Coordinated posttranscriptional mRNA population dynamics during T-cell activation

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Neelanjan Mukherjee^1,2,a, Patrick J Lager^1,a, Matthew B Friedersdorf¹, Marshall A Thompson^1,2 & Jack D Keene^1,2

^aThese authors contributed equally to this work

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Synopsis

Messenger ribonucleoproteins (mRNPs) exist in various forms in both the nucleus and the cytoplasm. They include Cajal bodies, GW/P bodies, stress granules, and small mRNPs scattered throughout the protoplasm. The principal components of mRNPs are RNAs, both informational and regulatory (e.g. microRNAs), and RNA-binding proteins (RBPs), which mediate the outcome of posttranscriptional gene expression. Some mRNPs are relatively stable. However, most are transient and apparent only under certain biological conditions. Although mRNP complexes are highly dynamic cellular environments (Brengues et al, 2005), very few studies have focused on global RNA dynamics of RNPs across different physiological conditions (Tenenbaum et al, 2000; Mazan-Mamczarz et al, 2008a, 2008b). As RNP complexes are sites that dictate posttranscriptional coordination and control of gene expression, it is crucial to evaluate the association of mRNAs with RBPs in these complexes.

A strategy developed in our laboratory, termed 'ribonomics,' identifies and characterizes protein–RNA interactions of endogenous RNP complexes en masse using a method called RIP chip (ribonucleoprotein immunoprecipitation microarray). Even though ribonomic profiling has been widely used to identify mRNAs associated with a given RBP (Keene, 2007; Halbeisen et al, 2008; Morris et al, 2009), the overwhelming majority of these studies used RIP-chip experiments from a single condition of growth or perturbation. This is in part because of the lack of analytical approaches for modeling RIP-chip data that allow systematic comparisons across physiological conditions. In this study, we addressed this problem using probabilistic models of RNP association to systematically investigate the contribution of RNP dynamics to biological pathways during changing conditions.

HuR is an RBP known to control the expression of many inflammatory cytokines and growth factors. We conduct ribonomics analysis of the RBP HuR to identify its mRNA targets during T-cell activation. The enrichment of HuR-associated mRNAs were quantified using Gaussian mixture modeling to calculate a log of odds ratio representing condition-specific association (Figure 1). Importantly, the subpopulations of HuR-associated mRNAs are functionally related and exhibit temporally coordinated changes in meaningful ways during the Jurkat activation process. For example, HuR-associated mRNAs were found to encode components of the T cell receptor signaling pathway and members of the WNT signaling pathway. Among the more interesting findings regarding the functionally related mRNAs revealed in this study is that targets of other RBPs and microRNAs show a very strong enrichment among the target populations associated with HuR (Figure 5). Furthermore, many of the HuR-associated mRNAs encoded RBPs. This is consistent with the idea that many RBPs target other RBPs and regulatory factors, or 'regulators of regulators,' providing both resilience and environmental responsiveness (Mansfield and Keene, 2009).

Figure 1

Overview of ribonomic analysis. (A) Isolation of HuR (blue) RNP complexes in parallel with IgG negative control, followed by extraction of mRNAs and hybridization to microarray (RIP chip). (B) GMM, of HuR IP versus negative IP t-scores for three biological replicates of RIP chip, to identify and quantify biochemically enriched populations mRNAs represented by probes. (C) LOD scores representing a condition-specific probability of HuR RNP association per probe. (D) Discrete approach, LOD HuR>0, defines a subset of probes associated with HuR in a given biological condition. Continuous approach, LOD score values to compare HuR RNP mRNA dynamics across different biological conditions, with other data types or databases, and identify small molecule effectors of HuR. Both approaches identify common functional groups or motifs associated with HuR.

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Figure 5

HuR RNPs are enriched for predicted targets of microRNAs. GSEA analysis was conducted on LOD HuR scores for each time point using the following three classes of gene sets for pathways, transcription factor targets (TF), and predicted microRNA (miRNA) target. Positively enriched gene sets were rank ordered and plotted by FWER P-values. Only results for 4 h are shown; 0 and 12 h results are similar.

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Our probabilistic modeling approach, which allowed us to identify the most dynamic of the population of HuR-associated mRNAs, provided insights into potential mechanisms of small molecules. The Connectivity Map (CMAP) is an approach to connect a priori defined gene expression signatures with small molecules based on correlation of gene expression dynamics by scanning a database of perturbagen-induced transcriptomic profiles (Lamb et al, 2006). In most cases, the levels of cellular mRNAs change after treatment of cells with drugs, but the significance of these changes is not well understood. On the basis of our observed HuR RNP dynamics, we defined a quantitative phenotype to systematically identify classes of small molecule candidates that can modulate HuR functionality using the CMAP. As our a priori defined gene expression signature was derived from HuR RNP dynamics rather than transcriptomic changes, this represents a novel application of the CMAP.

The candidate HuR effectors identified fell into drug classes that have overlapping mechanisms of action, including PI3K, COX, HDAC, and Hsp90 inhibitors. Importantly, the activated T-cell transcriptomic data derived in parallel with the ribonomics data did not identify the same drugs. Trichostatin A (TSA), a reversible HDAC inhibitor, represents an independent validation of our approach. TSA has been shown to affect mRNA stability by modulating the subcellular localization of HuR.

We examined resveratrol, a COX inhibitor with chemopreventive, anti-inflammatory, and anti-aging properties, to further validate and extend the usage of our approach. Similar to TSA, resveratrol modulated the subcellular localization of HuR during T-cell activation. Moreover, resveratrol was tested with reporter constructs containing 3' untranslated regions of four of the most dynamic HuR mRNA targets identified in the activated Jurkat cell ribonomic analysis. Three of these constructs were affected by resveratrol treatment, suggesting that resveratrol suppresses the stability or translation of those HuR target mRNAs. Moreover, when exogenous HuR was overexpressed in these activated reporter-containing Jurkat cells, the resveratrol effect on these reporter constructs was diminished. These results suggest that resveratrol may help suppress an excessive immune response involving T cells at the posttranscriptional level, whereas apparently showing little to no effect in the absence of immune stimulation and should be the subject of future studies.

Our study shows that dynamic changes in mRNA populations associated with RBPs at different times during an activation process, or after a perturbation, can be analyzed as a quantitative phenotype. Further, this quantitative phenotype can be used to query gene expression databases to identify novel relationships between posttranscriptional gene expression and other interesting biological processes, such as drug mechanisms. Such data can lead to a better understanding of the underlying mechanisms of drug action and disease.

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