Origin and Evolution of the Self-Organizing Cytoskeleton in the Network of Eukaryotic Organelles

The eukaryotic cytoskeleton evolved from prokaryotic cytomotive filaments. Prokaryotic filament systems show bewildering structural and dynamic complexity and, in many aspects, prefigure the self-organizing properties of the eukaryotic cytoskeleton. Here, the dynamic properties of the prokaryotic and eukaryotic cytoskeleton are compared, and how these relate to function and evolution of organellar networks is discussed. The evolution of new aspects of filament dynamics in eukaryotes, including severing and branching, and the advent of molecular motors converted the eukaryotic cytoskeleton into a self-organizing “active gel,” the dynamics of which can only be described with computational models. Advances in modeling and comparative genomics hold promise of a better understanding of the evolution of the self-organizing cytoskeleton in early eukaryotes, and its role in the evolution of novel eukaryotic functions, such as amoeboid motility, mitosis, and ciliary swimming.The eukaryotic cytoskeleton organizes space on the cellular scale and this organization influences almost every process in the cell. Organization depends on the mechanochemical properties of the cytoskeleton that dynamically maintain cell shape, position organelles, and macromolecules by trafficking, and drive locomotion via actin-rich cellular protrusions, ciliary beating, or ciliary gliding. The eukaryotic cytoskeleton is best described as an “active gel,” a cross-linked network of polymers (gel) in which many of the links are active motors that can move the polymers relative to each other (Karsenti et al. 2006). Because prokaryotes have only cytoskeletal polymers but lack motor proteins, this “active gel” property clearly sets the eukaryotic cytoskeleton apart from prokaryotic filament systems.Prokaryotes contain elaborate systems of several cytomotive filaments (Löwe and Amos 2009) that share many structural and dynamic features with eukaryotic actin filaments and microtubules (Löwe and Amos 1998; van den Ent et al. 2001). Prokaryotic cytoskeletal filaments may trace back to the first cells and may have originated as higher-order assemblies of enzymes (Noree et al. 2010; Barry and Gitai 2011). These cytomotive filaments are required for the segregation of low copy number plasmids, cell rigidity and cell-wall synthesis, cell division, and occasionally the organization of membranous organelles (Komeili et al. 2006; Thanbichler and Shapiro 2008; Löwe and Amos 2009). These functions are performed by dynamic filament-forming systems that harness the energy from nucleotide hydrolysis to generate forces either via bending or polymerization (Löwe and Amos 2009; Pilhofer and Jensen 2013). Although the identification of actin and tubulin homologs in prokaryotes is a major breakthrough, we are far from understanding the origin of the structural and dynamic complexity of the eukaryotic cytoskeleton.Advances in genome sequencing and comparative genomics now allow a detailed reconstruction of the cytoskeletal components present in the last common ancestor of eukaryotes. These studies all point to an ancestrally complex cytoskeleton, with several families of motors (Wickstead and Gull 2007; Wickstead et al. 2010) and filament-associated proteins and other regulators in place (Jékely 2003; Richards and Cavalier-Smith 2005; Rivero and Cvrcková 2007; Chalkia et al. 2008; Eme et al. 2009; Fritz-Laylin et al. 2010; Eckert et al. 2011; Hammesfahr and Kollmar 2012). Genomic reconstructions and comparative cell biology of single-celled eukaryotes (Raikov 1994; Cavalier-Smith 2013) allow us to infer the cellular features of the ancestral eukaryote. These analyses indicate that amoeboid motility (Fritz-Laylin et al. 2010; although, see Cavalier-Smith 2013), cilia (Cavalier-Smith 2002; Mitchell 2004; Jékely and Arendt 2006; Satir et al. 2008), centrioles (Carvalho-Santos et al. 2010), phagocytosis (Cavalier-Smith 2002; Jékely 2007; Yutin et al. 2009), a midbody during cell division (Eme et al. 2009), mitosis (Raikov 1994), and meiosis (Ramesh et al. 2005) were all ancestral eukaryotic cellular features. The availability of functional information from organisms other than animals and yeasts (e.g., Chlamydomonas, Tetrahymena, Trypanosoma) also allow more reliable inferences about the ancestral functions of cytoskeletal components (i.e., not only their ancestral presence or absence) and their regulation (Demonchy et al. 2009; Lechtreck et al. 2009; Suryavanshi et al. 2010).The ancestral complexity of the cytoskeleton in eukaryotes leaves a huge gap between prokaryotes and the earliest eukaryote we can reconstruct (provided that our rooting of the tree is correct) (Cavalier-Smith 2013). Nevertheless, we can attempt to infer the series of events that happened along the stem lineage, leading to the last common ancestor of eukaryotes. Meaningful answers will require the use of a combination of gene family history reconstructions (Wickstead and Gull 2007; Wickstead et al. 2010), transition analyses (Cavalier-Smith 2002), and computer simulations relevant to cell evolution (Jékely 2008).     

Molecular Mechanisms of Fibroblast Growth Factor Signaling in Physiology and Pathology

Fibroblast growth factors (FGFs) signal in a paracrine or endocrine fashion to mediate a myriad of biological activities, ranging from issuing developmental cues, maintaining tissue homeostasis, and regulating metabolic processes. FGFs carry out their diverse functions by binding and dimerizing FGF receptors (FGFRs) in a heparan sulfate (HS) cofactor- or Klotho coreceptor-assisted manner. The accumulated wealth of structural and biophysical data in the past decade has transformed our understanding of the mechanism of FGF signaling in human health and development, and has provided novel concepts in receptor tyrosine kinase (RTK) signaling. Among these contributions are the elucidation of HS-assisted receptor dimerization, delineation of the molecular determinants of ligand–receptor specificity, tyrosine kinase regulation, receptor cis-autoinhibition, and tyrosine trans-autophosphorylation. These structural studies have also revealed how disease-associated mutations highjack the physiological mechanisms of FGFR regulation to contribute to human diseases. In this paper, we will discuss the structurally and biophysically derived mechanisms of FGF signaling, and how the insights gained may guide the development of therapies for treatment of a diverse array of human diseases.Fibroblast growth factor (FGF) signaling fulfills essential roles in metazoan development and metabolism. A wealth of literature has documented the requirement for FGF signaling in multiple processes during embryogenesis, including implantation (Feldman et al. 1995), gastrulation (Sun et al. 1999), somitogenesis (Dubrulle and Pourquie 2004; Wahl et al. 2007; Lee et al. 2009; Naiche et al. 2011; Niwa et al. 2011), body plan formation (Martin 1998; Rodriguez Esteban et al. 1999; Tanaka et al. 2005; Mariani et al. 2008), morphogenesis (Metzger et al. 2008; Makarenkova et al. 2009), and organogenesis (Goldfarb 1996; Kato and Sekine 1999; Sekine et al. 1999; Sun et al. 1999; Colvin et al. 2001; Serls et al. 2005; Vega-Hernandez et al. 2011). Recent clinical and biochemical data have uncovered unexpected roles for FGF signaling in metabolic processes, including phosphate/vitamin D homeostasis (Consortium 2000; Razzaque and Lanske 2007; Nakatani et al. 2009; Gattineni et al. 2011; Kir et al. 2011), cholesterol/bile acid homeostasis (Yu et al. 2000a; Holt et al. 2003), and glucose/lipid metabolism (Fu et al. 2004; Moyers et al. 2007). Highlighting its diverse biology, deranged FGF signaling contributes to many human diseases, such as congenital craniosynostosis and dwarfism syndromes (Naski et al. 1996; Wilkie et al. 2002, 2005), Kallmann syndrome (Dode et al. 2003; Pitteloud et al. 2006a), hearing loss (Tekin et al. 2007, 2008), and renal phosphate wasting disorders (Shimada et al. 2001; White et al. 2001), as well as many acquired forms of cancers (Rand et al. 2005; Pollock et al. 2007; Gartside et al. 2009; di Martino et al. 2012). Endocrine FGFs have also been implicated in the progression of acquired metabolic disorders, including chronic kidney disease (Fliser et al. 2007), obesity (Inagaki et al. 2007; Moyers et al. 2007; Reinehr et al. 2012), and insulin resistance (Fu et al. 2004; Chen et al. 2008b; Chateau et al. 2010; Huang et al. 2011), giving rise to many opportunities for drug discovery in the field of FGF biology (Beenken and Mohammadi 2012).Based on sequence homology and phylogeny, the 18 mammalian FGFs are grouped into six subfamilies (Ornitz and Itoh 2001; Popovici et al. 2005; Itoh and Ornitz 2011). Five of these subfamilies act in a paracrine fashion, namely, the FGF1 subfamily (FGF1 and FGF2), the FGF4 subfamily (FGF4, FGF5, and FGF6), the FGF7 subfamily (FGF3, FGF7, FGF10, and FGF22), the FGF8 subfamily (FGF8, FGF17, and FGF18), and the FGF9 subfamily (FGF9, FGF16, and FGF20). In contrast, the FGF19 subfamily (FGF19, FGF21, and FGF23) signals in an endocrine manner (Beenken and Mohammadi 2012). FGFs exert their pleiotropic effects by binding and activating the FGF receptor (FGFR) subfamily of receptor tyrosine kinases that are coded by four genes (FGFR1, FGFR2, FGFR3, and FGFR4) in mammals (Johnson and Williams 1993; Mohammadi et al. 2005b). The extracellular domain of FGFRs consists of three immunoglobulin (Ig)-like domains (D1, D2, and D3), and the intracellular domain harbors the conserved tyrosine kinase domain flanked by the flexible amino-terminal juxtamembrane linker and carboxy-terminal tail (Lee et al. 1989; Dionne et al. 1991; Givol and Yayon 1992). A unique feature of FGFRs is the presence of a contiguous segment of glutamic and aspartic acids in the D1–D2 linker, termed the acid box (AB). The two-membrane proximal D2 and D3 and the intervening D2–D3 linker are necessary and sufficient for ligand binding/specificity (Dionne et al. 1990; Johnson et al. 1990), whereas D1 and the D1–D2 linker are implicated in receptor autoinhibition (Wang et al. 1995; Roghani and Moscatelli 2007; Kalinina et al. 2012). Alternative splicing and translational initiation further diversify both ligands and receptors. The amino-terminal regions of FGF8 and FGF17 can be differentially spliced to yield FGF8a, FGF8b, FGF8e, FGF8f (Gemel et al. 1996; Blunt et al. 1997), and FGF17a and FGF17b isoforms (Xu et al. 1999), whereas cytosine-thymine-guanine (CTG)-mediated translational initiation gives rise to multiple high molecular weight isoforms of FGF2 and FGF3 (Florkiewicz and Sommer 1989; Prats et al. 1989; Acland et al. 1990). The tissue-specific alternative splicing in D3 of FGFR1, FGFR2, and FGFR3 yields “b” and “c” receptor isoforms which, along with their temporal and spatial expression patterns, is the major regulator of FGF–FGFR specificity/promiscuity (Orr-Urtreger et al. 1993; Ornitz et al. 1996; Zhang et al. 2006). A large body of structural data on FGF–FGFR complexes has begun to reveal the intricate mechanisms by which different FGFs and FGFRs combine selectively to generate quantitatively and qualitatively different intracellular signals, culminating in distinct biological responses. In addition, these structural data have unveiled how pathogenic mutations hijack the normal physiological mechanisms of FGFR regulation to lead to pathogenesis. We will discuss the current state of the structural biology of the FGF–FGFR system, lessons learned from studying the mechanism of action of pathogenic mutations, and how the structural data are beginning to shape and advance the translational research.     

Architecture of the Mammalian Golgi

Since its first visualization in 1898, the Golgi has been a topic of intense morphological research. A typical mammalian Golgi consists of a pile of stapled cisternae, the Golgi stack, which is a key station for modification of newly synthesized proteins and lipids. Distinct stacks are interconnected by tubules to form the Golgi ribbon. At the entrance site of the Golgi, the cis-Golgi, vesicular tubular clusters (VTCs) form the intermediate between the endoplasmic reticulum and the Golgi stack. At the exit site of the Golgi, the trans-Golgi, the trans-Golgi network (TGN) is the major site of sorting proteins to distinct cellular locations. Golgi functioning can only be understood in light of its complex architecture, as was revealed by a range of distinct electron microscopy (EM) approaches. In this article, a general concept of mammalian Golgi architecture, including VTCs and the TGN, is described.In 1898 Camillo Golgi was the first to visualize, describe, and ultimately name the Golgi complex. Using a histochemical impregnation method causing the reduction and deposition of silver, he defined the Golgi in neuronal cells as a reticular apparatus stained by the “black reaction” (Golgi 1898). In the 1950s, the first ultrastructural images of the Golgi were revealed using the then newly developed electron microscope (EM) (Dalton 1954; Farquhar and Rinehart 1954; Sjostrand and Hanzon 1954; Dalton and Felix 1956), reviewed by Farquhar and Palade (1981). In 1961, the thiamine pyrophosphatase reaction developed by Novikoff and Goldfischer allowed cytochemical labeling of Golgi membranes, which revealed the ubiquitous cellular distribution of this organelle (Novikoff and Goldfischer 1961). In the many years of ultrastructural research that have followed, the visualization of the Golgi has gone hand-in-hand with the developing EM techniques.The intriguing structural complexity of the Golgi has made it one of the most photographed organelles in the cell. However, a full understanding of Golgi architecture is hard to deduce from the ultrathin (70–100 nm) sections used in standard transmission EM preparations. Rambourg and Clermont (1974) were the first to investigate the Golgi in three dimensions (3D), using stereoscopy (Rambourg 1974). In this approach a “thick” (150–200 nm), EM section is photographed at two distinct angles, after which the pairs of photographs are viewed with a stereoscope. Over the years, stereoscopy was applied to a variety of cells and has greatly contributed to our current understanding of Golgi architecture (Lindsey and Ellisman 1985; Rambourg and Clermont 1990; Clermont et al. 1994; Clermont et al. 1995). An alternative approach to study 3D structure is serial sectioning, by which a series of adjacent (serial) thin sections are collected. The Golgi can be followed throughout these sections and be constructed into a 3D model (Beams and Kessel 1968; Dylewski et al. 1984; Rambourg and Clermont 1990). In the nineties, 3D-EM was boosted by the introduction of high-voltage, dual axis 3D electron tomography (Ladinsky et al. 1999; Koster and Klumperman 2003; Marsh 2005; Marsh 2007; Noske et al. 2008), which allows the analysis of sections of up to 3–4 µm with a 4–6 nm resolution in the z-axis. The sections are photographed in a tilt series of different angles, which are reconstructed into a 3D tomogram that allows one to “look beyond” a given structure and reveals how it relates to other cellular compartments.Membranes with a similar appearance can differ in protein content and function. These differences are revealed by protein localization techniques. Therefore, in addition to the “classical” EM techniques providing ultrastructural details, EM methods that determine protein localization within the context of the cellular morphology have been crucial to further our understanding on the functional organization of the Golgi. For example, by enzyme-activity-based cytochemical staining the cis-to-trans-polarity in the distribution of Golgi glycosylation enzymes was discovered, reviewed by Farquhar and Palade (1981), which was key to understanding the functional organization of the Golgi stack in protein and lipid glycosylation. With the development of immunoEM methods, using antibodies, the need for enzyme activity for protein localization was overcome. This paved the way for the localization of a wide variety of proteins, such as the cytoplasmic coat complexes associated with the Golgi (Rabouille and Klumperman 2005).A logical next step in EM-based imaging of the Golgi would be to combine protein localization with 3D imaging, but this is technically challenging. A number of protocols enabling protein localization in 3D have recently been described (Trucco et al. 2004; Grabenbauer et al. 2005; Gaietta et al. 2006; Zeuschner et al. 2006; Meiblitzer-Ruppitsch et al. 2008), but these have only been applied in a limited manner to Golgi studies. Another approach that holds great potential for Golgi research is correlative microscopy (CLEM). Live cell imaging of fluorescent proteins has revolutionized cell biology by the real time visualization of dynamic events. However, live cell imaging does not reveal membrane complexity. By CLEM, live cells are first viewed by light microscopy and then prepared for EM (Mironov et al. 2008; van Rijnsoever et al. 2008). When coupled with the recent introduction of super resolution light microscopy techniques for real time imaging, the combination with EM for direct correlation with ultrastructural resolution has great potential (Hell 2009; Lippincott-Schwartz and Manley 2009).The 100th anniversary of the discovery of the Golgi, in 1998, triggered a wave of reviews on this organelle, including those focusing on Golgi architecture (Rambourg 1997; Farquhar and Palade 1998). More recent reviews that describe Golgi structure in great detail are provided by Marsh (2005) and Hua (2009). In this article, the most recent insights in mammalian Golgi architecture as revealed by distinct EM approaches are integrated into a general concept.     

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.     

Molecular Bases of Cell–Cell Junctions Stability and Dynamics

Epithelial cell–cell junctions are formed by apical adherens junctions (AJs), which are composed of cadherin adhesion molecules interacting in a dynamic way with the cortical actin cytoskeleton. Regulation of cell–cell junction stability and dynamics is crucial to maintain tissue integrity and allow tissue remodeling throughout development. Actin filament turnover and organization are tightly controlled together with myosin-II activity to produce mechanical forces that drive the assembly, maintenance, and remodeling of AJs. In this review, we will discuss these three distinct stages in the lifespan of cell–cell junctions, using several developmental contexts, which illustrate how mechanical forces are generated and transmitted at junctions, and how they impact on the integrity and the remodeling of cell–cell junctions.Cell–cell junction formation and remodeling occur repeatedly throughout development. Epithelial cells are linked by apical adherens junctions (AJs) that rely on the cadherin-catenin-actin module. Cadherins, of which epithelial E-cadherin (E-cad) is the most studied, are Ca²⁺-dependent transmembrane adhesion proteins forming homophilic and heterophilic bonds in trans between adjacent cells. Cadherins and the actin cytoskeleton are mutually interdependent (Jaffe et al. 1990; Matsuzaki et al. 1990; Hirano et al. 1992; Oyama et al. 1994; Angres et al. 1996; Orsulic and Peifer 1996; Adams et al. 1998; Zhang et al. 2005; Pilot et al. 2006). This has long been attributed to direct physical interaction of E-cad with β-catenin (β-cat) and of α-catenin (α-cat) with actin filaments (for reviews, see Gumbiner 2005; Leckband and Prakasam 2006; Pokutta and Weis 2007). Recently, biochemical and protein dynamics analyses have shown that such a link may not exist and that instead, a constant shuttling of α-cat between cadherin/β-cat complexes and actin may be key to explain the dynamic aspect of cell–cell adhesion (Drees et al. 2005; Yamada et al. 2005). Regardless of the exact nature of this link, several studies show that AJs are indeed physically attached to actin and that cadherins transmit cortical forces exerted by junctional acto-myosin networks (Costa et al. 1998; Sako et al. 1998; Pettitt et al. 2003; Dawes-Hoang et al. 2005; Cavey et al. 2008; Martin et al. 2008; Rauzi et al. 2008). In addition, physical association depends in part on α-cat (Cavey et al. 2008) and additional intermediates have been proposed to represent alternative missing links (Abe and Takeichi 2008) (reviewed in Gates and Peifer 2005; Weis and Nelson 2006). Although further work is needed to address the molecular nature of cadherin/actin dynamic interactions, association with actin is crucial all throughout the lifespan of AJs. In this article, we will review our current understanding of the molecular mechanisms at work during three different developmental stages of AJs biology: assembly, stabilization, and remodeling, with special emphasis on the mechanical forces controlling AJs integrity and development.     

Endocytosis of Viruses and Bacteria

Of the many pathogens that infect humans and animals, a large number use cells of the host organism as protected sites for replication. To reach the relevant intracellular compartments, they take advantage of the endocytosis machinery and exploit the network of endocytic organelles for penetration into the cytosol or as sites of replication. In this review, we discuss the endocytic entry processes used by viruses and bacteria and compare the strategies used by these dissimilar classes of pathogens.Many of the most widespread and devastating diseases in humans and livestock are caused by viruses and bacteria that enter cells for replication. Being obligate intracellular parasites, viruses have no choice. They must transport their genome to the cytosol or nucleus of infected cells to multiply and generate progeny. Bacteria and eukaryotic parasites do have other options; most of them can replicate on their own. However, some have evolved to take advantage of the protected environment in the cytosol or in cytoplasmic vacuoles of animal cells as a niche favorable for growth and multiplication. In both cases (viruses and intracellular bacteria), the outcome is often destructive for the host cell and host organism. The mortality and morbidity caused by infectious diseases worldwide provide a strong rationale for research into pathogen–host cell interactions and for pursuing the detailed mechanisms of transmission and dissemination. The study of viruses and bacteria can, moreover, provide invaluable insights into fundamental aspects of cell biology.Here, we focus on the mechanisms by which viral and bacterial pathogens exploit the endocytosis machinery for host cell entry and replication. Among recent reviews on this topic, dedicated uniquely to either mammalian viruses or bacterial pathogens, we recommend the following: Cossart and Sansonetti (2004); Pizarro-Cerda and Cossart (2006); Kumar and Valdivia (2009); Cossart and Roy (2010); Mercer et al. (2010b); Grove and Marsh (2011); Kubo et al. (2012); Vazquez-Calvo et al. (2012a); Sun et al. (2013).The term “endocytosis” is used herein in its widest sense, that is, to cover all processes whereby fluid, solutes, ligands, and components of the plasma membrane as well as particles (including pathogenic agents) are internalized by cells through the invagination of the plasma membrane and the scission of membrane vesicles or vacuoles. This differs from current practice in the bacterial pathogenesis field, where the term “endocytosis” is generally reserved for the internalization of molecules or small objects, whereas the uptake of bacteria into nonprofessional phagocytes is called “internalization” or “bacterial-induced phagocytosis.” In addition, the term “phagocytosis” is reserved for internalization of bacteria by professional phagocytes (macrophages, polymorphonuclear leucocytes, dendritic cells, and amoebae), a process that generally but not always leads to the destruction of the ingested bacteria (Swanson et al. 1999; May and Machesky 2001; Henry et al. 2004; Zhang et al. 2010). With a few exceptions, we will not discuss phagocytosis of bacteria or the endocytosis of protozoan parasites such as Toxoplasma and Plasmodium (Robibaro et al. 2001).     

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.     

Imaging and Modeling the Dynamics of Clathrin-Mediated Endocytosis

Clathrin-mediated endocytosis (CME) plays a central role in cellular homeostasis and is mediated by clathrin-coated pits (CCPs). Live-cell imaging has revealed a remarkable heterogeneity in CCP assembly kinetics, which can be used as an intrinsic source of mechanistic information on CCP regulation but also poses several major problems for unbiased analysis of CME dynamics. The backbone of unveiling the molecular control of CME is an imaging-based inventory of the full diversity of individual CCP behaviors, which requires detection and tracking of structural fiduciaries and regulatory proteins with an accuracy of >99.9%, despite very low signals. This level of confidence can only be achieved by combining appropriate imaging modalities with self-diagnostic computational algorithms for image analysis and data mining.Clathrin-mediated endocytosis (CME) drives the uptake of diverse receptor-bound macromolecules and is one of the main endocytic mechanisms constitutively active in all mammalian cells. Clathrin-coated vesicles (CCVs) were the first transport vesicles to be isolated (Pearse 1975), which subsequently led to the identification of clathrin and the heterotetrameric adaptor protein AP2 as the major coat components (Pearse 1976, 1978). Further research in this area was spurred by the discovery that familial hypercholesterolemia is caused by a single substitution of a cysteine for a tyrosine in the cytoplasmic tail of the low-density lipoprotein receptor (LDLR), which disrupts its endocytic internalization motif and prevents its concentration in clathrin-coated pits (CCPs) (Anderson et al. 1977). In the following decades, biochemistry combined with molecular biology and electron microscopy (EM) have revealed much about the molecular players involved in CME (reviewed by Conner and Schmid 2003; Schmid and McMahon 2007; McMahon and Boucrot 2011; Boettner et al. 2012). Today, we know that CME is initiated via assembly of clathrin and AP2 to form CCPs and that receptor–ligand complexes (referred to as “cargo”) are concentrated in CCPs via direct interactions between endocytic motifs within their cytoplasmic domains and adaptor molecules that recruit clathrin. With the aid of a multitude of endocytic accessory proteins (EAPs)—many with as-yet poorly defined functions—CCPs undergo stabilization, maturation, and invagination. Finally, membrane fission, catalyzed by the GTPase dynamin, pinches off the CCV carrying its cargo into the cell.Although powerful and invaluable, bulk biochemical assays can only report cumulative and ensemble-averaged effects on CME, whereas EM only provides static snapshots of highly dynamic structures. Both approaches are not sufficient to resolve critical, rate-limiting stages of CCP maturation and alternative outcomes that prevent CCV internalization. They are also not sufficient to probe the frequently overlapping functions of individual components in CCP formation and maturation. Perturbation of molecular players in a system with such redundancy may lead to no detectable shifts, or to detectable shifts that merely represent system adaptation, and thus may not reveal the actual function of the targeted component itself. Moreover, perturbing CME may globally interfere with cell homeostasis, which can also elicit phenotypes unrelated to the actual functions of the target. To remedy these issues, it is necessary to follow the dynamics of CME at the level of individual CCPs and to correlate these behaviors with differential patterns of cargo and EAP recruitment and activity.These goals became approachable with the “GFP revolution” in the 1990s, which was paralleled by leaps in the sensitivity of digital light microscopy. For CME, the power of these technologies was first shown by Keen and colleagues, who used a green fluorescent protein (GFP) fusion of the clathrin light chain (CLC) to image clathrin dynamics by time-lapse wide-field epifluorescence microscopy (Gaidarov et al. 1999). Since then, numerous live-cell imaging studies have revealed remarkable heterogeneity in CCP assembly kinetics and internalization (Rappoport and Simon 2003; Ehrlich et al. 2004; Keyel et al. 2004; Merrifield et al. 2005; Loerke et al. 2009; Taylor et al. 2011). Although the physiological and molecular bases for this heterogeneity remain to be uncovered, the working hypothesis is that CCP heterogeneity arises from variations in molecular composition, in cortical membrane mechanics, and in differences between cell types. More recent advances in imaging and computational image data analyses have made it possible to determine the order in which EAPs are incorporated or released from growing CCPs. Thus, multidimensional live-cell imaging and mathematical models, in combination with very mild chemical, molecular, and mechanical perturbations, may uncover how the molecular composition of an assembling CCP affects its behavior. In the following we describe the developments of imaging modalities and image analysis methods that have led to the current state of the art in quantitative imaging of CME.     

Cargo Recognition in Clathrin-Mediated Endocytosis

The endosomal system is expansive and complex, characterized by swift morphological transitions, dynamic remodeling of membrane constituents, and intracellular positioning changes. To properly navigate this ever-altering membrane labyrinth, transmembrane protein cargoes typically require specific sorting signals that are decoded by components of protein coats. The best-characterized sorting process within the endosomal system is the rapid internalization of select transmembrane proteins within clathrin-coated vesicles. Endocytic signals consist of linear motifs, conformational determinants, or covalent modifications in the cytosolic domains of transmembrane cargo. These signals are interpreted by a diverse set of clathrin-associated sorting proteins (CLASPs) that translocate from the cytosol to the inner face of the plasma membrane. Signal recognition by CLASPs is highly cooperative, involving additional interactions with phospholipids, Arf GTPases, other CLASPs, and clathrin, and is regulated by large conformational changes and covalent modifications. Related sorting events occur at other endosomal sorting stations.The internalization of a subset of plasma membrane proteins by clathrin-mediated endocytosis is one the best-characterized sorting processes that takes place in the endomembrane system of eukaryotic cells (Kirchhausen 2014). Selection of transmembrane proteins (referred to as “cargo”) for internalization by clathrin-mediated endocytosis involves recognition of endocytic signals in the cytosolic domains of the proteins by adaptors located in the inner layer of clathrin coats. Signal–adaptor interactions lead to concentration of the transmembrane proteins within clathrin-coated pits that eventually bud into the cytoplasm as clathrin-coated vesicles (Kirchhausen 2014). Transmembrane proteins that have endocytic signals are thus rapidly delivered to endosomes, whereas those that lack signals remain at the plasma membrane. This article summarizes recent progress in the elucidation of the mechanisms of signal recognition in clathrin-mediated endocytosis, with additional reference to related intracellular sorting events. Further information on this topic can be found in previous reviews (Bonifacino and Traub 2003; Traub 2009; Kelly and Owen 2011).     

Unconventional Functions for Clathrin,ESCRTs, and Other Endocytic Regulators in the Cytoskeleton,Cell Cycle,Nucleus, and Beyond: Links to Human Disease

The roles of clathrin, its regulators, and the ESCRT (endosomal sorting complex required for transport) proteins are well defined in endocytosis. These proteins can also participate in intracellular pathways that are independent of endocytosis and even independent of the membrane trafficking function of these proteins. These nonendocytic functions involve unconventional biochemical interactions for some endocytic regulators, but can also exploit known interactions for nonendocytic functions. The molecular basis for the involvement of endocytic regulators in unconventional functions that influence the cytoskeleton, cell cycle, signaling, and gene regulation are described here. Through these additional functions, endocytic regulators participate in pathways that affect infection, glucose metabolism, development, and cellular transformation, expanding their significance in human health and disease.The discovery and characterization of clathrin (Pearse 1975) initiated molecular definition of the many endocytosis regulators described in this collection, which mediate the clathrin-dependent and -independent pathways for membrane internalization (see Kirchhausen et al. 2014; Mayor et al. 2014; Merrifield and Kaksonen 2014). In accompanying reviews, we have seen how these endocytic pathways influence nutrition and metabolism (see Antonescu et al. 2014), signal transduction (see Bökel and Brand 2014; Di Fiore and von Zastrow 2014), neuronal function (see Morgan et al. 2013; Cosker and Segal 2014), infection and immunity (see ten Broeke et al. 2013; Cossart and Helenius 2014), tissue polarity and development (see Eaton and Martin-Belmonte 2014; Gonzalez-Gaitan and Jülicher 2014), and migration and metastasis (see Mellman and Yarden 2013). Recently, it has been established that some endocytic regulators have molecular properties that expand their functions beyond endocytosis. These include molecular interactions that affect the microtubule and actin cytoskeletons, nuclear translocation that influences gene regulation, and the formation of membrane-associated scaffolds that serve as signaling and sorting platforms. Through these diverse nonendocytic functions, endocytosis regulators play additional roles in cell division, pathogen infection, cell adhesion, and oncogenesis. In this article, we review the nonconventional behavior of endocytic regulators, first discussing the molecular properties that enable their moonlighting functions and then discussing the cellular processes and disease states that are influenced by these functions.     

Protein Solubility and Protein Homeostasis: A Generic View of Protein Misfolding Disorders

According to the “generic view” of protein aggregation, the ability to self-assemble into stable and highly organized structures such as amyloid fibrils is not an unusual feature exhibited by a small group of peptides and proteins with special sequence or structural properties, but rather a property shared by most proteins. At the same time, through a wide variety of techniques, many of which were originally devised for applications in other disciplines, it has also been established that the maintenance of proteins in a soluble state is a fundamental aspect of protein homeostasis. Taken together, these advances offer a unified framework for understanding the molecular basis of protein aggregation and for the rational development of therapeutic strategies based on the biological and chemical regulation of protein solubility.Virtually every complex biochemical process taking place in living cells depends on the ability of the molecules involved to self-assemble into functional structures (Dobson 2003; Robinson et al. 2007; Russel et al. 2009), and a sophisticated quality control system is responsible for regulating the reactions leading to this organization within the cellular environment (Dobson 2003; Balch et al. 2008; Hartl and Hayer-Hartl 2009; Powers et al. 2009; Vendruscolo and Dobson 2009). Proteins are the molecules that are essential for enabling, regulating, and controlling almost all the tasks necessary to maintain such a balance. To function, the majority of our proteins need to fold into specific three-dimensional structures following their biosynthesis in the ribosome (Hartl and Hayer-Hartl 2002). The wide variety of highly specific structures that results from protein folding, and which serve to bring key functional groups into close proximity, has enabled living systems to develop an astonishing diversity and selectivity in their underlying chemical processes by using a common set of just 20 basic molecular components, the amino acids (Dobson 2003). Given the central importance of protein folding, it is not surprising that the failure of proteins to fold correctly, or to remain correctly folded, is at the origin of a wide variety of pathological conditions, including late-onset diabetes, cystic fibrosis, and Alzheimer’s and Parkinson’s diseases (Dobson 2003; Chiti and Dobson 2006; Haass and Selkoe 2007). In many of these disorders proteins self-assemble in an aberrant manner into large molecular aggregates, notably amyloid fibrils (Chiti and Dobson 2006; Ramirez-Alvarado et al. 2010).     

Self-avoidance and Tiling: Mechanisms of Dendrite and Axon Spacing

The spatial pattern of branches within axonal or dendritic arbors and the relative arrangement of neighboring arbors with respect to one another impact a neuron''s potential connectivity. Although arbors can adopt diverse branching patterns to suit their functions, evenly spread branches that avoid clumping or overlap are a common feature of many axonal and dendritic arbors. The degree of overlap between neighboring arbors innervating a surface is also characteristic within particular neuron types. The arbors of some populations of neurons innervate a target with a comprehensive and nonoverlapping “tiled” arrangement, whereas those of others show substantial territory overlap. This review focuses on cellular and molecular studies that have provided insight into the regulation of spatial arrangements of neurite branches within and between arbors. These studies have revealed principles that govern arbor arrangements in dendrites and axons in both vertebrates and invertebrates. Diverse molecular mechanisms controlling the spatial patterning of sister branches and neighboring arbors have begun to be elucidated.Axonal and dendritic arbors adopt complex and morphologically diverse shapes that influence neural connectivity and information processing. In this article we review anatomical and molecular studies that elucidate how the arrangements of branches within neuronal arbors are established during development (isoneuronal spacing) and how the relative spacing of arbors is determined when multiple neurons together innervate a defined territory (heteroneuronal spacing). Together these mechanisms ensure that arbors achieve functionally appropriate coverage of input or output territories.Isoneuronal and heteroneuronal processes display a variety of spacing arrangements, suggesting a diversity of underlying molecular mechanisms. Self-avoidance can occur between branches that arise from a single soma (Yau 1976; Kramer and Kuwada 1983; Kramer and Stent 1985), implying that neurons are able to discriminate “self,” which they avoid, from “nonself” arbors, with which they coexist (Kramer and Kuwada 1983). Similarly, arbors from different cells that share the same function and together innervate a defined territory can create a pattern of minimally overlapping neighboring dendritic or axonal fields, known as tiling. Such spacing mechanisms ensure that arbors maximize their spread across a territory while minimizing the redundancy with which the territory is innervated. In contrast, adhesive interactions between arbors can operate to maintain coherence of dendrites at specific targets (Zhu and Luo 2004), or to bundle functionally similar processes and possibly coordinate their activity (Campbell et al. 2009). Understanding how processes are patterned relative to one another can help to uncover the functional logic of neural circuit organization.Here we focus primarily on mechanisms of isoneuronal and heteroneuronal avoidance that result in complete and nonredundant innervation of sensory or synaptic space. Such mechanisms have been studied extensively in systems where neuronal arbors innervate a two-dimensional plane, such as the retina or body wall (Wassle et al. 1981; Perry and Linden 1982; Hitchcock 1989; Lin and Masland 2004; Fuerst et al. 2009; Kramer and Stent 1985; Grueber et al. 2003; Sugimura et al. 2003; Sagasti et al. 2005). However, the principles regulating process spacing in these regions likely also apply in three dimensions, most prominently where processes are segregated into nonoverlapping domains or columns (Huckfeldt et al. 2009). It is also notable that nonneuronal cell types might similarly engage in self-avoidance and form tiling arrangements, including leech comb cells (Jellies and Kristan 1991) and mammalian astrocytes (Bushong et al. 2002; Ogata and Kosaka 2002; Livet et al. 2007). Elucidating the mechanisms of process spacing during development is therefore relevant for understanding principles of tissue organization inside and outside of the nervous system.     

Presynaptic Membrane Retrieval and Endosome Biology: Defining Molecularly Heterogeneous Synaptic Vesicles

The release and uptake of neurotransmitters by synaptic vesicles is a tightly controlled process that occurs in response to diverse stimuli at morphologically disparate synapses. To meet these architectural and functional synaptic demands, it follows that there should be diversity in the mechanisms that control their secretion and retrieval and possibly in the composition of synaptic vesicles within the same terminal. Here we pay particular attention to areas where such diversity is generated, such as the variance in exocytosis/endocytosis coupling, SNAREs defining functionally diverse synaptic vesicle populations and the adaptor-dependent sorting machineries capable of generating vesicle diversity. We argue that there are various synaptic vesicle recycling pathways at any given synapse and discuss several lines of evidence that support the role of the endosome in synaptic vesicle recycling.Chemical synapses contain discrete numbers of synaptic vesicles, which are capable of sustaining neurotransmitter release. Sustained neurotransmission occurs despite the secretory demands imposed by persistent and diverse patterns of neuronal electrical activity. Maintaining synaptic vesicle numbers requires local mechanisms to regenerate these vesicles to prevent their exhaustion, preserve plasma membrane surface area, and to maintain the molecularly distinct identity of a vesicle versus plasma membrane. Rizzoli and Betz (2005) eloquently draw a parallel between chemical neurotransmission with synapse chatter saying that some synapses “whisper,” whereas others “shout.” The “louder” the synapse, the more synaptic vesicles are required, extending from a few hundred vesicles (whisperers) to nearly thousands (shouters). This beautiful analogy implies that every synapse has just one “voice” or species of vesicle. Here we will present the case that synapses are more like choirs in which multiple vesicle species or “voices” contribute to the “pianissimo” or “fortissimo” parts of chemical neurotransmission.Synaptic terminals show a range of structural and functional differences in distinct regions of the brain, suggesting that the mechanisms for exocytosis/endocytosis coupling, as well as local vesicle recycling, may also be diverse. On one side, the Calyx of Held nerve terminal participates in fast and sustained synaptic transmission at high frequency (800 Hz), which is crucial for sound localization in the auditory brainstem (Taschenberger and von Gersdorff 2000; Borst and Soria van Hoeve 2012). The Calyx of Held houses ∼70,000 synaptic vesicles with nearly 3000 vesicles docked per Calyx terminal. These docked vesicles are distributed across the ∼500 active zones that exist per Calyx where vesicle fusion occurs (Satzler et al. 2002). On the other hand, hippocampal synapses fire action potentials at ∼0.5 Hz in bursts (Dobrunz and Stevens 1999). This synapse contains ∼200 synaptic vesicles and one active zone with ∼10 vesicles docked (Schikorski and Stevens 1997). With such a wide functional and structural gamut of synapses, it is reasonable to hypothesize that synaptic vesicles may differ in their retrieval mechanisms, not just at the rate at which the process occurs but also in the molecular pathways used.Two synaptic vesicle retrieval mechanisms, namely clathrin/AP-2/dynamin-dependent biogenesis and kiss-and-run, have been summarized in outstanding recent reviews (see, for example, Augustine et al. 2006; Rizzoli and Jahn 2007; Smith et al. 2008; Royle and Lagnado 2010; Ferguson and De Camilli 2012; Saheki and De Camilli 2012). Therefore, here we focus on the coupling of secretion and membrane retrieval, as well as endosome sorting. We will discuss new developments supporting the existence of diverse functional and molecular pools of synaptic vesicles and how endocytosis and endosome retrieval mechanisms may generate these vesicle pools.     

The Measure of Success: Constraints, Objectives, and Tradeoffs in Morphogen-mediated Patterning

A large, diverse, and growing number of strategies have been proposed to explain how morphogen gradients achieve robustness and precision. We argue that, to be useful, the evaluation of such strategies must take into account the constraints imposed by competing objectives and performance tradeoffs. This point is illustrated through a mathematical and computational analysis of the strategy of self-enhanced morphogen clearance. The results suggest that the usefulness of this strategy comes less from its ability to increase robustness to morphogen source fluctuations per se, than from its ability to overcome specific kinds of noise, and to increase the fraction of a morphogen gradient within which robust threshold positions may be established. This work also provides new insights into the longstanding question of why morphogen gradients show a maximum range in vivo.In recent years, much research on morphogen gradients has shifted from purely mechanistic questions—how gradients form and how morphogens signal—to strategic ones—how gradients perform well in the face of various kinds of constraints and perturbations. Forty years ago, Francis Crick was among the first to call attention to constraints that morphogens face, noting that the time required to spread a signal by random transport through a tissue varies with the square of distance (Crick 1970). Using order-of-magnitude calculations, he argued that observed biological maxima for morphogen-mediated patterning were just about where they should be if morphogen signals spread by aqueous diffusion.Although the idea that diffusion time is what limits the sizes of morphogen gradients remains untested, Crick''s work established a precedent of seeking explanations for developmental processes in terms of constraints imposed by the physical world. In the area of biological pattern formation, continued interest in how real-world limits constrain mechanisms has led many current investigators to focus on matters of robustness, the engineering term that describes the relative insensitivity of a system''s behavior to perturbations it may be expected to encounter. With respect to morphogen gradients, most work has focused on parametric robustness, i.e., insensitivity to parameter values (e.g., the dosage of genes, levels, or rate constants of enzymes [Eldar et al. 2002; Eldar et al. 2003; Eldar et al. 2004; Bollenbach et al. 2005; Shimmi et al. 2005; White et al. 2007]). Some investigators have also focused on the “precision” of morphogen gradients, which may be understood as robustness to the causes and effects of natural variation among individuals in a population (Houchmandzadeh et al. 2002; Gregor et al. 2007; Tostevin et al. 2007; Bollenbach et al. 2008; Emberly 2008).Remarkably, after hardly a decade of intensive study of such questions, we find ourselves awash in a sea of diverse and intriguing mechanisms for conferring one or another type of robustness on morphogen-mediated patterning. Mechanisms that operate at the level of gradient formation include self-enhanced morphogen degradation (Eldar et al. 2003), facilitated transport (Eldar et al. 2002; Shimmi et al. 2005), serial transcytosis (Bollenbach et al. 2005), presteady state patterning (Bergmann et al. 2007), and competition between morphogens for binding to inhibitors (Ben-Zvi et al. 2008). Mechanisms that operate at the level of morphogen detection and interpretation include morphogenetic apoptosis (Adachi-Yamada and O''Connor 2002), cell rearrangement (Ashe and Briscoe 2006), integration of signals from multiple morphogens (McHale et al. 2006; Morishita and Iwasa 2008), and various types of local cell-to-cell signaling (e.g., Amonlirdviman et al. 2005).Why so many strategies? Biologists are often quick to ascribe multiplicity to redundancy, but the perspective of engineering suggests a different view. Most engineers accept the “no free lunch” principle (also referred to as “conservation of fragility”), which states that any mechanism that increases robustness in one setting (i.e., to one type of perturbation, or with respect to one type of output) always compromises it in another. The fact that every strategy comes at a price has been offered as an explanation for the seemingly inescapable fragility of highly engineered, modern technology (Carlson and Doyle 2002). By building complex machines that resist everything we think of, we inevitably create susceptibilities to the things we neglected. Although biology is not the result of human engineering, we have no reason to believe that natural selection can circumvent the limits that engineers confront.In a world of no free lunch, one must evaluate a strategy not just by what it is good for, but the “price” of using it. With regard to morphogen-mediated patterning, it is reasonable to suggest that diverse strategies exist because each comes at a different price. If so, achieving meaningful biological understanding requires that we engage in a sort of cost-benefit analysis, in which each strategy is evaluated in the context of the performance objectives of the organism and constraints of the physical world. This is a tall order, as there is a great deal we still do not know about the performance needs of developing organisms (for example, for all the work performed so far on morphogen gradient robustness, we still know little about the magnitudes of the perturbations that need to be withstood). Nevertheless, there is no reason not to get started, as even through the early investigation of hard questions, one commonly learns useful things.