Calcineurin B-Like Protein-Interacting Protein Kinase CIPK21 Regulates Osmotic and Salt Stress Responses in Arabidopsis

The role of calcium-mediated signaling has been extensively studied in plant responses to abiotic stress signals. Calcineurin B-like proteins (CBLs) and CBL-interacting protein kinases (CIPKs) constitute a complex signaling network acting in diverse plant stress responses. Osmotic stress imposed by soil salinity and drought is a major abiotic stress that impedes plant growth and development and involves calcium-signaling processes. In this study, we report the functional analysis of CIPK21, an Arabidopsis (Arabidopsis thaliana) CBL-interacting protein kinase, ubiquitously expressed in plant tissues and up-regulated under multiple abiotic stress conditions. The growth of a loss-of-function mutant of CIPK21, cipk21, was hypersensitive to high salt and osmotic stress conditions. The calcium sensors CBL2 and CBL3 were found to physically interact with CIPK21 and target this kinase to the tonoplast. Moreover, preferential localization of CIPK21 to the tonoplast was detected under salt stress condition when coexpressed with CBL2 or CBL3. These findings suggest that CIPK21 mediates responses to salt stress condition in Arabidopsis, at least in part, by regulating ion and water homeostasis across the vacuolar membranes.Drought and salinity cause osmotic stress in plants and severely affect crop productivity throughout the world. Plants respond to osmotic stress by changing a number of cellular processes (Xiong et al., 1999; Xiong and Zhu, 2002; Bartels and Sunkar, 2005; Boudsocq and Lauriére, 2005). Some of these changes include activation of stress-responsive genes, regulation of membrane transport at both plasma membrane (PM) and vacuolar membrane (tonoplast) to maintain water and ionic homeostasis, and metabolic changes to produce compatible osmolytes such as Pro (Stewart and Lee, 1974; Krasensky and Jonak, 2012). It has been well established that a specific calcium (Ca²⁺) signature is generated in response to a particular environmental stimulus (Trewavas and Malhó, 1998; Scrase-Field and Knight, 2003; Luan, 2009; Kudla et al., 2010). The Ca²⁺ changes are primarily perceived by several Ca²⁺ sensors such as calmodulin (Reddy, 2001; Luan et al., 2002), Ca²⁺-dependent protein kinases (Harper and Harmon, 2005), calcineurin B-like proteins (CBLs; Luan et al., 2002; Batistič and Kudla, 2004; Pandey, 2008; Luan, 2009; Sanyal et al., 2015), and other Ca²⁺-binding proteins (Reddy, 2001; Shao et al., 2008) to initiate various cellular responses.Plant CBL-type Ca²⁺ sensors interact with and activate CBL-interacting protein kinases (CIPKs) that phosphorylate downstream components to transduce Ca²⁺ signals (Liu et al., 2000; Luan et al., 2002; Batistič and Kudla, 2004; Luan, 2009). In several plant species, multiple members have been identified in the CBL and CIPK family (Luan et al., 2002; Kolukisaoglu et al., 2004; Pandey, 2008; Batistič and Kudla, 2009; Weinl and Kudla, 2009; Pandey et al., 2014). Involvement of specific CBL-CIPK pair to decode a particular type of signal entails the alternative and selective complex formation leading to stimulus-response coupling (D’Angelo et al., 2006; Batistič et al., 2010).Several CBL and CIPK family members have been implicated in plant responses to drought, salinity, and osmotic stress based on genetic analysis of Arabidopsis (Arabidopsis thaliana) mutants (Zhu, 2002; Cheong et al., 2003, 2007; Kim et al., 2003; Pandey et al., 2004, 2008; D’Angelo et al., 2006; Qin et al., 2008; Tripathi et al., 2009; Held et al., 2011; Tang et al., 2012; Drerup et al., 2013; Eckert et al., 2014). A few CIPKs have also been functionally characterized by gain-of-function approach in crop plants such as rice (Oryza sativa), pea (Pisum sativum), and maize (Zea mays) and were found to be involved in osmotic stress responses (Mahajan et al., 2006; Xiang et al., 2007; Yang et al., 2008; Tripathi et al., 2009; Zhao et al., 2009; Cuéllar et al., 2010).In this report, we examined the role of the Arabidopsis CIPK21 gene in osmotic stress response by reverse genetic analysis. The loss-of-function mutant plants became hypersensitive to salt and mannitol stress conditions, suggesting that CIPK21 is involved in the regulation of osmotic stress response in Arabidopsis. These findings are further supported by an enhanced tonoplast targeting of the cytoplasmic CIPK21 through interaction with the vacuolar Ca²⁺ sensors CBL2 and CBL3 under salt stress condition.     

Decreased Mitochondrial Activities of Malate Dehydrogenase and Fumarase in Tomato Lead to Altered Root Growth and Architecture via Diverse Mechanisms

Transgenic tomato (Solanum lycopersicum) plants in which either mitochondrial malate dehydrogenase or fumarase was antisense inhibited have previously been characterized to exhibit altered photosynthetic metabolism. Here, we demonstrate that these manipulations also resulted in differences in root growth, with both transgenics being characterized by a dramatic reduction of root dry matter deposition and respiratory activity but opposite changes with respect to root area. A range of physiological, molecular, and biochemical experiments were carried out in order to determine whether changes in root morphology were due to altered metabolism within the root itself, alterations in the nature of the transformants'' root exudation, consequences of alteration in the efficiency of photoassimilate delivery to the root, or a combination of these factors. Grafting experiments in which the transformants were reciprocally grafted to wild-type controls suggested that root length and area were determined by the aerial part of the plant but that biomass was not. Despite the transgenic roots displaying alteration in the expression of phytohormone-associated genes, evaluation of the levels of the hormones themselves revealed that, with the exception of gibberellins, they were largely unaltered. When taken together, these combined experiments suggest that root biomass and growth are retarded by root-specific alterations in metabolism and gibberellin contents. These data are discussed in the context of current models of root growth and biomass partitioning.The structure of the plant tricarboxylic acid (TCA) cycle has been established for decades (Beevers, 1961), and in vitro studies have established regulatory properties of many of its component enzymes (Budde and Randall, 1990; Millar and Leaver, 2000; Studart-Guimarães et al., 2005). That said, relatively little is known, as yet, regarding how this important pathway is regulated in vivo (Fernie et al., 2004a; Sweetlove et al., 2007). Indeed, even fundamental questions concerning the degree to which this pathway operates in illuminated leaves (Tcherkez et al., 2005; Nunes-Nesi et al., 2007a) and the influence it has on organic acid levels in fruits (Burger et al., 2003) remain contentious. Furthermore, in contrast to many other pathways of primary metabolism, the TCA cycle has been subjected to relatively few molecular physiological studies. To date, the functions of pyruvate dehydrogenase, citrate synthase, aconitase, isocitrate dehydrogenase, succinyl-CoA ligase, fumarase, and malate dehydrogenase have been studied via this approach (Landschütze et al., 1995; Carrari et al., 2003; Yui et al., 2003; Nunes-Nesi et al., 2005, 2007a; Lemaitre et al., 2007; Studart-Guimarães et al., 2007); however, several of these studies were relatively cursory. Despite this fact, they generally corroborate one another, with at least two studies providing clear evidence for an important role of the TCA cycle in flower development (Landschütze et al., 1995; Yui et al., 2003) or in the coordination of photosynthetic and respiratory metabolisms of the illuminated leaf (Carrari et al., 2003; Nunes-Nesi et al., 2005, 2007a).In our own studies on tomato (Solanum lycopersicum), we have observed that modulation of fumarase and mitochondrial malate dehydrogenase activities leads to contrasting shoot phenotypes, with the former displaying stunted growth while the later exhibited an enhanced photosynthetic performance (Nunes-Nesi et al., 2005, 2007a). We were able to demonstrate that the stunted-growth phenotype observed in aerial parts of the fumarase plants was a consequence of altered stomatal function (Nunes-Nesi et al., 2007a), whereas the increased photosynthetic performance of the mitochondrial malate dehydrogenase seems likely to be mediated by the alterations in ascorbate metabolism exhibited by these plants (Nunes-Nesi et al., 2005; Urbanczyk-Wochniak et al., 2006). In keeping with the altered rates of photosynthesis in these antisense plants, the fruit yield of fumarase and mitochondrial malate dehydrogenase plants was decreased and increased, respectively. However, the root biomass of both transgenics was significantly reduced (Nunes-Nesi et al., 2005, 2007a). These observations were somewhat surprising given that it is estimated that 30% to 60% of net photosynthate is transported to root organs (Merckx et al., 1986; Nguyen et al., 1999; Singer et al., 2003). When taken together, these results suggest that the root phenotype must result from either an impairment of translocation or a root-specific effect. Neither of these explanations is without precedence, with inhibition of the expression of Suc transporters (Riesmeier et al., 1993; Gottwald et al., 2000) resulting in dramatically impaired root growth while organic acid exudation itself has been implicated in a wide range of root organ functions, including nutrient acquisition (de la Fuente et al., 1997; Imas et al., 1997; Neumann and Römheld, 1999; López-Bucio et al., 2000; Anoop et al., 2003; Delhaize et al., 2004), metal sequestration (Gillooly et al., 1983; de la Fuente et al., 1997; Cramer and Titus, 2001), and microbial proliferation in the rhizosphere (Lugtenberg et al., 1999; Weisskopf et al., 2005). In addition to the putative mechanisms listed above, the TCA cycle could be anticipated to play a vital role in meeting the high energy demands of nitrogen fixation and polymer biosynthesis associated with rapidly growing heterotrophic organs (Pradet and Raymond, 1983; Dieuaide-Noubhani et al., 1997; Stasolla et al., 2003; Deuschle et al., 2006). In keeping with this theory, alteration of the energy status of roots and other heterotrophic tissue has been documented to positively correlate with elevated biomass production (Anekonda, 2001; Regierer et al., 2002; Carrari et al., 2003; Lovas et al., 2003; Geigenberger et al., 2005). Here, we performed a detailed physiological, molecular, and biochemical evaluation of whole plant and root metabolism of the mitochondrial malate dehydrogenase and fumarate antisense tomato lines. In this manner, we broadly assessed biochemical changes in the root, including the levels of several major phytohormones, as well as dissected which characteristics were influenced by aerial parts of the plant. The results obtained are discussed both with respect to the regulation of the TCA cycle per se and within the context of the determination of root morphology and growth.     

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.Rational and quantitative assessment of metabolic changes in response to genetic modification (GM) is an open question and in need of innovative solutions. Nontargeted metabolite profiling can detect thousands of compounds, but it is not easy to understand the significance of the changed metabolites in the biochemical and biological context of the organism. To better assess the changes in metabolites from nontargeted metabolomics studies, it is important to examine the changed metabolites in the context of the genome-scale metabolic network of the organism.Metabolomics is a technique that aims to quantify all the metabolites in a biological system (Nikolau and Wurtele, 2007; Nicholson and Lindon, 2008; Roessner and Bowne, 2009). It has been used widely in studies ranging from disease diagnosis (Holmes et al., 2008; DeBerardinis and Thompson, 2012) and drug discovery (Cascante et al., 2002; Kell, 2006) to metabolic reconstruction (Feist et al., 2009; Kim et al., 2012) and metabolic engineering (Keasling, 2010; Lee et al., 2011). Metabolomic studies have demonstrated the possibility of identifying gene functions from changes in the relative concentrations of metabolites (metabotypes or metabolic signatures; Ebbels et al., 2004) in various species including yeast (Saccharomyces cerevisiae; Raamsdonk et al., 2001; Allen et al., 2003), Arabidopsis (Arabidopsis thaliana; Brotman et al., 2011), tomato (Solanum lycopersicum; Schauer et al., 2006), and maize (Zea mays; Riedelsheimer et al., 2012). Metabolomics has also been used to better understand how plants interact with their environments (Field and Lake, 2011), including their responses to biotic and abiotic stresses (Dixon et al., 2006; Arbona et al., 2013), and to predict important agronomic traits (Riedelsheimer et al., 2012). Metabolite profiling has been performed on many plant species, including angiosperms such as Arabidopsis, poplar (Populus trichocarpa), and Catharanthus roseus (Sumner et al., 2003; Rischer et al., 2006), basal land plants such as Selaginella moellendorffii and Physcomitrella patens (Erxleben et al., 2012; Yobi et al., 2012), and Chlamydomonas reinhardtii (Fernie et al., 2012; Davis et al., 2013). With the availability of whole genome sequences of various species, metabolomics has the potential to become a useful tool for elucidating the functions of genes using large-scale systematic analyses (Fiehn et al., 2000; Saito and Matsuda, 2010; Hur et al., 2013).Although metabolomics data have the potential for identifying the roles of genes that are associated with metabolic phenotypes, the biochemical mechanisms that link functions of genes with metabolic phenotypes are still poorly characterized. For example, we do not yet know the principles behind how perturbing the expression of a single gene changes the metabolic system as a whole. Large-scale metabolomics data have provided useful resources for linking phenotypes to genotypes (Fiehn et al., 2000; Roessner et al., 2001; Tikunov et al., 2005; Schauer et al., 2006; Lu et al., 2011; Fukushima et al., 2014). For example, Lu et al. (2011) compared morphological and metabolic phenotypes from more than 5,000 Arabidopsis chloroplast mutants using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS). Fukushima et al. (2014) generated metabolite profiles from various characterized and uncharacterized mutant plants and clustered the mutants with similar metabolic phenotypes by conducting multidimensional scaling with quantified metabolic phenotypes. Nonetheless, representation and analysis of such a large amount of data remains a challenge for scientific discovery (Lu et al., 2011). In addition, these studies do not examine the topological and functional characteristics of metabolic changes in the context of a genome-scale metabolic network. To understand the relationship between genotype and metabolic phenotype, we need to investigate the metabolic changes caused by perturbing the expression of a gene in a genome-scale metabolic network perspective, because metabolic pathways are not independent biochemical factories but are components of a complex network (Berg et al., 2002; Merico et al., 2009).Much progress has been made in the last 2 decades to represent metabolism at a genome scale (Terzer et al., 2009). The advances in genome sequencing and emerging fields such as biocuration and bioinformatics enabled the representation of genome-scale metabolic network reconstructions for model organisms (Bassel et al., 2012). Genome-scale metabolic models have been built and applied broadly from microbes to plants. The first step toward modeling a genome-scale metabolism in a plant species started with developing a genome-scale metabolic pathway database for Arabidopsis (AraCyc; Mueller et al., 2003) from reference pathway databases (Kanehisa and Goto, 2000; Karp et al., 2002; Zhang et al., 2010). Genome-scale metabolic pathway databases have been built for several plant species (Mueller et al., 2005; Zhang et al., 2005, 2010; Urbanczyk-Wochniak and Sumner, 2007; May et al., 2009; Dharmawardhana et al., 2013; Monaco et al., 2013, 2014; Van Moerkercke et al., 2013; Chae et al., 2014; Jung et al., 2014). Efforts have been made to develop predictive genome-scale metabolic models using enzyme kinetics and stoichiometric flux-balance approaches (Sweetlove et al., 2008). de Oliveira Dal’Molin et al. (2010) developed a genome-scale metabolic model for Arabidopsis and successfully validated the model by predicting the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. Other genome-scale models have been developed for Arabidopsis (Poolman et al., 2009; Radrich et al., 2010; Mintz-Oron et al., 2012), C. reinhardtii (Chang et al., 2011; Dal’Molin et al., 2011), maize (Dal’Molin et al., 2010; Saha et al., 2011), sorghum (Sorghum bicolor; Dal’Molin et al., 2010), and sugarcane (Saccharum officinarum; Dal’Molin et al., 2010). These predictive models have the potential to be applied broadly in fields such as metabolic engineering, drug target discovery, identification of gene function, study of evolutionary processes, risk assessment of genetically modified crops, and interpretations of mutant phenotypes (Feist and Palsson, 2008; Ricroch et al., 2011).Here, we interrogate the metabotypes caused by 136 single gene perturbations of Arabidopsis by analyzing the relative concentration changes of 1,348 chemically identified metabolites using a reconstructed genome-scale metabolic network. We examine the characteristics of the changed metabolites (the metabolites whose relative concentrations were significantly different in mutants relative to the wild type) in the metabolic network to uncover biological and topological consequences of the perturbed genes.     

Cell biology in neuroscience: Death of developing neurons: New insights and implications for connectivity

The concept that target tissues determine the survival of neurons has inspired much of the thinking on neuronal development in vertebrates, not least because it is supported by decades of research on nerve growth factor (NGF) in the peripheral nervous system (PNS). Recent discoveries now help to understand why only some developing neurons selectively depend on NGF. They also indicate that the survival of most neurons in the central nervous system (CNS) is not simply regulated by single growth factors like in the PNS. Additionally, components of the cell death machinery have begun to be recognized as regulators of selective axonal degeneration and synaptic function, thus playing a critical role in wiring up the nervous system.

Why do so many neurons die during development?

Programmed cell death occurs throughout life, as cell turnover is part of homeostasis and maintenance in most organs and tissues. The situation in the nervous system is principally different, as the vast majority of neurons undergo their last round of cell division early in development. Soon after exiting the cell cycle, neurons start elongating axons to innervate their targets. It is during this period that they are highly susceptible to undergo programmed cell death: a large percentage, as much as 50% in several ganglia in the peripheral nervous system (PNS) as well as in various central nervous system (CNS) areas, is eliminated around the time that connections are being made with other cells. Later in development, the propensity of neurons to initiate apoptosis progressively decreases. The likelihood for a neuron to undergo apoptosis seems to be determined by a tightly regulated apoptotic machinery (summarized in Fig. 1). Therefore, modulation of the expression levels or the activity of components of this apoptotic balance changes the sensitivity to death-promoting cues, allowing temporal restriction of cell death.Open in a separate windowFigure 1.Core components of the apoptotic machinery. The likelihood that a neuron undergoes apoptosis is determined by the interplay of the tightly interlinked apoptotic machinery, many components of which are highly conserved between species. The critical, and often terminal, step in programmed cell death is the proteolytic activation of the executor caspases (such as caspase 3, 6, 7) by the initiator caspases (i.e., caspase 8, 9, and 10; Riedl and Salvesen, 2007). In mammalian cells, initiation of the executor caspases is regulated by two distinct protein cascades: the intrinsic pathway, also known as the mitochondrial pathway, and the extrinsic pathway. The intrinsic pathway integrates a number of intra- and extracellular signal modalities, such as redox state (for example, the reactive oxygen species; Franklin, 2011), DNA damage (Sperka et al., 2012), ER stress (Puthalakath et al., 2007) and growth factor deprivation (Deckwerth et al., 1998; Putcha et al., 2003; Bredesen et al., 2005), or activation of the p75NTR neurotrophin receptor by pro-neurotrophins (Nykjaer et al., 2005). The stressors converge onto pro- and anti-apoptotic members of the Bcl-2 protein family (for example: BCL-2, BCL-X_l, BAX, and tBID; Youle and Strasser, 2008). These proteins regulate the release of cytochrome c from mitochondria, which activates the initiator caspase 9 through Apaf1 (Riedl and Salvesen, 2007). The extrinsic pathway links activation of ligand-bound death receptors (such as Fas/CD95 and TNFR) to the initiator caspase 8 and 10, through formation of the death-inducing signaling complex (DISC; LeBlanc and Ashkenazi, 2003; Peter and Krammer, 2003). Together with additional regulatory elements (including the Inhibitors of apoptosis proteins [IAP]; Vaux and Silke, 2005) and cFLIP (Scaffidi et al., 1999; Wang et al., 2005), the apoptotic machinery forms a balance that determines the propensity of the neuron to undergo apoptosis.Programmed cell death eliminates many neurons during development, even in organisms comprised of only few cells, such as Caenorhabditis elegans. As neurons and their targets are initially separated, it is possible that the initial generation of an overabundance of neurons is simply part of a mechanism to ensure that distal targets are adequately innervated (Oppenheim, 1991; Conradt, 2009; Chen et al., 2013). In various tissues other than the nervous system, programmed cell death is used to eliminate cells that are no longer needed, defective, or harmful to the function of the organism. However, there is strong evidence that the elimination of superfluous neurons in the developing nervous system is not essential. For example, early work in C. elegans revealed that preventing programmed cell death does not result in significant behavioral alterations (Ellis and Horvitz, 1986). In the C57BL/6 mouse strain, deletion of the executor caspases 3 and 7 (Fig. 1) has a remarkably limited neuropathological and morphological impact in the CNS (Leonard et al., 2002; Lakhani et al., 2006) compared with the 129X1/SvJ strain, in which deletion of these caspases causes neurodevelopmental defects (Leonard et al., 2002). Similar conclusions were reached by blocking the Bcl-2–associated X protein (BAX)–dependent pathway in many neuronal populations, including motoneurons (Buss et al., 2006a). A recent study in the developing retina showed that in mice lacking the central apoptotic regulator BAX, the normal mosaic distribution of intrinsically photosensitive retinal ganglion cells (ipRGCs) was perturbed (Chen et al., 2013). Although this abnormal distribution is dispensable for the intrinsic photosensitivity of the ipRGCs, it is required for establishing proper connections to other neurons in the retina, which is necessary for rod/cone photo-entrainment (Chen et al., 2013). Even though this finding highlights a physiological role for programmed cell death in the CNS, the functional consequences remain rather underwhelming in the face of a process that eliminates such large numbers of neurons (Purves and Lichtman, 1984; Oppenheim, 1991; Miller, 1995; Gohlke et al., 2004). It thus appears that apoptotic removal of the surplus neurons generated during development mainly serves the purpose to optimize the size of the nervous system to be minimal, but sufficient.

A molecular substrate for the neurotrophic theory

Quantitatively, programmed cell death of neurons in the PNS and CNS is most dramatic when neurons start contacting the cells they innervate. Because experimental manipulations such as target excision typically lead to the death of essentially all innervating neurons (Oppenheim, 1991), the concept emerged that the fate of developing neurons is regulated by their targets. This notion is often referred to as the “neurotrophic theory” (Hamburger et al., 1981; Purves and Lichtman, 1984; Oppenheim, 1991), but it is important to realize that it evolved in the absence of direct mechanistic or molecular support (Purves, 1988). Originally described as a diffusible agent promoting nerve growth, the eponymous NGF later provided a strong and very appealing molecular foundation for this theory (Korsching and Thoenen, 1983; Edwards et al., 1989; Hamburger, 1992). The tyrosine kinase receptor tropomyosin receptor kinase A (TrkA), which was initially identified as an oncogene (Martin-Zanca et al., 1986), was fortuitously discovered to be the critical receptor necessary for the prevention of neuronal cell death by NGF (Klein et al., 1991). Both the remarkable expression pattern of TrkA in NGF-dependent neurons and the onset of its expression during development (Martin-Zanca et al., 1990) provided further additional support for the neurotrophic theory. However, for a surprisingly long time, the question was not asked as to why only specific populations of neurons strictly depend on NGF for survival, while others do not. Indeed, it was only recently shown that TrkA causes cell death of neurons by virtue of its mere expression, and that this death-inducing activity is prevented by addition of NGF (Nikoletopoulou et al., 2010). These findings thus indicate that TrkA acts as a “dependence receptor,” a concept introduced after observations that various cell types die when receptors are expressed in the absence of their cognate ligands (Bredesen et al., 2005; Tauszig-Delamasure et al., 2007). Accordingly, embryonic mouse sympathetic or sensory neurons survive in the absence of NGF when TrkA is deleted (Nikoletopoulou et al., 2010). The closely related neurotrophin receptor TrkC also acts as a dependence receptor (Tauszig-Delamasure et al., 2007; Nikoletopoulou et al., 2010). Here, it is interesting to note a series of older, convergent results indicating that deletion of neurotrophin-3 (NT3), the TrkC ligand, leads to a significantly larger loss of sensory and sympathetic neurons in the PNS than the deletion of TrkC (Tessarollo et al., 1997). This phenotypic discrepancy fits well with the idea that inactivation of the ligand of a dependence receptor is expected to yield a more profound phenotype than inactivation of the receptor itself (Tauszig-Delamasure et al., 2007). How TrkA and TrkC induce apoptosis remains to be fully elucidated. It seems that proteolysis is involved, either of TrkC itself (Tauszig-Delamasure et al., 2007), as was suggested for other dependence receptors (Bredesen et al., 2005), or by the proteolysis of the neurotrophin receptor p75NTR, which associates with both TrkA and TrkC (Fig. 2; Nikoletopoulou et al., 2010). Surprisingly, although TrkA and TrkC cause cell death, the structurally related TrkB receptor does not (Nikoletopoulou et al., 2010), a difference that appears to be accounted for by their differential localization in the cell membrane. TrkA and TrkC colocalize with p75NTR in lipid rafts, whereas TrkB, which also associates with p75NTR (Bibel et al., 1999), is excluded from lipid rafts (Fig. 2; unpublished data). Interestingly, the transmembrane domains of TrkA and TrkC are closely related, and differ clearly from that of TrkB. It turns out that a chimeric protein of TrkB with the transmembrane domain of TrkA causes cell death, which can be prevented by the addition of the TrkB ligand brain-derived neurotrophic factor (BDNF; unpublished data). The suggestion that the lipid raft localization of TrkA and TrkC is important for their death-inducing function is in line with a number of reports indicating that certain apoptotic proteins preferentially localize in lipid rafts in the plasma membrane. After activation of the extrinsic apoptosis pathway, translocation of the activated receptors to lipid rafts in the membrane is required for assembling the death-inducing signaling complex (DISC; Davis et al., 2007; Song et al., 2007). Indeed, regulators of the extrinsic pathway (e.g., cFLIP; Fig. 1) prevent this translocation, explaining how they attenuate cell death induction (Song et al., 2007). Similarly, the localization of the dependence receptor DCC (deleted in colorectal cancer) in lipid rafts is a prerequisite for its pro-apoptotic activity in absence of its ligand, Netrin-1 (Furne et al., 2006).Open in a separate windowFigure 2.TrkA and TrkC as dependence receptors: mode of action and contrast with TrkB. All Trk receptors associate with the pan-neurotrophin receptor p75NTR (Bibel et al., 1999). A critical step in the induction of apoptosis by TrkA is the release of the intracellular death domain of p75NTR by the protease γ-secretase (Nikoletopoulou et al., 2010), which is localized in lipid rafts (Urano et al., 2005). Our membrane fractionation studies indicate that while TrkA and TrkC associate with p75NTR in lipid rafts, TrkB associated with p75NTR is excluded from this membrane domain (unpublished data). The 24–amino acid transmembrane domain of the Trk receptors may be responsible for this differential localization (see text).Despite the fact that TrkB does not act as a dependence receptor, its activation by BDNF is required for the survival of several populations of cranial sensory neurons (Ernfors et al., 1995; Liu et al., 1995). It appears that other death-inducing receptors predispose these neurons to be eliminated, such as p75NTR, which is expressed at high levels in some of these ganglia, or TrkC in vestibular neurons (Stenqvist et al., 2005). This latter case is of special interest, as NT3 is known not to be required for the survival of these neurons (Stenqvist et al., 2005). In addition to inducing apoptosis in the absence of their ligand, TrkA and TrkC have long been recognized to have a pro-survival function similar to TrkB, as can be inferred from the loss of specific populations of peripheral sensory neurons in mutants lacking these receptors (Klein et al., 1994; Smeyne et al., 1994).

Cell death in the CNS

Although TrkA is primarily expressed in peripheral sympathetic and sensory neurons, it is also found in a small population of cholinergic neurons in the basal forebrain (Sobreviela et al., 1994), a proportion of which requires NGF for survival (Hartikka and Hefti, 1988; Crowley et al., 1994; Müller et al., 2012). Selective deletion of TrkA was recently shown not to cause the death of these neurons (Sanchez-Ortiz et al., 2012). This supports the notion that TrkA acts as a dependence receptor for this small population of CNS neurons, like for peripheral sensory and sympathetic neurons. TrkA activation by NGF is essential for the maturation, projections, and function of these neurons (Sanchez-Ortiz et al., 2012), as was previously described for sensory neurons in the PNS as well (Patel et al., 2000).Whether or not receptors other than TrkA act as dependence receptors in the CNS is an important open question, particularly because TrkB, which is expressed highly by most CNS neurons, does not act as a dependence receptor (Nikoletopoulou et al., 2010). In retrospect, the structural similarities between TrkA and TrkB, just like those between NGF and BDNF (Barde, 1989), have substantially misled the field by suggesting that BDNF would act in the CNS like NGF in the PNS. Adding to the confusion were early findings showing that BDNF supports the growth of spinal cord motoneurons in vitro or in vivo after axotomy (Oppenheim et al., 1992; Sendtner et al., 1992; Yan et al., 1992). However, in the absence of lesion, deletion of BDNF does not lead to significant losses of neurons in the developing or adult CNS (Ernfors et al., 1994a; Jones et al., 1994; Rauskolb et al., 2010), unlike the case in some populations of PNS neurons. The poor correlation of the role of BDNF in CNS development and in axotomy and in vitro experiments is surprising, especially because the role of NGF in vivo could in essence be recapitulated by in vitro experiments. Although the reasons for this discrepancy are not fully understood, the strong up-regulation of death-inducing molecules such as p75NTR after axotomy (Ernfors et al., 1989) may be a part of the explanation. At present, most of the growth factors promoting the survival of PNS neurons fail to show significant survival properties for developing neurons in the CNS, as for example was shown for NT3 (Ernfors et al., 1994b; Fariñas et al., 1994), glial cell line–derived neurotrophic factor (GDNF; Henderson et al., 1994), ciliary neurotrophic factor (CNTF; DeChiara et al., 1995), and several others.In the developing CNS, neuronal activity and neurotransmitter input seem to play a more significant role than single growth factors in regulating neuronal survival. In particular, it has been known for a long time that blocking synaptic transmission at the neuromuscular junction has a pro-survival effect on spinal cord motoneurons (Pittman and Oppenheim, 1978; Oppenheim et al., 2008). By contrast, surgical denervation of afferent connections leads to increased apoptosis of postsynaptic neurons (Okado and Oppenheim, 1984), whereas inhibiting glycinergic and GABAergic synaptic transmission has both pro- and anti-apoptotic effects on motoneurons (Banks et al., 2005). Throughout the developing brain, blocking glutamate-mediated synaptic transmission involving NMDA receptors markedly increases normally occurring neuronal death (Ikonomidou et al., 1999; Heck et al., 2008). The mechanism involves a reduction of neuronal expression of anti-apoptotic proteins, such as B-cell lymphoma 2 (BCL-2; Hansen et al., 2004). Conversely, a limited increase in neuronal activity leads to down-regulation of the pro-apoptotic genes BAX and caspase 9 (Léveillé et al., 2010), thereby reducing the propensity of these cells to initiate programmed cell death (Hardingham et al., 2002). In addition to directly modulating the expression of apoptotic proteins, neuronal activity affects the expression of several secreted growth factors, such as BDNF (Hardingham et al., 2002; Hansen et al., 2004) and GDNF (Léveillé et al., 2010). So, even though BDNF is not a major survival factor in the developing CNS, it appears to be critical for activity-dependent neuroprotection (Tremblay et al., 1999). A recent publication revealed that certain populations of neurons in the CNS do not follow the predictions of the neurotrophic theory and showed that apoptosis of cortical inhibitory neurons is independent of cues present in the developing cerebral cortex (Southwell et al., 2012). This study indicates that programmed cell death of a large proportion of interneurons in the CNS is regulated by intrinsic mechanisms that are largely resistant to the presence or absence of extrinsic cues (Dekkers and Barde, 2013).Taken together, even though the extent of naturally occurring cell death in the different regions of the CNS is not nearly as well characterized as in the PNS, let alone quantified, it appears that its regulation may significantly differ. Although single secreted neurotrophic factors seem to be largely dispensable for survival, neuronal activity and other intrinsic mechanisms drive the propensity of the neurons in the CNS to undergo apoptosis. An important open question in this context is a possible involvement of non-neuronal cells, such as glial cells (see Corty and Freeman, in this issue).

The apoptotic machinery as a regulator of connectivity

Activation of the executor caspases has been most studied in cell bodies and typically results in the demise of the entire cell (Williams et al., 2006). However, recent evidence shows that caspases are also activated locally in neuronal processes and branches destined to be eliminated, for example in axons overshooting their targets that are subsequently pruned back to establish the precise adult connectivity (Finn et al., 2000; Raff et al., 2002; Luo and O’Leary, 2005; Buss et al., 2006b). Initially, axonal degeneration and axon pruning were thought to be independent of caspases (Finn et al., 2000; Raff et al., 2002). Later work in Drosophila melanogaster (Kuo et al., 2006; Williams et al., 2006) and in mammalian neurons (Plachta et al., 2007; Nikolaev et al., 2009; Vohra et al., 2010) demonstrated that interfering with the apoptotic balance or the executor caspases can prevent or at least delay axonal degeneration. Simon et al. (2012) have found that a caspase 9 to caspase 3 cascade is crucial for axonal degeneration induced by NGF withdrawal, with caspase 6 activation playing a significant but subsidiary role. Upstream of the caspases, BCL-2 family members such as BAX and BCL-X_l are required (Nikolaev et al., 2009; Vohra et al., 2010). It is conceivable that the failure of ipRGCs in BAX-deficient mice to form appropriate connections to other cells in the retina (Chen et al., 2013) may be in part attributable to defective axonal degeneration. Surprisingly, Apaf1 appears not to be involved in this process (Cusack et al., 2013), suggesting that axon degeneration depends on the concerted activation of the intrinsic initiator complex in a different way from apoptosis.Strikingly, a series of recent studies showed that several caspases and components of the intrinsic pathway also affect normal synaptic physiology in adulthood (Fig. 3, A–D). Here, pro-apoptotic proteins are predominantly involved in weakening the synapses, whereas the anti-apoptotic proteins have been mainly associated with synaptic strengthening (Fig. 3 B). In particular, caspase 3 promotes long-term depression (LTD), a stimulation paradigm that results in a period of decreased synaptic transmission (Li et al., 2010), and also prevents long-term potentiation (LTP), the converse situation leading to strengthened synaptic transmission (Jo et al., 2011). Likewise, the proapoptotic BCL-2 family members BAX and BAD stimulate LTD (Jiao and Li, 2011). By contrast, the anti-apoptotic protein BCL-X_l increases synapse numbers and strength (H. Li et al., 2008), and the inhibitor of apoptosis protein (IAP) family member survivin was reported to be involved in LTP in the hippocampus (Iscru et al., 2013) and in activity-dependent gene regulation (O’Riordan et al., 2008).Open in a separate windowFigure 3.Canonical and noncanonical functions of the apoptotic machinery. (A) The apoptotic machinery is not only involved in eliminating cells destined to die, but is also a central player in refining neuronal connectivity, by regulating synaptic transmission and by generating the adult connectivity through axon pruning (Luo and O’Leary, 2005; Hyman and Yuan, 2012). But how the canonical and noncanonical roles of the apoptotic machinery are interlinked and spatially restricted is not well understood. (B) In the adult nervous system, the pro-apoptotic proteins BAX, caspase 9, and caspase 3 promote weakening of synapses (long-term depression [LTD]; Li et al., 2010; Jiao and Li, 2011; Jo et al., 2011), while the anti-apoptotic proteins Bcl-X_l and the IAP survivin promote synaptic strengthening (long-term potentiation [LTP]; Li et al., 2008a; Iscru et al., 2013). It is unclear how the activation of these pathways is restricted to a single synapse, but a recent review suggested that the proteasomal degradation of activated caspases may prevent their diffusion (Hyman and Yuan, 2012). (C) Caspase activation is now known to be required for axon pruning during development to generate the adult refined connectivity (Luo and O’Leary, 2005; Simon et al., 2012). Different pathways are activated depending on the stimulus leading to degeneration. Growth factor deprivation during development leads to activation the executor caspases 3 and 6 (Simon et al., 2012) through the intrinsic apoptotic pathway, although its core protein Apaf1 does not seem to be required for this process (Cusack et al., 2013). On the other hand, a traumatic injury leads to reduced influx of NMNAT2 into the axon, which negatively affects the stability and function of mitochondria and leads to an increased calcium concentration (Wang et al., 2012). The effector caspase, caspase 6, is dispensable for this form of axonal degeneration (Vohra et al., 2010; Simon et al., 2012). Regulatory proteins such as the IAPs and also the proteasome seem to play a role in limiting the extent of activation to the degenerating part of the axon (Wang et al., 2012; Cusack et al., 2013; Unsain et al., 2013). (D) Simplified schematic of the main pro- and anti-apoptotic components. DISC, death-induced signaling complex. IAP, inhibitor of apoptosis protein. See Fig. 1 for details.These findings indicate that the apoptotic machinery acts at different levels in the cell, ranging from driving sub-lethal degradation of a compartment (Fig. 3 C) and attenuating synaptic transmission at the neuronal network level (Fig. 3 B) to destroying the entire cell during development or in disease (Fig. 3 D). How the cell spatially restricts the extent of activation of the apoptotic machinery is yet unclear. For example, elimination of the somata of developing neurons after neurotrophin deprivation is preceded by axonal degeneration, but not all instances of axonal degeneration lead to the death of the neuron (Campenot, 1977; Raff et al., 2002). Local regulation of caspase activation by IAPs is well established as a means for ensuring the elimination of neuronal processes in D. melanogaster (Kuo et al., 2006; Williams et al., 2006). Recent findings suggest a similar role for IAP in mammalian neurons, where it limits caspase activation to the degenerating axon (Fig. 3 C; Cusack et al., 2013; Unsain et al., 2013). The spontaneous mutation Wallerian degeneration slow (WldS; Lunn et al., 1989) has been instrumental to understand that trauma-induced axon degeneration is a regulated process different from, and independent of, cell body degeneration (Wang et al., 2012), but also distinct from axon pruning (Hoopfer et al., 2006). Work on the chimeric protein encoded by the WldS mutation also led to the identification of the protein NMNAT2 (nicotinamide mononucleotide adenylyltransferase 2) as a labile axon survival factor (Gilley and Coleman, 2010). How the WldS chimeric protein and NMNAT2 result in axon protection is unclear, but several lines of evidence seem to converge on local regulation of mitochondrial function and motility (Avery et al., 2012; Fang et al., 2012).Related to the spatial limiting of apoptotic activity is the question of how a local source of neurotrophins leads to the rescue of a developing peripheral neuron. When neurons encounter a source of neurotrophins, only the receptors close to the target will be activated, whereas the others, located further away, are not. The cell, therefore, needs to integrate a pro-survival signal from the activated receptors, and death-inducing signals from the nonactivated dependence receptors. The continued signaling of activated neurotrophin receptors that are retrogradely transported to the soma (Grimes et al., 1996; Howe et al., 2001; Wu et al., 2001; Harrington et al., 2011) likely play a role in counteracting the pro-apoptotic signaling proximal to the source of neurotrophins. It will be interesting to investigate whether similar mechanisms play a role in axon pruning and traumatic axon degeneration as well.

Programmed cell death in the adult brain

Most of the nervous system becomes post-mitotic early in development. In rodents, two brain areas retain the capacity to generate new neurons in the adult: the sub-ventricular zone, which generates neurons that migrate toward the olfactory bulb, and the sub-granular zone of the dentate gyrus of the hippocampus, where neurons are generated that integrate locally. Similar to what is observed during embryonic development, these adult-generated neurons are produced in excess, and a large fraction undergoes apoptosis when contacting its designated targets (Petreanu and Alvarez-Buylla, 2002; Kempermann et al., 2003; Ninkovic et al., 2007). Preventing apoptosis of adult-generated neurons in the olfactory bulb only has limited functional consequences (Kim et al., 2007), whereas a similar maneuver in the dentate gyrus does lead to impaired performance in memory tasks (Kim et al., 2009). Why superfluous hippocampal neurons would need to be eliminated for proper function is a matter of speculation, but may be linked with the fact that these are excitatory projection neurons, whereas in the olfactory bulb only axon-less inhibitory granule cells are integrated. The extent of survival in both these areas critically depends on the activity of the neuronal network in which these newly born neurons have to integrate (Petreanu and Alvarez-Buylla, 2002; Kempermann et al., 2006; Ninkovic et al., 2007). In this context, BDNF, the expression level of which is well known to be regulated by network activity, supports the survival of young adult–generated neurons and possibly even stimulates the proliferation of neural progenitors (Y. Li et al., 2008; Waterhouse et al., 2012). Interestingly, in young adult mouse mutants that exhibit spontaneous epileptic seizures, significantly higher levels of BDNF have been measured (Lavebratt et al., 2006; Heyden et al., 2011). Concomitantly, the entire hippocampal formation is considerably enlarged by as much as 40% (Lavebratt et al., 2006; Angenstein et al., 2007), which in turn is dependent on the epileptic seizures (Lavebratt et al., 2006). Whether or not there is a causal relationship between increased BDNF levels and hippocampal volume remains to be established.

Conclusion

Now that is has become clear that action of the apoptotic machinery can be limited spatially and temporally, several questions need to be addressed: how do neurons integrate intrinsic and extrinsic pro- and anti-apoptotic signals; and how they are spatially restricted to allow degradation of a dendrite or axon, or modulation of synaptic transmission? Another important issue is the regulation of cell death by intrinsic mechanisms in the central nervous system of vertebrates, not least because programmed cell death is observed in the CNS in a number of neurodegenerative diseases (Vila and Przedborski, 2003). Indeed, several of the central apoptotic components discussed here are also involved in these disorders (Hyman and Yuan, 2012). New insights in the regulation of programmed cell death in the developing nervous system may therefore continue to help to better understand the pathophysiological mechanisms of neurodegenerative disorders.     

Surveying Rubisco Diversity and Temperature Response to Improve Crop Photosynthetic Efficiency

The threat to global food security of stagnating yields and population growth makes increasing crop productivity a critical goal over the coming decades. One key target for improving crop productivity and yields is increasing the efficiency of photosynthesis. Central to photosynthesis is Rubisco, which is a critical but often rate-limiting component. Here, we present full Rubisco catalytic properties measured at three temperatures for 75 plants species representing both crops and undomesticated plants from diverse climates. Some newly characterized Rubiscos were naturally “better” compared to crop enzymes and have the potential to improve crop photosynthetic efficiency. The temperature response of the various catalytic parameters was largely consistent across the diverse range of species, though absolute values showed significant variation in Rubisco catalysis, even between closely related species. An analysis of residue differences among the species characterized identified a number of candidate amino acid substitutions that will aid in advancing engineering of improved Rubisco in crop systems. This study provides new insights on the range of Rubisco catalysis and temperature response present in nature, and provides new information to include in models from leaf to canopy and ecosystem scale.In a changing climate and under pressure from a population set to hit nine billion by 2050, global food security will require massive changes to the way food is produced, distributed, and consumed (Ort et al., 2015). To match rising demand, agricultural production must increase by 50 to 70% in the next 35 years, and yet the gains in crop yields initiated by the green revolution are slowing, and in some cases, stagnating (Long and Ort, 2010; Ray et al., 2012). Among a number of areas being pursued to increase crop productivity and food production, improving photosynthetic efficiency is a clear target, offering great promise (Parry et al., 2007; von Caemmerer et al., 2012; Price et al., 2013; Ort et al., 2015). As the gatekeeper of carbon entry into the biosphere and often acting as the rate-limiting step of photosynthesis, Rubisco, the most abundant enzyme on the planet (Ellis, 1979), is an obvious and important target for improving crop photosynthetic efficiency.Rubisco is considered to exhibit comparatively poor catalysis, in terms of catalytic rate, specificity, and CO₂ affinity (Tcherkez et al., 2006; Andersson, 2008), leading to the suggestion that even small increases in catalytic efficiency may result in substantial improvements to carbon assimilation across a growing season (Zhu et al., 2004; Parry et al., 2013; Galmés et al., 2014a; Carmo-Silva et al., 2015). If combined with complimentary changes such as optimizing other components of the Calvin Benson or photorespiratory cycles (Raines, 2011; Peterhansel et al., 2013; Simkin et al., 2015), optimized canopy architecture (Drewry et al., 2014), or introducing elements of a carbon concentrating mechanism (Furbank et al., 2009; Lin et al., 2014a; Hanson et al., 2016; Long et al., 2016), Rubisco improvement presents an opportunity to dramatically increase the photosynthetic efficiency of crop plants (McGrath and Long, 2014; Long et al., 2015; Betti et al., 2016). A combination of the available strategies is essential for devising tailored solutions to meet the varied requirements of different crops and the diverse conditions under which they are typically grown around the world.Efforts to engineer an improved Rubisco have not yet produced a “super Rubisco” (Parry et al., 2007; Ort et al., 2015). However, advances in engineering precise changes in model systems continue to provide important developments that are increasing our understanding of Rubisco catalysis (Spreitzer et al., 2005; Whitney et al., 2011a, 2011b; Morita et al., 2014; Wilson et al., 2016), regulation (Andralojc et al., 2012; Carmo-Silva and Salvucci, 2013; Bracher et al., 2015), and biogenesis (Saschenbrecker et al., 2007; Whitney and Sharwood, 2008; Lin et al., 2014b; Hauser et al., 2015; Whitney et al., 2015).A complementary approach is to understand and exploit Rubisco natural diversity. Previous characterization of Rubisco from a limited number of species has not only demonstrated significant differences in the underlying catalytic parameters, but also suggests that further undiscovered diversity exists in nature and that the properties of some of these enzymes could be beneficial if present in crop plants (Carmo-Silva et al., 2015). Recent studies clearly illustrate the variation possible among even closely related species (Galmés et al., 2005, 2014b, 2014c; Kubien et al., 2008; Andralojc et al., 2014; Prins et al., 2016).Until recently, there have been relatively few attempts to characterize the consistency, or lack thereof, of temperature effects on in vitro Rubisco catalysis (Sharwood and Whitney, 2014), and often studies only consider a subset of Rubisco catalytic properties. This type of characterization is particularly important for future engineering efforts, enabling specific temperature effects to be factored into any attempts to modify crops for a future climate. In addition, the ability to coanalyze catalytic properties and DNA or amino acid sequence provides the opportunity to correlate sequence and biochemistry to inform engineering studies (Christin et al., 2008; Kapralov et al., 2011; Rosnow et al., 2015). While the amount of gene sequence information available grows rapidly with improving technology, knowledge of the corresponding biochemical variation resulting has yet to be determined (Cousins et al., 2010; Carmo-Silva et al., 2015; Sharwood and Whitney, 2014; Nunes-Nesi et al., 2016).This study aimed to characterize the catalytic properties of Rubisco from diverse species, comprising a broad range of monocots and dicots from diverse environments. The temperature dependence of Rubisco catalysis was evaluated to tailor Rubisco engineering for crop improvement in specific environments. Catalytic diversity was analyzed alongside the sequence of the Rubisco large subunit gene, rbcL, to identify potential catalytic switches for improving photosynthesis and productivity. In vitro results were compared to the average temperature of the warmest quarter in the regions where each species grows to investigate the role of temperature in modulating Rubisco catalysis.     

The tubulin code: Molecular components,readout mechanisms,and functions

Microtubules are cytoskeletal filaments that are dynamically assembled from α/β-tubulin heterodimers. The primary sequence and structure of the tubulin proteins and, consequently, the properties and architecture of microtubules are highly conserved in eukaryotes. Despite this conservation, tubulin is subject to heterogeneity that is generated in two ways: by the expression of different tubulin isotypes and by posttranslational modifications (PTMs). Identifying the mechanisms that generate and control tubulin heterogeneity and how this heterogeneity affects microtubule function are long-standing goals in the field. Recent work on tubulin PTMs has shed light on how these modifications could contribute to a “tubulin code” that coordinates the complex functions of microtubules in cells.

Introduction

Microtubules are key elements of the eukaryotic cytoskeleton that dynamically assemble from heterodimers of α- and β-tubulin. The structure of microtubules, as well as the protein sequences of α- and β-tubulin, is highly conserved in evolution, and consequently, microtubules look alike in almost all species. Despite the high level of conservation, microtubules adapt to a large variety of cellular functions. This adaptation can be mediated by a large panel of microtubule-associated proteins (MAPs), including molecular motors, as well as by mechanisms that directly modify the microtubules, thus either changing their biophysical properties or attracting subsets of MAPs that convey specific functions to the modified microtubules. Two different mechanism can generate microtubule diversity: the expression of different α- and β-tubulin genes, referred to as tubulin isotypes, and the generation of posttranslational modifications (PTMs) on α- and β-tubulin (Figs. 1 and ​and2).2). Although known for several decades, deciphering how tubulin heterogeneity controls microtubule functions is still largely unchartered. This review summarizes the current advances in the field and discusses new concepts arising.Open in a separate windowFigure 1.Tubulin heterogeneity generated by PTMs. (A) Schematic representation of the distribution of different PTMs of tubulin on the α/β-tubulin dimer with respect to their position in the microtubule lattice. Acetylation (Ac), phosphorylation (P), and polyamination (Am) are found within the tubulin bodies that assemble into the microtubule lattice, whereas polyglutamylation, polyglycylation, detyrosination, and C-terminal deglutamylation take place within the C-terminal tubulin tails that project away from the lattice surface. The tubulin dimer represents TubA1A and TubB2B (Fig. 2), and modification sites for polyglutamylation and polyglycylation have been randomly chosen. (B) Chemical structure of the branched peptide formed by polyglutamylation and polyglycylation, using the γ-carboxyl groups of the modified glutamate residues as acceptor sites for the isopeptide bonds. Note that in the case of polyglutamylation, the elongation of the side chains generates classical peptide bonds (Redeker et al., 1991).Open in a separate windowFigure 2.Heterogeneity of C-terminal tails of tubulin isotypes and their PTMs. The amino acid sequences of all tubulin genes found in the human genome are indicated, starting at the last amino acid of the folded tubulin bodies. Amino acids are represented in single-letter codes and color coded according to their biochemical properties. Known sites for polyglutamylation are indicated (Eddé et al., 1990; Alexander et al., 1991; Rüdiger et al., 1992). Potential modification sites (all glutamate residues) are indicated. Known C-terminal truncation reactions of α/β-tubulin (tub) are indicated. The C-terminal tails of the yeast Saccharomyces cerevisiae are shown to illustrate the phylogenetic diversity of these domains.

Tubulin isotypes

The cloning of the first tubulin genes in the late 1970’s (Cleveland et al., 1978) revealed the existence of multiple genes coding for α- or β-tubulin (Ludueña and Banerjee, 2008) that generate subtle differences in their amino acid sequences, particularly in the C-terminal tails (Fig. 2). It was assumed that tubulin isotypes, as they were named, assemble into discrete microtubule species that carry out unique functions. This conclusion was reinforced by the observation that some isotypes are specifically expressed in specialized cells and tissues and that isotype expression changes during development (Lewis et al., 1985; Denoulet et al., 1986). These high expectations were mitigated by a subsequent study showing that all tubulin isotypes freely copolymerize into heterogeneous microtubules (Lewis et al., 1987). To date, only highly specialized microtubules, such as ciliary axonemes (Renthal et al., 1993; Raff et al., 2008), neuronal microtubules (Denoulet et al., 1986; Joshi and Cleveland, 1989), and microtubules of the marginal band of platelets (Wang et al., 1986; Schwer et al., 2001) are known to depend on some specific (β) tubulin isotypes, whereas the function of most other microtubules appears to be independent of their isotype composition.More recently, a large number of mutations in single tubulin isotypes have been linked to deleterious neurodevelopmental disorders (Keays et al., 2007; Fallet-Bianco et al., 2008; Tischfield et al., 2010; Cederquist et al., 2012; Niwa et al., 2013). Mutations of a single tubulin isotype could lead to an imbalance in the levels of tubulins as a result of a lack of incorporation of mutant isoforms into the microtubule lattice or to incorporation that perturbs the architecture or dynamics of the microtubules. The analysis of tubulin disease mutations is starting to reveal how subtle alterations of the microtubule cytoskeleton can lead to functional aberrations in cells and organisms and might provide novel insights into the roles of tubulin isotypes that have so far been considered redundant.

Tubulin PTMs

Tubulin is subject to a large range of PTMs (Fig. 1), from well-known ones, such as acetylation or phosphorylation, to others that have so far mostly been found on tubulin. Detyrosination/tyrosination, polyglutamylation, and polyglycylation, for instance, might have evolved to specifically regulate tubulin and microtubule functions, in particular in cilia and flagella, as their evolution is closely linked to these organelles. The strong link between those modifications and tubulin evolution has led to the perception that they are tubulin PTMs; however, apart from detyrosination/tyrosination, most of them have other substrates (Regnard et al., 2000; Xie et al., 2007; van Dijk et al., 2008; Rogowski et al., 2009).

Tubulin acetylation.

Tubulin acetylation was discovered on lysine 40 (K40; Fig. 1 A) of flagellar α-tubulin in Chlamydomonas reinhardtii (L’Hernault and Rosenbaum, 1985) and is generally enriched on stable microtubules in cells. Considering that K40 acetylation per se has no effect on the ultrastructure of microtubules (Howes et al., 2014), it is rather unlikely that it directly stabilizes microtubules. As a result of its localization at the inner face of microtubules (Soppina et al., 2012), K40 acetylation might rather affect the binding of microtubule inner proteins, a poorly characterized family of proteins (Nicastro et al., 2011; Linck et al., 2014). Functional experiments in cells have further suggested that K40 acetylation regulates intracellular transport by regulating the traffic of kinesin motors (Reed et al., 2006; Dompierre et al., 2007). These observations could so far not be confirmed by biophysical measurements in vitro (Walter et al., 2012; Kaul et al., 2014), suggesting that in cells, K40 acetylation might affect intracellular traffic by indirect mechanisms.Enzymes involved in K40 acetylation are HDAC6 (histone deacetylase family member 6; Hubbert et al., 2002) and Sirt2 (sirtuin type 2; North et al., 2003). Initial functional studies used overexpression, depletion, or chemical inhibition of these enzymes. These studies should be discussed with care, as both HDAC6 and Sirt2 deacetylate other substrates and have deacetylase-independent functions and chemical inhibition of HDAC6 is not entirely selective for this enzyme (Valenzuela-Fernández et al., 2008). In contrast, acetyl transferase α-Tat1 (or Mec-17; Akella et al., 2010; Shida et al., 2010) specifically acetylates α-tubulin K40 (Fig. 3), thus providing a more specific tool to investigate the functions of K40 acetylation. Knockout mice of α-Tat1 are completely void of K40-acetylated tubulin; however, they show only slight phenotypic aberrations, for instance, in their sperm flagellum (Kalebic et al., 2013). A more detailed analysis of α-Tat1 knockout mice demonstrated that absence of K40 acetylation leads to reduced contact inhibition in proliferating cells (Aguilar et al., 2014). In migrating cells, α-Tat1 is targeted to microtubules at the leading edge by clathrin-coated pits, resulting in locally restricted acetylation of those microtubules (Montagnac et al., 2013). A recent structural study of α-Tat1 demonstrated that the low catalytic rate of this enzyme, together with its localization inside the microtubules, caused acetylation to accumulate selectively in stable, long-lived microtubules (Szyk et al., 2014), thus explaining the link between this PTM and stable microtubules in cells. However, the direct cellular function of K40 acetylation on microtubules is still unclear.Open in a separate windowFigure 3.Enzymes involved in PTM of tubulin. Schematic representation of known enzymes (mammalian enzymes are shown) involved in the generation and removal of PTMs shown in Fig. 1. Note that some enzymes still remain unknown, and some modifications are irreversible. (*CCP5 preferentially removes branching points [Rogowski et al., 2010]; however, the enzyme can also hydrolyze linear glutamate chains [Berezniuk et al., 2013]).Recent discoveries have brought up the possibility that tubulin could be subject to multiple acetylation events. A whole-acetylome study identified >10 novel sites on α- and β-tubulin (Choudhary et al., 2009); however, none of these sites have been confirmed. Another acetylation event has been described at lysine 252 (K252) of β-tubulin. This modification is catalyzed by the acetyltransferase San (Fig. 3) and might regulate the assembly efficiency of microtubules as a result of its localization at the polymerization interface (Chu et al., 2011).

Tubulin detyrosination.

Most α-tubulin genes in different species encode a C-terminal tyrosine residue (Fig. 2; Valenzuela et al., 1981). This tyrosine can be enzymatically removed (Hallak et al., 1977) and religated (Fig. 3; Arce et al., 1975). Mapping of tyrosinated and detyrosinated microtubules in cells using specific antibodies (Gundersen et al., 1984; Geuens et al., 1986; Cambray-Deakin and Burgoyne, 1987a) revealed that subsets of interphase and mitotic spindle microtubules are detyrosinated (Gundersen and Bulinski, 1986). As detyrosination was mostly found on stable and long-lived microtubules, especially in neurons (Cambray-Deakin and Burgoyne, 1987b; Robson and Burgoyne, 1989; Brown et al., 1993), it was assumed that this modification promotes microtubule stability (Gundersen et al., 1987; Sherwin et al., 1987). Although a direct stabilization of the microtubule lattice was considered unlikely (Khawaja et al., 1988), it was found more recently that detyrosination protects cellular microtubules from the depolymerizing activity of kinesin-13–type motor proteins, such as KIF2 or MCAK, thus increasing their longevity (Peris et al., 2009; Sirajuddin et al., 2014).Besides kinesin-13 motors, plus end–tracking proteins with cytoskeleton-associated protein glycine-rich (CAP-Gly) domains, such as CLIP170 or p150/glued, specifically interact with tyrosinated microtubules (Peris et al., 2006; Bieling et al., 2008) via this domain (Honnappa et al., 2006). In contrast, kinesin-1 moves preferentially on detyrosinated microtubules tracks in cells (Liao and Gundersen, 1998; Kreitzer et al., 1999; Konishi and Setou, 2009). The effect of detyrosination on kinesin-1 motor behavior was recently measured in vitro, and a small but significant increase in the landing rate and processivity of the motor has been found (Kaul et al., 2014). Such subtle changes in the motor behavior could, in conjunction with other factors, such as regulatory MAPs associated with cargo transport complexes (Barlan et al., 2013), lead to a preferential use of detyrosinated microtubules by kinesin-1 in cells.Despite the early biochemical characterization of a detyrosinating activity, the carboxypeptidase catalyzing detyrosination of α-tubulin has yet to be identified (Hallak et al., 1977; Argaraña et al., 1978, 1980). In contrast, the reverse enzyme, tubulin tyrosine ligase (TTL; Fig. 3; Raybin and Flavin, 1975; Deanin and Gordon, 1976; Argaraña et al., 1980), has been purified (Schröder et al., 1985) and cloned (Ersfeld et al., 1993). TTL modifies nonpolymerized tubulin dimers exclusively. This selectivity is determined by the binding interface between the TTL and tubulin dimers (Szyk et al., 2011, 2013; Prota et al., 2013). In contrast, the so far unidentified detyrosinase acts preferentially on polymerized microtubules (Kumar and Flavin, 1981; Arce and Barra, 1983), thus modifying a select population of microtubules within cells (Gundersen et al., 1987).In most organisms, only one unique gene for TTL exists. Consequently, TTL knockout mice show a huge accumulation of detyrosinated and particularly Δ2-tubulin (see next section). TTL knockout mice die before birth (Erck et al., 2005) with major developmental defects in the nervous system that might be related to aberrant neuronal differentiation (Marcos et al., 2009). TTL is strictly tubulin specific (Prota et al., 2013), indicating that all observed defects in TTL knockout mice are directly related to the deregulation of the microtubule cytoskeleton.

Δ2-tubulin and further C-terminal modification.

A biochemical study of brain tubulin revealed that ∼35% of α-tubulin cannot be retyrosinated (Paturle et al., 1989) because of the lack of the penultimate C-terminal glutamate residue of the primary protein sequence (Fig. 2; Paturle-Lafanechère et al., 1991). This so-called Δ2-tubulin (for two C-terminal amino acids missing) cannot undergo retyrosination as a result of structural constraints within TTL (Prota et al., 2013) and thus is considered an irreversible PTM.Δ2-tubulin accumulates in long-lived microtubules of differentiated neurons, axonemes of cilia and flagella, and also in cellular microtubules that have been artificially stabilized, for instance, with taxol (Paturle-Lafanechère et al., 1994). The generation of Δ2-tubulin requires previous detyrosination of α-tubulin; thus, the levels of this PTM are indirectly regulated by the detyrosination/retyrosination cycle. This mechanistic link is particularly apparent in the TTL knockout mice, which show massive accumulation of Δ2-tubulin in all tested tissues (Erck et al., 2005). Loss of TTL and the subsequent increase of Δ2-tubulin levels were also linked to tumor growth and might contribute to the aggressiveness of the tumors by an as-yet-unknown mechanism (Lafanechère et al., 1998; Mialhe et al., 2001). To date, no specific biochemical role of Δ2-tubulin has been determined; thus, one possibility is that the modification simply locks tubulin in the detyrosinated state.The enzymes responsible for Δ2-tubulin generation are members of a family of cytosolic carboxypeptidases (CCPs; Fig. 3; Kalinina et al., 2007; Rodriguez de la Vega et al., 2007), and most of them also remove polyglutamylation from tubulin (see next section; Rogowski et al., 2010). These enzymes are also able to generate Δ3-tubulin (Fig. 1 A; Berezniuk et al., 2012), indicating that further degradation of the tubulin C-terminal tails are possible; however, the functional significance of this event is unknown.

Polyglutamylation.

Polyglutamylation is a PTM that occurs when secondary glutamate side chains are formed on γ-carboxyl groups of glutamate residues in a protein (Fig. 1, A and B). The modification was first discovered on α- and β-tubulin from the brain (Eddé et al., 1990; Alexander et al., 1991; Rüdiger et al., 1992; Mary et al., 1994) as well as on axonemal tubulin from different species (Mary et al., 1996, 1997); however, it is not restricted to tubulin (Regnard et al., 2000; van Dijk et al., 2008). Using a glutamylation-specific antibody, GT335 (Wolff et al., 1992), it was observed that tubulin glutamylation increases during neuronal differentiation (Audebert et al., 1993, 1994) and that axonemes of cilia and flagella (Fouquet et al., 1994), as well as centrioles of mammalian centrosomes (Bobinnec et al., 1998), are extensively glutamylated.Enzymes catalyzing polyglutamylation belong to the TTL-like (TTLL) family (Regnard et al., 2003; Janke et al., 2005). In mammals, nine glutamylases exist, each of them showing intrinsic preferences for modifying either α- or β-tubulin as well as for initiating or elongating glutamate chains (Fig. 3; van Dijk et al., 2007). Two of the six well-characterized TTLL glutamylases also modify nontubulin substrates (van Dijk et al., 2008).Knockout or depletion of glutamylating enzymes in different model organisms revealed an evolutionarily conserved role of glutamylation in cilia and flagella. In motile cilia, glutamylation regulates beating behavior (Janke et al., 2005; Pathak et al., 2007; Ikegami et al., 2010) via the regulation of flagellar dynein motors (Kubo et al., 2010; Suryavanshi et al., 2010). Despite the expression of multiple glutamylases in ciliated cells and tissues, depletion or knockout of single enzymes often lead to ciliary defects, particularly in motile cilia (Ikegami et al., 2010; Vogel et al., 2010; Bosch Grau et al., 2013; Lee et al., 2013), suggesting essential and nonredundant regulatory functions of these enzymes in cilia.Despite the enrichment of polyglutamylation in neuronal microtubules (Audebert et al., 1993, 1994), knockout of TTLL1, the major polyglutamylase in brain (Janke et al., 2005), did not show obvious neuronal defects in mice (Ikegami et al., 2010; Vogel et al., 2010). This suggests a tolerance of neuronal microtubules to variations in polyglutamylation.Deglutamylases, the enzymes that reverse polyglutamylation, were identified within a novel family of CCPs (Kimura et al., 2010; Rogowski et al., 2010). So far, three out of six mammalian CCPs have been shown to cleave C-terminal glutamate residues, thus catalyzing both the reversal of polyglutamylation and the removal of gene-encoded glutamates from the C termini of proteins (Fig. 3). The hydrolysis of gene-encoded glutamate residues is not restricted to tubulin, in which it generates Δ2- and Δ3-tubulin, but has also been reported for other proteins such as myosin light chain kinase (Rusconi et al., 1997; Rogowski et al., 2010). One enzyme of the CCP family, CCP5, preferentially removes branching points generated by glutamylation, thus allowing the complete reversal of the polyglutamylation modification (Kimura et al., 2010; Rogowski et al., 2010). However, CCP5 can also hydrolyze C-terminal glutamate residues from linear peptide chains similar to other members of the CCP family (Berezniuk et al., 2013).CCP1 is mutated in a well-established mouse model for neurodegeneration, the pcd (Purkinje cell degeneration) mouse (Mullen et al., 1976; Greer and Shepherd, 1982; Fernandez-Gonzalez et al., 2002). The absence of a key deglutamylase leads to strong hyperglutamylation in brain regions that undergo degeneration, such as the cerebellum and the olfactory bulb (Rogowski et al., 2010). When glutamylation levels were rebalanced by depletion or knockout of the major brain polyglutamylase TTLL1 (Rogowski et al., 2010; Berezniuk et al., 2012), Purkinje cells survived. Although the molecular mechanisms of hyperglutamylation-induced degeneration remain to be elucidated, perturbation of neuronal transport, as well as changes in the dynamics and stability of microtubules, is expected to be induced by hyperglutamylation. Increased polyglutamylation levels have been shown to affect kinesin-1–mediated transport in cultured neurons (Maas et al., 2009), and the turnover of microtubules can also be regulated by polyglutamylation via the activation of microtubule-severing enzymes such as spastin (Lacroix et al., 2010).Subtle differences in polyglutamylation can be seen on diverse microtubules in different cell types. The functions of these modifications remain to be studied; however, its wide distribution strengthens the idea that it could be involved in fine-tuning a range of microtubule functions.

Polyglycylation.

Tubulin polyglycylation or glycylation, like polyglutamylation, generates side chains of glycine residues within the C-terminal tails of α- and β-tubulin (Fig. 1, A and B). The modification sites of glycylation are considered to be principally the same as for glutamylation, and indeed, both PTMs have been shown to be interdependent in cells (Rogowski et al., 2009; Wloga et al., 2009). Initially discovered on Paramecium tetraurelia tubulin (Redeker et al., 1994), glycylation has been extensively studied using two antibodies, TAP952 and AXO49 (Bressac et al., 1995; Levilliers et al., 1995; Bré et al., 1996). In contrast to polyglutamylation, glycylation is restricted to cilia and flagella in most organisms analyzed so far.Glycylating enzymes are also members of the TTLL family, and homologues of these enzymes have so far been found in all organisms with proven glycylation of ciliary axonemes (Rogowski et al., 2009; Wloga et al., 2009). In mammals, initiating (TTLL3 and TTLL8) and elongating (TTLL10) glycylases work together to generate polyglycylation (Fig. 3). In contrast, the two TTLL3 orthologues from Drosophila melanogaster can both initiate and elongate glycine side chains (Rogowski et al., 2009).In mice, motile ependymal cilia in brain ventricles acquire monoglycylation upon maturation, whereas polyglycylation is observed only after several weeks (Bosch Grau et al., 2013). Sperm flagella, in contrast, acquire long glycine chains much faster, suggesting that the extent of polyglycylation could correlate with the length of the axonemes (Rogowski et al., 2009). Depletion of glycylases in mice (ependymal cilia; Bosch Grau et al., 2013), zebrafish (Wloga et al., 2009; Pathak et al., 2011), Tetrahymena thermophila (Wloga et al., 2009), and D. melanogaster (Rogowski et al., 2009) consistently led to ciliary disassembly or severe ciliary defects. How glycylation regulates microtubule functions remains unknown; however, the observation that glycylation-depleted axonemes disassemble after initial assembly (Rogowski et al., 2009; Bosch Grau et al., 2013) suggests a role of this PTM in stabilizing axonemal microtubules. Strikingly, human TTLL10 is enzymatically inactive; thus, humans have lost the ability to elongate glycine side chains (Rogowski et al., 2009). This suggests that the elongation of the glycine side chains is not an essential aspect of the function of this otherwise critical tubulin PTM.

Other tubulin PTMs.

Several other PTMs have been found on tubulin. Early studies identified tubulin phosphorylation (Eipper, 1974; Gard and Kirschner, 1985; Díaz-Nido et al., 1990); however, no specific functions were found. The perhaps best-studied phosphorylation event on tubulin takes place at serine S172 of β-tubulin (Fig. 1 A), is catalyzed by the Cdk1 (Fig. 3), and might regulate microtubule dynamics during cell division (Fourest-Lieuvin et al., 2006; Caudron et al., 2010). Tubulin can be also modified by the spleen tyrosine kinase Syk (Fig. 3; Peters et al., 1996), which might play a role in immune cells (Faruki et al., 2000; Sulimenko et al., 2006) and cell division (Zyss et al., 2005; Sulimenko et al., 2006).Polyamination has recently been discovered on brain tubulin (Song et al., 2013), after having been overlooked for many years as a result of the low solubility of polyaminated tubulin. Among several glutamine residues of α- and β-tubulin that can be polyaminated, Q15 of β-tubulin is considered the primary modification site (Fig. 1 A). Polyamination is catalyzed by transglutaminases (Fig. 3), which modify free tubulin as well as microtubules in an irreversible manner, and most likely contribute to the stabilization of microtubules (Song et al., 2013).Tubulin was also reported to be palmitoylated (Caron, 1997; Ozols and Caron, 1997; Caron et al., 2001), ubiquitinated (Ren et al., 2003; Huang et al., 2009; Xu et al., 2010), glycosylated (Walgren et al., 2003; Ji et al., 2011), arginylated (Wong et al., 2007), methylated (Xiao et al., 2010), and sumoylated (Rosas-Acosta et al., 2005). These PTMs have mostly been reported without follow-up studies, and some of them are only found in specific cell types or organisms and/or under specific metabolic conditions. Further studies will be necessary to gain insights into their potential roles for the regulation of the microtubule cytoskeleton.

Current advances and future perspectives

The molecular heterogeneity of microtubules, generated by the expression of different tubulin isotypes and by the PTM of tubulin has fascinated the scientific community for ∼40 years. Although many important advances have been made in the past decade, the dissection of the molecular mechanisms and a comprehensive understanding of the biological functions of tubulin isotypes and PTMs will be a challenging field of research in the near future.

Direct measurements of the impact of tubulin heterogeneity.

The most direct and reliable type of experiments to determine the impact of tubulin heterogeneity on microtubule behavior are in vitro measurements with purified proteins. However, most biophysical work on microtubules has been performed with tubulin purified from bovine, ovine, or porcine brains, which can be obtained in large quantities and with a high degree of purity and activity (Vallee, 1986; Castoldi and Popov, 2003). Brain tubulin is a mixture of different tubulin isotypes and is heavily posttranslationally modified and thus inept for investigating the functions of tubulin heterogeneity (Denoulet et al., 1986; Cambray-Deakin and Burgoyne, 1987b; Paturle et al., 1989; Eddé et al., 1990). Thus, pure, recombinant tubulin will be essential to dissect the roles of different tubulin isoforms and PTMs.Attempts to produce recombinant, functional α- and β-tubulin in bacteria have failed so far (Yaffe et al., 1988), most likely because of the absence of the extensive tubulin-specific folding machinery (Yaffe et al., 1992; Gao et al., 1993; Tian et al., 1996; Vainberg et al., 1998) in prokaryotes. An alternative source of tubulin with less isotype heterogeneity and with almost no PTMs is endogenous tubulin from cell lines such as HeLa, which in the past has been purified using a range of biochemical procedures (Bulinski and Borisy, 1979; Weatherbee et al., 1980; Farrell, 1982; Newton et al., 2002; Fourest-Lieuvin, 2006). Such tubulin can be further modified with tubulin-modifying enzymes, such as polyglutamylases, either by expressing those enzymes in the cells before tubulin purification (Lacroix and Janke, 2011) or in vitro with purified enzymes (Vemu et al., 2014). Despite some technical limitations of these methods, HeLa tubulin modified in cells has been successfully used in an in vitro study on the role of polyglutamylation in microtubule severing (Lacroix et al., 2010).Naturally occurring variants of tubulin isotypes and PTMs can be purified from different organisms, organs, or cell types, but obviously, only some combinations of tubulin isotypes and PTMs can be obtained by this approach. The recent development of an affinity purification method using the microtubule-binding TOG (tumor overexpressed gene) domain of yeast Stu2p has brought a new twist to this approach, as it allows purifying small amounts of tubulin from any cell type or tissue (Widlund et al., 2012).The absence of tubulin heterogeneity in yeast has made budding and fission yeast potential expression systems for recombinant, PTM-free tubulin (Katsuki et al., 2009; Drummond et al., 2011; Johnson et al., 2011). However, the expression of mammalian tubulin in this system has remained impossible. This problem was then partially circumvented by expressing tubulin chimeras that consist of a yeast tubulin body fused to mammalian C-terminal tubulin tails, thus mimicking different tubulin isotypes (Sirajuddin et al., 2014). Moreover, detyrosination can be generated by deleting the key C-terminal residue from endogenous or chimeric α-tubulin (Badin-Larçon et al., 2004), and polyglutamylation is generated by chemically coupling glutamate side chains to specifically engineered tubulin chimeras (Sirajuddin et al., 2014). These approaches allowed the first direct measurements of the impact of tubulin isotypes and PTMs on the behavior of molecular motors in vitro (Sirajuddin et al., 2014) and the analysis of the effects of tubulin heterogeneity on microtubule behavior and interactions inside the yeast cell (Badin-Larçon et al., 2004; Aiken et al., 2014).Currently, the most promising development has been the successful purification of fully functional recombinant tubulin from the baculovirus expression system (Minoura et al., 2013). Using this system, defined α/β-tubulin dimers can be obtained using two different epitope tags on α- and β-tubulin, respectively. Although these epitope tags are essential for separating recombinant from the endogenous tubulin, they could also affect tubulin assembly or microtubule–MAP interactions. Thus, future developments should focus on eliminating these tags.Current efforts have brought the possibility of producing recombinant tubulin into reach. Further improvement and standardization of these methods will certainly provide a breakthrough in understanding the mechanisms by which tubulin heterogeneity contributes to microtubule functions.

Complexity of tubulin—understanding the regulatory principles.

The diversity of tubulin genes (isotypes) and the complexity of tubulin PTMs have led to the proposal of the term “tubulin code” (Verhey and Gaertig, 2007; Wehenkel and Janke, 2014), in analogy to the previously coined histone code (Jenuwein and Allis, 2001). Tubulin molecules consist of a highly structured and thus evolutionarily conserved tubulin body and the unstructured and less conserved C-terminal tails (Nogales et al., 1998). As PTMs and sequence variations within the tubulin body are expected to affect the conserved tubulin fold and therefore the properties of the microtubule lattice, they are not likely to be involved in generating the tubulin code. In contrast, modulations of the C-terminal tails could encode signals on the microtubule surface without perturbing basic microtubule functions and properties (Figs. 1 A and ​and4).4). Indeed, the highest degree of gene-encoded diversity (Fig. 2) and the highest density and complexity of PTMs (Fig. 1) are found within these tail domains.Open in a separate windowFigure 4.Molecular components of the tubulin code. Schematic representation of potential coding elements that could generate specific signals for the tubulin code. (A) The length of the C-terminal tails of different tubulin isotypes differ significantly (Fig. 2) and could have an impact on the interactions between microtubules and MAPs. (B) Tubulin C-terminal tails are rich in charged amino acid residues. The distribution of these residues and local densities of charges could influence the electrostatic interactions with the tails and the readers. (C) Although each glutamate residue within the C-terminal tails could be considered a potential modification site, only some sites have been found highly occupied in tubulin purifications from native sources. This indicates selectivity of the modification reactions, which can participate in the generation of specific modification patterns (see D). Modification sites might be distinguished by their neighboring amino acid residues, which could create specific modification epitopes. (D) As a result of the large number of modification sites and the variability of side chains, a large variety of modification patterns could be generated within a single C-terminal tail of tubulin. (E) Modification patterns as shown in D can be distinct between α- and β-tubulin. These modification patterns could be differentially distributed at the surface of the microtubule lattice, thus generating a higher-order patterning. Tub, tubulin. For color coding, see Fig. 2.Considering the number of tubulin isotypes plus all potential combinations of PTMs (e.g., each glutamate residue within the C-terminal tubulin tail could be modified by either polyglutamylation or polyglycylation, each of them generating side chains of different lengths; Fig. 4), the number of distinct signals generated by the potential tubulin code would be huge. However, as many of these potential signals represent chemical structures that are similar and might not be reliably distinguished by readout mechanisms, it is possible that the tubulin code generates probabilistic signals. In this scenario, biochemically similar modifications would have similar functional readouts, and marginal differences between those signals would only bias biological processes but not determine them. This stands in contrast to the concept of the histone code, in which precise patterns of different PTMs on the histone proteins encode distinct biological signals.The concept of probabilistic signaling is already inscribed in the machinery that generates the tubulin code. Polyglutamylases and polyglycylases from the TTLL family have preferential activities for either α- or β-tubulin and for generating different lengths of the branched glutamate or glycine chains. Although under conditions of low enzyme concentrations, as found in most cells and tissues, the enzymes seem to selectively generate their preferential type of PTM, higher enzyme concentrations induce a more promiscuous behavior, leading, for instance, to a loss of selectivity for α- or β-tubulin (van Dijk et al., 2007). Similarly, the modifying enzymes might prefer certain modification sites within the C-terminal tails of tubulin but might be equally able to modify other sites, which could be locally regulated in cells. For example, β-tubulin isotypes isolated from mammalian brain were initially found to be glutamylated on single residues (Alexander et al., 1991; Rüdiger et al., 1992), which in the light of the comparably low sensitivity of mass spectrometry at the time might rather indicate a preferential than a unique modification of these sites. Nevertheless, the neuron-specific polyglutamylase for β-tubulin TTLL7 (Ikegami et al., 2006) can incorporate glutamate onto many more modification sites of β-tubulin in vitro (Mukai et al., 2009), which clearly indicates that not all of the possible modification events take place under physiological conditions.Several examples supporting a probabilistic signaling mode of the tubulin code are found in the recent literature. In T. thermophila, a ciliate without tubulin isotype diversity (Gaertig et al., 1993) but with a huge repertoire of tubulin PTMs and tubulin-modifying enzymes (Janke et al., 2005), tubulin can be easily mutagenized to experimentally eliminate sites for PTMs. Mutagenesis of the most commonly occupied glutamylation/glycylation sites within the β-tubulin tails did not generate a clear decrease of glycylation levels nor did it cause obvious phenotypic alterations. This indicates that the modifying enzymes can deviate toward alternative modification sites and that similar PTMs on different sites can compensate the functions of the mutated site. However, when all of the key modification sites were mutated, glycylation became prominently decreased, which led to severe phenotypes, including lethality (Xia et al., 2000). Most strikingly, these phenotypes could be recovered by replacing the C-terminal tail of α-tubulin with the nonmutated β-tubulin tail. This α–β-tubulin chimera became overglycylated and functionally compensated for the absence of modification sites on β-tubulin. The conclusion of this study is that PTM- and isotype-generated signals can fulfill a biological function within a certain range of tolerance.But how efficient is such compensation? The answer can be found in a variety of already described deletion mutants for tubulin-modifying enzymes in different model organisms. Most single-gene knockouts for TTLL genes (glutamylases or glycylases) did not result in prominent phenotypic alterations in mice, even for enzymes that are ubiquitously expressed. Only some highly specialized microtubule structures show functional aberrations upon the deletion of a single enzyme. These “tips of the iceberg” are usually the motile cilia and sperm flagella, which carry very high levels of polyglutamylation and polyglycylation (Bré et al., 1996; Kann et al., 1998; Rogowski et al., 2009). It thus appears that some microtubules are essentially dependent on the generation of specific PTM patterns, whereas others can tolerate changes and appear to function normally. How “normal” these functions are remains to be investigated in future studies. It is possible that defects are subtle and thus overlooked but could become functionally important under specific conditions.A tubulin code also requires readout mechanisms. The most likely “readers” of the tubulin code are MAPs and molecular motors. Considering the probabilistic signaling hypothesis, the expected effects of the signals would be in most cases rather gradual changes, for instance, to fine-tune molecular motor traffic and/or to bias motors toward defined microtubule tracks but not to obliterate motor activity or MAP binding to microtubules. An in vitro study using recombinant tubulin chimeras purified from yeast confirmed this notion (Sirajuddin et al., 2014). By analyzing which elements of the tubulin code can regulate the velocity and processivity of the molecular motors kinesin and dynein, these researchers found that the C-terminal tails of α- and β-tubulin differentially influence the kinetic parameters of the tested motors; however, the modulation was rather modest. One of their striking observations was that a single lysine residue, present in the C-terminal tails of two β-tubulin isotypes (Figs. 2 and ​and4),4), significantly affected motor traffic and that this effect can be counterbalanced by polyglutamylation. These observations are the first in vitro evidence for the interdependence of different elements of the tubulin code and provide another indication for its probabilistic mode of signaling.

Future directions.

One of the greatest technological challenges to understanding the function of the tubulin code is to detect and interpret subtle and complex regulatory events generated by this code. It will thus be instrumental to further develop tools to better distinguish graded changes in PTM levels on microtubules in cells and tissues (Magiera and Janke, 2013) and to reliably measure subtle modulations of microtubule behavior in reconstituted systems.The current advances in the field and especially the availability of whole-organism models, as well as first insights into the pathological role of tubulin mutations (Tischfield et al., 2011), are about to transform our way of thinking about the regulation of microtubule cytoskeleton. Tubulin heterogeneity generates complex probabilistic signals that cannot be clearly attributed to single biological functions in most cases and that are not essential for most cellular processes. Nevertheless, it has been conserved throughout evolution of eukaryotes and can hardly be dismissed as not important. To understand the functional implications of these processes, we might be forced to reconsider how we define biologically important events and how we measure events that might encode probabilistic signals. The answers to these questions could provide novel insights into how complex systems, such as cells and organisms, are sustained throughout difficult and challenging life cycles, resist to environmental stress and diseases, and have the flexibility needed to succeed in evolution.