International journal of plant genomics最新文献

The development of powerful "omics" technologies has enabled researchers to identify many genes of interest for which comprehensive functional analyses are highly desirable. However, the production of lines which ectopically express recombinant genes, or those in which endogenous genes are knocked down via stable transformation, remains a major bottleneck for the association between genetics and gene function in monocotyledonous crops. Methods of effective DNA transfer into regenerable cells of immature embryos from cereals by means of Agrobacterium tumefaciens have been modified in a stepwise manner. The effect of particular improvement measures has often not been significantly evident, whereas their combined implementation has resulted in meaningful advances. Here, we provide updated protocols for the Agrobacterium-mediated generation of stably transgenic barley, wheat, triticale and maize. Based upon these methods, several hundred independent transgenic lines have been delivered, with efficiencies of inoculated embryos leading to stably transgenic plants reaching 86% in barley, 10% in wheat, 4% in triticale, and 24% in maize.

The "RNA revolution" that started at the end of the 20th century with the discovery of post-transcriptional gene silencing and its mechanism via RNA interference (RNAi) placed tiny 21-24 nucleotide long noncoding RNAs (ncRNAs) in the forefront of biology as one of the most important regulatory elements in a host of physiologic processes. The discovery of new classes of ncRNAs including endogenous small interfering RNAs, microRNAs, and PIWI-interacting RNAs is a hallmark in the understanding of RNA-dependent gene regulation. New generation high-throughput sequencing technologies further accelerated the studies of this "tiny world" and provided their global characterization and validation in many biological systems with sequenced genomes. Nevertheless, for the many "yet-unsequenced" plant genomes, the discovery of small RNA world requires in vitro cloning from purified cellular RNAs. Thus, reproducible methods for in vitro small RNA cloning are of paramount importance and will remain so into the foreseeable future. In this paper, we present a description of existing small RNA cloning methods as well as next-generation sequencing methods that have accelerated this research along with a description of the application of one in vitro cloning method in an initial small RNA survey in the "still unsequenced" allotetraploid cotton genome.

Biochemical studies of plant auxin transporters in vivo are made difficult by the presence of multiple auxin transporters and auxin-interacting proteins. Furthermore, the expression level of most such transporters in plants is likely to be too low for purification and downstream functional analysis. Heterologous expression systems should address both of these issues. We have examined a number of such systems for their efficiency in expressing AUX1 from Arabidopsis thaliana. We find that a eukaryotic system based upon infection of insect cells with recombinant baculovirus provides a high level, easily scalable expression system capable of delivering a functional assay for AUX1. Furthermore, a transient transfection system in mammalian cells enables localization of AUX1 and AUX1-mediated transport of auxin to be investigated. In contrast, we were unable to utilise P. pastoris or L. lactis expression systems to reliably express AUX1.

Virus-induced gene silencing (VIGS) is one of the reverse genetics tools for analysis of gene function that uses viral vectors carrying a target gene fragment to produce dsRNA which trigger RNA-mediated gene silencing. There are a number of viruses which have been modified to silence the gene of interest effectively with a sequence-specific manner. Therefore, different types of methodologies have been advanced and modified for VIGS approach. Virus-derived inoculations are performed on host plants using different methods such as agro-infiltration and in vitro transcriptions. VIGS has many advantages compared to other loss-of-gene function approaches. The approach provides the generation of rapid phenotype and no need for plant transformation. The cost of VIGS experiment is relatively low, and large-scale analysis of screening studies can be achieved by the VIGS. However, there are still limitations of VIGS to be overcome. Nowadays, many virus-derived vectors are optimized to silence more than one host plant such as TRV-derived viral vectors which are used for Arabidopsis and Nicothiana benthamiana. By development of viral silencing systems monocot plants can also be targeted as silencing host in addition to dicotyledonous plants. For instance, Barley stripe mosaic virus (BSMV)-mediated VIGS allows silencing of barley and wheat genes. Here we summarize current protocols and recent modified viral systems to lead silencing of genes in different host species.

Autophagy (or self eating), a cellular recycling mechanism, became the center of interest and subject of intensive research in recent years. Development of new molecular techniques allowed the study of this biological phenomenon in various model organisms ranging from yeast to plants and mammals. Accumulating data provide evidence that autophagy is involved in a spectrum of biological mechanisms including plant growth, development, response to stress, and defense against pathogens. In this review, we briefly summarize general and plant-related autophagy studies, and explain techniques commonly used to study autophagy. We also try to extrapolate how autophagy techniques used in other organisms may be adapted to plant studies.

We have identified an accession (LA2093) within the tomato wild species Solanum pimpinellifolium with many desirable characteristics, including biotic and abiotic stress tolerance and good fruit quality. To utilize the full genetic potential of LA2093 in tomato breeding, we have developed a linkage map based on an F(2) population of a cross between LA2093 and a tomato breeding line, using 115 RFLP, 94 EST, and 41 RGA markers. The map spanned 1002.4 cM of the 12 tomato chromosomes with an average marker distance of 4.0 cM. The length of the map and linear order of the markers were in good agreement with the published maps of tomato. The ESTs were chosen based on their sequence similarities with known resistance or defense-response genes, signal-transduction factors, transcriptional regulators, and genes encoding pathogenesis-related proteins. Locations of several ESTs and RGAs coincided with locations of several known tomato resistance genes and quantitative resistance loci (QRLs), suggesting that candidate-gene approach may be effective in identifying and mapping new R genes. This map will be useful for marker-assisted exploitation of desirable traits in LA2093 and other S. pimpinellifolium accessions, and possibly for utilization of genetic variation within S. lycopersicum.

While it is possible to interpret microarray experiments a single gene at a time, most studies generate long lists of differentially expressed genes whose interpretation requires the integration of prior biological knowledge. This prior knowledge is stored in various public and private databases and covers several aspects of gene function and biological information. In this review, we will describe the tools and places where to find prior accurate biological information and how to process and incorporate them to interpret microarray data analyses. Here, we highlight selected tools and resources for gene class level ontology analysis (Section 2), gene coexpression analysis (Section 3), gene network analysis (Section 4), biological pathway analysis (Section 5), analysis of transcriptional regulation (Section 6), and omics data integration (Section 7). The overall goal of this review is to provide researchers with tools and information to facilitate the interpretation of microarray data.

The Dendrome Project and associated TreeGenes database serve the forest genetics research community through a curated and integrated web-based relational database. The research community is composed of approximately 2 000 members representing over 730 organizations worldwide. The database itself is composed of a wide range of genetic data from many forest trees with focused efforts on commercially important members of the Pinaceae family. The primary data types curated include species, publications, tree and DNA extraction information, genetic maps, molecular markers, ESTs, genotypic, and phenotypic data. There are currently ten main search modules or user access points within this PostgreSQL database. These access points allow users to navigate logically through the related data types. The goals of the Dendrome Project are to (1) provide a comprehensive resource for forest tree genomics data to facilitate gene discovery in related species, (2) develop interfaces that encourage the submission and integration of all genomic data, and to (3) centralize and distribute existing and novel online tools for the research community that both support and ease analysis. Recent developments have focused on increasing data content, functional annotations, data retrieval, and visualization tools. TreeGenes was developed to provide a centralized web resource with analysis and visualization tools to support data storage and exchange.

Control-treatment design is widely used in microarray gene expression experiments. The purpose of such a design is to detect genes that express differentially between the control and the treatment. Many statistical procedures have been developed to detect differentially expressed genes, but all have pros and cons and room is still open for improvement. In this study, we propose a Bayesian mixture model approach to classifying genes into one of three clusters, corresponding to clusters of downregulated, neutral, and upregulated genes, respectively. The Bayesian method is implemented via the Markov chain Monte Carlo (MCMC) algorithm. The cluster means of down- and upregulated genes are sampled from truncated normal distributions whereas the cluster mean of the neutral genes is set to zero. Using simulated data as well as data from a real microarray experiment, we demonstrate that the new method outperforms all methods commonly used in differential expression analysis.