Naresh Doni Jayavelu

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

BackgroundLow 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.ResultsIn 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.ConclusionsA. 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.

Metabolomic studies of human gastric cancer: Review

Metabolomics is a field of study in systems biology that involves the identification and quantification of metabolites present in a biological system. Analyzing metabolic differences between unperturbed and perturbed networks, such as cancerous and non-cancerous samples, can provide insight into underlying disease pathology, disease prognosis and diagnosis. Despite the large number of review articles concerning metabolomics and its application in cancer research, biomarker and drug discovery, these reviews do not focus on a specific type of cancer. Metabolomics may provide biomarkers useful for identification of early stage gastric cancer, potentially addressing an important clinical need. Here, we present a short review on metabolomics as a tool for biomarker discovery in human gastric cancer, with a primary focus on its use as a predictor of anticancer drug chemosensitivity, diagnosis, prognosis, and metastasis.

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.

Iterative sub-network component analysis enables reconstruction of large scale genetic networks.

BackgroundNetwork component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.ResultsTo address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subntework using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed.ConclusionsOur framework enables reconstruction of large gene expression data networks, without reducing their size or pruning potentially important components, and at the same time rendering the results more biological plausible. Our ISNCA method is not only suitable for prediction of key regulators in cancer studies, but it can be applied to any high-throughput gene expression data.

Methylome Analysis of Human Bone Marrow MSCs Reveals Extensive Age- and Culture-Induced Changes at Distal Regulatory Elements

Summary Human bone marrow stromal cells, or mesenchymal stem cells (BM-MSCs), need expansion prior to use as cell-based therapies in immunological and tissue repair applications. Aging and expansion of BM-MSCs induce epigenetic changes that can impact therapeutic outcomes. By applying sequencing-based methods, we reveal that the breadth of DNA methylation dynamics associated with aging and expansion is greater than previously reported. Methylation changes are enriched at known distal transcription factor binding sites such as enhancer elements, instead of CpG-rich regions, and are associated with changes in gene expression. From this, we constructed hypo- and hypermethylation-specific regulatory networks, including a sub-network of BM-MSC master regulators and their predicted target genes, and identified putatively disrupted signaling pathways. Our genome-wide analyses provide a broader overview of age- and expansion-induced DNA methylation changes and a better understanding of the extent to which these changes alter gene expression and functionality of human BM-MSCs.

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.

A Noise Removal Algorithm for Time Series Microarray Data

High-throughput technologies such as microarray data are a great resource for studying and understanding biological systems at a low cost. However noise present in the data makes it less reliable, and thus many computational methods and algorithms have been developed for removing the noise. We propose a novel noise removal algorithm based on Fourier transform functions. The algorithm optimizes the coefficients of the first and second order Fourier functions and selects the function which maximizes the Spearman correlation to the original data. To demonstrate the performance of this algorithm we compare the prediction accuracy of well known modelling tools, such as network component analysis (NCA), principal component analysis (PCA) and k-means clustering. We compared the performance of these tools on the original noisy data and the data treated with the algorithm. We performed the comparison analysis using three independent real biological data sets (each data set with two replicates). In all cases the proposed algorithm removes the noise in the data and substantially improves the predictions of modelling tools.

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

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

An atlas of silencer elements for the human and mouse genomes

The study of gene regulation is dominated by a focus on the control of gene activation or controlling an increase in the level of expression. Just as critical is the process of gene repression or silencing. Chromatin signatures have allowed for the global mapping of enhancer cis-regulatory elements, however, the identification of silencer elements by computational or experimental approaches in a genome-wide manner are lacking. We present a simple but powerful computational approach to identify putative silencers genome-wide. We used a series of consortia data to predict silencers in over 100 human and mouse cell or tissue types. We performed several analyses to determine if these elements exhibited characteristics expected of a silencers. Motif enrichment analyses on putative silencers determined that motifs belonging to known transcriptional repressors are enriched, as well as overlapping known transcription repressor binding sites. Leveraging promoter capture HiC data from several human and mouse cell types, we found that over 50% of putative silencer elements are interacting with gene promoters having very low to no expression. Next, to validate our silencer predictions, we quantified silencer activity using massively parallel reporter assays (MPRAs) on 7500 selected elements in K562 cells. We trained a support vector machine model classifier on MPRA data and used it to refine potential silencers in other cell types. We also show that similar to enhancer elements, silencer elements are enriched in disease-associated variants. Our results suggest a general strategy for genome-wide identification and characterization of silencer elements.