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The circular RNA (circRNA) Cdr1as is conserved across mammals and highly expressed in neurons, where it directly interacts with microRNA miR-7. However, the biological function of this interaction is unknown. Here, using primary forebrain murine neurons, we demonstrate that stimulating neurons by sustained depolarization rapidly induced two-fold transcriptional up-regulation of Cdr1as and strong post-transcriptional stabilization of miR-7. Cdr1as loss caused doubling of glutamate release from stimulated synapses and increased frequency and duration of local neuronal bursts. Moreover, periodicity of neuronal networks was increased and synchronicity was impaired. Strikingly, these effects were reverted by sustained expression of miR-7 which also cleared Cdr1as molecules from neuronal projections. Consistently, without Cdr1as, transcriptomic changes caused by miR-7 overexpression were stronger (including miR-7-targets down-regulation) and enriched in secretion/synaptic plasticity pathways. Altogether, our results suggest that in forebrain neurons Cdr1as buffers miR-7 activity to control glutamatergic excitatory transmission and neuronal connectivity important for long-lasting synaptic adaptations.

In the past, reactive nitrogen species (RNS) were supposed to be stress-induced by-products of disturbed metabolism that cause oxidative damage to biomolecules. However, emerging evidence demonstrates a substantial role of RNS as endogenous signals in eukaryotes. In plants, S-nitrosoglutathione (GSNO) is the dominant RNS and serves as the •NO donor for S-nitrosation of diverse effector proteins. Remarkably, the endogenous GSNO level is tightly controlled by S-nitrosoglutathione reductase (GSNOR) that irreversibly inactivates the glutathione-bound NO to ammonium. Exogenous feeding of diverse RNS, including GSNO, affected chromatin accessibility and transcription of stress-related genes, but the triggering function of RNS on these regulatory processes remained elusive. Here, we show that GSNO reductase-deficient plants (gsnor1-3) accumulate S-adenosylmethionine (SAM), the principal methyl donor for methylation of DNA and histones. This SAM accumulation triggered a substantial increase in the methylation index (MI = [SAM]/[S-adenosylhomocysteine]), indicating the transmethylation activity and histone methylation status in higher eukaryotes. Indeed, a mass spectrometry-based global histone profiling approach demonstrated a significant global increase in H3K9me2, which was independently verified by immunological detection using a selective antibody. Since H3K9me2-modified regions tightly correlate with methylated DNA regions, we also determined the DNA methylation status of gsnor1-3 plants by whole-genome bisulfite sequencing. DNA methylation in the CG, CHG, and CHH contexts in gsnor1-3 was significantly enhanced compared to the wild type. We propose that GSNOR1 activity affects chromatin accessibility by controlling the transmethylation activity (MI) required for maintaining DNA methylation and the level of the repressive chromatin mark H3K9me2.

Nitric oxide (NO) is a signaling molecule with multiple regulatory functions in plant physiology and stress response. In addition to direct effects on transcriptional machinery, NO executes its signaling function via epigenetic mechanisms. We report that light intensity-dependent changes in NO correspond to changes in global histone acetylation (H3, H3K9, and H3K9/K14) in Arabidopsis (Arabidopsis thaliana) wild-type leaves, and that this relationship depends on S-nitrosoglutathione reductase and histone deacetylase 6 (HDA6). The activity of HDA6 was sensitive to NO, demonstrating that NO participates in regulation of histone acetylation. Chromatin immunoprecipitation sequencing and RNA-seq analyses revealed that NO participates in the metabolic switch from growth and development to stress response. This coordinating function of NO might be particularly important in plant ability to adapt to a changing environment, and is therefore a promising foundation for mitigating the negative effects of climate change on plant productivity.

Isoprene is emitted from the biosphere into the atmosphere, and may strengthen the defense mechanisms of plants against oxidative and thermal stress. Once in the atmosphere, isoprene is rapidly oxidized, either to isoprene-hydroxy-hydroperoxides (ISOPOOH) at low levels of nitrogen oxides, or to methyl vinyl ketone (MVK) and methacrolein at high levels. Here we combine uptake rates and deposition velocities that we obtained in laboratory experiments with observations in natural forests to show that 1,2-ISOPOOH deposits rapidly into poplar leaves. There, it is converted first to cytotoxic MVK and then most probably through alkenal/one oxidoreductase (AOR) to less toxic methyl ethyl ketone (MEK). This detoxification process is potentially significant globally because AOR enzymes are ubiquitous in terrestrial plants. Our simulations with a global chemistry-transport model suggest that around 6.5Tgyr(-1) of MEK are re-emitted to the atmosphere. This is the single largest MEK source presently known, and recycles 1.5% of the original isoprene flux. Eddy covariance flux measurements of isoprene and MEK over different forest ecosystems confirm that MEK emissions can reach 1-2% those of isoprene. We suggest that detoxification processes in plants are one of the most important sources of oxidized volatile organic compounds in the atmosphere. Isoprene oxidation products are deposited rapidly into poplar leaves, where they undergo detoxification, and up to 1.5% are reemitted to the atmosphere as methyl ethyl ketone, according to laboratory and field experiments and chemistry-transport model simulations.

Nitric oxide (NO) is a signaling molecule with multiple regulatory functions in plant physiology and stress response. Besides direct effects on the transcriptional machinery, NO can fulfill its signaling function via epigenetic mechanisms.We report that light intensity-dependent changes in NO correlate with changes in global histone acetylation (H3, H3K9 and H3K9/K14) in Arabidopsis thaliana wild-type leaves and that this correlation depends on S-nitrosoglutathione reductase and histone deacetylase 6. The activity of histone deacetylase 6 was sensitive to NO, which demonstrates that NO participates in regulation of histone acetylation. ChIP-seq and RNA-seq analyses revealed that NO is involved in the metabolic switch from growth and development to stress response. This coordinating function of NO might be of special importance in adaptation to a changing environment and could therefore be a promising starting point to mitigating the negative effects of climate change on plant productivity.

Throughout the temperate zones, plants face combined drought and heat spells in increasing frequency and intensity. Here, we compared periodic (intermittent, i.e., high-frequency) versus chronic (continuous, i.e., high-intensity) drought-heat stress scenarios in gray poplar (Populus x canescens) plants for phenotypic and transcriptomic effects during stress and after recovery. Photosynthetic productivity after stress recovery exceeded the performance of poplar trees without stress experience. We analyzed the molecular basis of this stress-related memory phenotype and investigated gene expression responses across five major tree compartments including organs and wood tissues. For each of these tissue samples, transcriptomic changes induced by the two stress scenarios were highly similar during the stress phase but strikingly divergent after recovery. Characteristic molecular response patterns were found across tissues but involved different genes in each tissue. Only a small fraction of genes showed similar stress and recovery expression profiles across all tissues, including type 2C protein phosphatases, the LATE EMBRYOGENESIS ABUNDANT PROTEIN4-5 genes, and homologs of the Arabidopsis (Arabidopsis thaliana) transcription factor HOMEOBOX7. Analysis of the predicted transcription factor regulatory networks for these genes suggested that a complex interplay of common and tissue-specific components contributes to the coordination of post-recovery responses to stress in woody plants.

Nitrogen dioxide (NO2) forms in plants under stress conditions, but little is known about its physiological functions. Here, we explored the physiological functions of NO2 in plant cells using short-term fumigation of Arabidopsis (Arabidopsis thaliana) for 1 h with 10 parts per million (ppm) NO2. Although leaf symptoms were absent, the expression of genes related to pathogen resistance was induced. Fumigated plants developed basal disease resistance, or pattern-triggered immunity (PTI), against the necrotrophic fungus Botrytis cinerea and the hemibiotrophic bacterium Pseudomonas syringae. Functional salicylic acid (SA) and jasmonic acid (JA) signaling pathways were both required for full expression of NO2-induced resistance against B. cinerea. An early peak of SA accumulation immediately after NO2 exposure was followed by transient accumulation of oxophytodienoic acid. Simultaneous NO2-induced expression of genes involved in jasmonate biosynthesis and jasmonate catabolism resulted in the complete suppression of JA and JA-isoleucine (JA-Ile) accumulation, which was accompanied by a rise in the levels of their catabolic intermediates 12-OH-JA, 12-OH-JA-Ile, and 12-COOH-JA-Ile. NO2-treated plants emitted the volatile monoterpene α-pinene and the sesquiterpene longifolene (syn. junipene), which could function in signaling or direct defense against pathogens. NO2-triggered B. cinerea resistance was dependent on enhanced early callose deposition and CYTOCHROME P450 79B2 (CYP79B2), CYP79B3, and PHYTOALEXIN DEFICIENT 3 (PAD3) gene functions but independent of camalexin, CYP81F2, and 4-OH-indol-3-ylmethylglucosinolate derivatives. In sum, exogenous NO2 triggers basal pathogen resistance, pointing to a possible role for endogenous NO2 in defense signaling. Additionally, the study revealed the involvement of jasmonate catabolism and volatiles in pathogen immunity.

Background Elevated temperature and reduced water availability are frequently linked abiotic stresses that may provoke distinct as well as interacting molecular responses. Based on non-targeted metabolomic and transcriptomic measurements from Arabidopsis rosettes, this study aims at a systematic elucidation of relevant components in different drought and heat scenarios as well as relationships between molecular players of stress response. Results In combined drought-heat stress, the majority of single stress responses are maintained. However, interaction effects between drought and heat can be discovered as well; these relate to protein folding, flavonoid biosynthesis and growth inhibition, which are enhanced, reduced or specifically induced in combined stress, respectively. Heat stress experiments with and without supplementation of air humidity for maintenance of vapor pressure deficit suggest that decreased relative air humidity due to elevated temperature is an important component of heat stress, specifically being responsible for hormone-related responses to water deprivation. Remarkably, this “dry air effect” is the primary trigger of the metabolomic response to heat. In contrast, the transcriptomic response has a substantial temperature component exceeding the dry air component and including up-regulation of many transcription factors and protein folding-related genes. Data level integration independent of prior knowledge on pathways and condition labels reveals shared drought and heat responses between transcriptome and metabolome, biomarker candidates and co-regulation between genes and metabolic compounds, suggesting novel players in abiotic stress response pathways. Conclusions Drought and heat stress interact both at transcript and at metabolite response level. A comprehensive, non-targeted view of this interaction as well as non-interacting processes is important to be taken into account when improving tolerance to abiotic stresses in breeding programs. Transcriptome and metabolome may respond with different extent to individual stress components. Their contrasting behavior in response to temperature stress highlights that the protein folding machinery effectively shields the metabolism from stress. Disentangling the complex relationships between transcriptome and metabolome in response to stress is an enormous challenge. As demonstrated by case studies with supporting evidence from additional data, the large dataset provided in this study may assist in determining linked genetic and metabolic features as candidates for future mechanistic analyses. Electronic supplementary material The online version of this article (doi:10.1186/s12870-017-1062-y) contains supplementary material, which is available to authorized users.

With data from recent large-scale drug sensitivity measurement campaigns, it is now possible to build and test models predicting responses for more than one hundred anticancer drugs against several hundreds of human cancer cell lines. Traditional quantitative structure-activity relationship (QSAR) approaches focus on small molecules in searching for their structural properties predictive of the biological activity in a single cell line or a single tissue type. We extend this line of research in two directions: (1) an integrative QSAR approach predicting the responses to new drugs for a panel of multiple known cancer cell lines simultaneously and (2) a personalized QSAR approach predicting the responses to new drugs for new cancer cell lines. To solve the modeling task, we apply a novel kernelized Bayesian matrix factorization method. For maximum applicability and predictive performance, the method optionally utilizes genomic features of cell lines and target information on drugs in addition to chemical drug descriptors. In a case study with 116 anticancer drugs and 650 cell lines, we demonstrate the usefulness of the method in several relevant prediction scenarios, differing in the amount of available information, and analyze the importance of various types of drug features for the response prediction. Furthermore, after predicting the missing values of the data set, a complete global map of drug response is explored to assess treatment potential and treatment range of therapeutically interesting anticancer drugs.

This study investigates the role of volatile organic compounds in systemic acquired resistance (SAR), a salicylic acid (SA)-associated, broad-spectrum immune response in systemic, healthy tissues of locally infected plants. Gas chromatography coupled to mass spectrometry analyses of SAR-related emissions of wild-type and non-SAR-signal-producing mutant plants associated SAR with monoterpene emissions. Headspace exposure of Arabidopsis thaliana to a mixture of the bicyclic monoterpenes α-pinene and β-pinene induced defense, accumulation of reactive oxygen species, and expression of SA- and SAR-related genes, including the SAR regulatory AZELAIC ACID INDUCED1 (AZI1) gene and three of its paralogs. Pinene-induced resistance was dependent on SA biosynthesis and signaling and on AZI1. A. thaliana geranylgeranyl reductase1 mutants with reduced monoterpene biosynthesis were SAR-defective but mounted normal local resistance and methyl salicylate-induced defense responses, suggesting that monoterpenes act in parallel with SA. The volatile emissions from SAR signal-emitting plants induced defense in neighboring plants and this was associated with the presence of α-pinene, β-pinene, and camphene in the emissions of the 'sender' plants. Our data suggest that monoterpenes, particularly pinenes, promote SAR, acting through ROS and AZI1, and likely function as infochemicals in plant-to-plant signaling, thus allowing defense signal propagation between neighboring plants.

This study aimed to understand the molecular mechanisms of nitrogen dioxide (NO2)-induced toxicity and cell death in plants. Exposure of Arabidopsis to high concentrations of NO2 induced cell death in a dose-dependent manner. No leaf symptoms were visible after fumigation for 1 h with 10 parts per million (ppm) NO2. However, 20 ppm NO2 caused necrotic lesion formation and 30 ppm NO2 complete leaf collapse, which had already started during the 1 h fumigation period. NO2 fumigation resulted in a massive accumulation of nitrite and in protein modifications by S-nitrosylation and tyrosine nitration. Nitric oxide (NO) at 30 ppm did not trigger leaf damage or any of the effects observed after NO2 fumigation. The onset of NO2-induced cell death correlated with NO and hydrogen peroxide (H2O2) signaling and a decrease in antioxidants. NO- and H2O2-accumulating mutants were more sensitive to NO2 than wild-type plants. Accordingly, experiments with specific scavengers confirmed that NO and H2O2 are essential promoters of NO2-induced cell death. Leaf injection of 100 mM nitrite caused an increase in S-nitrosylation, NO, H2O2, and cell death suggesting that nitrite functioned as a mediator of NO2-induced effects. A targeted screening of phytohormone mutants revealed a protective role of salicylic acid (SA) signaling in response to NO2. It was also shown that phytohormones were modulators rather than inducers of NO2-induced cell death. The established experimental set-up is a suitable system to investigate NO2 and cell death signaling in large-scale mutant screens.

In the biological domain, it is more and more common to apply several high-throughput technologies to the same set of samples. We propose a Covariate-Related Structure Extraction approach (CRSE) that explores relationships between different types of high-dimensional molecular data (views) in the context of sample covariate information from the experimental design, for example class membership. Real-world data analysis with an initial pipeline implementation of CRSE shows that the proposed approach successfully captures cross-view structures underlying multiple biologically relevant classification schemes, allowing to predict class labels to unseen examples from either view or across views.

Motivation: Large public repositories of gene expression measurements offer the opportunity to position a new experiment into the context of earlier studies. While previous methods rely on experimental annotation or global similarity of expression profiles across genes or gene sets, we compare experiments by measuring similarity based on an unsupervised, data-driven regulatory model around pre-specified genes of interest. Our experiment retrieval approach is novel in two conceptual respects: (i) targetable focus and interpretability: the analysis is targeted at regulatory relationships of genes that are relevant to the analyst or come from prior knowledge; (ii) regulatory model-based similarity measure: related experiments are retrieved based on the strength of inferred regulatory links between genes. Results: We learn a model for the regulation of specific genes from a data repository and exploit it to construct a similarity metric for an information retrieval task. We use the Fisher kernel, a rigorous similarity measure that typically has been applied to use generative models in discriminative classifiers. Results on human and plant microarray collections indicate that our method is able to substantially improve the retrieval of related experiments against standard methods. Furthermore, it allows the user to interpret biological conditions in terms of changes in link activity patterns. Our study of the osmotic stress network for Arabidopsis thaliana shows that the method successfully identifies relevant relationships around given key genes. Availability: The code (R) is available at http://research.ics.tkk.fi/mi/software.shtml. Contact: elisabeth.georgii@aalto.fi; jarkko.salojarvi@helsinki.fi; samuel.kaski@hiit.fi Supplementary Information: Supplementary data are available at Bioinformatics online.

The analysis of n-ary relations receives attention in many different fields, for instance biology, web mining, and social studies. In the basic setting, there are n sets of instances, and each observation associates n instances, one from each set. A common approach to explore these n-way data is the search for n-set patterns, the n-way equivalent of itemsets. More precisely, an n-set pattern consists of specific subsets of the n instance sets such that all possible associations between the corresponding instances are observed in the data. In contrast, traditional itemset mining approaches consider only two-way data, namely items versus transactions. The n-set patterns provide a higher-level view of the data, revealing associative relationships between groups of instances. Here, we generalize this approach in two respects. First, we tolerate missing observations to a certain degree, that means we are also interested in n-sets where most (although not all) of the possible associations have been recorded in the data. Second, we take association weights into account. In fact, we propose a method to enumerate all n-sets that satisfy a minimum threshold with respect to the average association weight. Technically, we solve the enumeration task using a reverse search strategy, which allows for effective pruning of the search space. In addition, our algorithm provides a ranking of the solutions and can consider further constraints. We show experimental results on artificial and real-world datasets from different domains.

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.

Analysis of large networks is a central topic in various research fields including biology, sociology, and web mining. Detection of dense modules (a.k.a. clusters) is an important step to analyze the networks. Though numerous methods have been proposed to this aim, they often lack mathematical rigorousness. Namely, there is no guarantee that all dense modules are detected. Here, we present a novel reverse-search-based method for enumerating all dense modules. Furthermore, constraints from additional data sources such as gene expression profiles or customer profiles can be integrated, so that we can systematically detect dense modules with interesting profiles. We report successful applications in human protein interaction network analyses.

Summary: The DICS database is a dynamic web repository of computationally predicted functional modules from the human protein–protein interaction network. It provides references to the CORUM, DrugBank, KEGG and Reactome pathway databases. DICS can be accessed for retrieving sets of overlapping modules and protein complexes that are significantly enriched in a gene list, thereby providing valuable information about the functional context. Availability: Supplementary information on datasets and methods is available on the web server http://mips.gsf.de/proj/dics Contact: sabine.dietmann@googlemail.com

We consider the problem of simultaneously clustering genes and conditions of a gene expression data matrix. A bicluster is defined as a subset of genes that show similar behavior within a subset of conditions. Finding biclusters can be useful for revealing groups of genes involved in the same molecular process as well as groups of conditions where this process takes place. Previous work either deals with local, bicluster-based criteria or assumes a very specific structure of the data matrix (e.g. checkerboard or block-diagonal) (Ryan et al., 2005). In contrast, our goal is to find a set of flexibly arranged biclusters which is optimal in regard to a global objective function. As this is a NP-hard combinatorial problem, we describe several techniques to obtain approximate solutions. We benchmarked our approach successfully on the Alizadeh B-cell lymphoma data set (Alizadeh et al., 2000)

Motivation: We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities. Results: We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data. Availability: The code is available from the authors on request. Contact: [email protected]