Imaging cell biology in live animals: Ready for prime time

Time-lapse fluorescence microscopy is one of the main tools used to image subcellular structures in living cells. Yet for decades it has been applied primarily to in vitro model systems. Thanks to the most recent advancements in intravital microscopy, this approach has finally been extended to live rodents. This represents a major breakthrough that will provide unprecedented new opportunities to study mammalian cell biology in vivo and has already provided new insight in the fields of neurobiology, immunology, and cancer biology.The discovery of GFP combined with the ability to engineer its expression in living cells has revolutionized mammalian cell biology (Chalfie et al., 1994). Since its introduction, several light microscopy–based techniques have become invaluable tools to investigate intracellular events (Lippincott-Schwartz, 2011). Among them are: time-lapse confocal microscopy, which has been instrumental in studying the dynamics of cellular and subcellular processes (Hirschberg et al., 1998; Jakobs, 2006; Cardarelli and Gratton, 2010); FRAP, which has enabled determining various biophysical properties of proteins in living cells (Berkovich et al., 2011); and fluorescence resonance energy transfer (FRET), which has been used to probe for protein–protein interactions and the local activation of specific signaling pathways (Balla, 2009). The continuous search for improvements in temporal and spatial resolution has led to the development of more sophisticated technologies, such as spinning disk microscopy, which allows the resolution of fast cellular events that occur on the order of milliseconds (Nakano, 2002); total internal reflection microscopy (TIRF), which enables imaging events in close proximity (100 nm) to the plasma membrane (Cocucci et al., 2012); and super-resolution microscopy (SIM, PALM, and STORM), which captures images with resolution higher than the diffraction limit of light (Lippincott-Schwartz, 2011).Most of these techniques have been primarily applied to in vitro model systems, such as cells grown on solid substrates or in 3D matrices, explanted embryos, and organ cultures. These systems, which are relatively easy to maintain and to manipulate either pharmacologically or genetically, have been instrumental in providing fundamental information about cellular events down to the molecular level. However, they often fail to reconstitute the complex architecture and physiology of multicellular tissues in vivo. Indeed, in a live organism, cells exhibit a 3D organization, interact with different cell types, and are constantly exposed to a multitude of signals originated from the vasculature, the central nervous system, and the extracellular environment. For this reason, scientists have been attracted by the possibility of imaging biological processes in live multicellular organisms (i.e., intravital microscopy [IVM]). The first attempt in this direction was in 1839, when Rudolph Wagner described the interaction of leukocytes with the walls of blood vessels in the webbed feet of a live frog by using bright-field transillumination (Wagner, 1839). Since then, this approach has been used for over a century to study vascular biology in thin areas of surgically exposed organs (Irwin and MacDonald, 1953; Zweifach, 1954) or by implanting optical windows in the skin or the ears (Clark and Clark, 1932). In addition, cell migration has also been investigated using transparent tissues, such as the fin of the teleost (Wood and Thorogood, 1984; Thorogood and Wood, 1987). The introduction of epifluorescence microscopy has enabled following in more detail the dynamics of individual cells in circulation (Nuttall, 1987), in tumors (MacDonald et al., 1992), or in the immune system (von Andrian, 1996), and the spatial resolution has been significantly improved by the use of confocal microscopy, which has made it possible to collect serial optical sections from a given specimen (Villringer et al., 1989; O’Rourke and Fraser, 1990; Jester et al., 1991). However, these techniques can resolve structures only within a few micrometers from the surface of optically opaque tissues (Masedunskas et al., 2012a). It was only in the early nineteen nineties, with the development of multiphoton microscopy, that deep tissue imaging has become possible (Denk et al., 1990; Zipfel et al., 2003b), significantly contributing to several fields, including neurobiology, immunology, and cancer biology (Fig. 1; Svoboda and Yasuda, 2006; Amornphimoltham et al., 2011; Beerling et al., 2011). In the last few years, the development of strategies to minimize the motion artifacts caused by the heartbeat and respiration has made it possible to successfully image subcellular structures with spatial and temporal resolutions comparable to those achieved in in vitro model systems, thus providing the opportunity to study cell biology in live mammalian tissues (Fig. 1; Weigert et al., 2010; Pittet and Weissleder, 2011).Open in a separate windowFigure 1.Spatial resolution and current applications of intravital microscopy. IVM provides the opportunity to image several biological processes in live animals at different levels of resolution. Low-magnification objectives (5–10×) enable visualizing tissues and their components under physiological conditions and measuring their response under pathological conditions. Particularly, the dynamics of the vasculature have been one of topic most extensively studied by IVM. Objectives with higher magnification (20–30×) have enabled imaging the behavior of individual cell over long periods of time. This has led to major breakthroughs in fields such as neurobiology, immunology, cancer biology, and stem cell research. Finally, the recent developments of strategies to minimize the motion artifacts caused by the heartbeat and respiration combined with high power lenses (60–100×) have opened the door to image subcellular structures and to study cell biology in live animals.The aim of this review is to highlight the power of IVM in addressing cell biological questions that cannot be otherwise answered in vitro, due to the intrinsic limitations of reductionist models, or by other more classical approaches. Furthermore, we discuss limitations and areas for improvement of this imaging technique, hoping to provide cell biologists with the basis to assess whether IVM is the appropriate choice to address their scientific questions.

Imaging techniques currently used to perform intravital microscopy

Confocal and two-photon microscopy are the most widely used techniques to perform IVM. Confocal microscopy, which is based on single photon excitation, is a well-established technique (Fig. 2 A) that has been extensively discussed elsewhere (Wilson, 2002); hence we will only briefly describe some of the main features of two-photon microscopy and other nonlinear optical techniques.Open in a separate windowFigure 2.Fluorescent light microscopy imaging techniques used for intravital microscopy. (A) Confocal microscopy. (top) In confocal microscopy, a fluorophore absorbs a single photon with a wavelength in the UV-visible range of the spectrum (blue arrow). After a vibrational relaxation (orange curved arrow), a photon with a wavelength shifted toward the red is emitted (green arrow). (center) In thick tissue, excitation and emission occur in a relative large volume around the focal plane (F.P.). The off-focus emissions are eliminated through a pinhole, and the signal from the focal plane is detected via a photomultiplier (PMT). Confocal microscopy enables imaging at a maximal depth to 80–100 µm. (bottom) Confocal z stack of the tongue of a mouse expressing the membrane marker m-GFP (green) in the K14-positive basal epithelial layer, and the membrane marker mTomato in the endothelium (red). The xy view shows a maximal projection of 40 z slices acquired every 2.5 µm, whereas the xz view shows a lateral view of the stack. In blue are the nuclei labeled by a systemic injection of Hoechst. Excitation wavelengths: 450 nm, 488 nm, and 562 nm. (B) Two- and three-photon microscopy. (top) In this process a fluorophore absorbs almost simultaneously two or three photons that have half (red arrow) or a third (dark red arrow) of the energy required for its excitation with a single photon. Two- or three-photon excitations typically require near-IR or IR light (from 690 to 1,600 nm). (center) Emission and excitation occur only at the focal plane in a restricted volume (1.5 fl), and for this reason a pinhole is not required. Two- and three-photon microscopy enable imaging routinely at a maximal depth of 300–500 µm. (bottom) Two-photon z stack of an area adjacent to that imaged in A. xy view shows a maximal projection of 70 slices acquired every 5 µm. xz view shows a lateral view of the stack. Excitation wavelength: 840 nm. (C) SHG and THG. (top) In SHG and THG, photons interact with the specimen and combine to form new photons that are emitted with twice or three times their initial energy without any energy loss. (center) These processes have similar features to those described for two- and three-photon microscopy and enable imaging at a maximal depth of 200–400 µm. (bottom) z stack of a rat heart excited by two-photon microscopy (740 nm) to reveal the parenchyma (green), and SHG (930 nm) to reveal collagen fibers (red). xy shows a maximal projection of 20 slices acquired every 5 µm. xz view shows a lateral view of the stack. Bars: (xy views) 40 µm; (xz views) 50 µm.The first two-photon microscope (Denk et al., 1990) was based on the principle of two-photon excitation postulated by Maria Göppert-Mayer in her PhD thesis (Göppert-Mayer, 1931). In this process a fluorophore is excited by the simultaneous absorption of two photons with wavelengths in the near-infrared (IR) or IR spectrum (from 690 to 1,600 nm; Fig. 2 B). Two-photon excitation requires high-intensity light that is provided by lasers generating very short pulses (in the femtosecond range) and is focused on the excitation spot by high numerical aperture lenses (Zipfel et al., 2003b). There are three main advantages in using two-photon excitation for IVM. First, IR light has a deeper tissue penetration than UV or visible light (Theer and Denk, 2006). Indeed, two-photon microscopy can resolve structures up to a depth of 300–500 µm in most of the tissues (Fig. 2 B), and up to 1.5 mm in the brain (Theer et al., 2003; Masedunskas et al., 2012a), whereas confocal microscopy is limited to 80–100 µm (Fig. 2 A). Second, the excitation is restricted to a very small volume (1.5 fl; Fig. 2 B). This implies that in two-photon microscopy there is no need to eliminate off-focus signals, and that under the appropriate conditions photobleaching and phototoxicity are negligible (Zipfel et al., 2003b). However, confocal microscopy induces out-of-focus photodamage, and thus is less suited for long-term imaging. Third, selected endogenous molecules can be excited, thus providing the contrast to visualize specific biological structures without the need for exogenous labeling (Zipfel et al., 2003a). Some of these molecules can also be excited by confocal microscopy using UV light, although with the risk of inducing photodamage.More recently, other nonlinear optical techniques have been used for IVM, and among them are three-photon excitation, and second and third harmonic generation (SHG and THG; Campagnola and Loew, 2003; Zipfel et al., 2003b; Oheim et al., 2006). Three-photon excitation follows the same principle as two-photon (Fig. 2 B), and can reveal endogenous molecules such as serotonin and melatonin (Zipfel et al., 2003a; Ritsma et al., 2013). In SHG and THG, photons interact with the specimen and combine to form new photons that are emitted with two or three times their initial energy (Fig. 2 C). SHG reveals collagen (Fig. 2 C) and myosin fibers (Campagnola and Loew, 2003), whereas THG reveals lipid droplets and myelin fibers (Débarre et al., 2006; Weigelin et al., 2012). Recently, two other techniques have been used for IVM: coherent anti-Stokes Raman scattering (CARS) and fluorescence lifetime imaging (FLIM). CARS that is based on two laser beams combined to match the energy gap between two vibrational levels of the molecule of interest, has been used to image lipids and myelin fibers (Müller and Zumbusch, 2007; Fu et al., 2008; Le et al., 2010). FLIM, which measures the lifetime that a molecule spends in the excited state, provides quantitative information on cellular parameters such as pH, oxygen levels, ion concentration, and the metabolic state of various biomolecules (Levitt et al., 2009; Provenzano et al., 2009; Bakker et al., 2012).We want to emphasize that two-photon microscopy and the other nonlinear techniques are the obligatory choice when the imaging area is located deep inside the tissue, endogenous molecules have to be imaged, or long-term imaging with frequent sampling is required. However, confocal microscopy is more suited to resolve structures in the micrometer range, because of the possibility of modulating the optical slice (Masedunskas et al., 2012a).

IVM to investigate biological processes at the tissue and the single cell level

The main strength of IVM is to provide information on the dynamics of biological processes that otherwise cannot be reconstituted in vitro or ex vivo. Indeed, IVM has been instrumental in studying several aspects of tissue physiopathology (Fig. 3, A and B). Although other approaches such as classical immunohistochemistry, electron microscopy, and indirect immunofluorescence may provide detailed structural and quantitative information on blood vessels, IVM enables measuring events such as variations of blood flow at the level of the capillaries or local changes in blood vessel permeability. These data have been instrumental in understanding the mechanisms of ischemic diseases and tumor progression, and in designing effective anticancer treatments.

Table 1.

IVM to study tissue physiopathology

Quality control: Organellophagy: Eliminating cellular building blocks via selective autophagy

Maintenance of organellar quality and quantity is critical for cellular homeostasis and adaptation to variable environments. Emerging evidence demonstrates that this kind of control is achieved by selective elimination of organelles via autophagy, termed organellophagy. Organellophagy consists of three key steps: induction, cargo tagging, and sequestration, which involve signaling pathways, organellar landmark molecules, and core autophagy-related proteins, respectively. In addition, posttranslational modifications such as phosphorylation and ubiquitination play important roles in recruiting and tailoring the autophagy machinery to each organelle. The basic principles underlying organellophagy are conserved from yeast to mammals, highlighting its biological relevance in eukaryotic cells.

Introduction

Organelles are fundamental subunits of eukaryotic cells that possess structurally and functionally distinct characteristics that allow them to perform unique activities crucial for viability. It is thus a matter of the utmost importance for cells to maintain organellar quality and integrity. In addition, cells modulate the quantity of organelles in order to balance organellar activities and cellular demands, which can act as an adaptive mechanism to diverse environmental changes. Both dysfunctional and surplus organelles are cleared from cells through autophagy, a widely conserved self-eating process by which cytoplasmic constituents are sequestered as cargoes by intracellular membranes that fuse with lysosomes for hydrolytic breakdown (Mizushima and Komatsu, 2011; Mizushima et al., 2011; Weidberg et al., 2011).Although autophagy has primarily been recognized as a nonselective degradation pathway, recent studies reveal that it also plays a vital role in digesting specific cargoes such as proteins and organelles (Mizushima, 2011; Suzuki, 2013). The latter process, termed selective autophagy, includes the following three critical stages: first, signaling from degradation cues induces downstream events specific for a particular target; second, regulation of landmark molecules that tag the target as disposable cargo; third, assembly of core autophagy-related (Atg) proteins to sequester the cargo. In many cases, malfunction in or decreased cellular metabolism related to a protein or organelle leads to expression and activation of a landmark molecule. Core Atg proteins then localize to the cargo via direct or indirect interactions with the landmark molecule and ultimately mediate selective autophagy.In this short review, we summarize recent findings on organellophagy, autophagy-related pathways selective for organelles such as the peroxisome, mitochondrion, lipid droplet (structure surrounded by a phospholipid monolayer), lysosome, nucleus, ER, and even nonmembraneous structures like the ribosome. Despite the diversity of their degradation cues and landmark molecules, organellophagy seems to be regulated by common basic principles involving protein phosphorylation and ubiquitination. In particular, these two major posttranslational modifications promote targeting of core Atg proteins to the organellar surface. Defects in several organellophagy pathways are associated with various disorders including renal injury, neurodegeneration, obesity, and atherosclerosis (Mizushima and Komatsu, 2011), underscoring their physiological significance in health and disease.

Modes of autophagy in organellophagy

Three morphologically distinct modes of autophagic processes have so far been defined: macroautophagy, microautophagy, and chaperone-mediated autophagy (CMA; Fig. 1; Mizushima and Komatsu, 2011; Li et al., 2012; Cuervo and Wong, 2014; Feng et al., 2014). Macro- and microautophagy are conserved from yeast to humans, whereas CMA has been found only in mammals. Upon macroautophagy induction, newly formed double membrane–bound structures enclose proteins and organelles, eventually generating mature vesicles, called autophagosomes. Core Atg proteins play essential roles in autophagosome formation. The engulfed cargoes are then mixed with the lysosomal hydrolases via autophagosome–lysosome fusion and digested into small molecules for recycling. During microautophagy, the lysosomal membrane invaginates to sequester proteins and organelles. In some cases, accompanying membrane structures function in closure of the cargoes, which requires core Atg proteins. The lysosomal lipase then digests the internalized vesicles, leading to breakdown of the cargoes by hydrolases. By contrast, CMA recruits specific protein substrates associated with the molecular chaperone Hsc70 to lysosomes and translocates the substrates one by one into the lysosomal lumen through the receptor protein Lamp-2A in a manner independent on core Atg proteins. Unlike macro- and microautophagy, CMA has been suggested to degrade only proteins but not whole organelles.Open in a separate windowFigure 1.Three distinct modes of autophagy. In macroautophagy, newly generated cup-shaped structures, called isolation membranes, expand to surround cytoplasmic components. The two edges of isolation membranes then fuse to form double membrane–bound autophagosomes. Subsequently, autophagosomes fuse to lysosomes, and the engulfed cargoes are digested by hydrolytic enzymes. In microautophagy, invagination of the lysosomal membrane occurs to sequester proteins and organelles in the cytosol. The resulting vesicular structures are then pinched off and released into the lysosomal lumen for digestion. In chaperone-mediated autophagy (CMA), the Hsc70/co-chaperone complex delivers specific substrate proteins to lysosomes. The substrate polypeptides are then translocated one by one through the lysosomal membrane protein Lamp-2A and digested in the lysosomal lumen. Macro- and microautophagy are conserved from yeast to humans, whereas CMA has been found only in mammals. Unlike macro- and microautophagy, CMA has been suggested to degrade only proteins but not whole organelles.Morphological classification of organelle-specific autophagy in yeast and mammals is summarized in Manjithaya et al., 2010b; Oku and Sakai, 2010), mitochondrion (mitophagy; Ashrafi and Schwarz, 2013; Feng et al., 2013), lipid droplet (lipophagy; Liu and Czaja, 2013), and nucleus (nucleophagy; Mijaljica and Devenish, 2013). These degradation pathways appear to be conserved from yeast to humans. Macroautophagy-related turnover processes specific for lysosome (lysophagy; Hung et al., 2013; Maejima et al., 2013) and ER (reticulophagy/ER-phagy; Bernales et al., 2006) have been found in mammals and yeast, respectively. Although whether ribosome degradation in yeast occurs via macro- or microautophagy remains to be clarified, it seems to be a selective event (ribophagy) because ribosomal subunits are degraded significantly faster than other cytosolic proteins in an autophagy-dependent fashion (Kraft et al., 2008). To date, there has been no evidence suggesting selective degradation of the Golgi apparatus (golgiphagy).

Table 1.

Classification of organellophagy

Evolution and Function of the Plant Cell Wall Synthesis-Related Glycosyltransferase Family 8

Carbohydrate-active enzyme glycosyltransferase family 8 (GT8) includes the plant galacturonosyltransferase1-related gene family of proven and putative α-galacturonosyltransferase (GAUT) and GAUT-like (GATL) genes. We computationally identified and investigated this family in 15 fully sequenced plant and green algal genomes and in the National Center for Biotechnology Information nonredundant protein database to determine the phylogenetic relatedness of the GAUTs and GATLs to other GT8 family members. The GT8 proteins fall into three well-delineated major classes. In addition to GAUTs and GATLs, known or predicted to be involved in plant cell wall biosynthesis, class I also includes a lower plant-specific GAUT and GATL-related (GATR) subfamily, two metazoan subfamilies, and proteins from other eukaryotes and cyanobacteria. Class II includes galactinol synthases and plant glycogenin-like starch initiation proteins that are not known to be directly involved in cell wall synthesis, as well as proteins from fungi, metazoans, viruses, and bacteria. Class III consists almost entirely of bacterial proteins that are lipooligo/polysaccharide α-galactosyltransferases and α-glucosyltransferases. Sequence motifs conserved across all GT8 subfamilies and those specific to plant cell wall-related GT8 subfamilies were identified and mapped onto a predicted GAUT1 protein structure. The tertiary structure prediction identified sequence motifs likely to represent key amino acids involved in catalysis, substrate binding, protein-protein interactions, and structural elements required for GAUT1 function. The results show that the GAUTs, GATLs, and GATRs have a different evolutionary origin than other plant GT8 genes, were likely acquired from an ancient cyanobacterium (Synechococcus) progenitor, and separate into unique subclades that may indicate functional specialization.Plant cell walls are composed of three principal types of polysaccharides: cellulose, hemicellulose, and pectin. Studying the biosynthesis and degradation of these biopolymers is important because cell walls have multiple roles in plants, including providing structural support to cells and defense against pathogens, serving as cell-specific developmental and differentiation markers, and mediating or facilitating cell-cell communication. In addition to their important roles within plants, cell walls also have many economic uses in human and animal nutrition and as sources of natural textile fibers, paper and wood products, and components of fine chemicals and medicinal products. The study of the biosynthesis and biodegradation of plant cell walls has become even more significant because cell walls are the major components of biomass (Mohnen et al., 2008), which is the most promising renewable source for the production of biofuels and biomaterials (Ragauskas et al., 2006; Pauly and Keegstra, 2008). Analyses of fully sequenced plant genomes have revealed that they encode hundreds or even thousands of carbohydrate-active enzymes (CAZy; Henrissat et al., 2001; Yokoyama and Nishitani, 2004; Geisler-Lee et al., 2006). Most of these CAZy enzymes (Cantarel et al., 2009) are glycosyltransferases (GTs) or glycoside hydrolases, which are key players in plant cell wall biosynthesis and modification (Cosgrove, 2005).The CAZy database is classified into 290 protein families (www.cazy.org; release of September 2008), of which 92 are GT families (Cantarel et al., 2009). A number of the GT families have been previously characterized to be involved in plant cell wall biosynthesis. For example, the GT2 family is known to include cellulose synthases and some hemicellulose backbone synthases (Lerouxel et al., 2006), such as mannan synthases (Dhugga et al., 2004; Liepman et al., 2005), putative xyloglucan synthases (Cocuron et al., 2007), and mixed linkage glucan synthases (Burton et al., 2006). With respect to the synthesis of xylan, a type of hemicellulose, four Arabidopsis (Arabidopsis thaliana) proteins from the GT43 family, irregular xylem 9 (IRX9), IRX14, IRX9-L, and IRX14-L, and two proteins from the GT47 family, IRX10 and IRX10-L, are candidates (York and O''Neill, 2008) for glucuronoxylan backbone synthases (Brown et al., 2007, 2009; Lee et al., 2007a; Peña et al., 2007; Wu et al., 2009). In addition, three proteins have been implicated in the synthesis of an oligosaccharide thought to act either as a primer or terminator in xylan synthesis (Peña et al., 2007): two from the GT8 family (IRX8/GAUT12 [Persson et al., 2007] and PARVUS/GATL1 [Brown et al., 2007; Lee et al., 2007b]) and one from the GT47 family (FRA8/IRX7 [Zhong et al., 2005]).The GT families involved in the biosynthesis of pectins have been relatively less studied until recently. In 2006, a gene in CAZy family GT8 was shown to encode a functional homogalacturonan α-galacturonosyltransferase, GAUT1 (Sterling et al., 2006). GAUT1 belongs to a 25-member gene family in Arabidopsis, the GAUT1-related gene family, that includes two distinct but closely related families, the galacturonosyltransferase (GAUT) genes and the galacturonosyltransferase-like (GATL) genes (Sterling et al., 2006). Another GAUT gene, GAUT8/QUA1, has been suggested to be involved in pectin and/or xylan synthesis, based on the phenotypes of plant lines carrying mutations in this gene (Bouton et al., 2002; Orfila et al., 2005). It has further been suggested that multiple members of the GT8 family are galacturonosyltransferases involved in pectin and/or xylan biosynthesis (Mohnen, 2008; Caffall and Mohnen, 2009; Caffall et al., 2009).Aside from the 25 GAUT and GATL genes, Arabidopsis has 16 other family GT8 genes, according to the CAZy database, which do not seem to have the conserved sequence motifs found in GAUTs and GATLs: HxxGxxKPW and GLG (Sterling et al., 2006). Eight of these 16 genes are annotated as galactinol synthase (GolS) by The Arabidopsis Information Resource (TAIR; www.arabidopsis.org), and three of these AtGolS enzymes have been implicated in the synthesis of raffinose family oligosaccharides that are associated with stress tolerance (Taji et al., 2002). The other eight Arabidopsis GT8 genes are annotated as plant glycogenin-like starch initiation proteins (PGSIPs) in TAIR. PGSIPs have been proposed to be involved in the synthesis of primers necessary for starch biosynthesis (Chatterjee et al., 2005). Hence, the GT8 family is a protein family consisting of enzymes with very distinct proven and proposed functions. Indeed, a suggestion has been made to split the GT8 family into two groups (Sterling et al., 2006), namely, the cell wall biosynthesis-related genes (GAUTs and GATLs) and the non-cell wall synthesis-related genes (GolSs and PGSIPs).We are interested in further defining the functions of the GAUT and GATL proteins in plants, in particular their role(s) in plant cell wall synthesis. The apparent disparate functions of the GT8 family (i.e. the GAUTs and GATLs as proven and putative plant cell wall polysaccharide biosynthetic α-galacturonosyltransferases, the eukaryotic GolSs as α-galactosyltransferases that synthesize the first step in the synthesis of the oligosaccharides stachyose and raffinose, the putative PGSIPs, and the large bacterial GT8 family of diverse α-glucosyltransferases and α-galactosyltransferases involved in lipopolysaccharide and lipooligosaccharide synthesis) indicate that the GT8 family members are involved in several unique types of glycoconjugate and glycan biosynthetic processes (Yin et al., 2010). This observation led us to ask whether any of the GT8 family members are sufficiently closely related to GAUT and GATL genes to be informative regarding GAUT or GATL biosynthetic function(s) and/or mechanism(s).To investigate the relatedness of the members of the GT8 gene family, we carried out a detailed phylogenetic analysis of the entire GT8 family in 15 completely sequenced plant and green algal genomes (AbbreviationCladeSpeciesGenome PublishedDownloaded frommpcGreen algaeMicromonas pusilla CCMP1545Worden et al. (2009)JGI version 2.0mprGreen algaeMicromonas strain RCC299Worden et al. (2009)JGI version 2.0olGreen algaeOstreococcus lucimarinusPalenik et al. (2007)JGI version 1.0otGreen algaeOstreococcus tauriDerelle et al. (2006)JGI version 1.0crGreen algaeChlamydomonas reinhardtiiMerchant et al. (2007)JGI version 3.0vcGreen algaeVolvox carteri f. nagariensisNoJGI version 1.0ppMossPhyscomitrella patens ssp. patensRensing et al. (2008)JGI version 1.1smSpike mossSelaginella moellendorffiiNoJGI version 1.0ptDicotPopulus trichocarpaTuskan et al. (2006)JGI version 1.1atDicotArabidopsis thalianaArabidopsis Genome Initiative (2000)TAIR version 9.0vvDicotVitis viniferaJaillon et al. (2007)http://www.genoscope.cns.fr/gmDicotGlycine maxSchmutz et al. (2010)JGI version 1.0osMonocotOryza sativaGoff et al. (2002); Yu et al. (2002)TIGR version 6.1sbMonocotSorghum bicolorPaterson et al. (2009)JGI version 1.0bdMonocotBrachypodium distachyonVogel et al. (2010)JGI version 1.0Open in a separate window     

The Golgi and the centrosome: building a functional partnership

The mammalian Golgi apparatus is characterized by a ribbon-like organization adjacent to the centrosome during interphase and extensive fragmentation and dispersal away from the centrosome during mitosis. It is not clear whether this dynamic association between the Golgi and centrosome is of functional significance. We discuss recent findings indicating that the Golgi–centrosome relationship may be important for directional protein transport and centrosome positioning, which are both required for cell polarization. We also summarize our current knowledge of the link between Golgi organization and cell cycle progression.

Introduction

The Golgi apparatus plays a central role in the secretory pathway. Newly synthesized proteins are transported from the ER to the Golgi, where they are posttranslationally modified. They are sorted into carriers for delivery to the plasma membrane or the endosomal–lysosomal system. The basic structural unit of the Golgi apparatus is a stack of flattened cisternae that is morphologically conserved among most species. In mammalian cells, individual Golgi stacks are connected laterally to form a continuous membranous system called the Golgi ribbon, which is located in close physical proximity to the centrosome (Fig. 1, left).Open in a separate windowFigure 1.The spatial relationship between the Golgi and the centrosome during the mammalian cell cycle. Golgi (red, stained with antibodies to GM130) and centrosome (green, stained with antibodies to centrin) staining of nonsynchronized bone cancer cells (U2-OS) shows the physical proximity between these two organelles during interphase (left) and its temporary loss during mitosis (right). Bar, 10 µm.The centrosome functions as the major microtubule-organizing center of the cell and plays an important role in cell polarization and ciliogenesis (Bettencourt-Dias and Glover, 2007). In a newly formed daughter cell, this nonmembrane-bound organelle is composed of a pair of centrioles that is surrounded by a cloud of electron-dense material called the pericentriolar matrix. γ-Tubulin ring complexes (γ-TuRCs) in the pericentriolar matrix allow the centrosome to nucleate the radial array of interphase microtubules whose minus ends are embedded in the centrosome and whose plus ends extend toward the cell periphery. After centrosome duplication in S phase, the two centrosomes move to opposite poles of the cell and become the spindle poles from which spindle microtubules grow. Centrosomes are generally located in the cell center close to the nucleus, although this central position is lost in response to a polarization stimulus, which prompts centrosomes to reorient toward the leading edge of the cell (Pouthas et al., 2008). In most cell types, centrosome reorientation is critical for the ability of cells to polarize and migrate (Yvon et al., 2002). The centrosome is also linked to ciliogenesis because one of its centrioles is converted into the basal body from which a primary cilium extends (D''Angelo and Franco, 2009).The spatial relationship between the Golgi apparatus and the centrosome is altered by changes in Golgi organization that occur during the cell cycle (Fig. 1). These two organelles are only adjacent in interphase when the Golgi apparatus is arranged as a ribbon in the pericentriolar region (Colanzi et al., 2003). In contrast, Golgi membranes are fragmented and dispersed throughout the cytoplasm during mitosis. Intriguingly, the pericentriolar localization of the Golgi is a feature typical of some eukaryotic cells, ranging from mammalian and amphibian cells (Thyberg and Moskalewski, 1999; Reilein et al., 2003) to amoeba (Rehberg et al., 2005). However, other eukaryotes, including plants and flies, have isolated Golgi stacks (Stanley et al., 1997; Nebenführ and Staehelin, 2001) or isolated cisternae in the case of Saccharomyces cerevisiae that are scattered throughout the cytoplasm without an obvious connection with the centrosome (Preuss et al., 1992).In this paper, we review recent findings indicating that the relationship between the Golgi and the centrosome in interphase is important for cell polarization. We also summarize the current understanding of how Golgi–centrosome interactions during mitosis affect cell division.

Are there functional interactions between the Golgi and the centrosome during interphase?

Golgi membranes are actively positioned in the pericentriolar position.

The localization of the mammalian Golgi ribbon next to the centrosome requires the microtubule and actin cytoskeleton (Brownhill et al., 2009). Microtubules have a dual role in organizing the pericentriolar Golgi ribbon. First, the subset of microtubules that is nucleated at the Golgi is necessary for the assembly of Golgi fragments into a connected ribbon in the cell periphery (Miller et al., 2009). Second, centrosomal microtubules provide the tracks along which Golgi membranes are transported to the cell center (Cole et al., 1996). Both steps depend on the minus end–directed motor complex dynein (Burkhardt et al., 1997; Miller et al., 2009). The actin cytoskeleton is also involved in localizing Golgi membranes. Actin fibers, which have been detected at the Golgi complex, are required for the maintenance of the pericentriolar position of this organelle by providing tracks for actin-based motors (Valderrama et al., 1998; Sahlender et al., 2005; Vicente-Manzanares et al., 2007). Actin fibers and microtubules are coordinated by proteins that associate with both cytoskeletal elements such as WHAMM, a Golgi-bound actin-nucleating factor, and MACF1, a microtubule–actin cross-linking protein (Lin et al., 2005; Campellone et al., 2008).Golgi organization and localization in the pericentriolar region also depend on Golgi-associated proteins (Ramirez and Lowe, 2009). Their depletion produces defects in Golgi organization ranging from a disconnected Golgi ribbon in the pericentriolar region (Puthenveedu et al., 2006) to dispersed ministacks in the cytoplasm (Diao et al., 2003; Yadav et al., 2009).

Table I.

Golgi-associated proteins that control the pericentriolar position of the Golgi apparatus

Calcineurin B-Like Protein-Interacting Protein Kinase CIPK21 Regulates Osmotic and Salt Stress Responses in Arabidopsis

The role of calcium-mediated signaling has been extensively studied in plant responses to abiotic stress signals. Calcineurin B-like proteins (CBLs) and CBL-interacting protein kinases (CIPKs) constitute a complex signaling network acting in diverse plant stress responses. Osmotic stress imposed by soil salinity and drought is a major abiotic stress that impedes plant growth and development and involves calcium-signaling processes. In this study, we report the functional analysis of CIPK21, an Arabidopsis (Arabidopsis thaliana) CBL-interacting protein kinase, ubiquitously expressed in plant tissues and up-regulated under multiple abiotic stress conditions. The growth of a loss-of-function mutant of CIPK21, cipk21, was hypersensitive to high salt and osmotic stress conditions. The calcium sensors CBL2 and CBL3 were found to physically interact with CIPK21 and target this kinase to the tonoplast. Moreover, preferential localization of CIPK21 to the tonoplast was detected under salt stress condition when coexpressed with CBL2 or CBL3. These findings suggest that CIPK21 mediates responses to salt stress condition in Arabidopsis, at least in part, by regulating ion and water homeostasis across the vacuolar membranes.Drought and salinity cause osmotic stress in plants and severely affect crop productivity throughout the world. Plants respond to osmotic stress by changing a number of cellular processes (Xiong et al., 1999; Xiong and Zhu, 2002; Bartels and Sunkar, 2005; Boudsocq and Lauriére, 2005). Some of these changes include activation of stress-responsive genes, regulation of membrane transport at both plasma membrane (PM) and vacuolar membrane (tonoplast) to maintain water and ionic homeostasis, and metabolic changes to produce compatible osmolytes such as Pro (Stewart and Lee, 1974; Krasensky and Jonak, 2012). It has been well established that a specific calcium (Ca²⁺) signature is generated in response to a particular environmental stimulus (Trewavas and Malhó, 1998; Scrase-Field and Knight, 2003; Luan, 2009; Kudla et al., 2010). The Ca²⁺ changes are primarily perceived by several Ca²⁺ sensors such as calmodulin (Reddy, 2001; Luan et al., 2002), Ca²⁺-dependent protein kinases (Harper and Harmon, 2005), calcineurin B-like proteins (CBLs; Luan et al., 2002; Batistič and Kudla, 2004; Pandey, 2008; Luan, 2009; Sanyal et al., 2015), and other Ca²⁺-binding proteins (Reddy, 2001; Shao et al., 2008) to initiate various cellular responses.Plant CBL-type Ca²⁺ sensors interact with and activate CBL-interacting protein kinases (CIPKs) that phosphorylate downstream components to transduce Ca²⁺ signals (Liu et al., 2000; Luan et al., 2002; Batistič and Kudla, 2004; Luan, 2009). In several plant species, multiple members have been identified in the CBL and CIPK family (Luan et al., 2002; Kolukisaoglu et al., 2004; Pandey, 2008; Batistič and Kudla, 2009; Weinl and Kudla, 2009; Pandey et al., 2014). Involvement of specific CBL-CIPK pair to decode a particular type of signal entails the alternative and selective complex formation leading to stimulus-response coupling (D’Angelo et al., 2006; Batistič et al., 2010).Several CBL and CIPK family members have been implicated in plant responses to drought, salinity, and osmotic stress based on genetic analysis of Arabidopsis (Arabidopsis thaliana) mutants (Zhu, 2002; Cheong et al., 2003, 2007; Kim et al., 2003; Pandey et al., 2004, 2008; D’Angelo et al., 2006; Qin et al., 2008; Tripathi et al., 2009; Held et al., 2011; Tang et al., 2012; Drerup et al., 2013; Eckert et al., 2014). A few CIPKs have also been functionally characterized by gain-of-function approach in crop plants such as rice (Oryza sativa), pea (Pisum sativum), and maize (Zea mays) and were found to be involved in osmotic stress responses (Mahajan et al., 2006; Xiang et al., 2007; Yang et al., 2008; Tripathi et al., 2009; Zhao et al., 2009; Cuéllar et al., 2010).In this report, we examined the role of the Arabidopsis CIPK21 gene in osmotic stress response by reverse genetic analysis. The loss-of-function mutant plants became hypersensitive to salt and mannitol stress conditions, suggesting that CIPK21 is involved in the regulation of osmotic stress response in Arabidopsis. These findings are further supported by an enhanced tonoplast targeting of the cytoplasmic CIPK21 through interaction with the vacuolar Ca²⁺ sensors CBL2 and CBL3 under salt stress condition.     

Plasticity of cell migration: a multiscale tuning model

Cell migration underlies tissue formation, maintenance, and regeneration as well as pathological conditions such as cancer invasion. Structural and molecular determinants of both tissue environment and cell behavior define whether cells migrate individually (through amoeboid or mesenchymal modes) or collectively. Using a multiparameter tuning model, we describe how dimension, density, stiffness, and orientation of the extracellular matrix together with cell determinants—including cell–cell and cell–matrix adhesion, cytoskeletal polarity and stiffness, and pericellular proteolysis—interdependently control migration mode and efficiency. Motile cells integrate variable inputs to adjust interactions among themselves and with the matrix to dictate the migration mode. The tuning model provides a matrix of parameters that control cell movement as an adaptive and interconvertible process with relevance to different physiological and pathological contexts.

Introduction

Cell migration is a complex and heterogeneous process executed by all nucleated cell types at a given time window of their development. For most cells, including epithelial, stromal, and neuronal cells, migration phases are confined to morphogenesis and cease with terminal differentiation toward intact tissue to become reactivated only for tissue regeneration or neoplastic processes. For other cell types, such as leukocytes, migration is integral to their function and maintained throughout their life span. Some cell types migrate only in the context of a defined substrate, such as epithelial cells moving along a basement membrane but not through interstitial tissues, whereas other cell types, including leukocytes, are versatile, as they interact with and migrate within virtually any substrate present in the body. Thus, although the same basic process is executed (i.e., cell translocation along or through tissue structures), each cell type exerts migration in different contexts using distinct molecular repertoires and extracellular guidance cues. We here summarize extra- and intracellular molecular parameters that regulate cell migration and integrate them into a parameter “matrix” to better classify how cell migration modes are being both achieved and modulated.

The modes of cell migration

The mode of cell migration was originally classified based on the morphology of migration patterns. This terminology was then extended to include molecular parameters, such as cytoskeletal organization, the type of cell–matrix interaction and force generation, and the modification of the tissue structure imposed by migrating cells (Friedl et al., 1998b; Thiery, 2002; Friedl, 2004; Lämmermann and Sixt, 2009; Sanz-Moreno and Marshall, 2009). As main categories, cell move either individually (amoeboid or mesenchymal) or collectively (the migration of cohesive multicellular units; Fig. 1 and Friedl, 2004). Although these terms are arguably arbitrary and the molecular discrimination between the certain modes is incomplete, they help to simplify and categorize an otherwise diffuse literature and allow dissection of the molecular machineries underlying each mode.Open in a separate windowFigure 1.Cell morphologies, migration modes, and transitions. The nomenclature of interstitial migration modes is based on typical cell morphology (rounded or spindle-shaped) and pattern (individual, loosely connected, or collective). Each migration mode is governed by a set of molecular mechanisms (see details in Fig. 2), the regulation of which can change the style of migration. Most widely studied examples for alterations of migration mode are the mesenchymal-to-amoeboid transition or the collective-to-individual transition. The thick gray arrows indicate the direction of migration.

Table I.

Different migration modes and selected determinants