Unique genes in plants: specificities and conserved features throughout evolution

<p>Abstract</p> <p>Background</p> <p>Plant genomes contain a high proportion of duplicated genes as a result of numerous whole, segmental and local duplications. These duplications lead up to the formation of gene families, which are the usual material for many evolutionary studies. However, all characterized genomes include single-copy (unique) genes that have not received much attention. Unlike gene duplication, gene loss is not an unspecific mechanism but is rather influenced by a functional selection. In this context, we have established and used stringent criteria in order to identify suitable sets of unique genes present in plant proteomes. Comparisons of unique genes in the green phylum were used to characterize the gene and protein features exhibited by both conserved and species-specific unique genes.</p> <p>Results</p> <p>We identified the unique genes within both <it>A. thaliana </it>and <it>O. sativa </it>genomes and classified them according to the number of homologs in the alternative species: none (U{1:0}), one (U{1:1}) or several (U{1:m}). Regardless of the species, all the genes in these groups present some conserved characteristics, such as small average protein size and abnormal intron number. In order to understand the origin and function of unique genes, we further characterized the U{1:1} gene pairs. The possible involvement of sequence convergence in the creation of U{1:1} pairs was discarded due to the frequent conservation of intron positions. Furthermore, an orthology relationship between the two members of each U{1:1} pair was strongly supported by a high conservation in the protein sizes and transcription levels. Within the promoter of the unique conserved genes, we found a number of TATA and TELO boxes that specifically differed from their mean number in the whole genome. Many unique genes have been conserved as unique through evolution from the green alga <it>Ostreococcus lucimarinus </it>to higher plants. Plant unique genes may also have homologs in bacteria and we showed a link between the targeting towards plastids of proteins encoded by plant nuclear unique genes and their homology with a bacterial protein.</p> <p>Conclusion</p> <p>Many of the <it>A. thaliana </it>and <it>O. sativa </it>unique genes are conserved in plants for which the ancestor diverged at least 725 million years ago (MYA). Half of these genes are also present in other eukaryotic and/or prokaryotic species. Thus, our results indicate that (i) a strong negative selection pressure has conserved a number of genes as unique in genomes throughout evolution, (ii) most unique genes are subjected to a low divergence rate, (iii) they have some features observed in housekeeping genes but for most of them there is no functional annotation and (iv) they may have an ancient origin involving a possible gene transfer from ancestral chloroplasts or bacteria to the plant nucleus.</p

Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome

Tiling arrays make possible a large scale exploration of the genome thanks to probes which cover the whole genome with very high density until 2 000 000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this work we propose to consider simultaneously both questions as an unsupervised classification problem by modeling the joint distribution of the two conditions. In contrast to previous methods, we account for all available information on the probes as well as biological knowledge like annotation and spatial dependence between probes. Since probes are not biologically relevant units we propose a classification rule for non-connected regions covered by several probes. Applications to transcriptomic and ChIP-chip data of Arabidopsis thaliana obtained with a NimbleGen tiling array highlight the importance of a precise modeling and the region classification

Exploration of plant genomes in the FLAGdb++ environment

Background : In the contexts of genomics, post-genomics and systems biology approaches, data integration presents a major concern. Databases provide crucial solutions: they store, organize and allow information to be queried, they enhance the visibility of newly produced data by comparing them with previously published results, and facilitate the exploration and development of both existing hypotheses and new ideas. Results : The FLAGdb++ information system was developed with the aim of using whole plant genomes as physical references in order to gather and merge available genomic data from in silico or experimental approaches. Available through a JAVA application, original interfaces and tools assist the functional study of plant genes by considering them in their specific context: chromosome, gene family, orthology group, co-expression cluster and functional network. FLAGdb++ is mainly dedicated to the exploration of large gene groups in order to decipher functional connections, to highlight shared or specific structural or functional features, and to facilitate translational tasks between plant species (Arabidopsis thaliana, Oryza sativa, Populus trichocarpa and Vitis vinifera). Conclusion : Combining original data with the output of experts and graphical displays that differ from classical plant genome browsers, FLAGdb++ presents a powerful complementary tool for exploring plant genomes and exploiting structural and functional resources, without the need for computer programming knowledge. First launched in 2002, a 15th version of FLAGdb++ is now available and comprises four model plant genomes and over eight million genomic features

CATMA: a complete Arabidopsis GST database

The Complete Arabidopsis Transcriptome Micro Array (CATMA) database contains gene sequence tag (GST) and gene model sequences for over 70% of the predicted genes in the Arabidopsis thaliana genome as well as primer sequences for GST amplification and a wide range of supplementary information. All CATMA GST sequences are specific to the gene for which they were designed, and all gene models were predicted from a complete reannotation of the genome using uniform parameters. The database is searchable by sequence name, sequence homology or direct SQL query, and is available through the CATMA website at http://www.catma.or

Analysis of CATMA transcriptome data identifies hundreds of novel functional genes and improves gene models in the Arabidopsis genome

<p>Abstract</p> <p>Background</p> <p>Since the finishing of the sequencing of the <it>Arabidopsis thaliana </it>genome, the Arabidopsis community and the annotator centers have been working on the improvement of gene annotation at the structural and functional levels. In this context, we have used the large CATMA resource on the Arabidopsis transcriptome to search for genes missed by different annotation processes. Probes on the CATMA microarrays are specific gene sequence tags (GSTs) based on the CDS models predicted by the Eugene software. Among the 24 576 CATMA v2 GSTs, 677 are in regions considered as intergenic by the TAIR annotation. We analyzed the cognate transcriptome data in the CATMA resource and carried out data-mining to characterize novel genes and improve gene models.</p> <p>Results</p> <p>The statistical analysis of the results of more than 500 hybridized samples distributed among 12 organs provides an experimental validation for 465 novel genes. The hybridization evidence was confirmed by RT-PCR approaches for 88% of the 465 novel genes. Comparisons with the current annotation show that these novel genes often encode small proteins, with an average size of 137 aa. Our approach has also led to the improvement of pre-existing gene models through both the extension of 16 CDS and the identification of 13 gene models erroneously constituted of two merged CDS.</p> <p>Conclusion</p> <p>This work is a noticeable step forward in the improvement of the Arabidopsis genome annotation. We increased the number of Arabidopsis validated genes by 465 novel transcribed genes to which we associated several functional annotations such as expression profiles, sequence conservation in plants, cognate transcripts and protein motifs.</p

Functional analysis of Arabidopsis immune-related MAPKs uncovers a role for MPK3 as negative regulator of inducible defences

Background : Mitogen-activated protein kinases (MAPKs) are key regulators of immune responses in animals and plants. In Arabidopsis, perception of microbe-associated molecular patterns (MAMPs) activates the MAPKs MPK3, MPK4 and MPK6. Increasing information depicts the molecular events activated by MAMPs in plants, but the specific and cooperative contributions of the MAPKs in these signalling events are largely unclear.[br/] Results: In this work, we analyse the behaviour of MPK3, MPK4 and MPK6 mutants in early and late immune responses triggered by the MAMP flg22 from bacterial flagellin. A genome-wide transcriptome analysis reveals that 36% of the flg22-upregulated genes and 68% of the flg22-downregulated genes are affected in at least one MAPK mutant. So far MPK4 was considered as a negative regulator of immunity, whereas MPK3 and MPK6 were believed to play partially redundant positive functions in defence.[br/] Our work reveals that MPK4 is required for the regulation of approximately 50% of flg22-induced genes and we identify a negative role for MPK3 in regulating defence gene expression, flg22-induced salicylic acid accumulation and disease resistance to Pseudomonas syringae. Among the MAPK-dependent genes, 27% of flg22-upregulated genes and 76% of flg22-downregulated genes require two or three MAPKs for their regulation. The flg22-induced MAPK activities are differentially regulated in MPK3 and MPK6 mutants, both in amplitude and duration, revealing a highly interdependent network.[br/] Conclusions : These data reveal a new set of distinct functions for MPK3, MPK4 and MPK6 and indicate that the plant immune signalling network is choreographed through the interplay of these three interwoven MAPK pathways

Widespread anti-sense transcription in apple is correlated with siRNA production and indicates a large potential for transcriptional and/or post-transcriptional control

Characterizing the transcriptome of eukaryotic organisms is essential for studying gene regulation and its impact on phenotype. The realization that anti-sense (AS) and noncoding RNA transcription is pervasive in many genomes has emphasized our limited understanding of gene transcription and post-transcriptional regulation. Numerous mechanisms including convergent transcription, anti-correlated expression of sense and AS transcripts, and RNAi remain ill-defined.Here, we have combined microarray analysis and high-throughput sequencing of small RNAs (sRNAs) to unravel the complexity of transcriptional and potential post-transcriptional regulation in eight organs of apple (Malus × domestica). The percentage of AS transcript expression is higher than that identified in annual plants such as rice and Arabidopsis thaliana. Furthermore, we show that a majority of AS transcripts are transcribed beyond 3′UTR regions, and may cover a significant portion of the predicted sense transcripts. Finally we demonstrate at a genome-wide scale that anti-sense transcript expression is correlated with the presence of both short (21–23 nt) and long (&gt; 30 nt) siRNAs, and that the sRNA coverage depth varies with the level of AS transcript expression. Our study provides a new insight on the functional role of anti-sense transcripts at the genome-wide level, and a new basis for the understanding of sRNA biogenesis in plants

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Pro

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot