Transcriptomic-Wide Discovery of Direct and Indirect HuR RNA Targets in Activated CD4+ T Cells

Due to poor correlation between steady state mRNA levels and protein product, purely transcriptomic profiling methods may miss genes posttranscriptionally regulated by RNA binding proteins (RBPs) and microRNAs (miRNAs). RNA immunoprecipitation (RIP) methods developed to identify in vivo targets of RBPs have greatly elucidated those mRNAs which may be regulated via transcript stability and translation. The RBP HuR (ELAVL1) and family members are major stabilizers of mRNA. Many labs have identified HuR mRNA targets; however, many of these analyses have been performed in cell lines and oftentimes are not independent biological replicates. Little is known about how HuR target mRNAs behave in conditional knock-out models. In the present work, we performed HuR RIP-Seq and RNA-Seq to investigate HuR direct and indirect targets using a novel conditional knock-out model of HuR genetic ablation during CD4+ T activation and Th2 differentiation. Using independent biological replicates, we generated a high coverage RIP-Seq data set (>160 million reads) that was analyzed using bioinformatics methods specifically designed to find direct mRNA targets in RIP-Seq data. Simultaneously, another set of independent biological replicates were sequenced by RNA-Seq (>425 million reads) to identify indirect HuR targets. These direct and indirect targets were combined to determine canonical pathways in CD4+ T cell activation and differentiation for which HuR plays an important role. We show that HuR may regulate genes in multiple canonical pathways involved in T cell activation especially the CD28 family signaling pathway. These data provide insights into potential HuR-regulated genes during T cell activation and immune mechanisms.


Introduction
Due to poor correlation between steady state mRNA levels and protein products, traditional genome profiling techniques may be limiting. Eukaryotic mRNAs are regulated posttranscriptionally by RNA-binding proteins (RBPs) and microRNAs (miRNAs) at the levels of transcript stability and translation. To better understand the complex milieu of posttranscriptional gene regulation, investigators have developed various methods to identify mechanisms of gene regulation. RNA-immunoprecipitation applied to gene chips (RIP-Chip) or, more recently, to RNA-Seq (next generation deep sequencing) are two such methods that have revealed networks of posttranscriptionally regulated genes. These methods can be used to explore both known and novel transcripts.
The elav family of RBPs and HuR (elavl1) in particular play important roles in stabilization and translational recruitment of labile mRNA transcripts to heavy polysomes in order to facilitate protein product expression. Since HuR is a stabilizer RBP, many labs, including our own, have taken advantage of this fact and performed HuR RIP-Chip (or Seq) and RNA-Seq to identify mRNAs which may be coordinately regulated by HuR in a variety of biological systems [1][2][3], [4]. However, the vast majority of these analyses are not truly independent biological samples and they were mostly performed in transformed cell lines. Not as much is known about HuR regulation of mRNA target genes in primary cells in vivo.
An active area of HuR investigation is in immune system regulation, especially during CD4 + T cell activation and differentiation. When our group first cloned murine HuR (HuA), we demonstrated that T cell activation results in 14-fold increases in the total cellular HuR levels [5]. Other groups have shown that following T cell activation, HuR rapidly translocates to the cytoplasm [6]. Thus, we and others have hypothesized that HuR may facilitate T cell activation as well as naïve CD4 + T cell differentiation programs.
Naive CD4 + T cells can differentiate into different subsets, including Th1, Th2, Th17, Tfh and Treg, with unique signature transcription factors and different patterns of cytokine gene expression. HuR was previously described as a regulator of transcription factors and cytokines promoting CD4 + Th2 differentiation such as GATA-3 and IL-4 [7,8]. Th2 cytokines, such as IL-4, IL-5 and IL-13 play major roles in allergic airway inflammation. However, other HuR target transcripts essential for Th2 differentiation, besides those identified previously, may have been overlooked. In order to address this, we took advantage of HuR RIP-Chip techniques to determine direct HuR target transcripts in Th2 differentiating cells. Additionally, RNA-Seq was performed in HuR knock-out (KO) vs. wild type control CD4 + cells under Th2 polarization to identify target transcripts which were altered in the absence of HuR. To our knowledge, this is the first time these techniques have been used with independent biological replicates from primary murine CD4 + T cells undergoing Th2 differentiation.
Finally, we combined the data obtained from HuR RIP-Seq and RNA-Seq in order to determine HuR direct and indirect target transcripts under Th2 polarizing conditions. Our analysis (>400 million reads) reveals both known HuR regulated transcripts as well as new, putative HuR-associated targets under Th2 conditions which are crucial for Th2 differentiation and cytokine production. Such data when corroborated with translational expression can delineate previously overlooked genes which are required for T cell activation and inflammation in diseases such as asthma.

Material and Methods Mice
OX40 Cre HuR fl/fl mice were generated as previously described [7]. In HuR fl/fl mice, a portion of the promoter region, exon 1 and exon 2 of HuR gene were flanked with the loxP sites which allow HuR ablation to occur upon cre recombinase excision. These mice were then crossed to OX40-cre mice in which cre recombinase enzyme is expressed under the control of OX40 promoter in activated T cells to generate OX40-cre HuR fl/fl KO mice. All mice used were on the C57BL/6 background and housed in the pathogen free facilities. All animal experiments and procedures were conducted in accordance with the guidelines set forth by the University of Missouri Animal Care and Use Committee (IACUC). The University of Missouri IACUC approved all the experiments in this manuscript.

RNA isolation and sequencing
CD4 + Th2 polarized cells were harvested from the in vitro culture on day 5 post-activation. Approximately, 1x10 7 cells were lysed and re-suspended in 1 ml of Trizol reagent (Life Technologies). Total RNA was then isolated according to the company protocol. For RNA-sequencing, 5ug of RNA from seven OX40-cre HuR fl/fl mice and seven HuR fl/fl controls were used to make the libraries (TruSeq). RNA-sequencing was performed using an Illumina HiSeq 2000 sequencer at the DNA Core facility of the University of Missouri. These libraries were sequenced in two different runs (one 6-plex and one 8-plex) of one lane each using 100 bp single-end reads.

RNA-immunoprecipitation (RIP)
HuR and IgG1 RNA-immunoprecipitations were performed according to published protocol [1,9]. Briefly, CD4 + Th2 polarized cells from HuR fl/fl wild-type controls on day 5 of in vitro polarization were lysed in polysomal lysis buffer (PLB) supplemented with RNaseOUT, DTT and complete protease inhibitor. HuR (clone 3A2) or IgG1 isotype control antibodies were pre-coated onto protein A Sapharose beads (PAS) overnight at 4°C. On the next day, antibody coated beads were washed with NT-2 buffer (50mM Tris-Hcl pH7.4, 150mM NaCl 2 , 1mM MgCl 2 and 0.05%NP-40) at least 5 times. Equal amount of protein lysates in PLB buffer were then incubated with HuR or IgG1 antibody coated beads for 4 hours at 4°C. Beads were then washed with NT-2 buffer 7 times. After the last wash, the beads were then incubated with 20 units of RNase-free DNase I (15 min, 30°C) and washed once with NT-2 buffer. The ribonucleoprotein complexes were dissociated from the beads by incubating in 100 μl NT-2 buffer containing 0.1% SDS and 0.5 mg/ml Proteinase K (30 min, 55°C). The beads were then spun down and supernatants were collected. RNA extraction from the supernatant was performed with acid-phenol and precipitated using absolute ethanol. Total RNA was then processed for RNA-sequencing (RNA-Seq). From each biological replicate (n = 3), two libraries (HuR IP and IgG1 control) were prepared and sequenced on an Illumina HiSeq 2000 with 100 bp single end reads. Each library was sequenced using a full lane on the sequencer.

Statistical analyses and bioinformatics
RNA-Seq data and RIP-Seq data were analyzed separately to determine the indirect and direct HuR targets, respectively. After identifying those targets, the lists were merged and used for further analysis to identify enriched canonical pathways and to build novel networks related to HuR in CD4 + T cells.

RNA-Sequencing
The primary analysis of the pre-processed RNA-Seq data was conducted using R/Bioconductor, with other free, open-source programs used for the pre-processing itself. The preprocessing steps consisted of: 1) quality assessment of FASTQ files using FastQC (http://www. bioinformatics.bbsrc.ac.uk/projects/fastqc), 2) trimming of adapters from each library using Cutadapt, 3) aligning libraries to the mm10 genome (http://hgdownload-test.cse.ucsc.edu/ goldenPath/mm10/bigZips/) using a seed-and-vote method, and 4) forming a read-count matrix at the gene level by read summarization via featureCounts in Rsubread.
Non-specific filtering of genes prior to statistical testing was carried out to increase the power of detecting differentially expressed genes [10], based on the requirement that a gene have an expression level greater than 1 count per million reads mapped (CPM) for at least 7 libraries across all samples (HuR KO and wild-type control). Library normalization to adjust for differences in library size was then made using the TMM method [11] in edgeR [12]. A flexible mean-variance modeling and transformation process known as voom [13], was used in conjunction with a linear modeling approach [14], a combination known as Limma Voom, which was recently shown to perform among the best of available techniques in an comprehensive comparison of differential gene expression methods for RNA-Seq data [15]. Using this framework, normalized expression levels were compared between the KO/CTL, resulting in estimated fold changes and associated p-values. Adjustment to the p-values was made to account for multiple testing using the false discovery rate (FDR) method of Benjamini and Hochberg; these are reported as q-values.

RIP-Sequencing
Pre-processing and read summarization of RIP-Seq data was carried out as described for the RNA-Seq data. For each animal (n = 3), a HuR and IgG1 sample was processed separately using the RIPSeeker [16] algorithm, which is a de novo method for detecting peaks (indicating protein-RNA interactions) based on hidden Markov models (HMM). This is carried out in a multistage process whereby RIPSeeker first partitions the genome into non-overlapping bins, whose sizes are optimally determined per chromosome (optimal bin size selected was between 200-300 bp for our data), and each bin counts any reads that fall into it. Clearly, multiple (and often adjacent) bins may together correspond to a single RNA transcript that binds to HuR, so that the bins must be treated as dependent observations. This is dealt with via the HMM approach, which also takes advantage of the background signal information provided by the IgG1 sample, and is used to make a prediction of the state of the bin (RIP or background). To assess the significance of the prediction for a bin (i.e., that specific genomic region), both the RIP library and control library are assigned a RIPScore at each bin location. Adjacent regions with the same prediction are merged and their RIPscores are averaged across bins. Thus, by application of the central limit theorem, the difference between the average RIPscores (RIP and control) has been shown to have a Gaussian distribution, which can be used to derive a pvalue. Finally, those p-values are adjusted for multiple testing using FDR. Data (HuR and IgG1 control) from each animal were analyzed individually by RIPSeeker to produce a list of annotated RIP regions (aka peaks) and associated q-values. Taking cues from the quality control protocol used for ChIP-Seq data in the ENCODE project, the peak lists were compared between animals, and excluded if the peak list of any sample overlapped by less than 50% with the other samples.
Combined analysis of RNA-Seq and RIP-Seq results for canonical pathways and novel networks The set of genes identified by RNA-Seq analysis (indirect targets) and by RIP-Seq analysis (direct targets) were merged and used for further analysis to identify enriched canonical pathways and to build novel networks related to HuR in CD4 + T cells. Canonical pathways were assessed and novel networks were generated through the use of Ingenuity Pathways Analysis (Ingenuity Systems, www.ingenuity.com), henceforth IPA, as previously described [17]. The complete lists of identified genes were uploaded into IPA along with the Entrez Gene identifier (ID), fold change, and q-value. In the event of duplicate genes prior to uploading into IPA, RIP-Seq (direct target) information for the gene was used as opposed to RNA-Seq (indirect target). Within IPA, in the event of commonly mapped IDs to the Ingenuity Knowledge Base (IKB), the maximum fold change (i.e., RIP-Seq values took precedence over RNA-Seq values when both significant) was used to represent the single results for that Gene ID. For canonical pathway analysis, Fisher's exact test was used to determine which pathways are enriched for our reported direct or indirect HuR targets. For novel networks, a detailed description of the network generating algorithm is provided by Calvano et al. [18]. Graphical representations of the pathways were generated using IPA's Path Designer, which illustrates the relationships between molecules (i.e., nodes), and the biological relationship between two nodes is represented as an edge (i.e., connecting line). All edges are supported by at least 1 reference stored in the IKB which was derived either from the literature, from a textbook or from canonical information.

RT-qPCR
RNA isolation from CD4 + Th2 polarized cells on day 5 post-activation was performed using Trizol extraction following the manufacture's protocol. Reverse transcription (RT) was performed using 0.5-1 ug of RNA with Superscript III reverse transcriptase (Invitrogen). Quantitative PCR was done in triplicate using Platinum SYBR green Universal (Invitrogen). The data were analyzed using comparative CT method with Gapdh as an endogenous control. Data were present in arbitrary relative quantitative ratio (RQ). Student's t-test was used to calculate p-value based on delta-delta CT of the targets.
Murine primers for qPCR were listed below (forward/reverse):

Results
Our RNA-Seq experiments obtained approximately 425 million total raw reads (mean of 30 million), while the RIP-Seq experiments resulted in approximately 160 million total raw reads (mean of 26 million). Fig 1 provides a broad overview of the results found by RNA-Seq and RIP-Seq, in terms of the number of genes indicated as being significant by each method and how they are related. We searched BioGRID, a public database (http://thebiogrid.org/, accessed 6/24/2014) that aggregates and archives published genetic and protein interaction data from model organisms and humans [19][20][21], in order to compare our identified direct and indirect HuR (ELAVL1) targets with those experimentally determined in the literature. We downloaded data files containing approximately 2000 interactions involving HuR in human, mouse, and rat. Nearly all data came from humans; therefore, we only used BioGRID genes with mouse orthologs to construct Fig 1. We also limited our RNA-Seq and RIP-Seq findings to regions with official gene symbols because gene symbol is the identifier level at which the Bio-GRID data is reported. BioGrid, RNA-Seq and HuR RIP-Seq revealed 1514, 2068 and 271 transcripts involving in Th2 differentiation respectively. Under Th2 polarizing conditions, 271 transcripts were shown to be direct HuR target transcripts by HuR RIP-seq. The RNA expression level of 30 genes shown to be direct HuR targets were altered in the absence of HuR. Of all the transcripts analyzed by these 3 methods, only seven have previously been shown to be known interacting targets by BioGrid. Thus, using these 3 analyses in combination, we discovered not only the potential target transcripts with which HuR may physically interact, but we also revealed the transcripts that may require HuR for their expression. HuR indirect targets in CD4 + Th2 differentiation identified by RNA-Seq 2080 genes (2068 with designated gene symbol in NCBI Gene) were found based on a nominal p-value cutoff of .05 (with corresponding q < .30), with a median p-value of .1275 (q = .15). The full listing of these genes is given in S1 Table. A broad entry criterion was selected because the ultimate goal was to use the identified differentially expressed genes as input for a combined network analysis, using both RNA-Seq and RIP-Seq results. The top 50 differentially expressed genes (with an assigned gene symbol) discovered via RNA-Seq are given in Table 1, sorted by the absolute magnitude of the fold change. Here, fold change is defined as knock-out (KO) divided by wild-type control (CTL).
Most of the genes in Table 1 are highly significant (median q = .045) with a large foldchange (median magnitude of the fold change = 2.15). We also determined efficiency of HuR ablation in activated HuR KO CD4 + T cells by Western analysis. As shown in S1D Fig, HuR levels in HuR KO CD4 + T cells are indeed abolished. Additionally, we also verified RNA-Seq data by determining mRNA steady state levels of three potential HuR target genes (CAMK2, CAMK4 and IL-2) by RT-qPCR. Consistent with the RNA-Seq data, IL-2 mRNA was increased in KO T cells by two-fold, whereas CAMK2 and CAMK4 levels were decreased 50% and 15%, respectively RT-qPCR (S1A-S1C Fig).
Many genes that are involved in CD4 + T cell activation and Th2 differentiation were identified as genes whose expression is altered in the absence of HuR as shown in Table 1. Only a few of those transcripts have been previously verified as HuR targets in other transformed cell lines such as Plcd3 and Trp53cor1 [22,23].
Many of those known HuR regulated transcripts are not common with transcripts found in CD4 + T cells, even if those transcripts are highly expressed in both transformed cell lines and CD4 + T cells. This may indicate that HuR function may be cell type specific and HuR targets in transformed cell lines may be different from primary cells. The RNA-Seq data also showed novel HuR target transcripts encoding for cytokines, cytokine receptors and transcription factors required for CD4 + T cell functions such as IL-2, IL-10 and IL-1R1.

Direct in vivo HuR targets identified by RIP-Seq in CD4 + Th2 differentiation
Three independent experiments were conducted and analyzed separately to determine the number of significant binding events (hits or peaks). Those regions were annotated based on which gene the region overlapped (or the gene with nearest start site). Experiment 1 identified 149 binding events that mapped to 81 genes, experiment 2 found 381 that mapped to 193 genes, and experiment 3 found 348 that mapped to 172 genes. Experiment 1 did not meet the minimum agreement with the other two experiments, so those results were excluded from further analysis. The regions from the remaining two experiments were aggregated at the gene level for a total of 271 unique genes with at least one significant binding event overlapping them (S2 Table). The top 100 significantly different annotated regions from among 271 identified via RIP-Seq are given in Table 2, along with the total number of significant 'hits' (binding events) at that region among two independent RIP experiments.
The regions are arranged by gene type, and then sorted by fold change. Because each hit has an associated p-value, q-value, and fold change (HuR/IgG), we conservatively reported the minimum fold change within a gene, so that all hits within a gene have a fold change as least as large as the one reported. Similarly, we conservatively report the maximum p-value and qvalue, so that all hits within a gene have a value at least as small as the one reported. All regions given in Table 2 have a minimum q-value <1E-99 from testing for HuR binding to RNA, so we have suppressed reporting the p-value and q-value for Table 2, but the values are given in Table 1. Top 50 RNA-Seq significantly different genes sorted by magnitude of fold change (KO/CTL). The official symbol of the top 50 (significant) genes found via RNA-Seq are given, along with fold change (KO/CTL), raw p-value for differential expression, and q (false discovery rate value based on method of Benjamini-Hochberg). S2 Table. Location of binding overlaps the annotated region unless otherwise noted, in which case the reported gene is the nearest annotated gene; we have indicated those in Table 2 and S2 Table. Various transcripts of protein coding genes, pseudogenes, lincRNA genes, miRNA genes, snoRNA genes and unclassified genes were found to physically interact with HuR by HuR RIP-Seq. The top significant HuR associated transcripts and the number of HuR hits are displayed in Table 2. Many of those transcripts are novel HuR target genes which have been shown to play pivotal roles in CD4 + T cell activation and Th2 differentiation. We validated HuR RIP-Seq data by performing HuR RIP followed by RT-qPCR in some of potential HuR transcripts in Th2 polarized CD4 + T cells. IL-2, CAMK2 and CAMK4 transcript enrichment in RIP using HuR antibody were analyzed as compared to RIP using IgG1 antibody. In agreement with the HuR RIP-Seq data, IL-2 and CAMK4 were found to be direct HuR target transcripts while CAMK2 did not seem to be an appreciable direct HuR target transcript (S2A Fig). Combined network analysis of RNA-Seq and RIP-Seq regions Table 3 lists alphabetically the seven genes found in the intersection of Fig 1, and combines information reported in Tables 1 and 2 and S2 Table. In two instances, the hits do not perfectly overlap the annotated gene, so the reported gene is the nearest annotated gene; we have indicated those in Table 3.

Gene ID
Based on the union of the RNA-Seq and RIP-Seq lists, we used IPA software to identify the top 50 enriched canonical pathways (Table 4). We reported the significance and indicated the magnitude of the enrichment as a percent of the genes in our list for a given canonical pathway divided by the size of the canonical pathway. Fig 2 gives details for the top identified canonical pathway (iCOS-iCOSL Signaling in T Helper Cells) overlaid with relevant data (Table 5). Significant pathway nodes are shaded according to the size of the fold change (red >1; green <1), with white nodes indicating undetected genes and gray indicating genes that were detected, but not significant. Nodes with multicolor gradients contain genes with significant fold changes in different directions; complete relevant data for every node is given in Table 5, Fig 3 and Table 6 present similar information for another highly significant canonical pathway, CD28 Signaling in T Helper Cells.
Multiple pathways involved in CD4 + T cell activation and Th2 differentiation are displayed in Tables 3 to 6 and Figs 2 and 3. Two major CD28 family co-stimulatory pathways, iCOS-iCOSL (Inducible Co-stimulator)/AILIM (Activation-inducible lymphocyte immunomediatory molecule) and CD28, in T helper cells are the most effected by HuR ablation. iCOS and CD28 are both co-stimulatory molecules which are highly up-regulated upon the engagement of T cell receptor (TCR). iCOS is induced upon T cell activation but not in naïve CD4 + T cells while CD28 is constitutively expressed even before TCR engagement [24,25]. iCOS has been shown to be essential for Th2 differentiation and for follicular helper T cell (Tfh) development while CD28 signal is required for IL-2 production in activated CD4 + T cells [26][27][28]. This data  emphasize the possibility that HuR may play a role in regulating CD28 co-stimulatory receptor family signaling pathways which control T cell function and differentiation.
In order to verify several potential HuR target transcripts that have been illustrated previously to be involved in the iCOS-iCOSL pathway, we have quantitated CAMK2, CAMK4 and IL-2 levels in our system. CAMK2 and CAMK4 have been shown previously to inhibit IL-2 transcription [29]. Interestingly, our data show potential direct and indirect HuR regulation of CAMK2 and CAMK4 on IL-2 expression. Down regulation of CAMK2 and CAMK4 observed in Th2 differentiated HuR KO CD4 + T cells may result in up-regulation of IL-2 expression. Although the mRNA levels of CAMK2 were significantly lower in the HuR KO Th2 polarized CD4 + T cells as compared to the control, the protein levels were not significantly different (S1A-S1D Fig). This may be due to the inability of Western blotting to detect subtle changes in protein expression. Also, there are 4 isoforms of CAMK2 (α, β, δ and γ). Two isoforms of CAMK2 (β and β'e splice variants) have been shown to suppress IL-2 expression [29]. The antibody used in this experiment detects all CAMK2 isoforms, making it difficult to detect subtle alterations in individual isoform expression. Moreover, only expression of the CAMK2b isoform was altered in the absence of HuR and was a HuR target. These results emphasize the fact that HuR regulation of target mRNA transcripts is complex. Additionally, HuR regulation at the translation levels has to be considered in interpretation of mRNA results. Therefore, further Direct and Indirect HuR Targets in Activated CD4 + T Cells   Top canonical pathway (iCOS-iCOSL Signaling in T Helper Cells) overlaid with hits from union of RIP-Seq and RNA-Seq analyses. Significant pathway nodes are shaded according to size of fold change (red >1; green <1), with white nodes indicating genes that were not detected in the samples and gray indicating genes that were detected, but not significant. Nodes with multicolor gradients denote nodes with significant genes with fold changes in different directions (see Table 2

Discussion
It has long been appreciated that there is a poor correlation between steady state mRNA levels and protein products [30,31]. This observation strongly implied the importance of posttranscriptional gene regulation by RBPs and miRNAs. Keene and colleagues proposed the posttranscriptional operon hypothesis and developed techniques to identify in vivo mRNA targets of different RBPs [32][33][34][35]. These techniques have been used by many labs to delineate the universe of mRNA transcripts which co-exist in ribonucleoparticles (RNPs) with ribosomes, RBPs Table 5. Details of the hits in the top canonical pathway (iCOS-iCOSL Signaling in T Helper Cells). Official symbols of the significant genes found via RNA-Seq (RIP-Seq) that are in the pathway (Fig 2) is given along with name of its corresponding node in the pathway. Fold is given as KO/CTL (RNA-Seq) or IP/CTL (RIP-Seq), and q is the false discovery rate value based on method of Benjamini-Hochberg. Genes were identified by both RNA-Seq and RIP-Seq. Numbers in parentheses and italicized denote data obtained from RIP-Seq. and miRNAs [1,[36][37][38]. The further development of PAR-CLIP by Tuschl and his group allowed for fine mapping of direct residues which RBPs used to interact with their targets [3,39,40]. Taken together, these approaches have greatly aided the field in better delineation of posttranscriptionally regulated gene networks. Furthermore, these methods identified mRNAs which may have been previously over-looked using traditional profiling methods since their steady-state mRNA levels did not appreciably change.
As suggested by Phil Sharp and colleagues, RBPs and miRNA interact to affect cellular gene expression programs to help cells survive environmental stresses [41]. However, it has become increasingly clear that the cell employs these same mechanisms to posttranscriptionally control gene expression during differentiation and development in many tissues. More recent works Canonical pathway for CD28 Signaling in T Helper Cells overlaid with hits from union of RIP-Seq and RNA-Seq analyses. Significant pathway nodes are shaded according to size of fold change (red >1; green <1), with white nodes indicating genes that were not detected in the samples and gray indicating genes that were detected, but not significant. Nodes with multicolor gradients denote nodes with significant genes with fold changes in different directions (see Table 3 for more details). Fold change is defined as KO/CTL (RNA-Seq) or IP/CTL (RIP-Seq). Colored double borders indicate that the molecule is a complex. have identified the existence non-coding RNAs (such as lincRNAs and others) and have strongly suggested important roles for these evolutionally conserved genes in biology.
Many early analyses of RNP structure using RIP techniques were performed with HuR and family members since they are stabilizer RBPs, making it much easier to recover low copy number mRNA targets [1,5]. However, these approaches have been expanded to include other RNA binding proteins [42,43]. Data from different groups indicate that HuR interacts with miRNAs in a cell specific context to either positively or negatively regulate target gene expression [44,45]. Many of the previous works were limiting in that transformed cancer cell lines were used. Few studies were performed in primary cells or using tissues from animal models. However, to identify physiologically relevant immune regulation, it was imperative to expand utility of these techniques to in vivo animal models using primary cells. Another advantage of HuR genetic ablation approaches is that one can generate very low levels of HuR to achieve essentially a null allele in tissues, which can be difficult with conventional siRNA approaches.
It had been previously demonstrated that HuR plays crucial roles in cancer and immune cells, especially during T cell activation and development. We used a novel conditional HuR KO which ablates HuR following T cell activation since earlier ablation may interfere with T cell development and potentially alter gene expression profiles [7]. We focused upon CD4 + T cell differentiation since our earlier work as well as from other groups, has demonstrated the significance of HuR in T cell cytokine expression [7,8,46,47]. Table 6. Details of the hits of canonical pathway CD28 Signaling in T Helper Cells. Official symbols of the significant genes found via RNA-Seq (RIP--Seq) that are in the pathway (Fig 3) is given along with the name of its corresponding node in the pathway. Fold is given as KO/CTL (RNA-Seq) or IP/CTL (RIP-Seq), and q is the false discovery rate value based on method of Benjamini-Hochberg. Genes were identified by both RNA-Seq and RIP-Seq. Numbers in parentheses and italicized denote data obtained from RIP-Seq.

Symbol
Member We analyzed data using Ingenuity Pathway Analysis (IPA) to identify top canonical pathways in which multiple genes were affected to probe HuR function in Th2 differentiated CD4 + T cells. To our knowledge, this is first report studying direct and indirect HuR immune targets from primary CD4 + T cells using high coverage next generation sequencing. Our results identify many direct and indirect HuR gene targets which may play important roles in T cell activation and Th2 differentiation. We have verified not only various HuR targets common among other cell types but have also discovered novel HuR target transcripts specific for Th2 cell differentiation. Our data show that HuR may be involved in multiple T cell activation pathways especially in CD28 family co-stimulatory molecule signaling pathways. However, we note care in interpreting the data, since RNP formation has both kinetic and spatial components so that timing of HuR ablation may alter target milieu. It would be prudent to confirm any putative HuR mRNA targets identified by RIP-Seq and RNA-Seq at the level of translation. Indeed, a recent publication has used an RNA-Seq heavy polysome screen to identify translationally active HuR targets [48].HuR may function differently in distinct cell types since the mRNA and miRNAs expression milieu varies. In future studies, it will be important to vary timing of HuR ablation and determine the effects, if any, upon direct and indirect mRNA targets.
In summary, our data combined with studies from other labs can help delineate posttranscriptional networks by which RBPs, miRNAs and lincRNAs can coordinately affect cellular gene expression at different points. Such information can directly aid in our understanding of many pro-inflammatory disease processes such as asthma and autoimmunity.