XcisClique: analysis of regulatory bicliques

miRsig: a consensus-based network inference methodology to identify pan-cancer miRNA-miRNA interaction signatures

Decoding the patterns of miRNA regulation in diseases are important to properly realize its potential in diagnostic, prog- nostic, and therapeutic applications. Only a handful of studies computationally predict possible miRNA-miRNA interactions; hence, such interactions require a thorough investigation to understand their role in disease progression. In this paper, we design a novel computational pipeline to predict the common signature/core sets of miRNA-miRNA interactions for different diseases using network inference algorithms on the miRNA-disease expression profiles; the individual predictions of these algorithms were then merged using a consensus-based approach to predict miRNA-miRNA associations. We next selected the miRNA-miRNA associations across particular diseases to generate the corresponding disease-specific miRNA-interaction networks. Next, graph intersection analysis was performed on these networks for multiple diseases to identify the common signature/core sets of miRNA interactions. We applied this pipeline to identify the common signature of miRNA-miRNA inter- actions for cancers. The identified signatures when validated using a manual literature search from PubMed Central and the PhenomiR database, show strong relevance with the respective cancers, providing an indirect proof of the high accuracy of our methodology. We developed miRsig, an online tool for analysis and visualization of the disease-specific signature/core miRNA-miRNA interactions, available at: http://bnet.egr.vcu.edu/miRsig.

Deciphering Cis-Regulatory Element Mediated Combinatorial Regulation in Rice under Blast Infected Condition

Combinations of cis-regulatory elements (CREs) present at the promoters facilitate the binding of several transcription factors (TFs), thereby altering the consequent gene expressions. Due to the eminent complexity of the regulatory mechanism, the combinatorics of CRE-mediated transcriptional regulation has been elusive. In this work, we have developed a new methodology that quantifies the co-occurrence tendencies of CREs present in a set of promoter sequences; these co-occurrence scores are filtered in three consecutive steps to test their statistical significance; and the significantly co-occurring CRE pairs are presented as networks. These networks of co-occurring CREs are further transformed to derive higher order of regulatory combinatorics. We have further applied this methodology on the differentially up-regulated gene-sets of rice tissues under fungal (Magnaporthe) infected conditions to demonstrate how it helps to understand the CRE-mediated combinatorial gene regulation. Our analysis includes a wide spectrum of biologically important results. The CRE pairs having a strong tendency to co-occur often exhibit very similar joint distribution patterns at the promoters of rice. We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc. Our analyses have pointed a highly distributed nature of the combinatorial gene regulation facilitating an efficient alteration in response to fungal attack. All together, our proposed methodology could be an important approach in understanding the combinatorial gene regulation. It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data.

Differential expression of heat shock protein genes in preconditioning for photosynthetic acclimation in water-stressed loblolly pine

Heat shock proteins (HSPs) are induced not only under heat stress conditions but also under other environmental stresses such as water stress. In plants, HSPs families are larger than those of other eukaryotes. In order to elucidate a possible connection between HSP expression and photosynthetic acclimation or conditioning, we conducted a water stress experiment in loblolly pine (Pinus taeda L.) seedlings involving progressive treatment consisting of one cycle of mild stress (-1 MPa) followed by two cycles of severe stress (-1.7 MPa). Net photosynthesis was measured at each stress level. Photosynthetic acclimation occurred in the progressive treatment after the first cycle, but not in the severe treatment, suggesting that a cycle of mild stress conditioned the trees to adapt to a more severe stress. Real time results indicated specific patterns in needles in the expression of HSP70, HSP90 and sHSP genes for each treatment, both at maximum stress and at recovery. We identified a pine homolog to GRP94 (ER resident HSP90) that was induced after rehydration coincident with acclimation. Further analysis of the promoter region of the pine GRP94 showed putative cis-elements associated with water stress and rehydration, corresponding to the expression pattern observed in our experiment.

Unraveling protein networks with power graph analysis

Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks.

Networks play a crucial role in biology and are often used as a way to represent experimental results. Yet, their analysis and representation is still an open problem. Recent experimental and computational progress yields networks of increased size and complexity. There are, for example, small- and large-scale interaction networks, regulatory networks, genetic networks, protein-ligand interaction networks, and homology networks analyzed and published regularly. A common way to access the information in a network is though direct visualization, but this fails as it often just results in “fur balls” from which little insight can be gathered. On the other hand, clustering techniques manage to avoid the problems caused by the large number of nodes and even larger number of edges by coarse-graining the networks and thus abstracting details. But these also fail, since, in fact, much of the biology lies in the details. This work presents a novel methodology for analyzing and representing networks. Power Graphs are a lossless representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Moreover, power graphs can be clearly visualized: they compress up to 90% of the edges in biological networks and are applicable to all types of networks such as protein interaction, regulatory networks, or homology networks.

Integration of Arabidopsis thaliana stress-related transcript profiles, promoter structures, and cell-specific expression

The integration of stress-dependent, tissue- and cell-specific expression profiles and 5'-regulatory sequence motif analysis defines a common stress transcriptome, identifies major motifs for stress response, and places stress response in the context of tissue and cell lineages in the Arabidopsis root.

Background

Arabidopsis thaliana transcript profiles indicate effects of abiotic and biotic stresses and tissue-specific and cell-specific gene expression. Organizing these datasets could reveal the structure and mechanisms of responses and crosstalk between pathways, and in which cells the plants perceive, signal, respond to, and integrate environmental inputs.

Results

We clustered Arabidopsis transcript profiles for various treatments, including abiotic, biotic, and chemical stresses. Ubiquitous stress responses in Arabidopsis, similar to those of fungi and animals, employ genes in pathways related to mitogen-activated protein kinases, Snf1-related kinases, vesicle transport, mitochondrial functions, and the transcription machinery. Induced responses to stresses are attributed to genes whose promoters are characterized by a small number of regulatory motifs, although secondary motifs were also apparent. Most genes that are downregulated by stresses exhibited distinct tissue-specific expression patterns and appear to be under developmental regulation. The abscisic acid-dependent transcriptome is delineated in the cluster structure, whereas functions that are dependent on reactive oxygen species are widely distributed, indicating that evolutionary pressures confer distinct responses to different stresses in time and space. Cell lineages in roots express stress-responsive genes at different levels. Intersections of stress-responsive and cell-specific profiles identified cell lineages affected by abiotic stress.

Conclusion

By analyzing the stress-dependent expression profile, we define a common stress transcriptome that apparently represents universal cell-level stress responses. Combining stress-dependent and tissue-specific and cell-specific expression profiles, and Arabidopsis 5'-regulatory DNA sequences, we confirm known stress-related 5' cis-elements on a genome-wide scale, identify secondary motifs, and place the stress response within the context of tissues and cell lineages in the Arabidopsis root.