Condition-specific coregulation with cis-regulatory motifs and modules in the mouse genome

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

A genome-wide screen indicates correlation between differentiation and expression of metabolism related genes

Differentiated tissues may be considered as materials with distinct properties. The differentiation program of a given tissue ensures that it acquires material properties commensurate with its function. It may be hypothesized that some of these properties are acquired through production of tissue-specific metabolites synthesized by metabolic enzymes. To establish correlation between metabolism and organogenesis we have carried out a genome-wide expression study of metabolism related genes by RNA in-situ hybridization. 23% of the metabolism related genes studied are expressed in a tissue-restricted but not tissue-exclusive manner. We have conducted the screen on whole mount chicken (Gallus gallus) embryos from four distinct developmental stages to correlate dynamic changes in expression patterns of metabolic enzymes with spatio-temporally unique developmental events. Our data strongly suggests that unique combinations of metabolism related genes, and not specific metabolic pathways, are upregulated during differentiation. Further, expression of metabolism related genes in well established signaling centers that regulate different aspects of morphogenesis indicates developmental roles of some of the metabolism related genes. The database of tissue-restricted expression patterns of metabolism related genes, generated in this study, should serve as a resource for systematic identification of these genes with tissue-specific functions during development. Finally, comprehensive understanding of differentiation is not possible unless the downstream genes of a differentiation cascade are identified. We propose, metabolic enzymes constitute a significant portion of these downstream target genes. Thus our study should help elucidate different aspects of tissue differentiation.

Computational identification of transcriptional regulators in human endotoxemia

One of the great challenges in the post-genomic era is to decipher the underlying principles governing the dynamics of biological responses. As modulating gene expression levels is among the key regulatory responses of an organism to changes in its environment, identifying biologically relevant transcriptional regulators and their putative regulatory interactions with target genes is an essential step towards studying the complex dynamics of transcriptional regulation. We present an analysis that integrates various computational and biological aspects to explore the transcriptional regulation of systemic inflammatory responses through a human endotoxemia model. Given a high-dimensional transcriptional profiling dataset from human blood leukocytes, an elementary set of temporal dynamic responses which capture the essence of a pro-inflammatory phase, a counter-regulatory response and a dysregulation in leukocyte bioenergetics has been extracted. Upon identification of these expression patterns, fourteen inflammation-specific gene batteries that represent groups of hypothetically ‘coregulated’ genes are proposed. Subsequently, statistically significant cis-regulatory modules (CRMs) are identified and decomposed into a list of critical transcription factors (34) that are validated largely on primary literature. Finally, our analysis further allows for the construction of a dynamic representation of the temporal transcriptional regulatory program across the host, deciphering possible combinatorial interactions among factors under which they might be active. Although much remains to be explored, this study has computationally identified key transcription factors and proposed a putative time-dependent transcriptional regulatory program associated with critical transcriptional inflammatory responses. These results provide a solid foundation for future investigations to elucidate the underlying transcriptional regulatory mechanisms under the host inflammatory response. Also, the assumption that coexpressed genes that are functionally relevant are more likely to share some common transcriptional regulatory mechanism seems to be promising, making the proposed framework become essential in unravelling context-specific transcriptional regulatory interactions underlying diverse mammalian biological processes.

Transcription factor site dependencies in human, mouse and rat genomes

BACKGROUND

It is known that transcription factors frequently act together to regulate gene expression in eukaryotes. In this paper we describe a computational analysis of transcription factor site dependencies in human, mouse and rat genomes.

RESULTS

Our approach for quantifying tendencies of transcription factor binding sites to co-occur is based on a binding site scoring function which incorporates dependencies between positions, the use of information about the structural class of each transcription factor (major/minor groove binder), and also considered the possible implications of varying GC content of the sequences. Significant tendencies (dependencies) have been detected by non-parametric statistical methodology (permutation tests). Evaluation of obtained results has been performed in several ways: reports from literature (many of the significant dependencies between transcription factors have previously been confirmed experimentally); dependencies between transcription factors are not biased due to similarities in their DNA-binding sites; the number of dependent transcription factors that belong to the same functional and structural class is significantly higher than would be expected by chance; supporting evidence from GO clustering of targeting genes. Based on dependencies between two transcription factor binding sites (second-order dependencies), it is possible to construct higher-order dependencies (networks). Moreover results about transcription factor binding sites dependencies can be used for prediction of groups of dependent transcription factors on a given promoter sequence. Our results, as well as a scanning tool for predicting groups of dependent transcription factors binding sites are available on the Internet.

CONCLUSION

We show that the computational analysis of transcription factor site dependencies is a valuable complement to experimental approaches for discovering transcription regulatory interactions and networks. Scanning promoter sequences with dependent groups of transcription factor binding sites improve the quality of transcription factor predictions.

Application of Biostatistics and Bioinformatics Tools to Identify Putative Transcription Factor-Gene Regulatory Network of Ankylosing Spondylitis and Sarcoidosis

Transcription factors and corresponding cis-regulatory elements are considered key components in gene regulation. We combined biostatistics and bioinformatics tools to streamline identification of putative transcription factor-gene regulatory networks unique for two immune-mediated diseases, ankylosing spondylitis and sarcoidosis. After identifying differentially expressed genes from microarrays, we employed tightCluster to find tight clusters of potentially co-regulated genes. By subsequently applying bioinformatics tools to search for common cis-regulatory elements, putative transcription factor-gene regulatory networks were found. Recognition of these networks by applying this methodology could pave the way for new insights into disease pathogenesis.