Central Role of RET in Thyroid Cancer

RET (rearranged during transfection) is a receptor tyrosine kinase involved in the development of neural crest derived cell lineages, kidney, and male germ cells. Different human cancers, including papillary and medullary thyroid carcinomas, lung adenocarcinomas, and myeloproliferative disorders display gain-of-function mutations in RET. Accordingly, RET protein has become a promising molecular target for cancer treatment.The human RET (rearranged during transfection) gene maps on 10q11.2 and is composed of 21 exons spanning a region of 55,000 bp. It encodes a single-pass trans-membrane protein, RET, that belongs to the receptor tyrosine kinase (RTK) family (Pasini et al. 1995). The RET extracellular segment contains four cadherin-like domains, followed by a domain containing cysteine residues involved in the formation of intramolecular disulfide bonds (Fig. 1A) (Anders et al. 2001; Airaksinen and Saarma 2002). RET protein is highly glycosylated and N-glycosylation is necessary for its transport to the cell surface. Only the fully mature glycosylated 170 kDa RET protein isoform is exposed to the extracellular compartment, whereas the mannose-rich 150 kDa isoform is confined to the Golgi (Takahashi et al. 1993; Carlomagno et al. 1996). The transmembrane segment is composed of 22 amino acids, among which S649 and S653 mediate self-association and dimerization of RET, possibly via formation of inter-molecular hydrogen bonding (Kjaer et al. 2006). The intracellular portion of RET contains the tyrosine kinase domain split into two subdomains by the insertion of 27 amino acids. The RET COOH-terminal tail varies in length as a result of alternative splicing of the 3′ end (carboxy terminal with respect to glycine 1063), generating three different isoforms that contain 9 (RET9), 43 (RET43), or 51 (RET51) amino acids (Myers et al. 1995). RET9 and RET51 are the most abundant isoforms, and they activate similar signaling pathways through interaction with diverse protein complexes, and may exert a differential role in development (Fig. 1A) (de Graaff et al. 2001).Open in a separate windowFigure 1.Illustration of the mechanisms of activation of wild-type (wt) RET and RET-derived oncoproteins. (A) Wild-type RET activation is mediated by ligand (GFL)-induced dimerization; ligand binding to RET is not direct and mediated by GFR-α coreceptors (not shown); major RET autophosphorylation sites and downstream signaling pathways are indicated. RET extracellular cadherin-like domains are represented in red. The split intracellular RET tyrosine kinase domain, as well as the three alternative carboxy-terminal RET tails, are also depicted. (B) RET/PTC activation is mediated by coiled-coil-induced dimerization (left); activation of RET cysteine mutants associated with MEN2A or FMTC is mediated by disulfide bonds-mediated dimerization (right).RET shows several autophosphorylation sites (Fig. 1A) (Liu et al. 1996; Kawamoto et al. 2004). RET tyrosine 1062 (Y1062) functions as a multidocking site for signaling molecules containing a phosphotyrosine-binding (PTB) domain (Asai et al. 1996). Phospho-Y1062 binding proteins include SHC, N-SHC (RAI), FRS2, IRS1/2, DOK1, and DOK4/5 that, in turn, contribute to the activation of RAS-MAPK (mitogen-activated protein kinases) and PI3K (phosphatidyl inositol 3 kinase)-AKT pathways. Y1096, specific to the RET51 splicing variant, couples to the PI3K-AKT and RAS-MAPK pathways, as well. These signaling cascades mediate RET-dependent cell survival, proliferation, and motility (Alberti et al. 1998; Murakami et al. 1999; Segouffin-Cariou and Billaud 2000; Melillo et al. 2001a,b; Schuetz et al. 2004). Y905 is located in the activation loop of the RET kinase and its phosphorylation is associated with RET kinase activation (Knowles et al. 2006). Finally, Y981 and Y1015 have been shown to be coupled to important signaling molecules such as SRC and PLC-γ, respectively (Borrello et al. 1996; Encinas et al. 2004).RET is the receptor for a group of neurotrophic growth factors that belong to the glial cell line-derived neurotrophic factor (GDNF) family (GFLs, GDNF family ligands), namely, GDNF, Neurturin (NRT), Artemin (ART), and Persephin (PSF) (Airaksinen and Saarma 2002). GFLs mediate RET protein dimerization and activation (Fig. 1A). GFLs are presented to RET by GPI (glycosylphosphatidylinositol)-anchored coreceptors, called GFR-α (GDNF family receptor α 1-4). Differential tissue expression dictates the specificity of action displayed by alternative GLF-GFR-α pairs during development and adult life (Baloh et al. 2000; Airaksinen and Saarma 2002).Together with other membrane (DCC and p75NTR) or nuclear (androgen receptor, AR) receptors, RET belongs to the family of so-called “dependence” receptors (Mehlen and Bredesen 2011). In the absence of ligand, RET exerts a proapoptotic activity, that is blocked on ligand stimulation (Bordeaux et al. 2000). Such pro-apoptotic activity is RET kinase-independent and mediated by cleavage of RET cytosolic portion by caspase-3, which, in turn, releases a carboxy-terminal RET peptide that is able to induce cell death (Bordeaux et al. 2000). It is feasible that such activity is important for RET developmental function, because it may control migration of RET-expressing cells by limiting survival of cells that move beyond ligand availability (Bordeaux et al. 2000; Cañibano et al. 2007). Whether modulation of this function is also important for RET-associated diseases is still unknown. However, it is interesting to note that a cancer-associated RET mutant (RET-C634R, see below) does not exert cleavage-dependent proapoptotic effects, whereas RET mutants associated with defective development (Hirschsprung disease, see below) exert strong proapoptotic activity that is refractory to modulation by ligand (Bordeaux et al. 2000).RET is expressed in enteric ganglia, adrenal medulla chromaffin cells, thyroid C cells, sensory and autonomic ganglia of the peripheral nervous system, a subset of central nervous system nuclei, developing kidney and testis germ cells (Manié et al. 2001; de Graaff et al. 2001). RET null mice display impaired development of superior cervical ganglia and enteric nervous system, kidney agenesia, reduction of thyroid C cells, and impaired spermatogenesis (Manié et al. 2001). Accordingly, individuals with germline loss-of-function mutations of RET are affected by intestinal aganglionosis causing congenital megacolon (Hirschsprung disease) (Brooks et al. 2005). RET loss-of-function mutations have also been identified in congenital anomalies of kidney and urinary tract (CAKUT), either isolated or in combination with Hirschsprung disease (Jain 2009).Several genetic alterations convert RET into a dominantly transforming oncogene. This review will describe RET-derived oncogenes that are associated with different types of human neoplasia (Fig. 1B).     

Biology of the TAM Receptors

The TAM receptors—Tyro3, Axl, and Mer—comprise a unique family of receptor tyrosine kinases, in that as a group they play no essential role in embryonic development. Instead, they function as homeostatic regulators in adult tissues and organ systems that are subject to continuous challenge and renewal throughout life. Their regulatory roles are prominent in the mature immune, reproductive, hematopoietic, vascular, and nervous systems. The TAMs and their ligands—Gas6 and Protein S—are essential for the efficient phagocytosis of apoptotic cells and membranes in these tissues; and in the immune system, they act as pleiotropic inhibitors of the innate inflammatory response to pathogens. Deficiencies in TAM signaling are thought to contribute to chronic inflammatory and autoimmune disease in humans, and aberrantly elevated TAM signaling is strongly associated with cancer progression, metastasis, and resistance to targeted therapies.The name of the TAM family is derived from the first letter of its three constituents—Tyro3, Axl, and Mer (Prasad et al. 2006). As detailed in Figure 1, members of this receptor tyrosine kinase (RTK) family were independently identified by several different groups and appear in the early literature under multiple alternative names. However, Tyro3, Axl, and Mer (officially c-Mer or MerTK for the protein, Mertk for the gene) have now been adopted as the NCBI designations. The TAMs were first grouped into a distinct RTK family (the Tyro3/7/12 cluster) in 1991, through PCR cloning of their kinase domains (Lai and Lemke 1991). The isolation of full-length cDNAs for Axl (O''Bryan et al. 1991), Mer (Graham et al. 1994), and Tyro3 (Lai et al. 1994) confirmed their segregation into a structurally distinctive family of orphan RTKs (Manning et al. 2002b). The two ligands that bind and activate the TAMs—Gas6 and Protein S (Pros1)—were identified shortly thereafter (Ohashi et al. 1995; Stitt et al. 1995; Mark et al. 1996; Nagata et al. 1996).Open in a separate windowFigure 1.TAM receptors and ligands. The TAM receptors (red) are Tyro3 (Lai and Lemke 1991; Lai et al. 1994)—also designated Brt (Fujimoto and Yamamoto 1994), Dtk (Crosier et al. 1994), Rse (Mark et al. 1994), Sky (Ohashi et al. 1994), and Tif (Dai et al. 1994); Axl (O''Bryan et al. 1991)—also designated Ark (Rescigno et al. 1991), Tyro7 (Lai and Lemke 1991), and Ufo (Janssen et al. 1991); and Mer (Graham et al. 1994)—also designated Eyk (Jia and Hanafusa 1994), Nyk (Ling and Kung 1995), and Tyro12 (Lai and Lemke 1991). The TAMs are widely expressed by cells of the mature immune, nervous, vascular, and reproductive systems. The TAM ligands (blue) are Gas6 and Protein S (Pros1). The carboxy-terminal SHBG domains of the ligands bind to the immunoglobulin (Ig) domains of the receptors, induce dimerization, and activate the TAM tyrosine kinases. When γ-carboxylated in a vitamin-K-dependent reaction, the amino-terminal Gla domains of the dimeric ligands bind to the phospholipid phosphatidylserine expressed on the surface on an apposed apoptotic cell or enveloped virus. See text for details. (From Lemke and Burstyn-Cohen 2010; adapted, with permission, from the authors.)Subsequent progress on elucidating the biological roles of the TAM receptors was considerably slower and ultimately required the derivation of mouse loss-of-function mutants (Camenisch et al. 1999; Lu et al. 1999). The fact that Tyro3^−/−, Axl^−/−, and Mer^−/− mice are all viable and fertile permitted the generation of a complete TAM mutant series that included all possible double mutants and even triple mutants that lack all three receptors (Lu et al. 1999). Remarkably, these Tyro3^−/−Axl^−/−Mer^−/− triple knockouts (TAM TKOs) are viable, and for the first 2–3 wk after birth, superficially indistinguishable from their wild-type counterparts (Lu et al. 1999). Because many RTKs play essential roles in embryonic development, even single loss-of-function mutations in RTK genes often result in an embryonic-lethal phenotype (Gassmann et al. 1995; Lee et al. 1995; Soriano 1997; Arman et al. 1998). The postnatal viability of mice in which an entire RTK family is ablated completely—the TAM TKOs can survive for more than a year (Lu et al. 1999)—is therefore highly unusual. Their viability notwithstanding, the TAM mutants go on to develop a plethora of phenotypes, some of them debilitating (Camenisch et al. 1999; Lu et al. 1999; Lu and Lemke 2001; Scott et al. 2001; Duncan et al. 2003; Prasad et al. 2006). Almost without exception, these phenotypes are degenerative in nature and reflect the loss of TAM signaling activities in adult tissues that are subject to regular challenge, renewal, and remodeling. These activities are the subject of this review.     

Toll-Like Receptor Signaling

Toll-like receptors sense pathogen-associated molecular patterns (e.g., lipopolysaccharides) and trigger gene-expression changes that ultimately eradicate the invading microbes.Toll-like receptors (TLRs) are protective immune sentries that sense pathogen-associated molecular patterns (PAMPs) such as unmethylated double-stranded DNA (CpG), single-stranded RNA (ssRNA), lipoproteins, lipopolysaccharide (LPS), and flagellin. In innate immune myeloid cells, TLRs induce the secretion of inflammatory cytokines (Newton and Dixit 2012), thereby engaging lymphocytes to mount an adaptive, antigen-specific immune response (see Fig. 1) that ultimately eradicates the invading microbes (Kawai and Akira 2010).Open in a separate windowFigure 1.TLR signaling (simplified view).Identification of TLR innate immune function began with the discovery that Drosophila mutants in the Toll gene are highly susceptible to fungal infection (Lemaitre et al. 1996). This was soon followed by identification of a human Toll homolog, now known as TLR4 (Medzhitov et al. 1997). To date, 10 TLR family members have been identified in humans, and at least 13 are present in mice. All TLRs consist of an amino-terminal domain, characterized by multiple leucine-rich repeats, and a carboxy-terminal TIR domain that interacts with TIR-containing adaptors. Nucleic acid–sensing TLRs (TLR3, TLR7, TLR8, and TLR9) are localized within endosomal compartments, whereas the other TLRs reside at the plasma membrane (Blasius and Beutler 2010; McGettrick and O’Neill 2010). Trafficking of most TLRs from the endoplasmic reticulum (ER) to either the plasma membrane or endolysosomes is orchestrated by ER-resident proteins such as UNC93B (for TLR3, TLR7, TLR8, and TLR9) and PRAT4A (for TLR1, TLR2, TLR4, TLR7, and TLR9) (Blasius and Beutler 2010). Once in the endolysosomes, TLR3, TLR7, and TLR9 are subject to stepwise proteolytic cleavage, which is required for ligand binding and signaling (Barton and Kagan 2009). For some TLRs, ligand binding is facilitated by coreceptors, including CD14 and MD2.Following ligand engagement, the cytoplasmic TIR domains of the TLRs recruit the signaling adaptors MyD88, TIRAP, TRAM, and/or TRIF (see Fig. 2). Depending on the nature of the adaptor that is used, various kinases (IRAK4, IRAK1, IRAK2, TBK1, and IKKε) and ubiquitin ligases (TRAF6 and pellino 1) are recruited and activated, culminating in the engagement of the NF-κB, type I interferon, p38 MAP kinase (MAPK), and JNK MAPK pathways (Kawai and Akira 2010; Morrison 2012). TRAF6 is modified by K63-linked autoubiquitylation, which enables the recruitment of IκB kinase (IKK) through a ubiquitin-binding domain of the IKKγ (also known as NEMO) subunit. In addition, a ubiquitin-binding domain of TAB2 recognizes ubiquitylated TRAF6, causing activation of the associated TAK1 kinase, which then phosphorylates the IKKβ subunit. Pellino 1 can modify IRAK1 with K63-linked ubiquitin, allowing IRAK1 to recruit IKK directly. TLR4 signaling via the TRIF adaptor protein leads to K63-linked polyubiquitylation of TRAF3, thereby promoting the type I interferon response via interferon regulatory factor (IRFs) (Hacker et al. 2011). Alternatively, TLR4 signaling via MyD88 leads to the activation of TRAF6, which modifies cIAP1 or cIAP2 with K63-linked polyubiquitin (Hacker et al. 2011). The cIAPs are thereby activated to modify TRAF3 with K48-linked polyubiquitin, causing its proteasomal degradation. This allows a TRAF6–TAK1 complex to activate the p38 MAPK pathway and promote inflammatory cytokine production (Hacker et al. 2011). TLR signaling is turned off by various negative regulators: IRAK-M and MyD88 short (MyD88s), which antagonize IRAK1 activation; FADD, which antagonizes MyD88 or IRAKs; SHP1 and SHP2, which dephosphorylate IRAK1 and TBK1, respectively; and A20, which deubiquitylates TRAF6 and IKK (Flannery and Bowie 2010; Kawai and Akira 2010).Open in a separate windowFigure 2.TLR signaling. (Adapted with kind permission of Cell Signaling Technology [http://www.cellsignal.com].)Deregulation of the TLR signaling cascade causes several human diseases. Patients with inherited deficiencies of MyD88, IRAK4, UNC93B1, or TLR3 are susceptible to recurrent bacterial or viral infections (Casanova et al. 2011). Chronic TLR7 and/or TLR9 activation in autoreactive B cells, in contrast, underlies systemic autoimmune diseases (Green and Marshak-Rothstein 2011). Furthermore, oncogenic activating mutations of MyD88 occur frequently in the activated B-cell-like subtype of diffuse large B-cell lymphoma and in other B-cell malignancies (Ngo et al. 2011). Inhibitors of various TLRs or their associated kinases are currently being developed for autoimmune or inflammatory diseases and also hold promise for the treatment of B-cell malignancies with oncogenic MyD88 mutations. Many TLR7 and TLR9 agonists are currently in clinical trials as adjuvants to boost host antitumor responses in cancer patients (Hennessy et al. 2010).     

Diversity of Neural Precursors in the Adult Mammalian Brain

Aided by advances in technology, recent studies of neural precursor identity and regulation have revealed various cell types as contributors to ongoing cell genesis in the adult mammalian brain. Here, we use stem-cell biology as a framework to highlight the diversity of adult neural precursor populations and emphasize their hierarchy, organization, and plasticity under physiological and pathological conditions.The adult mammalian brain displays remarkable structural plasticity by generating and incorporating new neural cell types into an already formed brain (Kempermann and Gage 1999). Largely restricted within the subventricular zone (SVZ) along the lateral ventricle and the subgranular zone (SGZ) in the dentate gyrus (DG), neural genesis is thought to arise from neural stem cells (NSCs) (Ming and Song 2011). Stem cells are defined by hallmark functions: capacity to self-renew, maintenance of an immature state over a long duration, and ability to generate specialized cell types (Fig. 1). These features distinguish stem cells from committed progenitor cells that more readily differentiate into specialized cell types (Fig. 1). Stem and progenitor cells (collectively called precursors) are additionally characterized by their lineage capacity. For example, multipotential neural precursors generate neurons and glia, whereas unipotential cells produce only one cell type, such as neurons (Gage 2000; Ma et al. 2009). The classical NSC definition is based on cell culture experiments in which a single cell can self-renew and generate neurons, astrocytes, and oligodendrocytes (Gage 2000; Ma et al. 2009). Yet, reprogramming studies have raised the question of whether cultured lineage-restricted neural progenitors acquire additional potential not evident in vivo (Palmer et al. 1999; Kondo and Raff 2000; Gabay et al. 2003). As a result, various lineage models have been proposed to explain cell generation in the adult brain (Fig. 1) (Ming and Song 2011). In one model, bona fide adult stem cells generate multiple lineages at the individual cell level. In another, cell genesis represents a collective property from a mixed population of unipotent progenitors. Importantly, these models are not mutually exclusive as evidence for the coexistence of multiple precursors has been observed in several adult somatic tissues, in which one population preferentially maintains homeostasis and another serves as a cellular reserve (Li and Clevers 2010; Mascre et al. 2012). Recent technical advances, including single-cell lineage tracing (Kretzschmar and Watt 2012), have made it possible to dissect basic cellular and behavioral processes of neural precursors in vivo (Fig. 4) (Bonaguidi et al. 2012). In this work, we review our current knowledge of precursor cell identity, hierarchical organization, and regulation to examine the diverse origins of cell genesis in the adult mammalian brain.Open in a separate windowFigure 1.Models of generating cell diversity in the adult tissues. (A,B) Definitions of stem and progenitor cells. In A, quiescent stem cells (S_q) become active stem cells (S_a) that proliferate to generate different types of specialized cells (C₁, C₂, C₃) and new stem cells (S). The active stem cell can return to quiescence and remain quiescent over long periods of time. In B, lineage-restricted progenitor cells lacking self-renewal capacity (P₁, P₂, P₃) each give rise to distinct populations of specialized cells (C₁, C₂, C₃). (C) Generation of specialized cells in a tissue could be explained by three models. (1) The stem-cell model, in which multipotent stem cells give rise to all the specialized cells in the tissue. (2) The progenitor cell model, in which diverse, lineage-restricted progenitor cells give rise to different cell types in the tissue. (3) A hybrid model, in which a mixture of stem cells and lineage-restricted progenitor cells generate specialized cells of the adult tissue.

Table 1.

Comparison of different methods used to study the generation of new cells in the adult mammalian nervous system

mGluR1/TRPC3-mediated Synaptic Transmission and Calcium Signaling in Mammalian Central Neurons

Metabotropic glutamate receptors type 1 (mGluR1s) are required for a normal function of the mammalian brain. They are particularly important for synaptic signaling and plasticity in the cerebellum. Unlike ionotropic glutamate receptors that mediate rapid synaptic transmission, mGluR1s produce in cerebellar Purkinje cells a complex postsynaptic response consisting of two distinct signal components, namely a local dendritic calcium signal and a slow excitatory postsynaptic potential. The basic mechanisms underlying these synaptic responses were clarified in recent years. First, the work of several groups established that the dendritic calcium signal results from IP₃ receptor-mediated calcium release from internal stores. Second, it was recently found that mGluR1-mediated slow excitatory postsynaptic potentials are mediated by the transient receptor potential channel TRPC3. This surprising finding established TRPC3 as a novel postsynaptic channel for glutamatergic synaptic transmission.Glutamate is the predominant neurotransmitter used by excitatory synapses in the mammalian brain (Hayashi 1952; Curtis et al. 1959). At postsynaptic sites, glutamate binds to two different classes of receptors, namely the ionotropic glutamate receptors (iGluRs) and the metabotropic glutamate receptors (mGluRs) (Sladeczek et al. 1985; Nicoletti et al. 1986; Sugiyama et al. 1987). The iGluRs represent ligand-gated nonselective cation channels that underlie excitatory postsynaptic currents (EPSCs). Based on their subunit composition, gating, and permeability properties, they are subdivided into three groups named after specific agonists: AMPA- (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid), NMDA receptors (N-methyl D-aspartate receptors) and kainate receptors (Alexander et al. 2009). The other class of glutamate receptors, the mGluRs, consists of receptors that are coupled to G proteins and act through distinct downstream signaling cascades. They are structurally different from iGluRs and characterized by the presence of seven transmembrane domains (Houamed et al. 1991; Masu et al. 1991). The mGluRs exist as homodimers that do not by themselves form an ion-permeable pore in the membrane (Ozawa et al. 1998). To date, eight different genes (and more splice variants) encoding mGluRs have been identified and form the mGluR1 through mGluR8 subtypes (Alexander et al. 2009). Based on the amino acid sequence homology, downstream signal transduction pathways, and pharmacological properties, each of the subtypes was assigned to one of three groups. Group I receptors consist of mGluR1 and mGluR5 that positively couple to the phospholipase C (PLC). The receptors mGluR2 and mGluR3 constitute group II, whereas the remaining mGluRs, namely mGluR4, mGluR6, mGluR7, and mGluR8, belong to group III. Both groups II and III inhibit the adenylyl cyclase and thereby reduce the concentration of cAMP in the cytosol.Of all different subtypes, mGluR1 is the most abundantly expressed mGluR in the mammalian central nervous system. In the brain, mGluR1 is highly expressed in the olfactory bulb, dentate gyrus, and cerebellum (Lein et al. 2007). The highest expression level of mGluR1 in the brain is found in Purkinje cells, the principal neurons of the cerebellar cortex (Shigemoto et al. 1992; Lein et al. 2007). Together with the AMPA receptors, mGluR1s are part of the excitatory synapses formed between parallel fibers and Purkinje cells (Fig. 1A). Each Purkinje cell is innervated by 100,000–200,000 parallel fibers (Ito 2006) that are axons of the cerebellar granule cells, the most abundant type of neuron in the brain. A second type of excitatory input to Purkinje cells is represented by the climbing fibers that originate in the inferior olive in the brain stem (Ito 2006). The two excitatory synaptic inputs to Purkinje cells are important determinants for the main functions of the cerebellum, including the real-time control of movement precision, error-correction, and control of posture as well as the procedural learning of complex movement sequences and conditioned responses.Open in a separate windowFigure 1.Parallel fiber-evoked mGluR1-dependent signals. (A) Diagram showing the parallel fiber synaptic input to Purkinje cell dendrites. (B) Microelectrode recording of glutamatergic postsynaptic potentials from a Purkinje cell in an acute slice of adult rat cerebellum. Short trains of stimuli to the parallel fibers (5–6 at 50 Hz) caused summation of the early AMPA receptor-dependent EPSPs (leading to spike firing) and a slow, delayed, depolarizing potential (slow EPSP), which was reversibly inhibited by antagonist of mGluRs (+)-MCPG (1mM). (C) Confocal image of a patch-clamped Purkinje cell in a cerebellar slice of an adult mouse. The patch-clamp pipette and the glass capillary used for electrical stimulation of parallel fibers are depicted schematically. The site of stimulation is shown at higher magnification in D. (D) Left: Parallel fiber-evoked (five pulses at 200 Hz, in 10 mM CNQX) synaptic responses consisting of a dendritic mGluR1-dependent Ca²⁺ transient (ΔF/F, top) and an early rapid and a slow excitatory postsynaptic current (EPSC, bottom). Block of the mGluR1-dependent components by the group I-specific mGluR-antagonist CPCCOEt (200 µM) is shown as indicated. Right: Pseudocolor image of the synaptic Ca²⁺ signal. (B, Reprinted with modifications, with permission, from Batchelor and Gaithwaite 1997 [Nature Publishing Group].)It is expected that mGluR1 is involved in many of these cerebellar functions. This view is supported by the observation that mGluR1-deficient knockout mice show severe impairments in motor coordination. In particular, the gait of these mice is strongly affected as well as their ability for motor learning and general coordination (Aiba et al. 1994). The phenotype of the general mGluR1-knockout mice is rescued by the insertion of the gene encoding mGluR1 exclusively into cerebellar Purkinje cells (Ichise et al. 2000) and blockade of mGluR1 expression only in Purkinje cells of adult mice leads to impaired motor coordination (Nakao et al. 2007). These findings established mGluR1 in Purkinje cell as synaptic receptors that are indispensable for a normal cerebellar function.Synaptic transmission involving mGluR1s is found at both parallel fiber-Purkinje cell synapses (Batchelor and Garthwaite 1993; Batchelor et al. 1994) as well as at climbing fiber-Purkinje cell synapses (Dzubay and Otis 2002). Most of our knowledge on the mGluR1 was gained from the analysis of the parallel fiber synapses. The parallel fiber synapse is quite unique in the central nervous system regarding its endowment with neurotransmitter receptors. In contrast to most other glutamatergic synapses in the mammalian brain, it lacks functional NMDA receptors (Shin and Linden 2005). The entire synaptic transmission at these synapses relies on AMPA receptors and on mGluR1 (Takechi et al. 1998). Although AMPA receptors are effectively activated even with single shock stimuli (Konnerth et al. 1990; Llano et al. 1991b), activation of mGluRs requires repetitive stimulation (Batchelor and Garthwaite 1993; Batchelor et al. 1994; Batchelor and Garthwaite 1997; Takechi et al. 1998). A possible explanation for the need of repetitive stimulation may relate to the observation that mGluR1s are found mostly at the periphery of the subsynaptic region (Nusser et al. 1994). At these sites outside the synaptic cleft, glutamate levels that are sufficiently high for receptor activation may be reached only with repetitive stimulation.At parallel fiber-Purkinje cell synapses, repetitive stimulation produces an initial AMPA receptor postsynaptic signal component, followed by a more prolonged mGluR1 component (Fig. 1). Figure 1B shows a current clamp recording of this response consisting of an early burst of action potentials, followed by a prolonged depolarization known as a “slow excitatory postsynaptic potential” (slow EPSP) (Batchelor and Garthwaite 1993; Batchelor et al. 1994; Batchelor and Garthwaite 1997). Voltage-clamp recordings allow a clear separation of the initial rapid, AMPA receptor mediated excitatory postsynaptic current (EPSC) and the mGluR1-mediated slow EPSC (Fig. 1D) (Takechi et al. 1998; Hartmann et al. 2008). In addition of inducing the slow EPSPs, mGluR1s mediate a large and highly localized dendritic calcium transient in cerebellar Purkinje cells (Fig. 1D) (Llano et al. 1991a; Finch and Augustine 1998; Takechi et al. 1998).     

Membrane Domains Based on Ankyrin and Spectrin Associated with Cell–Cell Interactions

Nodes of Ranvier and axon initial segments of myelinated nerves, sites of cell–cell contact in early embryos and epithelial cells, and neuromuscular junctions of skeletal muscle all perform physiological functions that depend on clustering of functionally related but structurally diverse ion transporters and cell adhesion molecules within microdomains of the plasma membrane. These specialized cell surface domains appeared at different times in metazoan evolution, involve a variety of cell types, and are populated by distinct membrane-spanning proteins. Nevertheless, recent work has shown that these domains all share on their cytoplasmic surfaces a membrane skeleton comprised of members of the ankyrin and spectrin families. This review will summarize basic features of ankyrins and spectrins, and will discuss emerging evidence that these proteins are key players in a conserved mechanism responsible for assembly and maintenance of physiologically important domains on the surfaces of diverse cells.Spectrins are flexible rods 0.2 microns in length with actin-binding sites at each end (Shotton et al. 1979; Bennett et al. 1982) (Fig. 1A). Spectrins are assembled from α and β subunits, each comprised primarily of multiple copies of a 106-amino acid repeat (Speicher and Marchesi 1984). In addition to the canonical 106-residue repeat, β spectrins also have a carboxy-terminal pleckstrin homology domain (Zhang et al. 1995; Macias et al. 1994) and tandem amino-terminal calponin homology domains (Bañuelos et al. 1998), whereas α spectrins contain an Src homology domain 3 (SH3) site (Musacchio et al. 1992), a calmodulin-binding site (Simonovic et al. 2006), and EF hands (Travé et al. 1995) (Fig. 1A). Spectrin α and β subunits are assembled antiparallel and side-to-side into heterodimers, which in turn are associated head-to-head to form tetramers (Clarke 1971; Shotton et al. 1979; Davis and Bennett 1983) (Fig. 1A). In human erythrocytes, in which spectrin was first characterized (Marchesi and Steers 1968; Clarke 1971), actin oligomers containing 10–14 monomers are each linked to five to six spectrin tetramers by accessory proteins to form a geodesic domelike structure that has been resolved by electron microscopy (Byers and Branton 1985). The principal proteins at the spectrin–actin junction are protein 4.1, adducin, tropomyosin, tropomodulin, and dematin (Bennett and Baines 2001) (Open in a separate windowFigure 1.Domain structure and variants of spectrin and ankyrin proteins. (A) Molecular domains of spectrins: Two α spectrins and five β spectrins are shown. Spectrins are comprised of modular units called spectrin repeats (yellow). Other domains such as the ankyrin binding domain (purple), Src-homology domain 3 (SH3, blue), EF-hand domain (red), and calmodulin-binding domain (green) promote interactions with binding targets important for spectrin function. The pleckstrin homology domain (black) promotes association with the plasma membrane and the actin binding domain (grey) tethers the spectrin-based membrane skeleton to the underlying actin cytoskeleton. (B) The spectrin tetramer, the fundamental unit of the spectrin-based membrane skeleton. The spectrin repeat domains of α and β spectrin associate end-to-end to form heterodimers. Heterodimers associate laterally in an antiparallel fashion to form tetramers. The tetramers can then associate end-to-end to form extended macromolecules that link into a geodesic dome shape directly underneath the plasma membrane. (C) Molecular domains present in canonical ankyrins. The membrane binding domain of ankyrin isoforms (orange) is comprised of 24 ANK repeats. The spectrin binding domain (green-blue) allows ankyrins to coordinate integral membrane proteins to the membrane skeleton. The death domain (pink) is the most highly conserved domain. The regulatory domain (brown) is the most variable region of ankyrins. The regulatory domain interacts intramolecularly with the membrane binding domain to modulate ankyrin’s affinity for other binding partners. All ankyrins and spectrins are subject to alternative splicing, which further increases their functional diversity.

Table 1.

Binding partners of spectrin and ankyrins

Wnt Signaling in Vertebrate Axis Specification

The Wnt pathway is a major embryonic signaling pathway that controls cell proliferation, cell fate, and body-axis determination in vertebrate embryos. Soon after egg fertilization, Wnt pathway components play a role in microtubule-dependent dorsoventral axis specification. Later in embryogenesis, another conserved function of the pathway is to specify the anteroposterior axis. The dual role of Wnt signaling in Xenopus and zebrafish embryos is regulated at different developmental stages by distinct sets of Wnt target genes. This review highlights recent progress in the discrimination of different signaling branches and the identification of specific pathway targets during vertebrate axial development.Wnt pathways play major roles in cell-fate specification, proliferation and differentiation, cell polarity, and morphogenesis (Clevers 2006; van Amerongen and Nusse 2009). Signaling is initiated in the responding cell by the interaction of Wnt ligands with different receptors and coreceptors, including Frizzled, LRP5/6, ROR1/2, RYK, PTK7, and proteoglycans (Angers and Moon 2009; Kikuchi et al. 2009; MacDonald et al. 2009). Receptor activation is accompanied by the phosphorylation of Dishev-elled (Yanagawa et al. 1995), which appears to transduce the signal to both the cell membrane and the nucleus (Cliffe et al. 2003; Itoh et al. 2005; Bilic et al. 2007). Another common pathway component is β-catenin, an abundant component of adherens junctions (Nelson and Nusse 2004; Grigoryan et al. 2008). In response to signaling, β-catenin associates with T-cell factors (TCFs) and translocates to the nucleus to stimulate Wnt target gene expression (Behrens et al. 1996; Huber et al. 1996; Molenaar et al. 1996).This β-catenin-dependent activation of specific genes is often referred to as the “canonical” pathway. In the absence of Wnt signaling, β-catenin is destroyed by the protein complex that includes Axin, GSK3, and the tumor suppressor APC (Clevers 2006; MacDonald et al. 2009). Wnt proteins, such as Wnt1, Wnt3, and Wnt8, stimulate Frizzled and LRP5/6 receptors to inactivate this β-catenin destruction complex, and, at the same time, trigger the phosphorylation of TCF proteins by homeodomain-interacting protein kinase 2 (HIPK2) (Hikasa et al. 2010; Hikasa and Sokol 2011). Both β-catenin stabilization and the regulation of TCF protein function by phosphorylation appear to represent general strategies that are conserved in multiple systems (Sokol 2011). Thus, the signaling pathway consists of two branches that together regulate target gene expression (Fig. 1).Open in a separate windowFigure 1.Conserved Wnt pathway branches and components. In the absence of Wnt signals, glycogen synthase kinase 3 (GSK3) binds Axin and APC to form the β-catenin destruction complex. Some Wnt proteins, such as Wnt8 and Wnt3a, stimulate Frizzled and LRP5/6 receptors to inhibit GSK3 activity and stabilize β-catenin (β-cat). Stabilized β-cat forms a complex with T-cell factors (e.g., TCF1/LEF1) to activate target genes. Moreover, GSK3 inhibition leads to target gene derepression by promoting TCF3 phosphorylation by homeodomain-interacting protein kinase 2 (HIPK2) through an unknown mechanism, for which β-catenin is required as a scaffold. This phosphorylation results in TCF3 removal from target promoters and gene activation. Other Wnt proteins, such as Wnt5a and Wnt11, use distinct receptors such as ROR2 and RYK, in addition to Frizzled, to control the the cytoskeletal organization through core planar cell polarity (PCP) proteins, small GTPases (Rho/Rac/Cdc42), and c-Jun amino-terminal kinase (JNK).Other Wnt proteins, such as Wnt5a or Wnt11, strongly affect the cytoskeletal organization and morphogenesis without stabilizing β-catenin (Torres et al. 1996; Angers and Moon 2009; Wu and Mlodzik 2009). These “noncanonical” ligands do not influence TCF3 phosphorylation (Hikasa and Sokol 2011), but may use distinct receptors such as ROR1/2 and RYK instead of or in addition to Frizzled (Hikasa et al. 2002; Lu et al. 2004; Mikels and Nusse 2006; Nishita et al. 2006, 2010; Schambony and Wedlich 2007; Grumolato et al. 2010; Lin et al. 2010; Gao et al. 2011). In such cases, signaling mechanisms are likely to include planar cell polarity (PCP) components, such as Vangl2, Flamingo, Prickle, Diversin, Rho GTPases, and c-Jun amino-terminal kinases (JNKs), which do not directly affect β-catenin stability (Fig. 1) (Sokol 2000; Schwarz-Romond et al. 2002; Schambony and Wedlich 2007; Komiya and Habas 2008; Axelrod 2009; Itoh et al. 2009; Tada and Kai 2009; Sato et al. 2010; Gao et al. 2011). This simplistic dichotomy of the Wnt pathway does not preclude some Wnt ligands from using both β-catenin-dependent and -independent routes in a context-specific manner.Despite the existence of many pathway branches, only the β-catenin-dependent branch has been implicated in body-axis specification. Recent experiments in lower vertebrates have identified additional pathway components and targets and provided new insights into the underlying mechanisms.     

Gap Junctions

Gap junctions are aggregates of intercellular channels that permit direct cell–cell transfer of ions and small molecules. Initially described as low-resistance ion pathways joining excitable cells (nerve and muscle), gap junctions are found joining virtually all cells in solid tissues. Their long evolutionary history has permitted adaptation of gap-junctional intercellular communication to a variety of functions, with multiple regulatory mechanisms. Gap-junctional channels are composed of hexamers of medium-sized families of integral proteins: connexins in chordates and innexins in precordates. The functions of gap junctions have been explored by studying mutations in flies, worms, and humans, and targeted gene disruption in mice. These studies have revealed a wide diversity of function in tissue and organ biology.Gap junctions are clusters of intercellular channels that allow direct diffusion of ions and small molecules between adjacent cells. The intercellular channels are formed by head-to-head docking of hexameric assemblies (connexons) of tetraspan integral membrane proteins, the connexins (Cx) (Goodenough et al. 1996). These channels cluster into polymorphic maculae or plaques containing a few to thousands of units (Fig. 1). The close membrane apposition required to allow the docking between connexons sterically excludes most other membrane proteins, leaving a narrow ∼2 nm extracellular “gap” for which the junction is named (Fig. 2). Gap junctions in prechordates are composed of innexins (Phelan et al. 1998; Phelan 2005). In chordates, connexins arose by convergent evolution (Alexopoulos et al. 2004), to expand by gene duplication (Cruciani and Mikalsen 2007) into a 21-member gene family. Three innexin-related proteins, called pannexins, have persisted in vertebrates, although it is not clear if they form intercellular channels (Panchin et al. 2000; Bruzzone et al. 2003). 7Å-resolution electron crystallographic structures of intercellular channels composed of either a carboxy-terminal truncation of Cx43 (Unger et al. 1999; Yeager and Harris 2007) or an M34A mutant of Cx26 (Oshima et al. 2007) are available. The overall pore morphologies are similar with the exception of a “plug” in the Cx26 channel pore. The density of this plug is substantively decreased by deletion of amino acids 2–7, suggesting that the amino-terminus contributes to this structure (Oshima et al. 2008). A 3.5-Å X-ray crystallographic structure has visualized the amino-terminus of Cx26 folded into the mouth of the channel without forming a plug, thought to be an image of the open channel conformation (Maeda et al. 2009). The amino-terminus has been physiologically implicated in voltage-gating of the Cx26 and Cx32 channels (Purnick et al. 2000; Oh et al. 2004), lending support to a role for the amino-terminus as a gating structure. However, Cx43 also shows voltage-gating, and its lack of any structure resembling a plug remains unresolved. A comparison of a 1985 intercellular channel structure (Makowski 1985) with the 2009 3.5Å structure (Maeda et al. 2009) summarizes a quarter-century of X-ray progress (Fig. 3).Open in a separate windowFigure 1.A diagram showing the multiple levels of gap junction structure. Individual connexins assemble intracellularly into hexamers, called connexons, which then traffic to the cell surface. There, they dock with connexons in an adjacent cell, assembling an axial channel spanning two plasma membranes and a narrow extracellular “gap.”Open in a separate windowFigure 2.Electron microscopy of gap junctions joining adjacent hepatocytes in the mouse. The gap junction (GJ) is seen as an area of close plasma membrane apposition, clearly distinct from the tight junction (TJ) joining these cells. (Inset A) A high magnification view of the gap junction revealing the 2–3 nm “gap” (white arrows) separating the plasma membranes. (Inset B) A freeze-fracture replica of a gap junction showing the characteristic particles on the protoplasmic (P) fracture face and pits on the ectoplasmic (E) fracture face. The particles and pits show considerable disorder in their packing with an average 9-nm center-to-center spacing.Open in a separate windowFigure 3.A comparison of axial sections through gap-junction structures deduced from X-ray diffraction. The 1985 data (Makowski 1985) were acquired from gap junctions isolated biochemically from mouse liver containing mixtures of Cx32 and Cx26. The intercellular channel (CHANNEL) is blocked at the two cytoplasmic surfaces by electron density at the channel mouths along the sixfold symmetry axis. The 2009 data (Maeda et al. 2009), acquired from three-dimensional crystals of recombinant Cx26, resolve this density at the channel opening as the amino-termini of the connexin proteins, the 2009 model possibly showing an open channel structure.Most cells express multiple connexins. These may co-oligomerize into the same (homomeric) or mixed (heteromeric) connexons, although only certain combinations are permitted (Falk et al. 1997; Segretain and Falk 2004). A connexon may dock with an identical connexon to form a homotypic intercellular channel or with a connexon containing different connexins to form a heterotypic channel (Dedek et al. 2006). Although only some assembly combinations are permitted (White et al. 1994), the number of possible different intercellular channels formed by this 21-member family is astonishingly large. This diversity has significance because intercellular channels composed of different connexins have different physiological properties, including single-channel conductances and multiple conductance states (Takens-Kwak and Jongsma 1992), as well as permeabilities to experimental tracers (Elfgang et al. 1995) and to biologically relevant permeants (Gaunt and Subak-Sharpe 1979; Veenstra et al. 1995; Bevans et al. 1998; Gong and Nicholson 2001; Goldberg et al. 2002; Ayad et al. 2006; Harris 2007).Opening of extrajunctional connexons in the plasma membrane, described as “hemichannel” activity, can be experimentally induced in a variety of cell types. Because first observations of hemichannel activity were in an oocyte expression system (Paul et al. 1991) and dissociated retinal horizontal cells (DeVries and Schwartz 1992), the possible functions of hemichannels composed of connexins and pannexins has enjoyed vigorous investigation (Goodenough and Paul 2003; Bennett et al. 2003; Locovei et al. 2006; Evans et al. 2006; Srinivas et al. 2007; Schenk et al. 2008; Thompson and MacVicar 2008; Anselmi et al. 2008; Goodenough and Paul 2003). Hemichannels have been implicated in various forms of paracrine signaling, for example in providing a pathway for extracellular release of ATP (Cotrina et al. 1998; Kang et al. 2008), glutamate (Ye et al. 2003), NAD⁺ (Bruzzone et al. 2000), and prostaglandins (Jiang and Cherian 2003).     

Widely Conserved Signaling Pathways in the Establishment of Cell Polarity

How are the asymmetric distributions of proteins, lipids, and RNAs established and maintained in various cell types? Studies from diverse organisms show that Par proteins, GTPases, kinases, and phosphoinositides participate in conserved signaling pathways to establish and maintain cell polarity.The asymmetric distribution of proteins, lipids, and RNAs is necessary for cell fate determination, differentiation, and specialized cell functions that underlie morphogenesis (St Johnston 2005; Gonczy 2008; Knoblich 2008; Macara and Mili 2008; Martin-Belmonte and Mostov 2008). A fundamental question is how this asymmetric distribution is established and maintained in different types of cells and tissues. The formation of a specialized apical surface on an epithelial cell seems quite different from the specification of axons versus dendrites in a neuron, or the asymmetric division of a nematode zygote. Yet, remarkably, a conserved molecular toolbox is used throughout the metazoa to establish and maintain cell polarity in these and many other contexts. This toolbox consists of proteins that are components of signal transduction pathways (Goldstein and Macara 2007; Assemat et al. 2008; Yamanaka and Ohno 2008). However, our understanding of these pathways, and their intersection with other signaling networks, remains incomplete. Moreover, the regulation and cross talk between the polarity proteins and other signaling components varies from one context to another, which complicates the task of dissecting polarity protein function. Nonetheless, rapid progress is being made in our understanding of polarity signaling, which is outlined in this article, with an emphasis on the Par proteins, because these proteins play major roles integrating diverse signals that regulate cell polarity (Fig. 1) (see Munro and Bowerman 2009; Prehoda 2009; Nelson 2009).Open in a separate windowFigure 1.An overview of Par complex signaling, showing inputs (bottom) and outputs (top) with cellular functions that are targeted by these pathways (italics).     

Curvature-Driven Lipid Sorting in Biomembranes

It has often been suggested that the high curvature of transport intermediates in cells may be a sufficient means to segregate different lipid populations based on the relative energy costs of forming bent membranes. In this review, we present in vitro experiments that highlight the essential physics of lipid sorting at thermal equilibrium: It is driven by a trade-off between bending energy, mixing entropy, and interactions between species. We collect evidence that lipid sorting depends strongly on lipid–lipid and protein–lipid interactions, and hence on the underlying composition of the membrane and on the presence of bound proteins.Lipid and protein sorting are key processes that allow eukaryotic cells to maintain membrane homeostasis among organelles during intracellular transport (van Meer et al. 2008). It is known that lipids are not evenly distributed throughout the organelles of the cell. As an example, the percentage of sphingolipids increases along the secretory pathway, going from the endoplasmic reticulum (almost 0%) to the plasma membrane (about 30%) (van Meer and Lisman 2002). In the face of continuous lipid trafficking of vesicular intermediates back and forth along this pathway, the cell must be endowed with ways to preferentially sort these lipids to establish and maintain compositional specificity of each organelle.There is now compelling evidence that sorting occurs during the formation of highly curved transport intermediates (vesicles and tubules). For instance, it has been observed that tubules emanating from endosomes (Mukherjee and Maxfield 2000; Gruenberg 2003; Maxfield and McGraw 2004), as well as vesicles budding from the Golgi apparatus (Brugger et al. 2000; van Meer and Sprong 2004; Klemm et al. 2009), have a lipid composition significantly different from the compartment from which they originate. Mukherjee et al. found that an unsaturated lipid dye was enriched in live cell endosomal tubes, in contrast with a longer chain saturated lipid dye that was excluded (Mukherjee et al. 1999). Furthermore, quantitative analysis of the lipid composition of the vesicles formed from the trans-Golgi network showed these vesicles to be enriched in sphingolipids (Klemm et al. 2009), whereas COPI-coated vesicles trafficking from the cis-Golgi network to the endoplasmic reticulum were found to be depleted in sphingolipids (Brugger et al. 2000). These observations, that sphingolipids are depleted from retrograde carriers and are enriched in anterograde ones, are perfectly consistent with the maintenance in the cell of an increasing gradient of this type of lipid along the secretory pathway.How composition differences between cell organelles are maintained despite intense intracellular lipid exchanges is still poorly understood. Clearly, there is a need for protein and lipid sorting. The case of protein is relatively well explained. For instance, coat proteins are known to specifically bind to transmembrane proteins containing certain peptide sequences, which in turn bind to specific soluble cargoes (Cosson and Letourneur 1994; Stamnes et al. 1995; Barlowe 2000; Ehrlich et al. 2004). The specificity of this type of sorting is largely absent for lipids. Lipids, then, must use other physico-chemical means for their sorting:One way to sort is by the lateral segregation at the nanoscale into domains known as rafts. This very popular hypothesis asserts that proteins that recognize and bind to a given type of raft may form vesicles with the lipid composition of that raft (Simons and Van Meer 1988; Simons and Ikonen 1997; van Meer and Sprong 2004; for a recent review, see Lingwood and Simons 2010). In this mechanism, the lipid composition of the raft is fixed before membrane budding (Fig. 1A).Open in a separate windowFigure 1.Possible physico-chemical mechanisms for lipid sorting. (A) Lipid sorting based on the raft hypothesis. The composition and destination of newly formed vesicles are determined by the type of raft from which the vesicle originates. (B) Different lipid species and their shape. Lipids are characterized by a shape based on the size of the head group, the number of chains, and the degree of chain saturation. From left to right the shapes are inverted-conical for Lyso-Phosphatidic acid (LPA), cylindrical for unsaturated 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), and conical for 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE). Bottom: saturated cylindrical Sphingomyelin (SM) and cholesterol. (C) Two physical states relevant for cellular membranes. Top: drawing of a liquid-disordered (L_d) membrane, which is rich in unsaturated lipids (with little chain order); bottom: drawing of a liquid-ordered (L_o) membrane, which is rich in saturated lipids (with high degree of chain ordering) and cholesterol. (D) Lipid sorting based on spontaneous curvature. The membrane consists of cylindrical, inverted-conical. and conical lipids. The outer leaflet of the budding region is enriched in cylindrical and inverted conical lipids, whereas the inner leaflet is enriched in conical ones. (E) Lipid sorting based on composition dependence of the bending stiffness. The highly curved budding region is enriched in unsaturated lipids, so that the bending stiffness of this region is lower than the surrounding membrane.A second way to sort is by the coupling between lipid composition and the curvature energy of the membrane. Following this mechanism, an inhomogeneously curved membrane could produce an inhomogeneous lipid distribution to minimize the energy cost of bending the membrane (Mukherjee and Maxfield 2000; van Meer and Lisman 2002; Maxfield and McGraw 2004; van Meer and Sprong 2004). Cells can generate inhomogeneous membrane curvature in several ways. For example, COPI or clathrin coats can self-assemble on a membrane surface to form a membrane bud with significantly higher curvature than its surroundings (Antonny 2006). Also, molecular motors walking on microtubules can attach to an intracellular membrane surface and extract a nanoscale-radius tube, which then becomes a transport intermediate (Leduc et al. 2004). The energy cost of bending the membrane into these highly curved shapes depends on the deformability of the bilayer containing different lipids and on the molecular shapes of these lipids. Based on these two membrane properties, lipid sorting between regions of different curvature can reduce the cost of membrane bending. Mathematically, a simple continuum model can be used to describe the coupling between curvature and composition (see Box 1).The energy needed to bend a patch of membrane of area A is E = (1/2)κ(C ? C₀)?²A, where κ is the bending stiffness, C is the mean curvature, and C₀ is the spontaneous (relaxed) curvature (see Box 1 for a short introduction to membrane physics). The spontaneous curvature is a local quantity of the membrane, depending on asymmetry between the two leaflets making up the bilayer. Both C₀ and κ depend on the composition of the curved patch. If this patch is more curved than its surroundings, the bending energy can be lowered by adjusting the composition to reduce κ and/or to have C₀ match C. This idea has been explored theoretically, with special emphasis on the interplay between lipid inhomogeneity and the mechanical properties of vesicles (Markin 1981; Seifert 1993; Derganc 2007; Jiang and Powers 2008; Sorre et al. 2009).The connection between the membrane bending energy and the lipid composition has its origin at the microscopic scale. First, to understand why the notion of spontaneous curvature is related to lipid sorting, consider, for example, a spherical vesicle containing two types of lipids of different shape, one whose head group is wider than its tail, known as inverted-conical (such as lyso-phosphatidic acid (LPA) (Fig. 1B), and another whose head group is roughly as wide as its tail, having a cylindrical shape (such as DOPC) (Fig. 1B). If the outer leaflet is richer in the inverted-cone lipids and the inner leaflet is richer in the cylindrical lipids, then the vesicle will have a positive spontaneous curvature. Conversely, one would expect that the two leaflets of a spherical vesicle would readjust their composition so that C₀ can match the curvature of the vesicle (Leibler 1986; Gruner 1989; Kozlov and Helfrich 1992; Seifert 1993). In this case, a transverse asymmetry (i.e., across the leaflets) occurs in the lipid composition, with the outer leaflet being relatively enriched in inverted-conical lipids (Fig. 1D).In addition, sorting of lipids within a vesicle containing compartments of varying degrees of curvature can occur because of the composition dependence of the bending stiffness (Szleifer et al. 1988; Rawicz et al. 2000; Marsh 2006; Pan et al. 2008; Reeves et al. 2008; Fig. 1E). As the bending stiffness is an elastic property of a patch of membrane, this effect cannot be understood based on individual lipid geometry. Generally speaking, bending a thicker membrane causes greater differential strains between the head and tail groups of a single leaflet than it does in a thinner leaflet, and therefore a thicker membrane has a higher bending stiffness. Modeling and experiments confirm that the bending stiffness depends in particular on the square of the bilayer thickness (Evans 1974). Ternary mixtures containing cholesterol, saturated and unsaturated lipids are known to display coexistence of a saturated lipid-rich liquid-ordered (L_o) phase and an unsaturated lipid-rich liquid-disordered (L_d) phase (Dietrich et al. 2001; Veatch and Keller 2002; Baumgart et al. 2003; Goni et al. 2008) (Fig. 1C). From AFM measurements (Yuan et al. 2002; Lawrence et al. 2003; Connell and Smith 2006), it has been shown that L_o membranes are thicker than L_d membranes, and therefore stiffer, as has been measured by tube pulling experiments (Cuvelier et al. 2005; Roux et al. 2005) and pipette aspiration (Rawicz et al. 2008). Typical values of κ of ternary vesicles in the L_o phase are 50–60 kT, whereas in the L_d phase, typically 20–30 kT. Here, k is Boltzmann''s constant and T is room temperature, such that kT = 4.2 ×10⁻²¹. It was suggested that when a uniformly mixed membrane is subject to inhomogeneous curvature, the bending energy can be reduced by enriching the highly curved regions in the lipids that predominate in the L_d phase of phase separated membranes (Mukherjee and Maxfield 2000; van Meer and Lisman 2002; Maxfield and McGraw 2004; van Meer and Sprong 2004) (Fig. 1E).Although the simple picture of curvature-induced sorting of lipids based on molecular geometry (steric effects) is visually quite appealing, experiments and theory have shown that this is not an effective driving mechanism against the homogenizing effect of entropy (Safran et al. 1990, 1991; Derganc 2007; Mukhopadhyay et al. 2008; Sorre et al. 2009). This result is quite surprising given the widespread view in the past that curvature was a likely candidate to sort lipids based on their shape (Lodish et al. 2003) (Fig. 1D). A simple order of magnitude calculation highlights the importance of entropy. The gain in bending energy in transferring a sphingolipid from a tube of radius R = 20 nm to a flat reservoir, given a liberal estimate of the difference in bending stiffness between the tube and reservoir of 40 kT, is ΔE = (1/2)(Δκ/R²)a = (1/40)kT, for an area per lipid equal to a = 0.5 nm². We see that this energy is much smaller than kT, the scale of thermal energy, meaning that entropy will win out over any gain made in making the transfer. The small value of ΔE is because of the small size of the lipid dimension compared with the radius of the tube. The above calculation is valid, however, only if we consider the membrane to be an ideal solution of lipids. Membranes consisting of a mixture of lipids are characterized not only by elastic bending but also by the short-range interactions among the different lipid species (see Box 1). Below the miscibility temperature, these interactions give rise to large-scale domains, as seen, for example, by the coexistence of L_o and L_d phases (Bagatolli and Gratton 2000; Dietrich et al. 2001; Veatch and Keller 2002; Baumgart et al. 2003; de Almeida et al. 2003; McConnell and Vrljic 2003).Similar to the bulk behavior of a binary solution, the thermodynamic behavior of mixture membranes results from a competition between entropy and enthalpy (interactions). At high temperatures, thermal fluctuations tend to smooth out compositional heterogeneities, whereas at lower temperatures, depending on the overall composition of the membrane, favorable interactions between like species give rise to domain formation and phase coexistence (see Box 1). Conversely, phase separation can be induced at fixed temperature by adjusting the fractions of cholesterol and saturated and unsaturated lipids (Bagatolli and Gratton 2000; Dietrich et al. 2001; Veatch and Keller 2002; Baumgart et al. 2003; de Almeida et al. 2003; McConnell and Vrljic 2003). This is the more biologically relevant case. As domains have been observed in model membranes mimicking the cell membrane, it is therefore also expected that entropy and interactions play important roles in curvature-induced lipid sorting.Motivated by an increasing amount of experimental evidence that lipid sorting occurs in cells, there has been a renewed interest recently to develop model membrane systems that can be used to quantify the effectiveness of curvature-based sorting and to compare with theoretical predictions. In this review, we focus on recent works that have been possible because of the convergence of biological interest in lipid sorting and technological advances that have allowed its measurement in vitro. We examine below a number of works that have quantitatively measured the importance that cooperative behavior has on curvature-induced lipid redistribution. Some have considered lipid sorting caused by differences in spontaneous curvature between lipids, whereas others have considered sorting caused by the dependence of bending stiffness on sorting. The overriding conclusion of these works was that curvature is generally ineffective in sorting lipids because of prohibitive entropy costs, yet will be significantly amplified if the membrane composition is tuned to maximize the importance of lipid–lipid interactions.     

Cell Adhesion, the Backbone of the Synapse: ��Vertebrate�� and ��Invertebrate�� Perspectives

Synapses are asymmetric intercellular junctions that mediate neuronal communication. The number, type, and connectivity patterns of synapses determine the formation, maintenance, and function of neural circuitries. The complexity and specificity of synaptogenesis relies upon modulation of adhesive properties, which regulate contact initiation, synapse formation, maturation, and functional plasticity. Disruption of adhesion may result in structural and functional imbalance that may lead to neurodevelopmental diseases, such as autism, or neurodegeneration, such as Alzheimer''s disease. Therefore, understanding the roles of different adhesion protein families in synapse formation is crucial for unraveling the biology of neuronal circuit formation, as well as the pathogenesis of some brain disorders. The present review summarizes some of the knowledge that has been acquired in vertebrate and invertebrate genetic model organisms.Synapses are asymmetric, intercellular junctions that are the basic structural units of neuronal transmission. The correct development of synaptic specializations and the establishment of appropriate connectivity patterns are crucial for the assembly of functional neuronal circuits. Improper synapse formation and function may cause neurodevelopmental disorders, such as mental retardation (MsR) and autism spectrum disorders (ASD) (McAllister 2007; Sudhof 2008), and likely play a role in neurodegenerative disorders, such as Alzheimer''s disease (AD) (Haass and Selkoe 2007).At chemical synapses (reviewed in Sudhof 2004; Zhai and Bellen 2004; Waites et al. 2005; McAllister 2007; Jin and Garner 2008), the presynaptic compartment contains synaptic vesicles (SV), organized in functionally distinct subcellular pools. A subset of SVs docks to the presynaptic membrane around protein-dense release sites, named active zones (AZ). Upon the arrival of an action potential at the terminal, the docked and “primed” SVs fuse with the plasma membrane and release neurotransmitter molecules into the synaptic cleft. Depending on the type of synapse (i.e., excitatory vs. inhibitory synapses), neurotransmitters ultimately activate an appropriate set of postsynaptic receptors that are accurately apposed to the AZ.Synapse formation occurs in several steps (Fig. 1) (reviewed in Eaton and Davis 2003; Goda and Davis 2003; Waites et al. 2005; Garner et al. 2006; Gerrow and El-Husseini 2006; McAllister 2007). Spatiotemporal signals guide axons through heterogeneous cellular environments to contact appropriate postsynaptic targets. At their destination, axonal growth cones initiate synaptogenesis through adhesive interactions with target cells. In the mammalian central nervous system (CNS), immature postsynaptic dendritic spines initially protrude as thin, actin-rich filopodia on the surface of dendrites. Similarly, at the Drosophila neuromuscular junction (NMJ), myopodia develop from the muscles (Ritzenthaler et al. 2000). The stabilization of intercellular contacts and their elaboration into mature, functional synapses involves cytoskeletal arrangements and recruitment of pre- and postsynaptic components to contact sites in spines and boutons. Conversely, retraction of contacts results in synaptic elimination. Both stabilization and retraction sculpt a functional neuronal circuitry.Open in a separate windowFigure 1.(A–C) Different stages of synapse formation. (A) Target selection, (B) Synapse assembly, (C) Synapse maturation and stabilization. (D–F) The role of cell adhesion molecules in synapse formation is exemplified by the paradigm of N-cadherin and catenins in regulation of the morphology and strength of dendritic spine heads. (D) At an early stage the dendritic spines are elongated from motile structures “seeking” their synaptic partners. (E) The contacts between the presynaptic and postsynaptic compartments are stabilized by recruitment of additional cell adhesion molecules. Adhesional interactions activate downstream pathways that remodel the cytoskeleton and organize pre- and postsynaptic apparatuses. (F) Cell adhesion complexes, stabilized by increased synaptic activity, promote the expansion of the dendritic spine head and the maturation/ stabilization of the synapse. Retraction and expansion is dependent on synaptic plasticity.In addition to the plastic nature of synapse formation, the vast heterogeneity of synapses (in terms of target selection, morphology, and type of neurotransmitter released) greatly enhances the complexity of synaptogenesis (reviewed in Craig and Boudin 2001; Craig et al. 2006; Gerrow and El-Husseini 2006). The complexity and specificity of synaptogenesis relies upon the modulation of adhesion between the pre- and postsynaptic components (reviewed in Craig et al. 2006; Gerrow and El-Husseini 2006; Piechotta et al. 2006; Dalva et al. 2007; Shapiro et al. 2007; Yamada and Nelson 2007; Gottmann 2008). Cell adhesive interactions enable cell–cell recognition via extracellular domains and also mediate intracellular signaling cascades that affect synapse morphology and organize scaffolding complexes. Thus, cell adhesion molecules (CAMs) coordinate multiple synaptogenic steps.However, in vitro and in vivo studies of vertebrate CAMs are often at odds with each other. Indeed, there are no examples of mutants for synaptic CAMs that exhibit prominent defects in synapse formation. This apparent “resilience” of synapses is probably caused by functional redundancy or compensatory effects among different CAMs (Piechotta et al. 2006). Hence, studies using simpler organisms less riddled by redundancy, such as Caenorhabditis elegans and Drosophila, have aided in our understanding of the role that these molecules play in organizing synapses.In this survey, we discuss the roles of the best characterized CAM families of proteins involved in synaptogenesis. Our focus is to highlight the complex principles that govern the molecular basis of synapse formation and function from a comparative perspective. We will present results from cell culture studies as well as in vivo analyses in vertebrate systems and refer to invertebrate studies, mainly performed in Drosophila and C. elegans, when they have provided important insights into the role of particular CAM protein families. However, we do not discuss secreted factors, for which we refer the reader to numerous excellent reviews (as for example Washbourne et al. 2004; Salinas 2005; Piechotta et al. 2006; Shapiro et al. 2006; Dalva 2007; Yamada and Nelson 2007; Biederer and Stagi 2008; Salinas and Zou 2008).     

Central B-cell tolerance: where selection begins

The development of an adaptive immune system based on the random generation of antigen receptors requires a stringent selection process that sifts through receptor specificities to remove those reacting with self-antigens. In the B-cell lineage, this selection process is first applied to IgM⁺ immature B cells. By using increasingly sophisticated mouse models, investigators have identified the central tolerance mechanisms that negatively select autoreactive immature B cells and prevent inclusion of their antigen receptors into the peripheral B-cell pool. Additional studies have uncovered mechanisms that promote the differentiation of nonautoreactive immature B cells and their positive selection into the peripheral B-cell population. These mechanisms of central selection are fundamental to the generation of a naïve B-cell repertoire that is largely devoid of self-reactivity while capable of reacting with any foreign insult.B-cell generation in the bone marrow of adult mammals occurs through a tightly controlled developmental process (Fig. 1). Productive rearrangement of immunoglobulin heavy (IgH) and light (IgL) chain gene segments in B lymphocyte precursor cells, in addition to the expression of Ig-α (CD79a) and Ig-β (CD79b), result in the generation and expression on the cell surface of a mature B-cell antigen receptor (BCR). Whereas the combination of Ig H and L chains determines the antigenic specificity of the newly formed BCR, their association with Ig-α and Ig-β allows transduction of a signal inside the cell that directs cell fate. Developing B cells first express a mature BCR on the cell surface in the form of IgM and as such are classified as immature B cells (Fig. 1) (Hardy et al. 1991; Pelanda et al. 1996). It is at the immature B-cell stage that the BCR is tested for the first time for reactivity against autoantigens. This test determines whether the immature B cell and the antibody it expresses on the surface will be selected into the peripheral B-cell repertoire. Central B-cell tolerance, in fact, refers to the process that negatively selects newly generated immature B cells that react with a self-antigen in the bone marrow environment. This is considered the first checkpoint of B-cell tolerance, and the results of this checkpoint are fundamental to the generation of a naïve repertoire that contains foreign reactive antibodies and is largely devoid of self-reactive specificities.Open in a separate windowFigure 1.Schematic representation of B-cell development and Ig loci in mice. Large pro-B cells initiate Ig gene rearrangement at the IgH locus. Expression of a H chain following a productive V_HD_HJ_H recombination event promotes the differentiation of large pre-B cells in which the expression of pre-BCR (H chain pairing with surrogate light chains) results in the clonal expansion of H chain-positive pre-B cells and the development of small pre-B cells. Expression of conventional L chains following productive rearrangements at the IgL chain loci in small pre-B cells promotes the development of a diverse population of IgM⁺ immature B cells, which then differentiate into IgM⁺IgD⁺ transitional B cells. The scheme of mouse Ig H, κ, and λ loci (not to scale) indicate the presence of V (white rectangles), D (black vertical lines), J (brown vertical lines; a dashed line indicates a nonfunctional element), and C (black rectangles; a gray rectangle indicates a nonfunctional element) gene segments. The scheme does not represent the number of V_H, D_H, and Vκ gene segments in the actual Ig loci.On passing this central checkpoint, immature B cells continue to differentiate into transitional and mature B cells before and after they travel to the spleen (Loder et al. 1999; Allman et al. 2001; Su and Rawlings 2002; Tarlinton et al. 2003). Analysis of the bone marrow early immature B-cell repertoire indicates that a staggering 50%–75% of these cells express BCRs that are specific for self-antigens, both in mice and humans (Grandien et al. 1994; Wardemann et al. 2003). Similar studies performed on cell populations at the other end of this central checkpoint, namely, transitional and naïve mature B cells in spleen and blood, show a much lower frequency (20%–40%) of cells expressing autoreactive antibodies (Grandien et al. 1994; Wardemann et al. 2003), demonstrating the stringency and limitation of this initial selection step. Moreover, individuals affected by autoimmune disease such as lupus erythematosus or rheumatoid arthritis bear many more autoreactive cells in their new emigrant and naïve B-cell populations (Samuels et al. 2005; Yurasov et al. 2005), indicating a defect in central (and/or peripheral) B-cell selection. Thus, it seems important that the development of autoreactive immature B cells be constrained to prevent the potential occurrence of autoimmunity. However, there are also reasons to believe that the high frequency of autoreactive specificities generated during primary Ig gene rearrangements may be necessary for the generation of the peripheral B-cell repertoire (Pelanda et al. 1997; Kohler et al. 2008). Indeed, a fraction of autoreactive immature B cells, those manifesting a low level of self-reactivity, do bypass the central checkpoint of tolerance and differentiate into mature B cells (Hayakawa et al. 2003; Wardemann et al. 2003; Wen et al. 2005). The inclusion of these weakly self-reactive B cells in the peripheral B-cell repertoire may allow recognition of a broader spectrum of foreign molecules, potentially decreasing the negative impact of infections, especially at early stages (Mouquet et al. 2010).What are the rules that govern the selection of immature B cells? Most studies of central tolerance have been conducted by following the selection of B cells expressing BCRs displaying well-defined reactivity for natural or synthetic self-antigens. This has been accomplished through the use of Ig transgenic mice in which developing B cells have been altered to carry prerearranged Ig H and L chain genes encoding antibodies of defined antigen specificity and reactivity. Here we review some of these studies, what we have learned from them, and open questions that still await answers.     

Poles Apart: Prokaryotic Polar Organelles and Their Spatial Regulation

While polar organelles hold the key to understanding the fundamentals of cell polarity and cell biological principles in general, they have served in the past merely for taxonomical purposes. Here, we highlight recent efforts in unraveling the molecular basis of polar organelle positioning in bacterial cells. Specifically, we detail the role of members of the Ras-like GTPase superfamily and coiled-coil-rich scaffolding proteins in modulating bacterial cell polarity and in recruiting effector proteins to polar sites. Such roles are well established for eukaryotic cells, but not for bacterial cells that are generally considered diffusion-limited. Studies on spatial regulation of protein positioning in bacterial cells, though still in their infancy, will undoubtedly experience a surge of interest, as comprehensive localization screens have yielded an extensive list of (polarly) localized proteins, potentially reflecting subcellular sites of functional specialization predicted for organelles.Since the first electron micrographs that revealed flagella at the cell poles of bacteria, we have known that bacterial cells are polarized and that they are able to decode the underlying positional information to confine the assembly of an extracellular organelle to a polar cellular site (Fig. 1). Foraging into this unknown territory has been challenging, but recent efforts that exploit the power of bacterial genetics along with modern imaging methods to visualize proteins in the minute bacterial cells has yielded several enticing entry points to dissect polarity-based mechanisms and explore potentially contributing subdiffusive characteristics (Golding and Cox 2006).Open in a separate windowFigure 1.Transmission electron micrograph (taken by Jeff Skerker) of a Caulobacter crescentus swarmer cell showing the polar pili (empty arrowheads), the polar flagellum with the flagellar filament (filled arrowheads), and the hook (white arrow) (see Fig. 2A).While polar organelles are a visual manifestation of polarity, it is important to point out that polarity can also be inherent to cells, at least in molecular terms, even in the absence of discernible polar structures. In other words, molecular anatomy can reveal that a bacterial cell, such as an Escherichia coli cell, features specialized protein complexes at or near the poles, despite a perfectly symmetrical morphology (Maddock and Shapiro 1993; Lindner et al. 2008). Such systemic polarization in bacteria, likely stemming from the distinctive division history of each pole, has the potential to be widespread and to be exploited for positioning of polar organelles and protein complexes. As excellent reviews have been published detailing the interplay between cell polarity and protein localization (Dworkin 2009; Shapiro et al. 2009; Kaiser et al. 2010; Rudner and Losick 2010), here we focus on recent progress in understanding the function and localization of spatial regulators of polar organelles. Considering that the ever-growing list of polar protein complexes emerging from systematic and comprehensive localization studies (Kitagawa et al. 2005; Russell and Keiler 2008; Werner et al. 2009; Hughes et al. 2010) is suggestive of multiple polarly confined (organelle-like) functions, understanding their spatial regulation is also of critical relevance in the realm of medical bacteriology, as many virulence determinants also underlie polarity (Goldberg et al. 1993; Scott et al. 2001; Judd et al. 2005; Jain et al. 2006; Jaumouille et al. 2008; Carlsson et al. 2009). Below, we highlight a few prominent examples of overtly polar organelles and the proteins known to date that regulate their polar positioning.     

Morphogen Gradient Formation

How morphogen gradients are formed in target tissues is a key question for understanding the mechanisms of morphological patterning. Here, we review different mechanisms of morphogen gradient formation from theoretical and experimental points of view. First, a simple, comprehensive overview of the underlying biophysical principles of several mechanisms of gradient formation is provided. We then discuss the advantages and limitations of different experimental approaches to gradient formation analysis.How a multicellular organism develops from a single fertilized cell has fascinated people throughout history. By looking at chick embryos of different developmental stages, Aristotle first noted that development is characterized by growing complexity and organization of the embryo (Balme 2002). During the 19th century, two events were recognized as key in development: cell proliferation and differentiation. Driesch first noted that to form organisms with correct morphological pattern and size, these processes must be controlled at the level of the whole organism. When he separated two sea urchin blastomeres, they produced two half-sized blastula, showing that cells are potentially independent, but function together to form a whole organism (Driesch 1891, 1908). Morgan noted the polarity of organisms and that regeneration in worms occurs with different rates at different positions. This led him to postulate that regeneration phenomena are influenced by gradients of “formative substances” (Morgan 1901).The idea that organisms are patterned by gradients of form-providing substances was explored by Boveri and Hörstadius to explain the patterning of the sea urchin embryo (Boveri 1901; Hörstadius 1935). The discovery of the Spemann organizer, i.e., a group of dorsal cells that when grafted onto the opposite ventral pole of a host gastrula induce a secondary body axis (Spemann and Mangold 1924), suggested that morphogenesis results from the action of signals that are released from localized groups of cells (“organizing centers”) to induce the differentiation of the cells around them (De Robertis 2006). Child proposed that these patterning “signals” represent metabolic gradients (Child 1941), but the mechanisms of their formation, regulation, and translation into pattern remained elusive.In 1952, Turing showed that chemical substances, which he called morphogens (to convey the idea of “form producers”), could self-organize into spatial patterns, starting from homogenous distributions (Turing 1952). Turing’s reaction–diffusion model shows that two or more morphogens with slightly different diffusion properties that react by auto- and cross-catalyzing or inhibiting their production, can generate spatial patterns of morphogen concentration. The reaction–diffusion formalism was used to model regeneration in hydra (Turing 1952), pigmentation of fish (Kondo and Asai 1995; Kondo 2002), and snails (Meinhardt 2003).At the same time that Turing showed that pattern can self-organize from the production, diffusion, and reaction of morphogens in all cells, the idea that morphogens are released from localized sources (“organizers” à la Spemann) and form concentration gradients was still explored. This idea was formalized by Wolpert with the French flag model for generation of positional information (Wolpert 1969). According to this model, morphogen is secreted from a group of source cells and forms a gradient of concentration in the target tissue. Different target genes are expressed above distinct concentration thresholds, i.e., at different distances to the source, hence generating a spatial pattern of gene expression (Fig. 1C).Open in a separate windowFigure 1.Tissue geometry and simplifications. (A) Gradients in epithelia (left) and mesenchymal tissues (right). Because of symmetry considerations, one row of cells (red outline) is representative for the whole gradient. (B) Magnified view of the red row of cells shown in A. Cells with differently colored nuclei (brown, orange, and blue) express different target genes. (C) A continuum model in which individual cells are ignored and the concentration is a function of the positions x. The morphogen activates different target genes above different concentration thresholds (brown and orange).Experiments in the 1970s and later confirmed that tissues are patterned by morphogen gradients. Sander showed that a morphogen released from the posterior cytoplasm specifies anterioposterior position in the insect egg (Sander 1976). Chick wing bud development was explained by a morphogen gradient emanating from the zone of polarizing activity to specify digit positions (Saunders 1972; Tickle, et al. 1975; Tickle 1999). The most definitive example of a morphogen was provided with the identification of Bicoid function in the Drosophila embryo (Nüsslein-Volhard and Wieschaus 1980; Frohnhöfer and Nüsslein-Volhard 1986; Nüsslein-Volhard et al. 1987) and the visualization of its gradient by antibody staining (Driever and Nüsslein-Volhard 1988b, 1988a; reviewed in Ephrussi and St Johnston 2004). Since then, many examples of morphogen gradients acting in different organs and species have been found.In an attempt to understand pattern formation in more depth, quantitative models of gradient formation have been developed. An early model by Crick shows that freely diffusing morphogen produced in a source cell and destroyed in a “sink” cell at a distance would produce a linear gradient in developmentally relevant timescales (Crick 1970). Today, it is known that a localized “sink” is not necessary for gradient formation: Gradients can form if all cells act as sinks and degrade morphogen, or even if morphogen is not degraded at all. Here, we review different mechanisms of gradient formation, the properties of these gradients, and the implications for patterning. We discuss the theory behind these mechanisms and the supporting experimental data.