MicroRNAs (miRNAs) regulate specific immune mechanisms, but their genome-wide regulation of T lymphocyte activation is largely unknown. We performed a multidimensional functional genomics analysis to integrate genome-wide differential mRNA, miRNA, and protein expression as a function of human T lymphocyte activation and time. We surveyed expression of 420 human miRNAs in parallel with genome-wide mRNA expression. We identified a unique signature of 71 differentially expressed miRNAs, 57 of which were previously not known as regulators of immune activation. The majority of miRNAs are upregulated, mRNA expression of these target genes is downregulated, and this is a function of binding multiple miRNAs (combinatorial targeting). Our data reveal that consideration of this complex signature, rather than single miRNAs, is necessary to construct a full picture of miRNA-mediated regulation. Molecular network mapping of miRNA targets revealed the regulation of activation-induced immune signaling. In contrast, pathways populated by genes that are not miRNA targets are enriched for metabolism and biosynthesis. Finally, we specifically validated miR-155 (known) and miR-221 (novel in T lymphocytes) using locked nucleic acid inhibitors. Inhibition of these two highly upregulated miRNAs in CD4+ T cells was shown to increase proliferation by removing suppression of four target genes linked to proliferation and survival. Thus, multiple lines of evidence link top functional networks directly to T lymphocyte immunity, underlining the value of mapping global gene, protein, and miRNA expression.
T lymphocytes regulate the adaptive immune response by serving as Ag-specific effector cells. Activation via TCR engagement and CD28 costimulation is characterized by gene upregulation (1) and is a highly regulated process requiring coordination of multiple signaling pathways for proliferation, cytokines, and differentiation. After Ag clearance, some effector cells must be reduced or eliminated by mechanisms like activation-induced cell death (2). Thus, activation must be regulated in a coordinated fashion to achieve a balance among proliferation, memory, and quiescence.
MicroRNAs (miRNAs) have emerged as posttranscriptional regulators of gene expression in a variety of biological processes (3–7). The mode of miRNA regulation is protein repression via complementary sequence recognition in the 3′ untranslated region of the target mRNA and/or degradation of the target transcript (8–11). A recent paper indicates the major effect of miRNAs is to decrease mRNA levels (12).
miRNAs can potentially regulate hundreds of proteins (13) and modulate concentration of proteins over a narrow range in a dose-dependent manner (14, 15). miRNAs are involved in hematopoietic cell function and development (as summarized in Refs. 16–64). A few miRNAs have been linked to specific T lymphocyte mechanisms—181a (37), 181c (39), 155 (28), 150 (18), 146 (20), and 142 (40)—via regulation of T cell sensitivity to Ag stimulation, regulating transcription factors, and activation-induced cell death. However, at the global level, little is known about the impact of activation-induced miRNAs on mRNA and protein expression in human T lymphocytes, particularly in the context of mapping miRNA-regulated molecular networks.
In this study, we show that differentially upregulated miRNAs regulate T lymphocyte activation by targeting highly differentially expressed genes involved in networks critical for cell activation, proliferation, and survival. We used a multidimensional approach to integrate genome-wide miRNA, mRNA, and protein expression. We surveyed expression for 420 human miRNA sequences at 0, 24, 48, and 72 h after activation. In parallel, we profiled global mRNA and protein expression. We found 71 significantly differentially expressed miRNAs, of which 57 have not been previously linked to T lymphocyte function. Testing several established miRNA target prediction algorithms, we demonstrated globally that targets of multiple upregulated miRNAs (combinatorial targeting) have decreased mRNA expression with activation. In validation, we showed that inhibition of two highly upregulated miRNAs in CD4+ T cells increased proliferation by removing suppression of four target genes involved in proliferation and survival. Thus, our studies provide novel evidence for a large number of functional molecular networks populated by downregulated targets of highly upregulated miRNAs.
Materials and Methods
T lymphocyte isolation
Blood draw for this study was accepted by our institution’s ethical commission, and all subjects gave their written consent according to review board guidelines. CD2+ T lymphocytes were purified from Ficoll-Hypaque density-separated PBMCs of seven healthy human donors. MACS CD2+ micromagnetic beads were used for the positive isolation of CD2+ T cells using an MACS separator with LS columns (Miltenyi Biotec). CD4+ T lymphocytes were isolated from PBMCs of three human blood donors by negative selection with the MACS CD4+ T Cell Negative Isolation Kit II (Miltenyi Biotec) according to the manufacturer’s protocol.
Lymphocyte activation, RNA, and protein isolation
Freshly isolated CD2+1). The cells were harvested and stabilized in RNALater (Ambion) at 0, 24, 48, and 72 h postactivation. Total RNA was extracted using the mirVana miRNA Isolation Kit (Ambion), which also allows for the isolation of the total proteome fraction.
TaqMan stem-loop RT-PCR method (65+ T lymphocyte samples. A two-tailed Student t test with a p value threshold of 0.05 and false discovery rate (FDR)-adjusted p value (q-value) threshold of 0.1, for which q-value = p value × number tested/rank, between 0 and 48 h was used on the normalized data to identify differentially expressed miRNAs. A q-value of 0.1 implies that 10% of significant tests will result in false positives. miRNA expression following locked nucleic acid (LNA) nucleoporation in CD4+
Data have been deposited in the Gene Expression Omnibus under accession number GSE14352 and can be viewed at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14352.
Differential gene expression analysis
p value thresholds for p < 0.01; FDR 0.01). Fold changes and p value (two-tailed t test assuming unequal variance) were calculated for each time point comparison. Statistics were performed for specified pairwise comparisons among all time points of activation.
In parallel, we performed our own analysis of differential gene expression to corroborate AltAnalyze results. CEL files for each donor from the 1.0ST HuEx Arrays were normalized by robust multiarray averaging using a custom cumulative distribution function downloaded from the University of Michigan (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/genomic_curated_CDF.asp). Differential expression was measured with the Limma package (http://www.bioconductor.org/packages/2.6/bioc/html/limma.html) using a two-class model. All calculations were performed in R/Bioconductor. Genes were filtered using a fold-change filter of 1.5 (0.58 in log2) and an FDR filter of 0.01. Genes that were detected as significantly differentially expressed by both AltAnalyze and Limma analysis were then selected for further analysis.
The Multidimensional Protein Identification Tool proteomics
The Multidimensional Protein Identification Tool (66) protocol was used as described previously (1t test was used for hypothesis testing, and the significant differentially expressed proteins (p < 0.05) were considered for functional analysis.
miRNA target analysis
For prediction of target genes of differentially expressed miRNAs, three publicly available algorithms were used: PITA, MiRanda, and TargetScan 5.1. In the end, TargetScan predictions based on conservation scores were used to compute the 50th percentile targets in our expressed gene set.
We used Ingenuity Pathways Analysis (IPA; https://analysis.ingenuity.com) to map molecular pathways and networks populated by predicted miRNA targets. The IPA Database is a constantly curated resource of published literature on gene functions and interactions. Canonical pathway and networks analysis was carried out by uploading the predicted downregulated genes targeted by the upregulated miRNAs. Significance of association between genes and pathway was measured by the Benjamini and Hochberg multiple testing corrected p value that can be interpreted as an upper bound for the expected fraction of falsely rejected null hypotheses among all functions with p values smaller than the threshold of 0.05. Network node genes were based on an especially high degree of links to other genes in the IPA database.
A total of 3 × 106 primary human CD4+
Cell proliferation assay
Cell proliferation was measured using the Ziva Cell Proliferation Assay (Jaden BioScience). Electroporated CD4+ T lymphocytes were plated at 8 × 104/well in 96-well plates in duplicate for each condition and activated for 48 h with CD3/CD28 beads. Cells were pulsed with 10 μM BrdU/well 18 h before harvesting. Forty-eight hours after activation, cells were harvested, expander beads removed, and 104 cells/well were plated in 96-well Thermo Scientific Nunc Plates (Fisher). Cell proliferation was measured with a chemiluminescent substrate to detect the presence of an anti-BrdU Ab labeled with alkaline phosphatase on the Insight-Mi Luminometer (Jaden BioScience). The signal was fully developed and measured 60 min after the addition of the substrate.
A total of 300 ng total RNA from CD4+67) value was determined and specific gene expression normalized to endogenous control using δ-δ threshold cycle (Ct) method. The normalized δ Ct from LNA-transfected samples was then compared with the scrambled control to obtain δ-δ Ct values and used to calculate relative fold change compared with control. Experiments were performed in triplicate. The primer probe sequences for validation assays were as follows: IKBKE Probe (0.5 nM), 5′-/56-FAM/TAC CTG ATC /ZEN/CCG GCT CTT CAC CA/3IABkFQ/-3′; IKBKE Primer1 (1 nM), 5′-CAT CTT GTC CAA ACA GCA CTG-3′; IKBKE Primer2 (1 nM): 5′-AAA ATATCA CGG AGA CCC AGG-3′; FOS Probe (0.5 nM), 5′-/56-FAM/TGC AGA CCG /ZEN/AGATTG CCA ACCT/3IABkFQ/-3′; FOS Primer1 (1 nM), 5′-CAT CAG GGATCT TGC AGG C-3′; FOS Primer2 (1 nM), 5′-GACTGA TAC ACT CCA AGC GG-3′; IRS2 Probe (0.5 nM), 5′-/56-FAM/AGG CCA CCA /ZEN/TCG TGA AAG AGT GAA G/3IABkFQ/-3′; IRS2 Primer1 (1 nM), 5′-TGA CAT CCT GGT GAT AAA GCC-3′; IRS2 Primer2 (1 nM), 5′-ACT TCT TGT CCC ACC ACT TG-3′; PIK3R1 Probe (0.5 nM), 5′-/56-FAM/CAC AAT GCT /ZEN/TTA CTT CGC CGT CCA C/3IABkFQ/-3′; PIK3R1 Primer1 (1 nM), 5′-CTG TAC AAGTTATAG GGCTCG G-3′; and PIK3R1 Primer2 (1 nM), 5′-GAT GGC ACT TTT CTT GTC CG-3′.t) (
All statistical analyses used the Student t test of at least three independent experiments, unless stated otherwise. Differences with p values <0.05 are considered significant.
Activated T lymphocytes demonstrate a unique activation-induced miRNA signature
We used a multidimensional approach to integrate genome-wide miRNA, mRNA, and protein expression (Fig. 1A). We activated human T lymphocytes via CD3/CD28 costimulation and harvested cells at 0, 24, 48, and 72 h. This activation strategy modeled allogeneic activation (68, 69). We surveyed miRNA expression using quantitative real-time PCR (qPCR) for 420 human miRNA sequences. Specific miRNAs were differentially expressed in T lymphocytes as a function of activation. We identified 71 differentially expressed miRNAs (p < 0.05; q < 0.1) between 0 and 48 h, of which 51 were upregulated (Table I). We chose 48 h as a key time point in T lymphocyte activation based on peak cell proliferation, cytokine production, and expression of activation markers (1). These changes in miRNA expression are robust across all donors (Fig. 1B). The top 12 upregulated miRNAs were miR-221, -210, -98, -29b, -155, -218, -455-3p, -449, -548d, -222, -132, and -18a. The top downregulated miRNAs were miR-181a, -223, -224, -150, -146b, -126, -127-3p, -376a, -100, -99a, -125b, and -26a (Table II).
Based on current literature, of these 71 differentially expressed miRNAs, only 14 have a documented function in T lymphocytes: miR-150 (18, 19), miR-155 (18, 25), miR-181a (37), miR-106a (31, 33), the miR-17–92 cluster (30, 32), miR-24 (6, 47), miR-21 (18, 53), miR-223 (41), and miR-let-7f (18) (Tables I, II). Five additional miRNAs are linked to development or aberrant activation of hematopoietic cells (Supplemental Table I). For example, our results indicate that miR-150 is downregulated in T cells upon activation, consistent with studies in murine lymphocytes (19). In contrast, miR-155 is upregulated in activated human and murine T lymphocytes (26). Importantly, we identified 57 differentially expressed miRNAs currently undocumented in T lymphocyte activation.
Predicted targets of differentially upregulated miRNAs are globally downregulated
Starting with the 71 differentially expressed miRNAs, we mapped the gene targets of the 51 differentially upregulated. Predictive algorithms rely on multiple parameters: seed complementarity, thermodynamics, and biochemical properties of binding and evolutionary conservation. Unfortunately, these algorithms suffer from high false-positive and -negative rates. Combining predictions from different algorithms may be useful, but there is little overlap in top targets predicted by different algorithms (70, 71). We tested 50th percentile predictions for these 51 differentially upregulated miRNAs using four algorithms: PITA (72), MiRanda (73), and TargetScan5.1 context or conservation scores (74). We measured the change (δ) in mRNA expression between 0 and 48 h for all genes above background (expression ≥6.5; log2 scale). We then plotted differences in distribution of deltas between predicted targets and nontargets. MiRanda failed to produce any expression correlations with our data and was not used further. Predictions with TargetScan and PITA based on testing the effects of single miRNA binding also revealed no shift in mRNA signals with activation. In contrast, plotting the gene expression of targets predicted to bind multiple upregulated miRNAs (e.g., ≥4 or ≥7) revealed that combinatorial miRNA binding decreases mRNA expression (Fig. 1C, 1D). Combinatorial targeting benchmarked at the 50th percentile with both PITA and TargetScan gave the best predictions (Fig. 1E–J). As shown in Fig. 1I and 1J, TargetScan conservation predictions with combinatorial binding of four or more miRNAs show the best results correlating increased miRNA binding with decreased target gene expression.
T lymphocyte activation is marked by global gene upregulation including miRNA-processing machinery
We showed previously that T lymphocyte activation is dominated by widespread differential gene upregulation (1). We therefore analyzed differential gene expression in parallel with miRNA expression. Genome-wide mRNA transcript analysis revealed 3798 differentially expressed mRNA transcripts between 0 and 48 h (p < 0.01; FDR 1%): 3362 upregulated (89%) and 436 downregulated. Upregulation of the miRNA processing/biogenesis genes included: XPO5, EIF2C2/AGO2, SIP1/GEMIN2, -4, -5, -6, and -7, RANGAP1, YBX1, and ADARB1 (Table III).
Predicted targets of upregulated miRNAs populate networks associated with immunity, cell survival, and proliferation
Using TargetScan conservation predictions, we identified 1640 candidate miRNA targets, of which 214 were downregulated (Supplemental Table II). Thus, half of all 436 downregulated genes are targets of upregulated miRNAs. Functional pathway and network enrichment analysis was done for the 182 out of 214 downregulated targets that mapped to known functional pathways and the 200 that mapped to molecular networks.
Pathway analysis revealed statistically significant enrichment for 71 canonical pathways (multiple test correction p value <0.05), with the top 30 pathways shown in Fig. 2A. Represented were primarily immune signaling pathways including IL-12, PI3K, IL-10, CD40, NFAT, sphingosine 1-phosphate, and TCR. Cell survival, growth, and proliferation pathways included prolactin, TNFR2, ceramide, thrombopoietin, and p70S6K signaling. In sharp contrast, differentially expressed genes that were not predicted targets of miRNAs were highly enriched for metabolism and biosynthesis pathways (Fig. 2B).
Molecular networks were constructed from the miRNA targets with downregulated expression. Network eligibility was based on connectivity to other genes with known interactions. Highly connected genes represent network nodes or hubs where closely connected genes are functionally similar. The top network was comprised of 21 genes significantly enriched for functions linked to T lymphocyte activation, proliferation, and survival (Fig. 2C). The hub genes in this network are PIK3R1 with six connections and ATM, PARK2, HIP1R, and NCAM1 with three connections each. Members of this network are predicted targets for 17 upregulated miRNAs. The central node gene PIK3R1 belongs to the phosphoinositide 3-kinase family that phosphorylates phosphatidylinositol-(4,5)-biphosphate to phosphatidylinositol-(3,4,5)-triphosphate to regulate cell proliferation, and cytokine production (75). PIK3R1 is a predicted target of four miRNAs (miR-155, -21, -218, and -221). Thus, downregulated gene targets of upregulated activation-induced miRNAs are associated with proliferation and cell survival signaling networks. We identified 12 other target gene networks (Supplemental Table III).
Predicted targets of downregulated miRNAs are upregulated with activation
With respect to the impact of the 20 downregulated miRNAs, we predicted 1347 gene targets out of the total of 3798 activation-induced genes. A total of 487 genes were only targeted by downregulated miRNAs. In contrast, the majority (860) was also targeted by upregulated miRNAs, matching our observations on the apparent importance of combinatorial targeting. We predicted that targets of only downregulated miRNAs should have upregulated mRNA levels. Indeed, 410 (84%) were upregulated >1.5-fold.
We mapped the functional pathways enriched for these two classes of targets. The pathways linked to only downregulated miRNAs are predominantly cell metabolism and biosynthesis. Because miRNAs targeting these genes are downregulated with activation, the presumed regulation of their targets is removed or at least significantly decreased. The functional role of these genes in metabolism and biosynthesis, much like the genes we found were not targeted by miRNAs, supports the observation that activation-induced miRNAs target a functionally distinct class of genes. In contrast, the pathways linked to combinatorial targeting by both up- and downregulated miRNAs are enriched for signaling in immunity, growth, and cell proliferation (Fig. 3).
Correlating global protein expression to predicted miRNA targets
Our hypothesis was that upregulated miRNAs regulate the immune response and should repress target proteins during T lymphocyte activation. Target protein repression can be accomplished by either inhibiting translation or enhancing mRNA degradation. Although it has been shown that most translational repression is coupled to miRNA-mediated mRNA degradation (12, 14), we considered the possibility that some miRNA targets might be specifically repressed at the translational level without decreases in corresponding mRNAs. A focus exclusively on the downregulation of mRNAs would miss such targets. Therefore, a high-throughput shotgun proteomics protocol (66) was used to analyze global protein expression between 0 and 48 h.
A total of 589 differentially expressed proteins and another 876 proteins expressed uniquely at 0 or 48 h were identified. Correlating the predicted gene targets of miRNAs to expressed proteins, we identified 234 protein–mRNA transcript targets of upregulated miRNAs (Supplemental Table IV). Eighty-one of these proteins had decreased expression at 48 h. Interestingly, 70 of these protein targets have upregulated mRNA expression. Thus, these proteins are regulated by posttranscriptional mechanisms not coupled to mRNA decay. Functional analysis of these 81 downregulated protein targets revealed significant enrichment for signaling pathways in immune response, cell cycle, growth, and proliferation (Fig. 4A). In contrast, the 153 upregulated protein targets were enriched for only four pathways: RAN signaling; glycolysis/gluconeogenesis; phenylalanine, tyrosine, and tryptophan biosynthesis; and alanine and aspartate metabolism.
Because networks represent integration of multiple associations, we examined the top 3 networks to identify 19 downregulated proteins as predicted targets of ≥1 miRNAs (Fig. 4B). Within these 19 genes was AHNA, targeted by miR-200b and miR-7, and critical for calcium entry during immune T lymphocyte activation (76). AHNA was 2.2-fold down at the mRNA level and 8-fold down at the protein level. ATM, targeted by miR-132, miR-18a, and miR-21, regulates cell cycle, promotes normal lymphocyte development, and protects from neoplastic transformation (77). ATM was 2.8-fold down by mRNA and 4.8-fold down by protein. PIK3R1 is an adaptor kinase involved in TCR signaling and CD28 costimulation with 3-fold mRNA and 2.6-fold protein downregulation. It is a predicted target of four upregulated miRNAs (Fig. 2C) and is identified as a downregulated network hub by both gene expression and proteomics.
Several network proteins demonstrated increased mRNA but decreased protein levels (Fig. 4B). LMNB1, a predicted target of miR-218 and miR-7, showed the highest mRNA upregulation by 7-fold but was 1.5-fold down by protein. Inhibition of T lymphoblast proliferation is associated with downregulation of LMNB1 protein (78). CRKL is targeted by four miRNAs and involved in signal transduction through WIP, JNK, and ZAP70 (79). IQGAP1, targeted by two miRNAs, regulates lymphocyte cytoskeleton rearrangement in the immune synapse (80). Thus, these multiple lines of evidence linking the top functional networks directly to T lymphocyte immunity underline the value of such mapping based on global gene, protein, and miRNA expression.
Knockdown of miR-221 and miR-155 increases T lymphocyte proliferation by removing negative regulation of target genes
The premise of target predictions and functional network mapping is that upregulated miRNAs regulate genes that populate critical networks in T lymphocyte activation. To validate our approach, we knocked down two of the highest upregulated miRNAs: miR-221 and miR-155. Validations were done using purified CD4+ T lymphocytes to simplify the cell subset composition and reflect our recent finding that CD4+ T lymphocytes are selectively activated and proliferatively expanded in the early posttransplant period (81). We confirmed that miR-221 and 155 were significantly upregulated in CD4+ T cells at 48 h of activation (data not shown). Although the function of miR-155 has been widely studied in T cells (26, 41), miR-221 associated with cell cycle progression (44) has not been studied in T lymphocytes.
The impact of inhibiting miR-221 and -155 on T lymphocyte proliferation was measured by transfecting cells with specific inhibitors or scrambled controls followed by activation. We obtained >60% knockdown of both miRNAs in three donors (n = 3) as measured by qPCR (Fig. 4C). Significantly increased proliferation resulted from inhibiting either miR-155 or -221 as compared with scrambled control (Fig. 4D).
Among the predicted targets of miR-221 and miR-155, we chose four genes for validation by qPCR: PIK3R1, FOS, IRS2, and IKBKE (Fig. 4E). FOS and IKBKE have been previously validated as targets of miR-221 (82) and -155 (83), respectively. At 48 h after activation, the expression of FOS and PIK3R1 is statistically increased by knocking down these two miRNAs. Although changes in IKBKE levels were not statistically significant, IRS2, a predicted target of miR-155, increased expression after miR-155 knockdown. Upregulation of target genes following knockdown of miR-221 and -155 is consistent with the evidence above for mRNA repression mediated by miR-221 and/or -155 during T lymphocyte activation (Fig. 4F).
We investigated genome-wide miRNA, mRNA, and protein expression following human T lymphocyte activation. T lymphocyte activation relies on signaling cascades that create a balance between activation, memory, and quiescence. This balance is modulated by mechanisms regulating gene expression including posttranscriptional miRNA regulation. In this study, we show a unique miRNA signature with a total of 71 differentially expressed miRNAs with 51 upregulated between 0 and 48 h. This signature comprises 57 miRNAs with no documented roles in T lymphocyte function. Additionally, our data validated previous findings for a number of miRNAs with known functions in T lymphocytes: upregulation of miR-155 can regulate the susceptibility of human and murine CD4+ T cells to natural regulatory T cell-mediated suppression (26), the miR-17–92 cluster inhibits T cell activation (30, 32), miR-106a is implicated in IL-10 regulation (31, 33), miR-24 can inhibit cell proliferation by targeting cell-cycle genes (6, 47), and miR-21, upregulated by STAT3, prevents CD4+ T cell apoptosis and is implicated in lymphocyte oncogenesis (18, 53). Also, consistent with the importance of activation-induced miRNA expression, we observed upregulation of 10 miRNA biogenesis/processing machinery genes.
miRNAs are known inhibitors of gene expression. The challenge is to map miRNAs to specific gene targets and the molecular networks they regulate. To address this challenge, we investigated the predictive values of four widely used computational algorithms. First, if the results from all of the algorithms are compared using a single miRNA hit/seed approach, the predicted targets are poorly correlated between methods to the extent that different methods will report very different results. Second, single hit/seed predictions did not correlate with mRNA repression. In contrast, combinatorial targeting (multiple seeds per target) gave the best predictions. TargetScan conservation predictions with combinatorial binding of four or more miRNAs showed the correlation between increased miRNA binding with decreased target gene expression.
By integrating activation-induced miRNA, mRNA, and protein expression changes with target predictions, we tested our hypothesis that target genes are involved in regulating immune activation, cell proliferation, and survival. Indeed, functional analysis demonstrated that downregulated miRNA targets populated signaling pathways highly enriched for immune response, proliferation, and survival. In contrast, activation-induced genes not predicted to be miRNA targets demonstrated significant enrichment for pathways of metabolism, DNA stability, and cell cycle. These novel results reveal that predicted targets of activation-induced miRNAs are functionally distinct from nontarget genes and presumably evolved with distinct selection pressures for such regulation. We also hypothesized that some targets might be specifically regulated by posttranscriptional mechanisms not coupled to mRNA decay. Based on our proteomics, we detected a number of such downregulated protein targets despite increased mRNA expression. Thus, inhibition of protein translation is not always coupled to corresponding mRNA degradation.
By investigating connectivity between predicted targets, we identified highly connected genes that function as network nodes, with closely connected genes being functionally similar. The top nodal gene PIK3R1 of one such network is a predicted target of miR-221 and -155 and downregulated at both mRNA and protein levels. This gene is an adaptor kinase involved in TCR signaling and CD28 costimulation and regulates cell growth, proliferation, and T cell cytokine production (75). Thus, functional network analysis underlines the value of the mapping done in this study based on global gene, protein, and miRNA expression.
In validation, we focused on the functional roles of two top upregulated miRNAs in our data: miR-155, widely studied in T cells, and miR-221, not previously associated with T cell function. Knockdown of either miRNA produced a significant increase in proliferation of activated CD4+ T cells, confirming that these two miRNAs actually have antiproliferative roles during activation. We identified four potential targets of miR-155 and/or -221 and mapped a functional network critical to T cell activation (Fig. 4F). In addition to identifying PIK3R1 as a new target gene for miR-155 and -221, we discovered that the transcription factor FOS is also a target of both miR-155 and -221. We identified two more targets of miR-155: the novel IRS2, an adaptor for tyrosine kinases, and a previously verified miR-155 target, IKBKE, that regulates NF-κB activation (83). Knockdown of miR-221 and/or -155 increased target mRNA expression for PIK3R1, FOS, and IRS2.
In conclusion, we propose a model in which, in the course of T lymphocyte activation by TCR engagement and CD28 costimulation, there is a significant upregulation of miRNAs that are critical to this process. These activation-induced miRNAs create a negative-feedback loop to inhibit cell proliferation and regulate cell survival by targeting a series of molecular networks that we have mapped. In parallel, there is also a subset of miRNAs downregulated by activation, and 84% of their predicted target genes are shown to be upregulated at the mRNA level. Moreover, there is a functionally specific class of genes linked closely to the immune response that evolved to be the natural targets of these miRNAs in T lymphocytes, with functions revealed by molecular networking mapping that are clearly distinct from the activation-induced genes that are not miRNA targets.
The authors have no financial conflicts of interest.
We thank Priscilla Crisler for human blood drawing and Drs. William Soo Hoo and Connie Kohne (Jaden BioScience) for expert assistance in developing the cell proliferation assay.
This work was supported by National Institutes of Health Grants U19 A1063603 and R01 AI081757.
Y.A.G., S.M.K., and D.R.S. conceived and designed the experiments and wrote the manuscript; Y.A.G., S.M.K., and A.A.N. performed the experiments; Y.A.G., A.A.N., and T.H. analyzed the data; and C.C., D.C., S.R.H., and J.R.Y. contributed reagents/materials/analysis tools.
The sequences presented in this article (entire set of CEL files) have been submitted to the National Center for Biotechnology Information Gene Expression Omnibus under accession number GSE14352 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14352).
The online version of this article contains supplemental material.
Abbreviations used in this article:
- threshold cycle
- false discovery rate
- Ingenuity Pathways Analysis
- locked nucleic acid
- quantitative real-time PCR.
- Received May 2, 2011.
- Accepted June 17, 2011.
- Copyright © 2011 by The American Association of Immunologists, Inc.