Regulation of MicroRNA Expression
and Abundance during Lymphopoiesis
Stefan Kuchen,1,10Wolfgang Resch,1,10,* Arito Yamane,1,10Nan Kuo,1Zhiyu Li,1Tirtha Chakraborty,2Lai Wei,3
Arian Laurence,3Tomoharu Yasuda,2Siying Peng,2Jane Hu-Li,4Kristina Lu,5Wendy Dubois,6Yoshiaki Kitamura,7
Nicolas Charles,7Hong-wei Sun,8Stefan Muljo,4Pamela L. Schwartzberg,5William E. Paul,4John O’Shea,3
Klaus Rajewsky,2and Rafael Casellas1,9,*
1Genomics and Immunity, NIAMS, NIH, Bethesda, MD 20892, USA
2Immune Disease Institute and Department of Pathology, Harvard Medical School, Boston, MA 02115, USA
3Lymphocyte Cell Biology, NIAMS, NIH, Bethesda, MD 20892, USA
4Laboratory of Immunology, NIAID, NIH, Bethesda, MD 20892, USA
5Genetic Disease Research Branch, NHGRI, NIH, Bethesda, MD 20892, USA
6Laboratory of Genetics, NCI, NIH, Bethesda, MD 20892, USA
7Laboratory of Immune Cell Signaling, NIAMS, NIH, Bethesda, MD 20892, USA
8Biodata Mining and Discovery, NIAMS, NIH, Bethesda, MD 20892, USA
9Center of Cancer Research, NCI, NIH, Bethesda, MD 20892, USA
10These authors contributed equally to this work
*Correspondence: firstname.lastname@example.org (W.R.), email@example.com (R.C.)
Although the cellular concentration of miRNAs is crit-
ical to their function, how miRNA expression and
abundance are regulated during ontogeny is unclear.
We applied miRNA-, mRNA-, and ChIP-Seq to char-
acterize the microRNome during lymphopoiesis
within the context of the transcriptome and epige-
nome. We show that lymphocyte-specific miRNAs
are 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 reflect
transcriptional changes. However, we uncover a
subset of miRNAs for which abundance is dictated
by miRNA gene expression. We confirm that concen-
tration of 5p and 3p miRNA strands depends largely
on free energy properties of miRNA duplexes. Unex-
pectedly, we also find that miRNA strand accumula-
tion can be developmentally regulated. Our data
provide a comprehensive map of immunity’s micro-
RNome and reveal the underlying epigenetic and
transcriptional forces that shape miRNA homeo-
MicroRNAs (miRNAs) are noncoding small RNAs that modulate
the proteome of the cell by annealing to 30untranslated 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
50end 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,
conditionalablation of DICERor other miRNAprocessingfactors
828 Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc.
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
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.
Deep Sequencing of Small RNAs
To 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-
tionof testes, whichharbor alargenumber ofpiRNAs (Tam etal.,
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 enrichedin GC B lymphocytes, and
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 MicroRNome
With the ultimate goal of defining the microRNome of developing
lymphocytes, we nextnormalized 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
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
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
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 (Friedla ¨nder 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
Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc. 829
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 eightother miRNAs(Figure2C).Instrikingcontrast
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
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).
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
Table 1. Tissues and Cell Types
Cell TypeSpeciesOrganSorting, Purification, and Culture Conditions
B220?CD138hifrom IL6 transgenic mice
ProB cellsMouse Bone marrow
PreB cellsMouse Bone marrow
Immature B cellsMouse Spleen
Mature B cellsMouseSpleen
Marginal zone B cellsMouseSpleen
B1 B cellsMouse Peritoneal cavity
Germinal center B cellsMouseLymph nodes (immunized)
Plasma cellsMouse Lymph nodes
LPS+IL4 B cellsMouse SpleenCD43 depleted; 50 mg/ml LPS, 2.5ng/ml IL4, 72 hs
LPS+a-d-dextran B cellsMouseSpleenCD43 depleted; 50 mg/ml LPS, 2.5ng/ml a-d-dextran, 72 hs
Mast cells MouseBone marrow 20 ng/ml IL3, 20ng/ml SCF, 8 weeks
20 ng/ml IL3, sorted at day 10, CD49b+Fc3RIa+CD11b+Kit?
Percoll gradient, 88% Ly-6G+purity
Neutrophils Mouse Peritoneal cavity (casein)
Dendritic cellsMouseBone marrow20 ng/ml GM-CSF, non-adherent, 12 days
10 ng/ml GM-CSF, 10ng/ml IL3, nonadherent, 14 days
1000 U/ml IL2, adherent, 8 days
20 ng/mlIL3, 50 ng/ml IL6, 50 ng/mlSCF, 7 days, 60% c-Kit+ScaI+Lin?purity
Hematopoietic progenitor cells MouseBone marrow (5-FU)
Double-positive T cells
Naive CD4+T cells
Naive CD8+T cells
ConA CD8+T cells
Mouse Lymph node/Spleen
Spleen (sorted CD4+)
Spleen (sorted CD4+)
Spleen (sorted CD4+)
5 mg/ml Concanavalin A, 5 days
Th1 T cells Mouse
Protocol from Wei et al. (2009)
Th2 T cellsMouse
Protocol from Wei et al. (2009)
Th17 T cells Mouse
Protocol from Shi et al. (2008)
T follicular helper T cellsMouse Spleen (immunized)
Natural regulatory T cells Mouse Lymph node/spleen
Spleen (sorted CD4+)Induced regulatory T cellsMouseProtocol from Wei et al. (2009)
ESCsMouse Blastocysts Described in Marson et al. (2008)
Mouse embryonic fibroblastsMouse E13.5 (C57B6)Passage 2
Naive B cellsHumanTonsil
Memory B cellsHuman Tonsil
Pregerminal Center B cellsHuman Tonsil
Plasma cellsHuman Tonsil
830 Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc.
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
several uncharacterized miRNAs in lymphocytes, including
482234_chr15, which was confined to IgMhiimmature 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,
signature of hematopoietic cells, including lymphocytes.
Epigenetic Regulation of miRNA Expression
To 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
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,
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://
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
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
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
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,
progenitor cells; proB, progenitor B220loCD43+B
cells; preB, B220loCD25+IgM?bone marrow B
cells; B1, peritoneal B220loIgMhiB cells; MZ,
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
exvivo for72hr inthepresence 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.
Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc. 831
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 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).
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.
832 Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc.
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
wasthenaccompanied byanincreasein activechromatinmarks
such as H3K4me3 and H3K36me3. Importantly, in the case of
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,
tion in the context of B and T cell differentiation is discussed
Transcriptional Regulation of miRNA Expression
One 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
andmouse embryonic lineshave 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
Ectopically Expressed mRNA Targets Influence
The aboveresults uncovered examples whereoneof 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 foundthat only a minority of miRNAs (38%, or 153out of 407)
display highly polarized (>99:1) 5p or 3p strand bias (Figure 6A,
boxed data points), a considerable proportion (15%) show less
Figure 3. miRNA Signatures of the Mouse B and T Cell Compartments
(A) Heat map depicts expression of 49 lymphocyte-specific miRNAs.
(Band C) Examples of representative miRNAsdisplaying specific expressionin T or Blymphocytes. Only the highest TPM value is provided ineach of the graphs.
Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc. 833
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
preferentially loaded into the RISC complex is the one whose 50
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 50and 30ends 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,
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
At least one way the above observations can be explained is
through cognate mRNA target abundance, which is expected
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
(blackbars)andLPS+IL-4-activated (redbars)Bcells.Valuesrepresent thetotalnumberofsequence tagsalignedfrom2.5Kbupstreamofthegenetranscription
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.
834 Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc.
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.
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 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)ThetranscriptomeofHPCs,proBcells,preBcells andmature resting, LPS+IL-4activated,andgerminalcenter Bcellsasdeterminedbydeep 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 32, 828–839, June 25, 2010 ª2010 Elsevier Inc. 835
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 (Friedla ¨nder
et al., 2008; data not shown). Of these, however, only a small
fraction (18 in total) was detected at amounts greater than
100TPM. It is interesting to note therefore that the overwhelming
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
Figure 6. 5p:3p Fluctuations during Lymphocyte Development
(A) 5p versus 3p species abundance of mouse miRNAs (graphed as log2of 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 50and 30miRNA duplex ends (measured in Kcal/mol with
RNAduplex software). The plot shows that as the relative thermodynamic stability of the miRNA duplex 30end increases (represented by positive values on the x
axis), its50end is preferentially unwound, leading toasymmetric uploadingofits5pstrandintothe RISCcomplexand anoverall increase in5pcellular 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.
836 Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc.
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
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
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
lowlevels 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.
mine whether miRNAs strictly dependent on H3K27me3 for
expressionareinvolved in lymphocytedevelopmental decisions,
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
isreasonable toproposethat anincrease inthe 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
RNA Isolation and Quality Control
Sorted or cultured cells were collected by centrifugation, dissolved in Trizol at
a concentration of 5 to 20 3 106cells 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
Small RNA Profiling by Illumina Deep-Sequencing
Small RNA sample preparation was done in accordance with Illumina’s
protocol. In brief, 50and 30adapters 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.
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
Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc. 837
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).
All sequence data are available in the Gene Expression Omnibus (GEO)
database (http://www.ncbi.nlm.nih.gov/gds) under the accession number
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.
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
assistancewiththegenomeanalyzer; B.Wold forthemRNA-seqprotocol; 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
Barski, A., Jothi, R., Cuddapah, S., Cui, K., Roh, T.Y., Schones, D.E., and
Zhao, K. (2009). Chromatin poises miRNA- and protein-coding genes for
expression. Genome Res. 19, 1742–1751.
Bartel, D.P. (2004). MicroRNAs: Genomics, biogenesis, mechanism, and
function. Cell 116, 281–297.
Bartel, D.P. (2009). MicroRNAs: Target recognition and regulatory functions.
Cell 136, 215–233.
Baskerville, S., and Bartel, D.P. (2005). Microarray profiling of microRNAs
reveals frequent coexpression with neighboring miRNAs and host genes.
RNA 11, 241–247.
Basso, K., Sumazin, P., Morozov, P., Schneider, C., Maute, R.L., Kitagawa, Y.,
(2009). Identification of the human mature B cell miRNome. Immunity 30,
Bernstein, E., Kim, S.Y., Carmell, M.A., Murchison, E.P., Alcorn, H., Li, M.Z.,
Mills, A.A., Elledge, S.J., Anderson, K.V., and Hannon, G.J. (2003). Dicer is
essential for mouse development. Nat. Genet. 35, 215–217.
Borchert, G.M., Lanier, W., and Davidson, B.L. (2006). RNA polymerase III
transcribes human microRNAs. Nat. Struct. Mol. Biol. 13, 1097–1101.
Bouillet, P., Cory, S., Zhang, L.C., Strasser, A., and Adams, J.M. (2001).
Degenerative disorders caused by Bcl-2 deficiency prevented by loss of its
BH3-only antagonist Bim. Dev. Cell 1, 645–653.
Chen, C.Z., Li, L., Lodish, H.F., and Bartel, D.P. (2004). MicroRNAs modulate
hematopoietic lineage differentiation. Science 303, 83–86.
Chong, M.M., Rasmussen, J.P., Rudensky, A.Y., Rundensky, A.Y., and
Littman, D.R. (2008). The RNAseIII enzyme Drosha is critical in T cells for
preventing lethal inflammatory disease. J. Exp. Med. 205, 2005–2017.
Cobb, B.S., Nesterova, T.B., Thompson, E., Hertweck, A., O’Connor, E.,
Godwin, J., Wilson, C.B., Brockdorff, N., Fisher, A.G., Smale, S.T., and
Merkenschlager, M. (2005). T cell lineage choice and differentiation in the
absence of the RNase III enzyme Dicer. J. Exp. Med. 201, 1367–1373.
Di Cristofano, A., Pesce, B., Cordon-Cardo, C., and Pandolfi, P.P. (1998). Pten
is essential for embryonic development and tumour suppression. Nat. Genet.
Doench, J.G., and Sharp, P.A. (2004). Specificity of microRNA target selection
in translational repression. Genes Dev. 18, 504–511.
Dorsett, Y., McBride, K.M., Jankovic, M., Gazumyan, A., Thai, T.H., Robbiani,
D.F., Di Virgilio, M., San-Martin, B.R., Heidkamp, G., Schwickert, T.A., et al.
(2008). MicroRNA-155 suppresses activation-induced cytidine deaminase-
mediated Myc-Igh translocation. Immunity 28, 630–638.
Fang, W., Mueller, D.L., Pennell, C.A., Rivard, J.J., Li, Y.S., Hardy, R.R.,
Schlissel, M.S., and Behrens, T.W. (1996). Frequent aberrant immunoglobulin
gene rearrangements in pro-B cells revealed by a bcl-xL transgene. Immunity
Friedla ¨nder, M.R., Chen, W., Adamidi, C., Maaskola, J., Einspanier, R.,
Knespel, S., and Rajewsky, N. (2008). Discovering microRNAs from deep
sequencing data using miRDeep. Nat. Biotechnol. 26, 407–415.
Gentner, B., Schira, G., Giustacchini, A., Amendola, M., Brown, B.D., Ponzoni,
M., and Naldini, L. (2009). Stable knockdown of microRNA in vivo by lentiviral
vectors. Nat. Methods 6, 63–66.
Kanellopoulou, C., Muljo, S.A., Kung, A.L., Ganesan, S., Drapkin, R.,
Jenuwein, T., Livingston, D.M., and Rajewsky, K. (2005). Dicer-deficient
mouse embryonic stem cells are defective in differentiation and centromeric
silencing. Genes Dev. 19, 489–501.
Kim, V.N. (2005). MicroRNA biogenesis: Coordinated cropping and dicing.
Nat. Rev. Mol. Cell Biol. 6, 376–385.
Koralov, S.B., Muljo, S.A., Galler, G.R., Krek, A., Chakraborty, T.,
Kanellopoulou, C., Jensen, K., Cobb, B.S., Merkenschlager, M., Rajewsky,
N., and Rajewsky, K. (2008). Dicer ablation affects antibody diversity and
cell survival in the B lymphocyte lineage. Cell 132, 860–874.
Lagos-Quintana, M., Rauhut, R., Lendeckel, W., and Tuschl, T. (2001).
Identification of novel genes coding for small expressed RNAs. Science 294,
Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer,
S., Rice, A., Kamphorst, A.O., Landthaler, M., et al. (2007). A mammalian
microRNA expression atlas based on small RNA library sequencing. Cell
Lau, N.C., Lim, L.P., Weinstein, E.G., and Bartel, D.P. (2001). An abundant
class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans.
Science 294, 858–862.
Lee, R.C., and Ambros, V. (2001). An extensive class of small RNAs in Caeno-
rhabditis elegans. Science 294, 862–864.
Lee, R.C., Feinbaum, R.L., and Ambros, V. (1993). The C. elegans hetero-
chronic gene lin-4 encodes small RNAs with antisense complementarity to
lin-14. Cell 75, 843–854.
Lee, E.J., Baek, M., Gusev, Y., Brackett, D.J., Nuovo, G.J., and Schmittgen,
cell lines, and tumors. RNA 14, 35–42.
Li, Q.J., Chau, J., Ebert, P.J., Sylvester, G., Min, H., Liu, G., Braich, R.,
Manoharan, M., Soutschek, J., Skare, P., et al. (2007). miR-181a is an intrinsic
modulator of T cell sensitivity and selection. Cell 129, 147–161.
838 Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc.
Liu, J., Carmell, M.A., Rivas, F.V., Marsden, C.G., Thomson, J.M., Song, J.J., Download full-text
Hammond, S.M., Joshua-Tor, L., and Hannon, G.J. (2004). Argonaute2 is the
catalytic engine of mammalian RNAi. Science 305, 1437–1441.
Lu, J., Getz, G., Miska, E.A., Alvarez-Saavedra, E., Lamb, J., Peck, D.,
Sweet-Cordero, A., Ebert, B.L., Mak, R.H., Ferrando, A.A., et al. (2005).
MicroRNA expression profiles classify human cancers. Nature 435, 834–838.
Marson, A., Levine, S.S., Cole, M.F., Frampton, G.M., Brambrink, T.,
Johnstone, S., Guenther, M.G., Johnston, W.K., Wernig, M., Newman, J.,
circuitry of embryonic stem cells. Cell 134, 521–533.
Merkerova, M., Belickova, M., and Bruchova, H. (2008). Differential expression
of microRNAs in hematopoietic cell lineages. Eur. J. Haematol. 81, 304–310.
Monticelli, S., Ansel, K.M., Xiao, C., Socci, N.D., Krichevsky, A.M., Thai, T.H.,
Rajewsky, N., Marks, D.S., Sander, C., Rajewsky, K., et al. (2005). MicroRNA
profiling of the murine hematopoietic system. Genome Biol. 6, R71.
Muljo, S.A., Ansel, K.M., Kanellopoulou, C., Livingston, D.M., Rao, A., and
Rajewsky, K. (2005). Aberrant T cell differentiation in the absence of Dicer. J.
Exp. Med. 202, 261–269.
Neilson, J.R., Zheng, G.X., Burge, C.B., and Sharp, P.A. (2007). Dynamic
regulation of miRNA expression in ordered stages of cellular development.
Genes Dev. 21, 578–589.
O’Carroll, D., Mecklenbrauker, I., Das, P.P., Santana, A., Koenig, U., Enright,
A.J., Miska, E.A., and Tarakhovsky, A. (2007). A Slicer-independent role for
Argonaute 2 in hematopoiesis and the microRNA pathway. Genes Dev. 21,
O’Connell, R.M., Rao, D.S., Chaudhuri, A.A., Boldin, M.P., Taganov, K.D.,
Nicoll, J., Paquette, R.L., and Baltimore, D. (2008). Sustained expression of
microRNA-155 in hematopoietic stem cells causes a myeloproliferative
disorder. J. Exp. Med. 205, 585–594.
Rodriguez, A., Griffiths-Jones, S., Ashurst, J.L., and Bradley, A. (2004).
Identification of mammalian microRNA host genes and transcription units.
Genome Res. 14(10A), 1902–1910.
Rodriguez, A., Vigorito, E., Clare, S., Warren, M.V., Couttet, P., Soond, D.R.,
van Dongen, S., Grocock, R.J., Das, P.P., Miska, E.A., et al. (2007).
Requirement of bic/microRNA-155 for normal immune function. Science
Ruby, J.G., Stark, A., Johnston, W.K., Kellis, M., Bartel, D.P., and Lai, E.C.
(2007). Evolution, biogenesis, expression, and target predictions of a substan-
tially expanded set of Drosophila microRNAs. Genome Res. 17, 1850–1864.
Schwarz, D.S., Hutva ´gner, G., Du, T., Xu, Z., Aronin, N., and Zamore, P.D.
(2003). Asymmetry in the assembly of the RNAi enzyme complex. Cell 115,
Shi, G., Cox, C.A., Vistica, B.P., Tan, C., Wawrousek, E.F., and Gery, I. (2008).
Phenotype switching by inflammation-inducing polarized Th17 cells, but not
by Th1 cells. J. Immunol. 181, 7205–7213.
Taganov, K.D., Boldin, M.P., Chang, K.J., and Baltimore, D. (2006). NF-kap-
paB-dependent induction of microRNA miR-146, an inhibitor targeted to
signaling proteins of innate immune responses. Proc. Natl. Acad. Sci. USA
Taganov, K.D., Boldin, M.P., and Baltimore, D. (2007). MicroRNAs and
immunity: Tiny players in a big field. Immunity 26, 133–137.
Takizawa, M., Tolarova ´, H., Li, Z., Dubois, W., Lim, S., Callen, E., Franco, S.,
Mosaico, M., Feigenbaum, L., Alt, F.W., et al. (2008). AID expression levels
determine the extent of cMyc oncogenic translocations and the incidence of
B cell tumor development. J. Exp. Med. 205, 1949–1957.
Tam, O.H., Aravin, A.A., Stein, P., Girard, A., Murchison, E.P., Cheloufi, S.,
Hodges, E., Anger, M., Sachidanandam, R., Schultz, R.M., and Hannon, G.J.
(2008). Pseudogene-derived small interfering RNAs regulate gene expression
in mouse oocytes. Nature 453, 534–538.
Teng, G., Hakimpour, P., Landgraf, P., Rice, A., Tuschl, T., Casellas, R., and
Papavasiliou, F.N. (2008). MicroRNA-155 is a negative regulator of activa-
tion-induced cytidine deaminase. Immunity 28, 621–629.
Thai, T.H., Calado, D.P., Casola, S., Ansel, K.M., Xiao, C., Xue, Y., Murphy, A.,
Frendewey, D., Valenzuela, D., Kutok, J.L., et al. (2007). Regulation of the
germinal center response by microRNA-155. Science 316, 604–608.
Thomson, J.M., Newman, M., Parker, J.S., Morin-Kensicki, E.M., Wright, T.,
and Hammond, S.M. (2006). Extensive post-transcriptional regulation of
microRNAs and its implications for cancer. Genes Dev. 20, 2202–2207.
Tomari, Y., Matranga, C., Haley, B., Martinez, N., and Zamore, P.D. (2004).
A protein sensor for siRNA asymmetry. Science 306, 1377–1380.
Ventura, A., Young, A.G., Winslow, M.M., Lintault, L., Meissner, A., Erkeland,
S.J., Newman, J., Bronson, R.T., Crowley, D., Stone, J.R., et al. (2008).
Targeted deletion reveals essential and overlapping functions of the miR-17
through 92 family of miRNA clusters. Cell 132, 875–886.
Vigorito, E., Perks, K.L., Abreu-Goodger, C., Bunting, S., Xiang, Z., Kohlhaas,
S., Das, P.P., Miska, E.A., Rodriguez, A., Bradley, A., et al. (2007). microRNA-
155 regulates the generation of immunoglobulin class-switched plasma cells.
Immunity 27, 847–859.
Viswanathan, S.R., Daley, G.Q., and Gregory, R.I. (2008). Selective blockade
of microRNA processing by Lin28. Science 320, 97–100.
Wang, Z., Zang, C., Rosenfeld, J.A., Schones, D.E., Barski, A., Cuddapah, S.,
Cui, K., Roh, T.Y., Peng, W., Zhang, M.Q., and Zhao, K. (2008). Combinatorial
patterns of histone acetylations and methylations in the human genome. Nat.
Genet. 40, 897–903.
Watanabe, T., Totoki, Y., Toyoda, A., Kaneda, M., Kuramochi-Miyagawa, S.,
Obata, Y., Chiba, H., Kohara, Y., Kono, T., Nakano, T., et al. (2008). Endoge-
nous siRNAs from naturally formed dsRNAs regulate transcripts in mouse
oocytes. Nature 453, 539–543.
Wei, G., Wei, L., Zhu, J., Zang, C., Hu-Li, J., Yao, Z., Cui, K., Kanno, Y., Roh,
T.Y., Watford, W.T.,et al. (2009). Global mapping of H3K4me3 and H3K27me3
reveals specificity and plasticity in lineage fate determination of differentiating
CD4+ T cells. Immunity 30, 155–167.
Wightman, B., Ha, I., and Ruvkun, G. (1993). Posttranscriptional regulation of
the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in
C. elegans. Cell 75, 855–862.
Wu, H., Neilson, J.R., Kumar, P., Manocha, M., Shankar, P., Sharp, P.A., and
Manjunath, N. (2007). miRNA profiling of naı ¨ve, effector and memory CD8
T cells. PLoS ONE 2, e1020.
Xiao, C., and Rajewsky, K. (2009). MicroRNA control in the immune system:
Basic principles. Cell 136, 26–36.
Xiao, C., Calado, D.P., Galler, G., Thai, T.H., Patterson, H.C., Wang, J.,
Rajewsky, N., Bender, T.P., and Rajewsky, K. (2007). MiR-150 controls B
cell differentiation by targeting the transcription factor c-Myb. Cell 131,
Xiao, C., Srinivasan, L., Calado, D.P., Patterson, H.C., Zhang, B., Wang, J.,
Henderson, J.M., Kutok, J.L., and Rajewsky, K. (2008). Lymphoproliferative
disease and autoimmunity in mice with increased miR-17-92 expression in
lymphocytes. Nat. Immunol. 9, 405–414.
Zang, C., Schones, D.E., Zeng, C., Cui, K., Zhao, K., and Peng, W. (2009).
A clustering approach for identification of enriched domains from histone
modification ChIP-Seq data. Bioinformatics 25, 1952–1958.
Zhang, J., Jima, D.D., Jacobs, C., Fischer, R., Gottwein, E., Huang, G., Lugar,
P.L., Lagoo, A.S., Rizzieri, D.A., Friedman, D.R., et al. (2009). Patterns of
microRNA expression characterize stages of human B-cell differentiation.
Blood 113, 4586–4594.
Immunity 32, 828–839, June 25, 2010 ª2010 Elsevier Inc. 839