Identification of transcription factor's targets using tissue-specific transcriptomic data in Arabidopsis thaliana

Transcriptional control of hydrogen peroxide homeostasis regulates ground tissue patterning in the Arabidopsis root

In multicellular organisms, including higher plants, asymmetric cell divisions (ACDs) play a crucial role in generating distinct cell types. The Arabidopsis root ground tissue initially has two layers: endodermis (inside) and cortex (outside). In the mature root, the endodermis undergoes additional ACDs to produce the endodermis itself and the middle cortex (MC), located between the endodermis and the pre-existing cortex. In the Arabidopsis root, gibberellic acid (GA) deficiency and hydrogen peroxide (H2O2) precociously induced more frequent ACDs in the endodermis for MC formation. Thus, these findings suggest that GA and H2O2 play roles in regulating the timing and extent of MC formation. However, details of the molecular interaction between GA signaling and H2O2 homeostasis remain elusive. In this study, we identified the PEROXIDASE 34 (PRX34) gene, which encodes a class III peroxidase, as a molecular link to elucidate the interconnected regulatory network involved in H2O2- and GA-mediated MC formation. Under normal conditions, prx34 showed a reduced frequency of MC formation, whereas the occurrence of MC in prx34 was restored to nearly WT levels in the presence of H2O2. Our results suggest that PRX34 plays a role in H2O2-mediated MC production. Furthermore, we provide evidence that SCARECROW-LIKE 3 (SCL3) regulates H2O2 homeostasis by controlling transcription of PRX34 during root ground tissue maturation. Taken together, our findings provide new insights into how H2O2 homeostasis is achieved by SCL3 to ensure correct radial tissue patterning in the Arabidopsis root.

Differences and commonalities of plant responses to single and combined stresses

In natural or agricultural environments, plants are constantly exposed to a wide range of biotic and abiotic stresses. Given the forecasted global climate changes, plants will cope with heat waves, drought periods and pathogens at the same time or consecutively. Heat and drought cause opposing physiological responses, while pathogens may or may not profit from climate changes depending on their lifestyle. Several studies have been conducted to find stress-specific signatures or stress-independent commonalities. Previously this has been done by comparing different single stress treatments. This approach has been proven difficult since most studies, comparing single and combined stress conditions, have come to the conclusion that each stress treatment results in specific transcriptional changes. Although transcriptional changes at the level of individual genes are highly variable and stress-specific, central metabolic and signaling responses seem to be common, often leading to an overall reduced plant growth. Understanding how specific transcriptional changes are linked to stress adaptations and identifying central hubs controlling this interaction will be the challenge for the coming years. In this review, we will summarize current knowledge on plant responses to different individual and combined stresses and try to find a common thread potentially underlying these responses. We will begin with a brief summary of known physiological, metabolic, transcriptional and hormonal responses to individual stresses, elucidate potential commonalities and conflicts and finally we will describe results obtained during combined stress experiments. Here we will concentrate on simultaneous application of stress conditions but we will also touch consequences of sequential stress treatments.

A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions

BACKGROUND

We address the problem of integratively analyzing multiple gene expression, microarray datasets in order to reconstruct gene-gene interaction networks. Integrating multiple datasets is generally believed to provide increased statistical power and to lead to a better characterization of the system under study. However, the presence of systematic variation across different studies makes network reverse-engineering tasks particularly challenging. We contrast two approaches that have been frequently used in the literature for addressing systematic biases: meta-analysis methods, which first calculate opportune statistics on single datasets and successively summarize them, and data-merging methods, which directly analyze the pooled data after removing eventual biases. This comparative evaluation is performed on both synthetic and real data, the latter consisting of two manually curated microarray compendia comprising several E. coli and Yeast studies, respectively. Furthermore, the reconstruction of the regulatory network of the transcription factor Ikaros in human Peripheral Blood Mononuclear Cells (PBMCs) is presented as a case-study.

RESULTS

The meta-analysis and data-merging methods included in our experimentations provided comparable performances on both synthetic and real data. Furthermore, both approaches outperformed (a) the naïve solution of merging data together ignoring possible biases, and (b) the results that are expected when only one dataset out of the available ones is analyzed in isolation. Using correlation statistics proved to be more effective than using p-values for correctly ranking candidate interactions. The results from the PBMC case-study indicate that the findings of the present study generalize to different types of network reconstruction algorithms.

CONCLUSIONS

Ignoring the systematic variations that differentiate heterogeneous studies can produce results that are statistically indistinguishable from random guessing. Meta-analysis and data merging methods have proved equally effective in addressing this issue, and thus researchers may safely select the approach that best suit their specific application.

Genetic Adaptation of Giant Lobelias (Lobelia aberdarica and Lobelia telekii) to Different Altitudes in East African Mountains

The giant lobelias in East African mountains are good models for studying molecular mechanisms of adaptation to different altitudes. In this study, we generated RNA-seq data of a middle-altitude species Lobelia aberdarica and a high-altitude species L. telekii, followed by selective pressure estimation of their orthologous genes. Our aim was to explore the important genes potentially involved in adaptation to different altitudes. About 9.3 Gb of clean nucleotides, 167,929-170,534 unigenes with total lengths of 159,762,099-171,138,936 bp for each of the two species were generated. OrthoMCL method identified 3,049 1:1 orthologous genes (each species was represented by one ortholog). Estimations of non-synonymous to synonymous rate were performed using an approximate method and a maximum likelihood method in PAML. Eighty-five orthologous genes were under positive selection. At least 8 of these genes are possibly involved in DNA repair, response to DNA damage and temperature stimulus, and regulation of gene expression, which hints on how giant lobelias adapt to high altitudinal environment that characterized by cold, low oxygen, and strong ultraviolet radiation. The negatively selected genes are over-represented in Gene Ontology terms of hydrolase, macromolecular complex assembly among others. This study sheds light on understanding the molecular mechanism of adaptation to different altitudes, and provides genomic resources for further studies of giant lobelias.

Constructing simple biological networks for understanding complex high-throughput data in plants

Technological advances in the last decade have enabled biologists to produce increasing amounts of information for the transcriptome, proteome, interactome, and other -omics data sets in many model organisms. A major challenge is integration and biological interpretation of these massive data sets in order to generate testable hypotheses about gene regulatory networks or molecular mechanisms that govern system behaviors. Constructing gene networks requires bioinformatics skills to adequately manage, integrate, analyze and productively use the data to generate biological insights. In this chapter, we provide detailed methods for users without prior knowledge of bioinformatics to construct gene networks and derive hypotheses that can be experimentally verified. Step-by-step instructions for acquiring, integrating, analyzing, and visualizing genome-wide data are provided for two widely used open source platforms, R and Cytoscape platforms. The examples provided are based on Arabidopsis data, but the protocols presented should be readily applicable to any organism for which similar data can be obtained.

Network types and their application in natural variation studies in plants

We are in the age of data-driven biology. Not even a decade after the invention of high-throughput sequencing technologies, there are methods that accurately monitor DNA polymorphisms, transcription profiles, methylation states, transcription factor binding sites, chromatin compactness, nucleosome positions, dynamic histone marks, and so on. We are starting to generate comparable amounts of protein or metabolite data. A key issue is how are we going to make sense of all this information. Network analysis is the most promising method to integrate, query and display large amounts of data for human interpretation. This review shortly summarizes the basic types of networks, their properties and limitations. In addition, I introduce the application of networks to the study of the molecular mechanisms behind natural phenotypic variation.

Transcriptomics and systems biology analysis in identification of specific pathways involved in cacao resistance and susceptibility to witches' broom disease

This study reports on expression analysis associated with molecular systems biology of cacao-Moniliophthora perniciosa interaction. Gene expression data were obtained for two cacao genotypes (TSH1188, resistant; Catongo, susceptible) challenged or not with the fungus M. perniciosa and collected at three time points through disease. Using expression analysis, we identified 154 and 227 genes that are differentially expressed in TSH1188 and Catongo, respectively. The expression of some of these genes was confirmed by RT-qPCR. Physical protein-protein interaction (PPPI) networks of Arabidopsis thaliana orthologous proteins corresponding to resistant and susceptible interactions were obtained followed by cluster and gene ontology analyses. The integrated analysis of gene expression and systems biology allowed designing a general scheme of major mechanisms associated with witches' broom disease resistance/susceptibility. In this sense, the TSH1188 cultivar shows strong production of ROS and elicitors at the beginning of the interaction with M. perniciosa followed by resistance signal propagation and ROS detoxification. On the other hand, the Catongo genotype displays defense mechanisms that include the synthesis of some defense molecules but without success in regards to elimination of the fungus. This phase is followed by the activation of protein metabolism which is achieved with the production of proteasome associated with autophagy as a precursor mechanism of PCD. This work also identifies candidate genes for further functional studies and for genetic mapping and marker assisted selection.

Comprehensive literature review and statistical considerations for microarray meta-analysis

With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

Prerequisites, performance and profits of transcriptional profiling the abiotic stress response

During the last decade, microarrays became a routine tool for the analysis of transcripts in the model plant Arabidopsis thaliana and the crop plant species rice, poplar or barley. The overwhelming amount of data generated by gene expression studies is a valuable resource for every scientist. Here, we summarize the most important findings about the abiotic stress responses in plants. Interestingly, conserved patterns of gene expression responses have been found that are common between different abiotic stresses or that are conserved between different plant species. However, the individual histories of each plant affect the inter-comparability between experiments already before the onset of the actual stress treatment. This review outlines multiple aspects of microarray technology and highlights some of the benefits, limitations and also pitfalls of the technique. This article is part of a Special Issue entitled: Plant gene regulation in response to abiotic stress.