Jasmonates and Histone deacetylase 6 activate Arabidopsis genome-wide histone acetylation and methylation during the early acute stress response.

BACKGROUND: Jasmonates (JAs) mediate trade-off between responses to both biotic and abiotic stress and growth in plants. The Arabidopsis thaliana HISTONE DEACETYLASE 6 is part of the CORONATINE INSENSITIVE1 receptor complex, co-repressing the HDA6/COI1-dependent acetic acid-JA pathway that confers plant drought tolerance. The decrease in HDA6 binding to target DNA mirrors histone H4 acetylation (H4Ac) changes during JA-mediated drought response, and mutations in HDA6 also cause depletion in the constitutive repressive marker H3 lysine 27 trimethylation (H3K27me3). However, the genome-wide effect of HDA6 on H4Ac and much of the impact of JAs on histone modifications and chromatin remodelling remain elusive. RESULTS: We performed high-throughput ChIP-Seq on the HDA6 mutant, axe1-5, and wild-type plants with or without methyl jasmonate (MeJA) treatment to assess changes in active H4ac and repressive H3K27me3 histone markers. Transcriptional regulation was investigated in parallel by microarray analysis in the same conditions. MeJA- and HDA6-dependent histone modifications on genes for specialized metabolism; linolenic acid and phenylpropanoid pathways; and abiotic and biotic stress responses were identified. H4ac and H3K27me3 enrichment also differentially affects JAs and HDA6-mediated genome integrity and gene regulatory networks, substantiating the role of HDA6 interacting with specific families of transposable elements in planta and highlighting further specificity of action as well as novel targets of HDA6 in the context of JA signalling for abiotic and biotic stress responses. CONCLUSIONS: The findings demonstrate functional overlap for MeJA and HDA6 in tuning plant developmental plasticity and response to stress at the histone modification level. MeJA and HDA6, nonetheless, maintain distinct activities on histone modifications to modulate genetic variability and to allow adaptation to environmental challenges.

A large-scale evaluation of computational protein function prediction.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.

BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Computational selection of transcriptomics experiments improves Guilt-by-Association analyses.

The Guilt-by-Association (GBA) principle, according to which genes with similar expression profiles are functionally associated, is widely applied for functional analyses using large heterogeneous collections of transcriptomics data. However, the use of such large collections could hamper GBA functional analysis for genes whose expression is condition specific. In these cases a smaller set of condition related experiments should instead be used, but identifying such functionally relevant experiments from large collections based on literature knowledge alone is an impractical task. We begin this paper by analyzing, both from a mathematical and a biological point of view, why only condition specific experiments should be used in GBA functional analysis. We are able to show that this phenomenon is independent of the functional categorization scheme and of the organisms being analyzed. We then present a semi-supervised algorithm that can select functionally relevant experiments from large collections of transcriptomics experiments. Our algorithm is able to select experiments relevant to a given GO term, MIPS FunCat term or even KEGG pathways. We extensively test our algorithm on large dataset collections for yeast and Arabidopsis. We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA-based pathway reconstruction. Importantly, the set of experiments selected by the algorithm reflects the existing literature knowledge about the experiments. [A MATLAB implementation of the algorithm and all the data used in this paper can be downloaded from the paper website: http://www.paccanarolab.org/papers/CorrGene/].