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).     

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).     

Bacterial Influences on Animal Origins

Animals evolved in seas teeming with bacteria, yet the influences of bacteria on animal origins are poorly understood. Comparisons among modern animals and their closest living relatives, the choanoflagellates, suggest that the first animals used flagellated collar cells to capture bacterial prey. The cell biology of prey capture, such as cell adhesion between predator and prey, involves mechanisms that may have been co-opted to mediate intercellular interactions during the evolution of animal multicellularity. Moreover, a history of bacterivory may have influenced the evolution of animal genomes by driving the evolution of genetic pathways for immunity and facilitating lateral gene transfer. Understanding the interactions between bacteria and the progenitors of animals may help to explain the myriad ways in which bacteria shape the biology of modern animals, including ourselves.The first bacteria evolved more than 3 billion years ago and dominated the biosphere continually thereafter, shaping the environment in which animals would eventually evolve more than 2 billion years later (Narbonne 2005; Knoll 2011). Because animals evolved in seas filled with bacteria and have lived in close association with bacteria throughout their evolutionary history, it is likely that diverse interactions with bacteria (including predation on bacteria, harboring bacterial commensals, and infection with bacterial pathogens) influenced animal origins. Nonetheless, although the potential contributions of global environmental change and genome evolution to animal origins have received a fair amount of attention (Hoffman et al. 1998; Knoll and Carroll 1999; Knoll 2003; King 2004; Canfield et al. 2007; Shen et al. 2008; Srivastava et al. 2008, 2010; Richter and King 2013), relatively little is known about how the interactions of animal progenitors with the abundant bacteria in their environment may have influenced the evolution of animals (McFall-Ngai 1999; Moran 2007; Hughes and Sperandio 2008; McFall-Ngai et al. 2013). We review here the current state of knowledge about ancient bacterial interactions and consider how these associations may have shaped the biology and evolution of the earliest animals.     

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.     

Mitochondrial Evolution

Viewed through the lens of the genome it contains, the mitochondrion is of unquestioned bacterial ancestry, originating from within the bacterial phylum α-Proteobacteria (Alphaproteobacteria). Accordingly, the endosymbiont hypothesis—the idea that the mitochondrion evolved from a bacterial progenitor via symbiosis within an essentially eukaryotic host cell—has assumed the status of a theory. Yet mitochondrial genome evolution has taken radically different pathways in diverse eukaryotic lineages, and the organelle itself is increasingly viewed as a genetic and functional mosaic, with the bulk of the mitochondrial proteome having an evolutionary origin outside Alphaproteobacteria. New data continue to reshape our views regarding mitochondrial evolution, particularly raising the question of whether the mitochondrion originated after the eukaryotic cell arose, as assumed in the classical endosymbiont hypothesis, or whether this organelle had its beginning at the same time as the cell containing it.In 1970, Lynn Margulis published Origin of Eukaryotic Cells, an influential book that effectively revived the long-standing but mostly moribund idea that mitochondria and plastids (chloroplasts) evolved from free-living bacteria via symbiosis within a eukaryotic host cell (Margulis 1970). The discovery in the 1960s of DNA within these organelles together with the recognition that they contain a translation system distinct from that of the cytosol were two of the observations that Margulis marshaled in support of the endosymbiont hypothesis of organelle origins. Indeed, throughout her career, Margulis forcefully argued that symbiosis is a potent but largely unrecognized and unappreciated force in evolution (Margulis 1981). Technological developments in DNA cloning and sequencing in the 1970s and 1980s opened the way to the detailed characterization of mitochondrial genomes and genes, and the generation of key molecular data that were instrumental in affirming a bacterial origin of the mitochondrial and plastid genomes, allowing researchers to pinpoint the extant bacterial phyla to which these two organelles are most closely related. Over the past several decades, numerous reviews have documented in detail the biochemical and molecular and cell biological data bearing on the endosymbiont hypothesis of organelle origins (Gray 1982, 1983, 1989a,b, 1992, 1993, 1999; Gray and Doolittle 1982; Wallace 1982; Cavalier-Smith 1987b, 1992; Gray and Spencer 1996; Andersson and Kurland 1999; Gray et al. 1999, 2001, 2004; Lang et al. 1999; Andersson et al. 2003; Burger et al. 2003a; Bullerwell and Gray 2004). Various endosymbiotic models proposed over the years have been comprehensively critiqued (Martin et al. 2001), while the debates surrounding the endosymbiont hypothesis have been recounted in an engaging perspective that traces the development of ideas regarding organelle origins (Sapp 1994). Within a historical context, the present article emphasizes more recent data and insights that are relevant to continuing questions regarding how mitochondria originated and have since evolved.     

Bioenergetic Constraints on the Evolution of Complex Life

All morphologically complex life on Earth, beyond the level of cyanobacteria, is eukaryotic. All eukaryotes share a common ancestor that was already a complex cell. Despite their biochemical virtuosity, prokaryotes show little tendency to evolve eukaryotic traits or large genomes. Here I argue that prokaryotes are constrained by their membrane bioenergetics, for fundamental reasons relating to the origin of life. Eukaryotes arose in a rare endosymbiosis between two prokaryotes, which broke the energetic constraints on prokaryotes and gave rise to mitochondria. Loss of almost all mitochondrial genes produced an extreme genomic asymmetry, in which tiny mitochondrial genomes support, energetically, a massive nuclear genome, giving eukaryotes three to five orders of magnitude more energy per gene than prokaryotes. The requirement for endosymbiosis radically altered selection on eukaryotes, potentially explaining the evolution of unique traits, including the nucleus, sex, two sexes, speciation, and aging.Evolutionary theory has enormous explanatory power and is understood in detail at the molecular genetic level, yet it cannot easily predict even the past. The history of life on Earth is troubling. Life apparently arose very early, perhaps 4 billion years ago, but then remained essentially bacterial for probably some 2–3 billion years. Bacteria and archaea explored almost every conceivable metabolic niche and still dominate in terms of biomass. Yet, in morphological diversity and genomic complexity, bacteria barely begin to compare with eukaryotes, even at the level of cells, let alone multicellular plants and animals. Eukaryotes are monophyletic and share a common ancestor that by definition arose only once, probably between 1.5 and 2 billion years ago, although the dates are poorly constrained (Knoll et al. 2006; Parfrey et al. 2011). The eukaryotic common ancestor already had a nucleus, nuclear pore complexes, introns and exons, straight chromosomes, mitosis and meiotic sex, a dynamic cytoskeleton, an endoplasmic reticulum, and mitochondria, making it difficult to trace the evolution of these traits from a prokaryotic state (Koonin 2010). The “eukaryotic niche”—limited metabolic diversity but enormous morphological complexity—was never invaded by prokaryotes. In short, life arose early, stagnated in morphological complexity for several billion years, and then rather abruptly gave rise to a single group—the eukaryotes—which explored the morphological realm of life in ways never seen in bacteria or archaea.Consider the possibility of life evolving on other planets. Would it follow a similar trajectory? If not, why not? Evolutionary theory gives little insight. The perplexing history of life on Earth conceals a paradox relating to natural selection. If basal eukaryotic traits such as the nucleus, meiotic sex, and phagocytosis arose by selection, starting with a prokaryotic ancestor, and each step offered some small advantage over the last, then why don’t the same traits arise repeatedly in prokaryotes too? Prokaryotes made many a start. There are examples of bacteria or archaea with nucleus-like structures (Lindsay et al. 2001), recombination (Smith et al. 1993), linear chromosomes (Bentley et al. 2002), internal membranes (Pinevich 1997), multiple replicons (Robinson and Bell 2007), giant size (Schulz and Jorgensen 2001), extreme polyploidy (Mendell et al. 2008), a dynamic cytoskeleton (Vats and Rothfield 2009), predation (Davidov and Jurkevitch 2009), parasitism (Moran 2007), introns and exons (Simon and Zimmerly 2008), intercellular signaling (Waters and Bassler 2005), endocytosis-like processes (Lonhienne et al. 2010), and even endosymbionts (Wujek 1979; von Dohlen et al. 2001). Yet, for each of these traits, bacteria and archaea stopped well short of the baroque complexity of eukaryotes. Compare this with the evolution of eyes. From a simple, light-sensitive spot in an early metazoan, morphologically disparate eyes arose on scores of occasions (Vopalensky and Kozmic 2009). This is exactly what evolutionary theory predicts. Each step offers an advantage in its own ecological setting, so morphologically different eyes arise on multiple occasions. Why is this not the case for traits such as the nucleus, meiotic sex, and phagocytosis? To suggest that lateral gene transfer (LGT) or bacterial conjugation is equivalent to meiotic sex will not do: Neither involves a systematic and reciprocal exchange of alleles across the entire genome.The simplest explanation is a bottleneck. The “big bang” radiation of major eukaryotic supergroups, combined with the apparent absence of surviving evolutionary intermediates between prokaryotes and the last eukaryotic common ancestor, does indeed hint at a bottleneck at the origin of eukaryotes. There is no shortage of environmental possibilities, from snowball glaciations to rising atmospheric oxygen. The most widely held explanation contends that when oxygen levels rose after the great oxidation event, some proto-eukaryotic cells acquired mitochondria, which protected them against oxygen toxicity (Andersson and Kurland 1999) and enabled them to exploit oxygen as a terminal electron acceptor in respiration (Sagan 1967), giving the first eukaryotes an enormous competitive advantage. They swiftly occupied new niches made available by oxygen, outcompeting to extinction any other prokaryotes that tried subsequently to invade this niche (de Duve 2007; Gross and Bhattacharya 2010). But this is an evolutionary “just-so story” and has no evidence to support it. The idea that mitochondria might protect against oxygen toxicity is nonsense: The single-electron donors of respiratory chains are among the most potent free-radical generators known. And what was to stop facultatively aerobic bacteria—from which the mitochondria evolved, hence already present—from occupying the aerobic niche first?In fact, the limited evidence available suggests that oxygen had little to do with it (Müller et al. 2012; van der Giezen and Lenton 2012). A large, diverse group of morphologically simple protists dubbed archezoa are the key here. The archezoa appear to lack mitochondria; and three decades ago, looked to branch deeply in the eukaryotic tree. Cavalier-Smith postulated that some archezoa might be primitively amitochondriate: surviving evolutionary intermediates between prokaryotes and eukaryotes (Cavalier-Smith 1987, 1989). But 20 years of careful molecular biology and phylogenetics have shown that all known archezoa possess specialized organelles that derive from mitochondria, namely hydrogenosomes or mitosomes (Keeling 1998; Embley and Martin 2006; van der Giezen 2009; Archibald 2011). The archezoa are obviously not real evolutionary intermediates, and radical developments in phylogenomics have transformed the eukaryotic tree to a “big-bang” radiation with no early branching archezoa (Koonin 2010). The archezoa remain significant not because they are genuine evolutionary intermediates, but because they are true ecological intermediates. Critically, they were not outcompeted to extinction by more sophisticated aerobic eukaryotes. On the contrary, they lost their capacity for aerobic respiration and depend instead on anaerobic fermentations, yet remain, morphologically, more complex than bacteria or archaea.The fact that the archezoa are a phylogenetically disparate group that arose on multiple occasions is equally significant. The “intermediate” niche is viable and was invaded many times, without the new arrivals being outcompeted to extinction by existing cells, or vice versa. Yet each time the invader was an anaerobic eukaryote, which adapted by reductive evolution to the niche—not bacteria or archaea evolving slightly greater complexity. What is the likelihood of this bias? Given at least 20 independent origins of archezoa (van der Giezen 2009; Müller et al. 2012), the probability of these ecological intermediates arising each time from the eukaryotes rather than prokaryotes is less than one in a million. It is far more parsimonious to assume that there was something about the structure of eukaryotes that facilitated their invasion of this intermediate niche; and, conversely, something about the structure of prokaryotes that tended to preclude their evolution of greater morphological complexity. But this quite reasonable statement is loaded because it implies that prokaryotes existed for nearly 4 billion years, and throughout that time showed no tendency to evolve greater morphological complexity. In stark contrast, eukaryotes arose just once, a seemingly improbable event.Here I argue that the constraint on prokaryotes was bioenergetic. There was, indeed, a bottleneck at the origin of eukaryotes, but it was biological (restrictive), not environmental (selective). It related to the physical structure of prokaryotic cells: Both bacteria and archaea respire across their plasma membrane. I make three key points, which arguably apply to life elsewhere in the universe, and are therefore proposed as biological principles that could guide our understanding of life generally: (1) chemiosmotic coupling is as universal as the genetic code, for fundamental reasons relating to the origin of life; (2) prokaryotes are constrained by chemiosmotic coupling across their plasma membrane, but eukaryotes escaped this constraint through a rare and stochastic endosymbiosis between two prokaryotes, giving them orders of magnitude more energy per gene; and (3) this endosymbiosis, in turn, produced a unique genomic asymmetry, transforming the selection pressures acting on eukaryotes and driving the evolution of unique eukaryotic traits.     

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).     

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.     

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.     

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.     

What Was the Real Contribution of Endosymbionts to the Eukaryotic Nucleus? Insights from Photosynthetic Eukaryotes

Eukaryotic genomes are composed of genes of different evolutionary origins. This is especially true in the case of photosynthetic eukaryotes, which, in addition to typical eukaryotic genes and genes of mitochondrial origin, also contain genes coming from the primary plastids and, in the case of secondary photosynthetic eukaryotes, many genes provided by the nuclei of red or green algal endosymbionts. Phylogenomic analyses have been applied to detect those genes and, in some cases, have led to proposing the existence of cryptic, no longer visible endosymbionts. However, detecting them is a very difficult task because, most often, those genes were acquired a long time ago and their phylogenetic signal has been heavily erased. We revisit here two examples, the putative cryptic endosymbiosis of green algae in diatoms and chromerids and of Chlamydiae in the first photosynthetic eukaryotes. We show that the evidence sustaining them has been largely overestimated, and we insist on the necessity of careful, accurate phylogenetic analyses to obtain reliable results.Today it is widely accepted that photosynthesis originated in eukaryotes by the endosymbiosis of a cyanobacterium within a heterotrophic eukaryotic host. This occurred in a lineage that subsequently diversified to give rise to the three contemporary groups of primary photosynthetic eukaryotes: Viridiplantae (including green algae and land plants), Rhodophyta and Glaucophyta, grouped collectively within a unique eukaryotic superphylum called Archaeplastida (Adl et al. 2005) or Plantae (Cavalier-Smith 1982). Recently, a second case of primary endosymbioses has been unveiled thanks to the characterization of Paulinella chromatophora, a filose amoeba that hosts a cyanobacterium with a reduced genome that has been described as “a plastid in the making” (Marin et al. 2005; Keeling and Archibald 2008; Nowack et al. 2008). Primary endosymbioses resulted in the establishment of plastids with two membranes. However, a vast variety of eukaryotes possess plastids with three or more membranes. They derive from the endosymbioses of primary photosynthetic eukaryotes within other eukaryotic cells (Delwiche 1999; Keeling 2013). Such secondary endosymbioses have spread photosynthesis across the eukaryotic tree, either by the endosymbiosis of red or of green algae. Whereas it is almost certain that secondary endosymbioses of green algae occurred twice (in euglenids and chlorarachniophytes), secondary red algal plastids are found in a variety of alveolates, stramenopiles, cryptophytes, and haptophytes, and the number of red algal endosymbioses at the origin of these groups has been matter of intense debate (Baurain et al. 2010; Keeling 2010, 2013; Burki et al. 2012b). Moreover, the existence of tertiary endosymbioses (namely, the symbiosis of a secondary photosynthetic eukaryote within another eukaryotic cell) and of plastid replacements makes the picture of plastid evolution in eukaryotes even more complex. Dinoflagellates, some of which have replaced their ancestral red algal plastids by green algae, diatoms, haptophytes, or cryptophytes, are paradigmatic examples of such complex situations (Keeling 2013).The evolution of plastids has been studied using genes from the plastid genome as well as typical eukaryotic nuclear genes, which allow inferring the phylogenies of both the plastids and their hosts. The use of those markers has led to interesting discoveries, such as the monophyly of the Archaeplastida (Moreira et al. 2000; Rodríguez-Ezpeleta et al. 2005) or the difficulties in reconciling the plastid and host histories in eukaryotes with red algal plastids (Baurain et al. 2010; Burki et al. 2012b). However, a third class of genes can also provide useful complementary information: the genes of plastid origin retrieved within the nuclear genome of the host. In fact, contemporary plastids have small genomes, which is due to the fact that most of the original cyanobacterial symbiont genes were lost or transferred to the host nucleus (by a process called endosymbiotic gene transfer, EGT) during the evolution of plastids (Weeden 1981; Martin et al. 1998). These transfer events are not restricted to plastid endosymbioses—the same phenomenon occurred during the endosymbiosis that gave rise to the mitochondria (Gray et al. 1999; Burger et al. 2003).EGT genes may serve to study the evolutionary history of plastids and, in particular, the presence of cryptic endosymbioses. In fact, species that had a plastid in the past but lost photosynthesis may have conserved genes of plastid origin in their nuclear genomes. This has been shown for a variety of nonphotosynthetic eukaryotes, such as, for example, apicomplexan parasites (Fast et al. 2001; Roos et al. 2002; Williams and Keeling 2003; Huang et al. 2004), perkinsids (Stelter et al. 2007; Matsuzaki et al. 2008; Fernández Robledo et al. 2011) or nonphotosynthetic dinoflagellates (Sanchez-Puerta et al. 2007; Slamovits and Keeling 2008), and green algae (de Koning and Keeling 2004). Although much more controversial, potential EGTs have also been used to propose a photosynthetic ancestry for ciliates (Reyes-Prieto et al. 2008) or that algae with secondary plastids of red algal origin, such as diatoms and chromerids, may have contained green algal endosymbionts in their past (Moustafa et al. 2009; Woehle et al. 2011). Likewise, several dozens of potential EGTs have been detected in algae and plants that appear to have been acquired from Chlamydiae, a group of parasitic bacteria (Huang and Gogarten 2007; Becker et al. 2008; Moustafa et al. 2008), which led to proposing that cryptic chlamydial endosymbionts may have helped to establish the first plastids, in particular, by providing essential functions for plastid activity (Greub and Raoult 2003; Ball et al. 2013; Baum 2013).We revise here some of these cases of cryptic endosymbiosis, with special attention on the difficulties in accurately detecting EGT and the importance of proper phylogenetic analysis and of an adequate taxonomic sampling to achieve that task.     

Paradox of Mother's Curse and the Maternally Provisioned Offspring Microbiome

Strict maternal transmission creates an “asymmetric sieve” favoring the spread of mutations in organelle genomes that increase female fitness, but diminish male fitness. This phenomenon, called “Mother''s Curse,” can be viewed as an asymmetrical case of intralocus sexual conflict. The evolutionary logic of Mother''s Curse applies to each member of the offspring microbiome, the community of maternally provisioned microbes, believed to number in the hundreds, if not thousands, of species for host vertebrates, including humans. Taken together, these observations pose a compelling evolutionary paradox: How has maternal transmission of an offspring microbiome become a near universal characteristic of the animal kingdom when the genome of each member of that community poses a potential evolutionary threat to the fitness of host males? I review features that limit or reverse Mother''s Curse and contribute to resolving this paradox. I suggest that the evolution of vertical symbiont transmission requires conditions that mitigate the evolutionary threat to host males.The genomes of mitochondria, chloroplasts, and many symbiotic microbes are transmitted maternally by host females to their offspring. Maternal transmission can be transovariole (intracellular, within the egg) or contagious, during gestation, birth, or feeding (Sonneborn 1950; Smith and Dunn 1991; Gillham 1994; O’Neill et al. 1997). Vertically transmitted (VT) symbiont lineages tend to be genetically homogeneous within hosts (Birky et al. 1983, 1989; Funk et al. 2000). Maternal uniparental transmission creates an “asymmetric sieve” wherein mutations advantageous for females, but harmful for males, can spread through a population (Cosmides and Tooby 1981; Frank and Hurst 1996; Zeh and Zeh 2005; Burt and Trivers 2006). Such mutations spread because deleterious male-specific fitness effects do not affect the response to natural selection of the maternally transmitted entities. This adaptive process favoring the transmitting sex is called Mother''s Curse (MC) (Gemmell et al. 2004) and it has been referred to as an irreconcilable instance of intralocus conflict: “… exclusively maternal transmission of cytoplasmic genes (e.g., in mitochondria) can result in suboptimal mitochondrial function in males … a form of [intralocus sexual conflict] that apparently cannot be resolved, because selection on mitochondria in males cannot produce a response” (Bonduriansky and Chenoweth 2009, p. 285).Mitochondria are ubiquitous in animals and despite the indisputable evolutionary logic of MC (Frank and Hurst 1996) there are no reported cases of sperm-killing or son-killing mitochondria (Burt and Trivers 2006). Moreover, many species of animals possess an offspring microbiome, a community of microbes transmitted uniparentally from mother to offspring at some point in development, whether prefertilization, postfertilization, or postnatal (Funkhouser and Bordenstein 2013). In some vertebrates, including humans, this community is believed to number in the hundreds of species (Funkhouser and Bordenstein 2013). Prolonged periods of maternal care, as in mammals and birds, as well as kin-structured sociality, afford many opportunities for maternal provisioning of microbes to developing offspring. The social insects, in particular, show obligate mutualisms with a microbiome that confers important nutritional benefits for its host (Baumann 2005; Engel and Moran 2013), the termites being a classic example (Ikeda-Ohtsubo and Brune 2009).Together, the evolutionary logic of MC and the widespread existence of maternally transmitted hereditary symbioses pose a paradox for evolutionary biology. The maternally provisioned microbiome (MC) consists of tens to hundreds of genomes affording ample opportunity, along with mitochondrial and organelle genomes, for the occurrence of mutations that benefit females while harming host males. Assembling a VT community as a host nutritional or defensive adaptation requires evading MC not once, but from a continuous siege over evolutionary time. This is the Mother''s Curse–microbiome (MC–MB) paradox. It conceptually affiliated with the “paradox of mutualism,” the persistence of interspecific mutualisms despite the advantages of cheating by one or the other member of the mutualism (Heath and Stinchcombe 2014). Symbiont “cheating” on only half the members of a host species, the males, might offer marginal benefits relative to wholesale cheating on both host sexes. Nevertheless, the MC–MB paradox deserves research attention.In this review, I discuss inbreeding, kin selection, compensatory evolution, and defensive advantages against more virulent pathogens (or predators and herbivores) as means for resolving the MC–MB paradox. First, I review the simple population genetics of MC. I discuss how host inbreeding and kin selection (Unckless and Herren 2009; Wade and Brandvain 2009), alone or in concert, allow for a response to selection on male fertility and viability fitness effects of maternally transmitted genomes. As a result, inbreeding and kin selection can limit or prevent the spread of mutations in a hereditary symbiosis (Cowles 1915) that are harmful to males. I will show that, for both inbreeding and kin selection, there exist conditions that “favor the spread of maternally transmitted mutations harmful to females”; a situation that is the reverse of MC. However, many outbreeding, asocial species harbor maternally provisioned microbiomes and these solutions cannot be applied to them.I also consider the evolution of compensatory nuclear mutations that mitigate or eliminate the harm to males of organelles or symbionts, spreading via MC dynamics. However, I find that the relative rate of compensatory evolution is only 1/4 the rate of evolution of male-harming symbionts. Thus, an evolutionary rescue of host males via compensatory host nuclear mutations requires that there be fourfold or more opportunities for compensation offered by a larger host nuclear genome. The larger the number of species in a host microbiome, the more difficult it is to entertain host nuclear compensatory mutations as a resolution of the MC–MB paradox.Next, I consider the situation in which a deleterious, VT symbiont harms its host but prevents host infection by a more severely deleterious contagiously transmitted pathogen (Lively et al. 2005; see also Clay 1988). This is a case in which absolute harm to a host by a maternally provisioned symbiont becomes a “relative” fitness advantage. This is a scenario that may be common in hosts with speciose microbial communities, especially if each microbial species increases host resistance or outright immunity to infectious, virulent pathogens.Finally, I discuss models of symbiont domestication and capture via the evolution of vertical transmission from an ancestral state of horizontal transmission (Drown et al. 2013). I show that the evolution of vertical transmission requires conditions that tend to restrict the capacity for male harming by symbionts. Each of these scenarios significantly expands the range of evolutionary possibilities permitted for the coevolution of host–symbiont assemblages, especially those microbial communities that are maternally, uniparentally transmitted across host generations. Unfortunately, current data do not permit discriminating among these various evolutionary responses to MC, so none can be definitively considered a resolution of the MC–MB paradox.     

Biofilms

The ability to form biofilms is a universal attribute of bacteria. Biofilms are multicellular communities held together by a self-produced extracellular matrix. The mechanisms that different bacteria employ to form biofilms vary, frequently depending on environmental conditions and specific strain attributes. In this review, we emphasize four well-studied model systems to give an overview of how several organisms form biofilms: Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, and Staphylococcus aureus. Using these bacteria as examples, we discuss the key features of biofilms as well as mechanisms by which extracellular signals trigger biofilm formation.Bacteria are able to grow adhered to almost every surface, forming architecturally complex communities termed biofilms. In biofilms, cells grow in multicellular aggregates that are encased in an extracellular matrix produced by the bacteria themselves (Branda et al. 2005; Hall-Stoodley and Stoodley 2009). Biofilms impact humans in many ways as they can form in natural, medical, and industrial settings. For instance, formation of biofilms on medical devices, such as catheters or implants often results in difficult-to-treat chronic infections (Hall-Stoodley et al. 2004; Donlan 2008; Hatt and Rather 2008). Moreover, infections have been associated with biofilm formation on human surfaces such as teeth, skin, and the urinary tract (Hatt and Rather 2008). However, biofilms on human surfaces are not always detrimental. For example, dental plaque biofilms comprise dozens of species and the community composition frequently determines the presence or absence of disease. In dental plaque, there is a progression of colonization and the presence of beneficial species antagonizes colonization by detrimental organisms (Kreth et al. 2008). But biofilms form everywhere. For example, biofilms form on the hulls of ships and inside pipes where they cause severe problems (de Carvalho 2007). On the other hand, in many natural settings, biofilm formation often allows mutualistic symbioses. For instance, Actinobacteria often grow on ants, allowing the ants to maintain pathogen-free fungal gardens (Currie 2001; Danhorn and Fuqua 2007). Given the vast potential benefits and detriments that biofilms can confer, it is essential that we understand how bacteria thrive in these communities.There are numerous benefits that a bacterial community might obtain from the formation of biofilms. Biofilms confer resistance to many antimicrobials, protection from protozoan grazing, and protection against host defenses (Mah and O’Toole 2001; Matz and Kjelleberg 2005; Anderson and O’Toole 2008). One possible reason for the increased resistance to environmental stresses observed in biofilm cells appears to be the increase in the portion of persister cells within the biofilm (Lewis 2005). Despite being genetically identical to the rest of the population, persister cells are resistant to many antibiotics and are nondividing. Persister cells have been proposed to be protected from the action of antibiotics because they express toxin–antitoxin systems where the target of the antibiotics is blocked by the toxin modules (Lewis 2005). In addition to an increase in persisters, the presence of an extracellular matrix protects constituent cells from external aggressions. Extracellular matrices also act as a diffusion barrier to small molecules (Anderson and O’Toole 2008; Hall-Stoodley and Stoodley 2009). Related to this, in biofilms the diffusion of nutrients, vitamins, or cofactors is slower resulting in a bacterial community in which some of cells are metabolically inactive. Furthermore, the rate of bacterial growth is influenced by the fact that cells within a biofilm are confined to a limited space (Stewart and Franklin 2008). This condition is similar to the stationary phase created in laboratory conditions. Hence, biofilm formation in a way represents the natural stationary phase of bacterial growth. During stationary phase, bacteria profoundly change their physiology by increasing production of secondary metabolites such as antibiotics, pigments, and other small-molecules (Martin and Liras 1989). These secondary metabolites also function as signaling molecules to initiate the process of biofilm formation or to inhibit biofilm formation by other organisms that inhabit the same habitat (Lopez and Kolter 2009). In this article, we review the metabolic processes that characterize biofilm formation for a handful of well-studied bacterial organisms: Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, and Bacillus subtilis. In addition, we address the function of secondary metabolites and their role as signaling molecules during biofilm formation.