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.     

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.     

Origins of Eukaryotic Sexual Reproduction

Sexual reproduction is a nearly universal feature of eukaryotic organisms. Given its ubiquity and shared core features, sex is thought to have arisen once in the last common ancestor to all eukaryotes. Using the perspectives of molecular genetics and cell biology, we consider documented and hypothetical scenarios for the instantiation and evolution of meiosis, fertilization, sex determination, uniparental inheritance of organelle genomes, and speciation.The transition from prokaryote to protoeukaryote to the last eukaryotic common ancestor (LECA) entailed conservation, modification, and reconfiguration of preexisting genetic circuits via mutation, horizontal gene transfer (HGT), endosymbiosis, and selection, as detailed in previous articles of this collection. During the course of this evolutionary trajectory, the LECA became sexual, reassorting and recombining chromosomes in a process that entails regulated fusions of haploid gametes and diploid → haploid reductions via meiosis. That the LECA was sexual is no longer a matter of speculation/debate as evidence of sex, and of genes exclusively involved in meiosis, has been found in all of the major eukaryotic radiations (Brawley and Johnson 1992; Ramesh et al. 2005; Kobiyama et al. 2007; Malik et al. 2008; Phadke and Zufall 2009; Fritz-Laylin et al. 2010; Lahr et al. 2011; Peacock et al. 2011; Vanstechelman et al. 2013).We propose that the transition to a sexual LECA entailed four innovations: (1) alternation of ploidy via cell–cell fusion and meiosis; (2) mating-type regulation of cell–cell fusion via differentiation of complementary haploid gametes (isogametic and then anisogametic), a prelude to species-isolation mechanisms; (3) mating-type-regulated coupling of the diploid/meiotic state to the formation of adaptive diploid resting spores; and (4) mating-type-regulated transmission of organelle genomes. Our working assumption is that the protoeukaryote → LECA era featured numerous sexual experiments, most of which failed but some of which were incorporated, integrated, and modified. Therefore, this list is not intended to suggest a sequence of events; rather, the four innovations most likely coevolved in a parallel and disjointed fashion.Once these core sexual-cycle themes were in place, the evolution of eukaryotic sex has featured countless prezygotic and postzygotic variations, the outcome being the segregation of panmictic populations into distinct species with distinctive adaptations.For additional reviews on the evolution of sex, the interested reader is referred to Goodenough (1985), Dacks and Roger (1999), Schurko et al. (2009), Wilkins and Holliday (2009), Gross and Bhattacharya (2010), Lee et al. (2010), Perrin (2012), and Calo et al. (2013).     

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).     

On the Age of Eukaryotes: Evaluating Evidence from Fossils and Molecular Clocks

Our understanding of the phylogenetic relationships among eukaryotic lineages has improved dramatically over the few past decades thanks to the development of sophisticated phylogenetic methods and models of evolution, in combination with the increasing availability of sequence data for a variety of eukaryotic lineages. Concurrently, efforts have been made to infer the age of major evolutionary events along the tree of eukaryotes using fossil-calibrated molecular clock-based methods. Here, we review the progress and pitfalls in estimating the age of the last eukaryotic common ancestor (LECA) and major lineages. After reviewing previous attempts to date deep eukaryote divergences, we present the results of a Bayesian relaxed-molecular clock analysis of a large dataset (159 proteins, 85 taxa) using 19 fossil calibrations. We show that for major eukaryote groups estimated dates of divergence, as well as their credible intervals, are heavily influenced by the relaxed molecular clock models and methods used, and by the nature and treatment of fossil calibrations. Whereas the estimated age of LECA varied widely, ranging from 1007 (943–1102) Ma to 1898 (1655–2094) Ma, all analyses suggested that the eukaryotic supergroups subsequently diverged rapidly (i.e., within 300 Ma of LECA). The extreme variability of these and previously published analyses preclude definitive conclusions regarding the age of major eukaryote clades at this time. As more reliable fossil data on eukaryotes from the Proterozoic become available and improvements are made in relaxed molecular clock modeling, we may be able to date the age of extant eukaryotes more precisely.Our conception of the tree of eukaryotes has changed dramatically over the last few decades. In the 1980s and early 1990s, prevailing views were based on small subunit ribosomal RNA (SSU rRNA) gene phylogenies (e.g., Sogin 1991). However, as multiple protein-coding gene datasets were developed and more sophisticated phylogenetic methods were used, it became clear that the deep structure of the rRNA tree was the result of a methodological artifact known as long branch attraction (LBA) (Budin and Philippe 1998; Roger et al. 1999; Philippe et al. 2000a,b). Analyses based on multiple protein genes instead hinted at the existence of higher-level eukaryotic “supergroups” that encompassed both protistan and multicellular eukaryotic lineages (Baldauf et al. 2000). More recently, a better understanding of protistan ultrastructural diversity and the development of phylogenomic approaches have refined this picture and further delineated these groups (see also Fig. 1) (Bapteste et al. 2002; Burki et al. 2007; Hampl et al. 2009; Brown et al. 2012; Zhao et al. 2012).Open in a separate windowFigure 1.Maximum likelihood phylogenetic tree of eukaryotes based on a phylogenomic dataset. Additional taxa were added to the original 159-gene Brown et al. (2013) dataset to maximize available fossil calibrations (total of 85 taxa, 43,099 sites). Black dots represent nodes on which fossil calibration constraints were imposed; yellow stars indicate the various positions of the root of the eukaryote tree considered; pink dots indicate the origin of major eukaryotic groups discussed here. A maximum likelihood (ML) phylogenetic tree was obtained from 60 heuristic searches using RAxML version 7.2.6 (Stamatakis 2006) under the Le and Gascuel (LG) + Γ +F amino acid substitution model (Le and Gascuel 2008). Numbers at nodes indicate bootstrap support (BS) for splits estimated from 500 bootstrap replicates. Most splits received maximum support and only BS < 100% is reported. Tree is shown rooted at the base of Amorphea, although roots at the base of either Obazoa or Excavates were also explored. Bayesian inference was also conducted using PhyloBayes 3.2 (Lartillot et al. 2009) by running four chains under either the CAT-GTR, CAT-Poisson, or the catfix C60-Poisson models of evolution, all combined with a gamma rates model. Bayesian calculations were not completed because of lack of convergence between chains, although the postburn-in consensus phylogeny from all runs was identical to the ML tree except for an unresolved multifurcation at the base of Excavata. Relaxed molecular clock (RMC) analyses were conducted with Phylobayes using the ML tree as a fixed topology. For all analyses, a birth–death tree prior was applied. Two chains were run until diagnostic statistics indicated convergence or estimated dates on nodes of interest for the two chains were <5% different. Fossil calibrations were taken from Parfrey et al. (2011) with the following modifications: four calibrations (Gonyaulacales, Spirotrichs, Foraminifera, Euglenids) were removed because of insufficient gene coverage within the clade of interest; the “Ciliate” calibration based on the tetrahymenol biomarker was removed (see text); as insufficient gene data was available from the haptophyte Isochrysis galbana, the upper bound on the coccolithophorid calibration was adjusted to an uninformative maximum (3000 Ma); the oldest cestode fossil (tapeworm) (Dentzien-Dias et al. 2013) was added as a calibration for Platyhelminths. The minimum age (250 Ma) was taken from the youngest possible age of the fossil and the upper boundary was set equal to the next-oldest calibrated node (Bilateria).As our understanding of eukaryote phylogeny improved, fossil-calibrated molecular clock-based methods were beginning to be applied to date the major diversification events in this domain (Hedges et al. 2001; Douzery et al. 2004; Hedges and Kumar 2004; Berney and Pawlowski 2006; Parfrey et al. 2011). Molecular clock analyses were first introduced by Zuckerkandl and Pauling (1965). They showed that the differences between homologous proteins of different species are approximately proportional to their divergence time. Since then, sophisticated RMC methods have been developed that combine fossil data with molecular phylogenies for the inference of divergence times. However, attempts to estimate the age of deep divisions within eukaryotes using these methods have yielded vastly different estimates (e.g., see Douzery et al. 2004 vs. Hedges et al. 2004). These discrepancies can be explained by a myriad of sources of variability and error including (1) the assumed phylogeny of eukaryotes, (2) the sparse fossil record of protists and other organisms lacking hard structures for fossilization, (3) how fossil constraints are applied to phylogenetic trees, (4) methods and models used in RMC analysis, and (5) the selection of taxa and genes included.Here, we review the progress and pitfalls in estimating the age of the last eukaryotic common ancestor (LECA) and supergroups using molecular clock-based analyses. We first discuss recent progress in our understanding of eukaryotic phylogeny and the ancient eukaryotic fossil record, and then we review the development of molecular clock-based methods and how fossil constraints are treated. Next, we describe attempts to date ancient eukaryotic divergences using RMC methods. Finally, we present an RMC analysis of a very large dataset comprised of 159 proteins and 85 taxa, using 19 fossil calibrations.     

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.     

Origin of Microglia: Current Concepts and Past Controversies

Microglia are the resident macrophages of the central nervous system (CNS), which sit in close proximity to neural structures and are intimately involved in brain homeostasis. The microglial population also plays fundamental roles during neuronal expansion and differentiation, as well as in the perinatal establishment of synaptic circuits. Any change in the normal brain environment results in microglial activation, which can be detrimental if not appropriately regulated. Aberrant microglial function has been linked to the development of several neurological and psychiatric diseases. However, microglia also possess potent immunoregulatory and regenerative capacities, making them attractive targets for therapeutic manipulation. Such rationale manipulations will, however, require in-depth knowledge of their origins and the molecular mechanisms underlying their homeostasis. Here, we discuss the latest advances in our understanding of the origin, differentiation, and homeostasis of microglial cells and their myelomonocytic relatives in the CNS.Microglia are the resident macrophages of the central nervous system (CNS), which are uniformly distributed throughout the brain and spinal cord with increased densities in neuronal nuclei, including the Substantia nigra in the midbrain (Lawson et al. 1990; Perry 1998). They belong to the nonneuronal glial cell compartment and their function is crucial to maintenance of the CNS in both health and disease (Ransohoff and Perry 2009; Perry et al. 2010; Ransohoff and Cardona 2010; Prinz and Priller 2014).Two key functional features define microglia: immune defense and maintenance of CNS homeostasis. As part of the innate immune system, microglia constantly sample their environment, scanning and surveying for signals of external danger (Davalos et al. 2005; Nimmerjahn et al. 2005; Lehnardt 2010), such as those from invading pathogens, or internal danger signals generated locally by damaged or dying cells (Bessis et al. 2007; Hanisch and Kettenmann 2007). Detection of such signals initiates a program of microglial responses that aim to resolve the injury, protect the CNS from the effects of the inflammation, and support tissue repair and remodeling (Minghetti and Levi 1998; Goldmann and Prinz 2013).Microglia are also emerging as crucial contributors to brain homeostasis through control of neuronal proliferation and differentiation, as well as influencing formation of synaptic connections (Lawson et al. 1990; Perry 1998; Hughes 2012; Blank and Prinz 2013). Recent imaging studies revealed dynamic interactions between microglia and synaptic connections in the healthy brain, which contributed to the modification and elimination of synaptic structures (Perry et al. 2010; Tremblay et al. 2010; Bialas and Stevens 2013). In the prenatal brain, microglia regulate the wiring of forebrain circuits, controlling the growth of dopaminergic axons in the forebrain and the laminar positioning of subsets of neocortical interneurons (Squarzoni et al. 2014). In the postnatal brain, microglia-mediated synaptic pruning is similarly required for the remodeling of neural circuits (Paolicelli et al. 2011; Schafer et al. 2012). In summary, microglia occupy a central position in defense and maintenance of the CNS and, as a consequence, are a key target for the treatment of neurological and psychiatric disorders.Although microglia have been studied for decades, a long history of experimental misinterpretation meant that their true origins remained debated until recently. Although we knew that microglial progenitors invaded the brain rudiment at very early stages of embryonic development (Alliot et al. 1999; Ransohoff and Perry 2009), it has now been established that microglia arise from yolk sac (YS)-primitive macrophages, which persist in the CNS into adulthood (Davalos et al. 2005; Nimmerjahn et al. 2005; Ginhoux et al. 2010, 2013; Kierdorf and Prinz 2013; Kierdorf et al. 2013a). Moreover, early embryonic brain colonization by microglia is conserved across vertebrate species, implying that it is essential for early brain development (Herbomel et al. 2001; Bessis et al. 2007; Hanisch and Kettenmann 2007; Verney et al. 2010; Schlegelmilch et al. 2011; Swinnen et al. 2013). In this review, we will present the latest findings in the field of microglial ontogeny, which provide new insights into their roles in health and disease.     

Metabolic Stress in Autophagy and Cell Death Pathways

Growth factors and oncogenic kinases play important roles in stimulating cell growth during development and transformation. These processes have significant energetic and synthetic requirements and it is apparent that a central function of growth signals is to promote glucose metabolism to support these demands. Because metabolic pathways represent a fundamental aspect of cell proliferation and survival, there is considerable interest in targeting metabolism as a means to eliminate cancer. A challenge, however, is that molecular links between metabolic stress and cell death are poorly understood. Here we review current literature on how cells cope with metabolic stress and how autophagy, apoptosis, and necrosis are tightly linked to cell metabolism. Ultimately, understanding of the interplay between nutrients, autophagy, and cell death will be a key component in development of new treatment strategies to exploit the altered metabolism of cancer cells.Although single-celled organisms grow and proliferate based on nutrient availability, metazoan cells rely on growth factor input to promote nutrient uptake, regulate growth and proliferation, and survive (Raff 1992; Rathmell et al. 2000). Access and competition for these signals are critical in developmental patterning and to maintain homeostasis of mature tissues. Cells that do not receive proper growth factor signals typically atrophy, lose the ability to uptake and use extracellular nutrients, and instead induce the self-digestive process of autophagy as an intracellular energy source before ultimately undergoing programmed cell death. Cancer cells, in contrast, often become independent of extracellular growth signals by gaining mutations or expressing oncogenic kinases to drive intrinsic growth signals that mimic growth factor input, which can be the source of oncogene addiction. Growth factor input or oncogenic signals often drive highly elevated glucose uptake and metabolism (Rathmell et al. 2000; DeBerardinis et al. 2008; Michalek and Rathmell 2010). First described in cancer by Warburg in the 1920s, this highly glycolytic metabolic program is termed aerobic glycolysis and is a general feature of many nontransformed proliferative cells (Warburg 1956; DeBerardinis et al. 2008).Nutrient uptake and aerobic glycolysis induced by growth signals play key roles in cell survival (Vander Heiden et al. 2001). Manipulating cell metabolism as a means to promote the death of inappropriately dividing cells, therefore, is a promising new avenue to treat disease. Targeting the altered metabolism of cancer cells in particular is of great interest. It is still unclear at the molecular level, however, how inhibiting or modulating cell metabolism leads to apoptosis, and how these pathways may best be exploited (Dang et al. 2009; Wise and Thompson 2010).Growth factor or oncogenic kinases promote multiple metabolic pathways that are essential to prevent metabolic stress and may be targets in efforts to link metabolism and cell death (Vander Heiden et al. 2001). Decreased glucose metabolism on loss of growth signals leads to decreased ATP generation as well as loss in generation of many biosynthetic precursor molecules, including nucleic acids, fatty acids, and acetyl-CoA for acetylation (Zhao et al. 2007; Wellen et al. 2009; Coloff et al. 2011). Glucose is also important as a precursor for the hexosamine pathway, to allow proper glycosylation and protein folding in the endoplasmic reticulum (Dennis et al. 2009; Kaufman et al. 2010). If glucose metabolism remains insufficient or disrupted, the cells can switch to rely on mitochondrial oxidation of fatty acids and amino acids, which are energy rich but do not readily support cell growth and can lead to potentially dangerous levels of reactive oxygen species (Wellen and Thompson 2010). Amino acid deficiency can directly inhibit components of the signaling pathways downstream from growth factors and activate autophagy (Lynch 2001; Beugnet et al. 2003; Byfield et al. 2005; Nobukuni et al. 2005). Finally, hypoxia induces a specific pathway to increase nutrient uptake and metabolism via the hypoxia-inducible factor (HIF1/2α) that promotes adaptation to anaerobic conditions, but may lead to apoptosis if hypoxia is severe (Saikumar et al. 1998; Suzuki et al. 2001; Fulda and Debatin 2007).Typically a combination of metabolic stresses rather than loss of a single nutrient input occur at a given time (Degenhardt et al. 2006) and autophagy is activated to mitigate damage and provide nutrients for short-term survival (Bernales et al. 2006; Tracy et al. 2007; Altman et al. 2011; Guo et al. 2011). Autophagy is a cellular process of bulk cytoplasmic and organelle degradation common to nearly all eukaryotes. Unique double-membraned vesicles known as autophagosomes engulf cellular material and fuse with lysosomes to promote degradation of the contents (Kelekar 2005). Described in greater detail below, autophagy can reduce sources of stress, such as protein aggregates and damaged or dysfunctional intracellular organelles, and provide nutrients during times of transient and acute nutrient withdrawal.Despite the protective effects of autophagy, cells deprived of growth signals, nutrients, or oxygen for prolonged times will eventually succumb to cell death. Apoptosis is the initial death response on metabolic stress and is regulated by Bcl-2 family proteins. In healthy cells, antiapoptotic Bcl-2 family proteins, such as Bcl-2, Bcl-xl, and Mcl-1, bind and inhibit the multidomain proapoptotic proteins Bax and Bak (van Delft and Huang 2006; Walensky 2006; Chipuk et al. 2010). In metabolic stress, proapoptotic “BH3-only” proteins of the Bcl-2 family are induced or activated and bind to and inhibit the antiapoptotic Bcl-2 family proteins to allow activation of the proapoptotic Bax and Bak (Galonek and Hardwick 2006). The BH3-only proteins Bim, Bid, and Puma can also directly bind and activate Bax and Bak (Letai et al. 2002; Ren et al. 2010). Active Bax and Bak disrupt the outer mitochondrial membrane (termed mitochondrial outer-membrane permeabilization, or MOMP) and release several proapoptotic factors including cytochrome-C that activate the apoptosome that in turn activates effector caspases to cleave a variety of cellular proteins and drive apoptosis (Schafer and Kornbluth 2006). In cases in which these apoptotic pathways are suppressed, metabolic stress can instead lead to necrotic cell death (Jin et al. 2007).     

The Eukaryotic Tree of Life from a Global Phylogenomic Perspective

Molecular phylogenetics has revolutionized our knowledge of the eukaryotic tree of life. With the advent of genomics, a new discipline of phylogenetics has emerged: phylogenomics. This method uses large alignments of tens to hundreds of genes to reconstruct evolutionary histories. This approach has led to the resolution of ancient and contentious relationships, notably between the building blocks of the tree (the supergroups), and allowed to place in the tree enigmatic yet important protist lineages for understanding eukaryote evolution. Here, I discuss the pros and cons of phylogenomics and review the eukaryotic supergroups in light of earlier work that laid the foundation for the current view of the tree, including the position of the root. I conclude by presenting a picture of eukaryote evolution, summarizing the most recent progress in assembling the global tree.It is redundant to say that eukaryotes are diverse. Plants, animals, and fungi are the charismatic representatives of the eukaryotic domain of life, but this narrow view does not do justice to the eukaryotic diversity. Microscopic eukaryotes, often unicellular and known as the protists, represent the bulk of most major groups, whereas multicellular lineages are confined to small corners on the global tree of eukaryotes. If all eukaryotes possess structures enclosed within intracellular membranes (the organelles), an infinite variation of forms and feeding strategies has evolved since their origin. Eukaryotic cells can wander on their own, sometimes forming hordes of free-living pico-sized organisms that flourish in oceans. They can be parasites or symbionts, or come together by the billions in tightly packed, highly regulated multicellular organisms. Eukaryotes have occupied just about every ecological niche on Earth. Some actively gather food from the environment, others use plastids (chloroplasts) to derive energy from the light; many can adapt to variable conditions by switching between autotrophy and the predatory consumption of prey by phagotrophy. Eukaryotes also show a great deal of genomic variation (Lynch and Conery 2003). Some amoebozoan protists, for instance, have the largest known genomes—more than 200 times larger than that of humans (Keeling and Slamovits 2005). Conversely, microbial parasites can have highly compact, bacterial-size genomes (Corradi et al. 2010). Even smaller are the remnant nuclear genomes (nucleomorphs) of what were once free-living microbial algae. At around 500,000 nucleotides and hardly encoding a few hundreds genes, nucleomorphs are the smallest nuclear genome of all (Douglas et al. 2001; Gilson et al. 2006; Lane et al. 2007).Recognizing this great diversity and pushed by a desire to establish order, biologists have long attempted to assemble a global eukaryotic tree of life. A fully resolved phylogenetic tree including all organisms is not only the ultimate goal of systematics, it would also provide the foundation to infer the acquisition and evolution of countless characters through the history of long-dead species. But early attempts to resolve the eukaryotic tree, most of which were based on comparisons of morphology and nutrition modes, faced the impossible challenge of describing in an evolutionary sensitive way a world in which most of the diversity occurs among tiny microbes. For decades, biology textbooks assigned the eukaryotes to evolutionary entities called “kingdoms” in which the lords were the animals, plants, and fungi (Copeland 1938; Whittaker 1969; Margulis 1971). This is not to say that biologist ignored protists, and they have been in fact recognized as a kingdom for more that a century (Haeckel 1866), but protists were considered to be "simple" organisms from which more elaborate, multicellular species emerged. Although these early proposals succeeded in recognizing several major assemblages, such as animals and plants, they were less successful in resolving the relationships between the groups and, with the benefit of hindsight, failed to account for the fundamental paraphyletic and complex nature of the protist lines.     

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.     

Endocytosis,Signaling, and Beyond

The endocytic network comprises a vast and intricate system of membrane-delimited cell entry and cargo sorting routes running between biochemically and functionally distinct intracellular compartments. The endocytic network caters to the organization and redistribution of diverse subcellular components, and mediates appropriate shuttling and processing of materials acquired from neighboring cells or the extracellular milieu. Such trafficking logistics, despite their importance, represent only one facet of endocytic function. The endocytic network also plays a key role in organizing, mediating, and regulating cellular signal transduction events. Conversely, cellular signaling processes tightly control the endocytic pathway at different steps. The present article provides a perspective on the intimate relationships that exist between particular endocytic and cellular signaling processes in mammalian cells, within the context of understanding the impact of this nexus on integrated physiology.Molecular mechanisms governing the remarkable diversity of endocytic routes and trafficking steps are described elsewhere in the literature (see Bissig and Gruenberg 2013; Henne et al. 2013; Burd and Cullen 2014; Gautreau et al. 2014; Kirchhausen et al. 2014; Mayor et al. 2014; Merrifield and Kaksonen 2014; Piper et al. 2014). Moreover, these have been the focus of many studies in the last 30 years, and the topic has been covered by many excellent reviews, making it unnecessary for us to dwell on this aspect any further here (see, for instance, Howes et al. 2010; McMahon and Boucrot 2011; Sandvig et al. 2011; Parton and del Pozo 2013). Herein, we will instead concentrate our attention on how cellular regulatory mechanisms control endocytosis, as well as on how endocytic events impinge on cell functions. Emphasis will be placed, although not exclusively, on studies that analyze cellular networks using holistic approaches and in vivo analysis. Our aim is to give the reader a flavor of the deep embedding of endocytic processes within cellular programs, a concept we refer to as the endocytic matrix (Scita and Di Fiore 2010).     

P-Bodies and Stress Granules: Possible Roles in the Control of Translation and mRNA Degradation

The control of translation and mRNA degradation is important in the regulation of eukaryotic gene expression. In general, translation and steps in the major pathway of mRNA decay are in competition with each other. mRNAs that are not engaged in translation can aggregate into cytoplasmic mRNP granules referred to as processing bodies (P-bodies) and stress granules, which are related to mRNP particles that control translation in early development and neurons. Analyses of P-bodies and stress granules suggest a dynamic process, referred to as the mRNA Cycle, wherein mRNPs can move between polysomes, P-bodies and stress granules although the functional roles of mRNP assembly into higher order structures remain poorly understood. In this article, we review what is known about the coupling of translation and mRNA degradation, the properties of P-bodies and stress granules, and how assembly of mRNPs into larger structures might influence cellular function.The translation and decay of mRNAs play key roles in the control of eukaryotic gene expression. The determination of eukaryotic mRNA decay pathways has allowed insight into how translation and mRNA degradation are coupled. Degradation of eukaryotic mRNAs is generally initiated by shortening of the 3′ poly (A) tail (Fig. 1A) (reviewed in Parker and Song 2004; Garneau et al. 2007) by the major mRNA deadenylase, the Ccr4/Pop2/Not complex (Daugeron et al. 2001; Tucker et al. 2001; Thore et al. 2003). Following deadenylation, mRNAs can be degraded 3′ to 5′ by the exosome (Anderson and Parker 1998; Wang and Kiledjian 2001). However, more commonly, mRNAs are decapped by the Dcp1/Dcp2 decapping enzyme and then degraded 5′ to 3′ by the exonuclease, Xrn1 (Decker and Parker 1993; Hsu and Stevens 1993; Muhlrad et al. 1994, 1995; Dunckley and Parker 1999; van Dijk et al. 2002; Steiger et al. 2003). In metazoans, a second decapping enzyme, Nudt16, also contributes to mRNA turnover (Song et al. 2010).Open in a separate windowFigure 1.Eukaryotic mRNA decay pathways. (A) General mRNA decay pathways. (B) Specialized decay pathways that degrade translationally aberrant mRNAs.The processes of mRNA decay and translation are interconnected in eukaryotic cells in many ways. For example, quality control mechanisms exist to detect aberrancies in translation, which then lead to mRNAs being degraded by specialized mRNA decay pathways (Fig. 1B). Nonsense-mediated decay (NMD) is one such mRNA quality control system that degrades mRNAs that terminate translation aberrantly. In yeast, aberrant translation termination leads to deadenylation-independent decapping (Muhlrad and Parker 1994), whereas in metazoan cells NMD substrates can be both decapped and endonucleolytically cleaved and degraded (reviewed in Isken and Maquat 2007). A second quality control system for mRNA translation is referred to as no-go decay (NGD) and leads to endonucleolytic cleavage of mRNAs with strong stalls in translation elongation (Doma and Parker 2006; reviewed in Harigaya and Parker 2010). Another mechanism of mRNA quality control is the rapid 3′ to 5′ degradation of mRNAs that do not contain translation termination codons, which is referred to as non-stop decay (NSD) (Frischmeyer et al. 2002; van Hoof et al. 2002). The available evidence suggests these specialized mechanisms function primarily on aberrant mRNAs that are produced by defects in splicing, 3′ end formation, or damage to RNAs.The main pathway of mRNA degradation is also in competition with translation initiation. Competition between the two processes was first suggested by the observation that removal of the poly (A) tail and the cap structure, both of which stimulate translation initiation, were the key steps in mRNA degradation. In addition, inhibition of translation initiation by strong secondary structures in the 5′UTR, translation initiation inhibitors, a poor AUG context, or mutations in initiation factors increases the rates of deadenylation and decapping (Muhlrad et al. 1995; Muckenthaler et al. 1997; Lagrandeur and Parker 1999; Schwartz and Parker 1999). Moreover, the cap binding protein eIF4E, known to stimulate translation initiation, inhibits the decapping enzyme, Dcp1/Dcp2, both in vivo and in vitro (Schwartz and Parker 1999; Schwartz and Parker 2000). Finally, many mRNA specific regulatory factors, (e.g., miRNAs or PUF proteins), both repress translation and accelerate deadenylation and decapping (reviewed in Wickens et al. 2002; Behm-Ansmant et al. 2006; Franks and Lykke-Anderson 2008; Shyu et al. 2008).In the simplest model, the competition between translation and mRNA degradation can be understood through changes in the proteins bound to the cap and poly (A) tail that then influence the accessibility of these structures to deadenylases and decapping enzymes. For example, given that the Ccr4/Pop2/Not deadenylase complex is inhibited by poly (A)-binding protein (Pab1) (Tucker et al. 2002), the effects of translation on deadenylation are most likely through dynamic changes in the association of Pab1 binding with the poly (A) tail. One possibility is that defects in translation initiation either directly or indirectly decrease Pab1 association with the poly (A) tail. Deadenylation is also affected by aspects of translation termination. For instance, premature translation termination in yeast accelerates poly (A) shortening as part of the process of NMD (Cao and Parker 2003; Mitchell and Tollervey 2003). The coupling of translation termination to deadenylation has been suggested to occur through direct interactions of the translation termination factor eRF3 with Pab1 (Cosson et al. 2002), which may lead to Pab1 transiently dissociating from the poly (A) tail. Interestingly, in yeast, once the poly (A) tail reaches an oligo (A) length of 10–12 residues, a length that reduces the affinity of Pab1, the mRNA can become a substrate for decapping and for binding of the Pat1/Lsm1-7 complex (Tharun and Parker 2001; Chowdhury et al. 2007), which enhances the rate of decapping. This exchange of the Pab1 protein for the Pat1/Lsm1-7 complex is part of the mechanism that allows decapping to be promoted following deadenylation.A similar mRNP dynamic is also likely to occur on the cap structure. Specifically, the competition between translation initiation and decapping suggests that prior to decapping, translation initiation factors are exchanged for decapping factors, thereby assembling a distinct “decapping” mRNP that is no longer capable of translation initiation (Tharun and Parker 2001). This idea is supported by the observation that some decapping activators also function as translational repressors (Coller and Parker 2005; Pilkington and Parker 2008; Nissan et al. 2010). Thus, mRNA decapping appears to occur in two steps, first inhibition of translation initiation and exchange of translation factors for the general repression/degradation machinery, and a second step whereby the mRNA is actually degraded. Thus, by understanding the changes in mRNP states between actively translating mRNAs and mRNAs that are translationally repressed and possibly stored or ultimately degraded we will better understand how the fate of mRNAs is controlled in the cytoplasm.     

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).     

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.     

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.