Certain Adenylated Non-Coding RNAs, Including 59
Leader Sequences of Primary MicroRNA Transcripts,
Accumulate in Mouse Cells following Depletion of the
RNA Helicase MTR4
Jane E. Dorweiler1, Ting Ni2¤, Jun Zhu2, Stephen H. Munroe1*, James T. Anderson1*
1Department of Biological Sciences, Marquette University, Milwaukee, Wisconsin, United States of America, 2DNA Sequencing and Genomics Core, Genetics and
Development Biology Center, National Institutes of Health, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
RNA surveillance plays an important role in posttranscriptional regulation. Seminal work in this field has largely focused on
yeast as a model system, whereas exploration of RNA surveillance in mammals is only recently begun. The increased
transcriptional complexity of mammalian systems provides a wider array of targets for RNA surveillance, and, while many
questions remain unanswered, emerging data suggest the nuclear RNA surveillance machinery exhibits increased
complexity as well. We have used a small interfering RNA in mouse N2A cells to target the homolog of a yeast protein that
functions in RNA surveillance (Mtr4p). We used high-throughput sequencing of polyadenylated RNAs (PA-seq) to quantify
the effects of the mMtr4 knockdown (KD) on RNA surveillance. We demonstrate that overall abundance of polyadenylated
protein coding mRNAs is not affected, but several targets of RNA surveillance predicted from work in yeast accumulate as
adenylated RNAs in the mMtr4KD. microRNAs are an added layer of transcriptional complexity not found in yeast. After
Drosha cleavage separates the pre-miRNA from the microRNA’s primary transcript, the byproducts of that transcript are
generally thought to be degraded. We have identified the 59 leading segments of pri-miRNAs as novel targets of mMtr4
dependent RNA surveillance.
Citation: Dorweiler JE, Ni T, Zhu J, Munroe SH, Anderson JT (2014) Certain Adenylated Non-Coding RNAs, Including 59 Leader Sequences of Primary MicroRNA
Transcripts, Accumulate in Mouse Cells following Depletion of the RNA Helicase MTR4. PLoS ONE 9(6): e99430. doi:10.1371/journal.pone.0099430
Editor: Alfred S. Lewin, University of Florida, United States of America
Received March 27, 2014; Accepted May 14, 2014; Published June 13, 2014
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. Project data have been deposited to the NIH
short read archive (SRP041394).
Funding: This work was supported by Marquette University Way Klingler College of Arts and Sciences to J.T.A.; by Marquette University Department of Biological
Sciences to J.T.A. and S.H.M.; and by intramural research program at National Heart Lung Blood Institute, National Institutes of Health to J.Z.; and by National
Institutes of Health grant number 1R15GM100445-01A1 to J.T.A. Funding for open access charge: Marquette University and National Institutes of Health grant
number 1R15GM100445-01A1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* Email: firstname.lastname@example.org (JTA); Stephen.email@example.com (SHM)
¤ Current address: State Key Laboratory of Genetics Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan
University, Shanghai, P.R. China
Transcriptional activity in mammalian genomes, once consid-
ered limited, is now being viewed differently as more sophisticated
techniques are developed and used to interrogate transcriptomes.
The sheer abundance of regulatory RNAs (rRNA, snRNA,
snoRNA and tRNA) fostered their early discovery and character-
ization [1–6], whereas high-throughput sequencing (HTS) meth-
ods continue to uncover an ever increasing population of rare
RNAs. The use of HTS to survey mRNA reveals extensive
transcript diversity due to alternative splicing, as well as use of
alternative transcription start and polyadenylation sites [7–9].
These methods have also demonstrated the vast extent to which
sequences previously characterized as ‘junk DNA’ are transcribed,
accelerating the discovery, classification and characterization of a
wide array of non-coding RNAs (ncRNAs) [10–19].
Following transcription, RNAs are routinely subject to a series
of processing steps ranging from internal cleavage, trimming of
ends, RNA editing, and in some cases, the introduction of covalent
modifications into the nascent transcript. The production or
processing of an RNA occasionally results in the introduction of
errors, whereby important features critical to RNA function are
disrupted. These defective RNAs can interfere with normal
cellular functions, and have been linked to a host of diseases,
most prominently neurodegenerative diseases and cancer [20–25].
To prevent accumulation or deployment of defective RNAs,
eukaryotes invoke RNA surveillance as a ‘‘quality control’’ step
that can identify and destroy aberrant RNAs [26–30]. Surveillance
also recycles processed RNA intermediates for use in de novo
rounds of transcription. Once mRNA leaves the nucleus,
additional quality control occurs in the cytoplasm (Reviewed in
Extensive work in yeast led to the discovery of two multi-subunit
complexes that are central to nuclear RNA surveillance: the
nuclear exosome [32,33], and TRAMP (Trf4/Air2/Mtr4 Polyad-
enylation) [29,34,35], which identifies and targets RNAs for
PLOS ONE | www.plosone.org1June 2014 | Volume 9 | Issue 6 | e99430
degradation via the exosome. The exosome contains two
ribonucleolytic proteins (Rrp6p and Rrp44p) together with a core
of nine structural proteins. Both ribonucleolytic proteins have
39R59 exonuclease activity, but Rrp44p also contributes endonu-
clease activity [36,37]. The TRAMP complex consists of three
subunits: a non-canonical poly(A) polymerase (Trf4p, or Trf5p)
that marks byproduct or defective RNAs by appending a poly(A)
tail [38–40], a Zn-knuckle RNA-binding protein (Air2p, or Air1p;
) and an ATP-dependent RNA helicase (Mtr4p, which is also
known as Dob1p, or Skiv2l2p) capable of unwinding target RNAs
to facilitate degradation by the exosome . These complexes
work in concert to eliminate RNAs ranging from hypomodified
tRNA , byproducts of rRNA processing such as the 59 external
transcribed spacer (59 ETS; ) and even cryptic unstable
transcripts (CUTs; [42,43]).
The surveillance and degradation machinery appears to be well
conserved. Exosome complexes are found from archaea to
humans [44–46], and homologs of the proteins that comprise
the TRAMP complex are widely conserved among eukaryotes
[47–52]. Experimental exploration of RNA surveillance has
expanded to mammalian systems relatively recently [50,51].
One study in humans suggests a strong division of labor in
targeting various RNAs to the exosome. Localization analyses
suggest that the TRAMP complex may be restricted to the
nucleolus, whereas the Nuclear Exosome Targeting (NEXT)
complex is excluded from the nucleolus . The common
component in both of these complexes is the hMTR4 protein,
which exhibits a strong interaction with the hRRP6 protein of the
exosome . While RNA targets of TRAMP and the exosome
have been extensively characterized in yeast, similar knowledge is
relatively scarce in mammalian systems. The transcriptome in
mammals is more complex than yeast, both within protein coding
genes as well as intergenic regions, now more fully appreciated as
transcriptionally active regions producing numerous ncRNAs of
mostly unknown function [10,15,19]. Thus, the identification and
characterization of poly(A)+ RNAs that accumulate upon deple-
tion of a TRAMP homolog subunit, or protein(s) required for
RNA surveillance, is useful as an initial exploration of post
transcriptional control of RNA expression in mammals.
To identify RNA targets of Mus musculus nuclear RNA
surveillance, we used a small interfering RNA (siRNA) to deplete
the RNA dependent ATPase component of the TRAMP and
NEXT complexes, designated as Skiv2l2 (mMtr4) in mouse N2A
cells grown in culture. We hypothesized that targets of mammalian
TRAMP or NEXT complexes would accumulate in the mMtr4-
knockdown (mMtr4KD) as depletion of mMTR4 would impede
degradation of these targets by the exosome. However, such
targets should be successfully adenylated by the mTrf4 homolog,
PAPD5, such that they could be identified based upon their
We used an RNA-seq strategy specifically designed to capture
adenylated transcripts and precisely map the 39 end of the
genomic template (PA-Seq; ). Our analyses identified poly(A)+
RNAs that accumulate significantly more in the mMtr4KD than in
the mControlKD. Our data support conserved roles for mMtr4 in
processing rRNA 59ETS for degradation, and in snoRNA
surveillance. We identify a novel role for mMtr4 in targeting the
59 leader sequences of microRNAs for degradation.
Materials and Methods
Tissue culture and siRNA knockdown
N2A cells (ATCC CCL-131; [54,55]) were cultured in DMEM
with 10% fetal bovine serum at 37C. Freshly passaged N2A cells
were plated in 5 ml of serum containing medium at 56105cells
per 60 mm tissue culture dish and incubated 24 hours prior to
transfection with siRNA. Independent culture dishes were
transfected in triplicate with a 1 ml suspension of siRNA in Opti
MEM medium and Lipofectamine 2000 (Invitrogen) according the
manufacturer’s recommendations to a final concentration of
67 nM. Pre-designed siRNAs were obtained from Ambion: skiv2l2
(Cat#177475) targeted to mouse Mtr4, and ‘‘Negative control
#1’’ (Cat #AM4611). After 48 hr incubation with the siRNA,
medium was removed, and cells from individual plates were
harvested. For western blot analyses, cell pellets were resuspended
in lysis buffer and quantified using a Bradford assay. For RNA
experiments, cells were directly resuspended with TRIzol Reagent,
total RNA was isolated according to manufacturer’s recommen-
dations, and quantified using a Nanodrop ND-1000 instrument.
Determination of knockdown efficiency
Efficiency of the siRNA knockdown was confirmed at both
RNA and protein levels. Reverse transcription reactions were
completed using SuperScript II (Invitrogen) according to manu-
facturer’s recommendations with an oligo-dT18primer. Primer
sets were designed to span an intron to minimize the risk that
genomic DNA contamination could result in PCR amplification.
Moreover, all primer sets were subjected to melt-curve analysis to
confirm single amplicon yield, and tested on Mock-RT reactions
(no Reverse Transcriptase added) generated for each RNA sample
to further demonstrate amplicons were specifically derived from an
RNA template. Table S4 reports all primers used in this study,
and includes information regarding amplicon size and intron(s)
Real-Time PCR reactions were performed in triplicate using iQ
SYBR green master mix (BioRad) and equivalent aliquots of
cDNA, with cycling reactions carried out in a My iQ thermocycler
(BioRad). Real-Time PCR was used to quantify the level of Mtr4
mRNA relative to CyclophilinB in the control versus knockdown cell
lines, thereby determining the relative extent to which the target
mRNA had been knocked down. Upon confirmation of efficient
knockdown of the target mRNA, these total RNAs were further
purified for deep sequencing using an RNeasy kit (QIAGEN) with
on-column DNaseI digestion.
Western blot analyses were performed using three independent
siRNA treated cell cultures to demonstrate that Mtr4 protein levels
were successfully knocked down. Anti-Mtr4 (Skiv2l2) was obtained
from LSBio and anti-b actin from Thermo Scientific was used as a
Deep sequencing of the control vs. Mtr4 knockdown RNAs was
performed largely as described [53,56] with the notable exception
that (5 ug of each) RNA were first fragmented with magnesium,
and reverse transcribed using a biotinylated oligo dT(16)/deoxyU/
TTTVN-39 primer to selectively identify adenylated RNAs.
cDNAs were selected using Invitrogen’s MyOne Streptavidin
Dynabeads, dephosphorylated using APex Heat-Labile Alkaline
Phosphatase (Epicentre), and digested with the USER enzyme
(NEB) to selectively cut the RT primer at the Uracil, which
released the dephosphorylated cDNAs from the Dynabeads,
leaving a three T tag immediately adjacent to the complementary
nucleotide representing the 39 terminal residue of the adenylated
RNA. Following this step, and between each of the subsequent
modification steps, the released cDNAs were purified using the
DNA clean and concentrator-5 kit (ZYMO Research). Modifica-
tions included removal/blunt ending of the 39 poly(A) overhang
remaining after USER digestion using T4 DNA polymerase
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
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(NEB), A-tailing using an exo- Klenow fragment (Epicentre), and
ligation of the Barcoded Y-linker (Illumina) using T4 DNA ligase
(NEB). Resulting cDNAs were separated on an agarose gel (E-gel;
Invitrogen), and products in the 250–450 bp size range were
excised and purified using a gel purification kit (ZYMO Research).
Products were amplified for 16 cycles of PCR (initial denaturation
of 30 s at 98uC; then 16610 s at 98uC, 10 s at 67uC, and 30 s at
72uC; and a final extension of 10 min at 72uC) using Phusion
DNA polymerase (Finnzymes), and HPLC purified primers from
T-39; PE_PCR_BR: 59- CAAGCAGAAGACGGCATACGA-
ATCT-39). Products for sequencing were size selected (,250–
400 bp) from an agarose gel.
Paired-end sequencing was completed on an Illumina Hi-
Seq2000 instrument. Raw sequencing reads from the Illumina
platform were sorted based upon perfect match to a sample
specific barcode (at the first 5 bases of read 1), trimmed, and finally
split into separate fastq files for the 39 and 59 paired-end reads.
Project data have been deposited to the NIH short read archive
Mapping of Paired-End Reads
Illumina sequencing data was sorted relative to barcode, and
trimmed accordingly. The 39 end of the sequence was evident
based upon the presence of the paired end read that initiated with
three T’s. Paired end mapping to the mouse genome (mm9) was
completed largely as described  – briefly: using the Burrows-
Wheeler Aligner (BWA) mapping algorithm  to identify
positions within the genome that differ by no more than 2 bp for
the 39 read, include a corresponding match to the 59 read in the
correct orientation (k=2 & n=2). Each paired end read that was
successfully mapped in this manner was reported as a ‘hit’ for
adenylation being initiated at the specific nucleotide in the genome
that corresponded with the nucleotide adjacent to the initiating
TTTs of the 39 read. Sequence reads that corresponded with
highly repetitive genomic elements were ignored. Paired end reads
that did not map to a unique genomic position, but matched fewer
than ten genomic positions, were each randomly assigned to one of
those genomic positions. In general, adenylation sites with fewer
than five mapped sequencing reads were ignored.
Identification of Adenlyation Peaks
Clusters (a.k.a. peaks of adenylation) were identified largely as
previously described  using the F-Seq program  with the
exception that Narrow Peaks were defined as those whose 95%
interval was less than 10 bp wide. PA clusters that correspond to a
string of adenosines (either six continuous, or 15 out of 20 nt)
encoded in the genomic DNA were removed from future analysis.
Although their identification within our data suggests that these
genomic regions are transcribed, they cannot be definitively
attributed to 39 adenylation versus template adenosines potentially
internal to the relevant transcript. A series of Python scripts and
the Galaxy portal (main.g2.bx.psu.edu) were used for additional
comparative analyses among the sequencing replicates [59–61].
All data were normalized to account for the sometimes
significant difference in sequencing depth between sequencing
runs for each of the knockdowns. The total number of PA ‘hits’ at
a given genomic location were normalized to reads per million for
that knockdown sequencing run. Rather than pooling the
sequencing results from biological replicates, most data are
reported from one sequencing run (samples designated -1 in
Table 1) where the total number of reads were comparable
among the knockdowns, but the additional sequencing run always
exhibited consistent trends.
Molecular validation of differential polyadenylated
Abundance of adenylated transcripts in the mMtr4KD vs.
mControlKD was independently validated using qPCR. RNA was
reverse transcribed using oligo-dT18and either M_MLV (Pro-
mega) or SuperScript II (Invitrogen) reverse transcriptase accord-
ing to manufacturer’s recommendations. Equivalent aliquots of
each RT reaction were used in triplicate qPCR reactions assaying
the levels of adenylated U3 snRNAs, and of the 59 leader vs. the
pri-miRNAs of several miRNAs. In addition to melt-curve analysis
to confirm single amplicon yield, real-time amplification data were
also analyzed using the DART (Data analysis for Real-Time)
platform , which uses the kinetics of the real-time reaction to
determine the efficiency and consistency of amplification for each
primer set. After validating each set of primers for reasonable
consistency and efficiency, Ct values determined by the MyIQ
software were used to calculate the relative DDCT statistics for
each amplicon relative to CycB as a housekeeping control gene.
Oligonucleoltide primers used for PA-Seq data validation are also
included in Table S4.
TaqMan assay of mature miRNA abundance
Abundance of the mature mir322 miRNA was quantified
relative to U6 snRNA using their respective TaqMan assays
(Applied Biosystems) performed according to manufacturer’s
siRNA Knockdown of mMtr4 in mouse N2A cells
N2A cells transfected with siRNA specific to mMtr4 were
assayed relative to cells transfected with a control siRNA. RNAs
targeted for exosome degradation by fully functional TRAMP and
NEXT complexes should be rare and largely undetectable in
Table 1. Summary of paired-end sequencing experiments.
siRNA knockdownRaw readsMapped readsPercent mapped readsTotal genomic positions
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
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control knockdown cells, whereas these same RNAs should
accumulate in cells depleted for mMtr4. In contrast, we hypoth-
esized that canonical polyadenylation of messenger RNA would be
unaffected by the mMtr4KD. Thus, adenylated RNAs that are
substantially more abundant in the mMtr4KD are presumed to be
substrates of mMTR4, and possible targets of the mammalian
NEXT or TRAMP complexes. The ideal concentration and
corresponding knockdown efficiency for individual siRNAs were
empirically determined using qRT-PCR assays of the target
mRNA (Fig. 1A). Immunoblots confirmed successful knockdown
of the mMtr4 protein (Fig. 1B).
RNA samples from two replicate knockdowns were used for
high-throughput paired-end sequencing designed to specifically
capture the 39 ends of adenylated RNAs. The paired-end reads
were mapped to the mouse genome assembly using BWA (See
Materials and Methods). Roughly 75% of raw sequence reads
were successfully mapped to unique sites in the genome (Table 1).
Comparison of the two mMtr4KD replicates, sequenced to
different depths, demonstrates that these sequencing data
approach saturation. The ratio of mapped reads between mMtr4-
1 and mMtr4-2 is 3.15, while the ratio of total genomic positions
mapped between the same samples is only 1.73. All data were
normalized to account for differences in sequencing depth. As
described in the next section, comparisons between replicate KDs
demonstrated reproducibility of the PA-seq data. Within each
replicate sequencing experiment, the mMtr4KD and mControlKD,
were compared to identify similarities and differences in the
respective populations of adenylated RNAs. The replicate
sequencing experiments exhibit a strong positive correlation for
all target RNAs reported, but unless indicated otherwise, all data
shown are from one dataset (those samples designated by 21).
Polyadenylation of messenger RNAs are largely
unaffected by depletion of mMTR4
To test the hypothesis that mMtr4KD should not affect the
abundance of protein coding mRNAs, normalized polyadenylated
sequencing reads terminating within +50 bases of the annotated 39
end of RefSeq protein coding genes were identified for each KD.
For each of the KDs, roughly 8500 RefSeq genes are represented
in the PA-seq data with a minimum of five sequencing reads at the
modal position. Data from the modal position were used to test the
experimental reproducibility between sequencing experiments. A
dot plot of the two mControlKDs (mControl-1 vs. mControl-2), with
RNA isolation completed in parallel but library construction
completed independently, demonstrated reproducibility of the PA-
Seq data (R2=0.87). The mMtr4KD and mControlKD sequencing
reads of 8,935 RefSeq modal PA-sites are shown as a dot plot
(Fig. 2A), and the ratio of mMtr4KD versus mControlKD reads are
presented as a histogram (Fig. 2B). The dot plot of mMtr4KD vs
mControlKD indicated little variability in polyadenylation of the
RefSeq protein coding mRNAs under conditions where mMtr4 has
been depleted (Fig. 2A, R2=0.95). The histogram provides an
alternative view of the data for mMtr4KD vs mControlKD, revealing
a normal distribution, with ,94% of the RefSeq genes used in the
analysis exhibiting less than a two-fold difference (|log2|,1)
between mMtr4KD and mControlKD. The ,6% of RefSeq genes
falling outside the two-fold difference are largely observed at the
lowest expression levels (Fig. 2A), consistent with higher variability
at lower detection thresholds. Moreover, when examined in the
replicate PA-Seq data set, the relative expression of these same
RefSeqs in the mMtr4KD and mControlKD were equally split
among three categories: equivalent expression levels (,2-fold
difference), comparable difference (.2-fold difference), or an
inverse correlation (.2-fold difference in the opposite direction).
The data were also examined for evidence of differential
polyadenylation position usage between the mMtr4KD and
mControlKD. Although RNAs corresponding to alternative posi-
tions exist within the data, no significant differences in the relative
distribution of site usage were apparent.
The 59 ETS of rRNA is a conserved target of yeast and
mammalian RNA surveillance
Convinced that changes in amplitude of PA-seq reads between
mMtr4KD and mControlKD at a given locus would be the result of
differences in RNA abundance, we focused on one of the known
targets of the yeast TRAMP complex; the 59 ETS of pre-rRNA.
The 59 ETS has also been observed to accumulate in human tissue
culture cells depleted of hMtr4 . The PA-seq data demonstrate
that the mouse 59 ETS accumulates in the mMtr4KD relative to
mControlKD. Processing intermediates of the mouse pre-rRNA 59
ETS include the region from the transcription start to the A9
Figure 1. Efficent siRNA knockdown of mMtr4 mRNA and
protein. A) Expression of mMtr4 relative to mCyB as a housekeeping
control gene in cells treated with Control and mMtr4 siRNAs,
respectively. Relative expression was calculated using the DDCt
method. Error bars represent standard deviation of three KD
experiments. B) Western blot of total protein from siRNA treated N2A
cells showing level of mMtr4 protein relative to bActin. Comparable
numbers were obtained for three independent knockdowns.
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
PLOS ONE | www.plosone.org4June 2014 | Volume 9 | Issue 6 | e99430
processing site, from A9 to A0, and from A0to the 18S cleavage
junction ([63,64]; Fig. 3A). Accumulation of A0 terminating
fragments (+2 bp) is 8-fold higher in the mMtr4KD than the
mControlKD (Fig. 3B). In contrast, adenylated fragments termi-
nating at A9 or the 18S junction were below the PA-seq detection
threshold in any of our PA-seq libraries (Fig. S1; also see
discussion). Thus, accumulation of 59 ETS adenylated RNAs in
the mMtr4KD is specific to the A0processing site. We conclude
from this observation that degradation of the 59 ETS at the A0site
is affected by depletion of mMtr4.
U3 snoRNA adenylated transcripts accumulate in cells
depleted of mMtr4
Many of the snoRNAs function as guides directing accurate
processing or modification of precursor rRNA transcripts to
release 18S, 5.8S and 28S rRNAs, and facilitate their maturation
and function through nucleotide modification [65–68]. Specifical-
ly, U3 snoRNPs form duplexes at several sites along the 59 ETS to
direct endonucleolytic cleavage . There are four identical
copies of the U3B snoDNA (U3B.1-U3B.4) gene located in
tandem within the 2ndintron of Tex14 mRNA (NM_031386) on
mouse chromosome 11 [69–71]. We observed accumulation of
adenylated U3 snoRNA transcripts within our PA-seq data
corresponding to all four copies of U3B, and sought to
characterize these data further. First, we determined that all of
the 59 paired-end reads map within the 214 bp of the mature
snoRNA. This suggests that the reads derive from bona fide U3B
processing rather than read through transcription. We report the
mapped PA-seq data pooled from all four U3B loci (Fig. 4). The
level of U3 snoRNAs adenylated precisely at their annotated 39
ends was nearly equivalent in mMtr4KD vs. mControlKD (Fig. 4).
These data may indicate that routine turnover of aberrant or
perhaps even mature copies of U3 snoRNA occurs via an
adenylation dependent pathway. In stark contrast, shorter U3
snoRNAs adenylated at nucleotide positions immediately 59 of the
mature snoRNA 39 end are 9-fold more abundant in the
mMtr4KD relative to the mControlKD (Fig. 4, Fig. S2). Slightly
longer U3 snoRNAs are also observed, and are generally more
abundant in the mMtr4KD. A comparable distribution of
adenylated transcripts from the U3A locus on chromosome 10
are observed (data not shown). Given that normal U3 processing
involves 39R59 exosome-mediated trimming , these data may
indicate that U3 snoRNAs are occasionally ‘over-trimmed’, and
that such errors are eliminated by MTR4-facilitated degradation.
Quantitative RT-PCR was used to confirm differential accu-
mulation of adenylated U3 snoRNA in mMtr4KD relative to
mControlKD. Total RNA from two replicate knockdowns was
reverse transcribed using oligo-dT, and subjected to qPCR to
measure the level of poly(A)+U3 snoRNA in the mMtr4KD and
mControlKD using U3 specific primers. Given that oligo-dT primed
reverse transcription (RT) would not distinguish between full-
length and shortened adenylated U3 snoRNAs, our qPCR results
reflect the sum of all adenylation sites reported in Fig. 4. Thus, the
observed ,5-fold difference between mMtr4KD and mControlKD
by qPCR is consistent with the PA-seq data (Table S1).
Adenylated transcripts accumulate in the mMtr4KD
To identify additional targets of mMTR4 mediated degrada-
tion, the PA-seq data was analyzed using the F-Seq feature density
estimator  to identify clusters, or peaks, of adenylation. Peak
data were filtered to eliminate sequencing reads corresponding to
templated adenosines (see Methods). All remaining peaks were
categorized as corresponding to annotated mRNA polyadenyla-
tion sites, repetitive genomic elements (RepeatMask track), or
neither. Peaks in the neither category were compared to the
mControlKD to identify RNAs that exhibit differential accumula-
tion. Those exhibiting the largest ratios are shown in Table 2,
along with information regarding their relative genomic context.
Strikingly, four of the top five RNAs exhibiting increased
accumulation in the mMtr4KD map to a microRNA or microRNA
Figure 2. Knockdown of mMtr4 does not significantly alter
polyadenylation of protein coding mRNAs. 2A). Dot Plot of PA-
seq data from the mMtr4KD versus mControlKD for 8,935 protein coding
RefSeq genes. 2B). Histogram reporting log2 transformed ratios of
mMtr4KD/control reads. A value of 0 equals no difference, +1 equals a
two-fold difference, +2 equals a four-fold difference, and positive values
indicate that polyadenylated transcripts are more abundant in
mMtr4KD, whereas negative values indicate that they are more
abundant in the mControlKD. The bin labeled 0 includes log2ratios
ranging from +0.2; all other bins include values expanding by +0.4 in
either direction, and labeled according to the outer boundary of that
bin relative to zero.
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
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Adenylated transcripts associated with the mir322 host
gene accumulate in the mMtr4KD
The above analyses identified two significant peaks of adenyla-
tion on chromosome X corresponding to a full length non-coding
cDNA (RIKEN-C43009B03RIK) containing a miRNA cluster
(mir322-mir503-mir351). To gain a better appreciation for the
distribution of adenylation across this cDNA, we examined all F-
Seq peaks across the 4 kb span of C43009B03RIK. Distribution of
the mMtr4KD peaks reveal three prominent areas of adenylation
in this region of chromosome X: 1) at the 39 annotated end of
cDNA C43009B03RIK, 2) a loose clustering near the 39
annotated end of a shorter RIKEN cDNA AK021262 that
encodes a hypothetical 92aa protein but does not include the
miRNA cluster, and 3) at mir322 (Fig. 5). The presence of PA-seq
peaks in our data corresponding to the annotated 39 ends of both
RIKEN cDNAs indicates that our technique is effectively
interrogating transcriptional activity at this genomic locus.
Consistent with the existence of two RIKEN cDNAs at this locus,
our data also indicate there are two outcomes of transcription
starting at the 59 end of this genomic locus; one transcription unit
which includes the miRNA322, 503 and 351 cluster and a shorter
one that does not include the miRNAs.
Figure 3. Adenylated 59ETS transcripts accumulate in mMtr4KD. 3A) Schematic of precursor rRNA and relevant processing sites. The above
schematic depicts the External and Internal Transcribed Spacers (ETS and ITS, respectively) of mouse pre-rRNAs. The 18S, 5.8S, and 28S rRNAs are
shown as filled black arrows, whereas the External and Internal Transcribed Spacers (ETS and ITS, respectively) are shown as unfilled arrows. The 59ETS
is enlarged below to show the approximate locations of processing sites A9, A0, and the 18S junction (site #1). Processing site nomenclature
according to Kent et al 2009. 3B). Histogram reporting site specific adenylation of 59ETS transcripts in the mMtr4KD relative to mControlKD. Relative
adenylation, normalized as reads per million, is reported for specific nucleotide positions within the 59 ETS. The sites exhibiting the highest levels of
adenylation correspond with the two adjacent nucleotide positions identified as corresponding to A0rRNA processing sites by Kent et al 2009.
Nucleotide positions are reported as the distance from the 59 end of the pre-rRNA (BK000964).
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
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Strikingly, the substantial cluster of adenylated RNAs around
the 59 end of mir322 is notably absent in the mControlKD (Fig. 5).
The mode of this highly active adenylation cluster in the
mMtr4KD precisely corresponds with the Drosha cleavage site
(Fig. 6A). Directed interrogation of the mControlKD data identified
only three PA-seq reads at this position, indicating that there is a
67-fold increase for these adenylated transcripts in the mMtr4KD.
Drosha cleavage of the primary miRNA transcript would release
three distinct products; the pre-miRNA, a 59 leader fragment
(hereafter referred to as the 59 leader) and a 39 fragment (Fig. 6A).
In the case of mir322, the 39 fragment will contain two additional
microRNAs, mir503 and mir351, which may subsequently
influence the fate of that fragment (discussed below). The notable
PA-RNAs that accumulate in the mMtr4KD (Table 3), represent
the 59leader, suggesting that mMTR4 may play a role in targeting
the 59leaders of microRNAs to the exosome for degradation, in an
adenylation dependent manner.
The vast increase in abundance of adenylated mir322 59 leader
sequences in mMtr4KD relative to the mControlKD, along with a
modest 6-fold increase in transcripts adenylated at the annotated
39 end of the full length RIKEN cDNA, might reflect increased
expression of mir322. To determine whether the mMtr4KD causes
differential expression or accumulation of mir322, we employed a
TaqMan miRNA assay to quantify the relative levels of the mature
mir322-5p microRNA in mMtr4KD and mControlKD RNAs. The
mir322 TaqMan assays were normalized to U6 snRNA levels that
were assayed in parallel. Analyses in three biological replicates
suggest that mature mir322 levels may fluctuate in either direction
by as much as two fold in the mMtr4KD and mControlKD, but that
no consistent trend exists (Table S2). Although we cannot
completely rule out that the mir322-mir503-mir351 cluster is more
actively transcribed upon mMTR4 depletion, even a 2-fold
increase would seem too small to explain the large, 67-fold
increase in adenylated mir322 59 leader by a mechanism of
increased transcription alone. In a related experiment, PA-seq
detected a modest increase in the mir322 59leader and annotated
39 end of RIKEN-C43009B03RIK cDNA in N2A cells depleted
for the exonuclease mRrp6 (data not shown/unpublished). Taken
together, these data specifically support the idea that differences
between mMtr4KD and mControlKD are from impaired degrada-
tion of those transcripts, rather than increased transcription.
MTR4 facilitates degradation of miRNA 59 leaders
Two additional microRNAs, let7b and mir138-1, display
clusters of adenylation for mMtr4KD (Table 2). Comparable F-
Seq analysis using the deeper of the two mMtr4KD sequencing
data sets (mMtr4-2) identified a total of six miRNA peaks,
expanding the list to include let7c-2, mir106b and mir26b
(Table 3). In all cases, the modal position of adenylation maps
precisely to the predicted Drosha cleavage site, such that the
adenylated RNA corresponds to the 59 leader. In sharp contrast,
no corresponding peaks were identified in the mControlKD. In an
effort to determine whether the mControlKD may contain
corresponding 59 leader sequences below the level of detection
by F-Seq, or whether mMtr4KD may contain 59 leader sequences
corresponding with additional microRNAs, we asked whether
reads in the complete PA-seq data sets map in or around any
annotated miRNAs. This analysis identified a small number of PA-
seq reads in the mControlKD (Table 3), and PA-seq reads in
mMtr4KD corresponding with over 100 additional miRNAs,
eighty-five percent of which (105/124) include reads mapping
consistent with the predicted 59 leader terminating at the Drosha
cleavage site. Although a majority of the 105 additional miRNAs
are represented by fewer than 5 total PA-seq reads, it is difficult to
dismiss the correlation with the position of the 59 leader as
Table 2. Adenylated RNAs that accumulate in mMtr4KD.
chrm strandmm9 mode coordinate mMtr4KD*mCtrlKD*Mtr4/Ctrl Ratio
12259201565165mir138 59 leader
50407504250462.5 mir322 59leader
40613015332 437.72 last intron of Hspa8**
855377544066.67let7b 59 leader
50406290136245.67 mir322 39 UTR terminus
4847426837 103.70 unknown??***
* Data are reported as total raw reads from replicate 21, and summed over positions +2 bp relative to the peak mode.
** Modal position precisely corresponds to the 39 end of the Hspa8 intron, which contains the U14 snoRNA terminating 95 bp upstream.
*** Maps within a partial Y-box binding protein (oxyR) mRNA and AS rel to ,3rd intron of Zfp862.
Figure 4. Adenylated U3 snoRNAs from Chromsome 11 (U3B.1-
U3B.4) differentially accumulate in the mMtr4KD. This histogram
reports the level of adenylation at individual positions of U3B snoRNAs.
Positions are shown relative to the 39 end of the snoRNA, as indicated
schematically above, and by an arrow below the x-axis. Adenylated
RNAs terminating immediately 59 (positions 21 thru 23) relative to the
canonical 39 end of the snoRNA (position 0) are cumulatively 9-fold
more abundant in the mMtr4KD relative to the mControlKD.
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
PLOS ONE | www.plosone.org7June 2014 | Volume 9 | Issue 6 | e99430
coincidental. All miRNA 59 leader PA-seq data are available in
Oligo-dT primed qRT-PCR was used to independently test
miRNA 59 leader sequence accumulation in the mMtr4KD relative
to mControlKD. We used the same RNA samples used to generate
the PA-seq data, and focused on mir322, mir503, let7b, let7c2 and
mir138-1. qRT-PCR analysis of miRNA 59 leader sequences was
analyzed relative to qRT-PCR that spans the junction between the
59 leader and the pre-miRNA (as depicted by representative
amplicons 1 and 2 in Fig. 6A). Amplicon 2 serves as a critical
control for the level of detectable adenylated pri-miRNA that
would be amplified by the primers located within the 59 leader.
qRT-PCR results support accumulation of the miRNA 59 leader
relative to the pri-miRNA for mir322, let7b and mir138-1
(Figure 6B). In contrast, qRT-PCR results for amplicon 2
primers spanning the Drosha cleavage site were uniformly low.
For comparison, Figure 6C presents normalized PA-seq reads for
each of these microRNA leaders from both mMtr4KD and
mControlKD, as well as their relative ratio. The qRT-PCR data for
let7c2 and mir503 are less dramatic than the other microRNA 59
leaders, suggesting that the respective pri-miRNAs are more
abundant than the 59 leaders produced by Drosha, either due to
less frequent Drosha processing for these miRNAs, or instability of
the 59 leaders of these miRNAs despite knockdown of mMtr4.
We have analyzed the effect of depleting mMtr4 on RNA
adenylation in mouse N2A cells using small interfering RNAs
(siRNA). We used a PA-seq strategy to selectively amplify and
sequence adenylated RNAs. Depletion of mMtr4 does not alter the
abundance of adenylated protein coding mRNAs. In contrast,
depletion of mMtr4 results in the accumulation of a number of
adenylated non-coding RNA. Both the rRNA 59 ETS and U3
snRNA have been previously reported as targets of TRAMP. The
accumulation of adenylated 59 leader transcripts from pri-miRNAs
is novel, and suggests a role for mMtr4 in targeting these transcripts
for degradation by the exosome. Together, these results are
consistent with observations linking MTR4 homologs to RNA
surveillance [47,49,73–76]. Isolation of these target RNA
sequences based upon presence of a poly(A) tail further suggests
that, similar to extensive data in yeast [39,40,42,77] and more
recent observations in humans , at least some mMTR4-
mediated RNA surveillance in mouse also involves a Poly(A)
polymerase. In contrast, others have observed significant accu-
mulation of adenylated PROMPT RNAs in hMtr4KD human cell
lines [49,78], whereas significant PROMPT accumulation was not
detected in our PA-Seq data. This may reflect differences in
sensitivity of the respective gene specific RT-PCR vs. PA-Seq
methods, or issues related to oligo-dT RT reactions or adenylate
The 59 ETS of pre-rRNA is a conserved target of MTR4-
mediated decay in mouse
RNA surveillance has been most extensively characterized in
yeast, where the TRAMP complex functions to identify and target
specific RNAs for degradation. The yeast TRAMP complex
consists of the DExD/DExH box RNA helicase Mtr4p, as well as
a poly(A) polymerase (either Trf4p or Trf5p), and a RNA-binding
protein containing five Zn-knuckles (either Air1p or Air2p).
Although Mtr4 is an essential gene, repressible or reduced function
alleles of Mtr4 have been used to identify targets of TRAMP and
characterize the role(s) played by Mtr4p as part of the TRAMP
complex [33,73,79]. Allmang and colleagues  determined that
depletion of mtr4 resulted in time-course dependent accumulation
of the 59 ETS of yeast rRNA.
We observe significant accumulation of 59ETS sequences
adenylated at the A0processing site in our mMtr4KD, consistent
with this rRNA byproduct being a conserved target of the
mammalian TRAMP complex. Nevertheless, given the relative
abundance of rRNA, the frequency with which we observe these
transcripts (,28 reads per million in mMtr4KD, Fig. 3B) seems
surprisingly low. We also explored whether rRNA transcripts 59 of
the A9 processing site, or immediately adjacent to the 18S rRNA
(see diagram in Fig. 3A, and Fig. S1), accumulate in a similar
fashion to those at A0. Among ,31 million raw 39 reads queried,
including both the mMtr4KD and mControlKD, only 3 adenylated
transcripts corresponding with the region 59 of the A9 processing
site and no reads corresponding to the 18S proximal segment were
observed. Abundant data suggest that pre-rRNA processing
should occur frequently at the A9 site [63,64,80], although failure
to accumulate adenylated fragments terminating at this position in
the mMtr4KD is consistent with the fact that the analogous portion
of the yeast 59ETS is not degraded by the exosome, but rather by
59R39 exonucleases Xrn1p and Rat1p [67,81]. Overall, substan-
tive accumulation of 59ETS adenylated RNAs in the mMtr4KD is
specific to the A0processing site.
PA-seq reveals a heterogeneous population of
adenylated U3 snoRNAs in mMtr4KD
snoRNAs have also been observed to be targets of Mtr4p in
yeast [33,82]. Whether Mtr4p is targeting adenylated snoRNAs
for degradation, for 39 end processing by an exonuclease, or some
combination of the two has not been explored, but our data would
suggest that both may be true in mouse. Detection of U3
Figure 5. Abundant PA-seq reads map near mir322. The top diagram shows the mir322 - mir503 – mir351 cluster and associated RIKEN
transcripts on chromosome X. Note that the chromosomal orientation has been reversed such that the mm9 chromosomal coordinates are in
decreasing order from left to right because the transcripts are on the minus strand. The rightmost portion of this region has been expanded below to
highlight both the location and relative abundance of adenylation in the mMtr4KD (black) vs. mControlKD (grey).
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
PLOS ONE | www.plosone.org8June 2014 | Volume 9 | Issue 6 | e99430
transcripts adenylated at or just beyond the annotated 39 end in
the mControlKD is consistent with the idea that adenylation is part
of the normal U3 life-cycle, from 39 end maturation to normal
turnover of snoRNAs. Derti and colleagues  observe
adenylated U3A in an assortment of wild-type tissues. The fact
that we observe significantly higher levels of adenylated U3 in the
mMtr4KD, with a substantial increase in transcripts adenylated just
59 of the annotated 39 end, is consistent with the concept that
mMTR4 functions not only in the normal life-cycle of U3 as
observed in normal cells, but also in turnover of defective U3
snoRNAs, such as those that may have been over-trimmed by an
Nuclear RNA surveillance of miRNA 59 leaders
It is commonly accepted that extraneous portions of miRNA
primary transcripts released during miRNA biogenesis are ‘simply
degraded’, but the cellular components responsible for targeting
those RNAs for degradation have not been previously identified.
Our PA-seq data suggest that mMTR4, possibly as part of the
mammalian TRAMP complex, plays a role in directing some of
these miRNA byproducts, particularly the 59 leader, to the nuclear
exosome for degradation.
While we were able to detect adenylated 59 leader sequences
corresponding to over 100 miRNAs in the mMtr4 knockdown,
only a handful of these accumulate to highly significant levels
(Table 3, Fig. 6, and Table S3). Interestingly those that
accumulate to the highest levels correspond to abundant miRNAs.
Both mir322 and let7 are relatively ubiquitous across a wide range
of tissues, and among the most abundant microRNAs detected
[84–86]. Several of the other microRNAs play critical functional
roles in neural or brain development, or in cases where their
function is as yet unknown, they are abundantly represented in
neuronal cells, or during brain development [87–95].
Much of the small RNA deep sequencing data in mirbase also
suggests that the 59 most member of putative co-transcribed
miRNAs is often more abundant than those progressively 39 within
the cluster [84,96–98], consistent with the idea that Drosha may
preferentially liberate specific pre-microRNAs from polycistronic
pri-microRNAs, and that in some cases, the remaining transcript
may be degraded rather than processed further. Consistent with
this observation, miR322 is the 59 most member of its microRNA
We explored the genomic context for several more of the most
abundant microRNA 59 leaders (let-7b, let-7c2, mir138-1,
mir106b and mir26b) in an effort to determine whether context
might influence accumulation of the 59 leader in the mMtr4KD.
Indeed, many of the most abundant 59 leaders correspond to
microRNAs presumed to be co-transcribed with other micro-
RNAs. miR138-1 is one exception, residing in an intergenic region
of chromosome 9 with no other annotated miRNAs or mRNAs
nearby. The other is mir26b, which is found within the fourth
intron of a protein coding transcript (NM_153088, Ctdsp1).
Similar to mir322, mir106b is the 59 most microRNA within the
mir106b-mir93-mir25 cluster, which is located within intron 13 of
the Mcm7 gene. Finally, let-7c2 and let-7b are less than 1.0kb
apart and presumed to be part of the same primary transcript.
While the 59 leader sequences for both of these microRNAs
accumulate, we observed a greater amplitude of PA-seq accumu-
lating in mMtr4KD for the more distal let-7b than for let-7c2, both
being appreciably above that seen in the mControlKD. Overall, the
reason we see preferential (sometimes exclusive) accumulation of
the 59 leader for one member of a miRNA cluster is unknown, but
could reflect differences in processing order or efficiency, the
possibility that only some of the processing byproducts are
Figure 6. Adenylated 59 leader sequences from pri-miRNAs
accumulate in the mMtr4KD. A. Schematic depicting relevant
aspects of miRNA processing. Primary miRNA transcripts are produced
by RNA polII and include both a 59 cap and poly(A) tail. The miRNA and
complementary mir* contribute to formation of a hairpin, and Drosha
cleaves near the base of the hairpin to liberate the pre-miRNA .
mMtr4KD PA-seq identified adenylated 59 leader transcripts whose 39
end specifically maps to the Drosha cleavage site. qRT-PCR validation of
these data compared amplification results for primers within the
59leader (eg. Amplicon 1, depicted on uppermost diagram) to primers
that span the Drosha cleavage site (Amplicon 2) as a control for pri-
miRNA abundance. B. Relative expression of miRNA 59 leader sequences
was calculated using the DDCt method from triplicate reaction realtime
PCR data. All reactions were performed in triplicate, further averaged
over 3-4 independent qPCR runs for each miRNA 59 leader, and shown
relative to parallel qPCR reactions detecting Cyclophilin B mRNA. All Y-
axis values represent relative arbitrary units. C. Combined PA-seq data
for miRNA 59 leader sequences reported as reads per million, and ratio
of mMtr4KD over mControlKD.
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
PLOS ONE | www.plosone.org9June 2014 | Volume 9 | Issue 6 | e99430
adenylated, or RNA structural determinants of the accumulating
Although it remains unclear why specific miRNA 59 leaders
accumulate in the mMtr4KD, it is worth noting that we do not
observe notable accumulation of a corresponding 39 fragment that
would simultaneously be liberated by Drosha cleavage of the pre-
miRNA from the pri-miRNA. One factor that may influence
miRNA 59leader accumulation is the 59 cap on these RNApolII
derived transcripts preventing 59R39 exonuclease degradation,
whereas the 39 fragment would be susceptible to both 59R39 and
39R59 exonuclease degradation. Consistent with this distinction,
59 proximal versus 39 proximal RNAs resulting from cotranscrip-
tional cleavage (CoTC) of the human beta-globin gene have
alternate fates. The 39 proximal RNAs are degraded by the Xrn2
59R39 exonuclease , whereas the 59 proximal RNAs are
subject to exosome mediated degradation . Presence of a 59
cap could also help explain the correlation that several of the 59
leaders that accumulate are from the 59 most members of miRNA
clusters thought to be co-transcribed. Overall, no single aspect
appears to readily predict which microRNA 59leaders will
accumulate in mMtr4KD cells, or the relative extent to which
they will accumulate, but a select few are by far the most abundant
adenylated RNAs we have thus far identified in the mMtr4KD.
Implications for nuclear RNA surveillance in mammals
Although extensive research has uncovered a wealth of TRAMP
substrates in yeast, the absence of microRNAs in yeast and thus
our inability to predict this novel finding underscores the
importance of expanding research into a wider array of organisms.
This finding also reveals the extent to which nuclear RNA
surveillance complexes may have evolved to target an ever
increasing diversity of substrates as genomes evolve and increase in
complexity. Finally, our observation suggests that depleting MTR4
proteins in a wide array of tissues or organisms may be a useful
mechanism for characterizing the 59 ends of pri-miRNAs, such as
determining transcription start sites and identifying promoter
Well conserved homologs of Mtr4, Trf4 and Air2 form a
TRAMP-like complex in humans, but immunofluorescence
localization suggests that the Air2 homolog ZCCHC7 exclusively
localizes to the nucleolus . Given that the Air2 protein of yeast
is critical for assembly of the yeast TRAMP complex , and
that ZCCHC7 is the only human protein with significant similarity
to the Air2 and Air1 paralogs of yeast, it is unclear how
mammalian Trf4 (also known as Papd5) and Mtr4 would
collaboratively function outside the nucleolus. Although the
NEXT complex exhibits a complementary localization within
the mammalian nucleus, and is excluded from the nucleolus,
neither Trf4 nor any other poly(A) polymerase co-immunoprecip-
itated with this complex . Nevertheless, surveillance may
involve one or more canonical poly(A) polymerases [78,102], or
the mammalian Trf4 may function autonomously outside the
nucleolus . Definitive identification of the poly(A) polymeras-
e(s) responsible for adenylating will further illuminate RNA
surveillance in mammalian systems. Overall, our PA-Seq analyses
confirm the role of mMtr4 in adenylation mediated degradation of
the 59 ETS of rRNA and U3 snoRNAs, and suggest that some
miRNA 59 leader sequences utilize mMtr4 in a similar degradation
ETS. Graph depicts the level of adenylation at individual positions
across the 4 kb 59 ETS. Positions for which zero reads were
detected in either the mMtr4KD or mControlKD were omitted for
improved resolution of all remaining positions. Positions along the
x-axis are not to scale, but several reference points are indicated.
Density of PA-seq reads across the entire 59
Graph depicts the level of adenylation at individual positions
across the full length of U3B snoRNA. Data are pooled for the
four tandem copies of U3B found on chromosome 11 of the mouse
Density of PA-seq reads encompassing U3B.
Quantitative RT-PCR results for U3 snoRNA.
fluctuations in mMtr4 depleted cells.
Mature mir322 levels exhibit no consistent
All miR associated pA-seq reads.
Table 3. Combined PA-seq data corresponding to miRNA 59 leaders (with $25 reads)*.
chromosome microRNA mMtr4KDmControlKD**
chr9 mmu-mir-138-1 3771
* Data are reported as total raw reads combined from both biological replicates, and summed over positions +2 bp relative to the predicted Drosha cleavage site.
**Raw reads from mControlKD are not directly comparable to mMtr4KD due to ,3-fold higher coverage in one of the sequencing experiments. Figure 6C provides
normalized values for the top four miRNA 59 leaders. N.D. = not detected.
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
PLOS ONE | www.plosone.org 10June 2014 | Volume 9 | Issue 6 | e99430
All oligos used for qPCR.
The authors thank Sarah Wassink, Jodie Box, Fenchao Wang and Alexis
Onderak for technical assistance.
Conceived and designed the experiments: JED TN JZ SHM JTA.
Performed the experiments: JED TN SHM. Analyzed the data: JED TN
SHM JTA. Contributed reagents/materials/analysis tools: JED TN JZ
SHM JTA. Contributed to the writing of the manuscript: JED JTA.
1. Muramatsu M, Hodnett JL, Steele WJ, Busch H (1966) Synthesis of 28-S RNA
in the nucleolus. Biochim Biophys Acta 123: 116–125.
2. Darnell JE Jr (1968) Ribonucleic acids from animal cells. Bacteriol Rev 32:
3. Weinberg RA, Penman S (1968) Small molecular weight monodisperse nuclear
RNA. J Mol Biol 38: 289–304.
4. Gilham PT (1970) RNA sequence analysis. Annu Rev Biochem 39: 227–250.
5. Prestayko AW, Tonato M, Busch H (1970) Low molecular weight RNA
associated with 28 s nucleolar RNA. J Mol Biol 47: 505–515.
6. Scherrer K (2003) Historical review: the discovery of ‘giant’ RNA and RNA
processing: 40 years of enigma. Trends Biochem Sci 28: 566–571.
7. Blencowe BJ, Ahmad S, Lee LJ (2009) Current-generation high-throughput
sequencing: deepening insights into mammalian transcriptomes. Genes Dev 23:
8. David CJ, Manley JL (2010) Alternative pre-mRNA splicing regulation in
cancer: pathways and programs unhinged. Genes Dev 24: 2343–2364.
9. Di Giammartino DC, Nishida K, Manley JL (2011) Mechanisms and
consequences of alternative polyadenylation. Mol Cell 43: 853–866.
10. Moran VA, Perera RJ, Khalil AM (2012) Emerging functional and mechanistic
paradigms of mammalian long non-coding RNAs. Nucleic Acids Res 40: 6391–
11. Schonrock N, Gotz J (2012) Decoding the non-coding RNAs in Alzheimer’s
disease. Cell Mol Life Sci 69: 3543–3559.
12. Schonrock N, Harvey RP, Mattick JS (2012) Long noncoding RNAs in cardiac
development and pathophysiology. Circ Res 111: 1349–1362.
13. Schonrock N, Matamales M, Ittner LM, Gotz J (2012) MicroRNA networks
surrounding APP and amyloid-beta metabolism—implications for Alzheimer’s
disease. Exp Neurol 235: 447–454.
14. Wolin SL, Sim S, Chen X (2012) Nuclear noncoding RNA surveillance: is the
end in sight? Trends Genet 28: 306–313.
15. Dieci G, Conti A, Pagano A, Carnevali D (2013) Identification of RNA
polymerase III-transcribed genes in eukaryotic genomes. Biochim Biophys Acta
16. Kim T, Reitmair A (2013) Non-Coding RNAs: Functional Aspects and
Diagnostic Utility in Oncology. Int J Mol Sci 14: 4934–4968.
17. Brockdorff N (2013) Noncoding RNA and Polycomb recruitment. RNA 19:
18. Tan L, Yu JT, Hu N, Tan L (2013) Non-coding RNAs in Alzheimer’s disease.
Mol Neurobiol 47: 382–393.
19. Will S, Yu M, Berger B (2013) Structure-based whole genome realignment
reveals many novel non-coding RNAs. Genome Res Epub ahead of print.
20. Moreira MC, Klur S, Watanabe M, Nemeth AH, Le Ber I, et al. (2004)
Senataxin, the ortholog of a yeast RNA helicase, is mutant in ataxia-ocular
apraxia 2. Nat Genet 36: 225–227.
21. Fogel BL, Perlman S (2006) Novel mutations in the senataxin DNA/RNA
helicase domain in ataxia with oculomotor apraxia 2. Neurology 67: 2083–
22. Bassuk AG, Chen YZ, Batish SD, Nagan N, Opal P, et al. (2007) In cis
autosomal dominant mutation of Senataxin associated with tremor/ataxia
syndrome. Neurogenetics 8: 45-49.
23. Bruserud O (2007) Introduction: RNA and the treatment of cancer. Curr
Pharm Biotechnol 8: 318–319.
24. Nelson PT, Keller JN (2007) RNA in brain disease: no longer just ‘‘the
messenger in the middle’’. J Neuropathol Exp Neurol 66: 461–468.
25. Kim MY, Hur J, Jeong S (2009) Emerging roles of RNA and RNA-binding
protein network in cancer cells. BMB Rep 42: 125–130.
26. Kadaba S, Krueger A, Trice T, Krecic AM, Hinnebusch AG, et al. (2004)
Nuclear surveillance and degradation of hypomodified initiator tRNAMet in S.
cerevisiae. Genes Dev 18: 1227–1240.
27. Assenholt J, Mouaikel J, Andersen KR, Brodersen DE, Libri D, et al. (2008)
Exonucleolysis is required for nuclear mRNA quality control in yeast THO
mutants. RNA 14: 2305–2313.
28. Houseley J, Tollervey D (2008) The nuclear RNA surveillance machinery: the
link between ncRNAs and genome structure in budding yeast? Biochim
Biophys Acta 1779: 239–246.
29. Anderson JT, Wang X (2009) Nuclear RNA surveillance: no sign of substrates
tailing off. Crit Rev Biochem Mol Biol 44: 16–24.
30. Hessle V, Bjork P, Sokolowski M, de Valdivia EG, Silverstein R, et al. (2009)
The exosome associates cotranscriptionally with the nascent pre-mRNP
through interactions with heterogeneous nuclear ribonucleoproteins. Mol Biol
Cell 20: 3459–3470.
31. Parker R (2012) RNA degradation in Saccharomyces cerevisae. Genetics 191:
32. Mitchell P, Petfalski E, Shevchenko A, Mann M, Tollervey D (1997) The
exosome: a conserved eukaryotic RNA processing complex containing multiple
39—.59 exoribonucleases. Cell 91: 457–466.
33. Allmang C, Kufel J, Chanfreau G, Mitchell P, Petfalski E, et al. (1999)
Functions of the exosome in rRNA, snoRNA and snRNA synthesis. Embo J 18:
34. LaCava J, Houseley J, Saveanu C, Petfalski E, Thompson E, et al. (2005) RNA
degradation by the exosome is promoted by a nuclear polyadenylation
complex. Cell 121: 713–724.
35. Vana ´cova ´ S, Wolf J, Martin G, Blank D, Dettwiler S, et al. (2005) A new yeast
poly(A) polymerase complex involved in RNA quality control. PLoS Biol 3:
36. Lebreton A, Tomecki R, Dziembowski A, Seraphin B (2008) Endonucleolytic
RNA cleavage by a eukaryotic exosome. Nature 456: 993–996.
37. Schneider C, Leung E, Brown J, Tollervey D (2009) The N-terminal PIN
domain of the exosome subunit Rrp44 harbors endonuclease activity and
tethers Rrp44 to the yeast core exosome. Nucleic Acids Res 37: 1127–1140.
38. Haracska L, Johnson RE, Prakash L, Prakash S (2005) Trf4 and Trf5 proteins
of Saccharomyces cerevisiae exhibit poly(A) RNA polymerase activity but no
DNA polymerase activity. Mol Cell Biol 25: 10183–10189.
39. Egecioglu DE, Henras AK, Chanfreau GF (2006) Contributions of Trf4p- and
Trf5p-dependent polyadenylation to the processing and degradative functions
of the yeast nuclear exosome. RNA 12: 26–32.
40. Kadaba S, Wang X, Anderson JT (2006) Nuclear RNA surveillance in
Saccharomyces cerevisiae: Trf4p-dependent polyadenylation of nascent
hypomethylated tRNA and an aberrant form of 5S rRNA. RNA 12: 508–521.
41. Inoue K, Mizuno T, Wada K, Hagiwara M (2000) Novel RING finger
proteins, Air1p and Air2p, interact with Hmt1p and inhibit the arginine
methylation of Npl3p. J Biol Chem 275: 32793–32799.
42. Wyers F, Rougemaille M, Badis G, Rousselle JC, Dufour ME, et al. (2005)
Cryptic pol II transcripts are degraded by a nuclear quality control pathway
involving a new poly(A) polymerase. Cell 121: 725–737.
43. Thompson DM, Parker R (2007) Cytoplasmic decay of intergenic transcripts in
Saccharomyces cerevisiae. Mol Cell Biol 27: 92–101.
44. Sloan KE, Schneider C, Watkins NJ (2012) Comparison of the yeast and
human nuclear exosome complexes. Biochem Soc Trans 40: 850–855.
45. Liu Q, Greimann JC, Lima CD (2006) Reconstitution, activities, and structure
of the eukaryotic RNA exosome. Cell 127: 1223–1237.
46. Lebreton A, Seraphin B (2008) Exosome-mediated quality control: substrate
recruitment and molecular activity. Biochim Biophys Acta 1779: 558–565.
47. Cristodero M, Clayton CE (2007) Trypanosome MTR4 is involved in rRNA
processing. Nucleic Acids Res 35: 7023–7030.
48. Etheridge RD, Clemens DM, Gershon PD, Aphasizhev R (2009) Identification
and characterization of nuclear non-canonical poly(A) polymerases from
Trypanosoma brucei. Mol Biochem Parasitol 164: 66–73.
49. Lubas M, Christensen MS, Kristiansen MS, Domanski M, Falkenby LG, et al.
(2011) Interaction profiling identifies the human nuclear exosome targeting
complex. Mol Cell 43: 624–637.
50. Rammelt C, Bilen B, Zavolan M, Keller W (2011) PAPD5, a noncanonical
poly(A) polymerase with an unusual RNA-binding motif. RNA 17: 1737–1746.
51. Shcherbik N, Wang M, Lapik YR, Srivastava L, Pestov DG (2010)
Polyadenylation and degradation of incomplete RNA polymerase I transcripts
in mammalian cells. EMBO Rep 11: 106–111.
52. Fasken MB, Leung SW, Banerjee A, Kodani MO, Chavez R, et al. (2011) Air1
zinc knuckles 4 and 5 and a conserved IWRxY motif are critical for the
function and integrity of the Trf4/5-Air1/2-Mtr4 polyadenylation (TRAMP)
RNA quality control complex. J Biol Chem 286: 37429–37445.
53. Ni T, Yang Y, Hafez D, Yang W, Kiesewetter K, et al. (2013) Distinct
polyadenylation landscapes of diverse human tissues revealed by a modified
PA-seq strategy. BMC Genomics 14: 615.
54. Klebe RJ, Ruddle FH (1969) Neuroblastoma: Cell culture analysis of a
differentiating stem cell system. J Cell Biol 43: 69A.
55. Olmsted J, Carlson K, Klebe R, Ruddle F, Rosenbaum J (1970) Isolation of
Microtubule Protein from Cultured Mouse Neuroblastoma Cells. Proc Natl
Acad Sci U S A 65: 129–136.
56. Ni T, Corcoran DL, Rach EA, Song S, Spana EP, et al. (2010) A paired-end
sequencing strategy to map the complex landscape of transcription initiation.
Nat Methods 7: 521–527.
57. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-
Wheeler transform. Bioinformatics 25: 1754–1760.
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
PLOS ONE | www.plosone.org 11June 2014 | Volume 9 | Issue 6 | e99430
58. Boyle AP, Guinney J, Crawford GE, Furey TS (2008) F-Seq: a feature density
estimator for high-throughput sequence tags. Bioinformatics 24: 2537–2538.
59. Blankenberg D, Von Kuster G, Coraor N, Ananda G, Lazarus R, et al. (2010)
Galaxy: a web-based genome analysis tool for experimentalists. Curr Protoc
Mol Biol Chapter 19: Unit 19 10 11–21.
60. Goecks J, Nekrutenko A, Taylor J, Galaxy T (2010) Galaxy: a comprehensive
approach for supporting accessible, reproducible, and transparent computa-
tional research in the life sciences. Genome Biol 11: R86.
61. Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, et al. (2005)
Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15:
62. Peirson SN, Butler JN, Foster RG (2003) Experimental validation of novel and
conventional approaches to quantitative real-time PCR data analysis. Nucleic
Acids Res 31: e73.
63. Craig N, Kass S, Sollner-Webb B (1987) Nucleotide sequence determining the
first cleavage site in the processing of mouse precursor rRNA. Proc Natl Acad
Sci U S A 84: 629–633.
64. Kent T, Lapik YR, Pestov DG (2009) The 59 external transcribed spacer in
mouse ribosomal RNA contains two cleavage sites. RNA 15: 14–20.
65. Nabavi S, Nazar RN (2008) U3 snoRNA promoter reflects the RNA’s function
in ribosome biogenesis. Curr Genet 54: 175–184.
66. Pendrak ML, Roberts DD (2011) Ribosomal RNA processing in Candida
albicans. RNA 17: 2235–2248.
67. Petfalski E, Dandekar T, Henry Y, Tollervey D (1998) Processing of the
precursors to small nucleolar RNAs and rRNAs requires common components.
Mol Cell Biol 18: 1181–1189.
68. Mattaj IW, Tollervey D, Seraphin B (1993) Small nuclear RNAs in messenger
RNA and ribosomal RNA processing. FASEB J 7: 47–53.
69. Mazan S, Mattei MG, Passage E, Bachellerie JP (1991) Composition and
chromosomal localization of the small multigene family encoding mouse U3B
nucleolar RNA. Cytogenet Cell Genet 56: 18–22.
70. Mazan S, Bachellerie JP (1990) Organization of the gene family encoding
mouse U3B RNA: role of gene conversions in its concerted evolution. Gene 94:
71. Mazan S, Bachellerie JP (1988) Structure and organization of mouse U3B RNA
functional genes. J Biol Chem 263: 19461–19467.
72. Perumal K, Reddy R (2002) The 39 end formation in small RNAs. Gene Expr
73. de la Cruz J, Kressler D, Tollervey D, Linder P (1998) Dob1p (Mtr4p) is a
putative ATP-dependent RNA helicase required for the 39 end formation of
5.8S rRNA in Saccharomyces cerevisiae. Embo J 17: 1128–1140.
74. Jia H, Wang X, Liu F, Guenther UP, Srinivasan S, et al. (2011) The RNA
helicase Mtr4p modulates polyadenylation in the TRAMP complex. Cell 145:
75. Bernstein J, Patterson DN, Wilson GM, Toth EA (2008) Characterization of
the essential activities of Saccharomyces cerevisiae Mtr4p, a 39-.59 helicase
partner of the nuclear exosome. J Biol Chem 283: 4930–4942.
76. Wang X, Jia H, Jankowsky E, Anderson JT (2008) Degradation of
hypomodified tRNA (iMet) in vivo involves RNA-dependent ATPase activity
of the DExH helicase Mtr4p. Rna 14: 107–116.
77. Houseley J, Kotovic K, El Hage A, Tollervey D (2007) Trf4 targets ncRNAs
from telomeric and rDNA spacer regions and functions in rDNA copy number
control. Embo J 26: 4996–5006.
78. Beaulieu YB, Kleinman CL, Landry-Voyer AM, Majewski J, Bachand F (2012)
Polyadenylation-dependent control of long noncoding RNA expression by the
poly(A)-binding protein nuclear 1. PLoS Genet 8: e1003078.
79. Liang S, Hitomi M, Hu YH, Liu Y, Tartakoff AM (1996) A DEAD-box-family
protein is required for nucleocytoplasmic transport of yeast mRNA. Mol Cell
Biol 16: 5139–5146.
80. Craig N, Kass S, Sollner-Webb B (1991) Sequence organization and RNA
structural motifs directing the mouse primary rRNA-processing event. Mol Cell
Biol 11: 458–467.
81. Dichtl B, Stevens A, Tollervey D (1997) Lithium toxicity in yeast is due to the
inhibition of RNA processing enzymes. EMBO J 16: 7184–7195.
82. van Hoof A, Lennertz P, Parker R (2000) Yeast exosome mutants accumulate
39-extended polyadenylated forms of U4 small nuclear RNA and small
nucleolar RNAs. Mol Cell Biol 20: 441–452.
83. Derti A, Garrett-Engele P, Macisaac KD, Stevens RC, Sriram S, et al. (2012) A
quantitative atlas of polyadenylation in five mammals. Genome Res 22: 1173–
1183; data track available via UCSC genome browser.
84. Ahn HW, Morin RD, Zhao H, Harris RA, Coarfa C, et al. (2010) MicroRNA
transcriptome in the newborn mouse ovaries determined by massive parallel
sequencing. Mol Hum Reprod 16: 463–471.
85. Chiang HR, Schoenfeld LW, Ruby JG, Auyeung VC, Spies N, et al. (2010)
Mammalian microRNAs: experimental evaluation of novel and previously
annotated genes. Genes Dev 24: 992–1009.
86. Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, et al. (2007) A
mammalian microRNA expression atlas based on small RNA library
sequencing. Cell 129: 1401–1414.
87. Kawahara H, Imai T, Okano H (2012) MicroRNAs in Neural Stem Cells and
Neurogenesis. Front Neurosci 6: 30.
88. Eda A, Takahashi M, Fukushima T, Hohjoh H (2011) Alteration of microRNA
expression in the process of mouse brain growth. Gene 485: 46–52.
89. Dugas JC, Cuellar TL, Scholze A, Ason B, Ibrahim A, et al. (2010) Dicer1 and
miR-219 Are required for normal oligodendrocyte differentiation and
myelination. Neuron 65: 597–611.
90. Balzer E, Heine C, Jiang Q, Lee VM, Moss EG (2010) LIN28 alters cell fate
succession and acts independently of the let-7 microRNA during neurogliogen-
esis in vitro. Development 137: 891–900.
91. Bremer J, O’Connor T, Tiberi C, Rehrauer H, Weis J, et al. (2010) Ablation of
Dicer from murine Schwann cells increases their proliferation while blocking
myelination. PLoS One 5: e12450.
92. Eda A, Tamura Y, Yoshida M, Hohjoh H (2009) Systematic gene regulation
involving miRNAs during neuronal differentiation of mouse P19 embryonic
carcinoma cell. Biochem Biophys Res Commun 388: 648–653.
93. 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.
94. Saba R, Booth SA (2008) MicroRNA Profiling in CNS Tissue Using
Microarrays. In: Ying S-Y, editor. Current Perspectives in microRNAs
(miRNA): Springer. pp. 73–96.
95. Smirnova L, Grafe A, Seiler A, Schumacher S, Nitsch R, et al. (2005)
Regulation ofmiRNA expression during neuralcellspecification.EurJNeurosci
96. Griffiths-Jones S (2004) The microRNA Registry. Nucleic Acids Res 32: D109–
97. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA
annotation and deep-sequencing data. Nucleic Acids Res 39: D152–157.
98. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ (2006)
miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids
Res 34: D140–144.
99. West S, Gromak N, Proudfoot NJ (2004) Human 59 —. 39 exonuclease Xrn2
promotes transcription termination at co-transcriptional cleavage sites. Nature
100. West S, Gromak N, Norbury CJ, Proudfoot NJ (2006) Adenylation and
exosome-mediated degradation of cotranscriptionally cleaved pre-messenger
RNA in human cells. Mol Cell 21: 437–443.
101. Holub P, Lalakova J, Cerna H, Pasulka J, Sarazova M, et al. (2012) Air2p is
critical for the assembly and RNA-binding of the TRAMP complex and the
KOW domain of Mtr4p is crucial for exosome activation. Nucleic Acids Res
102. Bresson SM, Conrad NK (2013) The human nuclear poly(a)-binding protein
promotes RNA hyperadenylation and decay. PLoS Genet 9: e1003893.
103. Starega-Roslan J, Koscianska E, Kozlowski P, Krzyzosiak WJ (2011) The role
of the precursor structure in the biogenesis of microRNA. Cell Mol Life Sci 68:
MicroRNA 59 Leader Accumulation upon MTR4 Depletion in Mouse Cells
PLOS ONE | www.plosone.org12June 2014 | Volume 9 | Issue 6 | e99430