A transcriptional dynamic network during Arabidopsis thaliana pollen development

Calcium regulation of the Arabidopsis Na+/K+ transporter HKT1;1 improves seed germination under salt stress

Calcium is known to improve seed-germination rates under salt stress. We investigated the involvement of calcium ions (Ca2+) in regulating HIGH-AFFINITY K+ TRANSPORTER 1 (HKT1; 1), which encodes a Na+/K+ transporter, and its post-translational regulator TYPE 2C PROTEIN PHOSPHATASE 49 (PP2C49), in germinating Arabidopsis (Arabidopsis thaliana) seedlings. Germination rates of hkt1 mutant seeds under salt stress remained unchanged by CaCl2 treatment in wild-type Arabidopsis, whereas pp2c49 mutant seeds displayed improved salt-stress tolerance in the absence of CaCl2 supplementation. Analysis of HKT1;1 and PP2C49 promoter activity revealed that CaCl2 treatment results in radicle-focused expression of HKT1;1 and reduction of the native radicle-exclusive expression of PP2C49. Ion-content analysis indicated that CaCl2 treatment improves K+ retention in germinating wild-type seedlings under salt stress, but not in hkt1 seedlings. Transgenic seedlings designed to exclusively express HKT1;1 in the radicle during germination displayed higher germination rates under salt stress than the wild type in the absence of CaCl2 treatment. Transcriptome analysis of germinating seedlings treated with CaCl2, NaCl, or both revealed 118 upregulated and 94 downregulated genes as responsive to the combined treatment. Bioinformatics analysis of the upstream sequences of CaCl2-NaCl-treatment-responsive upregulated genes revealed the abscisic acid response element CACGTGTC, a potential CaM-binding transcription activator-binding motif, as most prominent. Our findings suggest a key role for Ca2+ in mediating salt-stress responses during germination by regulating genes that function to maintain Na+ and K+ homeostasis, which is vital for seed germination under salt stress.

NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks

The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is typically incomplete, therefore, integrating it with gene expression to infer GRNs remains difficult. To address this challenge, we introduce NetREX-CF-Regulatory Network Reconstruction using EXpression and Collaborative Filtering-a GRN reconstruction approach that brings together Collaborative Filtering to address the incompleteness of the prior knowledge and a biologically justified model of gene expression (sparse Network Component Analysis based model). We validated the NetREX-CF using Yeast data and then used it to construct the GRN for Drosophila Schneider 2 (S2) cells. To corroborate the GRN, we performed a large-scale RNA-Seq analysis followed by a high-throughput RNAi treatment against all 465 expressed TFs in the cell line. Our knockdown result has not only extensively validated the GRN we built, but also provides a benchmark that our community can use for evaluating GRNs. Finally, we demonstrate that NetREX-CF can infer GRNs using single-cell RNA-Seq, and outperforms other methods, by using previously published human data.

AtZAT4, a C2H2-Type Zinc Finger Transcription Factor from Arabidopsis thaliana, Is Involved in Pollen and Seed Development

Pollen plays an essential role in plant fertility by delivering the male gametes to the embryo sac before double fertilization. In several plant species, including Arabidopsis, C2H2-type zinc-finger transcription factors (TFs) have been involved in different stages of pollen development and maturation. ZINC FINGER of Arabidopsis thaliana 4 (AtZAT4) is homologous to such TFs and subcellular localization analysis has revealed that AtZAT4 is located in the nucleus. Moreover, analysis of AtZAT4 expression revealed strong levels of it in flowers and siliques, suggesting a role of the encoded protein in the regulation of genes that are associated with reproductive development. We characterized a T-DNA insertional heterozygous mutant Atzat4 (+/−). The relative gene expression analysis of Atzat4 (+/−) showed significant transcript reductions in flowers and siliques. Furthermore, the Atzat4 (+/−) phenotypic characterization revealed defects in the male germline, showing a reduction in pollen tube germination and elongation. Atzat4 (+/−) presented reduced fertility, characterized by a smaller silique size compared to the wild type (WT), and a lower number of seeds per silique. Additionally, seeds displayed lower viability and germination. Altogether, our data suggest a role for AtZAT4 in fertilization and seed viability, through the regulation of gene expression associated with reproductive development.

Characterization of germline development and identification of genes associated with germline specification in pineapple

Understanding germline specification in plants could be advantageous for agricultural applications. In recent decades, substantial efforts have been made to understand germline specification in several plant species, including Arabidopsis, rice, and maize. However, our knowledge of germline specification in many agronomically important plant species remains obscure. Here, we characterized the female germline specification and subsequent female gametophyte development in pineapple using callose staining, cytological, and whole-mount immunolocalization analyses. We also determined the male germline specification and gametophyte developmental timeline and observed male meiotic behavior using chromosome spreading assays. Furthermore, we identified 229 genes that are preferentially expressed at the megaspore mother cell (MMC) stage during ovule development and 478 genes that are preferentially expressed at the pollen mother cell (PMC) stage of anther development using comparative transcriptomic analysis. The biological functions, associated regulatory pathways and expression patterns of these genes were also analyzed. Our study provides a convenient cytological reference for exploring pineapple germline development and a molecular basis for the future functional analysis of germline specification in related plant species.

Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications

Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.

Computational dynamic approaches for temporal omics data with applications to systems medicine

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Reconstruction of temporal activity of microRNAs from gene expression data in breast cancer cell line

Background

MicroRNAs (miRNAs) are small non-coding RNAs that regulate genes at the post-transcriptional level in spatiotemporal manner. Several miRNAs are identified as prognostic and diagnostic markers in many human cancers. Estimation of the temporal activities of the miRNAs is an important step in the way to understand the complex interactions of these important regulatory elements with transcription factors (TFs) and target genes (TGs). However, current research on miRNA activities excludes network dynamics from the studies, disregarding the important element of time in the regulatory network analysis.

Results

In the current study, we combined experimentally verified miRNA-TG interactions with breast cancer microarray TG expression data to identify key miRNAs and compute their temporal activity using network component analysis (NCA). The computed activities showed that miRNAs were regulated in a time dependent manner. Our results allowed constructing a synergistic network of miRNAs using the computed miRNA activities and their shared regulation of TGs. We further extended this network by incorporating miRNA-TG, miRNA-TF, TF-miRNA and TF-TG regulations in the context of breast cancer. Our integrated network identified several miRNAs known to be involved in breast cancer regulation and revealed several novel miRNAs. Our further analysis detected substantial involvement of the miRNAs miR-324, miR-93, miR-615 and miR-1 in breast cancer, which was not known previously. Next, combining our integrated networks with functional annotation of differentially expressed genes resulted in new sub-networks. These sub-networks allowed us to identify the key miRNAs and their interactions with TFs and TGs of several biological processes involved in breast cancer. The identified markers are validated for their potential as prognostic markers for breast cancer through survival analysis.

Conclusions

Our dynamical analysis of the miRNA interactions greatly helps to discover new network based markers, and is highly applicable (but not limited) to cancer research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2260-3) contains supplementary material, which is available to authorized users.

Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses

Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.

A decade of pollen transcriptomics

Overview of pollen transcriptome studies. Pollen development is driven by gene expression, and knowledge of the molecular events underlying this process has undergone a quantum leap in the last decade through studies of the transcriptome. Here, we outline historical evidence for male haploid gene expression and review the wealth of pollen transcriptome data now available. Knowledge of the transcriptional capacity of pollen has progressed from genetic studies to the direct analysis of RNA and from gene-by-gene studies to analyses on a genomic scale. Microarray and/or RNA-seq data can now be accessed for all phases and cell types of developing pollen encompassing 10 different angiosperms. These growing resources have accelerated research and will undoubtedly inspire new directions and the application of system-based research into the mechanisms that govern the development, function and evolution of angiosperm pollen.

Phosphorylation of a WRKY transcription factor by MAPKs is required for pollen development and function in Arabidopsis

Plant male gametogenesis involves complex and dynamic changes in gene expression. At present, little is known about the transcription factors involved in this process and how their activities are regulated. Here, we show that a pollen-specific transcription factor, WRKY34, and its close homolog, WRKY2, are required for male gametogenesis in Arabidopsis thaliana. When overexpressed using LAT52, a strong pollen-specific promoter, epitope-tagged WRKY34 is temporally phosphorylated by MPK3 and MPK6, two mitogen-activated protein kinases (MAPKs, or MPKs), at early stages in pollen development. During pollen maturation, WRKY34 is dephosphorylated and degraded. Native promoter-driven WRKY34-YFP fusion also follows the same expression pattern at the protein level. WRKY34 functions redundantly with WRKY2 in pollen development, germination, and pollen tube growth. Loss of MPK3/MPK6 phosphorylation sites in WRKY34 compromises the function of WRKY34 in vivo. Epistasis interaction analysis confirmed that MPK6 belongs to the same genetic pathway of WRKY34 and WRKY2. Our study demonstrates the importance of temporal post-translational regulation of WRKY transcription factors in the control of developmental phase transitions in plants.

Pollen development, or male gametogenesis, is a process by which a haploid uninucleate microspore undergoes cell division and specification to form a mature pollen grain containing two sperm cells. The highly defined cell linage makes pollen development an ideal model to understand the regulation of plant cellular development. Pollen development has multiple phases and involves dynamic changes in gene expression, which highlights the importance of transcription factors and their regulatory pathway(s). In this report, we demonstrate that WRKY34 and WRKY2, two closely related WRKY transcription factors in Arabidopsis, play important roles in pollen development. WRKY34 is phosphorylated by MPK3/MPK6, two functionally redundant mitogen-activated protein kinases (MAPKs or MPKs), at early stages in pollen development. Utilizing a combination of genetic, biochemical, and cytological tools, we determined that this MAPK-WRKY signaling module functions at the early stage of pollen development. Loss of function of this pathway reduces pollen viability, and the surviving pollen has poor germination and reduced pollen tube growth, all of which reduce the transmission rate of the mutant pollen. This study discovers a novel stage-specific signaling pathway in pollen development.

Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells

Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.

Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

Background

Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking.

Results

In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p < 0.01) in 10 ecotypes, including 498 transcription factors and 315 transposable elements. The majority of the transcripts (75%) showed ecotype specific expression pattern. By using sequence data available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about regulatory interactions between transcription factors and their target genes in the model plant A. thaliana, we have adopted a powerful systems genetics approach- Network Component Analysis (NCA) to construct an in-silico transcriptional regulatory network model during response to cold stress. The resulting regulatory network contained 1,275 nodes and 7,720 connections, with 178 transcription factors and 1,331 target genes.

Conclusions

A. thaliana ecotypes exhibit considerable variation in transcriptome level responses to non-freezing cold stress treatment. Ecotype specific transcripts and related gene ontology (GO) categories were identified to delineate natural variation of cold stress regulated differential gene expression in the model plant A. thaliana. The predicted regulatory network model was able to identify new ecotype specific transcription factors and their regulatory interactions, which might be crucial for their local geographic adaptation to cold temperature. Additionally, since the approach presented here is general, it could be adapted to study networks regulating biological process in any biological systems.

Genome scale transcriptional response diversity among ten ecotypes of Arabidopsis thaliana during heat stress

In the scenario of global warming and climate change, heat stress is a serious threat to crop production worldwide. Being sessile, plants cannot escape from heat. Plants have developed various adaptive mechanisms to survive heat stress. Several studies have focused on diversity of heat tolerance levels in divergent Arabidopsis thaliana (A. thaliana) ecotypes, but comprehensive genome scale understanding of heat stress response in plants is still lacking. Here we report the genome scale transcript responses to heat stress of 10 A. thaliana ecotypes (Col, Ler, C24, Cvi, Kas1, An1, Sha, Kyo2, Eri, and Kond) originated from different geographical locations. During the experiment, A. thaliana plants were subjected to heat stress (38°C) and transcript responses were monitored using Arabidopsis NimbleGen ATH6 microarrays. The responses of A. thaliana ecotypes exhibited considerable variation in the transcript abundance levels. In total, 3644 transcripts were significantly heat regulated (p < 0.01) in the 10 ecotypes, including 244 transcription factors and 203 transposable elements. By employing a systems genetics approach- Network Component Analysis (NCA), we have constructed an in silico transcript regulatory network model for 35 heat responsive transcription factors during cellular responses to heat stress in A. thaliana. The computed activities of the 35 transcription factors showed ecotype specific responses to the heat treatment.

From genes to networks: in systematic points of view

We present a report of the BIOCOMP'10 - The 2010 International Conference on Bioinformatics & Computational Biology and other related work in the area of systems biology.