Arabidopsis LON2 Is Necessary for Peroxisomal Function and Sustained Matrix Protein Import

Relatively little is known about the small subset of peroxisomal proteins with predicted protease activity. Here, we report that the peroxisomal LON2 (At5g47040) protease facilitates matrix protein import into Arabidopsis (Arabidopsis thaliana) peroxisomes. We identified T-DNA insertion alleles disrupted in five of the nine confirmed or predicted peroxisomal proteases and found only two—lon2 and deg15, a mutant defective in the previously described PTS2-processing protease (DEG15/At1g28320)—with phenotypes suggestive of peroxisome metabolism defects. Both lon2 and deg15 mutants were mildly resistant to the inhibitory effects of indole-3-butyric acid (IBA) on root elongation, but only lon2 mutants were resistant to the stimulatory effects of IBA on lateral root production or displayed Suc dependence during seedling growth. lon2 mutants displayed defects in removing the type 2 peroxisome targeting signal (PTS2) from peroxisomal malate dehydrogenase and reduced accumulation of 3-ketoacyl-CoA thiolase, another PTS2-containing protein; both defects were not apparent upon germination but appeared in 5- to 8-d-old seedlings. In lon2 cotyledon cells, matrix proteins were localized to peroxisomes in 4-d-old seedlings but mislocalized to the cytosol in 8-d-old seedlings. Moreover, a PTS2-GFP reporter sorted to peroxisomes in lon2 root tip cells but was largely cytosolic in more mature root cells. Our results indicate that LON2 is needed for sustained matrix protein import into peroxisomes. The delayed onset of matrix protein sorting defects may account for the relatively weak Suc dependence following germination, moderate IBA-resistant primary root elongation, and severe defects in IBA-induced lateral root formation observed in lon2 mutants.Peroxisomes are single-membrane-bound organelles found in most eukaryotes. Peroxin (PEX) proteins are necessary for various aspects of peroxisome biogenesis, including matrix protein import (for review, see Distel et al., 1996; Schrader and Fahimi, 2008). Most matrix proteins are imported into peroxisomes from the cytosol using one of two targeting signals, a C-terminal type 1 peroxisome-targeting signal (PTS1) or a cleavable N-terminal type 2 peroxisome-targeting signal (PTS2) (Reumann, 2004). PTS1- and PTS2-containing proteins are bound in the cytosol by soluble matrix protein receptors, escorted to the peroxisome membrane docking complex, and translocated into the peroxisome matrix (for review, see Platta and Erdmann, 2007). Once in the peroxisome, many matrix proteins participate in metabolic pathways, such as β-oxidation, hydrogen peroxide decomposition, and photorespiration (for review, see Gabaldon et al., 2006; Poirier et al., 2006).In addition to metabolic enzymes, several proteases are found in the peroxisome matrix. Only one protease, DEG15/Tysnd1, has a well-defined role in peroxisome biology. The rat Tysnd1 protease removes the targeting signal after PTS2-containing proteins enter the peroxisome and also processes certain PTS1-containing β-oxidation enzymes (Kurochkin et al., 2007). Similarly, the Arabidopsis (Arabidopsis thaliana) Tysnd1 homolog DEG15 (At1g28320) is a peroxisomal Ser protease that removes PTS2 targeting signals (Helm et al., 2007; Schuhmann et al., 2008).In contrast with DEG15, little is known about the other eight Arabidopsis proteins that are annotated as proteases in the AraPerox database of putative peroxisomal proteins (Reumann et al., 2004; Carter et al., 2004; Shimaoka et al., 2004), which, in combination with the minor PTS found in both of these predicted proteases (Reumann, 2004), suggests that these enzymes may not be peroxisomal. Along with DEG15, only two of the predicted peroxisomal proteases, an M16 metalloprotease (At2g41790), which we have named PXM16 for peroxisomal M16 protease, and a Lon-related protease (At5g47040/LON2; Ostersetzer et al., 2007), are found in the proteome of peroxisomes purified from Arabidopsis suspension cells (Eubel et al., 2008). DEG15 and LON2 also have been validated as peroxisomally targeted using GFP fusions (Ostersetzer et al., 2007; Schuhmann et al., 2008).

Table I.

Putative Arabidopsis proteases predicted or demonstrated to be peroxisomal

Mouse Models of Osteoarthritis: A Summary of Models and Outcomes Assessment

Osteoarthritis (OA) is a multidimensional health problem and a common chronic disease. It has a substantial impact on patient quality of life and is a common cause of pain and mobility issues in older adults. The functional limitations, lack of curative treatments, and cost to society all demonstrate the need for translational and clinical research. The use of OA models in mice is important for achieving a better understanding of the disease. Models with clinical relevance are needed to achieve 2 main goals: to assess the impact of the OA disease (pain and function) and to study the efficacy of potential treatments. However, few OA models include practical strategies for functional assessment of the mice. OA signs in mice incorporate complex interrelations between pain and dysfunction. The current review provides a comprehensive compilation of mouse models of OA and animal evaluations that include static and dynamic clinical assessment of the mice, merging evaluation of pain and function by using automatic and noninvasive techniques. These new techniques allow simultaneous recording of spontaneous activity from thousands of home cages and also monitor environment conditions. Technologies such as videography and computational approaches can also be used to improve pain assessment in rodents but these new tools must first be validated experimentally. An example of a new tool is the digital ventilated cage, which is an automated home-cage monitor that records spontaneous activity in the cages.

Osteoarthritis (OA) is a multidimensional health problem and a common chronic disease.³⁶ Functional limitations, the absence of curative treatments, and the considerable cost to society result in a substantial impact on quality of life.⁷⁶ Historically, OA has been described as whole joint and whole peri-articular diseases and as a systemic comorbidity.^9,111 OA consists of a disruption of articular joint cartilage homeostasis leading to a catabolic pathway characterized by chondrocyte degeneration and destruction of the extracellular matrix (ECM). Low-grade chronic systemic inflammation is also actively involved in the process.^42,92 In clinical practice, mechanical pain, often accompanied by a functional decline, is the main reason for consultations. Recommendations to patients provide guidance for OA management.^{22, 33,49,86} Evidence-based consensus has led to a variety of pharmacologic and nonpharmacologic modalities that are intended to guide health care providers in managing symptomatic patients. Animal-based research is of tremendous importance for the study of early diagnosis and treatment, which are crucial to prevent the disease progression and provide better care to patients.The purpose of animal-based OA research is 2-fold: to assess the impact of the OA disease (pain and function) and to study the efficacy of a potential treatment.^18,67 OA model species include large animals such as the horse, goat, sheep, and dog, whose size and anatomy are expected to better reflect human joint conditions. However, small animals such as guinea pig, rabbit, mouse, and rat represent 77% of the species used.^1,87 In recent years, mice have become the most commonly used model for studying OA. Mice have several advantageous characteristics: a short development and life span, easy and low-cost breeding and maintenance, easy handling, small joints that allow histologic analysis of the whole joint,³² and the availability of genetically modified lines.¹⁰⁸ Standardized housing, genetically defined strains and SPF animals reduce the genetic and interindividual acquired variability. Mice are considered the best vertebrate model in terms of monitoring and controlling environmental conditions.^7,14,15,87 Mouse skeletal maturation is reached at 10 wk, which theoretically constitutes the minimal age at which mice should be entered into an OA study.^64,87,102 However, many studies violate this limit by testing mice at 8 wk of age.Available models for OA include the following (32,111 physical activity and exercise induced OA; noninvasive mechanical loading (repetitive mild loading and single-impact injury); and surgically induced (meniscectomy models or anterior cruciate ligament transection). The specific model used would be based on the goal of the study.⁷ For example, OA pathophysiology, OA progression, and OA therapies studies could use spontaneous, genetic, surgical, or noninvasive models. In addition, pain studies could use chemical models. Lastly, post-traumatic studies would use surgical or noninvasive models; the most frequently used method is currently destabilization of the medial meniscus,³² which involves transection of the medial meniscotibial ligament, thereby destabilizing the joint and causing instability-driven OA. An important caveat for mouse models is that the mouse and human knee differ in terms of joint size, joint biomechanics, and histologic characteristics (layers, cellularity),^32,64 and joint differences could confound clinical translation.¹⁰ Table 1. Mouse models of osteoarthritis.

Retrograde Intraflagellar Transport Mutants Identify Complex A Proteins With Multiple Genetic Interactions in Chlamydomonas reinhardtii

The intraflagellar transport machinery is required for the assembly of cilia. It has been investigated by biochemical, genetic, and computational methods that have identified at least 21 proteins that assemble into two subcomplexes. It has been hypothesized that complex A is required for retrograde transport. Temperature-sensitive mutations in FLA15 and FLA17 show defects in retrograde intraflagellar transport (IFT) in Chlamydomonas. We show that IFT144 and IFT139, two complex A proteins, are encoded by FLA15 and FLA17, respectively. The fla15 allele is a missense mutation in a conserved cysteine and the fla17 allele is an in-frame deletion of three exons. The flagellar assembly defect of each mutant is rescued by the respective transgenes. In fla15 and fla17 mutants, bulges form in the distal one-third of the flagella at the permissive temperature and this phenotype is also rescued by the transgenes. These bulges contain the complex B component IFT74/72, but not α-tubulin or p28, a component of an inner dynein arm, which suggests specificity with respect to the proteins that accumulate in these bulges. IFT144 and IFT139 are likely to interact with each other and other proteins on the basis of three distinct genetic tests: (1) Double mutants display synthetic flagellar assembly defects at the permissive temperature, (2) heterozygous diploid strains exhibit second-site noncomplemention, and (3) transgenes confer two-copy suppression. Since these tests show different levels of phenotypic sensitivity, we propose they illustrate different gradations of gene interaction between complex A proteins themselves and with a complex B protein (IFT172).CILIA and flagella are microtubule-based organelles that are found on most mammalian cells. They provide motility to cells and participate in many sensory processes. Defects in or loss of cilia/flagella cause a variety of human diseases that include polycystic kidney disease, retinal degeneration, infertility, obesity, respiratory defects, left–right axis determination, and polydactyly (Fliegauf et al. 2007). Mouse mutants demonstrate that cilia are essential for viability, neural tube closure, and bone development (Eggenschwiler and Anderson 2007; Fliegauf et al. 2007). Cilia and flagella are also present in protists, algae, moss, and some fungi.The assembly and maintenance of cilia and flagella require intraflagellar transport (IFT) (Kozminski et al. 1995). IFT involves the movement of 100- to 200-nm-long protein particles from the basal body located in the cell body to the tip of the flagella using the heterotrimeric kinesin-2 (anterograde movement) (Kozminski et al. 1995) and movement back to the cell body (retrograde movement) using the cytoplasmic dynein complex (Pazour et al. 1999; Porter et al. 1999). IFT particles change their direction of movement as well as their size, speed, and frequency at the ends of the flagella as they switch from anterograde to retrograde movement (Iomini et al. 2001). Biochemical isolation of IFT particles reveals that they are composed of at least 16 proteins and that these particles can be dissociated into two complexes in vitro by changing the salt concentration (Cole et al. 1998; Piperno et al. 1998). Recent genetic and bioinformatics analysis adds at least 7 more proteins to the IFT particle (Follit et al. 2009) (Eggenschwiler and Anderson 2007).

TABLE 1

Proteins and gene names for the intraflagellar transport particles in Chlamydomonas, C. elegans, and mouse

Adaptive Divergence in Experimental Populations of Pseudomonas fluorescens. IV. Genetic Constraints Guide Evolutionary Trajectories in a Parallel Adaptive Radiation

The capacity for phenotypic evolution is dependent upon complex webs of functional interactions that connect genotype and phenotype. Wrinkly spreader (WS) genotypes arise repeatedly during the course of a model Pseudomonas adaptive radiation. Previous work showed that the evolution of WS variation was explained in part by spontaneous mutations in wspF, a component of the Wsp-signaling module, but also drew attention to the existence of unknown mutational causes. Here, we identify two new mutational pathways (Aws and Mws) that allow realization of the WS phenotype: in common with the Wsp module these pathways contain a di-guanylate cyclase-encoding gene subject to negative regulation. Together, mutations in the Wsp, Aws, and Mws regulatory modules account for the spectrum of WS phenotype-generating mutations found among a collection of 26 spontaneously arising WS genotypes obtained from independent adaptive radiations. Despite a large number of potential mutational pathways, the repeated discovery of mutations in a small number of loci (parallel evolution) prompted the construction of an ancestral genotype devoid of known (Wsp, Aws, and Mws) regulatory modules to see whether the types derived from this genotype could converge upon the WS phenotype via a novel route. Such types—with equivalent fitness effects—did emerge, although they took significantly longer to do so. Together our data provide an explanation for why WS evolution follows a limited number of mutational pathways and show how genetic architecture can bias the molecular variation presented to selection.UNDERSTANDING—and importantly, predicting—phenotypic evolution requires knowledge of the factors that affect the translation of mutation into phenotypic variation—the raw material of adaptive evolution. While much is known about mutation rate (e.g., Drake et al. 1998; Hudson et al. 2002), knowledge of the processes affecting the translation of DNA sequence variation into phenotypic variation is minimal.Advances in knowledge on at least two fronts suggest that progress in understanding the rules governing the generation of phenotypic variation is possible (Stern and Orgogozo 2009). The first stems from increased awareness of the genetic architecture underlying specific adaptive phenotypes and recognition of the fact that the capacity for evolutionary change is likely to be constrained by this architecture (Schlichting and Murren 2004; Hansen 2006). The second is the growing number of reports of parallel evolution (e.g., Pigeon et al. 1997; ffrench-Constant et al. 1998; Allender et al. 2003; Colosimo et al. 2004; Zhong et al. 2004; Boughman et al. 2005; Shindo et al. 2005; Kronforst et al. 2006; Woods et al. 2006; Zhang 2006; Bantinaki et al. 2007; McGregor et al. 2007; Ostrowski et al. 2008)—that is, the independent evolution of similar or identical features in two or more lineages—which suggests the possibility that evolution may follow a limited number of pathways (Schluter 1996). Indeed, giving substance to this idea are studies that show that mutations underlying parallel phenotypic evolution are nonrandomly distributed and typically clustered in homologous genes (Stern and Orgogozo 2008).While the nonrandom distribution of mutations during parallel genetic evolution may reflect constraints due to genetic architecture, some have argued that the primary cause is strong selection (e.g., Wichman et al. 1999; Woods et al. 2006). A means of disentangling the roles of population processes (selection) from genetic architecture is necessary for progress (Maynard Smith et al. 1985; Brakefield 2006); also necessary is insight into precisely how genetic architecture might bias the production of mutations presented to selection.Despite their relative simplicity, microbial populations offer opportunities to advance knowledge. The wrinkly spreader (WS) morphotype is one of many different niche specialist genotypes that emerge when experimental populations of Pseudomonas fluorescens are propagated in spatially structured microcosms (Rainey and Travisano 1998). Previous studies defined, via gene inactivation, the essential phenotypic and genetic traits that define a single WS genotype known as LSWS (Spiers et al. 2002, 2003) (Figure 1). LSWS differs from the ancestral SM genotype by a single nonsynonymous nucleotide change in wspF. Functionally (see Figure 2), WspF is a methyl esterase and negative regulator of the WspR di-guanylate cyclase (DGC) (Goymer et al. 2006) that is responsible for the biosynthesis of c-di-GMP (Malone et al. 2007), the allosteric activator of cellulose synthesis enzymes (Ross et al. 1987). The net effect of the wspF mutation is to promote physiological changes that lead to the formation of a microbial mat at the air–liquid interface of static broth microcosms (Rainey and Rainey 2003).Open in a separate windowFigure 1.—Outline of experimental strategy for elucidation of WS-generating mutations and their subsequent identity and distribution among a collection of independently evolved, spontaneously arising WS genotypes. The strategy involves, first, the genetic analysis of a specific WS genotype (e.g., LSWS) to identify the causal mutation, and second, a survey of DNA sequence variation at specific loci known to harbor causal mutations among a collection of spontaneously arising WS genotypes. For example, suppressor analysis of LSWS using a transposon to inactivate genes necessary for expression of the wrinkly morphology delivered a large number of candidate genes (top left) (Spiers et al. 2002). Genetic and functional analysis of these candidate genes (e.g., Goymer et al. 2006) led eventually to the identity of the spontaneous mutation (in wspF) responsible for the evolution of LSWS from the ancestral SM genotype (Bantinaki et al. 2007). Subsequent analysis of the wspF sequence among 26 independent WS genotypes (bottom) showed that 50% harbored spontaneous mutations (of different kinds; see Open in a separate windowFigure 2.—Network diagram of DGC-encoding pathways underpinning the evolution of the WS phenotype and their regulation. Overproduction of c-di-GMP results in overproduction of cellulose and other adhesive factors that determine the WS phenotype. The ancestral SBW25 genome contains 39 putative DGCs, each in principle capable of synthesizing the production of c-di-GMP, and yet WS genotypes arise most commonly as a consequence of mutations in just three DGC-containing pathways: Wsp, Aws, and Mws. In each instance, the causal mutations are most commonly in the negative regulatory component: wspF, awsX, and the phosphodiesterase domain of mwsR (see text).To determine whether spontaneous mutations in wspF are a common cause of the WS phenotype, the nucleotide sequence of this gene was obtained from a collection of 26 spontaneously arising WS genotypes (WS_A-Z) taken from 26 independent adaptive radiations, each founded by the same ancestral SM genotype (Figure 1): 13 contained mutations in wspF (Bantinaki et al. 2007). The existence of additional mutational pathways to WS provided the initial motivation for this study.

TABLE 1

Mutational causes of WS

Evolutionary Strata in a Small Mating-Type-Specific Region of the Smut Fungus Microbotryum violaceum

DNA sequence analysis and genetic mapping of loci from mating-type-specific chromosomes of the smut fungus Microbotryum violaceum demonstrated that the nonrecombining mating-type-specific region in this species comprises ∼25% (∼1 Mb) of the chromosome length. Divergence between homologous mating-type-linked genes in this region varies between 0 and 8.6%, resembling the evolutionary strata of vertebrate and plant sex chromosomes.EVOLUTION of mating types or sex-determining systems often involves the suppression of recombination around the primary sex-determining or mating-type-determining locus. In animals and plants, it is often an entire or almost entire chromosome (Y or W in male or female heterogametic species, respectively) that ceases to recombine with its homologous (X or Z) chromosome (Charlesworth and Charlesworth 2000; Charlesworth 2008). Self-incompatibility loci in plants are also thought to be located in regions of suppressed recombination (Charlesworth et al. 2005; Kamau and Charlesworth 2005; Kamau et al. 2007; Li et al. 2007; Yang et al. 2007). Regardless of the phylogenetic position of a species, such nonrecombining regions are known to follow similar evolutionary trajectories. The nonrecombining region on the sex-specific chromosome expands in several steps, forming evolutionary strata—regions of different X/Y (or Z/W) divergence (Lahn and Page 1999; Handley et al. 2004; Sandstedt and Tucker 2004; Nicolas et al. 2005)—and genes in the nonrecombining regions gradually accumulate deleterious mutations that eventually render them dysfunctional (Charlesworth and Charlesworth 2005; Charlesworth 2008).Fungal mating-type systems are very diverse, with the number of mating types varying from two to several hundred (Casselton 2002). Like sex chromosomes in several animals and plants, suppressed recombination has evolved in regions near fungal mating-type loci, including in Ustilago hordei (Lee et al. 1999), Cryptococcus neoformans (Lengeler et al. 2002), and Neurospora tetrasperma (Menkis et al. 2008). These species have two mating types, but no morphologically distinct sexes. The mating-type locus (the region of suppressed recombination) of C. neoformans is small (∼100 kb) compared with known sex chromosomes and contains only ∼20 genes that, unlike many sex chromosomes (Y or W chromosomes), show no obvious signs of genetic degeneration (Lengeler et al. 2002; Fraser et al. 2004). Judging from the divergence between the homologous genes on the two mating-type-specific chromosomes, C. neoformans started to evolve sex chromosomes a long time ago because silent divergence between the two mating types in the most ancient region exceeds 100% (Fraser et al. 2004). Genes in the younger mating-type-specific region are much less diverged between the two sex chromosomes, suggesting that the evolution of the sex locus in C. neoformans might have proceeded through several steps. The nonrecombining region around the mating-type locus of N. tetrasperma is much larger than in C. neoformans (at least 6.6 Mb), and silent divergence between homologous genes on the mating-type-specific chromosomes ranges from zero to 9%, demonstrating that these mating-type-specific chromosomes evolved recently (Menkis et al. 2008).M. violaceum, which causes anther smut disease in Silene latifolia and other species in the family Caryophyllaceae, has two mating types, A1 and A2 (reviewed by Giraud et al. 2008), which are determined by the presence of mating-type-specific chromosomes (hereafter A1 and A2 chromosomes, or sex chromosomes) in the haploid stage of the life cycle (Hood 2002; Hood et al. 2004). The A1 and A2 chromosomes are distinguishable by size in pulsed-field electrophoresis, and it is possible to isolate individual chromosomes electrophoretically (Hood et al. 2004). Random fragments of A1 and A2 chromosomes have previously been isolated from mating-type-specific bands of pulsed-field separated chromosomes of M. violaceum (Hood et al. 2004). These fragments were assumed to be linked to mating type. The same method was used to isolate fragments of non-mating-type-specific chromosomes. On the basis of the analysis of their sequences, (Hood et al. 2004) proposed that mating-type-specific chromosomes in M. violaceum might be degenerate because they contained a lower proportion of protein-coding genes than other chromosomes. However, it was not determined whether the sequences isolated from the mating-type chromosomes originated from the mating-type-specific or from the recombining regions (Hood et al. 2004), and the relative sizes of these regions are not known for these M. violaceum chromosomes. We tested the mating-type specificity of 86 of these fragments and demonstrate that fewer than a quarter of these loci are located in the mating-type-specific region, suggesting that the nonrecombining region on the A1 and A2 chromosomes is quite small, while the rest of the chromosome probably recombines (like pseudoautosomal regions of sex chromosomes) and is therefore not expected to undergo genetic degeneration. Genetic mapping confirms the presence of two pseudoautosomal regions in the M. violaceum mating-type-specific chromosomes.As these chromosomes are mating type specific in the haploid stage of M. violaceum, mating-type-specific loci (or DNA fragments) can be identified by testing whether they are present exclusively in A1 or A2 haploid strains. We therefore prepared haploid A1 and A2 M. violaceum cultures from S. latifolia plants from two geographically remote locations (accessions Sl405 from Sweden and Sl127 from the French Pyrenees). Haploid sporidial cultures were isolated by a standard dilution method (Kaltz and Shykoff 1997; Oudemans and Alexander 1998). Mating types were determined by PCR amplification of each culture with primers designed for A1 and A2 pheromone receptor genes linked to A1 and A2 mating types (Yockteng et al. 2007). The primers were as follows: 5′-TGGCATCCCTCAATGTTTCC-3′ and 5′-CACCTTTTGATGAGAGGCCG-3′ for the A1 pheromone receptor (GenBank accession no. {"type":"entrez-nucleotide","attrs":{"text":"EF584742","term_id":"156254864","term_text":"EF584742"}}EF584742) and 5′-TGACGAGAGCATTCCTACCG-3′ and 5′-GAAGCGGAACTTGCCTTTCT-3′ for the A2 pheromone receptor (GenBank accession no. {"type":"entrez-nucleotide","attrs":{"text":"EF584741","term_id":"156254862","term_text":"EF584741"}}EF584741). Cultures with PCR product amplified only from an A1 or A2 pheromone receptor gene were selected for further use. The mating types of the cultures were verified by conjugating them in all combinations.The GenBank nucleotide database was searched using BLAST for sequences similar to those isolated by Hood et al. (2004). Sequences with similarity to transposable elements (TE) and other repeats were excluded. The resulting set of nonredundant sequences was used to design PCR primers for 98 fragments. Half of these were originally isolated from the A1 and half from the A2 chromosomes and are hereafter called A1-NNN or A2-NNN (where NNN is the locus number; supporting information, Table S1), which does not imply that these loci are A1 or A2 specific, but merely indicates that they were originally isolated from the A1 or A2 chromosomes. Amplification of these regions from new A1 and A2 M. violaceum cultures, independently isolated by ourselves, revealed that only 5 of the 49 loci isolated from the A1 chromosome are indeed A1 specific and only 6 of 49 isolated from the A2 chromosome are A2 specific. All other loci amplified from both A1 and A2 cultures. Figure 1 illustrates some of these results from the Swedish sample (Sl405).Open in a separate windowFigure 1.—Testing of mating-type specificity for loci isolated from A1 and A2 chromosomes. (a) PCR amplifications from haploid cultures from Sl405 using primers designed from six A1-originated loci. Loci in which a PCR product could be amplified only from A1 cultures (boxed) were classified as specific to mating type A1. (b) PCR tests of six A2-originated loci on the same set of haploids as in a. Loci in which a PCR product amplified only from A2 cultures (boxed) were classified as specific to mating type A2. Loci amplified from both A1 and A2 cultures are not mating type specific.The fragments that amplified from both A1 and A2 mating types may be in recombining regions, or they could be present in mating-type-specific regions on both A1 and A2 chromosomes. If they are in recombining regions, the A1- and A2-linked homologs should not be diverged from each other, but if they are in nonrecombining, mating-type-specific regions, the divergence of the A1- and A2-linked homologs should be roughly proportional to the time since recombination stopped in the region. We therefore sequenced and compared PCR fragments amplified from the two mating types of Sl405 or Sl127 cultures (GenBank accession nos. {"type":"entrez-nucleotide","attrs":{"text":"FI855822","term_id":"258612459","term_text":"FI855822"}}FI855822–{"type":"entrez-nucleotide","attrs":{"text":"FI856001","term_id":"258612570","term_text":"FI856001"}}FI856001). Sequencing of PCR products showed that 12 (4 A1 and 8 A2) loci have more than one copy, and they were excluded from further analysis. Sequences of 61 loci were identical between the A1 and A2 strains, and four loci demonstrated low total divergence (0.24–0.61%) between the two mating types (otintseva and D. Filatov, unpublished results). Thus, these loci might be located in the recombining part of the mating-type-specific chromosomes. Ten of 75 loci that amplified in both mating types demonstrated multiple polymorphisms fixed between the mating types rather than between the locations. Given that the strains that we used in the analysis originated from two geographically distant locations, it is highly unlikely that multiple polymorphisms distinguishing the A1 and A2 sequences arose purely by chance; thus, these loci are probably located in the nonrecombining mating-type-specific region of the M. violaceum A1 and A2 chromosomes.

TABLE 1

Loci from mating-type-specific chromosomes of M. violaceum used for PCR analysis and genetic map construction

		With nonzero A1/A2 divergenceb
Loci	Mating type specific	<1%	>1%	With zero A1/A2 divergenceb	Total
A1a	5	2 (1)	3 (3)	35 (3)	45 (7)
A2a	6	2 (0)	7 (7)	26 (3)	41 (10)
Subtotal		4 (1)	10 (10)
Total	11	14 (11)		61 (6)	86 (17)

Open in a separate window^aA1, loci originated from the A1 sex chromosome; A2, loci originated from the A2 sex chromosome.^bThe number of loci used for genetic map construction is in parentheses.To confirm the mating-type-specific or pseudoautosomal locations of the loci with and without A1/A2 divergence, we conducted genetic mapping in a family of 99 individuals, 50 of which were of mating type A1 and 49 of mating type A2. The family was generated by a cross between A1 and A2 M. violaceum strains from S. latifolia accessions Sl405 (Sweden) and Sl127 (France), respectively. The choice of strains from geographically distant locations was motivated by the hope of maximizing the number of DNA sequence differences between them that can be used as molecular genetic markers in segregation analysis. We inoculated S. latifolia seedlings with sporidial cultures of both mating types. For inoculation, petri dishes with 12-day-old seedlings of S. latifolia were flooded with 2.5 ml of inoculum suspension. Inoculum suspension consisted of equal volumes of the A1 and A2 sporidial cultures that were mixed and conjugated overnight at 14° under rotation (Biere and Honders 1996; Van Putten et al. 2003). Seedlings were potted 3 days after inoculation. Two months later, teliospores were collected from the flowers of the infected plant and grown in petri dishes on 3.6% potato dextrose agar medium. Haploid sporidia formed after meiosis were isolated and grown as separate cultures for DNA extraction. The mating types of single sporidia cultures were identified as described above. The loci analyzed in the segregation analysis were sequenced in the two parental haploid strains and in 99 (50 A1 and 49 A2) haploid strains that were generated in the cross. Single nucleotide differences between the parental strains were used as molecular genetic markers for segregation analysis in the progeny. The genetic map was constructed using MAPMAKER/EXP v3.0 (Lincoln et al. 1992) and MapDisto v1.7 (http://mapdisto.free.fr/).The resulting genetic map is shown in Figure 2. As expected, no recombination was observed between the 10 loci with diverged A1- and A2-linked copies. In addition, one marker with no A1/A2 divergence, A2-397, was also completely linked to the loci with significant A1/A2 divergence. This locus either may be very tightly linked to the nonrecombining mating-type-specific region or may have been added to that region more recently than the loci that had already accumulated some divergence between the alleles in the two mating types. The mating-type-specific pheromone receptor locus (Devier et al. 2009) and 11 mating-type-specific loci are also located in this nonrecombining region (Figure 2). Interestingly, the cluster of nonrecombining markers is flanked on both sides with markers that recombine in meiosis, demonstrating that there are pseudoautosomal regions on both ends of the mating-type-specific chromosomes.Open in a separate windowFigure 2.—Genetic map of the mating-type-determining chromosome in M. violaceum. Genetic distance (in centimorgans) and the relative positions of the markers are shown to the left and the right of the chromosome, respectively. The position of the nonrecombining region corresponds to the cluster of linked markers shown on the right of the figure. Total A1/A2 divergence is shown in parentheses. Eleven mating-type-specific markers (for which sequences are available from only one mating type), located in the nonrecombining mating-type-specific region, are not shown.Our results demonstrate that although the loci reported by Hood et al. (2004) were isolated from the A1 and A2 chromosomes, most of these loci are not located in the nonrecombining mating-type-specific regions. In fact, the nonrecombining region might be relatively small: of 86 tested fragments, only 21 appeared to be either mating type specific or linked to the mating-type locus. Assuming that these loci represent a random set of DNA fragments isolated from the A1 and A2 chromosomes, it is possible to estimate the size of the nonrecombining region using the binomial distribution: the nonrecombining region is expected to be 24.4% (95% CI: 16.7–33.6%) of the chromosome length. As the sizes of the A1 and A2 chromosomes are ∼3.4 and 4.2 Mb long (Hood 2002; Hood et al. 2004), the nonrecombining region might be ∼1 Mb long.Interestingly, total A1/A2 divergence for the 11 loci with A1- and A2-linked copies mapped to the nonrecombining region varied from 0% to 8.6% (Figure 2). In addition, 11 loci amplified from only one mating type. These genes could represent degenerated genes, some of which degenerated in A1 strains, and some in A2 strains. Alternatively, they might be highly diverged genes, such that the PCR primers amplify only one allele, and not the other. Variation in divergence may be the result of the stepwise cessation of recombination between the A1 and A2 chromosomes in M. violaceum, resembling the evolutionary strata reported for human, chicken, and white campion sex chromosomes (Lahn and Page 1999; Handley et al. 2004; Bergero et al. 2007). However, only the differences between the most and the least diverged loci are statistically significant (Devier et al. 2009), the M. violaceum mating-type region has at least three strata: one oldest stratum, including the pheromone receptor locus; a younger stratum with ∼5–9% A1/A2 divergence; and the youngest stratum with 1–4% divergence between the two mating types. There may also be an additional very recently evolved stratum containing the locus named A2-397, which is also present in all A1 strains tested, with no fixed differences between the A1 and A2 strains (No. of sites analyzedWithin A1

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Within A2

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Fixed differences between A1 and A2A1/A2 divergence (%)Loci^aS^b totalSπ (%)^cSπ (%)^cA1/A2 divergence <1%A1-23645630020.4410.44A1-0456544000040.61A2-568413220.4820.4800.24A2-411480210.210010.31A1/A2 divergence >1%A1-2176679000091.35A1-12856990010.1881.49A1-199618130010.16122.02A2-4223449000092.62A2-516470140000142.98A2-404508200030.59173.64A2-4355062220.3920.39183.95A2-4734572310.2210.22214.81A2-4573031710.3300165.54A2-5755034750.9930.59398.55Open in a separate window^aA1, loci originated from the A1 sex chromosome; A2, loci originated from the A2 sex chromosome.^bS, number of polymorphic sites.^cπ (%), average number of differences per 100 nucleotides.

TABLE 3

P-values for the 2 × 2 G-tests for significance of differences in A1/A2 divergence between the loci in the nonrecombining region

One- and Two-Locus Population Models With Differential Viability Between Sexes: Parallels Between Haploid Parental Selection and Genomic Imprinting

A model of genomic imprinting with complete inactivation of the imprinted allele is shown to be formally equivalent to the haploid model of parental selection. When single-locus dynamics are considered, an internal equilibrium is possible only if selection acts in the opposite directions in males and females. I study a two-locus version of the latter model, in which maternal and paternal effects are attributed to the single alleles at two different loci. A necessary condition for the allele frequency equilibria to remain on the linkage equilibrium surface is the multiplicative interaction between maternal and paternal fitness parameters. In this case the equilibrium dynamics are independent at both loci and results from the single-locus model apply. When fitness parameters are additive, analytic treatment was not possible but numerical simulations revealed that stable polymorphism characterized by association between loci is possible only in several special cases in which maternal and paternal fitness contributions are precisely balanced. As in the single-locus case, antagonistic selection in males and females is a necessary condition for the maintenance of polymorphism. I also show that the above two-locus results of the parental selection model are very sensitive to the inclusion of weak directional selection on the individual''s own genotypes.PARENTAL genetic effects refer to the influence of the mother''s and father''s genotypes on the phenotypes of their offspring, not attributable just to the transfer of genes. Examples have been documented across a wide range of areas of organism biology; see, for example, Wade (1998) and ​and22 in Rasanen and Kruuk (2007). Parental selection is a more formal concept used in theoretical modeling and concerns situations where the fitness of the offspring depends, besides other factors, on the genotypes of its parent(s) (generalizing from Kirkpatrick and Lande 1989).

TABLE 1

Frequencies of genotypes and fitness parameterizations in model 1

A Problem With the Correlation Coefficient as a Measure of Gene Expression Divergence

The correlation coefficient is commonly used as a measure of the divergence of gene expression profiles between different species. Here we point out a potential problem with this statistic: if measurement error is large relative to the differences in expression, the correlation coefficient will tend to show high divergence for genes that have relatively uniform levels of expression across tissues or time points. We show that genes with a conserved uniform pattern of expression have significantly higher levels of expression divergence, when measured using the correlation coefficient, than other genes, in a data set from mouse, rat, and human. We also show that the Euclidean distance yields low estimates of expression divergence for genes with a conserved uniform pattern of expression.IT is now possible to measure the expression levels of thousands of genes in multiple tissues at multiple times. This has led to investigations into the evolution of gene expression and how the pattern of expression changes on a genomic scale. In some analyses, the evolution of expression is considered only within one tissue, but in many studies the evolution across multiple tissues is investigated. In this latter case, the evolution of an expression profile—a vector of expression levels of a gene across several tissues—is considered.Several different statistics have been proposed to measure the divergence between gene expression profiles. The two most popular measures are the Euclidean distance (Jordan et al. 2005; Kim et al. 2006; Yanai et al. 2006; Urrutia et al. 2008) and Pearson''s correlation coefficient (Makova and Li 2003; Huminiecki and Wolfe 2004; Yang et al. 2005; Kim et al. 2006; Liao and Zhang 2006a,b; Xing et al. 2007; Urrutia et al. 2008). The correlation coefficient is often subtracted from one, so that the statistic varies from zero, when there has been no expression divergence, to a maximum of two; we refer to this statistic as the Pearson distance. Here we describe a significant shortcoming of the Pearson distance that is not shared by the Euclidean distance.To investigate properties of these two measures of expression divergence, we compiled a data set of 2859 orthologous genes from human, mouse, and rat for which we had microarray expression data from nine homologous tissues: bone marrow, heart, kidney, large intestine, pituitary, skeletal muscle, small intestine, spleen, and thymus). The expression data for rat came from Walker et al. (2004), the mouse data from Su et al. (2004), and the human data from Ge et al. (2005). Each tissue experiment had two replicates in mouse, a varying number of replicates in rat, and one in humans; some genes were also matched by multiple probe sets. To obtain an average across experiments and probe sets we processed the data as follows:

1. Raw CEL files of gene expression levels were obtained from the NCBI Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/projects/geo/).
2. The results from the mouse, rat, and human arrays were normalized separately using both the MAS5 (Affymetrix 2001) and the RMA algorithms (Irizarry et al. 2003) as implemented in Bioconductor (Gentleman et al. 2004). The results are qualitatively similar for the two normalization procedures, although recent analyses suggest that MAS5 normalization is generally better (Ploner et al. 2005; Lim et al. 2007).
3. The expression of each gene within a tissue was averaged across experiments and probe sets.

We computed expression distances (ED) between orthologous gene expression profiles, for each of the three species comparisons, rat–mouse, rat–human, and mouse–human, according to the two different distance metrics, the Euclidean distance and the Pearson distance:(1)Here x_ij is the expression level of the gene under consideration in species i in tissue j, and is the average expression level of the gene in species i across tissues. Expression levels are known in a total of k tissues.Because expression levels are measured on different microarray platforms in the three species, we compute relative abundance (RA) values, before calculating the Euclidean distance (Liao and Zhang 2006a). The RA is the expression of a gene in a particular tissue divided by the sum of the expression values of that gene across all tissues. We calculated RA values to remove “probe” effects (the tendency for a gene to bind its probe set on one platform more efficiently than on another platform). Because of probe effects it is not easy to distinguish absolute changes in expression and differences in binding efficiency. Calculating RA values removes this problem from the Euclidean distance. Pearson''s distance does not change under such a rescaling and so this is unnecessary.In some analyses the logarithm of the expression or RA values are used (e.g., Makova and Li 2003; Kim et al. 2006; Xing et al. 2007), and in others the expression values are used without this transformation (e.g., Huminiecki and Wolfe 2004; Jordan et al. 2005; Yang et al. 2005; Liao and Zhang 2006a,b; Yanai et al. 2006; Urrutia et al. 2008). We calculated both the Pearson and the Euclidean distances on log-transformed and untransformed expression values. The results are qualitatively similar so here we present only the results obtained using the logarithm of the expression or RA values.It is natural to expect the two measures of expression divergence to be positively correlated with one another; however, the Euclidean and Pearson distances are almost completely uncorrelated (MAS5 normalization, mouse–rat correlation coefficient = 0.06, human–rat r = 0.13, human–mouse r = 0.10; RMA normalization, mouse–rat correlation coefficient = −0.12, human–rat r = −0.00, human–mouse r = −0.08; Figure 1). This could, plausibly, be because the two statistics measure different aspects of divergence. However, irrespective of this, there is a potential problem associated with the Pearson distance. Imagine that we have a gene that is expressed at identical levels in all tissues in two species (i.e., expression levels are uniform between tissues and also between species). We quite reasonably assume that measured expression levels contain noise. Thus each measured expression level (x_ij) is the sum of the (assumed) uniform expression level and an independent random number representing noise. In this case there is no real divergence in the expression profile between the species. However, the two measures of divergence may differ greatly in this case. The Euclidean distance reflects only the noise present in the data and hence will be small if the noise is small. By contrast, the Pearson distance will have a value close to 1 since the second term in PeaD in Equation 1 will be close to zero, reflecting the fact that the noise components of different expression levels are independent. Thus the Pearson distance will give the impression that expression divergence is great, but all this apparent divergence is noise. This will be a problem with Pearson''s distance whenever measurement error is of the same magnitude as the differences in expression between tissues. This will therefore tend to be a problem for lowly expressed genes, where measurement error can be large relative to the true value.Open in a separate windowFigure 1.—The correlation between the Euclidean and Pearson distances for (a) mouse–rat, (b) human–rat, and (c) human–mouse. Only the results from MAS5 normalization are shown; qualitatively similar results were obtained with RMA.The above example is unrealistic because real gene expression profiles are rarely perfectly uniform. To investigate whether this shortcoming of the Pearson distance is a problem in real data sets, we determined genes with a relatively uniform pattern of expression in all three species considered above. To do this we computed the entropy of a gene''s expression, which is a measure of uniformity in expression across tissues (Schug et al. 2005): the higher the value of the entropy, the more uniform is the expression. We calculated the entropy for each gene in each of the three species, averaged these across species, and then took those genes in the upper quartile of mean entropy values as a data set of genes with a relatively conserved pattern of uniform expression.It is natural to expect those genes with a conserved uniform pattern of expression to have relatively low expression divergence; however, on average these genes have significantly higher Pearson distances than other genes (Figure 2; supporting information, Figure S1 and Figure S2). By contrast, the Euclidean distance shows the pattern one would anticipate; all of the conserved uniform genes have low expression divergence. It therefore seems likely that the Pearson distance is sensitive to measurement error and hence may not be a good measure of expression divergence.Open in a separate windowFigure 2.—The distribution of expression divergence values for those genes with a uniform pattern of expression that is conserved across species vs. the distribution for all genes for (a) Pearson and (b) Euclidean distances for mouse–rat. We present similar values for human–mouse and human–rat in Figure S1 and Figure S2. Only the results from MAS5 normalization are shown; qualitatively similar results were obtained with RMA.

TABLE 1

The median expression divergence for genes that have a conserved uniform pattern of expression (upper quartile of mean entropy values) vs. all other genes

Modeling the Hydraulics of Root Growth in Three Dimensions with Phloem Water Sources

Primary growth is characterized by cell expansion facilitated by water uptake generating hydrostatic (turgor) pressure to inflate the cell, stretching the rigid cell walls. The multiple source theory of root growth hypothesizes that root growth involves transport of water both from the soil surrounding the growth zone and from the mature tissue higher in the root via phloem and protophloem. Here, protophloem water sources are used as boundary conditions in a classical, three-dimensional model of growth-sustaining water potentials in primary roots. The model predicts small radial gradients in water potential, with a significant longitudinal gradient. The results improve the agreement of theory with empirical studies for water potential in the primary growth zone of roots of maize (Zea mays). A sensitivity analysis quantifies the functional importance of apical phloem differentiation in permitting growth and reveals that the presence of phloem water sources makes the growth-sustaining water relations of the root relatively insensitive to changes in root radius and hydraulic conductivity. Adaptation to drought and other environmental stresses is predicted to involve more apical differentiation of phloem and/or higher phloem delivery rates to the growth zone.Plant growth involves water uptake by the cells and expansion of the cell walls under the resultant turgor (internal hydrostatic pressure). The water uptake and increase in cell volume are accompanied by nutrient and metabolite deposition. Thus, hydraulics of growth (i.e. the energies, conductivities, and fluxes of water in growing tissue) are fundamental to understanding primary plant growth. Quantitatively, the driving force for water movement in the plant, as in other porous media, is considered to be the gradient in water potential (Ψ), an energy per unit volume given in MPa. Thus, primary growth can be modeled by considering plant tissue to be a distributed sink for water, with low Ψ and/or high hydraulic conductivity driving water deposition into rapidly expanding regions. Molz and Boyer (1978) developed the theoretical basis for predicting the radial water flux in one dimension within the intercalary meristem of growing soybean (Glycine max) hypocotyls. In this aerial tissue, water moves from the xylem both outward to the epidermis and inward to the pith. Thus, in the growing hypocotyls, Ψ is predicted to be least negative in the xylem and to decrease toward the epidermis and the pith. These predictions for growth-induced or growth-sustaining Ψ were confirmed when the experimental technology became sensitive enough to detect the gradients in Ψ (Nonami and Boyer, 1993). Passioura and Boyer (2003) expanded the theory to incorporate anatomical detail and corresponding spatial patterns of hydraulic conductivity. Their model explains experimental results on water relations during growth transients for many areas of the plant.The hydraulics of root growth differ from shoot growth because of differences in xylem anatomy. Root xylem becomes functional perhaps 1 cm behind the tip and well behind the growth zone. To enter the growing cells near the maize (Zea mays) root tip, externally supplied metabolites must move several millimeters without phloem (Fig. 1), and any water supplied by functional xylem would need to move more than 1 cm. Silk and Wagner (1980) provided a theoretical framework for a two-dimensional treatment of the growth-sustaining Ψ gradients in maize roots. They assumed that the water source was external (the soil or root-bathing medium) and that the root surface was in equilibrium with the soil or bathing medium, so that the flow path to growing cells in the root was predicted to be primarily inward. As in the shoot model, growing tissue was seen as a distributed sink for water. However, since the publication of that theory, experimental studies have revealed that the root tip is not in equilibrium with the bathing medium (Pritchard et al., 1996, 2000; Gould et al., 2004; Shimazaki et al., 2005). Pressure probes combined with osmotic potential determinations have shown that the Ψ of exterior root cells ranges from −0.17 to −0.6 MPa, depending on environmental conditions. This range is more negative than in the nutrient medium. Furthermore, evidence has accumulated that at least some water for root growth comes from the phloem. The most obvious evidence is perhaps the growth of nodal (adventitious) roots of maize, rice (Oryza sativa), and other gramineous plants (Westgate and Boyer, 1985). This growth is a normal part of crop development. The nodal roots grow through air and then dry layers of surface soil, making it unlikely that the expanding root cells obtain water from the dry media surrounding the root. Empirical and theoretical studies have concluded that the phloem probably provides water for growth of the primary maize root (Bret-Harte and Silk, 1994; Frensch and Hsiao, 1995; Pritchard, 1996; Pritchard et al., 1996, 2000; Hukin et al., 2002; Gould et al., 2004).Open in a separate windowFigure 1.Primary root growth zone. The tip of the seedling root of maize showing the meristem as part of the apical third of the elongation zone. The boundary of this root section was digitized to provide the computational body-fit grid used for the model. [See online article for color version of this figure.]The model described here follows the concepts of Pritchard and colleagues (1996, 2000) in assuming a pressure-driven bulk flow of solution through the phloem to the region where phloem is beginning to be functional (1–4 mm from the apex; Fig. 1). Water movement can occur from both the surrounding soil and the developing phloem. Henceforth, we refer to the “external water source equilibrium” or EE model, for which the boundary condition is solely an exterior medium of fairly high Ψ (−0.005 to −0.05 MPa) and no conditions are placed on the phloem Ψ (Silk and Wagner (1980), that the exterior of the root is in equilibrium with its bathing solution. Empirical studies have shown that this model is not realistic, because the root maintains peripheral cells at more negative Ψ than the bathing medium. Since this is hypothesized to occur by deposition of apoplastic solutes, we will refer to a model with external water source and apoplastic solutes near the exterior as the EASE model.

Table I.

Acronyms for models and definitions of symbols used in mathematical modeling

The role of molecular diffusion within dendritic spines in synaptic function

Spines are tiny nanoscale protrusions from dendrites of neurons. In the cortex and hippocampus, most of the excitatory postsynaptic sites reside in spines. The bulbous spine head is connected to the dendritic shaft by a thin membranous neck. Because the neck is narrow, spine heads are thought to function as biochemically independent signaling compartments. Thus, dynamic changes in the composition, distribution, mobility, conformations, and signaling properties of molecules contained within spines can account for much of the molecular basis of postsynaptic function and regulation. A major factor in controlling these changes is the diffusional properties of proteins within this small compartment. Advances in measurement techniques using fluorescence microscopy now make it possible to measure molecular diffusion within single dendritic spines directly. Here, we review the regulatory mechanisms of diffusion in spines by local intra-spine architecture and discuss their implications for neuronal signaling and synaptic plasticity.

IntroductionNeurons communicate with each other through synapses that organize to create functional circuits. Most excitatory synapses in the central nervous system are formed on dendritic spines, tiny protrusions that extend from dendrites (Bourne and Harris, 2008; Fig. 1 a). The spine typically has a head of 200–1,000-nm diameter, which is connected to the dendritic shaft via a neck of 100–200-nm width (Arellano et al., 2007; Fig. 1 b). The head contains postsynaptic density (PSD) proteins, the actin cytoskeleton, membrane structures, and organelles (Sheng and Hoogenraad, 2007; Fig. 1 c). The molecular composition of spine heads is different from that of the shaft. Because of its characteristic morphology, spines are thought to function as biochemically independent compartments by limiting molecular movement between the spine head and the rest of the dendrite (Adrian et al., 2014; Tønnesen and Nägerl, 2016). Clarifying this regulation is key to understanding how this unitary site of synaptic transmission is controlled. This is particularly crucial to our understanding about how changes in the postsynaptic site lead to synaptic plasticity.Open in a separate windowFigure 1.The shape and internal architecture of dendritic spines. (a) A super-resolution SIM image of a hippocampal neuron dendrite expressing GFP. (b) A surface image of a spine (arrow in a) reconstructed from a SIM image. (c) Schematic representation of a spine containing the PSD, actin cytoskeleton, recycling endosome, and SER.The control of spine architecture is critical at excitatory synapses in the brain (Alvarez and Sabatini, 2007; Forrest et al., 2018). Excitatory synapses exhibit synaptic plasticity, which changes the strength of synaptic transmission through mechanisms at both pre- and postsynaptic sides (Citri and Malenka, 2008). This process is generally thought to be a basis for changes in neural circuits controlled by experiences—i.e., learning and memory (Humeau and Choquet, 2019; Magee and Grienberger, 2020). Here, the size and shape of spines are strongly correlated with the strength of synaptic transmission (Kasai et al., 2010). Spine volume is proportional to PSD area (Harris and Stevens, 1989) and the number of α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate–type glutamate receptors (AMPARs; Nusser et al., 1998; Matsuzaki et al., 2001). Recently, combinational analysis of electrophysiology and correlative light and EM (CLEM) revealed the linear relationship between PSD area and synaptic strength (Holler et al., 2021). Also, longer spine necks attenuate somatic potentials to a greater degree (Araya et al., 2006). Thus, structural and functional plasticity of spines is tightly regulated. Specifically, when synaptic transmission is strengthened (e.g., long-term potentiation [LTP]), spines grow (Matsuzaki et al., 2004). In turn, when synaptic transmission weakens (e.g., long-term depression), spines shrink (Zhou et al., 2004; Oh et al., 2013).While many molecules involved in the plasticity of spine synapses have been identified (Sala and Segal, 2014), their mechanisms and regulations can only be discovered by monitoring the regulated changes in the composition and signaling properties of these factors within the confined space of the spine’s cytoplasm. To this end, the development of local photolysis of caged-glutamate played an important role (Matsuzaki et al., 2001). This method made it possible to induce structural plasticity locally at a single spine. Spine enlargement is induced by uncaging of caged-glutamate in the absence of Mg²⁺ or with postsynaptic depolarization in the presence of Mg²⁺ to activate N-methyl-D-aspartate–type glutamate receptors (NMDARs; Matsuzaki et al., 2004). Conversely, spine shrinkage is induced by low-frequency uncaging of caged-glutamate in the absence of Mg²⁺ or with postsynaptic depolarization (Oh et al., 2013). Shrinkage can also be induced by glutamate uncaging temporally coupled with back propagation action potential and uncaging of caged–γ-aminobutyric acid (GABA; Hayama et al., 2013). Glutamate uncaging–induced structural plasticity has also been seen to occur in vivo (Noguchi et al., 2019).These methods have vastly improved our understanding of the molecular mechanisms of structural plasticity (Nishiyama and Yasuda, 2015). In particular, stimulus-dependent increases in spine size (structural LTP [sLTP]), which are thought to be associated with functional LTP, have been studied extensively as a model of LTP (Nakahata and Yasuda, 2018; Fig. 2). Strong synaptic input causes an influx of Ca²⁺ through NMDARs that activates Ca²⁺/calmodulin-dependent protein kinase II (CaMKII) and the downstream signaling cascades. This modification of signaling cascades can affect cytoskeletal organization and membrane trafficking, which are responsible for two subsequent cellular events. First, spine morphology is modulated through cytoskeletal changes (Borovac et al., 2018). Second, synaptic transmission is enhanced by increased AMPAR insertion into the plasma membrane and movement to the PSD (Huganir and Nicoll, 2013). However, the molecular mechanisms that link these two phenomena are not fully understood (Herring and Nicoll, 2016). As sLTP progresses, the molecular composition within spines changes (Bosch et al., 2014; Meyer et al., 2014). Specifically, immediately after sLTP induction, actin-related molecules such as cofilin and actin-related protein 2/3 (Arp2/3) complex accumulate within a stimulated spine. On the other hand, scaffold proteins such as PSD-95 slowly accumulate over tens of minutes. SynGAP, which is localized at the PSD through interaction with PSD-95, escapes from spines immediately after stimulation and contributes to the expression of sLTP (Araki et al., 2015). These changes in molecular compositions immediately after stimulation may be due not only to molecule-specific binding but also to the physical regulation of diffusion (Obashi et al., 2019).Open in a separate windowFigure 2.Molecular motion important in sLTP. Strong synaptic input causes an influx of Ca²⁺ through NMDARs that activates CaMKII and the downstream signaling cascades. This modification of signaling cascades can affect cytoskeletal organization and membrane trafficking, which regulate spine morphology. Spine morphology affects the molecular exchange between the spine head and the dendritic shaft and lateral diffusion of membrane proteins including AMPARs. Regulation of molecular movements through the spine neck affects the molecular composition within spines. This change affects signal propagation into nearby spines. For example, cofilin and Arp2/3 complex accumulate within spines. SynGAP and activated RhoA escape from spines. Reorganization of the actin cytoskeleton affects movement of large molecules and the formation of a large signaling complex containing CaMKII and Tiam1. Also, the structure of the PSD affects membrane protein diffusion and alters the synaptic trafficking of AMPARs.In addition to molecular localization, fluorescence lifetime imaging of FRET-based biosensors has made it possible to measure spatiotemporal changes in the activity of signaling molecules involved in sLTP (Yasuda, 2012). These studies have demonstrated a critical relationship between the time that a molecule spends within a spine and the rate of signal inactivation. This relationship determines whether an activated signaling molecule is confined within a single spine or escapes from the spine and interacts with effectors present in the adjacent dendritic shaft or nearby spines (Yasuda, 2017). The signal propagation into nearby spines is most likely related to heterosynaptic plasticity, where activated synapses influence neighbor synapses within the same dendritic segments (Oh et al., 2015; Colgan et al., 2018; Chater and Goda, 2021). Thus, diffusion is a central feature of the regulation of spine structural plasticity. However, because the size of spines is small relative to the spatial resolution of diffraction-limited fluorescence microscopy and measuring methods are limited, elucidation of the mechanism regulating diffusion within spines has been challenging.To address this gap in understanding, researchers advanced fluorescence microscopy techniques, which enabled us to measure changes in the nanoscale localization, diffusion, and signal activities inside spines. These studies allow us to directly understand how spine structures physically limit molecular diffusion and reveal fundamental mechanisms that control the localization and biochemical signal transduction pathways in neurons. Here, we summarize recent findings that have revealed physical barriers within spines using super-resolution microscopy (Sigal et al., 2018; Table 1) and molecular dynamics measurements (Fig. 3 and Table 2), and we discuss how these barriers serve as a fundamental feature controlling neuronal signaling and synaptic plasticity.Table 1.List of super-resolution microscopy techniques

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     

Immunomodulation by Mesenchymal Stem Cells in Veterinary Species

Mesenchymal stem cells (MSC) are adult-derived multipotent stem cells that have been derived from almost every tissue. They are classically defined as spindle-shaped, plastic-adherent cells capable of adipogenic, chondrogenic, and osteogenic differentiation. This capacity for trilineage differentiation has been the foundation for research into the use of MSC to regenerate damaged tissues. Recent studies have shown that MSC interact with cells of the immune system and modulate their function. Although many of the details underlying the mechanisms by which MSC modulate the immune system have been defined for human and rodent (mouse and rat) MSC, much less is known about MSC from other veterinary species. This knowledge gap is particularly important because the clinical use of MSC in veterinary medicine is increasing and far exceeds the use of MSC in human medicine. It is crucial to determine how MSC modulate the immune system for each animal species as well as for MSC derived from any given tissue source. A comparative approach provides a unique translational opportunity to bring novel cell-based therapies to the veterinary market as well as enhance the utility of animal models for human disorders. The current review covers what is currently known about MSC and their immunomodulatory functions in veterinary species, excluding laboratory rodents.Abbreviations: AT, adipose tissue; BM, Bone marrow; CB, umbilical cord blood; CT, umbilical cord tissue; DC, dendritic cell; IDO, indoleamine 2;3-dioxygenase; MSC, mesenchymal stem cells; PGE₂, prostaglandin E2; VEGF, vascular endothelial growth factorMesenchymal stem cells (MSC, alternatively known as mesenchymal stromal cells) were first reported in the literature in 1968.³⁹ MSC are thought to be of pericyte origin (cells that line the vasculature)^21,22 and typically are isolated from highly vascular tissues. In humans and mice, MSC have been isolated from fat, placental tissues (placenta, Wharton jelly, umbilical cord, umbilical cord blood), hair follicles, tendon, synovial membrane, periodontal ligament, and every major organ (brain, spleen, liver, kidney, lung, bone marrow, muscle, thymus, pancreas, skin).^23,121 For most current clinical applications, MSC are isolated from adipose tissue (AT), bone marrow (BM), umbilical cord blood (CB), and umbilical cord tissue (CT; 11,87,99 Clinical trials in human medicine focus on the use of MSC both for their antiinflammatory properties (graft-versus-host disease, irritable bowel syndrome) and their ability to aid in tissue and bone regeneration in combination with growth factors and bone scaffolds (clinicaltrials.gov).¹³¹ For tissue regeneration, the abilities of MSC to differentiate and to secrete mediators and interact with cells of the immune system likely contribute to tissue healing (Figure 1). The current review will not address the specific use of MSC for orthopedic applications and tissue regeneration, although the topic is covered widely in current literature for both human and veterinary medicine.^57,62,90

Table 1.

Tissues from which MSC have been isolated