structures reveal that AP-3 adopts an open conformation in solution, comparable to the membrane-bound conformations of AP-1 or AP-2. This open conformation appears to be far more flexible than AP-1 or AP-2, resulting in compact, intermediate, and stretched subconformations. Mass spectrometrical analysis of the cross-linked AP-3 complex further indicates that the ear domains are flexibly attached to the surface of the complex. Using biochemical reconstitution assays, we also show that efficient AP-3 recruitment to the membrane depends primarily on cargo binding. Once bound to cargo, AP-3 clustered and immobilized cargo molecules, as revealed by single-molecule imaging on polymer-supported membranes. We conclude that its flexible open state may enable AP-3 to bind and collect cargo at the Golgi and could thus allow coordinated vesicle formation at the trans-Golgi upon Arf1 activation.

Eukaryotic cells have membrane-enclosed organelles, which carry out specialized functions, including compartmentalized biochemical reactions, metabolic channeling, and regulated signaling, inside a single cell. The transport of proteins, lipids, and other molecules between these organelles is mediated largely by small vesicular carriers that bud off at a donor compartment and fuse with the target membrane to deliver their cargo. The generation of these vesicles has been subject to extensive studies and has led to the identification of numerous coat proteins that are required for their formation at different sites (1, 2). Coat proteins can be monomers, but in most cases, they consist of several proteins, which form a heteromeric complex.Heterotetrameric adapter protein (AP) complexes are required at several endomembranes for cargo binding. Five well-conserved AP-complexes with differing functions have been identified in mammalian cells, named AP-1–AP-5, of which three (AP-1–AP-3) are conserved from yeast to human (3, 4). The three conserved adapter complexes function at different membranes along the endomembrane system. AP-1 is required for cargo transport between the Golgi and the endosome, AP-2 is required for cargo recognition and transport between the plasma membrane and the early endosome. Finally, AP-3 functions between the trans Golgi and the vacuole in yeast, whereas mammalian AP-3 localizes to a tubular endosomal compartment, in addition to or instead of the TGN (2, 5, 6).Each of the complexes consists of four different subunits: two large adaptins (named α−ζ and β1-5 respectively), a medium-sized subunit (μ1-5), and a small subunit (σ1-5). While μ- and σ-subunits together with the N-termini of the large adaptins build the membrane-binding core of the complex, the C-termini of both adaptins contain the ear domains, which are connected via flexible linkers (2). The recruitment of these complexes to membranes is not entirely conserved. They all require cargo binding, yet AP-1 binds Arf1-GTP with the γ and β1 subunit and phosphatidylinositol-4-phosphate (PI4P) via a proposed conserved site on its γ-subunit (7, 8). AP-2, on the other hand, interacts with PI(4,5)P₂ at the plasma membrane via its α, β2, and μ2 subunits (9, 10, 11).Several studies have uncovered how AP-3 functions in cargo sorting in yeast. AP-3 recognizes cargo at the Golgi via two sorting motifs in the cytosolic segments of membrane proteins: a Yxxφ sorting motif, as found in yeast in the SNARE Nyv1 or the Yck3 casein kinase, which binds to a site in μ3, as shown for mammalian AP-3, which is similar to μ2 in AP-2 (12, 13, 14), and dileucine motifs as found in the yeast SNARE Vam3 or the alkaline phosphatase Pho8, potentially also at a site comparable to AP-1 and AP-2 (15, 16). Unlike AP-1 and AP-2-coated vesicles, which depend on clathrin for their formation (2, 17), AP-3 vesicle formation in yeast does not require clathrin or the HOPS subunit Vps41 (18), yet Vps41 is required at the vacuole to bind AP-3 vesicles prior to fusion (19, 20, 21, 22). Studies in metazoan cells revealed that Vps41 and AP-3 function in regulated secretion (23, 24, 25), and AP-3 is required for biogenesis of lysosome-related organelles (26). This suggests that the AP-3 complex has features that are quite different from AP-1 and AP-2 complexes, which cooperate with clathrin in vesicle formation (2).Among the three conserved AP complexes, the function of the AP-3 complex is the least understood. Arf1 is necessary for efficient AP-3 vesicle generation in mammalian cells and shows a direct interaction with the β3 and δ subunits of AP-3 (27, 28). In addition, in vitro experiments on mammalian AP-3 using liposomes or enriched Golgi membranes suggest Arf1 as an important factor in AP-3 recruitment, whereas acidic lipids do not have a major effect, in contrast to what was found for AP-1 and AP-2 (7, 11, 29, 30). Another study showed that membrane recruitment of AP-3 depends on the recognition of sorting signals in cargo tails and PI3P (31), similar to AP-1 recruitment via cargo tails, Arf1 and PI4P (32).However, since AP-1 and AP-3 are both recruited to the trans-Golgi network (TGN) in yeast (33), the mechanism of their recruitment likely differs. Even though Arf1 is required, yeast AP-3 seems to be present at the TGN before the arrival of the Arf1 guanine nucleotide exchange factor (GEF) Sec7 (33). This implies the necessity for additional factors at the TGN and a distinct mechanism to allow for spatial and temporal separation of AP-1 and AP-3 recruitment to membranes. Structural data on mammalian AP-1 and AP-2 “core” complexes without the hinge and ear domains of their large subunits revealed that both exist in at least two very defined conformational states: a “closed” cytosolic state, where the cargo-binding sites are buried within the complex, and an “open” state, where the same sites are available to bind cargo (7, 8, 10, 34, 35). Binding of Arf1 to AP-1 or PI(4,5)P₂ in case of AP-2 induces a conformational change in the complexes that enables them to bind cargo molecules carrying a conserved acidic di-Leucine or a Tyrosine-based motif, as for all three AP complexes in yeast (8, 34). Additional conformational states and intermediates have been reported for both, mammalian AP-1 and AP-2 complex. AP-1, for example, can be hijacked by the human immunodeficiency virus-1 (HIV-1) proteins viral protein u (Vpu) and negative factor (Nef), resulting in a hyper-open conformation of AP-1 (36, 37).An emerging model over the past years has suggested that APs have several binding sites that allow for the stabilization of membrane binding and the open conformation of the complexes, but there are initial interactions required that dictate their recruitment to the target membrane. Although these interaction sites for mammalian AP-1 and AP-2 have been identified in great detail based on interaction analyses and structural studies (8, 10, 11, 35, 36, 38, 39), structural data for AP-3 is largely missing. The C-terminal part of the μ-subunit of mammalian AP-3 has been crystallized together with a Yxxφ motif-containing a cargo peptide, which revealed a similar fold and cargo-binding site as shown for AP-1 and AP-2 (14). However, positively charged binding surfaces required for PIP-interaction were not well conserved. Although the “trunk” segment of AP-1 and AP-2 is known quite well by now, information on hinge and ear domains in context of these complexes is largely missing. Crystal structures of the isolated ear domains of α-, γ- and β2-adaptin have been published (40, 41, 42), and a study on mammalian AP-3 suggested a direct interaction between δ-ear and δ3 that interfered with Arf1-binding (43). Furthermore, during tethering of AP-3 vesicles with the yeast vacuole, the δ−subunit Apl5 of the yeast AP-3 complex binds to the Vps41 subunit of the HOPS complex as a prerequisite of fusion (18, 19, 21, 22).In this study, we applied single particle electron cryo-microscopy (cryo-EM) to analyze the purified full-length AP-3 complex from yeast and unraveled the factors required for AP-3 recruitment to membranes by biochemical reconstitution. Our data reveal that a surprisingly flexible AP-3 complex requires a combination of cargo, PI4P, and Arf1 for membrane binding, which explains its function in selective cargo sorting at the Golgi.     

Anecdotal,Historical and Critical Commentaries on Genetics: The Impact of Whole Genome Sequencing on Model System Genetics: Get Ready for the Ride

Much of our understanding of how organisms develop and function is derived from the extraordinarily powerful, classic approach of screening for mutant organisms in which a specific biological process is disrupted. Reaping the fruits of such forward genetic screens in metazoan model systems like Drosophila, Caenorhabditis elegans, or zebrafish traditionally involves time-consuming positional cloning strategies that result in the identification of the mutant locus. Whole genome sequencing (WGS) has begun to provide an effective alternative to this approach through direct pinpointing of the molecular lesion in a mutated strain isolated from a genetic screen. Apart from significantly altering the pace and costs of genetic analysis, WGS also provides new perspectives on solving genetic problems that are difficult to tackle with conventional approaches, such as identifying the molecular basis of multigenic and complex traits.GENETIC model systems, from bacteria, yeast, plants, worms, flies, and fish to mice allow the dissection of the genetic basis of virtually any biological process by isolating mutants obtained through random mutagenesis, in which the biological process under investigation is defective. Such forward genetic analysis is unbiased and free of assumptions. The rigor and conceptual simplicity of forward genetic analysis is striking, some may say, beautiful; and the unpredictability of what one finds—be that an unexpected phenotype popping out of a screen or the eventual molecular nature of the gene (take the discovery of miRNAs as an example; Lee et al. 1993)—appeals to the adventurous. Even though mutant phenotypic analysis alone can reveal the logic of underlying biological processes (take Ed Lewis'' analysis of homeotic mutants as an example; Lewis 1978)—it is the identification of the molecular lesions in mutant animals that provides the key mechanistic and molecular details that propel our understanding of biological processes.The identification of the molecular lesion in mutant organisms depends on how the mutation was introduced. Classically, two types of mutagens have been used in most model systems: biological agents such as plasmids, viruses, or transposons whose insertions disrupt functional DNA elements (either coding or regulatory elements) or chemical mutagens, such as ethyl methane sulfonate (EMS) or N-ethyl N-nitroso urea (ENU), that introduce point mutations or deletions. Point mutation-inducing chemical mutagens are in many ways a superior mutagenic agent because their mutational frequency is high and because the spectrum of their effects on a given locus—producing hypomorphs, hypermorphs, amorphs, neomorphs, etc.—is hard to match by biological mutagens. Moreover, chemical mutagens do not display the positional bias of many biological agents. In addition, point mutations in a gene are often crucial in dissecting the functionally relevant domains of the gene product. In spite of the advantages of chemical mutagens, model system geneticists often prefer biological mutagens simply because the molecular lesions induced by those agents are characterized by the easily locatable DNA footprint that these agents generate. In contrast, the location of a point mutation (or deletion) has to be identified through conventional mapping strategies, which tend to be tedious and time consuming. Even in model systems in which positional cloning is quite fast and straightforward (e.g., Caenorhabditis elegans, which has a short generation time and a multitude of mapping tools available), it nevertheless is a significant effort that can occasionally present hurdles that are difficult to surmount (e.g., if the gene maps into a region with few genetic markers that allow for mapping). These difficulties explain why RNAi-based “genetic screens” have gained significant popularity in C. elegans; they circumvent mapping and reveal molecular identities of genes involved in a given process straight away (Kamath and Ahringer 2003). However, genes and cells show differential susceptibility to RNAi; off-target effects and lack of reproducibility can be a problem, and the range of effects that RNAi has on gene activity is generally more limited compared to chemically induced gene mutations.The recent application of next generation, deep sequencing technology (see Bentley 2006; Morozova and Marra 2008 for technology reviews) is beginning to significantly alter the landscape of genetic analysis as it allows the use of chemical mutagens without having to deal with its disadvantages. Deep sequencing technology incorporated into platforms such as Illumina''s Genome Analyzer or ABI''s SOLiD, allows one-shot sequencing of the entire model system''s genome, resulting in the detection of mutagen-induced sequence alterations compared to a nonmutagenized reference genome. Proof-of-concept studies have so far been conducted in bacteria, yeast, plant, worms, and flies, all published within the last year (Sarin et al. 2008; Smith et al. 2008; Srivatsan et al. 2008; Blumenstiel et al. 2009; Irvine et al. 2009; Rigola et al. 2009). Many more studies are under way; for example, since our first proof-of-principle study (Sarin et al. 2008), my laboratory has identified the molecular basis of >10 C. elegans strains defective in neuronal development and homeostasis (V. Bertrand, unpublished data; M. Doitsidou, unpublished data; E. Flowers, unpublished data; S. Sarin, unpublished data).The advantages of whole genome sequencing (WGS) are obvious. The process is extraordinarily fast with the sequencing taking only ∼5 days and the subsequent sequence data analysis only a few hours, particularly if the end user employs bioinformatic tools customized for mutant detection (Bigelow et al. 2009). The process is also remarkably cost effective. For example, a C. elegans genome can be sequenced with a required sequence coverage of ∼10 times for <$2,000 in reagent and machine operating costs. The capacity of deep sequencing machines—and hence the costs associated with sequencing a genome—apparently follow Moore''s law of doubling its capacity about every 2 years, like many technological innovations do (Pettersson et al. 2009). That is, the <$1,000 genome for C. elegans (∼100-Mb genome) and Drosophila (∼123-Mb genome) is just around the corner and other models will sooner or later follow suit. The cost effectiveness becomes particularly apparent if one compares the cost of WGS to the personnel and reagent costs associated with multiple-month to multiple-year mapping-based cloning efforts.WGS identifies sequence variants between a mutated genome and a premutagenesis reference genome. Chemical mutagens randomly introduce many mutations in the genome and, therefore, the phenotype-causing sequence variant needs to be identified as such out of a large pool of sequence variants. Sequence variants that have no impact on the phenotype can be outcrossed before sequencing or eliminated through some rough mapping of the mutation, which allows the experimenter to focus only on those variants contained in a specific sequence interval. Ensuing functional tests such as transformation rescue or phenocopy by RNAi and the availability of other alleles of the same locus are critical means to validate a phenotype-causing sequence variant (Sarin et al. 2008). The latter approach—the availability of multiple alleles of the same locus—is in many ways the most powerful one to sift through a number of candidate variants revealed by WGS. In this approach, candidate loci revealed by WGS are resequenced by conventional Sanger sequencing in allelic strains and only those that are indeed phenotype causing will show up mutated in all allelic variants of the locus (Sarin et al. 2008). The availability of multiple alleles of a locus is highly desirable for many aspects of genetic analysis anyway and therefore does not represent an additional and specific burden for undertaking a WGS project.The importance of WGS on model system genetics will be substantial and wide ranging. Speed and cost effectiveness means that the wastelands of genetic mapping can be trespassed fast enough to allow an experimenter to multitask a whole mutant collection in parallel, thereby closing in on the “holy grail” of genetic analysis—the as-complete-as-possible mutational saturation of a biological process and the resulting deciphering of complete genetic pathways and networks. What will become limiting steps are not any more the tediousness of mapping, but rather the effectiveness with which mutant collections can be built. Novel technologies that involve machine-based, semiautomated selection of mutant animals have been developed over the past few years to study a variety of distinct biological processes in several metazoan model systems, e.g., gfp-based morphology or cell fate screens in worms (Crane et al. 2009; Doitsidou et al. 2008) or behavioral screens in flies (Dankert et al. 2009) and are important steps in this direction. Such an “industrial revolution” of genetic screening (i.e., the mutant selection part, followed by WGS) moves us geneticists away from, not into the trenches of factory life and frees us up to do what we should like to enjoy most—thinking of designing interesting screens, seeing how genes interact, and interpreting it all.Another important impact of WGS is that it will allow tackling problems that were previously hard to deal with. For example, the tediousness of following subtle phenotypes, low penetrance phenotypes, or phenotypes that are cumbersome to score often hampers positional cloning approaches that rely on identifying rare recombinants in a large sibling pool. Moreover, many genetic traits such as behavioral genetic traits are very sensitive to genetic background and are therefore also often hard to map in the conventional way. WGS hones in on candidate genes straight away. Taking this notion a step further, WGS will also be able to get at the molecular basis of multigenic traits and quantitative trait loci, which again are hard to molecularly identify through conventional mapping strategies; a proof-of-principle study has made this point already in bacteria (Srivatsan et al. 2008). In principle, such multigenic traits may have been mutationally induced or could be present in natural variants of a species, which provides intriguing perspective for the population geneticist.Model organisms of biological interest that were previously relatively intractable for classic genetic mutant analysis due to the absence of genetic markers or other practical problems such as prohibitive generation times, may also now be movable into the arena of genetic model systems, through the WGS-mediated molecular analysis of mutagen-induced variants or through the study of natural variants.The sequencing of human cancer genomes has already begun to illustrate the impact of WGS on human genetics (Campbell et al. 2008; Ley et al. 2008). However, those human WGS studies illustrate why model systems will continue to be extremely important—their experimental accessibility allows us to address which of the many variants detected by WGS is indeed the phenotype-causing one.The message to model system geneticists is clear: Get access to a deep sequencer, buckle up, and get ready for the ride.     

Using RNA Interference to Identify Specific Modifiers of a Temperature-Sensitive,Embryonic-Lethal Mutation in the Caenorhabditis elegans Ubiquitin-Like Nedd8 Protein Modification Pathway E1-Activating Gene rfl-1

The essential Caenorhabditis elegans gene rfl-1 encodes one subunit of a heterodimeric E1-activating enzyme in the Nedd8 ubiquitin-like protein conjugation pathway. This pathway modifies the Cullin scaffolds of E3 ubiquitin ligases with a single Nedd8 moiety to promote ligase function. To identify genes that influence neddylation, we used a synthetic screen to identify genes that, when depleted with RNAi, enhance or suppress the embryonic lethality caused by or198ts, a temperature-sensitive (ts) mutation in rfl-1. We identified reproducible suppressor and enhancer genes and employed a systematic specificity analysis for each modifier using four unrelated ts embryonic lethal mutants. Results of this analysis highlight the importance of specificity controls in identifying genetic interactions relevant to a particular biological process because 8/14 enhancers and 7/21 suppressors modified lethality in other mutants. Depletion of the strongest specific suppressors rescued the early embryonic cell division defects in rfl-1(or198ts) mutants. RNAi knockdown of some specific suppressors partially restored Cullin neddylation in rfl-1(or198ts) mutants, consistent with their gene products normally opposing neddylation, and GFP fusions to several suppressors were detected in the cytoplasm or the nucleus, similar in pattern to Nedd8 conjugation pathway components in early embryonic cells. In contrast, depletion of the two strongest specific enhancers did not affect the early embryonic cell division defects observed in rfl-1(or198ts) mutants, suggesting that they may act at later times in other essential processes. Many of the specific modifiers are conserved in other organisms, and most are nonessential. Thus, when controlled properly for specificity, modifier screens using conditionally lethal C. elegans mutants can identify roles for nonessential but conserved genes in essential processes.UBIQUITIN-mediated proteolysis regulates many biological processes (Nandi et al. 2006). In the early Caenorhabditis elegans embryo, these include oocyte maturation, cell cycle progression, cell polarization, and cell fate patterning, all of which require the timely destruction of maternally expressed proteins (Bowerman and Kurz 2006; Greenstein and Lee 2006). One C. elegans protein targeted for proteolysis early in embryogenesis is MEI-1, the AAA-ATPase subunit of the microtubule-severing complex called katanin (Mains et al. 1990; Dow and Mains 1998; Srayko et al. 2000; Kurz et al. 2002; Pintard et al. 2003a; Xu et al. 2003). Katanin is a heterodimer of two subunits called p60 and p80 in vertebrates and MEI-1 and MEI-2 in C. elegans. Katanin in C. elegans is required for proper assembly and function of the small, barrel-shaped meiotic spindles (Albertson and Thomson 1993; McNally et al. 2006) and must be degraded after meiotic divisions to permit assembly of the much larger first mitotic spindle in the one-cell zygote. In mutants that fail to degrade katanin after the completion of meiosis, the first mitotic spindle is fragmented and mis-oriented, cytokinesis is defective, and the embryos die without hatching (Dow and Mains 1998; Srayko et al. 2000; Kurz et al. 2002).The katanin subunit MEI-1 is targeted for poly-ubiquitylation and proteolytic destruction by a Cullin-based E3 ligase (Kurz et al. 2002). This complex includes the Cullin scaffolding protein CUL-3 and a substrate-specific adaptor called MEL-26 that binds to CUL-3 through a BTB domain and to MEI-1 through a MATH domain (Pintard et al. 2003b). Cullin 3-based E3 ligases in mammals also utilize substrate-specific adaptor proteins that, like MEL-26, have both a Cullin-binding BTB/POZ domain and another protein–protein interaction domain that binds to the substrate (Geyer et al. 2003; Cullinan et al. 2004; Angers et al. 2006). While MEI-1/Katanin downregulation by the CUL-3/MEL-26 E3 ligase is essential at most growth temperatures, a mel-26 null mutation is viable at the low growth temperature of 15° (Lu and Mains 2007). This bypass of mel-26 at 15° depends at least in part on the anaphase-promoting complex and its targeting of MEI-1 for proteolytic degradation (Lu and Mains 2007). Phosphorylation by the kinase MBK-2 primes MEI-1 for proteolysis (Quintin et al. 2003; Stitzel et al. 2007) and also promotes the downregulation of MEI-1 by the anaphase-promoting complex (Lu and Mains 2007).CUL-3 is the only C. elegans Cullin thus far identified that requires modification by the ubiquitin-like protein Nedd8 (Bowerman and Kurz 2006). In contrast, C. elegans CUL-2 is required for progression through meiosis and for the localized degradation of cell fate determinants in one-cell-stage embryos (Liu et al. 2004; Sonneville and Gonczy 2004), but neddylation-defective mutants do not exhibit these early defects (Bowerman and Kurz 2006). Cullin neddylation is mediated by the Nedd8 protein conjugation pathway, which begins with a heterodimeric E1-activating enzyme consisting of ULA-1 and RFL-1 (Uba3p in budding yeast) and also includes the E2-conjugating enzyme UBC-12 (Jones and Candido 2000; Srayko et al. 2000; Kurz et al. 2002) and the E3 ligase DCN-1 (Kurz et al. 2005).The downregulation of MEI-1/katanin by the CUL-3/MEL-26 E3 ligase requires a balance of both CUL-3 neddylation, which is mediated by the Nedd8 conjugation pathway, and deneddylation, which is mediated by the conserved COP-9 Signalosome (Pintard et al. 2003a). Other Cullin-based E3 ubiquitin ligases also require a balance of neddylation and deneddylation (Lyapina et al. 2001; Schwechheimer et al. 2001; Bornstein et al. 2006; Hetfeld et al. 2008). Deneddylation may modulate activation of the E3 ligase and thereby prevent the premature degradation of substrate adaptor proteins that also can become poly-ubiquitylated and degraded as a result of E3 ligase function.To identify additional factors that influence neddylation, and the downregulation of MEI-1/katanin after the completion of meiosis in C. elegans, we report here our use of RNA interference (RNAi) to reduce gene functions in a temperature-sensitive (ts) neddylation-defective mutant, rfl-1(or198ts). The discovery of RNAi and its systemic properties in C. elegans have made it possible to systematically target C. elegans genes for depletion by feeding worms bacterial strains that express double-strand RNAs corresponding to C. elegans gene sequences (Fire et al. 1998; Timmons et al. 2001; Feinberg and Hunter 2003; Baugh et al. 2005; Lehner et al. 2006; van Haaften et al. 2006). Furthermore, chemical mutagenesis screens have identified temperature-sensitive mutations in many essential C. elegans genes, which can be used for synthetic screens by choosing intermediate-growth temperatures that sensitize the genetic background and also optimize visual scoring of embryonic viability. Recently, genomewide RNAi screens have been used to identify C. elegans genes that, when reduced in function, restore viability to temperature-sensitive, embryonic-lethal mutants (Labbe et al. 2006; O''Rourke et al. 2007). Because a loss of suppressor function restores mutant viability, the suppressors may negatively regulate either the wild-type gene product or the process that requires the wild-type gene product.Here we report our identification of C. elegans genes that, when reduced in function by feeding RNAi, reproducibly suppressed or enhanced rfl-1(or198ts) embryonic lethality. Most suppressors were specific for rfl-1(or198ts), while specific enhancement was less common. Many of the rfl-1-specific suppressors and enhancers are conserved but appear nonessential. GFP fusions to several specific suppressors exhibit localization patterns that resemble those known for neddylation pathway components, and depletion of some of these partially restored CUL-3 neddylation in rfl-1(or198ts) mutants. In addition to identifying possible roles for conserved genes in cullin neddylation, we report the first quantitative analysis of specificity for both the enhancement and the suppression of a conditionally lethal mutant in C. elegans. Our results highlight the importance of testing genetic modifiers of conditionally lethal mutants for locus specificity.     

Identification of a Novel Abscisic Acid-Regulated Farnesol Dehydrogenase from Arabidopsis

In Arabidopsis (Arabidopsis thaliana), farnesylcysteine is oxidized to farnesal and cysteine by a membrane-associated thioether oxidase called farnesylcysteine lyase. Farnesol and farnesyl phosphate kinases have also been reported in plant membranes. Together, these observations suggest the existence of enzymes that catalyze the interconversion of farnesal and farnesol. In this report, Arabidopsis membranes are shown to possess farnesol dehydrogenase activity. In addition, a gene on chromosome 4 of the Arabidopsis genome (At4g33360), called FLDH, is shown to encode an NAD⁺-dependent dehydrogenase that oxidizes farnesol more efficiently than other prenyl alcohol substrates. FLDH expression is repressed by abscisic acid (ABA) but is increased in mutants with T-DNA insertions in the FLDH 5′ flanking region. These T-DNA insertion mutants, called fldh-1 and fldh-2, are associated with an ABA-insensitive phenotype, suggesting that FLDH is a negative regulator of ABA signaling.Isoprenylated proteins are modified at the C terminus via cysteinyl thioether linkage to either a 15-carbon farnesyl or a 20-carbon geranylgeranyl group (Clarke, 1992; Zhang and Casey, 1996; Rodríguez-Concepción et al., 1999; Crowell, 2000; Crowell and Huizinga, 2009). These modifications mediate protein-membrane and protein-protein interactions and are necessary for the proper localization and function of hundreds of proteins in eukaryotic cells. In Arabidopsis (Arabidopsis thaliana), the PLURIPETALA (PLP; At3g59380) and ENHANCED RESPONSE TO ABA1 (At5g40280) genes encode the α- and β-subunits of protein farnesyltransferase (PFT), respectively (Cutler et al., 1996; Pei et al., 1998; Running et al., 2004). These subunits form a heterodimeric zinc metalloenzyme that catalyzes the efficient transfer of a farnesyl group from farnesyl diphosphate to protein substrates with a C-terminal CaaX motif, where “C” is Cys, “a” is an aliphatic amino acid, and “X” is usually Met, Gln, Cys, Ala, or Ser (Fig. 1). The PLP and GERANYLGERANYL-TRANSFERASE BETA (At2g39550) genes encode the α- and β-subunits of protein geranylgeranyltransferase type 1 (PGGT1), respectively (Running et al., 2004; Johnson et al., 2005). These subunits form a distinct heterodimeric zinc metalloenzyme that catalyzes the efficient transfer of a geranylgeranyl group from geranylgeranyl diphosphate to protein substrates with a C-terminal CaaL motif, where “C” is Cys, “a” is an aliphatic amino acid, and “L” is Leu. A third protein prenyltransferase, called protein geranylgeranyltransferase type II or RAB geranylgeranyltransferase, catalyzes the dual geranylgeranylation of RAB proteins with a C-terminal XCCXX, XXCXC, XXCCX, XXXCC, XCXXX, or CCXXX motif, where “C” is Cys and “X” is any amino acid. However, RAB proteins must be associated with the RAB ESCORT PROTEIN to be substrates of RAB geranylgeranyltransferase. Plant protein prenylation has received considerable attention in recent years because of the meristem defects of Arabidopsis PFT mutants and the abscisic acid (ABA) hypersensitivity of Arabidopsis PFT and PGGT1 mutants (Cutler et al., 1996; Pei et al., 1998; Running et al., 1998, 2004; Johnson et al., 2005).Open in a separate windowFigure 1.Proposed metabolism of farnesal and farnesol as it relates to protein prenylation. The portion of the cycle shown in red is the subject of this article.Proteins that are prenylated by either PFT or PGGT1 undergo further processing in the endoplasmic reticulum (Crowell, 2000; Crowell and Huizinga, 2009). First, the aaX portion of the CaaX motif is removed by proteolysis (Fig. 1). This reaction is catalyzed by one of two CaaX endoproteases, which are encoded by the AtSTE24 (At4g01320) and AtFACE-2 (At2g36305) genes (Bracha et al., 2002; Cadiñanos et al., 2003). Second, the prenylated Cys residue at the new C terminus is methylated by one of two isoprenylcysteine methyltransferases (Fig. 1), which are encoded by the AtSTE14A (At5g23320) and AtSTE14B (ICMT; At5g08335) genes (Crowell et al., 1998; Crowell and Kennedy, 2001; Narasimha Chary et al., 2002; Bracha-Drori et al., 2008). A specific isoprenylcysteine methylesterase encoded by the Arabidopsis ICME (At5g15860) gene has also been described, demonstrating the reversibility of isoprenylcysteine methylation (Deem et al., 2006; Huizinga et al., 2008).Like all proteins, prenylated proteins have a finite half-life. However, unlike other proteins, prenylated proteins release farnesylcysteine (FC) or geranylgeranylcysteine (GGC) upon degradation. Mammals possess a prenylcysteine lyase enzyme that catalyzes the oxidative cleavage of FC and GGC (Zhang et al., 1997; Tschantz et al., 1999; Tschantz et al., 2001; Beigneux et al., 2002; Digits et al., 2002). This FAD-dependent thioether oxidase consumes molecular oxygen and generates hydrogen peroxide, Cys, and a prenyl aldehyde product (i.e. farnesal or geranylgeranial). In Arabidopsis, a similar lyase exists. However, the Arabidopsis enzyme, which is encoded by the FCLY (At5g63910) gene, is specific for FC (Fig. 1; Crowell et al., 2007; Huizinga et al., 2010). GGC is metabolized by a different mechanism.Plant membranes have been shown to contain farnesol kinase, geranylgeraniol kinase, farnesyl phosphate kinase, and geranylgeranyl phosphate kinase activities (Fig. 1; Thai et al., 1999). These membrane-associated kinases differ with respect to nucleotide specificity, suggesting that they are distinct enzymes (i.e. farnesol kinase and geranylgeraniol kinase can use CTP, UTP, or GTP as a phosphoryl donor, whereas farnesyl phosphate kinase and geranylgeranyl phosphate kinase exhibit specificity for CTP as a phosphoryl donor). However, it remains unclear if farnesol kinase is distinct from geranylgeraniol kinase or if farnesyl phosphate kinase is distinct from geranylgeranyl phosphate kinase. Nonetheless, it is clear that these kinases convert farnesol and geranylgeraniol to their monophosphate and diphosphate forms for use in isoprenoid biosynthesis, including sterol biosynthesis and protein prenylation.Because plants have the metabolic capability to generate farnesal from FC and farnesyl diphosphate from farnesol, we considered the possibility that plant membranes also contain an oxidoreductase capable of catalyzing the reduction of farnesal to farnesol and/or the oxidation of farnesol to farnesal (Fig. 1; Thai et al., 1999; Crowell et al., 2007). To date, the only reports of such an oxidoreductase are from the corpora allata glands of insects, where it participates in juvenile hormone synthesis, and black rot fungus-infected sweet potato (Ipomoea batatas; Baker et al., 1983; Inoue et al., 1984; Sperry and Sen, 2001; Mayoral et al., 2009). Insect farnesol dehydrogenase is an NADP⁺-dependent oxidoreductase that is encoded by a subfamily of short-chain dehydrogenase/reductase (SDR) genes (Mayoral et al., 2009). Farnesol dehydrogenase from sweet potato is a 90-kD, NADP⁺-dependent homodimer with broad specificity for prenyl alcohol substrates and is induced by wounding and fungus infection of potato roots (Inoue et al., 1984).Here, we extended previous work in which [1-³H]FC was shown to be oxidized to [1-³H]farnesal, and [1-³H]farnesal reduced to [1-³H]farnesol, in the presence of Arabidopsis membranes (Crowell et al., 2007). The reduction of [1-³H]farnesal to [1-³H]farnesol was abolished by pretreatment of Arabidopsis membranes with NADase, suggesting that sufficient NAD(P)H is present in Arabidopsis membranes to support the enzymatic reduction of farnesal to farnesol. In this report, we demonstrate the presence of farnesol dehydrogenase activity in Arabidopsis membranes using [1-³H]farnesol as a substrate. Moreover, we identify a gene on chromosome 4 of the Arabidopsis genome (At4g33360), called FLDH, that encodes an NAD⁺-dependent dehydrogenase with partial specificity for farnesol as a substrate. FLDH expression is repressed by exogenous ABA, and fldh mutants exhibit altered ABA signaling. Taken together, these observations suggest that ABA regulates farnesol metabolism in Arabidopsis, which in turn regulates ABA signaling.     

Widening the scope of constriction

JGP modeling study suggests that selectivity filter constriction is a plausible mechanism for C-type inactivation of the Shaker voltage-gated potassium channel.

In response to prolonged activation, many K⁺ channels spontaneously reduce the membrane conductance by undergoing C-type inactivation, a kinetic process crucial for the pacing of cardiac action potentials and the modulation of neuronal firing patterns. In the pH-activated bacterial channel KcsA, C-type inactivation appears to involve constriction of the channel’s selectivity filer that prohibits ion conduction, but whether voltage-gated channels like Drosophila Shaker use a similar mechanism is controversial (1). In this issue of JGP, a computational study by Li et al. suggests that filter constriction is indeed a plausible mechanism for the C-type inactivation of Shaker (2).(Left to right) Jing Li, Benoît Roux, and colleagues use computational modeling to show that selectivity filter constriction, allosterically promoted by opening of the intracellular activation gate, is a plausible mechanism for the C-type inactivation of voltage-gated K⁺ channels such as Drosophila Shaker. The selectivity filter is conductive (left) when the intracellular gate is partially open, but adopts a constricted conformation (right) when the gate is open wide.Various structural approaches have shown that C-type inactivation of KcsA channels is associated with the symmetrical constriction of all four channel subunits at the level of the central glycine residue in the selectivity filter. Benoît Roux and colleagues at The University of Chicago used MD simulations to show that the KcsA pore can transition from the conductive to the constricted conformation on an appropriate timescale, and that this transition is allosterically promoted by the wide opening of the pore’s intracellular gate (3). Modeling by Roux and colleagues suggests that C-type inactivation of cardiac hERG channels could also involve selectivity filter constriction, though in this case it appears to be an asymmetric process in which only two of the channel’s subunits move closer together (4).“In view of the high similarity between the pore domains of Shaker and KcsA (almost 40% sequence identity), we wanted to examine if it’s possible for the Shaker selectivity filter to constrict and, if so, how similar it is to KcsA,” Roux explains. Led by first author Jing Li—now an assistant professor at the University of Mississippi—Roux and colleagues developed several homology models of the Shaker pore domain with the intracellular gate open to various degrees (2).MD simulations and free energy calculations revealed that the Shaker selectivity filter can dynamically transition from a conductive to a constricted conformation, and that this transition is allosterically coupled to the intracellular gate; the constricted conformation is stable when the gate is wide open. “Our computations strongly suggest that constriction is a plausible mechanism for the C-type inactivation of Shaker,” Roux says. “There’s no reason based on the currently available information to reject the existence of a constricted state in Shaker channels.”As with KcsA, Shaker channels appear to constrict symmetrically at the level of the selectivity filter’s central glycine. But Li et al.’s simulations revealed some small variations between the two channels, including differences in the number of water molecules bound to each channel subunit and the arrangement of the hydrogen-bond network they form to stabilize the constricted state.Li et al. also modeled the pore domain of the Shaker W434F mutant, which is widely assumed to be trapped in a C-type inactivated state. The simulation suggests that the mutant channel’s filter adopts a stable constricted conformation even when the intracellular gate is only partially open, although the constriction is asymmetric and occurs at the level of a different filter residue (2).Constriction may therefore be a universal mechanism of C-type inactivation, even if the exact conformation varies from channel to channel. But, says Roux, confirming this will require more experimental work using the right conditions and mutations to capture the structure of inactivated channels.     

Estimating Breeding Values With Molecular Relatedness and Reconstructed Pedigrees in Natural Mating Populations of Common Sole,Solea Solea

Captive populations where natural mating in groups is used to obtain offspring typically yield unbalanced population structures with highly skewed parental contributions and unknown pedigrees. Consequently, for genetic parameter estimation, relationships need to be reconstructed or estimated using DNA marker data. With missing parents and natural mating groups, commonly used pedigree reconstruction methods are not accurate and lead to loss of data. Relatedness estimators, however, infer relationships between all animals sampled. In this study, we compared a pedigree relatedness method and a relatedness estimator (“molecular relatedness”) method using accuracy of estimated breeding values. A commercial data set of common sole, Solea solea, with 51 parents and 1953 offspring (“full data set”) was used. Due to missing parents, for 1338 offspring, a pedigree could be reconstructed with 10 microsatellite markers (“reduced data set”). Cross-validation of both methods using the reduced data set showed an accuracy of estimated breeding values of 0.54 with pedigree reconstruction and 0.55 with molecular relatedness. Accuracy of estimated breeding values increased to 0.60 when applying molecular relatedness to the full data set. Our results indicate that pedigree reconstruction and molecular relatedness predict breeding values equally well in a population with skewed contributions to families. This is probably due to the presence of few large full-sib families. However, unlike methods with pedigree reconstruction, molecular relatedness methods ensure availability of all genotyped selection candidates, which results in higher accuracy of breeding value estimation.To estimate genetic parameters, additive genetic relationships between individuals are inferred from known pedigrees (Falconer and Mackay 1996; Lynch and Walsh 1997). However, in natural populations (Ritland 2000; Thomas et al. 2002) and in captive species where natural mating in groups is used to obtain offspring (Brown et al. 2005; Fessehaye et al. 2006; Blonk et al. 2009) pedigrees are reconstructed. In these populations there is no control on mating structure, and typically unbalanced population structures with highly skewed parental contributions are obtained (Bekkevold et al. 2002; Brown et al. 2005; Fessehaye et al. 2006; Blonk et al. 2009). To reconstruct pedigrees, parental allocation methods are often used (Marshall et al. 1998; Avise et al. 2002; Duchesne et al. 2002). These methods require that all parents be known. For situations where parental information is not available, numerous DNA-marker-based methods for estimating molecular relatedness have been developed (Lynch 1988; Queller and Goodnight 1989; Ritland 2000; Toro et al. 2002). These relatedness estimators determine relationship values between individuals on a continuous scale. Evaluation of relatedness estimators within real and simulated data in both plants and animals (e.g., see Van de Casteele et al. 2001 ; Milligan 2003; Oliehoek et al. 2006; Rodríguez-Ramilo et al. 2007; Bink et al. 2008) has generally focused on bias and sampling error of estimated genetic variances or relatedness values. Relatively little attention has been paid to their efficiency for estimation of breeding values.Two types of relatedness estimators are currently available: method-of-moments estimators and maximum-likelihood estimators. Method-of-moments estimators (e.g., Queller and Goodnight 1989; Li et al. 1993; Ritland 1996; Lynch and Ritland 1999; Toro et al. 2002) determine relationships while calculating sharing of alleles between pairs in different ways. A variant of method-of-moments estimators is the transformation of continuous relatedness values to categorical genealogical relationships using “explicit pedigree reconstruction” (Fernández and Toro 2006) or thresholds (Rodríguez-Ramilo et al. 2007). However, correlations of transformed coancestries with known genealogical coancestries are low (Rodríguez-Ramilo et al. 2007). Several studies have compared different method-of-moments estimators but none revealed one single best estimator (Van de Casteele et al. 2001; Oliehoek et al. 2006; Rodríguez-Ramilo et al. 2007; Bink et al. 2008).Maximum-likelihood (ML) approaches classify animals into a limited number of relationship classes (Mousseau et al. 1998; Thomas et al. 2002; Wang 2004; Herbinger et al. 2006; Anderson and Weir 2007). For each pair a likelihood to fall into a possible relatedness class (e.g., full sib vs. unrelated) is calculated given its genotype and phenotype. ML techniques combined with a Markov chain Monte Carlo approach reconstruct groups with specific relationships jointly and are therefore more efficient than other ML approaches. To minimize standard errors, all discussed ML methods require balanced population structures, large sibling groups, and a large variance of relatedness (Thomas et al. 2002; Wang 2004; Anderson and Weir 2007). Therefore, these methods may not be suitable for natural mating systems.Unlike parental allocation methods, a benefit from relatedness estimators is that essentially all selection candidates are maintained for breeding value estimation, even with missing parents. The question is, however, whether such relatedness estimators also give accurate breeding values to perform selection.In this study, we test suitability of a relatedness estimator to obtain breeding values in a population of common sole, Solea solea (n = 1953) obtained by natural mating. First, we estimate breeding values using pedigree relatedness of animals for which a pedigree could be reconstructed (using parental allocation). This data set (n = 1338) is further referred to as “reduced data set.” We compare results with estimated breeding values using a simple but robust method-of-moments relatedness estimator: “molecular relatedness” (Toro et al. 2002, 2003). Next, we estimate breeding values using molecular relatedness in the full data set (n = 1953). Results show that accuracies of estimated breeding values obtained with molecular relatedness and pedigree relatedness are comparable. Accuracy increases when breeding values are estimated with molecular relatedness in the full data set. This implies that a molecular relatedness estimator can be used to estimate breeding values in captive natural mating populations.