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Dissection of a DNA-damage-induced transcriptional network using a combination of microarrays, RNA interference and computational promoter analysis.

ABSTRACT: BACKGROUND: Gene-expression microarrays and RNA interferences (RNAi) are among the most prominent techniques in functional genomics. The combination of the two holds promise for systematic, large-scale dissection of transcriptional networks. Recent studies, however, raise the concern that nonspecific responses to small interfering RNAs (siRNAs) might obscure the consequences of silencing the gene of interest, throwing into question the ability of this experimental strategy to achieve precise network dissections. RESULTS: We used microarrays and RNAi to dissect a transcriptional network induced by DNA damage in a human cellular system. We recorded expression profiles with and without exposure of the cells to a radiomimetic drug that induces DNA double-strand breaks (DSBs). Profiles were measured in control cells and in cells knocked-down for the Rel-A subunit of NFkappaB and for p53, two pivotal stress-induced transcription factors, and for the protein kinase ATM, the major transducer of the cellular responses to DSBs. We observed that NFkappaB and p53 mediated most of the damage-induced gene activation; that they controlled the activation of largely disjoint sets of genes; and that ATM was required for the activation of both pathways. Applying computational promoter analysis, we demonstrated that the dissection of the network into ATM/NFkappaB and ATM/p53-mediated arms was highly accurate. CONCLUSIONS: Our results demonstrate that the combined experimental strategy of expression arrays and RNAi is indeed a powerful method for the dissection of complex transcriptional networks, and that computational promoter analysis can provide a strong complementary means for assessing the accuracy of this dissection.

SUBMITTER: Elkon R

PROVIDER: S-EPMC1175955 | biostudies-literature | 2005

REPOSITORIES: biostudies-literature

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Dissection of a DNA-damage-induced transcriptional network using a combination of microarrays, RNA interference and computational promoter analysis.

Elkon Ran R Rashi-Elkeles Sharon S Lerenthal Yaniv Y Linhart Chaim C Tenne Tamar T Amariglio Ninette N Rechavi Gideon G Shamir Ron R Shiloh Yosef Y

Genome biology 20050413 5

<h4>Background</h4>Gene-expression microarrays and RNA interferences (RNAi) are among the most prominent techniques in functional genomics. The combination of the two holds promise for systematic, large-scale dissection of transcriptional networks. Recent studies, however, raise the concern that nonspecific responses to small interfering RNAs (siRNAs) might obscure the consequences of silencing the gene of interest, throwing into question the ability of this experimental strategy to achieve prec ...[more]

PMID: 15892871

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Project description:Exogenous siRNAs are commonly used to regulate endogenous gene expression levels for gene function analysis, genotype-phenotype association studies and for gene therapy. Exogenous siRNAs can target mRNAs within the cytosol as well as nascent RNA transcripts within the nucleus, thus complicating siRNA targeting specificity. To highlight challenges in achieving siRNA target specificity, we targeted an overlapping gene set that we found associated with a familial form of multiple synostosis syndrome type 4 (SYSN4). In the affected family, we found that a previously unknown non-coding gene TOSPEAK/C8orf37AS1 was disrupted and the adjacent gene GDF6 was downregulated. Moreover, a conserved long-range enhancer for GDF6 was found located within TOSPEAK which in turn overlapped another gene which we named SMALLTALK/C8orf37. In fibroblast cell lines, SMALLTALK is transcribed at much higher levels in the opposite (convergent) direction to TOSPEAK. siRNA targeting of SMALLTALK resulted in post transcriptional gene silencing (PTGS/RNAi) of SMALLTALK that peaked at 72 h together with a rapid early increase in the level of both TOSPEAK and GDF6 that peaked and waned after 24 h. These findings indicated the following sequence of events: Firstly, the siRNA designed to target SMALLTALK mRNA for RNAi in the cytosol had also caused an early and transient transcriptional interference of SMALLTALK in the nucleus; Secondly, the resulting interference of SMALLTALK transcription increased the transcription of TOSPEAK; Thirdly, the increased transcription of TOSPEAK increased the transcription of GDF6. These findings have implications for the design and application of RNA and DNA targeting technologies including siRNA and CRISPR. For example, we used siRNA targeting of SMALLTALK to successfully restore GDF6 levels in the gene therapy of SYNS4 family fibroblasts in culture. To confidently apply gene targeting technologies, it is important to first determine the transcriptional interference effects of the targeting reagent and the targeted gene.

Project description:Transcriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)-gene interactions at the genome-wide level. In correlation-based TRNI, network edges can in principle be evaluated using standard statistical tests. However, while such tests nominally assume independent microarray experiments, we expect dependency between the experiments in microarray compendia, due to both project-specific factors (e.g., microarray preparation, environmental effects) in the multi-project compendium setting and effective dependency induced by gene-gene correlations. Herein, we characterize the nature of dependency in an Escherichia coli microarray compendium and explore its consequences on the problem of determining which and how many arrays to use in correlation-based TRNI.We present evidence of substantial effective dependency among microarrays in this compendium, and characterize that dependency with respect to experimental condition factors. We then introduce a measure neff of the effective number of experiments in a compendium, and find that corresponding to the dependency observed in this particular compendium there is a huge reduction in effective sample size i.e., neff = 14.7 versus n = 376. Furthermore, we found that the neff of select subsets of experiments actually exceeded neff of the full compendium, suggesting that the adage 'less is more' applies here. Consistent with this latter result, we observed improved performance in TRNI using subsets of the data compared to results using the full compendium. We identified experimental condition factors that trend with changes in TRNI performance and neff , including growth phase and media type. Finally, using the set of known E. coli genetic regulatory interactions from RegulonDB, we demonstrated that false discovery rates (FDR) derived from neff -adjusted p-values were well-matched to FDR based on the RegulonDB truth set.These results support utilization of neff as a potent descriptor of microarray compendia. In addition, they highlight a straightforward correlation-based method for TRNI with demonstrated meaningful statistical testing for significant edges, readily applicable to compendia from any species, even when a truth set is not available. This work facilitates a more refined approach to construction and utilization of mRNA expression compendia in TRNI.

Dataset Information

Dissection of a DNA-damage-induced transcriptional network using a combination of microarrays, RNA interference and computational promoter analysis.

Publications

Dissection of a DNA-damage-induced transcriptional network using a combination of microarrays, RNA interference and computational promoter analysis.

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