Compact tomato seedlings and plants upon overexpression of a tomato chromatin remodelling ATPase gene

Control of plant growth is an important aspect of crop productivity and yield in agriculture. Overexpression of the AtCHR12/23 genes in Arabidopsis thaliana reduced growth habit without other morphological changes. These two genes encode Snf2 chromatin remodelling ATPases. Here, we translate this approach to the horticultural crop tomato (Solanum lycopersicum). We identified and cloned the single tomato ortholog of the two Arabidopsis Snf2 genes, designated SlCHR1. Transgenic tomato plants (cv. Micro-Tom) that constitutively overexpress the coding sequence of SlCHR1 show reduced growth in all developmental stages of tomato. This confirms that SlCHR1 combines the functions of both Arabidopsis genes in tomato. Compared to the wild type, the transgenic seedlings of tomato have significantly shorter roots, hypocotyls and reduced cotyledon size. Transgenic plants have a much more compact growth habit with markedly reduced plant height, severely compacted reproductive structures with smaller flowers and smaller fruits. The results indicate that either GMO-based or non-GMO-based approaches to modulate the expression of chromatin remodelling ATPase genes could develop into methods to control plant growth, for example to replace the use of chemical growth retardants. This approach is likely to be applicable and attractive for any crop for which growth habit reduction has added value

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

Unrooted phylogenetic tree of all candidate Snf2 genes in plant genomes.

<p>The full tree from which this subset was extracted is presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081147#pone.0081147.s003" target="_blank">Figure S3</a>. The subfamily branches were collapsed to a single node that represents the first split that is part of the subfamily branch. Confidence values (50-100) are indicated at the relevant splits of the branches. The tree is based on 100 bootstrap replicates. The leaf tagged ‘not classified’ indicates candidate Snf2 members that are not part of a known subfamily, including <i>Cre09.g390000.t1.1</i> (<i>Chlamydomonas reinhardtii</i>) and members of sequence databases. </p

RT-PCR expression analysis of DRD1 and Snf2 subfamilies of tomato Snf2 ATPase genes.

<p>The tissue used is indicated on the x-axis. The individual genes are indicated of the right, the branches identified on the left. The expression of the actin gene (25 cycles) was used as control (lower panel). The number of PCR cycles used for the analysis of the individual gene was adjusted to generate a detectable amount of PCR product. For most of genes, 35 cycles were used. Genes marked with superscript a (^a) were amplified with 29 cycles. For the actin gene 25 cycles were used.</p

Distribution of Snf2 family members in plant genomes.

<p>Groupings and subfamilies on the left are named according to the Arabidopsis subfamily classification [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081147#B3" target="_blank">3</a>]. Species names on the top are organized on the basis of their phylogenetic relationship according to Phytozome [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081147#B14" target="_blank">14</a>]. Snf2 candidate member <i>Cre09.g390000.t1.1</i> (<i>Chlamydomonas reinhardtii</i>) could not be assigned to any subfamily and was excluded. Subfamily counts are shaded according to the deviation from the subfamily mean in standard deviations (sd). The total count is given on the top right cell. Mean and standard deviations per subfamily are indicated in the last column.</p

Biological process annotation of proteins across the plant kingdom

Accurate annotation of protein function is key to understanding life at the molecular level, but automated annotation of functions is challenging. We here demonstrate the combination of a method for protein function annotation that uses network information to predict the biological processes a protein is involved in, with a sequence-based prediction method. The combined function prediction is based on co-expression networks and combines the network-based prediction method BMRF with the sequence-based prediction method Argot2. The combination shows significantly improved performance compared to each of the methods separately, as well as compared to Blast2GO. The approach was applied to predict biological processes for the proteomes of rice, barrel clover, poplar, soybean and tomato. The novel function predictions are available at www.ab.wur.nl/bmrf. Analysis of the relationships between sequence similarity and predicted function similarity identifies numerous cases of divergence of biological processes in which proteins are involved, in spite of sequence similarity. This indicates that the integration of network-based and sequence-based function prediction is helpful towards the analysis of evolutionary relationships. Examples of potential divergence are identified for various biological processes, notably for processes related to cell development, regulation, and response to chemical stimulus. Such divergence in biological process annotation for proteins with similar sequences should be taken into account when analyzing plant gene and genome evolution. DATA: All gene functions predictions are available online (http://www.ab.wur.nl/bmrf/). The online resource can be queried for predictions of proteins or for Gene Ontology terms of interest, and the results can be downloaded in bulk. Queries can be based on protein identifiers, biological process Gene Ontology identifiers, or text descriptors of biological processes

Generation and analysis of expressed sequence tags in the extreme large genomes <it>Lilium</it> and <it>Tulipa</it>

Abstract Background Bulbous flowers such as lily and tulip (Liliaceae family) are monocot perennial herbs that are economically very important ornamental plants worldwide. However, there are hardly any genetic studies performed and genomic resources are lacking. To build genomic resources and develop tools to speed up the breeding in both crops, next generation sequencing was implemented. We sequenced and assembled transcriptomes of four lily and five tulip genotypes using 454 pyro-sequencing technology. Results Successfully, we developed the first set of 81,791 contigs with an average length of 514 bp for tulip, and enriched the very limited number of 3,329 available ESTs (Expressed Sequence Tags) for lily with 52,172 contigs with an average length of 555 bp. The contigs together with singletons covered on average 37% of lily and 39% of tulip estimated transcriptome. Mining lily and tulip sequence data for SSRs (Simple Sequence Repeats) showed that di-nucleotide repeats were twice more abundant in UTRs (UnTranslated Regions) compared to coding regions, while tri-nucleotide repeats were equally spread over coding and UTR regions. Two sets of single nucleotide polymorphism (SNP) markers suitable for high throughput genotyping were developed. In the first set, no SNPs flanking the target SNP (50 bp on either side) were allowed. In the second set, one SNP in the flanking regions was allowed, which resulted in a 2 to 3 fold increase in SNP marker numbers compared with the first set. Orthologous groups between the two flower bulbs: lily and tulip (12,017 groups) and among the three monocot species: lily, tulip, and rice (6,900 groups) were determined using OrthoMCL. Orthologous groups were screened for common SNP markers and EST-SSRs to study synteny between lily and tulip, which resulted in 113 common SNP markers and 292 common EST-SSR. Lily and tulip contigs generated were annotated and described according to Gene Ontology terminology. Conclusions Two transcriptome sets were built that are valuable resources for marker development, comparative genomic studies and candidate gene approaches. Next generation sequencing of leaf transcriptome is very effective; however, deeper sequencing and using more tissues and stages is advisable for extended comparative studies.</p