Auxin and Plant-Microbe Interactions

Microbial synthesis of the phytohormone auxin has been known for a long time. This property is best documented for bacteria that interact with plants because bacterial auxin can cause interference with the many plant developmental processes regulated by auxin. Auxin biosynthesis in bacteria can occur via multiple pathways as has been observed in plants. There is also increasing evidence that indole-3-acetic acid (IAA), the major naturally occurring auxin, is a signaling molecule in microorganisms because IAA affects gene expression in some microorganisms. Therefore, IAA can act as a reciprocal signaling molecule in microbe-plant interactions. Interest in microbial synthesis of auxin is also increasing in yet another recently discovered property of auxin in Arabidopsis. Down-regulation of auxin signaling is part of the plant defense system against phytopathogenic bacteria. Exogenous application of auxin, e.g., produced by the pathogen, enhances susceptibility to the bacterial pathogen.The phytohormone auxin (from the Greek “auxein,” meaning to grow) regulates a whole repertoire of plant developmental processes, as documented in previous articles on this topic. Perhaps less well known is the fact that some microorganisms also produce auxin (Costacurta and Vanderleyden 1995; Patten and Glick 1996). In their interaction with plants, these microorganisms can interfere with plant development by disturbing the auxin balance in plants. This is best documented for phytopathogenic bacteria like Agrobacterium spp. and Pseudomonas savastanoi pv. savastanoi, causing tumors and galls, respectively (Jameson 2000; Mole et al. 2007), and plant growth promoting rhizobacteria (PGPR) such as Azospirillum spp. that impact on plant root development (Persello-Cartieaux et al. 2003; Spaepen et al. 2007a). The term rhizobacteria refers to the fact that their numbers are highly enriched in the rhizosphere, i.e., the narrow band of soil that surrounds the root (Hiltner 1904; Smalla et al. 2006; van Loon 2007). Of more recent date is the observation that auxin (indole-3-acetic acid or IAA) is a signaling molecule in some microorganisms (Spaepen et al. 2007a). Bringing these data together, it follows that auxin can have a major impact in microorganism-plant interactions. This is the main theme addressed in this article. Finally, the recent finding that auxin signaling in plants is also part of the Arabidopsis defense response against a leaf pathogen (Navarro et al. 2006) is discussed in relation to bacterial IAA synthesis.     

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.     

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).     

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).     

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).     

Retrograde Traffic from the Golgi to the Endoplasmic Reticulum

Proteins to be secreted are transported from the endoplasmic reticulum (ER) to the Golgi apparatus. The transport of these proteins requires the localization and activity of proteins that create ER exit sites, coat proteins to collect cargo and to reshape the membrane into a transport container, and address labels—SNARE proteins—to target the vesicles specifically to the Golgi apparatus. In addition some proteins may need export chaperones or export receptors to enable their exit into transport vesicles. ER export factors, SNAREs, and misfolded Golgi-resident proteins must all be retrieved from the Golgi to the ER again. This retrieval is also part of the organellar homeostasis pathway essential to maintaining the identity of the ER and of the Golgi apparatus. In this review, I will discuss the different processes in retrograde transport from the Golgi to the ER and highlight the mechanistic insights we have obtained in the last couple of years.Proteins that are exposed at the plasma membrane or populate a membrane-bounded organelle are synthesized into the endoplasmic reticulum (ER). In the ER, the folding of these proteins takes place and posttranslational modifications such as N-glycosylation and disulfide bridge formation occur. Upon adopting a suitable, often correct, conformation, proteins destined to locations beyond the ER are concentrated at so-called ER exit sites (ERES) and incorporated into nascent COPII-coated vesicles. These COPII vesicles eventually bud off the ER membrane and are transported to the Golgi (in yeast, Drosophila, and C. elegans) or the ER-Golgi intermediate compartment (in mammalian cells) (Schweizer et al. 1990; Kondylis and Rabouille 2003; Spang 2009; Witte et al. 2011).It is assumed that the vesicle coat is at least partially destabilized through the hydrolysis of GTP by the small GTPase Sar1 (Oka and Nakano 1994; Springer et al. 1999). However, some of the destabilized coat components have to stay on the vesicle until it has reached the Golgi apparatus because coat components participate in the recognition and the tethering process (Barlowe 1997; Cai et al. 2007; Lord et al. 2011; Zong et al. 2012). Subsequently, SNARE proteins on the vesicles (v-SNAREs) zipper up with cognate SNAREs on the Golgi (target SNAREs, t-SNAREs) to drive membrane fusion (Hay et al. 1998; Cao and Barlowe 2000; Parlati et al. 2002). The content of the ER-derived COPII vesicles is thereby released into the lumen of the cis-cisterna of the Golgi apparatus. Most proteins will continue their journey through the Golgi apparatus and encounter further modifications such as extension of the glycosylation tree or lipidation. However, some proteins, especially those involved in the fusion process, i.e., the v-SNAREs or proteins that act as export factors of the ER, such as Vma21, which is essential for export of the correctly folded and assembled V0 sector of the V-ATPase, need to be recycled back to the ER for another round of transport (Ballensiefen et al. 1998; Malkus et al. 2004). Moreover, cis-Golgi proteins are returned to the ER for quality/functional control (Todorow et al. 2000; Sato et al. 2004; Valkova et al. 2011). Finally, some ER-resident proteins, such as the ER Hsp70 chaperone BiP/Kar2, can escape the ER, but are captured at the cis-Golgi by the H/KDEL receptor Erd2 and returned to the ER (Lewis et al. 1990; Semenza et al. 1990; Aoe et al. 1997).Unfortunately, the retrograde transport route is also hijacked by toxins. For example, endocytosed cholera toxin subunit A contains a KDEL sequence and can thereby exploit the system to access the ER (Majoul et al. 1996, 1998). From there, it is retro-translocated into the cytoplasm where it can exert its detrimental function.     

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.     

Cadherin-mediated Intercellular Adhesion and Signaling Cascades Involving Small GTPases

Epithelia form physical barriers that separate the internal milieu of the body from its external environment. The biogenesis of functional epithelia requires the precise coordination of many cellular processes. One of the key events in epithelial biogenesis is the establishment of cadherin-dependent cell–cell contacts, which initiate morphological changes and the formation of other adhesive structures. Cadherin-mediated adhesions generate intracellular signals that control cytoskeletal reorganization, polarity, and vesicle trafficking. Among such signaling pathways, those involving small GTPases play critical roles in epithelial biogenesis. Assembly of E-cadherin activates several small GTPases and, in turn, the activated small GTPases control the effects of E-cadherin-mediated adhesions on epithelial biogenesis. Here, we focus on small GTPase signaling at E-cadherin-mediated epithelial junctions.Cell–cell adhesions are involved in a diverse range of physiological processes, including morphological changes during tissue development, cell scattering, wound healing, and synaptogenesis (Adams and Nelson 1998; Gumbiner 2000; Halbleib and Nelson 2006; Takeichi 1995; Tepass et al. 2000). In epithelial cells, cell–cell adhesions are classified into three kinds of adhesions: adherens junction, tight junction, and desmosome (for more details, see Meng and Takeichi 2009, Furuse 2009, and Delva et al. 2009, respectively). A key event in epithelial polarization and biogenesis is the establishment of cadherin-dependent cell–cell contacts. Cadherins belong to a large family of adhesion molecules that require Ca²⁺ for their homophilic interactions (Adams and Nelson 1998; Blanpain and Fuchs 2009; Gumbiner 2000; Hartsock and Nelson 2008; Takeichi 1995; Tepass et al. 2000). Cadherins form transinteraction on the surface of neighboring cells (for details, see Shapiro and Weis 2009). For the development of strong and rigid adhesions, cadherins are clustered concomitantly with changes in the organization of the actin cytoskeleton (Tsukita et al. 1992). Classical cadherins are required, but not sufficient, to initiate cell–cell contacts, and other adhesion protein complexes subsequently assemble (for details, see Green et al. 2009). These complexes include the tight junction, which controls paracellular permeability, and desmosomes, which support the structural continuum of epithelial cells. A fundamental problem is to understand how these diverse cellular processes are regulated and coordinated. Intracellular signals, generated when cells attach with one another, mediate these complicated processes.Several signaling pathways upstream or downstream of cadherin-mediated cell–cell adhesions have been identified (Perez-Moreno et al. 2003) (see also McCrea et al. 2009). Among these pathways, small GTPases including the Rho and Ras family GTPases play critical roles in epithelial biogenesis and have been studied extensively. Many key morphological and functional changes are induced when these small GTPases act at epithelial junctions, where they mediate an interplay between cell–cell adhesion molecules and fundamental cellular processes including cytoskeletal activity, polarity, and vesicle trafficking. In addition to these small GTPases, Ca²⁺ signaling and phosphorylation of cadherin complexes also play pivotal roles in the formation and maintenance of cadherin-mediated adhesions. Here, we focus on signaling pathways involving the small GTPases in E-cadherin-mediated cell–cell adhesions. Other signaling pathways are described in recent reviews (Braga 2002; Fukata and Kaibuchi 2001; Goldstein and Macara 2007; McLachlan et al. 2007; Tsukita et al. 2008; Yap and Kovacs 2003; see also McCrea et al. 2009).     

Calcium Oscillations

Calcium signaling results from a complex interplay between activation and inactivation of intracellular and extracellular calcium permeable channels. This complexity is obvious from the pattern of calcium signals observed with modest, physiological concentrations of calcium-mobilizing agonists, which typically present as sequential regenerative discharges of stored calcium, a process referred to as calcium oscillations. In this review, we discuss recent advances in understanding the underlying mechanism of calcium oscillations through the power of mathematical modeling. We also summarize recent findings on the role of calcium entry through store-operated channels in sustaining calcium oscillations and in the mechanism by which calcium oscillations couple to downstream effectors.Calcium ions participate in a multiplicity of physiological and pathological functions. Among the most intensely studied, and the major focus of this article, is the role of Ca²⁺ as a cellular signal. Elevations in cytoplasmic Ca²⁺ mediate a plethora of cellular responses, ranging from extremely rapid events (muscle contraction, neurosecretion), to slower more subtle responses (cell division, differentiation, apoptosis). In contrast to most cellular signals, it is a relatively simple matter to observe changes in cytoplasmic Ca²⁺ in real time in living cells. As a result, the truly complex nature of Ca²⁺ signaling pathways has been revealed. The challenge is to understand what regulates these signals and what the biological significance of their complexity is.In the majority of laboratory experiments examining effects of various stimulants on Ca²⁺ signaling, supramaximal concentrations of activating agonists are employed resulting in rapid, robust, and often sustained increases in cytoplasmic Ca²⁺. It has long been appreciated that these signals result from a coordinated release of intracellular stores and increased Ca²⁺ influx across the plasma membrane (Bohr, 1973; Putney et al. 1981). The intracellular release of Ca²⁺ most commonly results from the Ca²⁺ releasing action of the phospholipase C-derived second messenger, inositol 1,4,5-trisphosphate (InsP₃) (Streb et al. 1983), whereas the entry of Ca²⁺ is because of the activation of store-operated channels in the plasma membrane (Putney 1986). However, it is becoming increasingly clear that these large sustained elevations seldom occur with physiological levels of stimulants. Rather the more common pattern of Ca²⁺ signaling, in both excitable and nonexcitable cells is a pattern of periodic discharges and/or entry of Ca²⁺. In excitable cells, such as the heart for example, these may be comprised of, or initiated by regenerative all-or-none plasma membrane channel activation, the Ca²⁺ action potential (Tsien et al. 1986) with amplification by intracellular Ca²⁺ release (Fabiato 1983). In nonexcitable cells, these spikes of cytoplasmic Ca²⁺ arise from regenerative discharge of stored Ca²⁺, a process generally termed Ca²⁺ oscillations (Prince and Berridge 1973; Woods et al. 1986). Like Ca²⁺ action potentials, these all-or-none discharges of Ca²⁺ represent a form of excitable behavior of the intracellular Ca²⁺ release signaling mechanism. However, because it is not possible to easily monitor and control the transmembrane chemical and biophysical parameters, as is the case for excitable plasma membrane behavior, it has been more difficult to fully understand the basic mechanisms by which these Ca²⁺ oscillations arise. Thus, although the question has been exhaustively studied for well over twenty years, there is still uncertainty and controversy over the underlying processes that give rise to Ca²⁺ oscillations. A number of reviews have discussed these issues at some length (Berridge and Galione 1988; Rink and Jacob 1989; Berridge 1990; Petersen and Wakui 1990; Berridge 1991; Cuthbertson and Cobbold 1991; Meyer and Stryer 1991; Hellman et al. 1992; Tepikin and Petersen 1992; Thomas et al. 1992; Dupont and Goldbeter 1993; Keizer 1993; Sneyd et al. 1994; Li et al. 1995; Thomas et al. 1996; Shuttleworth 1999; Lewis 2003; Dupont et al. 2007). In the current treatment, we have chosen to focus on two important aspects of Ca²⁺ oscillations. First, we review the available evidence for various computational models of Ca²⁺ oscillations that employ a quantitative approach to validate or repudiate specific mechanisms. Second, we consider the interrelationship between Ca²⁺ oscillations and plasma membrane Ca²⁺ influx mechanisms, with the view that we may learn more of the physiological function that these intracellular discharges of Ca²⁺ provide.     

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).     

Molecular Bases of Cell–Cell Junctions Stability and Dynamics

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

Biofilms

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

Functional Differentiation of Adult-Born Neurons along the Septotemporal Axis of the Dentate Gyrus

Over the past several decades, the proliferation and integration of adult-born neurons into existing hippocampal circuitry has been implicated in a wide range of behaviors, including novelty recognition, pattern separation, spatial learning, anxiety behaviors, and antidepressant response. In this review, we suggest that the diversity in behavioral requirements for new neurons may be partly caused by separate functional roles of individual neurogenic niches. Growing evidence shows that the hippocampal formation can be compartmentalized not only along the classic trisynaptic circuit, but also along a longitudinal septotemporal axis. We suggest that subpopulations of hippocampal adult-born neurons may be specialized for distinct mnemonic- or mood-related behavioral tasks. We will examine the literature supporting a functional and anatomical dissociation of the hippocampus along the longitudinal axis and discuss techniques to functionally dissect the roles of adult-born hippocampal neurons in these distinct subregions.Since the presence of dividing cells in the mostly postmitotic adult brain was first described (Altman and Das 1965), the generation of new neurons in adulthood has been proposed to be involved in a variety of behaviors (Doetsch and Hen 2005; Becker and Wojtowicz 2007; Sahay and Hen 2007; Deng et al. 2010; Ming and Song 2011; Miller and Hen 2014). Adult neurogenesis in the healthy mammalian brain is consistently seen in the subventricular zone (SVZ) of the lateral ventricles and the subgranular zone (SGZ) of the hippocampal dentate gyrus (DG). Recent studies have implicated hippocampal neurogenesis in learning- and memory-related tasks, such as contextual discrimination and spatial navigation and, specifically, in behavioral pattern separation (Clelland et al. 2009; Sahay et al. 2011; Nakashiba et al. 2012; Niibori et al. 2012; see also reviews in Deng et al. 2010; Ming and Song 2011; Marin-Burgin and Schinder 2012), but also in some behavioral effects of antidepressants (Santarelli et al. 2003; see also reviews in Sahay and Hen 2007; Kheirbek et al. 2012; Tanti and Belzung 2013). However, the exact role of adult hippocampal neurogenesis in some of these behaviors has been debated as some studies have shown no effects of altering adult neurogenesis on spatial navigation or antidepressant response. Proposed explanations have included differences in the behavioral tasks used to measure cognition or emotion, motivational state of subjects, species differences, or in how neurogenesis is defined, either as proliferation, survival, or differentiation (see reviews in Zhao et al. 2008; Aimone et al. 2011; Petrik et al. 2012b; Miller and Hen 2014).It must also be noted, however, that these hippocampal neurons are not born into a singular structure. Work in the past several decades has shown that the hippocampus can be divided, not only along the classic trisynaptic loop, but also longitudinally along a septotemporal axis. The septal (dorsal in rodents; posterior in primates) and temporal (ventral in rodents; anterior in primates) poles, as well as potential intermediate zones of the hippocampus, have different anatomic connections and electrophysiological properties, express a gradient of molecular markers, and play different functional roles, such as performance in spatial learning tasks and stress responses (see reviews in Moser and Moser 1998; Fanselow and Dong 2010). Consequently, adult-born neurons in the hippocampal DG may also be segregated along this longitudinal axis, and conflicting functional roles for neurogenesis may be a result of attempting to examine hippocampal neurogenesis as a unitary phenomenon. It is possible that there are intrinsic, cell-autonomous differences in adult-born neurons generated at opposite poles of the DG. An alternative, although not mutually exclusive, hypothesis is that progenitor cells are initially identical, but differentiate in a dissimilar manner as a result of integration into distinct network circuitry. We will, therefore, first discuss heterogeneity of the hippocampus along its longitudinal axis before reviewing differences in neurogenesis between the septal and temporal poles of the DG. As these topics have been reviewed extensively elsewhere (Moser and Moser 1998; Deng et al. 2010; Fanselow and Dong 2010; Koehl and Abrous 2011; Samuels and Hen 2011; Kheirbek et al. 2012; Petrik et al. 2012b), we will not try to exhaustively cover all the current literature. Rather, we attempt to gather key studies examining a septotemporal gradient of the hippocampus and hippocampal neurogenesis. We will then suggest possible approaches to examine neurogenesis in specific subregions of the hippocampal DG. Finally, a short section will examine segregation of the DG along its transverse axis.