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Chapter 19
Analysis of the Conservative Motifs
in Promoters of miRNA Genes, Expressed
in Different Tissues of Mammalians
Oleg V. Vishnevsky, Konstantin V. Gunbin, Andrey V. Boc harnikov,
and Eugene V. Berezikov
Abstract Numerous miRNAs play an important role in translation regulation,
modulating embryo development, stem cells proliferation, and tissue differentia-
tion. Aberrant miRNA expression has been associated with diseases like cancer,
microcephaly, and schizophrenia. It is too little known about regulation of miRNA
expression. A computer approach was developed in order to reveal the significant
oligonucleotide motifs in the regulatory regions of eukaryotic genes. The regulatory
signals that are specific to the promoter regions of miRNA containing genes, which
are expressed in different tissues of mammalians, were obtained and classified.
O.V. Vishnevsky
Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090,
Prospekt Lavrentyeva 10, Novosibirsk, Russia
Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia
e-mail: oleg@bionet.nsc.ru
K.V. Gunbin
Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090,
Prospekt Lavrentyeva 10, Novosibirsk, Russia
A.V. Bocharnikov
Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia
E.V. Berezikov
Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090,
Prospekt Lavrentyeva 10, Novosibirsk, Russia
Hubrecht Institute, RNAAS, Utrecht, the Netherlands
P. Pontarotti (ed.), Evolutionary Biology – Concepts, Biodiversity, Macroevolution
and Genome Evolution, DOI 10.1007/978-3-642-20763-1_19,
#
Springer-Verlag Berlin Heidelberg 2011
325
19.1 Introduction
miRNAs are short (~22 bp) RNA sequences that bind to the 3
0
-untranslated region
(3
0
-UTR) of the mRNAs of target human genes (Kim 2005; Bartel 2004; Pasquinelli
et al. 2005). This binding event causes translation repression (Wightman et al.
1993) or stimulates rapid degradation of the transcript (Giraldez et al. 2006). Other
types of regulation, such as translation activation (Filipowicz et al. 2008) and
heterochromatin formation (Kim et al. 2008), have also been described. About
30% of all protein-coding human genes are targets for miRNAs (John et al. 2004).
miRNAs are believed to particularly target genes of transcription factors, such as
nuclear hormone receptors (John et al. 2004). One miRNA can target hundreds of
downstream target mRNAs, while one mRNA can be targeted by multiple miRNAs.
miRNAs control the expression of large number of genes (Lewis et al. 2003;
Enright et al. 2003) and play an important role in the regulation of the main
biological mechanisms that guide the development of organisms, stem cell prolif-
eration, apoptosis, and the processes of tissue differentiation (Foshay and Gallicano
2009; Johnnidis et al. 2008; Shcherbata et al. 2006; Stadler and Ruohola-B aker
2008; Yi et al. 2008). Disturbances in the expression of miRNAs result in
pathologies during development and serious diseases (Davis et al. 2008; Stark
et al. 2008; Alvarez-Garcia and Miska 2005; Jiang et al. 2008). Recent studies
have implicated miRNAs in numerous human diseases such as colorectal cancer,
chronic lymphocytic leukemia, and Fragile X syndrome (Calin et al. 2002, 2004,
2005; Caudy et al. 2002; McManus 2003; Croce and Calin 2005).
The vast majority of miRNAs (Fig. 19.1) are transcribe d by RNA polymer ase
II (Pol II) (Lee et al. 2004; C ai et al. 2004) as long primary transcripts (pri-
miRNA) (Bartel 2004; Lee et al. 2002; Cullen 2004), that contain single or
multiple hairpin-like structures (Cullen 2004; Cai et al. 2004; Lee et al. 2004;
Altuvia et a l. 2005). Approximately 50% of mammalian miRN A loci are found in
close proximity to other miRNAs (Lee et al. 2002). Like other Pol II transcripts,
pri-miRNAs are 5
0
-cappedand3
0
- polyadenylated (Cai et al. 2004; Lee et al.
2004;Brachtetal.2004). pri-miRNAs are processed in the nucleus into approxi-
mately 70 nT hairpins (pre-miRN A) by Dro sha, an RNAse-III like e nzyme acting
with DGCR8 and other cofactors (Lee et al. 2002, 2003). The pre-miRNAs are
exported by Exportin 5 (EXP5) to the cytoplasm (Kim 2004). EXP5 is a member
of the nuclear transport receptor family (Lund et al. 2004;Yietal.2005;
Bohnsack et al. 2004). In the cytoplasm, a second RNAse-III like e nzyme,
Dicer cleaves the pre-miRNA into a mature double-stranded dsRNA duplex
(Bernstein et al. 2001; Grishok et al. 2001;Hutva
´
gner et al. 2001; Ketting et al.
2001; Knight and Bass 2001). D icer is a highly conserved p rotein that is found in
almost all eukaryotic organisms, including Schizosaccharomyces pombe,plants
and animals. One strand of the 21–23 nucleotide dsRNA is packaged into the
RNA-Inhibiting Silencing Complex (RISC)andguidedtotargetmRNAs.Pairing
between microRNAs and target mRNAs takes place in association with one or
more Argonaute proteins, the major protein component of RISCs. Depending
326 O.V. Vishnevsky et al.
upon the degree of complementarity between the target mRNA and the miRNA,
the mRNA is either subject to repression of translation or cleavage and degrada-
tion. It has been demonstrated that six nucleotides at positions 2–7 relative to the
5
0
end of the miRNA (called seed) are crucial in base pairing with the target
mRNA in animals, while the remaining portion of the 30-miRNA seems less
important (Bartel 2004, 2009).
The transcription of most miRNA genes is mediated by RNA polymerase II,
although a minor group of miRNAs that are associated with Alu repeats can be
transcribed by Pol III (Borchert et al. 2006).
Approximately 50% of human miRNAs appear to be expressed from introns of
protein-coding transcripts (Rodriguez et al. 2004). Some part of miRNAs was found
in exons of protein-coding genes. Others are transcribed by Pol II as independent
transcription units using its own promoter.
Expression analyses show that miRNAs are expressed in a tissue-specific man-
ner at specific times (Lagos-Quintana et al. 2002; Sempere et al. 2004; Krichevsky
et al. 2006; Landgraf et al. 2007). Brain development has been associated with
highly dynamic and temporally regulated waves of miR NA expression, with spe-
cific groups of miRNA being expressed only at specific time points during embry-
onic development of the nervous system (Stadler and Ruohola-Baker 2008; Dogini
et al. 2008; Miska et al. 2004; Wheeler et al. 2006).
A range of Pol II–associated transcription factors control miRNA gene tran-
scription (Lee and Dutta 2009; Cao et al. 2006; Kosik 2006; Zeng 2009). For
instance, myogenic transcription factors, such as MyoD1, induce the transcription
miRNA gene
pri miRNA
hairpin
transcription
transport
RISC formation
3′-UTR
5′-Cap
5′
3′-PolyA
cleavage
Drosha
DGCR8
Exportin 5
Diser
dicing
Ago
RISC
RISC
Pol II
pre miRNA
target mRNA
mature miRNA
miRNA duplex
Nucleus Cytoplasm
Fig. 19.1 Pathway of microRNA (miRNA) biogenesis and action
19 Analysis of the Conservative Motifs in Promoters of miRNA Genes 327
of miR-1 and miR-133 during myogenesis (Chen et al. 2006; Kim et al. 2006; Rao
et al. 2006). Also in the brain, transcription of miR-134, partic ipating in the
regulation of BDNF-stimulated synaptic plasticity (Schratt et al. 2006 ) is regulated
by neuronal activity via factor Mef2 (Fiore et al. 2009; Pulipparacharuvil et al.
2008). Some miRNAs are under the control of tumor-suppressive or oncogenic
transcription factors. The tumor suppressor p53 activates transcription of the
miR-34 family of miRNAs (He et al. 2007), whereas the oncogenic protein MyC
transactivates or represses a number of miRNAs that are involved in the cell cycle
and apoptosis (He et al. 2005; Chang et al. 2008). The canonical TATA box motifs
have been identified upstream of miRNA genes (Stormo 2000).
Epigenetic control also contributes to miRNA gene regulation (Lujambio et al.
2007; Saito et al. 2006). For instance, the miR-203 locus frequently undergoes
DNA methylation in T-cell lymphoma but not in normal T lymphocytes (Bueno
et al. 2008).
RNA editing is another possible way of regulating miRNA biogenesis. The
alteration of adenines to inosines has been observed in miR-142 (Yang et al.
2006) and miR-151 (Kawahara et al. 2007). RNA editing can also change the target
specificity of the miRNA (Kawahara et al. 2008).
The development of high-performance deep-sequencing techniques (Lu et al.
2005; Margulies et al. 2005) and in silico prediction methods (Lai et al. 2003; Nam
et al. 2005; Li et al. 2006; Huang et al. 2007) has accelerated the discovery of
miRNAs. Based on these technologies (Berezikov et al. 2005), the human genome
is now believed to contain more than 1,000 miRNA genes. The diversity of the
miRNA repertoire increases with the organism’s complexity: humans and other
mammals have about four times more annotated miRNA genes compared to
insects and nematodes, suggesting the role of miRNAs in the development of
this complexity.
Many of the animal miRNAs are phylogenetically conserved; ~55% of
Caenorhabditis elegans miRNAs have homologues in humans. It means that
miRNAs have had important roles throughout animal evolution (Iba
´
n
˜
ez-Ventoso
et al. 2008). At the same time, many miRNAs were conserved only between
primates and some were even species-specific, suggesting the existence of recently
evolved miRNA genes (Berezikov et al. 2006).
Although we know that miRNAs are expressed in tissue-specific manner, we
know very little about peculiarities of regulation of miRNA expression. The aim of
our research is to reveal and analyze the regulatory signals in promoter regions of
miRNA genes, expressed in different tissues of mamm alians.
19.2 Materials and Methods
The sets of the regulatory regions of miRNA genes expressed in the brain, lungs,
and excretory system of 12 species of mammalians (Homo sapiens, Pan troglodytes,
Pongo pygmaeus, Gorilla gorilla, Macaca mulatta, Callithrix jacchus, Bos taurus,
328 O.V. Vishnevsky et al.
Equus caballus, Canis familiaris, Mus musculus, Rattus norvegicus, Sus scrofa)
were generated. The [1,000; +1] regions of miRNA genes relative to the tran-
scription start site were analyzed.
For this, at the first stage, information about localization in the genome of human
1,572 miRNAs, which were predicted by experimental and computer approaches,
was obtained from the Ensembl database (http://www.ensembl.org). Then the
nearest transcription start sites in the 5
0
-region of miRNAs were located at a
distance of not more than 3,000 nucleotides in relation to the beginning of the
miRNA. This information was obtained from the FANTOM4 database (Kawaji
et al. 2009). This database contains information about 5
0
-ends of more than 24
millions of human mRNAs and information about tissues, where these mRNAs
were detected. Based on this information, the sets of promoters of human miRNA
genes, expressed in brain, excretory system, and lungs in the [1,000; +1] region
relative to the transcription start site were generated. After this, homologous
1,000 bp genome regions were obtained for 11 species of mammals in Ensembl
Compara database using Compara Per1API. This database contains the whole
genomic alignments of different species and is widely used in methods of compar-
ative analysis. Finally, the sets of promoters of miRNA genes of 12 species of
mammalians, expressed in brain (“brain set”), excretory system (“excretory set”),
and lungs (“lungs set”) have been produced.
The 36 sets constructed strongly differ by size (Fig. 19.2). The biggest sets were
obtained for primates. Non-primates have appro ximately two times less size of the
Homo sapiens
0
50
100
150
200
250
300
Pan troglodytes
Gorilla gorilla
Pongo pygmaeus
Macaca mulatta
Callithrix jacchus
Mus musculus
Rattus norvegicus
Canis familiaris
Equus caballus
Bos taurus
Sus scrofa
Fig. 19.2 The size of the brain set (black bar), excretory set ( white bar), and lungs set (gray bar)
19 Analysis of the Conservative Motifs in Promoters of miRNA Genes 329
sets for all tissues. The sets of prom oters of miRNA genes , expressed in brain, were
the biggest. The size of the sets varies from 273 sequences for human brain set to
57 sequences for excretory set of rat.
The search for significant regulatory signals that are related to the structural–
functional organization of promoters was performed in 36 sets of species- and
tissue-specific sequences using the method (Vishnevsky and Kolchanov 2005)of
revealing degenerate oligonucleotide motifs, i.e., short words of a fixed length
written in the 15 single letter-based IUPAC code (A,T,G,C, R ¼ G/A, Y ¼ T/C,
M ¼ A/C, K ¼ G/T , W ¼ A/T, S ¼ G/C, B ¼ T/G/C, V ¼ A/G/C, H ¼ A/T/C,
D ¼ A/T/G, N ¼ A/T/G/C). This approach is based on the analysis of oligo-
nucleotide vocabularies of the promoters, finding and clustering of the similar
oligonucleotides characterized by low Hamming distance and located in sequences
of different promoters, with continuous iteration building of IUPAC-consensus for
every class of the oligonucleotides. We consider the motif as significant, if it occurs
in the high number of promoter sequences, located in the low number of random
sequences and its probability P(n, N) to occur by chance in the set of promoters
is low.
The probability P(n, N) is calculated as follows. Let us consider an oligonucleo-
tide motif M ¼ m
1
, m
2
,..., m
l
of length l in the expanded 15 sing le letter-based
IUPAC code. The probability of this motif to occur at a particular position in the
DNA sequence S
k
of length L is: PðMÞ¼
Q
l
i¼1
P
i
, where P
i
is a frequency of a letter
m
i
assessed from the nucleotide content of S
k
. The binomial probability P(n, N)
to observe the motif M in more or equal than n (0 n N) sequences is:
Pðn; NÞ¼
P
N
i¼n
C
i
N
P
i
ð1 PÞ
Ni
, where PðS
k
Þ¼1 e
ðLlþ1ÞPðMÞ
.
This approach does not need the preliminary experimental information about
transcription factor binding sites or multiple alignments to reveal significant signals
in the analyzed set of the sequences.
The graphics accelerators (GPU) and chips with programmable logic (FPGA)
were applied to increase the calculation speed. It allows us to increase the speed of
calculation in 50 times for GPU and in 500 times for FPGA in comparison with
single CPU.
To assess the similarities in the regulatory regions of genes based on oligonucle-
otide motifs, we developed a method (Vishnevsky and Kolchanov 2005) based on
a comparison of the abundance and character of the distribution of motifs in
sequences under study. As a measure of similarity between the jth promoter and
the sequence studied, the value P
j
¼
P
L
k¼1
log p
k
=L is used, where L is the length of
the sequence analyzed and p
k
is the product of frequencies of nucleotides, which are
consistent with the motifs covering the kth position. Th e greater is P
j
, the lower is
the probability of chance occurrence of the motif set characteristic of the jth
promoter in the sequence.
330 O.V. Vishnevsky et al.
19.3 Results and Discussion
The number of the motifs, revealed using the ARGO program, strongly varied for
different tissues in different species (Fig. 19.3). For example, it was found 363
motifs in the human excretory set. Against this, only 10 significant motifs were
obtained from the lung promoters of mouse. These differences could be explained
as by differences in size of the sets analyzed, as by different heterogeneity of the
sets. In general, the most of the motifs were found in the sets of brain-specific
promoters of primates.
Characteristics of the most significant oligonucleotide motifs found in the sets
of human promoters are shown in Table 19.1. For example, the AGRRRGAA ¼
(A)(G)(G/A)(G/A)(G/A)(G)(A)(A) motif is presented in 37% of human brain-
specific promoters. It was found in 5% of random sequences, generated with the
same mononucleotide content. The logarithm of binomial probability P(n, N)to
observe the motif by chance is 39. An analysis of the motifs demonstrates that
some of them are rather complex words, but others look like polyA–polyT runs or
TATA-like sequences.
The next step we estimated the tissue specificity of the motifs revealed.
Figure 19.4 demonstrates that along with the motifs, overrepresented in one set of
promoters, there are motifs, equally distributed in promoters of all the tissues
analyzed. For example, the motif VRTCAGCM, revealed in the brain set, occurs
Homo sapiens
0
50
100
150
200
250
300
350
400
Pan troglodytes
Gorilla gorilla
Pongo pygmaeus
Macaca mulatta
Callithrix jacchus
Mus musculus
Rattus norvegicus
Canis familiaris
Equus caballus
Bos taurus
Sus scrofa
Fig. 19.3 Number of the motifs, revealed in promoters of genes, expressed in the brain set (black
bar), excretory set (white bar), and lungs set (gray bar)
19 Analysis of the Conservative Motifs in Promoters of miRNA Genes 331
in 40% of brain-specific sequences, but it was found only in 20% of excretory set
or lung set promoters. At the same time, the motif GSBGCVGS, revealed in the
excretory set, equally occurs in the sets of all tissues analyzed. We suppose that
Table 19.1 Characteristics
of the most significant motifs,
revealed in the promoters of
genes, expressed in different
tissues of Homo sapiens
Motif
Occurrence
in promoters
Occurrence
in random
sequences log(P(n, N))
Brain set
AGRRRGAA 0.37 0.05 39.61
TTTTYATT 0.30 0.03 34.23
VTYCCCAG 0.28 0.02 31.00
CTTYYTCT 0.24 0.02 30.64
CCYCWBCC 0.26 0.05 26.50
YCCCAGSN 0.21 0.05 25.28
Excretory set
TTTWTWTT 0.40 0.04 31.56
GCAGMGVC 0.40 0.02 30.89
GGCKGBRG 0.39 0.05 28.28
GAAMCAAW 0.29 0.08 27.58
AAAMAMYC 0.27 0.06 26.75
SCWGGAGY 0.29 0.05 25.90
Lungs set
CCYBYCYC 0.46 0.10 33.94
TTYTTWTT 0.34 0.03 33.36
TBSMCAGG 0.28 0.03 22.42
HCYCARCC 0.37 0.07 21.01
MWKSCCAG 0.39 0.09 19.10
GGNGSCKG 0.30 0.08 18.77
GYSCRGSC
CTCYCCVY
VRTCAGCM
GGMRCKWG
ATTRTCTW
WGVAAGGW
VGTGYHTT
CARKAAAM
GGWTWRCT
TAAAAAAB
GTTYAYTS
AARAAAAH
AAATTYWK
GSBGCVGC
CGCDCVGS
MSTYYGCC
GSCSRYGC
WCCMCCTK
CMASACRG
TCCMCMGS
GSRSCKCG
RTNTCAAA
GGRGAADM
DSTGAGMT
CTGSCYHA
STMAKCCC
WHTCCCMG
GSSAGGVG
0
10
20
40
30
50
60
brain set excretory set lungs set
Fig. 19.4 The occurrence rate of the motifs in promoters of H sapiens genes, expressed in brain
(black bar), excretory system (white bar), and lungs (gray bar)
332 O.V. Vishnevsky et al.
these common motifs could correspond to the basic properties of promoter regions
of miRNA genes, independent from the tissue specificity of gene.
Then we estimated the distribut ion of the motifs along the promoter sequences,
relative to the transcription start site position. Figure 19 .5 demonstr at es the
occurrence rate of the motifs in the 500-bp window, which slides along the
promoter sequences with the step 10 bp. The analysis of the graphs demonstrates
that most of the motifs are located in the region [500; +1] relative to the start of
transcription. At the same time, some other motifs, e.g., GGYBGSRG, are equally
distributed along the promoter sequences.
The comparison of the motifs with the known binding sites of transcription
factors and position weight matrixes from TRANSFAC (Matys et al. 2006), TRRD
(Kolchanov et al. 2002) and Jaspar (Portales-Casamar et al. 2010) databases
indicates that the majority of them have similarities with binding sites of transcrip-
tion factors, which participates in development, regulation of the cell cycle, and
apoptosis (Table 19.2). For all species were found motifs, presented in all three
tissues. Some of them correspond to the ubiquitous binding sites like TATA-box or
CCAAT-box. At the same time, part of the motifs corresponds to tissue-specific
binding sites. For instance, the motifs for binding sites of TATA binding protein or
SP1, important for the early development of an organism were found in all three sets
of promoters. On the contrary, the motifs of CREB binding site, required for brain
development and functioning were found in the set of brain-specific promoters. A lot
of the motifs were not interpreted significantly. These unclassified motifs could
[–1000;–500]
[–950;–450]
[–900;–400]
[–850;–350]
[–800;–300]
[–750;–250]
[–700;–200]
[–650;–150]
[–600;–100]
[–550;–50]
[–500;+1]
0
-10
10
20
TTTTYATT
GGYBGSRG
AGRSCHGK
VRTCAGCM
ATTRTCTW
WGVAAGGW
VGTGYHTT
TAAAAAAB
30
40
50
Fig. 19.5 Distribution of the brain-specific motifs of H. sapiens along the promoter sequences.
Axis X – regions of promoter, relative to the transcription start site, axis Y – the percent of
promoters, containing a motif, relative to the random level. Zero level is the occurrence rate of the
motif in the set of the random sequences
19 Analysis of the Conservative Motifs in Promoters of miRNA Genes 333
correspond to still unknown species-specific transcription factor binding sites or to
some structural features of promoters, like short polyA-polyT runs, which are known
to induce DNA curvature or serve as “easily melting” sites.
Finally, we estimated species-specificity of the motifs obtained in the pro-
moters of different mammalian species. For this purpose, we calculated the average
oligonucleotide similarity H
Oli(Homo,X)
between human promoters and the pro-
moters of other mammalians using the ARGO program as follows. Let’s compare
the set of human promoters and the set of chimpanzee promoters. At first, we
calculate the oligonucleotide similarity of the first human promoter and all
promoters of chimpanzee, to find the maximal value of similarity function. Then
we repeat this procedure for all other human promoters and finally estimate the
oligonucleotide similarity F
OliðHomo;PanÞ
¼
P
N
i¼1
max F
i
=N of the sets analyzed. Finally,
the value of the average oligonucleot ide similarity is: H
OliðHomo;PanÞ
¼
F
OliðHomo;PanÞ
F
OliðHomo;RandomÞ
F
OliðHomo;HomoÞ
F
OliðHomo;RandomÞ
, where F
Oli(Homo,Random)
is the oligonucleotide similarity
between the set of human promoters and the set of random sequences, generated
with the same mononucleotide content. F
Oli(Homo,Homo)
is the oligonucleotide simi-
larity of the set of human promoters with itself.
To estimate the dependence of the average oligonucleotide similarity H
Oli
on the
homology of the sequences analyzed, we calculated the average homology H
Align
of
the sets using the pairwise alignment by the same way.
Figure 19.6 demonstrates the results of comparison of the human promoters and
promoters of other species. The highest values of the similarities to human are
typical for other primates. Promoters of all tissues demonstrate the similar values of
average homology and oligonucleotide similarity. It is of interest that oligonucleo-
tide similarity of the set of brain-specific dog promoters is slightly higher then the
oligonucleotide similarity in promoters of other non-primate species and it is equal
to the value of macaque.
Table 19.2 Classification
of the motifs using TRRD,
TRANSFAC, and Jaspar
Brain set Excretory set Lungs set
SP1 SP1 SP1
SREBP1 SREBP1 SREBP1
TBP TBP TBP
NKX NKX NKX
NFk_B NFk_B NFk_B
IRF1 IRF1 IRF1
AP2 AP2 E2F
EGR CP2 CP2
Max GR Max
CREB GATA1-3 GATA1-3
MYT1 STAT6 STAT6
MYC PAX PAX
SRF E2F SRF
334 O.V. Vishnevsky et al.
Comparison of the oligonucleotide similarity and average homology demonstrates
that for phylogenetically close to human species like other primates, these values
differ no more than per 30% in all tissues. For chimpanzee, this difference is less than
15%. For tissue-specific promoters of evolutionary distant from human species, like
non-primates, the average homology values are relatively low, but oligonucleotide
similarity values are rather high. The value of difference varies from 40% to 55%. We
suggest that it could be explained by evolutionary conservation of the tissue-specific
regulatory signals located in upstream regions of genes of phylogenetically distant
species, despite of upstream region low similarities.
19.4 Conclusion
The potential regulatory signals in promoters of miRNA genes, expressed in
different tissues of mammalians were revealed; these may be used for the further
experimental analysis. The vast majority of the motifs obtained, correspond to the
transcription factor binding sites, involved in the regulation of the organism devel-
opment, tissue differentiation, cell cycle regulation, and apoptosis. It was shown
that in tissue-specific promoters of evolutionarily distant species of mammalians,
similarity at the level of regulatory signals is significantly higher than the average
homology of sequences.
Acknowledgments The work was supported by the Russian Foundation for Basic Research
(grants no. 09-04-01641-a, 11-04-12167 and 11-04-01888-a), Integration projects of the Siberian
Branch of the Russian Academy of Sciences no. 26, 113, 119 and Programs of the Russian
Pan_troglodytes
Gorilla_gorilla
Ponga_pygmaeus
Macaca_mulatta
Callithrix_jacchus
Mus_musculus
Rattus_norvegicus
Canis_faniliaris
Equus_caballus
Bos_taurus
Sus_scrofa
excretory set
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Pan_troglodytes
Gorilla_gorilla
Pongo_pygmaeus
Macaca_mulatta
Callithrix_jacchus
Mus_musculus
Rattus_norvegicus
Canis_familiaris
Equus_caballus
Bos_taurus
Sus_scrofa
lungs set
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Pan_troglodytes
Gorilla_gorilla
Ponga_pygmaeus
Macaca_mulatta
Callithrix_jacchus
Mus_musculus
Rattus_norvegicus
Canis_familiaris
Equus_caballus
Bos_taurus
Sus_scrofa
brain set
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Fig. 19.6 Comparison of the oligonucleotide similarity H
Oli
(black bar), the average homology
H
Align
(white bar)ofH. sapiens promoters, and the promoters of other species
19 Analysis of the Conservative Motifs in Promoters of miRNA Genes 335
Academy of Sciences no. 22 (project no. 8) and no. 23 (project no. 29), the Ministry of Science
and Education of the Russian Federation (Contracts no. P857, no. P721).
References
Altuvia Y, Landgraf P, Lithwick G, Elefant N, Pfeffer S, Aravin A, Brownstein MJ, Tuschl T,
Margalit H (2005) Clustering and conservation patterns of human microRNAs. Nucleic Acids
Res 33:2697–2706
Alvarez-Garcia I, Miska EA (2005) MicroRNA functions in animal development and human
disease. Development 132:4653–4662
Bartel DP (2004) MicroRNAs: geno mics, biogenesis, mechanism, and function. Cell 116:281–297
Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233
Berezikov E, Guryev V, van de Belt J, Wienholds E, Plasterk RH, Cuppen E (2005) Phylogenetic
shadowing and computational identification of human microRNA genes. Cell 120:21–24
Berezikov E, Thuemmler F, van Laake LW, Kondova I, Bontrop R, Cuppen E, Plasterk RH (2006)
Diversity of microRNAs in human and chimpanzee brain. Nat Genet 38:1375–1377
Bernstein E, Caudy AA, Hammond SM, Hannon GJ (2001) Role for a bidentate ribonuclease in the
initiation step of RNA interference. Nature 409:363–366
Bohnsack MT, Czaplinski K, Gorlich D (2004) Exportin 5 is a RanGTP-dependent dsRNA-
binding protein that mediates nuclear export of pre-miRNAs. RNA 10:185–191
Borchert GM, Lanier W, Davidson BL (2006) RNA polymerase III transcribes human
microRNAs. Nat Struct Mol Biol 13:1097–1101
Bracht J, Hunter S, Eachus R, Weeks P, Pasquinelli AE (2004) Trans-splicing and polyadenylation
of let-7 microRNA primary transcripts. RNA 10:1586–1594
Bueno MJ, Pe
´
rez de Castro I, Go
´
mez de Cedro
´
n M, Santos J, Calin GA, Cigudosa JC, Croce CM,
Ferna
´
ndez-Piqueras J, Malumbres M (2008) Genetic and epigenetic silencing of microRNA-
203 enhances ABL1 and BCR-ABL1 oncogene expression. Cancer Cell 13:496–506
Cai X, Hagedorn CH, Cullen BR (2004) Human microRNAs are processed from capped,
polyadenylated transcripts that can also function as mRNAs. RNA 10:1957–1966
Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, Aldler H, Rattan S, Keating M, Rai
K et al (2002) Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16
at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci USA 99:15524–15529
Calin GA, Sevignani C, Dumitru CD, Hyslop T, Noch E, Yendamuri S, Shimizu M, Rattan S,
Bullrich F, Negrini M, Croce CM (2004) Human microRNA genes are frequently located at
fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci USA 101:2999–3004
Calin GA, Ferracin M, Cimmino A, Di Leva G, Shimizu M, Wojcik SE, Iorio MV, Visone R,
Sever NI, Fabbri M et al (2005) A microRNA signature associated with prognosis and
progression in chronic lymphocytic leukemia. N Engl J Med 353:1793–1801
Cao X, Yeo G, Muotri AR, Kuwabara T, Gage FH (2006) Noncoding RNAs in the mammalian
central nervous system. Annu Rev Neurosci 29:77–103
Caudy AA, Myers M, Hannon GJ, Hammond SM (2002) Fragile X-related protein and VIG
associate with the RNA interference machinery. Genes Dev 16:2491–2496
Chang TC, Yu D, Lee YS, Wentzel EA, Arking DE, West KM, Dang CV, Thomas-Tikhonenko A,
Mendell JT (2008) Widespread microRNA repression by Myc contributes to tumorigenesis.
Nat Genet 40:43–50
Chen JF, Mandel EM, Thomson JM, Wu Q, Callis TE, Hammond SM, Conlon FL, Wang DZ
(2006) The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and
differentiation. Nat Genet 38:228–233
Croce CM, Calin GA (2005) MiRNAs, cancer, and stem cell division. Cell 122:6–7
Cullen BR (2004) Transcription and processing of human microRNA precursors. Mol Cell
16:861–865
336 O.V. Vishnevsky et al.
Davis TH, Cuellar TL, Koch SM, Barker AJ, Harfe BD, McManus MT, Ullian EM (2008)
Conditional loss of Dicer disrupts cellular and tissue morphogenesis in the cortex and hippo-
campus. J Neurosci 28:4322–4330
Dogini DB, Ribeiro PA, Rocha C, Pereira TC, Lopes-Cendes I (2008) MicroRNA expression
profile in murine central nervous system development. J Mol Neurosci 35:331–337
Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS (2003) MicroRNA targets in
Drosophila. Genome Biol 5:R1
Filipowicz W, Bhattacharyya SN, Sonenberg N (2008) Mechanisms of post-transcriptional regu-
lation by microRNAs: are the answers in sight? Nat Rev Genet 9:102–114
Fiore R, Khudayberdiev S, Christensen M, Siegel G, Flavell SW, Kim TK, Greenberg ME, Schratt
G (2009) Mef2-mediated transcription of the miR379–410 cluster regulates activity-dependent
dendritogenesis by fine-tuning Pumilio2 protein levels. EMBO J 28:697–710
Foshay KM, Gallicano GI (2009) MiR-17 family miRNAs are expressed during early mammalian
development and regulate stem cell differentiation. Dev Biol 326:431–443
Giraldez AJ, Mishima Y, Rihel J, Grocock RJ, Van Dongen S, Inoue K, Enright AJ, Schier AF
(2006) Zebrafish MiR-430 promotes deadenylation and clearance of maternal mRNAs. Science
312:75–79
Grishok A, Pasquinelli AE, Conte D, Li N, Parrish S, Ha I, Baillie DL, Fire A, Ruvkun G, Mello
CC (2001) Genes and mechanisms related to RNA interference regulate expression of the small
temporal RNAs that control C. elegans developmental timing. Cell 106:23–34
He L, Thomson JM, Hemann MT, Hernando-Monge E, Mu D, Goodson S, Powers S, Cordon-
Cardo C, Lowe SW, Hannon GJ, Hammond SM (2005) A microRNA polycistron as a potential
human oncogene. Nature 435:828–833
He L, He X, Lowe SW, Hannon GJ (2007) MicroRNAs join the p53 network – another piece in the
tumoursuppression puzzle. Nat Rev Cancer 7:819–822
Huang TH, Fan B, Rothschild MF, Hu ZL, Li K, Zhao SH (2007) MiRFinder: an improved
approach and software implementation for genome-wide fast microRNA precursor scans.
BMC Bioinform 8:341
Hutva
´
gner G, McLachlan J, Pasquinelli AE, Ba
´
lint E, Tuschl T, Zamore PD (2001) A cellular
function for the RNAinterference enzyme Dicer in the maturation of the let-7 small temporal
RNA. Science 293:834–838
Iba
´
n
˜
ez-Ventoso C, Vora M, Driscoll M (2008) Sequence relationships among C. elegans, D.
melanogaster and human microRNAs highlight the extensive conservation of microRNAs in
biology. PLoS ONE 3:e2818
Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y (2008) miR2Disease:
a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res
37:D98–D104
John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS (2004) Human microRNA targets.
PLoS Biol 2:e363
Johnnidis JB, Harris MH, Wheeler RT, Stehling-Sun S, Lam MH, Kirak O, Brummelkamp TR,
Fleming MD, Camargo FD (2008) Regulation of progenitor cell proliferation and granulocyte
function by microRNA-223. Nature 451:1125–1129
Kawahara Y, Zinshteyn B, Chendrimada TP, Shiekhattar R, Nishikura K (2007) RNA editing of
the microRNA-151 precursor blocks cleavage by the Dicer–TRBP complex. EMBO Rep
8:763–769
Kawahara Y, Megraw M, Kreider E, Iizasa H, Valente L, Hatzigeorgiou AG, Nishikura K
(2008) Frequency and fate of micr oRNA e diting in human brain. Nucleic Acids Res
36:5270–5280
Kawaji H, Severin J, Lizio M, Waterhouse A, Katayama S, Irvine KM, Hume DA, Forrest AR,
Suzuki H, Carninci P, Hayashizaki Y, Daub CO (2009) The FANTOM web resource: from
mammalian transcriptional landscape to its dynamic regulation. Genome Biol 4:R40
19 Analysis of the Conservative Motifs in Promoters of miRNA Genes 337
Ketting RF, Fischer SE, Bernstein E, Sijen T, Hannon GJ, Plasterk RH (2001) Dicer functions
in RNA interference and in synthesis of small RNA involved in developmental timing in
C. elegans. Genes Dev 15:2654–2659
Kim VN (2004) MicroRNA precursors in motion: exportin-5 mediates their nuclear export. Trends
Cell Biol 14:156–159
Kim VN (2005) MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol
6:376–385
Kim HK, Lee YS, Sivaprasad U, Malhotra A, Dutta A (2006) Muscle-specific microRNA miR-206
promotes muscle differentiation. J Cell Biol 174:677–687
Kim DH, Saetrom P, Jr Snove O, Rossi JJ (2008) MicroRNA-directed transcriptional gene
silencing in mammalian cells. Proc Natl Acad Sci USA 105:16230–16235
Knight SW, Bass BL (2001) A role for the RNase III enzyme DCR-1 in RNA interference and
germ line development in Caenorhabditis elegans. Science 293:2269–2271
Kolchanov NA, Ignatieva EV, Ananko EA, Podkolodnaya OA, Stepanenko IL, Merkulova TI,
Pozdnyakov MA, Podkolodny NL, Naumochkin AN, Romashchenko AG (2002) Transcription
regulatory regions database (TRRD): its status in 2002. Nucleic Acids Res 30:312–317
Kosik KS (2006) The neuronal microRNA system. Nat Rev Neurosci 7:911–920
Krichevsky AM, Sonntag KC, Isacson O, Kosik KS (2006) Specific microRNAs modulate
embryonic stem cell-derived neurogenesis. Stem Cells 24:857–864
Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, LendeckelW TT (2002) Identification of tissue-
specific microRNAs from mouse. Curr Biol 12:735–739
Lai EC, Tomancak P, Williams RW, Rubin GM (2003) Computational identification of Drosoph-
ila microRNA genes. Genome Biol 4:R42
Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO,
Landthaler M, Lin C, Socci ND, Hermida L, Fulci V, Chiaretti S, Foa
`
R, Schliwka J, Fuchs U,
Novosel A, M
€
uller RU, Schermer B, Bissels U, Inman J, Phan Q, Chien M, Weir DB, Choksi R,
De Vita G, Frezzetti D, Trompeter HI, Hornung V, Teng G, Hartmann G, Palkovits M, Di
Lauro R, Wernet P, Macino G, Rogler CE, Nagle JW, Ju J, Papavasiliou FN, Benzing T,
Lichter P, Tam W, Brownstein MJ, Bosio A, Borkhardt A, Russo JJ, Sander C, Zavolan M,
Tuschl T (2007) A mammalian microRNA expression atlas based on small RNA library
sequencing. Cell 129:1401–1414
Lee YS, Dutta A (2009) MicroRNAs in cancer. Annu Rev Pathol 4:199–227
Lee Y, Jeon K, Lee JT, Kim S, Kim VN (2002) MicroRNA maturation: stepwise processing and
subcellular localization. EMBO J 21:4663–4670
Lee Y, Ahn C, Han J, Choi H, Kim J, Yim J, Lee J, Provost P, Ra
˚
dmark O, Kim S, Kim VN (2003)
The nuclear RNase III Drosha initiates microRNA processing. Nature 425:415–419
Lee Y, Kim M, Han J, Yeom KH, Lee S, Baek SH, Kim VN (2004) MicroRNA genes are
transcribed by RNA polymerase II. EMBO J 23:4051–4060
Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB (2003) Prediction of mammalian
microRNA targets. Cell 115:787–798
Li SC, Pan CY, Lin WC (2006) Bioinformatic discovery of microRNA precursors from human
ESTs and introns. BMC Genomics 7:164
Lu C, Tej SS, Luo S, Haudenschild CD, Meyers BC, Green PJ (2005) Elucidation of the small
RNA component of the transcriptome. Science 309:1567–1569
Lujambio A, Ropero S, Ballestar E, Fraga MF, Cerrato C, Setie
´
n F, Casado S, Suarez-Gauthier A,
Sanchez-Cespedes M, Git A, Spiteri I, Das PP, Caldas C, Miska E, Esteller M (2007) Genetic
unmasking of an epigenetically silenced microRNA in human cancer cells. Cancer Res
67:1424–1429
Lund E, G
€
uttinger S, Calado A, Dahlberg JE, Kutay U (2004) Nuclear export of microRNA
precursors. Science 303:95–98
Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS,
Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S,
Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza
338 O.V. Vishnevsky et al.
JR, Leamon JH, Lefkowitz SM, Lei M, Li J, Lohman KL, Lu H, Makhijani VB, McDade KE,
McKenna MP, Myers EW, Nickerson E, Nobile JR, Plant R, Puc BP, Ronan MT, Roth GT,
Sarkis GJ, Simons JF, Simpson JW, Srinivasan M, Tartaro KR, Tomasz A, Vogt KA, Volkmer
GA, Wang SH, Wang Y, Weiner MP, Yu P, Begley RF, Rothberg JM (2005) Genome
sequencing in microfabricated high-density picolitre reactors. Nature 437:376–380
Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D,
Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE,
Wingender E (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regu-
lation in eukaryotes. Nucleic Acids Res 34:D108–D110
McManus MT (2003) MicroRNAs and cancer. Semin Cancer Biol 13:253–258
Miska EA, Alvarez-Saavedra E, Townsend M, Yoshii A, Sestan N, Rakic P, Constantine-Paton M,
Horvitz HR (2004) Microarray analysis of microRNA expression in the developing mamma-
lian brain. Genome Biol 5:R68
Nam JW, Shin KR, Han J, Lee Y, Kim VN, Zhang BT (2005) Human microRNA prediction
through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res
33:3570–3581
Pasquinelli AE, Hunter S, Bracht J (2005) MicroRNAs: a developing story. Curr Opin Genet Dev
15:200–205
Portales-Casamar E, Thongjuea S, Kwon AT, Arenillas D, Zhao X, Valen E, Yusuf D, Lenhard B,
Wasserman WW, Sandelin A (2010) JASPAR 2010: the greatly expanded open-access data-
base of transcription factor binding profiles. Nucleic Acids Res 38:D105–D110
Pulipparacharuvil S, Renthal W, Hale CF, Taniguchi M, Xiao G, Kumar A, Russo SJ, Sikder D,
Dewey CM, Davis MM, Greengard P, Nairn AC, Nestler EJ, Cowan CW (2008) Cocaine
regulates MEF2 to control synaptic and behavioral plasticity. Neuron 59:621–633
Rao PK, Kumar RM, Farkhondeh M, Baskerville S, Lodish HF (2006) Myogenic factors that
regulate expression of muscle-specific microRNAs. Proc Natl Acad Sci USA 103:8721–8726
Rodriguez A, Griffiths-Jones S, Ashurst JL, Bradley A (2004) Identification of mammalian
microRNA host genes and transcription units. Genome Res 14:1902–1910
Saito Y, Liang G, Egger G, Friedman JM, Chuang JC, Coetzee GA, Jones PA (2006) Specific
activation of microRNA-127 with downregulation of the proto-oncogene BCL6 by chromatin-
modifying drugs in human cancer cells. Cancer Cell 9:435–443
Schratt GM, Tuebing F, Nigh EA, Kane CG, Sabatini ME, Kiebler M, Greenberg ME (2006)
A brain-specific microRNA regulates dendritic spine development. Nature 439:283–289
Sempere LF, Freemantle S, Pitha-Rowe I, Moss E, Dmitrovsky E, Ambros V (2004) Expression
profiling of mammalian microRNAs uncovers a subset of brain-expressed microRNAs with
possible roles in murine and human neuronal differentiation. Genome Biol 5:R13
Shcherbata HR, Hatfield S, Ward EJ, Reynolds S, Fischer KA, Ruohola-Baker H (2006) The
microRNA pathway plays a regulatory role in stem cell division. Cell Cycle 5:172–175
Stadler BM, Ruohola-Baker H (2008) Small RNAs: keeping stem cells in line. Cell 132:563–566
Stark KL, Xu B, Bagchi A, Lai WS, Liu H, Hsu R, Wan X, Pavlidis P, Mills AA, Karayiorgou M,
Gogos JA (2008) Altered brain microRNA biogenesis contributes to phenotypic deficits in a
22q11-deletion mouse model. Nat Genet 40:751–760
Stormo GD (2000) Gene-finding approaches for eukaryotes. Genome Res 10:394–397
Vishnevsky OV, Kolchanov NA (2005) ARGO: a web system for the detection of degenerate
motifs and large-scale recognition of eukaryotic promoters. Nucleic Acids Res 33:w417–w422
Wheeler G, Ntounia-Fousara S, Granda B, Rathjen T, Dalmay T (2006) Identification of new
central nervous system specific mouse microRNAs. FEBS Lett 580:2195–2200
Wightman B, Ha I, Ruvkun G (1993) Posttranscriptional regulation of the heterochronic gene
lin-14 mediates temporal pattern formation in C. elegans. Cell 75:855–862
Yang W, Chendrimada TP, Wang Q, Higuchi M, Seeburg PH, Shiekhattar R, Nishikura K (2006)
Modulation of microRNA processing and expression through RNA editing by ADAR
deaminases. Nat Struct Mol Biol 13:13–21
19 Analysis of the Conservative Motifs in Promoters of miRNA Genes 339
Yi R, Doehle BP, Qin Y, Macara IG, Cullen BR (2005) Overexpression of exportin 5 enhances
RNA interference mediated by short hairpin RNAs and microRNAs. RNA 11:220–226
Yi R, Poy MN, Stoffel M, Fuchs E (2008) A skin microRNA promotes differentiation by
repressing ‘stemness’. Nature 452:225–229
Zeng Y (2009) Regulation of the mammalian nervous system by microRNAs. Mol Pharmacol
75:259–264
340 O.V. Vishnevsky et al.
