Cetacean morbillivirus: A Land-to-Sea Journey and Back?

<正>Cetacean morbillivirus(CeMV), the most relevant pathogen impacting the health and conservation of several already threatened cetacean populations worldwide(Van Bressem et al. 2014), has shown in recent years an apparently increased tendency to cross ‘‘interspecies barriers' '(Jo et al. 2018 a), thereby giving rise to disease and mortality outbreaks in free-ranging dolphins and whales     

Evaluation of Antibody-Dependent Enhancement of SARS-CoV Infection in Rhesus Macaques Immunized with an Inactivated SARS-CoV Vaccine

正Dear Editor,In 2003,severe acute respiratory syndrome coronavirus(SARS-CoV)emerged in Guangdong Province,China,infected more than 8000 individuals,and resulted in a 10%mortality rate(Rota et al.2003).Later,in 2012,a novel CoV,Middle East respiratory syndrome coronavirus(MERS-CoV),was isolated from the sputum of a man in Saudi Arabia(Perl et al.2014).Notably,MERS-CoV     

Genomic Characterization of the First Parechovirus in Bats

<正>Dear Editor,Parechoviruses (PeVs) are non-enveloped, spherical viruses of genus Parechovirus and family Picornaviridae.Within the capsid is a naked monopartite, linear, singlestranded positive-sense RNA genome of 7.3 kb, comprising a single long open reading frame (ORF) encoding a     

The Era of Immune Checkpoint Therapy: From Cancer to Viral Infection——A Mini Comment on the 2018 Medicine Nobel Prize

正The 2018 Medicine Nobel Prize was awarded jointly to two immunologists, James P. Allison at the University of Texas MD Anderson Cancer Center in Houston and Tasuku Honjo at Kyoto University in Japan, who pioneered a new way to treat cancers (Ledford et al. 2018). Both Laureates have shown how so called ‘‘immune checkpoints’’ on T cells can be used to manipulate the immune responses so     

Establishment of Novel Monoclonal Fabs Specific for Epstein-Barr Virus Encoded Latent Membrane Protein 1

<正>Dear Editor,Epstein-Barr virus (EBV, also termed human herpesvirus-4) was the first identified human tumor virus. Since its discovery in 1964, studies have shown that EBV infects over 90%of all people by the time they are adults(Williams and Crawford 2006). EBV infection can result in     

Virology at the Lorne Infection and Immunity Conference 2019

<正>The Lorne Infection and Immunity Conference is one of five scientific meetings held during each month of February at the Cumberland resort in the picturesque seaside town of Lorne, on the Great Ocean Road in Victoria (Australia).The specific aim of the meeting is to bring together basic,     

A focused and efficient genetic screening strategy in the mouse: identification of mutations that disrupt cortical development

Although the mechanisms that regulate development of the cerebral cortex have begun to emerge, in large part through the analysis of mutant mice (Boncinelli et al. 2000; Molnar and Hannan 2000; Walsh and Goffinet 2000), many questions remain unanswered. To provide resources for further dissecting cortical development, we have carried out a focused screen for recessive mutations that disrupt cortical development. One aim of the screen was to identify mutants that disrupt the tangential migration of interneurons into the cortex. At the same time, we also screened for mutations that altered the growth or morphology of the cerebral cortex. We report here the identification of thirteen mutants with defects in aspects of cortical development ranging from the establishment of epithelial polarity to the invasion of thalamocortical axons. Among the collection are three novel alleles of genes for which mutant alleles had already been used to explore forebrain development, and four mutants with defects in interneuron migration. The mutants that we describe here will aid in deciphering the molecules and mechanisms that regulate cortical development. Our results also highlight the utility of focused screens in the mouse, in addition to the large-scale and broadly targeted screens that are being carried out at mutagenesis centers.     

DJ-1 Is a Redox-Dependent Molecular Chaperone That Inhibits α-Synuclein Aggregate Formation

Parkinson's disease (PD) pathology is characterized by the degeneration of midbrain dopamine neurons (DNs) ultimately leading to a progressive movement disorder in patients. The etiology of DN loss in sporadic PD is unknown, although it is hypothesized that aberrant protein aggregation and cellular oxidative stress may promote DN degeneration. Homozygous mutations in DJ-1 were recently described in two families with autosomal recessive inherited PD (Bonifati et al. 2003). In a companion article (Martinat et al. 2004), we show that mutations in DJ-1 alter the cellular response to oxidative stress and proteasomal inhibition. Here we show that DJ-1 functions as a redox-sensitive molecular chaperone that is activated in an oxidative cytoplasmic environment. We further demonstrate that DJ-1 chaperone activity in vivo extends to α-synuclein, a protein implicated in PD pathogenesis.     

Neural activity when people solve verbal problems with insight

People sometimes solve problems with a unique process called insight, accompanied by an “Aha!” experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1) revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2) revealed a sudden burst of high-frequency (gamma-band) neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.     

Genetic Control of Photoperiod Sensitivity in Maize Revealed by Joint Multiple Population Analysis

Variation in maize for response to photoperiod is related to geographical adaptation in the species. Maize possesses homologs of many genes identified as regulators of flowering time in other species, but their relation to the natural variation for photoperiod response in maize is unknown. Candidate gene sequences were mapped in four populations created by crossing two temperate inbred lines to two photoperiod-sensitive tropical inbreds. Whole-genome scans were conducted by high-density genotyping of the populations, which were phenotyped over 3 years in both short- and long-day environments. Joint multiple population analysis identified genomic regions controlling photoperiod responses in flowering time, plant height, and total leaf number. Four key genome regions controlling photoperiod response across populations were identified, referred to as ZmPR1–4. Functional allelic differences within these regions among phenotypically similar founders suggest distinct evolutionary trajectories for photoperiod adaptation in maize. These regions encompass candidate genes CCA/LHY, CONZ1, CRY2, ELF4, GHD7, VGT1, HY1/SE5, TOC1/PRR7/PPD-1, PIF3, ZCN8, and ZCN19.MAIZE (Zea mays L. subsp. mays) was domesticated in southern Mexico and its center of diversity is in tropical Latin America (Goodman 1999; Matsuoka et al. 2002), where precipitation rates and day lengths cycle annually. The presumed ancestor of maize, teosinte (Zea mays L. subsp. parviglumis), likely evolved photoperiod sensitivity to synchronize its reproductive phases to the wetter, short-day growing season (Ribaut et al. 1996; Campos et al. 2006). A critical event in the postdomestication evolution of maize was its spread from tropical to temperate regions of the Americas (Goodman 1988), requiring adaptation to longer day lengths. The result of this adaptation process is manifested today as a major genetic differentiation between temperate and tropical maize (Liu et al. 2003) and substantially reduced photoperiod sensitivity of temperate maize (Gouesnard et al. 2002). Tropical maize exhibits delayed flowering time, increased plant height, and a greater total leaf number when grown in temperate latitudes with daily dark periods <11 hr (Allison and Daynard 1979; Warrington and Kanemasu 1983a,b). Identifying the genes underlying maize photoperiod sensitivity will provide insight into the postdomestication evolution of maize and may reduce barriers to the use of diverse tropical germplasm resources for improving temperate maize production (Holland and Goodman 1995; Liu et al. 2003; Ducrocq et al. 2009).Natural variation at key genes in flowering time pathways is related to adaptation and evolution of diverse plant species (Caicedo et al. 2004; Shindo et al. 2005; Turner et al. 2005; Cockram et al. 2007; Izawa 2007; Slotte et al. 2007). Identification of some of the genes controlling adaptation in numerous plant species relied on regulatory pathways elucidated in Arabidopsis (Simpson and Dean 2002). Many key genes in the Arabidopsis flowering time regulatory pathways are conserved across diverse plant species (Kojima et al. 2002; Hecht et al. 2007; Kwak et al. 2008), but their functions have diverged, resulting in unique regulatory pathways in some phylogenetic groups (Colasanti and Coneva 2009). For example, FRI and FLC control most natural variation for vernalization response in Arabidopsis (Caicedo et al. 2004; Shindo et al. 2005), but wheat and barley appear to lack homologs of these genes and regulate vernalization response with different genes (Yan et al. 2004).Maize exhibits tremendous natural variation for flowering time (Gouesnard et al. 2002; Camus-Kulandaivelu et al. 2006), for which numerous QTL have been identified (Chardon et al. 2004). In contrast, only a few flowering time mutants are known and only a handful of flowering time genes, including DWARF8 (D8), DELAYED FLOWERING1 (DLF1), VEGETATIVE TO GENERATIVE TRANSITION1 (VGT1), and INDETERMINATE GROWTH1 (ID1), have been cloned in maize (Thornsberry et al. 2001; Colasanti et al. 2006; Muszynski et al. 2006; Salvi et al. 2007; Colasanti and Coneva 2009). Variation at or near D8 and VGT1 is related to latitudinal adaptation, but these genes do not appear to regulate photoperiod responses and account for only a limited proportion of the standing flowering time variation in maize (Camus-Kulandaivelu et al. 2006, 2008; Ducrocq et al. 2008; Buckler et al. 2009).Quantitative trait loci (QTL) mapping was a key first step to identifying the genes underlying natural variation for flowering time in Arabidopsis (Koornneef et al. 2004). Photoperiodic QTL have been mapped previously in individual biparental maize mapping populations (Koester et al. 1993; Moutiq et al. 2002; Wang et al. 2008; Ducrocq et al. 2009). Such studies are informative with respect to the parents from which the populations were derived, but often do not reflect the genetic heterogeneity of broader genetic reference populations (Holland 2007).Association mapping (Thornsberry et al. 2001; Ersoz et al. 2007) and combined analysis of multiple biparental crosses (Rebaï et al. 1997; Rebaï and Goffinet 2000; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008) represent alternative approaches to understanding the variation in genetic control for complex traits among diverse germplasm. Association mapping has limited power to identify genes that affect traits closely associated with population structure, such as flowering time in maize (Camus-Kulandaivelu et al. 2006; Ersoz et al. 2007). In contrast, joint QTL analysis of multiple populations is not hindered by the associations between causal genes and population structure. Combined QTL analysis of multiple mapping populations provides improved power to detect QTL, more precise estimation of their effects and positions, and better understanding of their functional allelic variation and distribution across more diverse germplasm compared to single-population mapping (Rebaï et al. 1997; Wu and Jannink 2004; Jourjon et al. 2005; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008; Buckler et al. 2009). Joint analysis also provides a direct test of the importance of higher-order epistatic interactions between founder alleles at individual loci with genetic backgrounds (Jannink and Jansen 2001; Blanc et al. 2006). In this study, joint analysis of multiple populations was used to test directly the hypothesis that diverse tropical maize lines carry functionally similar alleles at key photoperiod loci, which would imply genetic homogeneity for a common set of mutations and a shared evolutionary pathway for photoperiod insensitivity.The objective of this study was to integrate candidate gene analyses with photoperiod QTL mapping across multiple maize populations. We tested candidate floral regulators known from other species for associations with natural variation for photoperiod response in maize. We analyzed flowering time in four interrelated recombinant inbred line (RIL) populations, each derived from crosses between temperate and tropical maize parents (Figure 1), in both long- and short-day environments to characterize their responses to distinct photoperiods. Joint population analysis provided high resolution of many QTL positions, permitting robust testing of underlying candidate genes. We directly and indirectly mapped homologs of flowering time candidates genes from Arabidopsis, rice, and barley on a dense consensus genetic map of these four populations, permitting identification of homologs that colocalize with genome regions associated with variation for photoperiod response. These mapping families are being integrated into the maize nested association mapping (NAM) population (Buckler et al. 2009; McMullen et al. 2009) because they were genotyped with the maize NAM map SNP markers, they involve the common parent B73, and their seed and genotypic information (File S1 cont.) are publicly available. Their availability further expands the genetic diversity represented by the maize NAM population and enhances this valuable public community resource.Open in a separate windowFigure 1.—Factorial mating of two temperate (B73 and B97) and two tropical (CML254 and Ki14) inbred maize lines to create four related recombinant inbred line mapping populations.     

The Drosophila Claudin Kune-kune Is Required for Septate Junction Organization and Tracheal Tube Size Control

The vertebrate tight junction is a critical claudin-based cell–cell junction that functions to prevent free paracellular diffusion between epithelial cells. In Drosophila, this barrier is provided by the septate junction, which, despite being ultrastructurally distinct from the vertebrate tight junction, also contains the claudin-family proteins Megatrachea and Sinuous. Here we identify a third Drosophila claudin, Kune-kune, that localizes to septate junctions and is required for junction organization and paracellular barrier function, but not for apical-basal polarity. In the tracheal system, septate junctions have a barrier-independent function that promotes lumenal secretion of Vermiform and Serpentine, extracellular matrix modifier proteins that are required to restrict tube length. As with Sinuous and Megatrachea, loss of Kune-kune prevents this secretion and results in overly elongated tubes. Embryos lacking all three characterized claudins have tracheal phenotypes similar to any single mutant, indicating that these claudins act in the same pathway controlling tracheal tube length. However, we find that there are distinct requirements for these claudins in epithelial septate junction formation. Megatrachea is predominantly required for correct localization of septate junction components, while Sinuous is predominantly required for maintaining normal levels of septate junction proteins. Kune-kune is required for both localization and levels. Double- and triple-mutant combinations of Sinuous and Megatrachea with Kune-kune resemble the Kune-kune single mutant, suggesting that Kune-kune has a more central role in septate junction formation than either Sinuous or Megatrachea.EPITHELIA are essential for separating physiologically distinct body compartments and regulating trafficking between them. For proper function, it is imperative that epithelia maintain effective barriers against free paracellular diffusion. To this end, epithelial cells contain occluding junctions, which regulate paracellular permeability. In vertebrates, this is accomplished by tight junctions (TJ), structures that are characterized by regions of close membrane apposition between adjacent cells known as “kissing points” (Tsukita and Furuse 2002). While the TJ is made up of at least 40 different components (Schneeberger and Lynch 2004), the core proteins responsible for the paracellular barrier are the claudins (Angelow et al. 2008).Claudins are four-transmembrane domain proteins that form homo- and heterophilic interactions within the same cell (Furuse et al. 1999; Blasig et al. 2006) and with claudins in adjacent cells (Furuse et al. 1999), thereby establishing the paracellular seal. There are 24 members of the claudin family in mammals, many of which display distinct, tissue-specific expression patterns (Kiuchi-Saishin et al. 2002; Angelow et al. 2008). Mutations in several claudins can cause significant paracellular permeability defects in mice. For example, mutations in claudin-14 increase TJ permeability in the organ of Corti and cause deafness (Ben-Yosef et al. 2003), while loss of claudin-1 compromises epidermal barrier function (Furuse et al. 2002).In Drosophila, primary (ectodermally derived) epithelia lack discernable TJs and instead use pleated septate junctions (SJ) for the paracellular barrier (Baumgartner et al. 1996; Lamb et al. 1998; Genova and Fehon 2003; Paul et al. 2003). However, despite sharing a common barrier function, vertebrate TJs and invertebrate SJs differ in several ways. While vertebrate TJs are positioned apical to adherens junctions (AJ) and contain conserved apical polarity proteins, SJs are basal to AJs and contain conserved basolateral polarity proteins (reviewed in Tepass 2003; Wu and Beitel 2004). In addition, SJs do not contain kissing points, but rather ladder-like septa that span the intermembrane space (Lane and Swales 1982; Tepass and Hartenstein 1994).Beyond their general epithelial barrier function, SJs are also required for several tissue-specific processes. Glial cells, for example, ensheath nerve fibers and use SJs to maintain the blood–brain barrier (Auld et al. 1995; Baumgartner et al. 1996; Schwabe et al. 2005). In the embryonic tracheal system, SJs are required for the apical secretion of the lumenal matrix modifying proteins, Vermiform (Verm) and Serpentine (Serp), which act through undefined pathways to restrict tube length (Wang et al. 2006). This secretory pathway appears to be specific for Verm and Serp, since other apical proteins are secreted normally in SJ mutants. SJ proteins have also been shown to play a role in morphogenesis of the heart tube, even though this tissue lacks typical SJ septa (Yi et al. 2008).Although SJs have clear differences from vertebrate TJs, SJs contain at least two claudins, Megatrachea (Mega) and Sinuous (Sinu), both of which are required for the paracellular barrier (Behr et al. 2003; Wu et al. 2004; Stork et al. 2008). In this article, we identify a third claudin, Kune-kune (Kune), that is an integral SJ protein. Like the other claudins, Kune is required for maintaining epithelial paracellular barrier and tracheal tube size control and is not required for apical-basal polarity. We also find that, of all three characterized claudins, Kune has a more severe SJ phenotype, suggesting that it is a more central player in SJ organization and function than previously characterized Drosophila claudins.     

The Rate and Spectrum of Spontaneous Mutations in a Plant RNA Virus

Knowing mutation rates and the molecular spectrum of spontaneous mutations is important to understanding how the genetic composition of viral populations evolves. Previous studies have shown that the rate of spontaneous mutations for RNA viruses widely varies between 0.01 and 2 mutations per genome and generation, with plant RNA viruses always occupying the lower side of this range. However, this peculiarity of plant RNA viruses is based on a very limited number of studies. Here we analyze the spontaneous mutational spectrum and the mutation rate of Tobacco etch potyvirus, a model system of positive sense RNA viruses. Our experimental setup minimizes the action of purifying selection on the mutational spectrum, thus giving a picture of what types of mutations are produced by the viral replicase. As expected for a neutral target, we found that transitions and nonsynonymous (including a few stop codons and small deletions) mutations were the most abundant type. This spectrum was notably different from the one previously described for another plant virus. We have estimated that the spontaneous mutation rate for this virus was in the range 10⁻⁶−10⁻⁵ mutations per site and generation. Our estimates are in the same biological ballpark that previous values reported for plant RNA viruses. This finding gives further support to the idea that plant RNA viruses may have lower mutation rates than their animal counterparts.THE rate of spontaneous mutation is a key parameter to understanding the genetic structure of populations over time. Mutation represents the primary source of genetic variation on which natural selection and genetic drift operate. Although the exact value of the mutation rate is important for several evolutionary theories, accurate estimates are available only for a handful of organisms. RNA viruses show mutation rates that are orders of magnitude higher than those of their DNA-based hosts and in the range of 0.03–2 per genome and replication round (Drake et al. 1998; Drake and Holland 1999; Chao et al. 2002). This difference results from the lack of proofreading activity of the virus-encoded RNA-dependent RNA polymerases (Steinhauer et al. 1992). The evolutionary causes of such elevated mutation rates remain unknown and it is commonly accepted that they may be beneficial as a mechanism to escape from the strong selective pressures imposed by the host''s defense mechanisms, although not necessarily evolved in response to natural selection (Elena and Sanjuán 2005; Clune et al. 2008). Indeed, in the short term, a too high mutation rate has pernicious effects on viral fitness since most of the mutations produced are deleterious (Bonhoeffer et al. 2004; Sanjuán et al. 2004).In the case of plant RNA viruses, it has been repeatedly reported that their populations are highly genetically stable (Rodríguez-Cerezo et al. 1991; Fraile et al. 1997; Marco and Aranda 2005; Herránz et al. 2008) in comparison with their animal counterparts, although reports of higher substitution rates also exist (Fargette et al. 2008; Gibbs et al. 2008). This peculiar behavior might be due in part to stronger stabilizing selection, weaker immune-mediated positive selection (García-Arenal et al. 2001), the existence of strong bottlenecks during cell-to-cell movement and systemic colonization of distal tissues (Hall et al. 2001; Sacristán et al. 2003; Li and Roossinck 2004), severe bottlenecks during vector-mediated transmission (Ali et al. 2006; Moury et al. 2007; Betancourt et al. 2008), or differences in the replication mode compared to lytic animal viruses (French and Stenger 2003; Sardanyés et al. 2009). Another more obvious possibility is that, indeed, plant viruses have lower mutation rates than other RNA viruses. Indeed the only two available direct estimates of mutation rates for plant viruses are both in the lower side of the range usually accepted for animal riboviruses: 0.10–0.13 per genome and generation for Tobacco mosaic virus (TMV) (Malpica et al. 2002) and 0.28 for Tobacco etch virus (TEV) (Sanjuán et al. 2009). However, none of these estimates is perfect. Although in the TMV experiments particular care was taken to measure mutation rate in a long target protected from the action of purifying selection (hence deleterious mutations remain in the population), uncertainties related to the number of infection cycles elapsed during the mutation–accumulation phase and the fraction of mutations that produced a selectable phenotype exist. In the case of TEV, the estimate should be taken as an upper limit because selection was operating during the mutation–accumulation phase. Furthermore, the estimate is of the same order of magnitude as the methodological error.To further evaluate whether plant RNA viruses show unusually low mutation rates, we have developed a new empirical method that allows estimating the mutation rate and the spectrum of spontaneous mutations produced during an in vivo infectious process. The viral model system chosen for this experiment has been TEV (family Potyviridae, genus Potyvirus), a prototypical example of positive sense RNA virus that has also become a model for virus experimental evolution. The method is based on the analysis of the temporal accumulation of mutations in a 1536-nt-long neutral viral target. TEV genome size is 9539 nt long (GeneBank {"type":"entrez-nucleotide","attrs":{"text":"DQ986288","term_id":"115606139","term_text":"DQ986288"}}DQ986288) and encodes a large polyprotein of 346 kDa that self-processes into at least nine mature proteins. One of these proteins, the nuclear inclusion protein b (NIb) has RNA-dependent RNA-polymerase activity (Urcuqui-Inchima et al. 2001). This protein forms inclusions in the nucleus of infected plants and is required in the cytoplasm for replication complexes during viral RNA synthesis. NIb is the only protein that can be provided functionally in trans (Li and Carrington 1995). Taking advantage of this property, we infected Nicotiana tabacum transgenic plants expressing TEV NIb and followed the accumulation of mutations in the viral copy of NIb. This experimental system minimizes the effect of purifying selection on the virus-encoded NIb due to complementation by the transgene.     

Topology and robustness in the Drosophila segment polarity network

A complex hierarchy of genetic interactions converts a single-celled Drosophila melanogaster egg into a multicellular embryo with 14 segments. Previously, von Dassow et al. reported that a mathematical model of the genetic interactions that defined the polarity of segments (the segment polarity network) was robust (von Dassow et al. 2000). As quantitative information about the system was unavailable, parameters were sampled randomly. A surprisingly large fraction of these parameter sets allowed the model to maintain and elaborate on the segment polarity pattern. This robustness is due to the positive feedback of gene products on their own expression, which induces individual cells in a model segment to adopt different stable expression states (bistability) corresponding to different cell types in the segment polarity pattern. A positive feedback loop will only yield multiple stable states when the parameters that describe it satisfy a particular inequality. By testing which random parameter sets satisfy these inequalities, I show that bistability is necessary to form the segment polarity pattern and serves as a strong predictor of which parameter sets will succeed in forming the pattern. Although the original model was robust to parameter variation, it could not reproduce the observed effects of cell division on the pattern of gene expression. I present a modified version that incorporates recent experimental evidence and does successfully mimic the consequences of cell division. The behavior of this modified model can also be understood in terms of bistability in positive feedback of gene expression. I discuss how this topological property of networks provides robust pattern formation and how large changes in parameters can change the specific pattern produced by a network.     

Map-Based Cloning of the Gene Associated With the Soybean Maturity Locus E3

Photosensitivity plays an essential role in the response of plants to their changing environments throughout their life cycle. In soybean [Glycine max (L.) Merrill], several associations between photosensitivity and maturity loci are known, but only limited information at the molecular level is available. The FT3 locus is one of the quantitative trait loci (QTL) for flowering time that corresponds to the maturity locus E3. To identify the gene responsible for this QTL, a map-based cloning strategy was undertaken. One phytochrome A gene (GmPhyA3) was considered a strong candidate for the FT3 locus. Allelism tests and gene sequence comparisons showed that alleles of Misuzudaizu (FT3/FT3; JP28856) and Harosoy (E3/E3; PI548573) were identical. The GmPhyA3 alleles of Moshidou Gong 503 (ft3/ft3; JP27603) and L62-667 (e3/e3; PI547716) showed weak or complete loss of function, respectively. High red/far-red (R/FR) long-day conditions enhanced the effects of the E3/FT3 alleles in various genetic backgrounds. Moreover, a mutant line harboring the nonfunctional GmPhyA3 flowered earlier than the original Bay (E3/E3; PI553043) under similar conditions. These results suggest that the variation in phytochrome A may contribute to the complex systems of soybean flowering response and geographic adaptation.FLOWERING represents the transition from the vegetative to the reproductive phase in plants. Various external cues, such as photoperiod and temperature, are known to initiate plant flowering under the appropriate seasonal conditions. Of these cues, light is the most important, being received by several photoreceptors, including the red light (R) and the far-red light (FR)-absorbing phytochromes and the blue/UV-A absorbing cryptochromes and phototorpins (Chen et al. 2004).Phytochrome is the best characterized of these photoreceptors. All higher plant phytochromes are thought to exist as specific dimer combinations (Sharrock and Clack 2004), with each monomer being attached to a light-absorbing linear tetrapyrrole, phytochromobilin. The phytochrome apoproteins are synthesized within the cytosol and assemble autocatalytically with a chromophore to form the phytochrome holoproteins. The R-absorbing form (Pr) is thought to be inactive but is then converted to the active FR-absorbing form (Pfr) by R absorption. The absorption of light triggers the transfer of the phytochrome to the nucleus, where it regulates gene expression. In most plant species, the phytochrome apoproteins are encoded by a small gene family. Type I phytochrome is degraded in the light and is abundant in dark-grown seedlings, whereas type II phytochrome is relatively stable in the light (reviewed by Bae and Choi 2008). In Arabidopsis, five phytochromes (PhyA–E) have been characterized (Clack et al. 1994; Quail et al. 1995). PhyA is type I and is responsible for the very low fluence response and high irradiance response, whereas the other phytochromes are type II and are responsible for red-far/red reversible low fluence response (reviewed by Whitelam et al. 1998).It is well known that mutations in the phytochrome A gene affect the photoperiodic control of flowering. In Arabidopsis, a phyA mutant flowered later in either long-day or short-day conditions with a night break (Johnson et al. 1994; Reed et al. 1994). In rice, combinations of mutant alleles of phytochrome genes conferred various effects on the flowering phenotype. For example, the phyA phyB and phyA phyC double mutants grown under natural-day-length conditions showed earlier flowering phenotypes than wild-type plants (Takano et al. 2005). In pea, a long-day plant, loss- or gain-of-function phyA mutants displayed late or early flowering phenotypes, respectively (Weller et al. 1997, 2001). It is likely that photoperiodic response via phyA signaling is important for crop adaptation to a wide range of growing conditions.In soybean [Glycine max (L.) Merrill], several maturity loci, designated as E loci (Cober et al. 1996a), have been characterized by classical methods. These are E1 and E2 (Bernard 1971), E3 (Buzzell 1971), E4 (Buzzell and Voldeng 1980), E5 (McBlain and Bernard 1987), E6 (Bonato and Vello 1999), and E7 (Cober and Voldeng 2001). Of these, the E1, E3, and E4 loci have been suggested to be related to photoperiod sensitivity under various light conditions (Saidon et al. 1989; Cober et al. 1996b; Abe et al. 2003). In previous studies, using the same populations as in this study, three flowering-time quantitative trait loci (QTL)—FT1, FT2, and FT3 loci—were identified and considered to be identical with the maturity loci E1, E2, and E3, respectively (Yamanaka et al. 2001; Watanabe et al. 2004). Although many loci related to soybean flowering and maturity have been identified, and some candidate genes were recognized using near isogenic lines (NILs) (Tasma and Shoemaker 2003), most of the genes responsible for these loci have not yet been isolated except for the E4 gene. Liu et al. (2008) reported an association between phytochrome A and photoperiod sensitivity. A retrotransposon sequence inserted into the exon of the e4 allele conferred an early flowering phenotype under long-day conditions extended by incandescent lighting.A relationship between the E3 gene and some photoreceptor genes was suggested from different photosensitivity responses of various soybean NILs (Cober et al. 1996a). Cober and Voldeng (1996) also reported a linkage relationship between the E3 and Dt1 loci, which is related to a determinate or indeterminate growth habit phenotype. Additionally, Molnar et al. (2003) reported that Satt229, on linkage group (LG) L, was a proximal simple sequence repeat (SSR) marker to the E3 loci. According to the Soybean Genome Database (Shultz et al. 2006a,b, 2007; http://soybeangenome.siu.edu/) and the Legume Information System (LIS; http://www.comparative-legumes.org/), there are numerous QTL and >60 loci associated with various agronomic traits in the region between Dt1 and Satt373 (∼30–40 cM). This extremely large number of QTL may be the result of linkage between the Dt1 and E3 loci because both loci can affect many aspects of plant morphology. Among these QTL, several associations with the E3 gene have been reported (Mansur et al. 1996; Orf et al. 1999; Funatsuki et al. 2005; Kahn et al. 2008).To identify the genes responsible for the target QTL, fine mapping and map-based cloning strategies are necessary (Salvi and Tuberosa 2005). QTL analysis using intercross-derived populations, such as F₂ and recombinant inbred lines (RILs), have some limitations in genome resolution (10–30 cM) because of the simultaneous segregation of several loci affecting the same trait (Kearsey and Farquhar 1998). Additional strategies are therefore required to locate QTL more precisely. The use of NILs that differ at a single QTL is an effective approach for fine mapping and characterization of an individual locus (Salvi and Tuberosa 2005). However, the development of NILs through repeated backcrossing is time-consuming and laborious (Tuinstra et al. 1997). The use of a residual heterozygous line (RHL), as proposed by Yamanaka et al. (2004), and which is derived from RIL, is a powerful tool for precisely evaluating QTL (Haley et al. 1994). An RHL harbors a heterozygous region where the target QTL is located and a homozygous background in most other regions of the genome. Tuinstra et al. (1997) used a similar term, heterogeneous inbred family, for a selfed RHL population to identify the QTL associated with seed weight in sorghum.This RHL strategy has already been used to identify loci underlying resistance to pathogens in soybean (Njiti et al. 1998; Meksem et al. 1999; Triwitayakorn et al. 2005). After identification of the target loci, novel DNA markers tightly linked to the loci were developed using the amplified fragment length polymorphism (AFLP) method (Meksem et al. 2001a,b). Physical contigs, screened by sequence-characterized amplified region (SCAR) markers converted from these AFLP fragments, are ideal sources for identifying candidate genes for the target traits (Ruben et al. 2006).The aim of this study is to characterize the FT3 locus using a map-based cloning strategy and to confirm the gene responsible for the E3/FT3 locus by allelism tests through comparisons of gene sequences and photosensitivity of several alleles.     

In Planta Mutagenesis Determines the Functional Regions of the Wheat Puroindoline Proteins

In planta analysis of protein function in a crop plant could lead to improvements in understanding protein structure/function relationships as well as selective agronomic or end product quality improvements. The requirements for successful in planta analysis are a high mutation rate, an efficient screening method, and a trait with high heritability. Two ideal targets for functional analysis are the Puroindoline a and Puroindoline b (Pina and Pinb, respectively) genes, which together compose the wheat (Triticum aestivum L.) Ha locus that controls grain texture and many wheat end-use properties. Puroindolines (PINs) together impart soft texture, and mutations in either PIN result in hard seed texture. Studies of the PINs'' mode of action are limited by low allelic variation. To create new Pin alleles and identify critical function-determining regions, Pin point mutations were created in planta via EMS treatment of a soft wheat. Grain hardness of 46 unique PIN missense alleles was then measured using segregating F₂:F₃ populations. The impact of individual missense alleles upon PIN function, as measured by grain hardness, ranged from neutral (74%) to intermediate to function abolishing. The percentage of function-abolishing mutations among mutations occurring in both PINA and PINB was higher for PINB, indicating that PINB is more critical to overall Ha function. This is contrary to expectations in that PINB is not as well conserved as PINA. All function-abolishing mutations resulted from structure-disrupting mutations or from missense mutations occurring near the Tryptophan-rich region. This study demonstrates the feasibility of in planta functional analysis of wheat proteins and that the Tryptophan-rich region is the most important region of both PINA and PINB.NATURAL selection has captured a relatively small subset of potentially useful protein sequences. Unraveling the critical features of proteins via understanding the process of their evolution is a powerful approach for proteins present in many diverse species (Bashford et al. 1987; Hampsey et al. 1988). However, this approach is not feasible for the wheat puroindolines (PINs) that are present only in hexaploid wheat and related species (Massa and Morris 2006). The PINs are unique in structure in having a tryptophan-rich domain and are members of the protease inhibitor/seed storage/lipid transfer protein family (PF00234) (Finn et al. 2008).The tryptophan-rich domain has been hypothesized to control PIN function (Giroux and Morris 1997), but there is no unbiased direct evidence for this since previous studies have focused on the tryptophan box alone (Evrard et al. 2008). A nonbiased approach would consist of random mutagenesis followed by functional analysis (Bowie et al. 1990). This approach has been used extensively for proteins that can be expressed in vitro using either random (Tarun et al. 1998; Guo et al. 2004; Smith and Raines 2006; Georgelis et al. 2007) or site-directed mutations (Miyahara et al. 2008; Osmani et al. 2008). However, functional analysis of many plant proteins in vitro may not be comparable to in planta analysis. In the case of puroindolines, there is no in vitro assay that properly mimics the synergistic binding of PINA and PINB to starch granules or is as easy to measure as grain hardness. Therefore, creation and analysis of a large number of new alleles in wheat in planta is an ideal approach to dissect PIN function.The absence of high-throughput transformation and/or functional screening methods in most crop plants is the largest obstacle in the way of in planta protein functional analysis. However, high-throughput in vitro random or targeted mutagenesis followed by functional analysis has been demonstrated in Arabidopsis thaliana (Dunning et al. 2007) and Nicotiana benthamiana (Boter et al. 2007). Traditional in planta mutagenesis followed by analysis of loss-of-function mutations has been used to clone unknown genes (Xiong et al. 2001) or to define function for candidate genes (Haralampidis et al. 2001; Qi et al. 2006). A high-throughput in planta functional approach for PINA and PINB seems attractive for three reasons. First, the EMS mutation rate in wheat is higher than in any other plant (Slade et al. 2005; Feiz et al. 2009a). Second, PINs control the vast majority of variation in grain hardness (Campbell et al. 1999). Finally, a small-scale preliminary study indicated the feasibility of this approach (Feiz et al. 2009a).PINA and PINB are cysteine-rich proteins unique in having a tryptophan-rich domain (Blochet et al. 1993) and together compose the wheat Hardness (Ha) locus (Giroux and Morris 1998; Wanjugi et al. 2007a). Ha is located on chromosome 5DS and is the major determinant of wheat endosperm texture (Mattern et al. 1973; Law et al. 1978; Campbell et al. 1999). Soft texture (Ha) results when both Pin genes are wild type (Pina-D1a, Pinb-D1a) while hard texture (ha) results from mutations in either Pin (Giroux and Morris 1997, 1998). Transgenic studies in rice (Krishnamurthy and Giroux 2001), wheat (Beecher et al. 2002; Martin et al. 2006), and corn (Zhang et al. 2009) have demonstrated that Pin mutations are causative to hard grain texture. PINA and PINB are not functionally interchangeable and control grain hardness via cooperative binding to starch granules (Hogg et al. 2004; Swan et al. 2006; Wanjugi et al. 2007a; Feiz et al. 2009b). PIN binding to starch granules is mediated by polar lipids (Greenblatt et al. 1995) and PIN abundance is correlated with seed polar lipid content (Feiz et al. 2009b). Variation in PIN function affects grain hardness along with nearly all end product quality traits (Hogg et al. 2005; Martin et al. 2007, 2008; Wanjugi et al. 2007b; Feiz et al. 2008). Determining PINs'' function-determining regions could lead to greater knowledge of their mode of action and to wheat quality improvements. Current PIN functional analyses have been limited to in vitro tests of binding to each other (Ziemann et al. 2008) or to yeast membranes (Evrard et al. 2008).Here, we report the creation and functional analysis in planta of new alleles of PINA and PINB. This is the first successful in planta functional analysis of a crop plant protein.