MicroRNA Regulation of Molecular Networks Mapped by Global MicroRNA, mRNA, and Expression in Activated T Lymphocytes This information is current as of September 23, 2021. Yevgeniy A. Grigoryev, Sunil M. Kurian, Traver Hart, Aleksey A. Nakorchevsky, Caifu Chen, Daniel Campbell, Steven R. Head, John R. Yates III and Daniel R. Salomon J Immunol 2011; 187:2233-2243; Prepublished online 25 July 2011; Downloaded from doi: 10.4049/jimmunol.1101233 http://www.jimmunol.org/content/187/5/2233 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2011/07/25/jimmunol.110123 Material 3.DC1 References This article cites 83 articles, 31 of which you can access for free at: http://www.jimmunol.org/content/187/5/2233.full#ref-list-1

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The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2011 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology

MicroRNA Regulation of Molecular Networks Mapped by Global MicroRNA, mRNA, and Protein Expression in Activated T Lymphocytes

Yevgeniy A. Grigoryev,* Sunil M. Kurian,* Traver Hart,* Aleksey A. Nakorchevsky,† Caifu Chen,‡ Daniel Campbell,x Steven R. Head,x John R. Yates, III,† and Daniel R. Salomon*

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, Downloaded from 57 of which were previously not known as regulators of immune activation. The majority of miRNAs are upregulated, mRNA expression of these target 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. http://www.jimmunol.org/ 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 , protein, and miRNA expression. The Journal of Immu- nology, 2011, 187: 2233–2243.

lymphocytes regulate the adaptive immune response by coordinated fashion to achieve a balance among proliferation, serving as Ag-specific effector cells. Activation via TCR memory, and quiescence. T engagement and CD28 costimulation is characterized by MicroRNAs (miRNAs) have emerged as posttranscriptional by guest on September 23, 2021 gene upregulation (1) and is a highly regulated process requiring regulators of in a variety of biological processes coordination of multiple signaling pathways for proliferation, cyto- (3–7). The mode of miRNA regulation is protein repression via kines, and differentiation. After Ag clearance, some effector cells complementary sequence recognition in the 39 untranslated region must be reduced or eliminated by mechanisms like activation- of the target mRNA and/or degradation of the target transcript (8– induced cell death (2). Thus, activation must be regulated in a 11). A recent paper indicates the major effect of miRNAs is to decrease mRNA levels (12). miRNAs can potentially regulate hundreds of (13) and *Department of Molecular and Experimental Medicine, The Scripps Research In- modulate concentration of proteins over a narrow range in a dose- † stitute, La Jolla, CA 92037; Department of Chemical Physiology, The Scripps Re- dependent manner (14, 15). miRNAs are involved in hematopoi- search Institute, La Jolla, CA 92037; ‡Applied Biosystems, Foster City, CA 94404; and xDNA Microarray Core, The Scripps Research Institute, La Jolla, CA 92037 etic cell function and development (as summarized in Refs. 16– Received for publication May 2, 2011. Accepted for publication June 17, 2011. 64). A few miRNAs have been linked to specific T lymphocyte This work was supported by National Institutes of Health Grants U19 A1063603 and mechanisms—181a (37), 181c (39), 155 (28), 150 (18), 146 (20), R01 AI081757. and 142 (40)—via regulation of T cell sensitivity to Ag stimula- Y.A.G., S.M.K., and D.R.S. conceived and designed the experiments and wrote the tion, regulating transcription factors, and activation-induced cell manuscript; Y.A.G., S.M.K., and A.A.N. performed the experiments; Y.A.G., A.A.N., death. However, at the global level, little is known about the im- and T.H. analyzed the data; and C.C., D.C., S.R.H., and J.R.Y. contributed reagents/ materials/analysis tools. pact of activation-induced miRNAs on mRNA and protein ex- pression in human T lymphocytes, particularly in the context of The sequences presented in this article (entire set of CEL files) have been submitted to the National Center for Biotechnology Information Gene Expression Omnibus mapping miRNA-regulated molecular networks. under accession number GSE14352 (http://www.ncbi.nlm.nih.gov/geo/query/acc. In this study, we show that differentially upregulated miRNAs cgi?acc=GSE14352). regulate T lymphocyte activation by targeting highly differentially Address correspondence and reprint requests to Dr. Daniel R. Salomon, Department expressed genes involved in networks critical for cell activation, of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, Mail Code MEM-L55, La Jolla, CA 92037. E-mail address: proliferation, and survival. We used a multidimensional approach [email protected] to integrate genome-wide miRNA, mRNA, and protein expression. The online version of this article contains supplemental material. We surveyed expression for 420 human miRNA sequences at 0, 24,

Abbreviations used in this article: Ct, threshold cycle; FDR, false discovery rate; IPA, 48, and 72 h after activation. In parallel, we profiled global mRNA Ingenuity Pathways Analysis; LNA, locked nucleic acid; miRNA, microRNA; qPCR, and protein expression. We found 71 significantly differentially ex- quantitative real-time PCR. pressed miRNAs, of which 57 have not been previously linked to Copyright Ó 2011 by The American Association of Immunologists, Inc. 0022-1767/11/$16.00 T lymphocyte function. Testing several established miRNA target www.jimmunol.org/cgi/doi/10.4049/jimmunol.1101233 2234 microRNA-REGULATED NETWORKS IN T CELL ACTIVATION prediction algorithms, we demonstrated globally that targets of performed for specified pairwise comparisons among all time points of multiple upregulated miRNAs (combinatorial targeting) have de- activation. creased mRNA expression with activation. In validation, we In parallel, we performed our own analysis of differential gene ex- pression to corroborate AltAnalyze results. CEL files for each donor from showed that inhibition of two highly upregulated miRNAs in the 1.0ST HuEx Arrays were normalized by robust multiarray averaging CD4+ T cells increased proliferation by removing suppression of using a custom cumulative distribution function downloaded from the four target genes involved in proliferation and survival. Thus, University of Michigan (http://brainarray.mbni.med.umich.edu/Brainarray/ our studies provide novel evidence for a large number of func- Database/CustomCDF/genomic_curated_CDF.asp). Differential expression was measured with the Limma package (http://www.bioconductor.org/ tional molecular networks populated by downregulated targets of packages/2.6/bioc/html/limma.html) using a two-class model. All calcu- highly upregulated miRNAs. lations 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 AltA- Materials and Methods nalyze and Limma analysis were then selected for further analysis. T lymphocyte isolation The Multidimensional Protein Identification Tool proteomics Blood draw for this study was accepted by our institution’s ethical com- mission, and all subjects gave their written consent according to review The Multidimensional Protein Identification Tool (66) protocol was used board guidelines. CD2+ T lymphocytes were purified from Ficoll-Hypaque as described previously (1). Protein fraction was denatured, alkylated, and density-separated PBMCs of seven healthy human donors. MACS CD2+ trypsin digested. A total of 50 mg digested protein sample at 0 and 48 h micromagnetic beads were used for the positive isolation of CD2+ T cells was run in four technical replicates. Data were acquired using an LTQ LX using an MACS separator with LS columns (Miltenyi Biotec). CD4+ linear ion trap mass spectrometer (Thermo Fisher Scientific) interfaced in- T lymphocytes were isolated from PBMCs of three human blood donors by line with two-dimensional HPLC in a data-dependent manner in which Downloaded from negative selection with the MACS CD4+ T Cell Negative Isolation Kit II each analytical full scan (mass spectrometry; range 200–2000 mass-to- (Miltenyi Biotec) according to the manufacturer’s protocol. charge ratio units) was followed by three fragmentation scans (tandem mass spectrometry) that targeted the three most abundant ions from the full Lymphocyte activation, RNA, and protein isolation scan. The 40-microsecond collision-induced dissociation pulses of 35% intensity were used for precursor ion fragmentation. A default exclusion Freshly isolated CD2+ T lymphocytes were resuspended in RPMI 1640 list (Xcalibur 2.0; Thermo Fisher Scientific) of 180-s, 50 precursor ion medium (HyClone) supplemented with 10% (v/v) FBS and 2 mM gluta- members was used for data acquisition. Raw data were searched against mine, with penicillin (100 U/ml) and streptomycin (100 U/ml), and acti- the European Bioinformatics Institute database (12/01/2006 release) sup- http://www.jimmunol.org/ vated with CD3/CD28 Dynal beads (Invitrogen) according to the manu- plemented with a decoy database in which each entry of the original pro- facturer’s protocol. Cell activation was confirmed by flow cytometry for tein contains its reversed sequence. Database searching used SEQUEST the following activation markers: CD134 (OX40), CD150 (SLAM), CD25 (v27), and outcomes were filtered using DTASelect version 2.0. Protein (IL-2Ra), CD69, and CD71 (transferrin receptor); and intracellular cyto- identifications were extracted, and a measure of normalized amino acid kines: IFN-g, IL-10, IL-2, IL-4, and TNF-a for activated T cells as shown coverage was used as label-free quantification. Relative quantifications previously (1). The cells were harvested and stabilized in RNALater were done using spectral counts normalized to the median of the total (Ambion) at 0, 24, 48, and 72 h postactivation. Total RNA was extracted spectral counts. Protein identifications across replicate experiments were using the mirVana miRNA Isolation Kit (Ambion), which also allows for pooled to represent a union for each category of 0 and 48 h postactivation. the isolation of the total proteome fraction. Proteins identified in two or more technical replicates per category were kept for further analysis. Relative protein abundance was compared be- miRNA profiling by guest on September 23, 2021 tween the 0- and 48-h postactivation for proteins present in more than one TaqMan stem-loop RT-PCR method (65) was performed on an ABI category. Proteins identified in more than one technical replicate in a single 7900HT Real-Time PCR system (Applied Biosystems) for 420 human category and not in any category were also considered for functional miRNA primer/probes on 0.5 mg total RNA from 0-, 24-, 48-, and 72-h analysis as unique identifications. A two-tailed Student t test was used for CD2+ T lymphocyte samples. A two-tailed Student t test with a p value hypothesis testing, and the significant differentially expressed proteins threshold of 0.05 and false discovery rate (FDR)-adjusted p value (q-value) (p , 0.05) were considered for functional analysis. threshold of 0.1, for which q-value = p value 3 number tested/rank, be- tween 0 and 48 h was used on the normalized data to identify differentially miRNA target analysis expressed miRNAs. A q-value of 0.1 implies that 10% of significant tests For prediction of target genes of differentially expressed miRNAs, three will result in false positives. miRNA expression following locked nucleic publicly available algorithms were used: PITA, MiRanda, and TargetScan acid (LNA) nucleoporation in CD4+ T cells was measured with TaqMan 5.1. In the end, TargetScan predictions based on conservation scores were MicroRNA Assays (Applied Biosystems) for hsa-miR-155 and -221 in used to compute the 50th percentile targets in our expressed gene set. accordance with manufacturers’ protocols. U6 was used as an internal control. Functional mapping Microarray profiling We used Ingenuity Pathways Analysis (IPA; https://analysis.ingenuity.com) to map molecular pathways and networks populated by predicted miRNA A total of 1.5 mg total RNA per sample was converted into labeled cDNA targets. The IPA Database is a constantly curated resource of published using the GeneChip WT Sense Target Labeling kit (Affymetrix). Labeled literature on gene functions and interactions. Canonical pathway and net- cDNA was hybridized to Affymetrix Human Exon 1.0 ST arrays (Affy- works analysis was carried out by uploading the predicted downregulated metrix). Data for mRNA transcript profiles were generated in the form of genes targeted by the upregulated miRNAs. Significance of association CEL files using standard protocols. between genes and pathway was measured by the Benjamini and Hochberg Microarray data multiple testing corrected p value that can be interpreted as an upper bound for the expected fraction of falsely rejected null hypotheses among all Data have been deposited in the Gene Expression Omnibus under accession functions with p values smaller than the threshold of 0.05. Network node number GSE14352 and can be viewed at http://www.ncbi.nlm.nih.gov/geo/ genes were based on an especially high degree of links to other genes in query/acc.cgi?acc=GSE14352. the IPA database. Differential gene expression analysis Electroporation Raw data expression values from CEL files were normalized by robust A total of 3 3 106 primary human CD4+ T lymphocytes were electro- multiarray averaging algorithm provided through Affymetrix Power Tools porated in Nucleofector II instrument (Amaxa) using the Human T cell (Affymetrix) and summarized in AltAnalyze software (http://altanalyze. Nucleofector kit (VPA-1002; Lonza) in duplicate with 50 nM miRCURY org), retaining only probesets that align to a single Ensembl gene. Gene- LNA microRNA Power Inhibitor or scrambled negative control probes expression values were calculated based on the mean expression of all core (Exiqon) against hsa-miR-221 and -155 according to the manufacturer’s probesets detected above background (p value thresholds for p , 0.01; protocol. After electroporation, cells were cultured in RPMI 1640 medium FDR 0.01). Fold changes and p value (two-tailed t test assuming unequal supplemented with 10% (v/v) FBS and 2 mM glutamine, and after 2 h, half variance) were calculated for each time point comparison. Statistics were of the medium was replaced with fresh medium. At 24 h after electro- The Journal of Immunology 2235 poration, the cells were activated with CD3/CD28 Dynal beads (Invitrogen) Based on current literature, of these 71 differentially expressed for 48 h. miRNAs, only 14 have a documented function in T lymphocytes: Cell proliferation assay 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 Cell proliferation was measured using the Ziva Cell Proliferation Assay (18, 53), miR-223 (41), and miR-let-7f (18) (Tables I, II). Five (Jaden BioScience). Electroporated CD4+ T lymphocytes were plated at 8 3 104/well in 96-well plates in duplicate for each condition and activated additional miRNAs are linked to development or aberrant activation for 48 h with CD3/CD28 beads. Cells were pulsed with 10 mM BrdU/well of hematopoietic cells (Supplemental Table I). For example, our 18 h before harvesting. Forty-eight hours after activation, cells were har- results indicate that miR-150 is downregulated in T cells upon 4 vested, expander beads removed, and 10 cells/well were plated in 96-well activation, consistent with studies in murine lymphocytes (19). In Thermo Scientific Nunc Plates (Fisher). Cell proliferation was measured contrast, miR-155 is upregulated in activated human and murine with a chemiluminescent substrate to detect the presence of an anti-BrdU Ab labeled with alkaline phosphatase on the Insight-Mi Luminometer T lymphocytes (26). Importantly, we identified 57 differentially (Jaden BioScience). The signal was fully developed and measured 60 min expressed miRNAs currently undocumented in T lymphocyte after the addition of the substrate. activation. Quantitative RT-PCR Predicted targets of differentially upregulated miRNAs are globally downregulated A total of 300 ng total RNA from CD4+ T cells was transcribed into cDNA using qScript cDNA Supermix (Quanta Biosiences) according to the manu- Starting with the 71 differentially expressed miRNAs, we mapped facturer’s guidelines, and gene expression for IRS2, IKBKE, FOS, and the gene targets of the 51 differentially upregulated. Predictive PIK3R1 was quantitated with PrimeTime qPCR assays (Integrated DNA

algorithms rely on multiple parameters: seed complementarity, Downloaded from Technologies) using PerfeCta qPCR FastMix kit (Quanta Biosiences) on the ABI 7900HT Fast Real-Time PCR instrument (Applied Biosystems). thermodynamics, and biochemical properties of binding and Expression of 18S gene was set as endogenous control. For data analysis, evolutionary conservation. Unfortunately, these algorithms suffer the threshold cycle (Ct) (67) value was determined and specific gene ex- from high false-positive and -negative rates. Combining predictions pression normalized to endogenous control using d-d threshold cycle (Ct) from different algorithms may be useful, but there is little overlap in method. The normalized d C from LNA-transfected samples was then t top targets predicted by different algorithms (70, 71). We tested compared with the scrambled control to obtain d-d Ct values and used to calculate relative fold change compared with control. Experiments were 50th percentile predictions for these 51 differentially upregulated http://www.jimmunol.org/ performed in triplicate. The primer probe sequences for validation assays miRNAs using four algorithms: PITA (72), MiRanda (73), and were as follows: IKBKE Probe (0.5 nM), 59-/56-FAM/TAC CTG ATC TargetScan5.1 context or conservation scores (74). We measured 9 /ZEN/CCG GCT CTT CAC CA/3IABkFQ/-3 ; IKBKE Primer1 (1 nM), the change (d) in mRNA expression between 0 and 48 h for all 59-CAT CTT GTC CAA ACA GCA CTG-39; IKBKE Primer2 (1 nM): 59- AAA ATATCA CGG AGA CCC AGG-39; FOS Probe (0.5 nM), 59-/56- genes above background (expression $6.5; log2 scale). We then FAM/TGC AGA CCG /ZEN/AGATTG CCA ACCT/3IABkFQ/-39; FOS plotted differences in distribution of deltas between predicted Primer1 (1 nM), 59-CAT CAG GGATCT TGC AGG C-39; FOS Primer2 (1 targets and nontargets. MiRanda failed to produce any expression nM), 59-GACTGA TAC ACT CCA AGC GG-39; IRS2 Probe (0.5 nM), 59-/ correlations with our data and was not used further. Predictions 56-FAM/AGG CCA CCA /ZEN/TCG TGA AAG AGT GAA G/ 3IABkFQ/-39; IRS2 Primer1 (1 nM), 59-TGA CAT CCT GGT GAT AAA with TargetScan and PITA based on testing the effects of single

GCC-39; IRS2 Primer2 (1 nM), 59-ACT TCT TGT CCC ACC ACT TG-39; miRNA binding also revealed no shift in mRNA signals with by guest on September 23, 2021 PIK3R1 Probe (0.5 nM), 59-/56-FAM/CAC AAT GCT /ZEN/TTA CTT activation. In contrast, plotting the gene expression of targets CGC CGT CCA C/3IABkFQ/-39; PIK3R1 Primer1 (1 nM), 59-CTG TAC predicted to bind multiple upregulated miRNAs (e.g., $4or$7) AAGTTATAG GGCTCG G-39; and PIK3R1 Primer2 (1 nM), 59-GAT revealed that combinatorial miRNA binding decreases mRNA GGC ACT TTT CTT GTC CG-39. expression (Fig. 1C,1D). Combinatorial targeting benchmarked at Statistics the 50th percentile with both PITA and TargetScan gave the best All statistical analyses used the Student t test of at least three independent predictions (Fig. 1E–J). As shown in Fig. 1I and 1J, TargetScan experiments, unless stated otherwise. Differences with p values ,0.05 are conservation predictions with combinatorial binding of four or considered significant. more miRNAs show the best results correlating increased miRNA binding with decreased target gene expression. Results T lymphocyte activation is marked by global gene upregulation Activated T lymphocytes demonstrate a unique including miRNA-processing machinery activation-induced miRNA signature We showed previously that T lymphocyte activation is dominated We used a multidimensional approach to integrate genome-wide by widespread differential gene upregulation (1). We therefore miRNA, mRNA, and protein expression (Fig. 1A). We activated analyzed differential gene expression in parallel with miRNA human T lymphocytes via CD3/CD28 costimulation and harvested expression. Genome-wide mRNA transcript analysis revealed cells at 0, 24, 48, and 72 h. This activation strategy modeled al- 3798 differentially expressed mRNA transcripts between 0 and logeneic activation (68, 69). We surveyed miRNA expression 48 h (p , 0.01; FDR 1%): 3362 upregulated (89%) and 436 using quantitative real-time PCR (qPCR) for 420 human miRNA downregulated. Upregulation of the miRNA processing/biogenesis sequences. Specific miRNAs were differentially expressed in genes included: XPO5, EIF2C2/AGO2, SIP1/GEMIN2, -4, -5, -6, T lymphocytes as a function of activation. We identified 71 dif- and -7, RANGAP1, YBX1, and ADARB1 (Table III). ferentially 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 Predicted targets of upregulated miRNAs populate networks key time point in T lymphocyte activation based on peak cell associated with immunity, cell survival, and proliferation proliferation, cytokine production, and expression of activation Using TargetScan conservation predictions, we identified 1640 markers (1). These changes in miRNA expression are robust candidate miRNA targets, of which 214 were downregulated across all donors (Fig. 1B). The top 12 upregulated miRNAs were (Supplemental Table II). Thus, half of all 436 downregulated genes miR-221, -210, -98, -29b, -155, -218, -455-3p, -449, -548d, -222, are targets of upregulated miRNAs. Functional pathway and net- -132, and -18a. The top downregulated miRNAs were miR-181a, work enrichment analysis was done for the 182 out of 214 -223, -224, -150, -146b, -126, -127-3p, -376a, -100, -99a, -125b, downregulated targets that mapped to known functional pathways and -26a (Table II). and the 200 that mapped to molecular networks. 2236 microRNA-REGULATED NETWORKS IN T CELL ACTIVATION Downloaded from http://www.jimmunol.org/ by guest on September 23, 2021

FIGURE 1. Combinatorial targeting by multiple upregulated miRNAs during T cell activation demonstrates decreased mRNA levels after activation with increased miRNA binding. A, Schematic of our experimental approach. B, miRNA signature of T cell activation: heat map of 71 statistically significant (p , 0.05; q , 0.1), differentially expressed miRNAs at 0 and 48 h. Heat map shows expression at 0, 24, 48, and 72 h across seven donors. Red represents positive change, cyan represents negative change, and white represents no change. C, A cumulative distribution function plot of relative fold change between 0 and 48 h of combined PITA, TargetScan/conservation, and TargetScan/context; top 50th percentile predictions in each, 2+ miRNAs targeting each gene. Target genes in red; nontarget genes in blue. D, Same as A with 4+ miRNAs targeting each gene. E, PITA, top 50th percentile predictions; 4+ miRNAs targeting a given gene. Target genes in red; nontarget genes in blue. F, PITA, top 50th percentile predictions with 7+ miRNAs targeting a given gene. Target genes in red; nontarget genes in blue. G, TargetScan/context score, top 50th percentile predictions; 4+ miRNAs targeting a given gene. Target genes in red; nontarget genes in blue. H, TargetScan/context score, top 50th percentile predictions; 7+ miRNAs targeting a given gene. Target genes in red; nontarget genes in blue. I, TargetScan/conservation score, top 50th percentile predictions; 4+ miRNAs targeting a given gene. Target genes in red; nontarget genes in blue. J, TargetScan/ conservation score, top 50th percentile predictions; 7+ miRNAs targeting a given gene. Target genes in red; nontarget genes in blue. The Journal of Immunology 2237

Table I. Differentially upregulated (.2-fold) miRNAs in activated Table II. Differentially downregulated miRNAs in activated T cells T cells detected 0 versus 48 detected 0 versus 48

miRNA p Value q-Value Fold Change miRNA p Value q-Value Fold Change hsa-miR-221 2.77 E-08 3.31 E-06 7881.6 hsa-miR-197 1.72 E-02 6.75 E-02 21.2 hsa-miR-210 2.12 E-04 2.54 E-03 1846.7 hsa-miR-146b 1.67 E-02 6.67 E-02 21.6 hsa-miR-98 8.96 E-04 6.69 E-03 237.6 hsa-miR-10a 1.95 E-02 7.52 E-02 21.6 hsa-miR-29b 1.97 E-03 1.27 E-02 194.7 hsa-miR-342-3p 1.88 E-04 2.37 E-03 21.7 hsa-miR-155 4.19 E-08 3.34 E-06 70.5 hsa-miR-26b 2.90 E-02 9.90 E-02 21.8 hsa-miR-218 4.80 E-03 2.55 E-02 45.4 hsa-miR-31 4.91 E-04 4.05 E-03 21.8 hsa-miR-455-5p 1.34 E-03 9.13 E-03 32.2 hsa-miR-328 9.47 E-05 1.33 E-03 21.9 hsa-miR-449 1.01 E-02 4.75 E-02 20.5 hsa-miR-95 2.58 E-03 1.54 E-02 22.1 hsa-miR-548d 9.97 E-03 4.76 E-02 18.8 hsa-miR-26a 2.96 E-03 1.72 E-02 22.4 hsa-miR-222 2.89 E-07 1.73 E-05 17.1 hsa-miR-150 4.30 E-05 8.56 E-04 23.0 hsa-miR-132 6.22 E-05 1.06 E-03 15.3 hsa-miR-125b 3.33 E-03 1.89 E-02 23.4 hsa-miR-18a 2.05 E-03 1.29 E-02 15.3 hsa-miR-99a 5.04 E-03 2.62 E-02 23.8 hsa-miR-18aa 7.19 E-09 1.72 E-06 15.0 hsa-miR-100 2.23 E-03 1.37 E-02 23.9 hsa-miR-200b 2.90 E-02 9.76 E-02 13.1 hsa-miR-376a 2.19 E-02 7.93 E-02 24.1 hsa-miR-330-3p 1.39 E-02 5.61 E-02 11.1 hsa-miR-126 1.19 E-03 8.34 E-03 25.2 hsa-miR-206 1.28 E-02 5.68 E-02 10.0 hsa-miR-127-3p 2.31 E-02 8.23 E-02 27.2 hsa-miR-17-5p 5.48 E-06 2.18 E-04 9.8 hsa-miR-224 1.31 E-02 5.60 E-02 27.9 hsa-miR-424 1.32 E-02 5.44 E-02 9.6 hsa-miR-223 4.05 E-05 8.80 E-04 28.7 Downloaded from hsa-miR-20b 2.34 E-06 1.12 E-04 9.0 hsa-miR-199aa 9.15 E-03 4.46 E-02 231.0 hsa-miR-106a 6.51 E-06 2.22 E-04 8.4 hsa-miR-181a 2.07 E-02 7.72 E-02 271.8 hsa-miR-7 7.05 E-05 1.05 E-03 8.3 Shown are significantly differentially downregulated miRNAs. hsa-miR-93 3.08 E-05 7.37 E-04 7.6 aq-value is the FDR threshold for the corresponding p value. hsa-miR-324-5p 1.37 E-05 4.09 E-04 6.4 hsa-miR-19a 2.89 E-04 2.76 E-03 6.3 hsa-miR-130b 2.51 E-04 2.50 E-03 6.1 http://www.jimmunol.org/ hsa-miR-20a 2.39 E-05 6.34 E-04 6.0 regulated miRNAs. The central node gene PIK3R1 belongs to the hsa-miR-301a 2.24 E-04 2.55 E-03 6.0 phosphoinositide 3-kinase family that phosphorylates phosphati- hsa-miR-27b 2.81 E-02 9.74 E-02 5.9 dylinositol-(4,5)-biphosphate to phosphatidylinositol-(3,4,5)-tri- hsa-miR-19b 2.24 E-04 2.43 E-03 5.8 phosphate to regulate cell proliferation, and cytokine production hsa-miR-99b 2.35 E-04 2.45 E-03 5.1 hsa-miR-629 4.60 E-04 4.07 E-03 4.7 (75). PIK3R1 is a predicted target of four miRNAs (miR-155, -21, hsa-miR-363 2.35 E-02 8.27 E-02 4.2 -218, and -221). Thus, downregulated gene targets of upregulated hsa-let-7f 1.30 E-02 5.65 E-02 3.8 activation-induced miRNAs are associated with proliferation and hsa-miR-21 3.68 E-03 2.00 E-02 3.7 cell survival signaling networks. We identified 12 other target gene hsa-miR-362-5p 1.77 E-03 1.18 E-02 3.7 networks (Supplemental Table III). hsa-miR-135b 6.35 E-04 5.06 E-03 3.5 by guest on September 23, 2021 hsa-miR-92a 5.26 E-05 9.67 E-04 3.4 Predicted targets of downregulated miRNAs are upregulated hsa-miR-106b 6.54 E-03 3.33 E-02 3.4 with activation hsa-miR-27a 3.41 E-03 1.90 E-02 3.1 hsa-miR-365 1.04 E-02 4.80 E-02 2.7 With respect to the impact of the 20 downregulated miRNAs, we hsa-miR-378 2.17 E-02 7.98 E-02 2.7 predicted 1347 gene targets out of the total of 3798 activation- hsa-miR-422a 1.64 E-04 2.18 E-03 2.6 hsa-miR-425-3p 8.26 E-03 4.11 E-02 2.5 induced genes. A total of 487 genes were only targeted by hsa-miR-24 6.69 E-05 1.07 E-03 2.3 downregulated miRNAs. In contrast, the majority (860) was also Shown are significantly differentially upregulated miRNAs with fold change .2- targeted by upregulated miRNAs, matching our observations on the fold. apparent importance of combinatorial targeting. We predicted that aq-value is the FDR threshold for the corresponding p value. targets of only downregulated miRNAs should have upregulated mRNA levels. Indeed, 410 (84%) were upregulated .1.5-fold. Pathway analysis revealed statistically significant enrichment for We mapped the functional pathways enriched for these two 71 canonical pathways (multiple test correction p value ,0.05), classes of targets. The pathways linked to only downregulated with the top 30 pathways shown in Fig. 2A. Represented were miRNAs are predominantly cell metabolism and biosynthesis. primarily immune signaling pathways including IL-12, PI3K, Because miRNAs targeting these genes are downregulated with IL-10, CD40, NFAT, sphingosine 1-phosphate, and TCR. Cell activation, the presumed regulation of their targets is removed or at survival, growth, and proliferation pathways included prolactin, least significantly decreased. The functional role of these genes in TNFR2, ceramide, thrombopoietin, and p70S6K signaling. In metabolism and biosynthesis, much like the genes we found were sharp contrast, differentially expressed genes that were not pre- not targeted by miRNAs, supports the observation that activation- dicted targets of miRNAs were highly enriched for metabolism induced miRNAs target a functionally distinct class of genes. In and biosynthesis pathways (Fig. 2B). contrast, the pathways linked to combinatorial targeting by both up- Molecular networks were constructed from the miRNA targets and downregulated miRNAs are enriched for signaling in immu- with downregulated expression. Network eligibility was based nity, growth, and cell proliferation (Fig. 3). on connectivity to other genes with known interactions. Highly connected genes represent network nodes or hubs where closely Correlating global protein expression to predicted miRNA connected genes are functionally similar. The top network was targets comprised of 21 genes significantly enriched for functions linked Our hypothesis was that upregulated miRNAs regulate the immune to T lymphocyte activation, proliferation, and survival (Fig. 2C). response and should repress target proteins during T lymphocyte The hub genes in this network are PIK3R1 with six connections activation. Target protein repression can be accomplished by either and ATM, PARK2, HIP1R, and NCAM1 with three connections inhibiting translation or enhancing mRNA degradation. Although it each. Members of this network are predicted targets for 17 up- has been shown that most translational repression is coupled to 2238 microRNA-REGULATED NETWORKS IN T CELL ACTIVATION

Table III. miRNA-processing machinery genes detected at 48 h postactivation

Fold Change Genea Definition Function 48 h versus 0 h miRNAs XPO5 Exportin-5 Mediates the nuclear export of miRNA precursors 4.30 miR-218, miR-24 RANGAP1 Ran GTPase-activating protein 1 mRNA processing and transport 1.60 ND EIF2C2/AGO2 Protein -2 Provides endonuclease activity to RISC; cleaves 3.84 ND siRNA/mRNA heteroduplexes bound to RISC SIP1/GEMIN2 Survival of motor neuron protein- Core component of the SMN complex, which plays 2.23 ND interacting protein 1 an essential role in spliceosomal snRNP assembly in the cytoplasm and is required for pre-mRNA splicing in the nucleus GEMIN4 Gem-associated protein 4 Component of the SMN complex, which is required 1.59 miR-155 for pre-mRNA splicing in the nucleus GEMIN5 Gem-associated protein 5 Component of the SMN complex, which is required 2.13 ND for pre-mRNA splicing in the nucleus GEMIN6 Gem-associated protein 6 Component of the SMN complex, which is required 2.11 ND for pre-mRNA splicing in the nucleus GEMIN7 Gem-associated protein 7 Component of the SMN complex, which is required 1.90 ND for pre-mRNA splicing in the nucleus YBX1 Nuclease-sensitive element- Participates in different steps of mRNA biogenesis, 1.53 ND

binding protein 1 including mRNA transcription, processing, and Downloaded from transport from the nucleus into the cytoplasm, binds to splice sites in pre-mRNA, and regulates splice site selection ADARB1 dsRNA-specific editase 1 Binds to siRNA without editing them and suppresses 1.50 miR-218 siRNA-mediated RNA interference aThe FDR-adjusted p values of every gene are ,0.007. Shown are genes implicated in miRNA processing/biogenesis that were significantly differentially expressed between

0 and 48 h, with relative fold changes. http://www.jimmunol.org/ ND, miRNA not detected; RISC, RNA-induced silencing complexes; siRNA, short interfering RNA; SMN, survival of motor neuron; snRNP, small nuclear ribonucleoprotein. miRNA-mediated mRNA degradation (12, 14), we considered the Several network proteins demonstrated increased mRNA but possibility that some miRNA targets might be specifically re- decreased protein levels (Fig. 4B). LMNB1, a predicted target of pressed at the translational level without decreases in corre- miR-218 and miR-7, showed the highest mRNA upregulation by 7- sponding mRNAs. A focus exclusively on the downregulation of fold but was 1.5-fold down by protein. Inhibition of T lymphoblast mRNAs would miss such targets. Therefore, a high-throughput proliferation is associated with downregulation of LMNB1 protein shotgun proteomics protocol (66) was used to analyze global (78). CRKL is targeted by four miRNAs and involved in signal protein expression between 0 and 48 h. transduction through WIP, JNK, and ZAP70 (79). IQGAP1, tar- by guest on September 23, 2021 A total of 589 differentially expressed proteins and another 876 geted by two miRNAs, regulates lymphocyte cytoskeleton rear- proteins expressed uniquely at 0 or 48 h were identified. Correlat- rangement in the immune synapse (80). Thus, these multiple lines of ing the predicted gene targets of miRNAs to expressed proteins, evidence linking the top functional networks directly to T lympho- we identified 234 protein–mRNA transcript targets of upregulated cyte immunity underline the value of such mapping based on global miRNAs (Supplemental Table IV). Eighty-one of these proteins gene, protein, and miRNA expression. had decreased expression at 48 h. Interestingly, 70 of these protein targets have upregulated mRNA expression. Thus, these proteins Knockdown of miR-221 and miR-155 increases T lymphocyte are regulated by posttranscriptional mechanisms not coupled to proliferation by removing negative regulation of target genes mRNA decay. Functional analysis of these 81 downregulated The premise of target predictions and functional network mapping protein targets revealed significant enrichment for signaling path- is that upregulated miRNAs regulate genes that populate critical ways in immune response, cell cycle, growth, and prolifera- networks in T lymphocyte activation. To validate our approach, we tion (Fig. 4A). In contrast, the 153 upregulated protein targets knocked down two of the highest upregulated miRNAs: miR-221 were enriched for only four pathways: RAN signaling; glycolysis/ and miR-155. Validations were done using purified CD4+ T lym- gluconeogenesis; phenylalanine, tyrosine, and tryptophan bio- phocytes to simplify the cell subset composition and reflect our synthesis; and alanine and aspartate metabolism. recent finding that CD4+ T lymphocytes are selectively activated Because networks represent integration of multiple associa- and proliferatively expanded in the early posttransplant period tions, we examined the top 3 networks to identify 19 downregulated (81). We confirmed that miR-221 and 155 were significantly up- proteins as predicted targets of $1 miRNAs (Fig. 4B). Within regulated in CD4+ T cells at 48 h of activation (data not shown). these 19 genes was AHNA, targeted by miR-200b and miR-7, and Although the function of miR-155 has been widely studied in critical for calcium entry during immune T lymphocyte activation T cells (26, 41), miR-221 associated with cell cycle progression (76). AHNA was 2.2-fold down at the mRNA level and 8-fold (44) has not been studied in T lymphocytes. down at the protein level. ATM, targeted by miR-132, miR-18a, The impact of inhibiting miR-221 and -155 on T lymphocyte and miR-21, regulates cell cycle, promotes normal lymphocyte proliferation was measured by transfecting cells with specific development, and protects from neoplastic transformation (77). inhibitors or scrambled controls followed by activation. We ATM was 2.8-fold down by mRNA and 4.8-fold down by protein. obtained .60% knockdown of both miRNAs in three donors PIK3R1 is an adaptor kinase involved in TCR signaling and CD28 (n = 3) as measured by qPCR (Fig. 4C). Significantly increased costimulation with 3-fold mRNA and 2.6-fold protein downreg- proliferation resulted from inhibiting either miR-155 or -221 as ulation. It is a predicted target of four upregulated miRNAs (Fig. compared with scrambled control (Fig. 4D). 2C) and is identified as a downregulated network hub by both gene Among the predicted targets of miR-221 and miR-155, we chose expression and proteomics. four genes for validation by qPCR: PIK3R1, FOS, IRS2, and The Journal of Immunology 2239 Downloaded from http://www.jimmunol.org/ by guest on September 23, 2021

FIGURE 2. Functional analysis of predicted targets of upregulated miRNAs reveals networks associated with immunity, cell survival, and proliferation. A, Top 30 overrepresented canonical pathways for downregulated gene targets of upregulated miRNAs. Pathways are sorted by score (2log [multiple testing corrected p value]). A higher score indicates that the pathway is more significantly associated with genes of interest. The vertical line represents statistically significant threshold limit. B, Significantly overrepresented canonical pathways for nontarget differentially expressed genes 0 versus 48 h. C, Top direct interaction network representing 21 downregulated target focus genes, overlaid with predicted major miRNA targeting and functions associated with nodal genes.

IKBKE (Fig. 4E). FOS and IKBKE have been previously vali- stically increased by knocking down these two miRNAs. Although dated as targets of miR-221 (82) and -155 (83), respectively. At changes in IKBKE levels were not statistically significant, IRS2, 48 h after activation, the expression of FOS and PIK3R1 is stati- a predicted target of miR-155, increased expression after miR-155 2240 microRNA-REGULATED NETWORKS IN T CELL ACTIVATION Downloaded from http://www.jimmunol.org/ FIGURE 3. Functions of genes targeted by downregulated miRNAs. Top 30 overrepresented canonical pathways for gene targets of upregulated as well as downregulated miRNAs and downregulated miRNAs only. Pathways are sorted by score (2log [multiple testing corrected p value]). A higher score indicates that the pathway is more significantly associated with genes of interest. The horizontal line represents statistically significant threshold limit. The pathways linked to only downregulated miRNAs are predominantly cell metabolism and biosynthesis, much like the genes not targeted by miRNAs. In contrast, the pathways linked to combinatorial targeting by both up- and downregulated miRNAs are enriched for signaling in immunity, growth, and cell proliferation.

knockdown. Upregulation of target genes following knockdown First, if the results from all of the algorithms are compared using by guest on September 23, 2021 of miR-221 and -155 is consistent with the evidence above for a single miRNA hit/seed approach, the predicted targets are poorly mRNA repression mediated by miR-221 and/or -155 during correlated between methods to the extent that different methods T lymphocyte activation (Fig. 4F). will report very different results. Second, single hit/seed predictions did not correlate with mRNA repression. In contrast, combinatorial Discussion targeting (multiple seeds per target) gave the best predictions. We investigated genome-wide miRNA, mRNA, and protein ex- TargetScan conservation predictions with combinatorial binding of pression following human T lymphocyte activation. T lymphocyte four or more miRNAs showed the correlation between increased activation relies on signaling cascades that create a balance be- miRNA binding with decreased target gene expression. tween activation, memory, and quiescence. This balance is mod- By integrating activation-induced miRNA, mRNA, and protein ulated by mechanisms regulating gene expression including expression changes with target predictions, we tested our hy- posttranscriptional miRNA regulation. In this study, we show pothesis that target genes are involved in regulating immune ac- a unique miRNA signature with a total of 71 differentially ex- tivation, cell proliferation, and survival. Indeed, functional analysis pressed miRNAs with 51 upregulated between 0 and 48 h. This demonstrated that downregulated miRNA targets populated sig- signature comprises 57 miRNAs with no documented roles in naling pathways highly enriched for immune response, prolif- T lymphocyte function. Additionally, our data validated previous eration, and survival. In contrast, activation-induced genes not pre- findings for a number of miRNAs with known functions in T lym- dicted to be miRNA targets demonstrated significant enrichment phocytes: upregulation of miR-155 can regulate the susceptibi- for pathways of metabolism, DNA stability, and cell cycle. These lity of human and murine CD4+ T cells to natural regulatory T novel results reveal that predicted targets of activation-induced cell-mediated suppression (26), the miR-17–92 cluster inhibits miRNAs are functionally distinct from nontarget genes and pre- T cell activation (30, 32), miR-106a is implicated in IL-10 regu- sumably evolved with distinct selection pressures for such regu- lation (31, 33), miR-24 can inhibit cell proliferation by targeting lation. We also hypothesized that some targets might be specifically cell-cycle genes (6, 47), and miR-21, upregulated by STAT3, regulated by posttranscriptional mechanisms not coupled to mRNA prevents CD4+ T cell apoptosis and is implicated in lymphocyte decay. Based on our proteomics, we detected a number of such oncogenesis (18, 53). Also, consistent with the importance of downregulated protein targets despite increased mRNA expression. activation-induced miRNA expression, we observed upregulation Thus, inhibition of protein translation is not always coupled to of 10 miRNA biogenesis/processing machinery genes. corresponding mRNA degradation. miRNAs are known inhibitors of gene expression. The challenge By investigating connectivity between predicted targets, we is to map miRNAs to specific gene targets and the molecular net- identified highly connected genes that function as network nodes, works they regulate. To address this challenge, we investigated the with closely connected genes being functionally similar. The top predictive values of four widely used computational algorithms. nodal gene PIK3R1 of one such network is a predicted target of The Journal of Immunology 2241

FIGURE 4. Inhibition of miR-155 (known) and miR-221 (novel), two highly upregulated miRNAs, in CD4+ T cells increased proliferation by removing suppression of four target genes linked to proliferation and survival. A, Top 30 overrepresented canonical pathways for down- regulated protein targets of upregulated miRNAs. Pathways are sorted by score. B, Expression of 19 predicted downregulated protein targets as- Downloaded from sociated with top three networks. C, qPCR ex- pression of miR-155 and miR-221 in CD4+ T cells nucleoporated with 50 nM LNA–anti- miR-155 (gray bar), LNA–anti-miR-221 (black bar), or LNA-scrambled control (white bar) showing decreased miRNA expression after LNA–anti-miR transfection relative to scrambled http://www.jimmunol.org/ control, set as 1. Shown are fold changes relative to scrambled control, normalized to U6 snRNA. D, Increased cell proliferation following knock- down of miR-221 (black bar) or miR-155 (gray bar) compared with scrambled control (white bar) by cell proliferation assay. E, qPCR analysis of predicted targets in CD4+ T cells transfected with LNA–anti-miR-155 (gray), LNA–anti-miR- 221 (black), and LNA scrambled control (white), presented relative to the expression in scrambled by guest on September 23, 2021 control sample, set as 1. F, Proposed model of miR-155 and -221 negative-feedback regulation of proliferation following T cell activation. CD3/ CD28 costimulation induces signaling cascades that result in transcription of genes that promote proliferation, activation, and immune response. Upregulation of miR-155 and -221 serves to di- minish proliferation and immune response by targeting key proliferative genes such as PIK3R1, IRS2, and IKBKE as well as transcription factor FOS. Error bars in C–E represent mean 6 SEM of triplicate experiments from three donors (n = 3). *p , 0.05, ***p , 0.0001 (t test) compared with scrambled control.

miR-221 and -155 and downregulated at both mRNA and protein In validation, we focused on the functional roles of two top levels. This gene is an adaptor kinase involved in TCR signaling upregulated miRNAs in our data: miR-155, widely studied in and CD28 costimulation and regulates cell growth, proliferation, T cells, and miR-221, not previously associated with T cell and T cell cytokine production (75). Thus, functional network function. Knockdown of either miRNA produced a significant in- analysis underlines the value of the mapping done in this study crease in proliferation of activated CD4+ T cells, confirming that based on global gene, protein, and miRNA expression. these two miRNAs actually have antiproliferative roles during 2242 microRNA-REGULATED NETWORKS IN T CELL ACTIVATION activation. We identified four potential targets of miR-155 and/or 16. Cobb, B. S., A. Hertweck, J. Smith, E. O’Connor, D. Graf, T. Cook, S. T. Smale, S. Sakaguchi, F. J. Livesey, A. G. Fisher, and M. Merkenschlager. 2006. A role -221 and mapped a functional network critical to T cell activation for in immune regulation. J. Exp. Med. 203: 2519–2527. (Fig. 4F). In addition to identifying PIK3R1 as a new target gene 17. Monticelli, S., K. M. Ansel, C. Xiao, N. D. Socci, A. M. Krichevsky, T. H. Thai, for miR-155 and -221, we discovered that the transcription factor N. Rajewsky, D. S. Marks, C. Sander, K. Rajewsky, et al. 2005. MicroRNA profiling of the murine hematopoietic system. Genome Biol. 6: R71. FOS is also a target of both miR-155 and -221. We identified two 18. Wu, H., J. R. Neilson, P. Kumar, M. Manocha, P. Shankar, P. A. Sharp, and more targets of miR-155: the novel IRS2, an adaptor for tyrosine N. Manjunath. 2007. miRNA profiling of naı¨ve, effector and memory CD8 kinases, and a previously verified miR-155 target, IKBKE, that T cells. PLoS ONE 2: e1020. 19. Zhou, B., S. Wang, C. Mayr, D. P. Bartel, and H. F. Lodish. 2007. miR-150, regulates NF-kB activation (83). Knockdown of miR-221 and/or a microRNA expressed in mature B and T cells, blocks early B cell development -155 increased target mRNA expression for PIK3R1, FOS, and when expressed prematurely. Proc. Natl. Acad. Sci. USA 104: 7080–7085. IRS2. 20. Li, J., Y. Wan, Q. Guo, L. Zou, J. Zhang, Y. Fang, J. Zhang, J. Zhang, X. Fu, H. Liu, et al. 2010. Altered microRNA expression profile with miR-146a up- In conclusion, we propose a model in which, in the course of regulation in CD4+ T cells from patients with rheumatoid arthritis. Arthritis Res. T lymphocyte activation by TCR engagement and CD28 costim- Ther. 12: R81. 21. Cullen, B. R. 2006. Viruses and microRNAs. Nat. Genet. 38(Suppl): S25–S30. ulation, there is a significant upregulation of miRNAs that are 22. Cimmino, A., G. A. Calin, M. Fabbri, M. V. Iorio, M. Ferracin, M. Shimizu, critical to this process. These activation-induced miRNAs create S. E. Wojcik, R. I. Aqeilan, S. Zupo, M. Dono, et al. 2005. miR-15 and miR-16 a negative-feedback loop to inhibit cell proliferation and regulate induce apoptosis by targeting BCL2. Proc. Natl. Acad. Sci. USA 102: 13944– 13949. cell survival by targeting a series of molecular networks that we 23. Dorsett, Y., K. M. McBride, M. Jankovic, A. Gazumyan, T. H. Thai, have mapped. In parallel, there is also a subset of miRNAs D. F. Robbiani, M. Di Virgilio, B. Reina San-Martin, G. Heidkamp, downregulated by activation, and 84% of their predicted target T. A. Schwickert, et al. 2008. MicroRNA-155 suppresses activation-induced -mediated Myc-Igh translocation. Immunity 28: 630–638. genes are shown to be upregulated at the mRNA level. Moreover, 24. O’Connell, R. M., A. A. Chaudhuri, D. S. Rao, and D. Baltimore. 2009. Inositol Downloaded from there is a functionally specific class of genes linked closely to the phosphatase SHIP1 is a primary target of miR-155. Proc. Natl. Acad. Sci. USA immune response that evolved to be the natural targets of these 106: 7113–7118. 25. Rodriguez, A., E. Vigorito, S. Clare, M. V. Warren, P. Couttet, D. R. Soond, miRNAs in T lymphocytes, with functions revealed by molecular S. van Dongen, R. J. Grocock, P. P. Das, E. A. Miska, et al. 2007. Requirement of networking mapping that are clearly distinct from the activation- bic/microRNA-155 for normal immune function. Science 316: 608–611. 26. Stahl, H. F., T. Fauti, N. Ullrich, T. Bopp, J. Kubach, W. Rust, P. Labhart, induced genes that are not miRNA targets. V. Alexiadis, C. Becker, M. Hafner, et al. 2009. miR-155 inhibition sensitizes

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