Epigenetic Variations in Plant Hybrids and Their Potential Roles in Heterosis

Heterosis,one of the most important biological phenomena,refers to the phenotypic superiority of a hybrid over its genetically diverse parents with respect to many traits such as biomass,growth rate and yield.Despite its successful application in breeding and agronomic production of many crop and animal varieties,the molecular basis of heterosis remains elusive.The classic genetic explanations for heterosis centered on three hypotheses:dominance (Davenport,1908;Bruce,1910;Keeble and Pellew,1910;Jones,1917),overdominance (East,1908;Shull,1908) and epistasis (Powers,1944;Yu et al.,1997).However,these hypotheses are largely conceptual and not connected to molecular principles,and are therefore insufficient to explain the molecular basis of heterosis (Birchler et al.,2003).Recently,many studies have explored the molecular mechanism of heterosis in plants at a genome-wide level.These studies suggest that global differential gene expression between hybrids and parental lines potentially contributes to heterosis in plants (e.g.,Swanson-Wagner et al.,2006;Zhang et al.,2008;Wei et al.,2009;Song et al.,2010).Research suggests that genetic components,including cis-acting elements and trans-acting factors,are critical regulators of differential gene expression in hybrids (Hochholdinger and Hoecker,2007;Springer and Stupar,2007;Zhang et al.,2008).However,other research indicates that epigenetic components,the regulators of chromatin states and genome activity,also have the potential to impact heterosis (e.g.,Ha et al.,2009;He et al.,2010;Groszmann et al.,2011;Barber et al.,2012;Chodavarapu et al.,2012;Greaves et al.,2012a;Shen et al.,2012).     

Some progress in the study of plant–soil interactions in China

Plants and soil are the base for sustainably surviving human beings on the globe as the role of materials, energy, resources and environment (Shao & Chu 2008; Shao et al. 2008, 2009, 2010, 2012a,b; Liu & Shao, 2010; Ruan et al. 2010; Xu et al. 2010, 2012; Shao 2012; Huang et al. 2013). This topic has been extensively investigated for 100 years with more achievements in many sectors and practical significance in conducting high-efficient agriculture and eco-environmental construction. The plant–soil interaction is the core issue of this topic, which has been given much attention for the past 30 years (Wu et al. 2007, 2010; Zhang et al. 2011, 2013; Xu et al. 2012, 2013).     

A novel role for Greatwall kinase in recovery from DNA damage

Activation of the DNA damage response (DDR) is critical for genomic integrity and tumor suppression. The occurrence of DNA damage quickly evokes the DDR through ATM/ATR-dependent signal transduction, which promotes DNA repair and activates the checkpoint to halt cell cycle progression (Halazonetis et al., 2008; Motoyama and Naka, 2004; Zhou and Elledge, 2000). The "turn off" process of the DDR upon satisfaction of DNA repair, also known as "checkpoint recovery", involves deactivation of DDR elements, but the mechanism is poorly understood. Greatwall kinase (Gwl) has been identified as a key element in the G2/M transition (Archambault et al., 2007; Jackson, 2006; Zhao et al., 2008; Yu et al., 2004; Yu et al., 2006; Zhao et al., 2006) and helps maintain M phase through inhibition of PP2A/B55δ (Burgess et al., 2010; Castilho et al., 2009; Goldberg, 2010; Lorca et al., 2010; Vigneron et al., 2009), the principal phosphatase for Cdk-phosphorylated substrates. Here we show that Gwl also promotes recovery from DNA damage and is itself directly inhibited by the DNA damage response (DDR). In Xenopus egg extracts, immunodepletion of Gwl increased the DDR to damaged DNA, whereas addition of wild type, but not kinase dead Gwl, inhibited the DDR. The removal of damaged DNA from egg extracts leads to recovery from checkpoint arrest and entry into mitosis, a process impaired by Gwl depletion and enhanced by Gwl over-expression. Moreover, activation of Cdk1 after the removal of damaged DNA is regulated by Gwl. Collectively, these results defines Gwl as a new regulator of the DDR, which plays an important role in recovery from DNA     

Population genomics as a new tool for wildlife management

Admixture and introgression have varied effects on population viability and fitness. Admixture might be an important source of new alleles, particularly for small, geographically isolated populations. However, admixture might also cause outbreeding depression if populations are adapted to different ecological or climatic conditions. Because of the emerging use of translocation and admixture as a conservation and wildlife management strategy to reduce genetic load (termed genetic rescue), the possible effects of admixture have practical consequences ( Bouzat et al. 2009 ; Hedrick & Fredrickson 2010 ). Importantly, genetic load and local adaptation are properties of individual loci and epistatic interactions among loci rather than properties of genomes. Likewise, the outcome and consequences of genetic rescue depend on the fitness effects of individual introduced alleles. In this issue of Molecular Ecology, Miller et al. (2012) use model‐based, population genomic analyses to document locus‐specific effects of a recent genetic rescue in the bighorn sheep population within the National Bison Range wildlife refuge (NBR; Montana, USA). They find a subset of introduced alleles associated with increased fitness in NBR bighorn sheep, some of which experienced accelerated introgression following their introduction. These loci mark regions of the genome that could constitute the genetic basis of the successful NBR bighorn sheep genetic rescue. Although population genomic analyses are frequently used to study local adaptation and selection (e.g. Hohenlohe et al. 2010 ; Lawniczak et al. 2010 ), this study constitutes a novel application of this analytical framework for wildlife management. Moreover, the detailed demographic data available for the NBR bighorn sheep population provide a rare and powerful source of information and allow more robust population genomic inference than is often possible.     

VirtualPlant: A Software Platform to Support Systems Biology Research

Data generation is no longer the limiting factor in advancing biological research. In addition, data integration, analysis, and interpretation have become key bottlenecks and challenges that biologists conducting genomic research face daily. To enable biologists to derive testable hypotheses from the increasing amount of genomic data, we have developed the VirtualPlant software platform. VirtualPlant enables scientists to visualize, integrate, and analyze genomic data from a systems biology perspective. VirtualPlant integrates genome-wide data concerning the known and predicted relationships among genes, proteins, and molecules, as well as genome-scale experimental measurements. VirtualPlant also provides visualization techniques that render multivariate information in visual formats that facilitate the extraction of biological concepts. Importantly, VirtualPlant helps biologists who are not trained in computer science to mine lists of genes, microarray experiments, and gene networks to address questions in plant biology, such as: What are the molecular mechanisms by which internal or external perturbations affect processes controlling growth and development? We illustrate the use of VirtualPlant with three case studies, ranging from querying a gene of interest to the identification of gene networks and regulatory hubs that control seed development. Whereas the VirtualPlant software was developed to mine Arabidopsis (Arabidopsis thaliana) genomic data, its data structures, algorithms, and visualization tools are designed in a species-independent way. VirtualPlant is freely available at www.virtualplant.org.Today, experimental biology laboratories usually investigate the molecular mechanisms underlying a physiological or developmental response by identifying the genes involved using a genomic platform, such as microarray (or, soon, deep sequencing) technology. Such a platform might identify genes regulated during a physiological or developmental response. Once the relevant gene sets are identified, biologists next analyze their functional relationships (e.g. whether they belong to the same metabolic pathway) and analyze their properties in the context of known biological pathways (DeRisi et al., 1997). Performing these tasks can be cumbersome because the biologist has to use several different tools to accomplish them. In addition, the difficulty is often increased because the different tools do not read and write the same data formats, forcing the biologist to obtain data conversion software.Aside from the challenge of integrating the vast amount of knowledge accumulated in the literature about the relevant genes, the genomic data available in the public domain have been obtained with a large number of experimental approaches and an even larger number of laboratories. Moreover, the information is stored in numerous databases, and it is encoded in diverse formats and database schemas. Bioinformatics faces a major challenge integrating this large-scale, heterogeneous information into architectures that support biological research. Different approaches that have been employed include hypertext navigation on the World Wide Web, data warehousing, and client-side integration (for example, see Ritter, 1994; Karp, 1996; Siepel et al., 2001; Philippi, 2004; Wilkinson et al., 2005). Once data from distinct database sources are coherently integrated, tools and computer models can be used to enable one to visualize and analyze this biological data from a systems perspective (Ideker et al., 2001). Several environments have been developed to support data integration and modeling (Kahlem and Birney, 2007). Some software allows detailed mathematical representation of cellular processes (e.g. Gepasi [Mendes, 1997] and Virtual Cell [Loew and Schaff, 2001]), while other software permits qualitative representations of cellular components and their interactions (e.g. Cytoscape [Shannon et al., 2003], Osprey [Breitkreutz et al., 2003], and N-Browse [Kao and Gunsalus, 2008]). Generally, quantitative models build detailed mathematical abstractions of specific cellular process. Quantitative models are powerful because they describe a system in detail (Endy and Brent, 2001), but they require a detailed understanding of the system. Unfortunately, this information is available for only a few biological processes. In fact, there are still many gaps in our qualitative understanding of biological systems, even for model organisms. For example, most of the genes in Arabidopsis (Arabidopsis thaliana) have not yet been experimentally characterized. Thus, while quantitative computer models can provide powerful, detailed representations of biological systems, not enough is known about Arabidopsis and other plants to construct such models of them or their major components. Therefore, we have focused on building software that facilitates analysis of the systems and statistical and interaction relationships between their genes and gene products.Today''s most widely available measure of gene function is the level of gene expression provided by a microarray analysis. Many approaches and tools support analysis of expression data. A now classic approach, for example, is to identify genes that are coregulated in their expression patterns across selected experimental conditions (e.g. Eisen et al., 1998). An extensive review of the different software tools that are available for studying gene coexpression is available (Usadel et al., 2009). To identify genes that are differentially expressed between two experimental conditions, statistical methods such as Rank Products can be used (Breitling et al., 2004; Hong et al., 2006). Several tools are available as packages in BioConductor, a project largely composed of tools written in the statistical language R (Gentleman et al., 2004). To determine the biological significance of differentially or coexpressed genes, biologists often evaluate the frequency of occurrence of functional attributes provided by structured functional annotations, such as Gene Ontology (GO; Ashburner et al., 2000). Several software packages to automate this type of analysis now exist (e.g. Onto-Express [Khatri et al., 2002], GoMiner [Zeeberg et al., 2003], GOSurfer [Zhong et al., 2004], and FatiGO [Al-Shahrour et al., 2004]). While advanced data analysis tools for exploiting genomic data are rapidly emerging (for review, see Brady and Provart, 2009), the narrow specialization of most current software tools forces geneticists to employ many tools to analyze the data in a single biological study. This cumbersome and inefficient process greatly hinders biologists following a systems approach of iterative in silico exploration and experimentation.VirtualPlant addresses these problems by integrating selected genomic data and analysis tools into a single Web-accessible software platform. The goal of our work is to help biologists discover new insights by synthesizing multiple data sources. VirtualPlant provides access to a database storing selected information about Arabidopsis and rice (Oryza sativa) experiments, genes, gene products, and their properties. VirtualPlant''s software architecture and data model have been designed and created in a generic, species-independent manner to ease the addition of new organisms and tools in the future. The VirtualPlant database also includes a high-level representation of plant cellular components and interactions that allow users to create molecular networks “on the fly.” These molecular networks provide a framework for analyzing experimental measurements. VirtualPlant also includes novel data visualization and data analysis techniques that allow seamless information exploration across many data sets with the help of a shopping cart in which gene sets from experiments and/or analyses can be stored and then used as inputs to other tools to enable iterative analysis. For concreteness, we present an example of how we have used VirtualPlant to identify gene networks and putative regulatory hubs that control seed development. We have previously demonstrated the use of VirtualPlant and specific tools embodied in the VirtualPlant system to generate hypotheses that were validated experimentally (Wang et al., 2004; Gutiérrez et al., 2007b, 2008; Gifford et al., 2008; Thum et al., 2008).     

Large-Scale Reverse Genetics in Arabidopsis: Case Studies from the Chloroplast 2010 Project

Traditionally, phenotype-driven forward genetic plant mutant studies have been among the most successful approaches to revealing the roles of genes and their products and elucidating biochemical, developmental, and signaling pathways. A limitation is that it is time consuming, and sometimes technically challenging, to discover the gene responsible for a phenotype by map-based cloning or discovery of the insertion element. Reverse genetics is also an excellent way to associate genes with phenotypes, although an absence of detectable phenotypes often results when screening a small number of mutants with a limited range of phenotypic assays. The Arabidopsis Chloroplast 2010 Project (www.plastid.msu.edu) seeks synergy between forward and reverse genetics by screening thousands of sequence-indexed Arabidopsis (Arabidopsis thaliana) T-DNA insertion mutants for a diverse set of phenotypes. Results from this project are discussed that highlight the strengths and limitations of the approach. We describe the discovery of altered fatty acid desaturation phenotypes associated with mutants of At1g10310, previously described as a pterin aldehyde reductase in folate metabolism. Data are presented to show that growth, fatty acid, and chlorophyll fluorescence defects previously associated with antisense inhibition of synthesis of the family of acyl carrier proteins can be attributed to a single gene insertion in Acyl Carrier Protein4 (At4g25050). A variety of cautionary examples associated with the use of sequence-indexed T-DNA mutants are described, including the need to genotype all lines chosen for analysis (even when they number in the thousands) and the presence of tagged and untagged secondary mutations that can lead to the observed phenotypes.Decoding of the Arabidopsis (Arabidopsis thaliana) genome sequence earlier this decade (Arabidopsis Genome Initiative, 2000) provided the opportunity to determine the functions of approximately 27,000 protein-coding genes. One or more functions of a small percentage of genes are currently experimentally determined, typically from mutant or transgenic analysis or through biochemistry. However, roles for the vast majority of plant genes are either more or less accurately predicted by DNA sequence homology or unpredictable based upon DNA sequence (Arabidopsis Genome Initiative, 2000; Cho and Walbot, 2001; Rhee et al., 2008; for recent specific examples, see Gao et al., 2009; Schilmiller et al., 2009). Because of the uncertainty associated with homology-based function assessment, high-throughput approaches to gene function identification are needed to expand the universe of genes with experimental annotation.In contrast to organisms amenable to targeted gene replacement, such as bacteria, yeast, and mouse (Wendland, 2003; Wu et al., 2007; Adams and van der Weyden, 2008), obtaining a gene knockout is not as efficient in flowering plants. In Arabidopsis, the conventional way of creating a gene knockout is by insertional mutagenesis via Agrobacterium tumefaciens-mediated transformation (Krysan et al., 1999). Using this technique, a large piece of T-DNA is inserted into the genome in an untargeted manner (Alonso et al., 2003). If it lands within a coding or regulatory region, the T-DNA can influence the expression of the corresponding gene. While the probability of any single insertion element causing a mutation in a gene of interest is low, sequencing of hundreds of thousands of independent insertion sites has led to a collection of mutants in the majority of genes (http://signal.SALK.edu/tabout.html; Alonso et al., 2003).T-DNA mutants can be a valuable tool for forward genetics, in which hundreds or thousands of mutants are subjected to phenotypic assays (Feldmann, 1991; Kuromori et al., 2006), but reverse genetics is the most common way in which these mutant collections are utilized. Typically, a small number of candidate genes are tested for a role in a particular biological process by reducing or increasing gene expression and assaying one or more phenotypes (for review, see Page and Grossniklaus, 2002; Alonso and Ecker, 2006). The availability of a gene-indexed T-DNA mutant collection allows researchers to rapidly obtain mutant lines for their genes of interest (http://signal.SALK.edu/cgi-bin/tdnaexpress). The availability of a large collection of indexed mutant or RNA interference lines in other model organisms has facilitated large-scale reverse genetics studies (Piano et al., 2000; Giaever et al., 2002; Ho et al., 2009).In the course of a large reverse genetics project (The Chloroplast 2010 Project; http://www.plastid.msu.edu/), more than 3,500 T-DNA lines harboring insertions in nuclear genes, most of which were computationally predicted to encode chloroplast-targeted proteins, were subjected to a diverse set of phenotypic screens (Lu et al., 2008). In total, 85 phenotypic observations ranging from quantitative metabolite measurements to qualitative phenotypic observations are collected for each mutant line, and the data are stored in a relational database (http://bioinfo.bch.msu.edu/2010_LIMS). This approach seeks to take advantage of the best features of forward and reverse genetics by screening a large number of lines with mutations in known genes. Unlike conventional genetics screens, where plants are assayed for one or a small number of traits, this project surveys varied phenotypes.In this study, a variety of phenotypic variants were analyzed. In some cases, independent mutants of the same gene were found to have similar phenotypes, revealing new information about those genes. In other examples, a single homozygous mutant allele was found to have a detectable phenotype. These run the gamut from cases where secondary mutations are strongly implicated in causing the phenotype, to an example where an analogous maize (Zea mays) mutant is known to have a similar phenotype, to other instances where the causative mutation is yet to be identified. In several examples of secondary mutations, the phenotype was not due to a T-DNA insertion, reinforcing the idea that these untagged alleles are a cause for concern in conducting large-scale reverse genetics screens (Vitha et al., 2003; Adham et al., 2005; Zolman et al., 2008), while providing opportunities for gene function discovery by map-based cloning or whole genome sequence analysis.     

A Single Nucleotide Deletion in Gibberellin20-oxidase1 Causes Alpine Dwarfism in Arabidopsis

Alpine dwarfism is widely observed in alpine plant populations and often considered a high-altitude adaptation, yet its molecular basis and ecological relevance remain unclear. In this study, we used map-based cloning and field transplant experiments to investigate dwarfism in natural Arabidopsis (Arabidopsis thaliana) accessions collected from the Swiss Alps. A loss-of-function mutation due to a single nucleotide deletion in gibberellin20-oxidase1 (GA5) was identified as the cause of dwarfism in an alpine accession. The mutated allele, ga5-184, was found in two natural Arabidopsis populations collected from one geographic region at high altitude, but was different from all other reported ga5 null alleles, suggesting that this allele has evolved locally. In field transplant experiments, the dwarf accession with ga5-184 exhibited a fitness pattern consistent with adaptation to high altitude. Across a wider array of accessions from the Swiss Alps, plant height decreased with altitude of origin, but fitness patterns in the transplant experiments were variable and general altitudinal adaptation was not evident. In general, our study provides new insights into molecular basis and possible ecological roles of alpine dwarfism, and demonstrates the importance of the GA-signaling pathway for the generation of ecologically relevant variation in higher plants.Adaptation to environmental conditions is one of the evolutionary processes that can lead to lineage divergence and the generation of biodiversity (Savolainen et al., 2013). Local adaptation within species may evolve in spatially heterogeneous environments if the strength of divergent selection among populations overrides other evolutionary forces (e.g. genetic drift, gene flow), and is manifested in an increased fitness of local genotypes in the local habitat when compared with foreign genotypes in the local habitat and when compared with fitness of the local genotypes in foreign habitats (Kawecki and Ebert, 2004; Savolainen et al., 2013). It is often a challenge for classic studies of local adaptation to identify traits mediating local adaptation and the underlying genetic architectures. To date, our knowledge on the molecular basis of local adaptation remains remarkably limited, even though this topic has been extensively studied in recent decades (Bergelson and Roux, 2010; Barrett and Hoekstra, 2011). A particular difficulty in studying the molecular basis of local adaptation comes from the need to combine meaningful field experiments together with relevant molecular studies to provide solid evidence on the adaptive value of phenotypes and associated genotypes. Therefore, a complete study of molecular mechanisms of adaptation ideally requires (1) identifying candidate traits under natural selection or relevant for adaptation, (2) isolating genes/loci that influence candidate traits, and (3) quantifying the fitness (adaptive value) conferred by different alleles under natural conditions (Bergelson and Roux, 2010). In the model plant Arabidopsis (Arabidopsis thaliana) and its relatives, as well as in several animal systems, great advances in detecting the molecular basis of adaptation have been made (e.g. Linnen et al., 2009; Fournier-Level et al., 2011; Hancock et al., 2011). Nevertheless, discussions on adaptionist storytelling or of producing molecular spandrels have been recurrent (for review, see Barrett and Hoekstra, 2011).Alpine habitats are characterized by a fine mosaic of heterogeneous and often extreme environmental conditions (Körner, 2003; Byars et al., 2007; Pico, 2012). Along altitudinal gradients, changes in environmental conditions are often steep, and plants growing in alpine areas have developed a variety of morphological and physiological adaptations that allow them to cope with extreme conditions. Alpine plant dwarfism, that is, reduced plant stature with increasing altitude, is one of the most common characteristics observed in plant populations originating from high altitudes (Clausen et al., 1948; Körner, 2003). Alpine dwarfism is thought to help alpine plants take advantage of the higher ambient temperature close to the soil surface, allocate more resources to reproduction, decrease damage from strong wind, and reduce evaporation (Turesson, 1922; Körner, 2003). Therefore, alpine dwarfism is widely considered as adaptive in plants (Turesson, 1922; Clausen et al., 1948; Körner, 2003; Byars et al., 2007; Gonzalo-Turpin and Hazard, 2009). However, whether alpine dwarfism is indeed adaptive and how it is genetically controlled remain unknown in many species. This may be partially attributable to the limited genomic resources that are available for alpine plant species, which are usually nonmodel organisms. Hence, studying the molecular basis of this ecologically important trait in model organisms such as Arabidopsis can give much-needed insights (Bergelson and Roux, 2010).Dwarfism and semidwarfism are important and well-studied traits in agriculture because they help overcome lodging and thus substantially contributed to the green revolution in the last century, the unprecedented increase in crop yields due to the adoption of genetically improved crop varieties (Peng et al., 1999; Khush, 2001). It has repeatedly been found in a diversity of plants that dwarfism and semidwarfism were due to deficiencies in either signaling or biosynthesis of GA (e.g. Spray et al., 1996; Peng et al., 1999; Monna et al., 2002). Dwarfism and dwarfing alleles have also been reported in natural Arabidopsis accessions (Barboza et al., 2013), and other recent studies have shown evidence of population differentiation and climatic adaptation along altitudinal gradients in this model plant (Montesinos et al., 2009; Méndez-Vigo et al., 2011, 2013; Montesinos-Navarro et al., 2011; Pico, 2012; Suter et al., 2014; Luo et al., 2015). In this study, we have investigated plant dwarfism in natural Arabidopsis accessions collected in the Swiss Alps (Supplemental Table S1) and identified a loss-of-function mutation in gibberellin20-oxidase1 (GA5, also called GA20ox1) as the cause of dwarfism in an Arabidopsis accession collected from high altitude (2,012 m above sea level [a.s.l.]). In field transplant experiments, this accession displayed fitness patterns consistent with altitudinal adaptation. Across a larger set of regional accessions, including 10 further accessions without the dwarfing mutation, plant height decreased with altitude of origin; however, this pattern could not be tied to adaptive differentiation along altitude.     

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Non-coding genomic regions in complex eukaryotes, including intergenic areas, introns, and untranslated segments of exons, are profoundly non-random in their nucleotide composition and consist of a complex mosaic of sequence patterns. These patterns include so-called Mid-Range Inhomogeneity (MRI) regions -- sequences 30-10000 nucleotides in length that are enriched by a particular base or combination of bases (e.g. (G+T)-rich, purine-rich, etc.). MRI regions are associated with unusual (non-B-form) DNA structures that are often involved in regulation of gene expression, recombination, and other genetic processes (Fedorova & Fedorov 2010). The existence of a strong fixation bias within MRI regions against mutations that tend to reduce their sequence inhomogeneity additionally supports the functionality and importance of these genomic sequences (Prakash et al. 2009).Here we demonstrate a freely available Internet resource -- the Genomic MRI program package -- designed for computational analysis of genomic sequences in order to find and characterize various MRI patterns within them (Bechtel et al. 2008). This package also allows generation of randomized sequences with various properties and level of correspondence to the natural input DNA sequences. The main goal of this resource is to facilitate examination of vast regions of non-coding DNA that are still scarcely investigated and await thorough exploration and recognition.     

Use of the valine biosynthetic pathway to convert glucose into isobutanol

Microbiological synthesis of higher alcohols (1-butanol, isobutanol, 2-methyl-1-butanol, etc.) from plant biomass is critically important due to their advantages over ethanol as a motor fuel. In recent years, the use of branched-chain amino acid (BCAA) biosynthesis pathways together with heterologous Ehrlich pathway enzyme system (Hazelwood et al. in Appl Environ Microbiol 74:2259–2266, 2008) has been proposed by the Liao group as an alternative approach to aerobic production of higher alcohols as new-generation biofuels (Atsumi et al. in Nature 451:86–90, 2008; Atsumi et al. in Appl Microbiol Biotechnol 85:651–657, 2010; Cann and Liao in Appl Microbiol Biotechnol 81:89–98, 2008; Connor and Liao in Appl Environ Microbiol 74:5769–5775, 2008; Shen and Liao in Metab Eng 10:312–320, 2008; Yan and Liao in J Ind Microbiol Biotechnol 36:471–479, 2009). On the basis of these remarkable investigations, we re-engineered Escherichia coli valine-producing strain H-81, which possess overexpressed ilvGMED operon, for the aerobic conversion of sugar into isobutanol. To redirect valine biosynthesis to the production of alcohol, we also—as has been demonstrated previously (Atsumi et al. in Nature 451:86–90, 2008; Atsumi et al. in Appl Microbiol Biotechnol 85:651–657, 2010; Cann and Liao in Appl Microbiol Biotechnol 81:89–98, 2008; Connor and Liao in Appl Environ Microbiol 74:5769–5775, 2008; Shen and Liao in Metab Eng 10:312–320, 2008; Yan and Liao in J Ind Microbiol Biotechnol 36:471–479, 2009)—used enzymes of Ehrlich pathway. In particular, in our study, the following heterologous proteins were exploited: branched-chain 2-keto acid decarboxylase (BCKAD) encoded by the kdcA gene from Lactococcus lactis with rare codons substituted, and alcohol dehydrogenase (ADH) encoded by the ADH2 gene from Saccharomyces cerevisiae. We show that expression of both of these genes in the valine-producing strain H-81 results in accumulation of isobutanol instead of valine. Expression of BCKAD alone also resulted in isobutanol accumulation in the culture broth, supporting earlier obtained data (Atsumi et al. in Appl Microbiol Biotechnol 85:651–657, 2010) that native ADHs of E. coli are also capable of isobutanol production. Thus, in this work, isobutanol synthesis by E. coli was achieved using enzymes similar to but somewhat different from those previously used.     

181 Integrative analysis of gene expression and protein–protein interaction networks in Glioblastoma

Glioblastoma multiforme (GBM) is the most malignant of all the brain tumors with very low median survival time of one year, as per Central Brain Tumor Registry of the USA, 2001. Efforts are ongoing to understand this disease pathogenesis in complete details. Global gene expression changes in GBM pathogenesis have been studied by several groups using microarray technology (e.g. Carro et al., 2010). One of the many approaches to ‘understand the control mechanisms underlying the observed changes in the activity of a biological process’ (Cline et al., 2007) is integration of gene expression and protein–protein interactions (PPI) datasets. Among several examples, aberrant activation of Wnt/β-catenin signaling pathway as well as sonic hedgehog (SHH) signaling pathway is reported in GBMs (Klaus & Birchmeier, 2008). Further, these two pathways are also involved in proliferation and clonogenicity of glioma cancer stem cells (Li et al., 2009), which are thought to play a role in glioma initiation, proliferation, and invasion, and are one of the important points of intervention. Hedgehog–Gli1 signaling is also found to regulate the expression of stemness genes. In this paper, analyses of the relationship between the significant differential expression of these and other genes and the connectivity as well as topological features of a PPI network would be discussed. This way, genes potentially overlooked when relying solely on expression profiles may be identified which can be biologically relevant as possible drug target/s or disease biomarker/s.     

Ubiquity or not ubiquity: That is the question

The nature of population structure in eukaryotic microbes has been the subject of intense debate, but until recently the tools to test these hypotheses were either problematic (e.g., allozymes that cannot detect all genetic changes) or beyond financial and technological limits of most laboratories (e.g., high throughput sequencing). In a recent issue of Molecular Ecology, Craig et al. (2019) use a genomic approach to investigate the population structure of a model alga, the chlorophyte Chlamydomonas reinhardtii (Figure 1). Using high throughput sequencing, read mapping, and variant calling, they detected strong signals of differentiation at a continental scale, while local patterns of admixture were complex. Population genomic techniques such as these have not been used extensively in studies of microbial eukaryotes and the fields of conservation genetics and evolution stand to benefit vastly from the adoption of these techniques to studies of diverse protist lineages.     

187 Plucking the high hanging fruit: a systematic approach for targeting protein interfaces

Development of specific ligands for protein targets that help decode the complexities of protein–protein interaction networks is a key goal for the field of chemical biology. Despite the emergence of powerful in silico and experimental high-throughput screening strategies, the discovery of synthetic ligands that selectively modulate protein–protein interactions remains a challenge for the chemical biologists. Proteins often utilize small folded domains for recognition of other biomolecules. The basic hypothesis guiding our research is that by mimicking these domains, we can modulate the function of a particular protein with metabolically-stable synthetic molecules (Raj et al., 2013). This presentation will discuss computational approaches (Bullock et al., 2011; Jochim & Arora, 2010) to identify targetable interfaces along with synthetic methods (Patgiri et al., 2008; Tosovska & Arora, 2010) to develop protein domain mimics (PDMs) as modulators of intracellular protein–protein interactions (Henchey et al., 2010; Patgiri et al., 2011).     

Plant diversity and ecology on the Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau (QTP) comprises a platform (sometimes called the Qinghai–Tibet Plateau sensu stricto), the Himalayas, and Hengduan Mountains (Liang et al.,2018; Mao et al.,2021). The latter two parts and adjacent highlands are also called the Pan-Himalaya. Numerous plants are distributed there with many endemic species, probably because of the high diverse landscapes created by continuous geological and climatic activities (Favre et al.,2015; Mao et al.,2021). As the well known biodiversity hotspot of the alpine plants in the world (Sun et al.,2017), many studies have been conducted on evolutionary origin and ecological adaptation of those species occurring in the QTP (e.g., Wen et al.,2014, 2019; Zhang et al.,2019). In the present special issue, we collected 15 related papers on this topic. Among them, two are invited reviews. Mao et al. (2021) provide a comprehensive review of evolutionary origin of species diversity on the QTP. Especially, they outlined major disputes and likely causes in this research topic, including circumscribing and naming the QTP, the QTP uplifts, dating of molecular phylogenetic trees, non-causal correlations between QTP uplifts and species diversification and the unified ice sheet. The authors also summarized genomic advancements related to high-altitude adaptation of both plants and animals. Tong et al. (2021) reviewed the reproductive strategies of animal-pollinated alpine plants on the QTP, involving pollination system, pollen limitation, self-pollination, and sexual system. In this region, 95.4% of animal-pollinated plants are pollinated by insects (i.e., bees, moths, butterflies, and flies) with only 4% by vertebrates (i.e., bats and birds). Self-pollination through self-compatibility shift from outcrossing has become an effective reproductive strategy to overcome pollen limitation in alpine plants. The other 13 research papers aimed to address origin and adaptation of alpine flora involving three major lines of evidence: genomics, ecology, and paleobotany. We hope that the collection of these papers will increase our understanding of the origin, speciation, and adaptation of alpine species on the QTP.     

RAD in the realm of next-generation sequencing technologies

The first North American RAD Sequencing and Genomics Symposium, sponsored by Floragenex (http://www.floragenex.com/radmeeting/), took place in Portland, Oregon (USA) on 19 April 2011. This symposium was convened to promote and discuss the use of restriction-site-associated DNA (RAD) sequencing technologies. RAD sequencing is one of several strategies recently developed to increase the power of data generated via short-read sequencing technologies by reducing their complexity (Baird et al. 2008; Huang et al. 2009; Andolfatto et al. 2011; Elshire et al. 2011). RAD sequencing, as a form of genotyping by sequencing, has been effectively applied in genetic mapping and quantitative trait loci (QTL) analyses in a range of organisms including nonmodel, genetically highly heterogeneous organisms (Table 1; Baird et al. 2008; Baxter et al. 2011; Chutimanitsakun et al. 2011; Pfender et al. 2011). RAD sequencing has recently found applications in phylogeography (Emerson et al. 2010) and population genomics (Hohenlohe et al. 2010). Considering the diversity of talks presented during this meeting, more developments are to be expected in the very near future.     

Arsenic Speciation in Phloem and Xylem Exudates of Castor Bean

How arsenic (As) is transported in phloem remains unknown. To help answer this question, we quantified the chemical species of As in phloem and xylem exudates of castor bean (Ricinus communis) exposed to arsenate [As(V)], arsenite [As(III)], monomethylarsonic acid [MMA(V)], or dimethylarsinic acid. In the As(V)- and As(III)-exposed plants, As(V) was the main species in xylem exudate (55%–83%) whereas As(III) predominated in phloem exudate (70%–94%). The ratio of As concentrations in phloem to xylem exudate varied from 0.7 to 3.9. Analyses of phloem exudate using high-resolution inductively coupled plasma-mass spectrometry and accurate mass electrospray mass spectrometry coupled to high-performance liquid chromatography identified high concentrations of reduced and oxidized glutathione and some oxidized phytochelatin, but no As(III)-thiol complexes. It is thought that As(III)-thiol complexes would not be stable in the alkaline conditions of phloem sap. Small concentrations of oxidized glutathione and oxidized phytochelatin were found in xylem exudate, where there was also no evidence of As(III)-thiol complexes. MMA(V) was partially reduced to MMA(III) in roots, but only MMA(V) was found in xylem and phloem exudate. Despite the smallest uptake among the four As species supplied to plants, dimethylarsinic acid was most efficiently transported in both xylem and phloem, and its phloem concentration was 3.2 times that in xylem. Our results show that free inorganic As, mainly As(III), was transported in the phloem of castor bean exposed to either As(V) or As(III), and that methylated As species were more mobile than inorganic As in the phloem.Arsenic (As) is an environmental and food chain contaminant that has attracted much attention in recent years. Soil contamination with As may lead to phytotoxicity and reduced crop yield (Panaullah et al., 2009). Food crops are also an important source of inorganic As, a class-one carcinogen, in human dietary intake, and there is a need to decrease the exposure to this toxin (European Food Safety Authority, 2009). Paddy rice (Oryza sativa) is particularly efficient in As accumulation, which poses a potential risk to the population based on a rice diet (Meharg et al., 2009; Zhao et al., 2010a). Other terrestrial food crops generally do not accumulate as much As as paddy rice; however, where soils are contaminated, relatively high concentrations of As in wheat (Triticum aestivum) grain have been reported (Williams et al., 2007; Zhao et al., 2010b). On the other hand, some fern species in the Pteridaceae family are able to tolerate and hyperaccumulate As in the aboveground part to >1,000 mg kg⁻¹ dry weight (e.g. Ma et al., 2001; Zhao et al., 2002); these plants offer the possibility for remediation of As-contaminated soil or water (Salido et al., 2003; Huang et al., 2004). A better understanding of As uptake and long-distance transport, metabolism, and detoxification is needed for developing strategies for mitigating As contamination, through either decreased As accumulation in food crops or enhanced As accumulation for phytoremediation.The pathways of As uptake by plant roots differ between different As species; arsenate [As(V)] enters plant cells via phosphate transporters, whereas arsenite [As(III)] is taken up via some aquaporins (for review, see Zhao et al., 2009). In rice, a silicic acid efflux protein also mediates As(III) efflux toward stele for xylem loading (Ma et al., 2008). Methylated As species, such as monomethylarsonic acid [MMA(V)] and dimethylarsinic acid [DMA(V)], which may be present in the environment as products of microbial or algal methylation of inorganic As or from past uses of methylated As pesticides, are taken up by rice roots partly through the aquaporin NIP2;1 (for nodulin 26-like intrinsic protein; also named Lsi1; Li et al., 2009). Once inside plant cells, As(V) is reduced to As(III), possibly catalyzed by As(V) reductase(s) such as the plant homologs of the yeast (Saccharomyces cerevisiae) ACR2 (Bleeker et al., 2006; Dhankher et al., 2006; Ellis et al., 2006; Duan et al., 2007). As(III) has a high affinity to thiol (-SH) groups and is detoxified by complexation with thiol-rich phytochelatins (PCs; Pickering et al., 2000; Schmöger et al., 2000; Raab et al., 2005; Bluemlein et al., 2009; Liu et al., 2010). As(III)-PC complexation in roots was found to result in reduced mobility for efflux and for long-distance transport, possibly because the complexes are stored in the vacuoles (Liu et al., 2010). Excess As(III) causes cellular toxicity by binding to the vicinal thiol groups of enzymes, such as the plastidial lipoamide dehydrogenase, which has been shown to be a sensitive target of As toxicity (Chen et al., 2010). The As hyperaccumulating Pteris species differ from nonhyperaccumulating plants because of enhanced As(V) uptake (Wang et al., 2002; Poynton et al., 2004), little As(III)-thiol complexation (Zhao et al., 2003; Raab et al., 2004), and efficient xylem loading of As(III) (Su et al., 2008). Recently, an As(III) efflux transporter, PvACR3, has been found to play an important role in As(III) detoxification by transporting As(III) into vacuoles in Pteris vittata (Indriolo et al., 2010).With the exception of As hyperaccumulators, most plant species have a limited root-to-shoot translocation of As (Zhao et al., 2009). The chemical species of As in xylem exudate have been determined in a number of plant species. As(III) was found to be the predominant species (80%–100%) in the xylem sap of rice, tomato (Solanum lycopersicum), cucumber (Cucumis sativus), and P. vittata even when these plants were fed As(V) (Mihucz et al., 2005; Xu et al., 2007; Ma et al., 2008; Su et al., 2010), suggesting that As(V) is reduced in roots before being loaded into the xylem. In other plant species, such as Brassica juncea (Pickering et al., 2000), wheat, and barley (Hordeum vulgare; Su et al., 2010), As(V) accounted for larger proportions (40%–50%) of the total As in the xylem sap. Studies using HPLC-inductively coupled plasma (ICP)-mass spectrometry (MS) coupled with electrospray (ES)-MS showed no evidence of As(III)-thiol complexation in the xylem sap of sunflower (Helianthus annuus; Raab et al., 2005). When rice plants were exposed to MMA(V) or DMA(V), both As species were found in the xylem sap (Li et al., 2009). Generally, methylated As species are taken up by roots at slower rates than inorganic As, but they are more mobile during the xylem transport from roots to shoots (Marin et al., 1992; Raab et al., 2007; Li et al., 2009).It has been shown that phloem transport contributes substantially to As accumulation in rice grain (Carey et al., 2010). However, little is known about how As is transported in phloem (Zhao et al., 2009). There are no reports on the chemical species of As in phloem exudate. The speciation of As in phloem is important because it dictates how As is loaded in the source tissues and unloaded in the sink tissues, such as grain. Questions with regard to the oxidation state, methylation, and complexation of As in phloem sap remain to be answered. Unlike xylem sap, phloem sap is much more difficult to obtain in sufficient quantities for analysis. In this study, we investigated As speciation in phloem and xylem exudates of castor bean (Ricinus communis), which is widely used as a model plant to investigate phloem transport of solutes (e.g. Hall et al., 1971; Hall and Baker, 1972; Allen and Smith, 1986; Bromilow et al., 1987).     

Subcellular localization of Hairless protein shows a major focus of activity within the nucleus

Hairless, a major antagonist of the Notch signaling-pathway in Drosophila (Bang and Posakony, 1992; Maier et al., 1992), associates with Suppressor of Hairless [Su(H)], thereby inhibiting trans-activation of Notch target genes (Brou et al., 1994). These molecular interactions could occur either at the step of signal transduction in the cytoplasm or during implementation of the signal within the nucleus. We examined the subcellular distribution of Hairless, showing that it is a low abundant, ubiquitous protein that is cytosolic as well as nuclear. High levels of Hairless cause nuclear retention of Su(H), loss of Hairless reduces the amount of Su(H) in the nucleus.