Regulation of MicroRNA Expression and Abundance during ...Immunity Resource Regulation of MicroRNA Expression and Abundance during Lymphopoiesis Stefan Kuchen,1,10 Wolfgang Resch,1,10,*

Please cite this article in press as: Kuchen et al., Regulation of MicroRNA Expression and Abundance during Lymphopoiesis, Immunity (2010),doi:10.1016/j.immuni.2010.05.009

Immunity

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Regulation of MicroRNA Expressionand Abundance during LymphopoiesisStefan Kuchen,1,10 Wolfgang Resch,1,10,* Arito Yamane,1,10 Nan Kuo,1 Zhiyu Li,1 Tirtha Chakraborty,2 Lai Wei,3

Arian Laurence,3 Tomoharu Yasuda,2 Siying Peng,2 Jane Hu-Li,4 Kristina Lu,5 Wendy Dubois,6 Yoshiaki Kitamura,7

Nicolas Charles,7 Hong-wei Sun,8 Stefan Muljo,4 Pamela L. Schwartzberg,5 William E. Paul,4 John O’Shea,3

Klaus Rajewsky,2 and Rafael Casellas1,9,*1Genomics and Immunity, NIAMS, NIH, Bethesda, MD 20892, USA2Immune Disease Institute and Department of Pathology, Harvard Medical School, Boston, MA 02115, USA3Lymphocyte Cell Biology, NIAMS, NIH, Bethesda, MD 20892, USA4Laboratory of Immunology, NIAID, NIH, Bethesda, MD 20892, USA5Genetic Disease Research Branch, NHGRI, NIH, Bethesda, MD 20892, USA6Laboratory of Genetics, NCI, NIH, Bethesda, MD 20892, USA7Laboratory of Immune Cell Signaling, NIAMS, NIH, Bethesda, MD 20892, USA8Biodata Mining and Discovery, NIAMS, NIH, Bethesda, MD 20892, USA9Center of Cancer Research, NCI, NIH, Bethesda, MD 20892, USA10These authors contributed equally to this work

*Correspondence: [email protected] (W.R.), [email protected] (R.C.)DOI 10.1016/j.immuni.2010.05.009

SUMMARY

Although the cellular concentration of miRNAs is crit-ical to their function, how miRNA expression andabundance are regulated during ontogeny is unclear.We applied miRNA-, mRNA-, and ChIP-Seq to char-acterize the microRNome during lymphopoiesiswithin the context of the transcriptome and epige-nome. We show that lymphocyte-specific miRNAsare either tightly controlled by polycomb group-mediated H3K27me3 or maintained in a semi-acti-vated epigenetic state prior to full expression.Because of miRNA biogenesis, the cellular concen-tration of mature miRNAs does not typically reflecttranscriptional changes. However, we uncover asubset of miRNAs for which abundance is dictatedby miRNA gene expression. We confirm that concen-tration of 5p and 3p miRNA strands depends largelyon free energy properties of miRNA duplexes. Unex-pectedly, we also find that miRNA strand accumula-tion can be developmentally regulated. Our dataprovide a comprehensive map of immunity’s micro-RNome and reveal the underlying epigenetic andtranscriptional forces that shape miRNA homeo-stasis.

INTRODUCTION

MicroRNAs (miRNAs) are noncoding small RNAs that modulate

the proteome of the cell by annealing to 30 untranslated regions

of cognate mRNAs and inhibiting protein translation and/or

promoting mRNA instability (Bartel, 2004). Since their discovery

in C. elegans (Lee et al., 1993; Wightman et al., 1993), miRNA

orthologs and paralogs have been described in a variety of

species, suggesting these regulatory RNAs are involved in basic

cellular functions across the phyla (Lagos-Quintana et al., 2001;

Lau et al., 2001; Lee and Ambros, 2001; Marson et al., 2008;

Wightman et al., 1993). This view has been strengthened by

the early embryonic lethality of mice deficient in miRNA process-

ing factors (Bernstein et al., 2003; Chong et al., 2008; Kanello-

poulou et al., 2005; Liu et al., 2004).

In the mammalian genome, miRNAs are encoded within

introns of protein-coding genes or as independent entities

transcribed either by RNA polymerase II (Rodriguez et al.,

2004) or RNA polymerase III (Borchert et al., 2006). In some

instances, groups of miRNAs are organized in genomic clusters

processed from a single transcript. Because of their palindromic

nature, miRNAs in nascent primary transcripts (pri-miRNAs)

display a characteristic stem-loop structure that is recognized

and cleaved in the nucleus by the Drosha-DGCR8 complex

into 60–70 nucleotide (nt) precursor (pre) miRNAs. Once in the

cytoplasm, pre-miRNAs are further processed by the RNase III

endonuclease DICER into mature RNA fragments of �22 nt in

length, which are loaded into the RNA-induced silencing

complex (RISC). Partial sequence complementary between the

50 end of the mature miRNA (6–8 nt seed region) and its target

mRNA leads to downregulation of protein expression (Bartel,

2009; Doench and Sharp, 2004; Kim, 2005).

As is the case for nonhematopoietic tissues, lymphocytes and

other cells of the immune system rely on miRNAs to effect

lineage commitment, proliferation, migration, and differentiation

(Taganov et al., 2007; Xiao and Rajewsky, 2009). In most cases,

these activities are orchestrated by both ubiquitously expressed

and hematopoietic-specific miRNA species (Basso et al., 2009;

Landgraf et al., 2007; Merkerova et al., 2008; Monticelli et al.,

2005; Neilson et al., 2007; Wu et al., 2007). Deletion or overex-

pression of these miRNAs impairs the immune system at various

developmental stages (Chen et al., 2004; Li et al., 2007; O’Con-

nell et al., 2008; Rodriguez et al., 2007; Thai et al., 2007; Ventura

et al., 2008; Vigorito et al., 2007; Xiao et al., 2008). Similarly,

conditional ablation of DICER or other miRNA processing factors

Immunity 32, 1–12, June 25, 2010 ª2010 Elsevier Inc. 1

mailto:[email protected]

mailto:[email protected]

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Immunity’s MicroRNome


results in a profound block of both B and T cell development

(Cobb et al., 2005; Koralov et al., 2008; Muljo et al., 2005; O’Car-

roll et al., 2007). It is notable that these striking phenotypes are

driven for the most part through small changes in the cellular

concentration of key factors. In the B cell compartment for

instance, miR-150 curtails the activity of the c-Myb transcription

factor in a dose-dependent fashion over a narrow range of

miRNA and c-Myb concentrations (Xiao et al., 2007). Similarly,

mass action seems to be the underlying principle behind

miR-155 regulation of the B cell mutator AID or miR-17 and

miR-92-mediated inhibition of the tumor suppressor Pten and

the proapoptotic Bim proteins (Dorsett et al., 2008; Teng et al.,

2008; Ventura et al., 2008; Xiao et al., 2007; Xiao et al., 2008).

These examples, which in hindsight explain the haploinsuffi-

ciency observed in AID, cMyb, PTEN, and Bim heterozygous

mice (Bouillet et al., 2001; Di Cristofano et al., 1998; Takizawa

et al., 2008; Xiao et al., 2007), clearly demonstrate that the

absolute cellular concentration of miRNAs is crucial at managing

a cell’s proteome. Yet, how specific cell lineages establish

miRNA concentrations upon differentiation remains to be

defined.

Here, we use parallel sequencing to define chromatin modifi-

cations, the transcriptome, and microRNome of developing

lymphocytes. This integrative approach reveals the epigenetic,

transcriptional, and, indirectly, posttranscriptional mechanisms

controlling miRNA cellular concentrations.

RESULTS

Deep Sequencing of Small RNAsTo determine miRNA abundance during lymphopoiesis, we

microsequenced small RNAs (sRNAs) 18–30 nt in length from

mouse hematopoietic progenitor cells (HPCs) and downstream

T cell lineages including CD4+CD8+ thymocytes; splenic CD4+

and CD8+ T cells; ConA activated CD8+ cells; ex-vivo differenti-

ated Th (T helper type) 1, Th2, Th17, and iTreg (induced regula-

tory T) cells, as well as nTreg (natural regulatory T) cells; and Tfh

(T follicular helper) cells, which were cell sorted from secondary

lymphoid organs or spleens from immunized mice respectively

(Table 1). In addition, essentially all stages of B cell ontogeny

were examined, from progenitor bone marrow B cells (proB) to

terminally differentiated plasma cells (Table 1). For comparative

purposes, samples from other hematopoietic lineages (mast

cells, basophils, neutrophils, dendritic cells, and NK cells),

mouse embryonic stem cells (ESCs), and fibroblasts (MEFs),

along with 11 adult tissues, were also included in the survey.

Altogether, >300,000,000 sRNAs were sequenced. On average,

four million sequence tags per library were aligned to the

mouse genome with 100% identity and annotated as either

miRNAs; noncoding (nc) RNAs (rRNAs, tRNAs, snoRNAs, or

snRNAs); Piwi-interacting RNAs (piRNAs); small-coding RNAs

(scRNAs); highly repetitive RNA sequences; or unknown short

RNAs (Figure 1A and Table S1 available online). With the excep-

tion of testes, which harbor a large number of piRNAs (Tam et al.,

2008; Watanabe et al., 2008; Figure 1A), miRNAs were the most

abundant RNA species identified, oscillating between 68% in

germinal center (GC) B cells and 99.4% in the lung (Figure 1B).

Interestingly, scRNAs, which are presumably mRNA breakdown

products, were considerably enriched in GC B lymphocytes, and

2 Immunity 32, 1–12, June 25, 2010 ª2010 Elsevier Inc.

to a lesser extent, in other cells and tissues (Figure S1). Using

mice expressing the Em-BclXL transgene, which blocks activa-

tion-induced cell death in B cells (Fang et al., 1996), we found

that scRNAs are probably byproducts of mRNA degradation

and thus may be analogous to the well-characterized internu-

cleosomal DNA fragmentation occurring during apoptosis

(Figure S1). We conclude that the large majority of sRNAs micro-

sequenced belong to the miRNA family.

The Immune System’s MicroRNomeWith the ultimate goal of defining the microRNome of developing

lymphocytes, we next normalized the entire data set on the basis

of the absolute number of miRNA reads sequenced in each

sample. To validate this strategy, we quantified fold changes in

miRNAs miR-191_5p, miR-21_5p, miR-25_3p, miR-128-1_3p,

miR-128-2_3p, let7c-1_5p, let7c-2_5p, and let-7e_5p between

brain and activated B cell samples by using LNA qPCR. Reassur-

ingly, we observed a high degree of correlation between LNA

and miRNA-seq (R2 = 0.995, Figure S2A). Using TaqMan tech-

nology, we also analyzed eight miRNAs in various B and T cell

populations as well as HPCs and found again an overall agree-

ment between TaqMan and deep sequencing (r = 0.76; CI.95 =

[0.66, 0.83]; p < 0.0001, Figure S2B). Finally, biological and tech-

nical replicates from mature resting B cells and neutrophils

respectively showed significant reproducibility between

miRNA-seq runs (r > 0.94; p < 0.0001 for all replicates, Fig-

ure S2C). These analyses demonstrate that deep sequencing

can reliably profile miRNA abundance relative to the total pool

of miRNAs; thus our normalization protocol permits direct

comparison of miRNA expression between different cells and

tissues.

Of the 600 mature mouse miRNAs (miRBase 13), 353 were

detected at more than 100 sequence tags per million (TPM) in

at least one of the 40 tissues and cell types analyzed

(Table S2). Moreover, using a modified version of the miRDeep

algorithm (Friedlander et al., 2008, see Experimental Proce-

dures), we isolated 18 putative miRNAs, the majority of which

showed phylogenetic conservation (Data S1). Each individual

tissue or cell type expressed a mean of 102 ± 28 miRNAs at

greater than 100 TPM and 162 ± 43 when the cutoff was set at

25 TPM. In agreement with previous studies (Landgraf et al.,

2007), hierarchical clustering of miRNA profiling both paralleled

known developmental histories and clearly separated cells of

the immune system from other tissues (Figure 2A). Unexpect-

edly, expression of the let-7 family of miRNAs also partitioned

hematopoietic from nonhematopoietic cells. For instance, let-

7f was predominant in the immune system, comprising in

some cases up to 60% of all let-7 sequences, whereas its

expression in nonhematopoietic cells was statistically lower

(p < 0.0001; Wilcoxon rank-sum test, Figure 2B). Conversely,

let-7c abundance was substantially greater in nonhematopoietic

cells (Figure 2B). Other let-7 family members also showed differ-

ential expression in these two compartments (data not shown).

Thus, profiling and hierarchical clustering of miRNAs set apart

hematopoietic lineages from other cells and tissues.

ESCs displayed a large number of specific miRNAs (151),

followed by brain (52) and testes (41) (Figure 2A; Table S2 and

Data S2). In the immune system, neutrophils, basophils, as well

as T and B cells (considered as a single group) also displayed

Table 1. Tissues and Cell Types

Cell Type Species Organ Sorting, Purification, and Culture Conditions

ProB cells Mouse Bone marrow B220+IgM�CD43+CD25�

PreB cells Mouse Bone marrow B220+IgM�CD43�CD25+

Immature B cells Mouse Spleen B220+IgMhiCD21/CD35lo

Mature B cells Mouse Spleen B220+IgMloCD21/CD35lo

Marginal zone B cells Mouse Spleen B220+IgMhiCD21/CD35hi

B1 B cells Mouse Peritoneal cavity B220loIgMhi

Germinal center B cells Mouse Lymph nodes (immunized) CD19+CD95hi

Plasma cells Mouse Lymph nodes B220�CD138hi from IL6 transgenic mice

LPS+IL4 B cells Mouse Spleen CD43 depleted; 50 mg/ml LPS, 2.5ng/ml IL4, 72 hs

LPS+a-d-dextran B cells Mouse Spleen CD43 depleted; 50 mg/ml LPS, 2.5ng/ml a-d-dextran, 72 hs

Mast cells Mouse Bone marrow 20 ng/ml IL3, 20ng/ml SCF, 8 weeks

Basophils Mouse Bone marrow 20 ng/ml IL3, sorted at day 10, CD49b+Fc3RIa+CD11b+Kit�

Neutrophils Mouse Peritoneal cavity (casein) Percoll gradient, 88% Ly-6G+ purity

Dendritic cells Mouse Bone marrow 20 ng/ml GM-CSF, non-adherent, 12 days

Macrophages Mouse Bone marrow 10 ng/ml GM-CSF, 10ng/ml IL3, nonadherent, 14 days

NK cells Mouse Spleen (RAG1�/�) 1000 U/ml IL2, adherent, 8 days

Hematopoietic progenitor cells Mouse Bone marrow (5-FU) 20 ng/ml IL3, 50 ng/ml IL6, 50 ng/ml SCF, 7 days, 60% c-Kit+ScaI+Lin� purity

Double-positive T cells Mouse Thymus CD3+CD4+CD8+

Naive CD4+ T cells Mouse Lymph node/Spleen CD4+CD62L+CD44lowCD25-

Naive CD8+ T cells Mouse Spleen CD3+CD8+CD4�

ConA CD8+ T cells Mouse Spleen 5 mg/ml Concanavalin A, 5 days

Th1 T cells Mouse Spleen (sorted CD4+) Protocol from Wei et al. (2009)

Th2 T cells Mouse Spleen (sorted CD4+) Protocol from Wei et al. (2009)

Th17 T cells Mouse Spleen (sorted CD4+) Protocol from Shi et al. (2008)

T follicular helper T cells Mouse Spleen (immunized) CD4+CD44hiCXCR5hi

Natural regulatory T cells Mouse Lymph node/spleen CD4+CD25+

Induced regulatory T cells Mouse Spleen (sorted CD4+) Protocol from Wei et al. (2009)

ESCs Mouse Blastocysts Described in Marson et al. (2008)

Mouse embryonic fibroblasts Mouse E13.5 (C57B6) Passage 2

Naive B cells Human Tonsil CD19+CD38-/loCD27�

Memory B cells Human Tonsil CD19+CD38-/loCD27+

Pregerminal Center B cells Human Tonsil CD19+CD38+CD77�IgD+

Centroblasts Human Tonsil CD19+CD38+CD77+IgD�

Centrocytes Human Tonsil CD19+CD38+CD77�IgD�

Plasma cells Human Tonsil CD19-/loCD38hiCD27hiIgD�

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a substantial number of preferentially expressed miRNAs (Fig-

ure 2A). In addition to miR-223, which has been previously

ascribed to the myeloid lineage (Chen et al., 2004), neutrophils

expressed high amounts of miR-26 (a and b), miR-744,

miR-340, and eight other miRNAs (Figure 2C). In striking contrast

to ESCs, hematopoietic progenitor cells displayed few unique

miRNAs, illustrated in Figure 2D by miR-193b. From basophils

and mast cells we characterized a highly specific miRNA

(1073496_chr3) located in intron 8 of the CPA3 gene, which

encodes the basophil and mast cell-secreted protease carboxy-

peptidase A (Figure 2D). We also uncovered four miRNAs with

preferential expression in NK cells. Notably, these miRNAs

displayed nearly identical expression profiles, which, in addition

to NK cells, included lower expression in Tfh cells, ConA-acti-

vated CD8+ T cells, Th1 cells, and LPS+IL-4 stimulated B

lymphocytes (Figure 2D and Data S1). This feature implies

that a common regulatory pathway might drive expression of

these miRNAs.

A total of 49 miRNAs were preferentially upregulated in

lymphocytes (Figure 3A and Table S3). As formerly shown

(Chen et al., 2004), the miR-181 family was highly abundant in

developing bone marrow B cells and thymocytes (Figure 3A).

In CD4+CD8+ T cells, miR-181 miRNAs comprised 13.7%

(137,024 TPM) of the microRNome, which is in good accord

with published estimates (15.6%; Neilson et al., 2007).

miR-128, previously implicated in neural specification and malig-

nancy, was even more dominant in CD4+CD8+ T cells (54,134

TPM compared to 27,568 TPM in the brain, Figure 3B). In B cells,

the most abundant miRNAs were miR-320 and miR-191, which

comprised as much as 10% (109,145 TPM) of all miRNAs in


Figure 1. sRNA Microsequencing of Hema-

topoietic and Nonhematopoietic Cells

(A) Pie charts representing the distribution of the

six different classes of sRNAs measured from

LPS+IL-4 activated B cells, HPCs, heart, and

testes.

(B) Percentage of miRNAs of total sRNA

sequences that had at least one perfect alignment

to the mouse reference genome (mm9). Excluding

testes, the miRNA mean for all cell types and

tissues analyzed was 91.1%. The following

abbreviations are used: MEFs, mouse embryonic

fibroblasts; S. Glands, salivary glands; ESCs,

embryonic stem cells; HPCs, hematopoietic

progenitor cells; proB, progenitor B220loCD43+ B

cells; preB, B220loCD25+IgM� bone marrow B

cells; B1, peritoneal B220loIgMhi B cells; MZ,

IgMhiCR1CR2hi marginal zone B cells; GC,

CD95hiB220+ germinal center B cells; PCs,

CD138hiB220� plasma cells; LPS+IL-4, mature

B220+CD43� splenic B cells activated ex vivo for

72 hr in the presence of lipopolysaccharide and

interleukin 4; LPS+a-d-D, mature B cells activated

ex vivo for 72 hr in the presence of lipopolysaccha-

ride and dextran conjugated anti-IgD; ConA,

CD8+ T cells activated ex-vivo in the presence of

concanavalin A for 72 hr; iTregs, ex-vivo induced

regulatory T cells; nTregs, natural regulatory

T cells isolated by cell sorting.

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plasma cells and up to 5% in bone marrow and some peripheral

B cells, respectively (Figure 3C). By comparison, the highest

expression of miR-155 in activated B cells was 3,417 TPM

(Table S2). Other notable examples were miR-147, miR-31,

and miR-15b, which showed predominant expression in Th1,

Tfh (and basophils), and nTreg cells, respectively (Figure 3B).

miR-139 and miR-28 were highly exclusive to bone marrow

and GC B lymphocytes respectively (Figure 3C). We also isolated

several uncharacterized miRNAs in lymphocytes, including

482234_chr15, which was confined to IgMhi immature B cells,

and 145561_chr11, present in LPS+IL-4-activated B cells, iTreg

cells, Th17 cells, and ESCs (Figures 3B and 3C). Both miRNAs

were phylogenetically conserved and intronic to noncoding

mRNAs (Figure 3, schematics). On the basis of these results,

we conclude that miRNA-Seq reliably identifies the microRNome

signature of hematopoietic cells, including lymphocytes.

Epigenetic Regulation of miRNA Expressionin LymphocytesTo examine how miRNA expression is regulated during lympho-

poiesis, we first mapped histone modifications at high resolution

across the entire genome of HPCs, proB, preB, mature resting,

and LPS+IL-4 activated B cells. We concentrated on histone

H3, lysine 4 trimethylation (H3K4me3), which demarcates both

gene promoters and transcriptional activity; H3K27me3, which

is associated with transcriptional inhibition; and H3K36me3,

which follows transcriptional elongation. These data were

complemented with similar epigenetic analyses of ESCs (Marson

et al., 2008), as well as naive CD4+ T cells and downstream-

differentiated populations Th1, Th2, Th17, iTreg, and nTreg cells

(Wei et al., 2009). For a more comprehensive view of the epige-

netic landscape at and around miRNA genes, we mapped 32

additional histone modifications in mature resting and LPS+IL-4


activated B cells (H2AK9Ac, H2BK5Ac, H2BK5me1, H2BK12Ac,

H2BK20Ac, H2BK120Ac, H3K4me1, H3K4me2, H3K9Ac,

H3K9me1, H3K9me2, H3K9me3, H3K4Ac, H3K14Ac,

H3K18Ac, H3K23Ac, H3K27Ac, H3K36Ac, H3K27me1,

H3K27me2, H3K36me1, H3K36me2, H3K79me1, H3K79me2,

H3K79me3, H4K5Ac, H4K8Ac, H4K12Ac, H4K16Ac, H4K91Ac,

H4K20me1, and H3K20me3), together with the histone variant

H2A.Z, RNA polymerase II (PolII), and the histone acetyltransfer-

ase CBP-p300 (Table S4). The raw epigenetic data of miRNA

genes (±5 Kb) is provided as Data S3. This bedgraph file can

be loaded and viewed using the UCSC browser (http://

genome.ucsc.edu/).

On the basis of their expression profiles during B and T cell

development, we broadly classified miRNAs as (1) inactive, (2)

active, (3) poised, or (4) induced (Figure 4). As expected, miRNA

genes not expressed during lymphopoiesis (e.g., the heart-

specific miR-383) displayed repressive modifications such as

H3K27me3, H3K9me3, H3K27me2, and/or H4K20me3 (Wang

et al., 2008; Figure 4A and Table S4). In keeping with their lack

of transcription, we detected little or no PolII at promoter regions

of these genes (Figure 4A and Table S4). Alternatively, miRNA

genes fully active across development (e.g., let-7g) displayed

chromatin modifications previously linked to actively transcribed

genes ((Wang et al., 2008), Figure 4B and Table S4). (We point

out that whereas lack of posttranscriptional mechanisms prevent

accumulation of let-7 mature miRNAs in ESCs [Viswanathan

et al., 2008 and Figure 4B, bar graph]; the let-7 genes are none-

theless fully expressed therein.)

Among miRNAs confined to specific stages of B and/or T cell

development, our epigenetic analysis revealed at least two dis-

tinct subsets. As exemplified by miR-155 and 145561_chr11,

one group was H3K27 demethylated in HPCs or resting cells,

yet showed little or no expression at these early stages of

http://genome.ucsc.edu/

http://genome.ucsc.edu/

Figure 2. miRNA Signatures of the Mouse Immune System, Embryonic Cells, and Adult Tissues

(A) Heat map showing expression of 375 miRNAs (detected at >100 TPM) in mouse hematopoietic and nonhematopoietic cells based on hierarchical clustering

analysis. miRNAs enriched in a particular sample are depicted with red bars, whereas depleted miRNAs are depicted with green bars. B and T cell populations

outlined in Table 1 were combined into a single group.

(B) Percentage of let-7a (gray line), let-7c (red line), and let-7f (blue line). The total let-7 sequence tags were set to 100%. Let-7f and let-7c miRNA abundance is

inversely proportional in hematopoietic cells (light-blue box), whereas roughly equivalent in nonhematopoietic cells and tissues (light-red box).

(C) Expression profiles of miRNAs enriched in neutrophils. Two independently processed neutrophil samples (1 and 2) are shown and the highest TPM value of the

two is given in parenthesis.

(D) Examples of miRNAs predominantly expressed in HPCs, mast cells, and NK cells. For uncharacterized miRNAs, their genomic location is schematized.

Orientation of miRNAs and genes is represented with red and black arrowheads, respectively. Mammalian phylogenetic conservation is depicted with dense

black bar graphs based on UCSC PhastCons30Way algorithm.

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development (Figure 4C, Table S4, and Data S3). Cell activation,

however, brought about a dramatic increase in miRNA expres-

sion along with a high degree of activating modifications as

well as PolII and p300 recruitment. These miRNA genes thus

appear to be poised for activation early during ontogeny. On

the other hand, other hematopoietic-specific miRNAs, illustrated


Figure 3. miRNA Signatures of the Mouse B and T Cell Compartments

(A) Heat map depicts expression of 49 lymphocyte-specific miRNAs.

(B and C) Examples of representative miRNAs displaying specific expression in T or B lymphocytes. Only the highest TPM value is provided in each of the graphs.

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by miR-139 and miR-147, were more tightly regulated in that

they retained H3K27me3 up until full miRNA gene expression

was invoked (Figures 4D, Table S4, and Data S3). Transcription

was then accompanied by an increase in active chromatin marks

such as H3K4me3 and H3K36me3. Importantly, in the case of

miR-139, which was expressed in proB and preB cells, transcrip-

tional downregulation in mature resting and activated B cells was

accompanied by a return of H3K27me3 (Figure 4D), strength-

ening the notion that targeted expression of this miRNA subset

is tightly regulated. On the basis of these results, we conclude

that lymphocyte-specific miRNAs differ as to when the repres-

sive mark deposited by polycomb group proteins, H3K27me3,

is selectively lost. The potential functional impact of such distinc-

tion in the context of B and T cell differentiation is discussed

below.

Transcriptional Regulation of miRNA Expressionin LymphocytesOne corollary of our epigenetic analysis is that, at least for

some miRNAs, changes in miRNA cellular concentration during

lymphopoiesis are probably determined by gene transcription.

This view is consistent with the observation that clustered

miRNAs in the genome display fairly similar expression profiles

(Figure 5A; Baskerville and Bartel, 2005; Ruby et al., 2007). The

rationale being that proximally located miRNAs are encoded

by common primary transcripts. Yet, studies of human tumors

and mouse embryonic lines have revealed considerable discrep-

ancies between primary transcripts and mature miRNAs (Thom-

son et al., 2006). As such discrepancies likely reflect miRNA


processing defects in transformed cells (Thomson et al., 2006),

it is unclear to what extent transcription per se dictates miRNA

cellular abundance in primary cells. To systematically address

this issue, we quantified expression of spliced primary tran-

scripts by mRNA-seq and compared mRNA RPKM to miRNA

TPM values in six stages of B cell development (HPCs, proB,

preB, resting mature, LPS+IL-4 activated, and GC B cells). On

the basis of a Spearman’s rank correlation coefficient > 0.7,

we found coordinate expression of spliced primary transcripts

and mature miRNAs in 15 cases out of 54 (27%, Figure 5B and

Table S5). Within this correlated group, we found examples of

miRNAs intronic to coding genes illustrated by miR-139 and

miR-152 (Figure 5C), or embedded within noncoding RNAs, as

in the case of miR-107 and miR-362 (Table S5). We conclude

that during B cell development, fluctuations in expression of

one-quarter of miRNAs are correlated to spliced primary

transcript amounts.

Ectopically Expressed mRNA Targets InfluencemiRNA AbundanceThe above results uncovered examples where one of two miRNA

strands more closely parallels changes in miRNA gene expres-

sion (for instance, Figure 5D), implying that mechanisms other

than transcription and/or posttranscriptional processing may

influence final miRNA cellular abundance. To explore this idea,

we determined the overall 5p-3p distribution for mouse miRNAs.

We found that only a minority of miRNAs (38%, or 153 out of 407)

display highly polarized (>99:1) 5p or 3p strand bias (Figure 6A,

boxed data points), a considerable proportion (15%) show less

Figure 4. Epigenetic control of miRNA expression in lymphocytes

(A) Chromatin modifications associated with promoter and gene body of Sgcz (encoding the heart-specific miR-383) during lymphopoiesis. Silencing is achieved

via trimethylation of lysines 27 and 9 of histone H3, and lysine 20 of histone H4. Top bar graph provides expression profiles (in TPM) of miR-383_5p.

(B) The ubiquitously expressed let-7g gene displays chromatin modifications associated with active genes, including RNA polII and CBP-p300 recruitment.

Bar graph plots let-7g_5p.

(C) Example of a hematopoietic miRNA gene, miR-155, induced at late stages of development but depleted of H3K27me3 in progenitor cells. Modification

patterns of H3K14Ac, H3K36me3, H3K36Ac, H3K79me2, H2BK12Ac, and H3K27me3, as well as polII and p300 recruitment, are shown for mature resting

(black bars) and LPS+IL-4-activated (red bars) B cells. Values represent the total number of sequence tags aligned from 2.5 Kb upstream of the gene transcription

start site to its transcription termination site. The bar graph depicts expression profiles of miR-155_5p.

(D) Example of a hematopoietic miRNA gene, miR-139, that retains H3K27me3 inhibitory mark up until full gene expression is invoked. Chromatin modifications

H3K27me3 and H3K36me3 are shown and bar graph depicts miR-139_5p TPM values.

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than 3-fold strand bias (Figure 6A, enclosed with dotted lines),

and the remaining 47% fall somewhere in between these two

extremes. To determine whether this strand distribution was

evolutionarily conserved, we deep-sequenced the human B

cell microRNome from six tonsillar populations (Data S4) and

compared the 5p:3p ratio between mouse and human for

miRNAs with exact sequence conservation (highlighted in red

in Figure 6A). We found extensive 5p:3p correlation between

the two species (r = 0.94; CI.95 = [0.89, 0.96]; p < 0.0001,

Figure 6B), indicating miRNA strand abundance might be under

similar mechanistic constraints across species.

The uneven accumulation of 5p and 3p miRNA strands has

been explained by the functional asymmetry principle, which

postulates that during processing of miRNA duplexes, the strand

preferentially loaded into the RISC complex is the one whose 50

end is less tightly paired to its complement (Schwarz et al., 2003).

The ejected passenger strand or miRNA* is degraded, and thus

in general less abundant. Supporting the functional asymmetry

principle, a systematic comparison of 5p-3p strand concentra-

tions against the calculated stability of the 50 and 30 ends of

the miRNA duplex showed an overall significant correlation

(r = 0.53; CI.95 = [0.42, 0.61]; p < 0.0001, Figure 6C). This finding,

which is consistent with previous studies (Schwarz et al., 2003;

Tomari et al., 2004), argues that cellular 5p-3p distributions

can be partially explained by the propensity of the miRNA duplex

ends to fray. Yet, we found multiple instances where miRNA

strands showed differential expression profiles across the

various tissues and cell types examined. A case in point is

miR-30c2, whose 3p strand is undetected in hematopoietic

cells, but expressed at levels comparable to its complement,

miR-30c2_5p, in somatic cells and tissues (Figure 6D, left graph).

Another example is the miR-150_5p _3p pair, which is expressed

in resting immature and mature B cells as 3p > 5p, whereas this

pattern is reversed in the T cell compartment (Figure 6D, right

graph). Other examples are miR-139, miR-329, and miR-30e

(Table S2).

At least one way the above observations can be explained is

through cognate mRNA target abundance, which is expected


Figure 5. miRNA Abundance in B Cell Development as a Function of Transcription

(A) Correlation between expression and chromosome distance separating mouse miRNAs. Data points represent all possible pair combinations for miRNAs

located in the same chromosome. The y axis provides the Pearson correlation coefficient calculated for tissue expression patterns, whereas the x axis gives

the physical distance between the miRNAs in kilobases (Kb).

(B) The transcriptome of HPCs, proB cells, preB cells and mature resting, LPS+IL-4 activated, and germinal center B cells as determined by deep sequencing and

normalized as RPKM values. Each data point represents one of 126 mRNA-miRNA pairs (Table S3) at any one of the six B cell developmental stages.

(C) Examples of mRNA/miRNA pairs that show correlation (r > 0.7, upper row) or no correlation (r < 0.7, lower row) across the six B cell developmental stages.

Black bars represent mRNAs RPKM values (left y axis), whereas magenta bars depict miRNAs TPM values (right y axis).

(D) Examples of miRNAs with one strand (5p in both cases) showing greater correlation to mRNA expression profiles than the other.

Immunity



to vary throughout ontogeny and could in principle impact the

stability and/or half-life of miRNA-RISC complexes. To explore

this possibility, we expressed in A70 proB cells miR-30_5p

complementary target sequences (mirTs; Gentner et al., 2009),

which are recognized by the seed domain of the entire miR-30

family of miRNAs. We found that mirT expression affected

in highly specific fashion the steady-state abundance of

miR-30d_5p and miR-30e_5p, the two miR-30 members

expressed at significant amounts in A70 cells (Figure S4A).

Specifically, miR-30_5p amounts declined relative to control

upon cognate mRNA expression: 463 versus 3320 TPM for

miR-30d_5p and 330 versus 1237 TPM for miR-30e_5p

(Figure S4A). Attesting to the high specificity of the mirT-miRNA

interaction, no other endogenous miRNAs (including miR-30_3p

arms) were drastically affected by miR-30_5pT expression.

Analogously, expression of miR-30_5pTs in 293T cells resulted

in specific depletion of 5p arms from the two expressed

miR-30 members: miR-30e_5p (2001 versus 7693. TPM) and


miR-30a_5p (2840 versus 868 TPM, Figure S4B), indicating

that downregulation of miR-30_5p by mRNA targets is con-

served between mice and humans. We confirmed these results

by targeting 4 additional endogenous miRNAs in 293T cells.

Interestingly, we found examples of both specific upregulation

(miR-744_5p) and downregulation (miR-25_3p, miR-128_3p,

miR-191_5p) of miRNAs upon cognate mRNA expression

(Figure S4C). We conclude that the steady-state abundance of

miRNA strands can be influenced by ectopic expression of

complementary mRNA sequences. As proposed below, this

feature might help explain the changes in 3p:5p strand ratios

observed during development.

DISCUSSION

The hematopoietic microRNome can be set apart from that of

somatic cells and tissues by a distinctive miRNA signature,

differential expression patterns of let7, and a unique 5p:3p

Figure 6. 5p:3p Fluctuations during Lymphocyte Development

(A) 5p versus 3p species abundance of mouse miRNAs (graphed as log2 of TPM values). Closed red circles depict miRNAs entirely conserved between mouse

and human. Dotted lines delimit all miRNAs that display roughly equal numbers of 5p and 3p species.

(B) Correlation of 5p/3p ratios between mouse and human. Human miRNAs were microsequenced from 6 tonsillar B cell populations isolated by cell sorting:

naive, pre-germinal center, centrocytes, centroblasts, plasma cells, and memory B cells. For mouse, the entire panel of miRNA samples was used. Only miRNAs

entirely conserved between the two species were plotted. Both the Spearman correlation and P values are provided.

(C) Comparative analysis of miRNA 5p and 3p strand abundance (y axis) versus dissociation energy of 50 and 30 miRNA duplex ends (measured in Kcal/mol with

RNAduplex software). The plot shows that as the relative thermodynamic stability of the miRNA duplex 30 end increases (represented by positive values on the x

axis), its 50 end is preferentially unwound, leading to asymmetric uploading of its 5p strand into the RISC complex and an overall increase in 5p cellular abundance

(positive end of the y axis). Data at the negative end of the x and y axes provide the counterexample.

(D) The left plot shows a relative abundance of miR-30c2_5p (red bars) and 3p (blue bars) strands across all cells and samples examined. Hematopoietic

(light-blue box) and nonhematopoietic (light-red box) cells can be subdivided according to the absence or presence of miR-30c2_3p. The right plot shows

analysis of miR-150_5p (red bars) and 3p (blue bars) distribution in various B and T cell populations. ‘‘Mature (2)’’ and ‘‘CD8 (2)’’ refer to duplicate samples.

Immunity



distribution for selected miRNAs. Previous studies uncovered

miRNAs circumscribed to or enriched in the immune system,

such as miR-150, miR-155, miR-223, miR-146, and the

miR-181 family (Barski et al., 2009; Basso et al., 2009; Chen

et al., 2004; Landgraf et al., 2007; Merkerova et al., 2008; Mon-

ticelli et al., 2005; Neilson et al., 2007; Taganov et al., 2006;

Wu et al., 2007; Zhang et al., 2009). Our comparative cluster

analysis corroborated these findings and further assigned

previously characterized miRNAs to the hematopoietic system:

miR-15b (nTreg cells), miR-26a/b (neutrophils), miR-28 (GC B

cells), miR-107 (basophils), miR-320 and miR-148a (plasma

cells), miR-128 (CD4+CD8+ cells), miR-221 and miR-222

(Tfh-basophils), miR-147 (Th1 cells), miR-182 (NK cells), and

miR-193b (HPCs). Of note, both miR-28 and miR-148a were

also highly specific for human GC and plasma cells, respectively

(Figure S6). In addition, we identified 14 mouse miRNAs that

show preferential expression in various cells of the immune

system In the context of gene discovery, it is important to point

out that of the >300,000,000 sequence reads analyzed, as

many as 2,406 putative mouse miRNAs were predicted on the

basis of a probabilistic model of miRNA biogenesis (Friedlander

et al., 2008; data not shown). Of these, however, only a small

fraction (18 in total) was detected at amounts greater than

100 TPM. It is interesting to note therefore that the overwhelming

majority of putative miRNAs uncovered by our analysis appear to

represent low-abundant hairpin species that might on occasion

be processed by the RNAi pathway. Admittedly, we cannot

rule out that some of these putative miRNAs are expressed at

considerable levels in tissues not examined in our survey.

Furthermore, the arbitrary 100 TPM threshold is not a predictor

of physiological activity, which in principle could still be signifi-

cant at very low levels of miRNA expression. In light of our

findings however, it is reasonable to propose that most mouse

and human miRNAs have already been characterized. This

idea is supported by the fact that recent large-scale cloning

efforts yielded few uncharacterized, mostly low-abundant,

miRNAs (Basso et al., 2009; Landgraf et al., 2007).

The combining high-throughput miRNA-seq, mRNA-seq, and

ChIP-seq provides an unprecedented view of the various regula-

tory steps that shape miRNA expression and abundance during


Immunity



lymphopoiesis. At the epigenetic level, the methylation status of

H3K27 partitioned lymphocyte-specific miRNA genes into two

distinct subsets. Those belonging to the miR-139 and miR-147

group were found to retain the repressive H3K27me3 mark

throughout development up until miRNA gene transcription

was elicited. miRNA genes of the miR-155/145561_chr11 group

were H3K27 demethylated at earlier stages of development, and

thus they appeared to be poised for activation prior to full

transcription. The rationale for this subdivision might be provided

by the spatiotemporal context in which these miRNAs are

expressed. miR-147 for instance is induced as naive CD4+

T cells differentiate into the Th1 cell lineage. This process of

fate determination relies on positive epigenetic reprogramming

and expression of key factors, and negative regulation of

competing pathways driving differentiation to other T helper

cell types (Wei et al., 2009). Similarly, miR-139 derepression

occurs as part of another fate determination step, from pluripo-

tent hematopoietic progenitor cells to pro-B cells. Although the

precise role of miR-139 and miR-147 remains to be determined,

the expectation would be that miRNAs involved in symmetric or

progressive lymphocyte lineage commitment would be tightly

regulated by robust mechanisms. One such mechanism may

be polycomb group-mediated H3K27 trimethylation, which

might ensure induction of cell-fate determination only at the

appropriate time.

Our data have also revealed lymphocyte-induced miRNA

genes that appear to be epigenetically poised for transcription

early in development. These miRNA genes are associated with

some activating histone marks, are H3K27me3 depleted, recruit

low levels of PolII, but lack H3K36me3 or H3K79me2—hallmarks

of polymerase elongation. This subinduced chromatin state is

reflected by the low, but detectable, miRNA expression prior to

full gene transcription. For miR-155, 145561_chr11, and other

miRNAs under this category, full expression occurs as resting

lymphocytes are exposed to mitogens and cytokines typically

released during the immune response. The benefits of maintain-

ing critical miRNA genes like miR-155 in a preexisting subin-

duced state maybe best understood in the cadre of viral or

bacterial infections, which require rapid T and B cell responses.

On the basis of these considerations, it will be important to deter-

mine whether miRNAs strictly dependent on H3K27me3 for

expression are involved in lymphocyte developmental decisions,

while H3K27me3-independent ones drive B and T cell activation.

Ongoing studies indicate that this might be indeed the case.

Transcriptionally, we have shown that expression of at least

one-quarter of mature miRNAs closely follows spliced primary

transcripts as determined by mRNA-Seq. Because mRNAs are

stabilized by polyadenylation and other mechanisms, this is

likely to be an underestimate, e.g., the correlation between

mature miRNAs and unspliced pri-miRNAs is expected to be

more significant. Regardless of the precise number, the results

were nonetheless unexpected in light of the fact that studies

with cell lines fail to find any such correlation (Thomson et al.,

2006). Transformed cells, however, display a global reduction

in mature miRNAs (Lee et al., 2008; Lu et al., 2005), presumably

from a general deficiency in miRNA processing. Alternatively, in

primary cells several posttrancriptional mechanisms are bound

to promote deviations between transcription rate and mature

miRNA expression, such as efficiency of miRNA processing,


miRNA editing, or miRNA nuclear transport. In addition to these,

we have found that ectopically expressed mRNA targets can

both increase or decrease a particular miRNA pool. Whether

endogenous mRNAs can likewise influence fluctuations in

miRNA abundance remains to be determined. Nevertheless, it

is reasonable to propose that an increase in the absolute number

of mRNA targets may impact the balance and/or half-life of

‘‘free’’ miRNA, miRNA-RISC, and miRNA-RISC-mRNA com-

plexes. We find this scenario intriguing because it raises the

possibility that under physiological conditions, miRNAs and

mRNAs regulate each other’s homeostasis.

In summary, our studies reveal some of the epigenetic, tran-

scriptional, and posttranscriptional strategies that help orches-

trate cellular abundance of miRNAs during lymphopoiesis. The

hematopoietic miRNA signatures provided by the data represent

a valuable resource that will help guide future gene-targeting

experiments of individual miRNAs. In this respect, a major chal-

lenge in the field has been the identification of critical miRNAs:

miRNA targets driving developmental decisions. A strategy to

solve this problem is based on the observation that cellular

concentration of miRNA targets fluctuates as a function of

cognate miRNA expression (e.g., miR-150:c-Myb [Xiao et al.,

2007]). In principle, by applying microsequencing and bioinfor-

matics to a large number of developmental stages, it should be

possible to predict functional miRNA:miRNA target pairs. Our

preliminary studies using the miRNA- and mRNA-Seq data

from B cell development support this view. We anticipate that

genomic approaches such as this will help unravel how miRNAs

regulate development and effector functions of the immune

system.

EXPERIMENTAL PROCEDURES

RNA Isolation and Quality Control

Sorted or cultured cells were collected by centrifugation, dissolved in Trizol at

a concentration of 5 to 20 3 106 cells per milliliter, and stored at �80�C. Total

RNA was isolated in accordance with the manufacturer’s recommendations.

The RNA integrity number (RIN) for all samples was assessed from 100 ng of

total RNA with Agilent RNA 6000 Nano Kit and Bioanalyzer 2100 (Agilent

Technologies). The RIN of all mouse and human cell populations was at least

9 and the RIN of tissues was at least 8 except for pancreas (RIN 7.3) and skin

(RIN 7).

Small RNA Profiling by Illumina Deep-Sequencing

Small RNA sample preparation was done in accordance with Illumina’s

protocol. In brief, 50 and 30 adapters were sequentially ligated to small RNA

of 18–30 bases gel purified from 5–10 mg total RNA. Adaptor-ligated small

RNA was reverse transcribed, amplified by 15 PCR cycles with high-fidelity

Phusion polymerase (Finnzymes), and sequenced on a Genome Analyzer

(Illumina) in accordance with the manufacturer’s instructions.

Epigenetic Analysis

Cells were fixed with 1% paraformaldehyde at 37�C for 10 min and either

MNase-treated for ChIP or sonicated for Polymerase II and p300 IP. Precipi-

tated DNA fragments were processed in accordance with Illumina’s protocol

and sequenced on a Genome Analyzer with manufacturer’s instructions.

During analysis, short sequence reads were trimmed to 25 nts and aligned to

the mouse genome with either ELAND or Bowtie. Comparison data for mESC

and T cell populations were obtained from public databases (Marson et al.,

2008; Wei et al., 2009). Uniquely aligned reads were analyzed by SICER

(Zang et al., 2009) with an expectation value E of 50 in a random background

model. Reads on significant islands as defined by SICER were normalized to

the total number of reads on islands. Downstream analysis was carried out in R.

Immunity



Quantitative RT-PCR

RNA samples used for quantitative RT-PCR were isolated with the mirVana

miRNA isolation kit (ABI, part No: 1561) in accordance with the manufacturer’s

instructions. cDNA generated from 10 ng of total RNA by the TaqMan micro-

RNA reverse transcription kit (ABI, part No: 4366596) was applied for each

TaqMan qRT-PCR reaction with TaqMan 2 3 Universal PCR Master Mix

(ABI, part No: 4324018). All TaqMan qRT-PCR reactions were performed

and analyzed by StepOnePlus Real-time PCR system (ABI) with the following

cycle conditions: 95�C for 10 min, 1 cycle; 95�C for 15 s and 60�C for 60 s,

50 cycles. The expression of individual miRNA was normalized and expressed

as a percentage relative to U6 with the following formula: fold induction =

2[�DCt], where DCt = Ct(target) � Ct(U6).

For a comparison of brain samples to activated B cells, total RNA was sent

to Exicon for commercial miRCURY�-based microRNA quantification of

selected microRNAs (mmu-let-7c, mmu-let-7e, mmu-miR-21, mmu-miR-25,

mmu-miR-128, and mmu-miR-191).

ACCESSION NUMBERS

All sequence data are available in the Gene Expression Omnibus (GEO)

database (http://www.ncbi.nlm.nih.gov/gds) under the accession number

GSE21630.

SUPPLEMENTAL INFORMATION

Supplemental Information includes three figures, five tables, four data collec-

tions, and Supplemental Experimental Procedures and can be found with this

article online at doi:10.1016/j.immuni.2010.05.009.

ACKNOWLEDGMENTS

We thank members of the Casellas lab for discussions; J. Newman and

R. Young for mouse ESCs; J. Simone for cell sorting; G. Gutierrez for technical

assistance with the genome analyzer; B. Wold for the mRNA-seq protocol; and

L. Naldini for the LV-SFFV lentiviral vector. This work was supported in part by

the Intramural Research Program of NIAMS-NIH. S. K. was supported by the

Swiss Foundation for Grants in Biology and Medicine.

Received: October 18, 2009

Revised: March 22, 2010

Accepted: April 8, 2010

Published online: June 3, 2010

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Regulation of MicroRNA Expression and Abundance during ...Immunity Resource Regulation of MicroRNA Expression and Abundance during Lymphopoiesis Stefan Kuchen,1,10 Wolfgang Resch,1,10,*