Earth��s Earliest Atmospheres

Earth is the one known example of an inhabited planet and to current knowledge the likeliest site of the one known origin of life. Here we discuss the origin of Earth’s atmosphere and ocean and some of the environmental conditions of the early Earth as they may relate to the origin of life. A key punctuating event in the narrative is the Moon-forming impact, partly because it made Earth for a short time absolutely uninhabitable, and partly because it sets the boundary conditions for Earth’s subsequent evolution. If life began on Earth, as opposed to having migrated here, it would have done so after the Moon-forming impact. What took place before the Moon formed determined the bulk properties of the Earth and probably determined the overall compositions and sizes of its atmospheres and oceans. What took place afterward animated these materials. One interesting consequence of the Moon-forming impact is that the mantle is devolatized, so that the volatiles subsequently fell out in a kind of condensation sequence. This ensures that the volatiles were concentrated toward the surface so that, for example, the oceans were likely salty from the start. We also point out that an atmosphere generated by impact degassing would tend to have a composition reflective of the impacting bodies (rather than the mantle), and these are almost without exception strongly reducing and volatile-rich. A consequence is that, although CO- or methane-rich atmospheres are not necessarily stable as steady states, they are quite likely to have existed as long-lived transients, many times. With CO comes abundant chemical energy in a metastable package, and with methane comes hydrogen cyanide and ammonia as important albeit less abundant gases.The origin of life has not been seen in Earth’s rock record, and poor preservation of Earth’s oldest rocks suggests that it will not be. The oldest bits of rock that might plausibly retain geochemical hints of habitable conditions on Earth’s surface—a handful of zircons—have been subject to fierce debates regarding whether they contain such evidence. However, the weight of evidence does suggest that Earth has supported microbial life since the Hadean (“Hadean” refers to the geologic Eon preceding the Achaean. It can be regarded as Earth before the appearance of a true-rock record 3.9 Ga—the precise definition remains to be agreed on by the proper authorities). This would make Hadean Earth the one known planet where life has begun.The modern focus on the atmosphere as the source of prebiotic chemistry dates to the famous Miller-Urey experiments of the 1950s (Miller 1953, 1955; Miller and Urey 1959; Oró and Kamel 1961; Johnson et al. 2008). These experiments were intended to model the kinds of disequilibrium chemistry that would have resulted from electrical discharges in, or ultraviolet radiation being absorbed in, highly reduced atmospheres in which methane, ammonia, and water were all major constituents. These experiments were driven by Harold C. Urey’s theory that Earth accreted as a cool body and that its atmosphere was dominated by hydrogen and the hydrides of common volatiles (Urey won the Nobel Prize for Chemistry in 1934 for the discovery of deuterium). Miller and his colleagues, and many other investigators who have since performed similar experiments, have consistently found that a wide range of amino acids and other prebiotically interesting molecules form readily in such environments (e.g., see the review by Oró et al. 1990). These experiments were very influential in directing the attention of prebiotic chemists to a highly reduced primordial atmosphere.However, photochemical studies showed that any methane (Lasaga et al. 1971) or ammonia (Kuhn and Atreya 1979; Kasting 1982) in the atmosphere would quickly be destroyed. Meanwhile geologically based arguments, which treat the atmosphere as outgassed from the solid Earth, were taken as strongly suggesting that Earth’s original atmosphere was composed mostly of H₂O, CO₂, and N₂, with only small amounts of CO and H₂, and essentially no CH₄ or NH₃ (Poole 1951; Holland 1962; Abelson 1966; Holland 1984). This composition of volcanic gases is determined by temperature and the QFM (quartz-fayalite-magnetite) mineral buffer pertinent to the modern mantle. Nor is there evidence of a time on Earth when things were clearly different. Geochemical evidence in Earth’s oldest igneous rocks indicates that the redox state of the Earth’s mantle has not changed over the past 3.8 Gyr (Delano 2001; Canil 2002). Miller-Urey-type experiments performed in the more oxidized mixtures of modern volcanic gases generate relatively little of prebiotic interest, especially when CO₂ is abundant (Miller and Urey 1959; Schlessinger and Miller 1983; Stribling and Miller 1987). New work suggests that spark yields of ammonia, HCN, and amino acids in CO₂-N2-water mixtures can be less disappointing if the water is allowed to become acidic (Cleaves et al. 2008). Nevertheless, the contrast between methane and ammonia on the one hand and carbon dioxide and dinitrogen on the other led many prebiotic chemists, Miller and Urey prominent among them, to regard the presence of life on Earth as providing a strong boundary condition on the nature of Earth’s early atmosphere.The sense of poor prospects led some to abandon the atmosphere in favor of the hydrosphere. At the low temperatures and high water activities of hydrothermal systems, it is in theory possible to get non-negligable amounts of methane and ammonia at the QFM buffer (French 1966; Shock and Schulte 1990, 1998). Shock and Schulte (1990) used this approach to explain the abundances of organic molecules in asteroids (as sampled by carbonaceous meteorites) and suggested that such a model might have application to the origin of life on Earth (Shock 1990; Shock et al. 1995; Shock and Schulte 1998). The issue of a submarine (as opposed to a subaerial) origin of life became contentious (Miller and Bada 1988, 1993; Shock and Schulte 1993). Significant features of the hydrothermal hypothesis are that (1) it ties the origin of life to the process of making organic molecules, and (2) it implies that life is widespread in the Solar System, because hydrothermal systems may exist in many moons (Shock and McKinnon 1993).Another workaround is to abandon the idea that organic molecules were generated in situ here on Earth. Instead the organic molecules would be delivered by comets and asteroids and interplanetary dust particles (IDPs, Anders 1989; Chyba and Sagan 1992; Whittet 1997). The basis of this proposal is that organic molecules are abundant in the Solar System. Many meteorites and dust grains are rich in complex organic molecules, and there is little doubt that comets are at least as rich. The chief difficulty is that, apart from special cases, only a small fraction of the more interesting and more delicate organic materials in comets and asteroids would survive impact (Clark 1988; Anders 1989; Chyba et al. 1990; Chyba and Sagan 1992; Whittet 1997; Pierazzo and Chyba 1999; Pasek and Lauretta 2008). The importance of an exogenic source of organics to the origin of life has probably been overstated. In the median case the quantities aren’t large and the biological potential of a modest cosmic windfall of IDPs is unclear, although a slow soft collision by a big organic-rich comet—possible but by construction unlikely—could have a huge unique effect (Clark 1988). An alternative lesson to be taken from abundant organics in the Solar System is that organic molecules are not hard to make, and so were probably also made here.A third perspective to the origin of the atmosphere—that the earliest atmosphere was degassed from impacting material as it arrived rather than outgassed from the solid Earth into a primordial vacuum (Arrhenius et al. 1974; Lange et al. 1985; Tyburczy et al. 1986; Abe and Matsui 1986; Zahnle et al. 1988; Ahrens et al. 1989)—has gotten comparatively little traction. Recently three new theoretical studies (Schaefer and Fegley 2007, 2010; Hashimoto et al. 2007) show that atmospheres dominated by impact degassing would be much more reduced than atmospheres dominated by Earth’s mantle. A fourth recent study (Sugita and Schultz 2009) addresses impact degassing and impact synthesis in possible cometary matter experimentally. The latter can be regarded as parallel to the experiments performed by nature when the pieces of comet Shoemaker-Levy 9 struck Jupiter in July 1994. The SL9 impacts generated vast quantities of small molecules, especially CO, but the list of apparently synthetic products also included HCN, C₂H₂, C₂H₄, S₂, CS, CS₂, OCS, and CO₂ (Zahnle et al. 1995; Harrington et al. 2004).Here we address the state and properties of Earth’s primordial atmosphere. Our review is presented in three parts: the origin of the atmosphere, the Moon-forming impact, and events taking place after the Moon-forming impact. Placing the origin of the atmosphere before the Moon-forming impact is a choice that is founded on the high volatile contents of all known chondritic meteorites: To build Earth without volatiles is difficult if all known examples of possible source materials are more volatile-rich than Earth. Nonetheless we will also consider the alternative—volatile delivery after the Moon-forming impact—because it is one of the concepts in debate (cf. Albarède 2009) and because the hypothesis of transient, strongly reduced impact-degassed atmospheres applies obviously and directly to it.     

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.     

Morphogen Gradient Formation

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

Structure and Biochemistry of Cadherins and Catenins

Classical cadherins mediate specific adhesion at intercellular adherens junctions. Interactions between cadherin ectodomains from apposed cells mediate cell–cell contact, whereas the intracellular region functionally links cadherins to the underlying cytoskeleton. Structural, biophysical, and biochemical studies have provided important insights into the mechanism and specificity of cell–cell adhesion by classical cadherins and their interplay with the cytoskeleton. Adhesive binding arises through exchange of β strands between the first extracellular cadherin domains (EC1) of partner cadherins from adjacent cells. This “strand-swap” binding mode is common to classical and desmosomal cadherins, but sequence alignments suggest that other cadherins will bind differently. The intracellular region of classical cadherins binds to p120 and β-catenin, and β-catenin binds to the F-actin binding protein α-catenin. Rather than stably bridging β-catenin to actin, it appears that α-catenin actively regulates the actin cytoskeleton at cadherin-based cell–cell contacts.Cadherins constitute a large family of cell surface proteins, many of which participate in Ca²⁺-dependent cell adhesion that plays a fundamental role in the formation of solid tissues (Takeichi 1995; Tepass 1999; Gumbiner 2005). Many events in the development of multicellular assemblies are associated with changes in cadherin expression (Takeichi 1995; Honjo et al. 2000; Price et al. 2002). Expression of particular cadherins often correlates with formation of discrete tissue structures, and in mature tissues discrete cell layers or other cell assemblies are often demarcated by particular cadherins (Gumbiner 1996). Conversely, down-regulation or loss of cadherins correlates with an increased metastatic potential of the affected cells that arises from the loss of their adhesive properties (Hajra and Fearon 2002; Gumbiner 2005).The cadherins of vertebrates, and some of their invertebrate homologs, are the most highly characterized. “Classical” cadherins, associated with the adherens junction, and the closely related desmosomal cadherins feature an amino-terminal extracellular region or ectodomain that is followed by a transmembrane anchor and a carboxy-terminal intracellular region. Interactions between ectodomains on apposed cells mediate specific cell–cell contacts, whereas the intracellular region functionally links cadherins to the underlying cytoskeleton. This article focuses on structural, biophysical, and biochemical studies that have provided important mechanistic insights into the specificity of cell–cell adhesion and its interplay with the cytoskeleton (see also Meng and Takeichi 2009; Cavey and Lecuit 2009).     

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).     

Sexual Conflict between Parents: Offspring Desertion and Asymmetrical Parental Care

Parental care is an immensely variable social behavior, and sexual conflict offers a powerful paradigm to understand this diversity. Conflict over care (usually considered as a type of postzygotic sexual conflict) is common, because the evolutionary interests of male and female parents are rarely identical. I investigate how sexual conflict over care may facilitate the emergence and maintenance of diverse parenting strategies and argue that researchers should combine two fundamental concepts in social behavior to understand care patterns: cooperation and conflict. Behavioral evidence of conflict over care is well established, studies have estimated specific fitness implications of conflict for males or females, and experiments have investigated specific components of conflict. However, studies are long overdue to reveal the full implications of conflict for both males and females. Manipulating (or harming) the opposite sex seems less common in postzygotic conflicts than in prezygotic conflicts because by manipulating, coercing, or harming the opposite sex, the reproductive interest of the actor is also reduced. Parental care is a complex trait, although few studies have yet considered the implications of multidimensionality for parental conflict. Future research in parental conflict will benefit from understanding the behavioral interactions between male and female parents (e.g., negotiation, learning, and coercion), the genetic and neurogenomic bases of parental behavior, and the influence of social environment on parental strategies. Empirical studies are needed to put sexual conflict in a population context and reveal feedback between mate choice, pair bonds and parenting strategies, and their demographic consequences for the population such as mortalities and sex ratios. Taken together, sexual conflict offers a fascinating avenue for understanding the causes and consequences of parenting behavior, sex roles, and breeding system evolution.Sexual conflict over care is a type of evolutionary conflict that emerges from the different interests of males and females in regard to parental care (Trivers 1972; Clutton-Brock 1991; Chapman et al. 2003; Arnqvist and Rowe 2005). The conflict arises when the young benefit from the effort of either parent, but each parent pays only the cost of its own effort, so that each parent would have higher fitness if the other parent provides more care (Houston et al. 2005; Lessells 2006; Klug et al. 2012). Conflict refers to the way selection acts on the two sexes that have different optimum values in parental provisioning; between the two optima, sexually antagonistic selection operates (Lessells 2012). Sexual conflict over care can be seen as tug-of-war, because each parent is tempted to pull out of care leaving the other parent to provide more care for the young (Székely et al. 1996; Arnqvist and Rowe 2005; Lessells 2012).Sexual conflict over care seems to be the rule rather than the exception. The conflict may be resolved by one or both parents failing to adopt the optimal parenting for their mate and nonetheless remaining in conflict, or by both parents adopting the optima that suit their mate (i.e., exhibit the maximum provisioning possible). Examples of the latter conflict resolution (whereby the conflict is completely wiped out) are exceedingly rare and seem to be limited to three scenarios. First, conflict over care is not expected in obligate monogamy by both males and females so that the lifetime reproductive successes of both parents are identical. This may occur in semelparous organisms (i.e., both the male and the female put their resources into a single breeding event) or in iteroparous organisms with lifelong exclusive monogamy. Second, males and females might be genetically identical, so even though one or both sexes are polygamous, polygamy would benefit the same genome whether it is in the male or the female phenotype. Third, parental care is cost-free and thus parents provide maximum level of care (P Smiseth, pers. comm.). However, few, if any, organisms fit these restrictive assumptions, and thus conflict-free parenting seems exceedingly rare in nature: (1) some level of polygamy (by males, females, or both sexes) appears to be widespread; (2) the reproduction by genetically identical individuals (clones) as separate sexes (males and females) seems unlikely although not impossible if sex is determined environmentally; and (3) care provisioning, as far as we are aware, does have costs that discourage parents from providing their absolute maxima for a given batch of offspring.Parents may have conflicting interest over caring or deserting the young, the amount of care provided for each young, the number of simultaneous mates, the size and sex ratio of their brood, and the synchronization of birth for a clutch or litter of young (Westneat and Sargent 1996; Houston et al. 2005; Klug et al. 2012; Lessells 2012). Conflict between parents over care is usually labeled as a postzygotic conflict although resources had been already allocated into the gametes before fertilization as part of parental provisioning (Clutton-Brock 1991); other examples of postzygotic conflicts include infanticide and genomic imprinting (Chapman et al. 2003; Tregenza et al. 2006; Lessells 2012; see Palombit 2014).Studies of conflict over care are fascinating for at least four major reasons. First, parental care is diverse. There is great variation both between and within species in the types of care provided, duration of care, and the sex of the care-providing parent (Wilson 1975; Clutton-Brock 1991; McGraw et al. 2010; Royle et al. 2012), and sexual conflict is thought to be one of the main drivers of this diversity. Second, parental care is one of the core themes in breeding systems and sex role evolution, and it is increasingly evident that parental care can only be understood by dissecting the entangled relationships between ecological and life-history settings, and the variety of mating and parenting behavior (Székely et al. 2000; Webb et al. 2002; Wedell et al. 2006; Jennions and Kokko 2010; Klug et al. 2012). Third, parental care was (and is) one of the test beds of evolutionary game theory. Numerous models have been developed to understand how parents interact with each other and with their offspring (Trivers 1972; Maynard Smith 1977; Houston and Davies 1985; Balshine-Earn and Earn 1998; McNamara et al. 1999, 2000; Webb et al. 1999; Johnstone and Hinde 2006; Johnstone et al. 2014). Parental care research is one field in which empiricists are extensively testing the predictions of evolutionary game theoretic models in both the laboratory and wild populations (Székely et al. 1996; Balshine-Earn and Earn 1998; Harrison et al. 2009; Klug et al. 2012; Lessells 2012; van Dijk et al. 2012), although the congruence between theoretical and empirical work is not as tight as often assumed (Houston et al. 2013). Finally, parental care—wherever it occurs—is often a major component of fitness, because whether the offspring are cared for or abandoned has a large impact on their survival, maturation, and reproduction (Smiseth et al. 2012). Therefore, parental care (or the lack of it) may have an impact on population productivity and population growth and influences the resilience of populations to various threats (Bessa-Gomes et al. 2004; Veran and Beissinger 2009; Blumstein 2010). Thus, understanding the behavioral interactions between parents and the fitness implications of these interactions is highly relevant for population dynamics and biodiversity conservation (Alonzo and Sheldon 2010; Blumstein 2010).Sexual conflict over care has been reviewed recently (van Dijk and Székely 2008; Lessells 2012; Houston et al. 2013). Here, I focus on three issues that have not been extensively covered by previous reviews: (1) why sexual conflict over care occurs in some environments, whereas in others parental cooperation appears to dominate; (2) how can one detect sexual conflict over care; and (3) what are the implications of sexual conflict over care for macroevolution. I view causes and implications of parental care primarily from empirical perspectives; there are excellent reviews on the rich theoretical literature (Lessells 2006, 2012; Klug et al. 2012; Houston et al. 2013). My intention is not to be comprehensive; instead, I use selected examples to illustrate salient features of conflict over care. I focus on ecological and evolutionary aspects; for a discussion of the genetic and neuroendocrine bases of parental care, see Adkins-Regan (2005), McGraw et al. (2010), and Champagne and Curley (2012). I prefer to use the term “parental care” instead of “parental investment,” because the latter, as admitted by Trivers (1985), is extremely difficult to estimate empirically and thus may have a limited use in empirical studies (Mock and Parker 1997; McGraw et al. 2010). The term “parental investment” can be deceptive, if used without directly demonstrating the full costs of care. The term “parental care” is less restrictive, because it refers to any form of parental behavior that appears to increase the fitness of an offspring and is likely to have evolved for this function (Clutton-Brock 1991; Smiseth et al. 2012). In this review, I focus on families in the narrow sense (i.e., two parents and their offspring), although in numerous organisms the families are more extensive and may include several generations of offspring living together and/or unrelated individuals that assist the parents rearing the young.     

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.     

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.     

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

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

SLAM-Family Receptors: Immune Regulators with or without SAP-Family Adaptors

The signaling lymphocytic activation molecule (SLAM) family of receptors and the SLAM-associated protein (SAP) family of intracellular adaptors are expressed in immune cells. By way of their cytoplasmic domain, SLAM-related receptors physically associate with SAP-related adaptors. Evidence is accumulating that the SLAM and SAP families play crucial roles in multiple immune cell types. Moreover, the prototype of the SAP family, that is SAP, is mutated in a human immunodeficiency, X-linked lymphoproliferative (XLP) disease. In the presence of SAP-family adaptors, the SLAM family usually mediates stimulatory signals that promote immune cell activation or differentiation. In the absence of SAP-family adaptors, though, the SLAM family undergoes a “switch-of-function,” thereby mediating inhibitory signals that suppress immune cell functions. The molecular basis and significance of this mechanism are discussed herein.Immune cells undergo differentiation and, once mature, are activated through the integrated actions of many molecules, including cell surface receptors and intracellular signaling effectors. Whereas some of these molecules have “primary” roles in the immune response, others have secondary, albeit still critical, functions in this process. For example, differentiation and activation of B cells are strictly dependent on the function of the B-cell receptor (BCR) and its intracellular effectors. Other receptors present on B cells, such as CD19 and CD40, influence B-cell functions in critical ways, by modulating BCR-triggered signals (Cambier et al. 1994).There is accumulating evidence that the signaling lymphocytic activation molecule (SLAM) family of receptors plays important roles in immunity (Schwartzberg et al. 2009; Ma et al. 2007; Veillette et al. 2007; Veillette 2006b; Veillette 2006a). This class of receptors provides key effects in multiple immune cell types. Recent data indicate that SLAM-family receptors can either promote or inhibit the functions of primary activating receptors (Cruz-Munoz et al. 2009; Dong et al. 2009). These alternative activities are controlled by whether or not SLAM-related receptors are coexpressed with members of the SLAM-associated protein (SAP) family of intracellular adaptor molecules. The functions and mechanisms of action of the SLAM and SAP families are reviewed herein.     

The Persistent Contributions of RNA to Eukaryotic Gen(om)e Architecture and Cellular Function

Currently, the best scenario for earliest forms of life is based on RNA molecules as they have the proven ability to catalyze enzymatic reactions and harbor genetic information. Evolutionary principles valid today become apparent in such models already. Furthermore, many features of eukaryotic genome architecture might have their origins in an RNA or RNA/protein (RNP) world, including the onset of a further transition, when DNA replaced RNA as the genetic bookkeeper of the cell. Chromosome maintenance, splicing, and regulatory function via RNA may be deeply rooted in the RNA/RNP worlds. Mostly in eukaryotes, conversion from RNA to DNA is still ongoing, which greatly impacts the plasticity of extant genomes. Raw material for novel genes encoding protein or RNA, or parts of genes including regulatory elements that selection can act on, continues to enter the evolutionary lottery.

Everything has been said already, but not yet by everyone.—Karl Valentin

Sturgeon''s Revelation: Ninety percent of science fiction is crud, but then, ninety percent of everything is crud.—Theodore Sturgeon

They think that intelligence is about noticing things that are relevant (detecting patterns); in a complex world, intelligence consists in ignoring things that are irrelevant (avoiding false patterns).—Nassim Nicholas Taleb (Taleb 2010)

Of all extant cellular macromolecules, RNA is the most ancient, persisting as much as 4 × 10⁹ years in our planet’s life-forms. The ability to combine genotype with phenotype such as catalytic activity (Noller and Chaires 1972; Kruger et al. 1982; Guerrier-Takada et al. 1983; Noller et al. 1992) leveled a major hurdle in understanding the origin of life. The salient discoveries eliminated the virtually impossible prerequisite for two to three different classes of macromolecules to converge as an evolving unit. At the same time, RNA provides a required continuity in the path of evolution (Yarus 2011) during various genetic takeovers or evolutionary transitions (Cairns-Smith 1982; Szathmáry and Smith 1995). In a remarkably insightful article dating back half a century, Alex Rich foresaw much of what now is becoming mainstream, for example, that RNA was ancestral to protein and DNA (Rich 1962). This landmark publication received little attention over the years; even early proponents of an RNA world did not refer to this article (Woese 1967; Crick 1968; Orgel 1968; Gilbert 1986), although at least one of the investigators must have had knowledge about the article, as it was cited in a different context concerning the stereochemical possibility of six distinct base pairs (Crick 1968). The origin of the DNA genome from RNA and that “DNA may be regarded as a derivative molecule which has evolved in the form that it only carries out part of the primitive nucleic acid function” is another correct prediction (Rich 1962). Furthermore, the investigator presaged mechanisms such as antisense RNA control of gene expression, short interfering RNAs (siRNAs), and perhaps microRNAs (miRNAs): “If both strands are active, then the DNA would produce two RNA strands which are complementary to each other. Only one of these might be active in protein synthesis, and the other strand might be a component of the control or regulatory signal” (Rich 1962).In this article, I shall present the rise and persistence of RNA from the dawn of an RNA world and discuss current evolutionary principles already apparent in an RNA world. In comparison to Archaea and Bacteria, the eukaryotic genome is a better vantage point, as archaeal and bacterial genomes are more derived and, thus, lost many of the RNA signatures that eukaryotes still show. It is likely that eukaryotic DNA genomes not only kept much more of their RNA/RNP world heritage than previously anticipated, but also continue to evolve novel RNAs in various functional roles.     

The Desmosome

Desmosomes are intercellular junctions that tether intermediate filaments to the plasma membrane. Desmogleins and desmocollins, members of the cadherin superfamily, mediate adhesion at desmosomes. Cytoplasmic components of the desmosome associate with the desmosomal cadherin tails through a series of protein interactions, which serve to recruit intermediate filaments to sites of desmosome assembly. These desmosomal plaque components include plakoglobin and the plakophilins, members of the armadillo gene family. Linkage to the cytoskeleton is mediated by the intermediate filament binding protein, desmoplakin, which associates with both plakoglobin and plakophilins. Although desmosomes are critical for maintaining stable cell–cell adhesion, emerging evidence indicates that they are also dynamic structures that contribute to cellular processes beyond that of cell adhesion. This article outlines the structure and function of the major desmosomal proteins, and explores the contributions of this protein complex to tissue architecture and morphogenesis.The desmosome is an adhesive intercellular junction that is crucial to tissues that experience mechanical stress, such as the myocardium, bladder, gastrointestinal mucosa, and skin (Getsios et al. 2004b; Holthofer et al. 2007). The desmosome was first observed in the spinous layer of epidermis by the Italian pathologist Giulio Bizzozero (1846–1901). Bizzozero''s observations of these small dense nodules, subsequently named “nodes of Bizzozero,” led him to the insightful interpretation of these structures as adhesive cell–cell contact points. The term desmosome was later coined by Josef Schaffer in 1920 and is derived from the Greek words “desmo,” meaning bond or fastening, and “soma,” meaning body (Wells 2005; Calkins and Setzer 2007). The introduction of electron microscopy yielded a series of advances by Porter, Odland, and Kelly in the 1950s and 1960s, which revealed desmosome organization at the ultrastructural level. These studies and others indicated that the desmosome can be divided into three morphologically identifiable zones: the extracellular core region (desmoglea), the outer dense plaque (ODP), and the inner dense plaque (IDP) (Fig. 1A) (Kowalczyk et al. 1994; Schmidt et al. 1994; Green and Jones 1996; North et al. 1999; Garrod and Chidgey 2008).Open in a separate windowFigure 1.A model for the structure of desmosomes. (A) Electron micrograph of a desmosome. (B) Schematic of desmosomal proteins and relative distance from the plasma membrane (PM). The desmosomal cadherins, the desmogleins and desmocollins, extend into extracellular core and outer dense plaque (ODP) to establish contact and adhere to neighboring cells in a Ca²⁺-dependent manner. The cadherin cytoplasmic tails associate linker proteins, plakoglobin (PG), the plakophilins (PKP), and desmoplakin (DP). DP binds to keratin intermediate filaments (KIF) within the inner dense plaque (IDP), serving to tether the intermediate filaments to the plasma membrane. (Adapted with permission from Kottke et al. 2006.)In the mid 1970s, Skerrow and Matoltsy (Skerrow and Matoltsy 1974a; Skerrow and Matoltsy 1974b) advanced the field by isolating desmosomes using biochemical approaches (Bass-Zubek and Green 2007).These landmark studies provided a foundation for the Franke and Steinberg laboratories to characterize the transmembrane glycoproteins and cytoplasmic plaque proteins that linked the structure to the intermediate filament cytoskeleton, and to develop immunological tools for localizing specific components (Franke et al. 1981; Kapprell et al. 1985; Steinberg et al. 1987). Collectively, these and other studies shaped our current view of how desmosomal components are organized.The transmembrane glycoproteins, termed desmogleins and desmocollins (Garrod and Chidgey 2008), represent separate subfamilies of the cadherin superfamily of calcium dependent adhesion molecules. The extracellular domains of the desmogleins and desmocollins mediate adhesion, whereas the cytoplasmic tails of these cadherins associate with the desmosomal plaque proteins. The outer dense plaque consists of the cytoplasmic tails of the desmosomal cadherins, which bind to members of the armadillo and plakin family of linker proteins (Kowalczyk et al. 1994; Getsios et al. 2004b; Garrod and Chidgey 2008). Plakoglobin, a member of the armadillo family, binds directly to the cytoplasmic tails of both the desmogleins and the desmocollins (Wahl et al. 1996; Witcher et al. 1996). Desmoplakin, a member of the plakin family, interacts with both plakoglobin and another subgroup of armadillo family proteins, the plakophilins (Cowin and Burke 1996). Finally, the interaction between desmoplakin and the keratin filaments forms the inner dense plaque, tethering the cytoskeletal network to the adhesion complex (Fig. 1B) (Kowalczyk et al. 1994; Getsios et al. 2004b; Garrod and Chidgey 2008).The following sections of this article describe the structural and functional characteristics of the major desmosomal proteins. In addition, we discuss differences in tissue expression patterns of desmosomal proteins and the role of desmosomes in human disease. A comprehensive review of additional proteins found to regulate or associate with desmosomes is provided elsewhere (Holthofer et al. 2007) and discussion of desmosome dynamics is provided in Green et al. 2009.     

Memory Consolidation

Conscious memory for a new experience is initially dependent on information stored in both the hippocampus and neocortex. Systems consolidation is the process by which the hippocampus guides the reorganization of the information stored in the neocortex such that it eventually becomes independent of the hippocampus. Early evidence for systems consolidation was provided by studies of retrograde amnesia, which found that damage to the hippocampus-impaired memories formed in the recent past, but typically spared memories formed in the more remote past. Systems consolidation has been found to occur for both episodic and semantic memories and for both spatial and nonspatial memories, although empirical inconsistencies and theoretical disagreements remain about these issues. Recent work has begun to characterize the neural mechanisms that underlie the dialogue between the hippocampus and neocortex (e.g., “neural replay,” which occurs during sharp wave ripple activity). New work has also identified variables, such as the amount of preexisting knowledge, that affect the rate of consolidation. The increasing use of molecular genetic tools (e.g., optogenetics) can be expected to further improve understanding of the neural mechanisms underlying consolidation.Memory consolidation refers to the process by which a temporary, labile memory is transformed into a more stable, long-lasting form. Memory consolidation was first proposed in 1900 (Müller and Pilzecker 1900; Lechner et al. 1999) to account for the phenomenon of retroactive interference in humans, that is, the finding that learned material remains vulnerable to interference for a period of time after learning. Support for consolidation was already available in the facts of retrograde amnesia, especially as outlined in the earlier writings of Ribot (1881). The key observation was that recent memories are more vulnerable to injury or disease than remote memories, and the significance of this finding for consolidation was immediately appreciated.

In normal memory a process of organization is continually going on—a physical process of organization and a psychological process of repetition and association. In order that ideas may become a part of permanent memory, time must elapse for these processes of organization to be completed. (Burnham 1903, p. 132)

It is useful to note that the term consolidation has different contemporary usages that derive from the same historical sources. For example, the term is commonly used to describe events at the synaptic/cellular level (e.g., protein synthesis), which stabilize synaptic plasticity within hours after learning. In contrast, systems consolidation, which is the primary focus of this review, refers to gradual reorganization of the brain systems that support memory, a process that occurs within long-term memory itself (Squire and Alvarez 1995; Dudai and Morris 2000; Dudai 2012).Systems consolidation is typically, and accurately, described as the process by which memories, initially dependent on the hippocampus, are reorganized as time passes. By this process, the hippocampus gradually becomes less important for storage and retrieval, and a more permanent memory develops in distributed regions of the neocortex. The idea is not that memory is literally transferred from the hippocampus to the neocortex, for information is encoded in the neocortex as well as in hippocampus at the time of learning. The idea is that gradual changes in the neocortex, beginning at the time of learning, establish stable long-term memory by increasing the complexity, distribution, and connectivity among multiple cortical regions. Recent findings have enriched this perspective by emphasizing the dynamic nature of long-term memory (Dudai and Morris 2013). Memory is reconstructive and vulnerable to error, as in false remembering (Schacter and Dodson 2001). Also, under some conditions, long-term memory can transiently return to a labile state (and then gradually stabilize), a phenomenon termed reconsolidation (Nader et al. 2000; Sara 2000; Alberini 2005). In addition, the rate of consolidation can be influenced by the amount of prior knowledge that is available about the material to be learned (Tse et al. 2007; van Kesteren et al. 2012).Neurocomputational models of consolidation (McClelland et al. 1995; McClelland 2013) describe how the acquisition of new knowledge might proceed and suggest a purpose for consolidation. As originally described, elements of information are first stored in a fast-learning hippocampal system. This information directs the training of a “slow learning” neocortex, whereby the hippocampus gradually guides the development of connections between the multiple cortical regions that are active at the time of learning and that represent the memory. Training of the neocortex by the hippocampus (termed “interleaved” training) allows new information to be assimilated into neocortical networks with a minimum of interference. In simulations (McClelland et al. 1995), rapid learning of new information, which was inconsistent with prior knowledge, was shown to cause interference and disrupt previously established representations (“catastrophic interference”). The gradual incorporation of information into the neocortex during consolidation avoids this problem. In a recent revision of this framework (McClelland 2013), neocortical learning is characterized, not so much as fast or slow, but as dependent on prior knowledge. If the information to be learned is consistent with prior knowledge, neocortical learning can be more rapid.This review considers several types of evidence that illuminate the nature of the consolidation process: studies of retrograde amnesia in memory-impaired patients, studies of healthy volunteers with neuroimaging, studies of sleep and memory, studies of experimental animals, both with lesions or other interventions, and studies that track neural activity as time passes after learning.     

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.     

Understanding the Structure and Function of the Immunological Synapse

The immunological synapse has been an area of very active scientific interest over the last decade. Surprisingly, much about the synapse remains unknown or is controversial.  Here we review some of these current issues in the field:  how the synapse is defined, its potential role in T-cell function, and our current understanding about how the synapse is formed.T cells are activated when they recognize peptide-MHC complexes on the surface of antigen presenting cells (APC) (Babbitt et al. 1985). But the exact process regarding how antigenic pMHC complexes are recognized and transduced into signals is still incompletely understood. Naïve T cells enter secondary lymphoid organs such as the lymph node and scan dendritic cells for the presence of rare specific pMHC complexes (Miller et al. 2004). After recognizing less than 10 specific pMHC complexes, naïve T cells maintain long contacts (6–18 h) with dendritic cells before being committed to enter cell cycle and differentiate into effector T cells (Iezzi et al. 1998; Irvine et al. 2002; Mempel et al. 2004).The immunological synapse (IS) refers to the organization of membrane proteins that occurs at the interface between the T cell and the APC during these long contacts and also during the effector phase (Grakoui et al. 1999; Monks et al. 1998). Interest in studying the IS stems from ideas that the supramolecular structures that form at the IS underlies the high sensitivity of T cell recognition and that understanding these structures will lead to better insights into how antigen recognition leads to the decision of a T cell to proliferate, differentiate, and function.Springer first put forward the concept that receptors would segregate laterally during cell interactions (Springer 1990). Subsequently, Kupfer was the first to show that proteins in the contact area between a T cell and APC segregate laterally (Monks et al. 1998). Specifically, he noted that the integrin, LFA-1, became concentrated in an outer ring, known as the peripheral supramolecular activation complex (pSMAC) and the TCR became concentrated in the center, in a zone known as the central supramolecular activation complex (cSMAC) (Monks et al. 1998)(Fig. 1). We showed that CD2 could segregate from LFA-1 and concentrate in the center of a hybrid cell-planar bilayer junction and suggested that these patterns and those described by Monks et al. (1998) provided evidence for the previously hypothesized immunological synapse (Dustin et al. 1998; Norcross 1984). The function of this receptor segregation is still not completely understood but it was initially hypothesized that formation of this pattern might be related to T-cell activation and constitute a “molecular machine” that would be formed in response to the presence of antigenic ligand and that this “molecular machine” might function to sustain signaling for long periods of time and direct subsequent T-cell differentiation (Grakoui et al. 1999).Open in a separate windowFigure 1.Structure of the immunological synapse. The basic structure of the “organized” immunological synapse with SMACs is shown (left). In the center is the central supramolecular activation complex or cSMAC, which contains receptors like the TCR, CD28, CD4, CD8, and CD2. Newer studies suggest that the cSMAC may be divided into an outer area containing CD28 and an inner area containing the TCR (not shown). The ring that surrounds the cSMAC is called the peripheral supramolecular activation complex or pSMAC. This domain is mainly populated by the integrin molecule LFA-1. Outside of the pSMAC is another domain known as the distal supramolecular activation complex. Originally the dSMAC was thought not be important and contain all of the molecules that are not specifically recruited to the cSMAC or pSMAC but it is increasingly becoming appreciated that the dSMAC is an area of active membrane movement. This suggests that the pSMAC and dSMAC may be analogous to the actin structures known as the lamellae and lamellipodia, respectively (right).