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N6-methyladenosine (m6A) is the most abundant RNA modification. It has been involved in the regulation of RNA metabolism, including degradation and translation, in both physiological and disease conditions. A recent study showed that m6A-mediated degradation of key transcripts also plays a role in the control of T cells homeostasis and IL-7 induced differentiation. We re-analyzed the omics data from that study and, through the integrative analysis of total and nascent RNA-seq data, we were able to comprehensively quantify T cells RNA dynamics and how these are affected by m6A depletion. In addition to the expected impact on RNA degradation, we revealed a broader effect of m6A on RNA dynamics, which included the alteration of RNA synthesis and processing. Altogether, the combined action of m6A on all major steps of the RNA life-cycle closely re-capitulated the observed changes in the abundance of premature and mature RNA species. Ultimately, our re-analysis extended the findings of the initial study, focused on RNA stability, and proposed a yet unappreciated role for m6A in RNA synthesis and processing dynamics.
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genes
G C A T
T A C G
G C A T
Article
m6A-Dependent RNA Dynamics in T
Cell Differentiation
Mattia Furlan 1,2 , Eugenia Galeota 1, Stefano de Pretis 1, Michele Caselle 2and
Mattia Pelizzola 1, *
1Center for Genomic Science, Fondazione Istituto Italiano di Tecnologia, 20139 Milan, Italy;
mattia.furlan@iit.it (M.F.); eugenia.galeota@iit.it (E.G.); stefano.depretis@iit.it (S.d.P.)
2Physics Department and INFN, University of Turin, 10125 Turin, Italy; caselle@to.infn.it
*Correspondence: mattia.pelizzola@iit.it; Tel.: +39-02-9437-5019
Received: 22 November 2018; Accepted: 27 December 2018; Published: 8 January 2019


Abstract:
N6-methyladenosine (m6A) is the most abundant RNA modification. It has been involved
in the regulation of RNA metabolism, including degradation and translation, in both physiological
and disease conditions. A recent study showed that m6A-mediated degradation of key transcripts
also plays a role in the control of T cells homeostasis and IL-7 induced differentiation. We re-analyzed
the omics data from that study and, through the integrative analysis of total and nascent RNA-seq
data, we were able to comprehensively quantify T cells RNA dynamics and how these are affected
by m6A depletion. In addition to the expected impact on RNA degradation, we revealed a broader
effect of m6A on RNA dynamics, which included the alteration of RNA synthesis and processing.
Altogether, the combined action of m6A on all major steps of the RNA life-cycle closely re-capitulated
the observed changes in the abundance of premature and mature RNA species. Ultimately, our
re-analysis extended the findings of the initial study, focused on RNA stability, and proposed a yet
unappreciated role for m6A in RNA synthesis and processing dynamics.
Keywords:
RNA-seq; RNA dynamics; m6A; RNA modifications; RNA metabolic labeling;
mathematical modeling
1. Introduction
Dozens of RNA modifications decorating coding and non-coding RNA species have been shown
to be important determinants in the fate of marked RNA transcripts [
1
]. m6A is the most abundant
RNA modification in various species, including human and mouse. Its pattern is determined by the
action of specific writers (including METTL3) and erasers, and impacts on most stages of the RNA
life-cycle, including processing, stability, transport, and translation [
2
]. Regulation by m6A is involved
in various important biological processes such as cellular differentiation, pluripotency, stress response,
and gametogenesis [
3
6
]. Moreover, alterations in m6A patterns are associated with human diseases
like cancer initiation and progression [
7
9
]. While the impact of m6A on specific stages of the RNA
life-cycle, such as RNA translation and degradation, is supported by ample evidence and widely
recognized. Its involvement in other steps, namely RNA synthesis and processing, is far from being
fully established. However, both a number of components of the m6A writer complex and the erasers
are enriched within nuclear speckles, suggesting their interplay with the machineries responsible for
RNA transcription and processing. Indeed, m6A installment was shown to occur prevalently at the
level of premature RNA [
10
] and to be influenced by RNA Polymerase II elongation [
11
]. In addition,
few studies have indicated a role for m6A in the regulation of alternative splicing [
12
17
], but opposite
evidence has also been reported [10,18].
Genes 2019,10, 28; doi:10.3390/genes10010028 www.mdpi.com/journal/genes
Genes 2019,10, 28 2 of 9
Recently, an RNA metabolic labeling method has been developed that detects nascent RNA
molecules based on the incorporation of modified 4sU nucleotides. By allowing the quantification of
the kinetic rates governing the various stages of the RNA life-cycle, this method permits to characterize
the RNA dynamics and therefore, in our case, to investigate how the m6A epitranscriptome impacts
on the dynamics of premature and mature RNA species.
A recent study combining RNA metabolic labeling with m6A profiling demonstrated the relevance
of m6A in the control of T cell homeostasis and IL-7 induced differentiation [
19
]. Its authors analyzed
the impact of m6A on RNA degradation both in wild type (WT) T cells and in T cells in which m6A
writer Mettl3 had been knocked-out (KO) thus leading to a marked reduction in m6A bulk levels
(28% of WT levels). When key transcripts involved in T cell homeostasis and differentiation were
considered, m6A was found to heavily influence their stability while having a negligible role in their
processing and translation
We re-analyzed the RNA metabolic labeling data from the same study. To this end, we quantified
the kinetic rates of RNA synthesis, processing and degradation using INSPEcT (version 1.8.0), a tool
that we had previously developed for the integrative analysis of nascent and total RNA-seq data [
20
].
Our analyses suggest that in T cells, the m6A epitranscriptome is important, not only for the control of
RNA degradation, but also in RNA synthesis and processing.
2. Materials and Methods
2.1. Dataset Description
Raw RNA-seq data on total and nascent RNA and raw data for m6A profiling in untreated WT
cells were derived from the GEO series GSE100278 [
19
], where WT and Mettl3-KO mouse naive T cells
were compared before and after IL-7 stimulation. Nascent RNA was quantified by RNA metabolic
labeling with a 15 min pulse of 4sU nucleotides. In WT cells, total and nascent RNA were quantified
before IL-7 induction, and at 15, 30, 45, 60, and 75 min following IL-7 exposure. In Mettl3-KO cells,
total and nascent RNA were quantified in untreated cells, and after 15, 30, and 60 min of exposure
to IL-7.
We focused our analysis on the samples obtained from untreated cells and from cells exposed for
15 and 60 min to IL-7 treatment, as these were available for both WT and KO cells. We ignored the
samples from 30 min exposure since, in this case, the WT samples gave low numbers of aligned reads
(0.9 and 2 million reads respectively, compared to an average of 1.7 and 5.6 million aligned reads in
other samples) and a low correlation with other samples (Figure S1).
2.2. Analysis of High-Throughput Sequencing Data
Raw data were processed using HTS-flow, a workflow management system for high-throughput
sequencing data that we had previously developed [
21
]. Briefly: (i) poor quality bases were masked, (ii)
reads were aligned with the mouse genome (mm9 assembly) using TopHat with default settings [
22
],
(iii) aligned reads were filtered for duplicates using the samtools rmdup routine [23].
m6A peaks were obtained using the MACS peak caller (version 2.0) with a p-value cutoff of
1×105
. m6A+ transcripts were identified by requiring at least one MACS peak within each of the
two available replicates. m6Atranscripts were identified as those never associated to MACS peaks.
Only genes with adequate intronic and exonic expression were retained (see Section 2.3).
2.3. Expression Quantification and Filtering
BAM alignment files were analyzed using the R/Bioconductor package INSPEcT [
20
].
The abundance of premature RNA species was determined based on the intronic signal from
total RNA-seq experiments. That of mature RNA species was determined by subtracting the
intronic signal from the exonic signal. Reads densities within these genomic regions were
quantified using a function in INSPEcT called makeRPKMs (based on transcripts annotation from the
Genes 2019,10, 28 3 of 9
TxDb.Mmusculus.UCSC.mm9.knownGene Bioconductor package (version 3.2.2). Intron-less genes and
genes with either exonic expression lower than 1 RPKM (Reads Per Kilobase of transcript, per Million
mapped reads) or intronic expression lower than 0.1 RPKM in at least one sample, were excluded.
The remaining 4527 genes underwent further analysis.
2.4. Mathematical Modeling of Synthesis, Processing, and Degradation Rates
The time-courses of total and nascent RNA-seq data in WT and KO conditions were analyzed
with INSPEcT. Due to lack of biological replicates, it was not possible to estimate mean values and
variances for the expression levels of the genes. This issue forced us to limit our analysis to first
guess rates estimates, which were not subjected to any modeling step. However, first guess and
modeled rates are typically well correlated. For example, in Reference [
20
] the median Pearson’s
correlations between first guess and modeled rates were 0.89, 0.80, and 0.72 for synthesis, processing,
and degradation, respectively.
A fraction of the processing and degradation computed rates were not finite (processing WT: 0.9%,
processing KO: 0.4%, degradation WT: 20.8%, degradation KO: 11.9%). In these cases, we evaluated the
missing values by using a linear model to interpolate the rates computed immediately before and after
the not finite datum. When this situation occurred at the boundary of the time-course, we returned the
closest finite value.
2.5. Analysis of Data Distributions
The distributions of the changes in RNA kinetic rates in Figure 1B were tested for a shift from
0 using the non-parametric Wilcoxon test. In particular, due to the large difference in the number of
data points within each distribution, the p-values were determined by sampling the all and m6A
distributions based on the size of the m6A+ population. The median p-value over 1000 repetitions was
reported in the figure.
Genes 2019, 10, x FOR PEER REVIEW 3 of 10
2.4. Mathematical Modeling of Synthesis, Processing, and Degradation Rates
The time-courses of total and nascent RNA-seq data in WT and KO conditions were analyzed
with INSPEcT. Due to lack of biological replicates, it was not possible to estimate mean values and
variances for the expression levels of the genes. This issue forced us to limit our analysis to first guess
rates estimates, which were not subjected to any modeling step. However, first guess and modeled
rates are typically well correlated. For example, in Reference [20] the median Pearson’s correlations
between first guess and modeled rates were 0.89, 0.80, and 0.72 for synthesis, processing, and
degradation, respectively.
A fraction of the processing and degradation computed rates were not finite (processing WT:
0.9%, processing KO: 0.4%, degradation WT: 20.8%, degradation KO: 11.9%). In these cases, we
evaluated the missing values by using a linear model to interpolate the rates computed immediately
before and after the not finite datum. When this situation occurred at the boundary of the time-course,
we returned the closest finite value.
2.5. Analysis of Data Distributions
The distributions of the changes in RNA kinetic rates in Figure 1B were tested for a shift from 0
using the non-parametric Wilcoxon test. In particular, due to the large difference in the number of
data points within each distribution, the p-values were determined by sampling the all and m6A
distributions based on the size of the m6A+ population. The median p-value over 1000 repetitions
was reported in the figure.
Figure 1. (A) Quantification of the kinetic rates of RNA synthesis, processing, and degradation through
mathematical modeling of the RNA life-cycle. (B) Distributions of changes in the kinetic rates between
untreated wild type (WT) and knock-out (KO) cells for all modeled transcripts and for the subsets of
mA+ and m6A− transcripts; Wilcoxon p-values testing a negative shift of each distribution are reported.
(C) GeneOntology (GO) functional enrichment analysis for the top-ranking differential genes for each
kinetic rate.
The distributions of premature and mature RNA abundances and of the RNA kinetic rates in
Figure 2 were compared between WT and KO time-courses. To avoid making assumptions on the
normality of the distributions, the KolmogorovSmirnov (KS) and the MannWhitney (MW) tests
were used to assess how significant differences were.
2.6. Functional Enrichment Analysis
Functional enrichments of regulated transcripts were performed using the R/Bioconductor
package rGREAT version 1.11.1 [24], based on the genes corresponding to the 500 transcripts with
the highest log2 fold change in KO compared to WT, in any of the kinetic rates. The following
ontologies and categories were considered: GeneOntology (GO) Molecular Function, GO Biological
Respiratory electron transport
p = 2 x 10-3
p = 5 x 10-9 p = 4 x 10-4
p = 4 x 10-3
p = 8 x 10-13
p = 4 x 10-6
p = 4 x 10-4 p = 3 x 10-3 p = 6 x 10-7
Figure 1.
(
A
) Quantification of the kinetic rates of RNA synthesis, processing, and degradation through
mathematical modeling of the RNA life-cycle. (
B
) Distributions of changes in the kinetic rates between
untreated wild type (WT) and knock-out (KO) cells for all modeled transcripts and for the subsets
of mA+ and m6A
transcripts; Wilcoxon p-values testing a negative shift of each distribution are
reported. (
C
) GeneOntology (GO) functional enrichment analysis for the top-ranking differential genes
for each kinetic rate.
The distributions of premature and mature RNA abundances and of the RNA kinetic rates in
Figure 2were compared between WT and KO time-courses. To avoid making assumptions on the
normality of the distributions, the Kolmogorov–Smirnov (KS) and the Mann–Whitney (MW) tests
were used to assess how significant differences were.
Genes 2019,10, 28 4 of 9
2.6. Functional Enrichment Analysis
Functional enrichments of regulated transcripts were performed using the R/Bioconductor
package rGREAT version 1.11.1 [
24
], based on the genes corresponding to the 500 transcripts with
the highest log2 fold change in KO compared to WT, in any of the kinetic rates. The following
ontologies and categories were considered: GeneOntology (GO) Molecular Function, GO Biological
Process, GO Cellular Component, Mouse Phenotype, Disease Ontology, PANTHER Pathway, BioCyc
Pathway, and MSigDB Pathway. Individual terms were considered significant when both p-values of
the hypergeometric and binomial tests were below the threshold of 1
×
10
3
. Only p-values of the
hypergeometric test were finally reported. The same approach was used to analyze the functional
enrichment of gene clusters.
2.7. Clustering
Cluster membership was determined based on the abundance of premature and mature RNA
species and on the RNA kinetic rates. Specifically, we considered the absolute values in the untreated
condition and the changes that followed IL-7 treatment in WT and Mettl3-KO cells.
The absolute values of mature RNA species were quantified as Z-scores from the joint distribution
of untreated WT and KO data by subtracting the median and normalizing over the interquartile value.
The same procedure was applied to premature RNA abundances, synthesis rate, processing rate, and
degradation rate data.
The changes that followed IL-7 treatment were quantified by computing the log2 ratios at 15 and
60 min compared to the untreated condition for premature and mature RNA species and RNA kinetic
rates, in both WT and KO cells.
We verified that Z-scores and fold changes had comparable medians and interquartile ranges
(Figure S2). We then clustered the matrix composed of Z-scores and fold changes in 11 groups of genes
using the k-means algorithm (R package stats version 3.4.1) based on a standard sum of squares metric.
Various number of groups were tested in the range between 2 and 15, and 11 groups were chosen
as a trade-off between: (i) minimizing the percentage of clusters with less than 25 genes; (ii) maximizing
uniqueness and statistical significance of the Biological Processes related to each cluster. Regarding
the second task, we performed a functional enrichment analysis based on the GO Biological Process
ontology for each clustering configuration. For all groups of clusters, we selected the statistically
significant terms (hypergeometric p-value < 1
×
10
5
) and estimated their mean p-value and the
median percentage of terms that appeared only in one cluster (Figure S3).
A simplified version of the resulting heatmap is discussed in Results—Clusters analysis.
Specifically, it does not include clusters 9–11, because represented by less than 25 genes, it does
not contain the 15 min log2 fold changes and for each couple of Z-scores (WT and KO), it reports only
a per gene mean value. The complete heatmap is displayed in Figure S4.
2.8. Source Code
The R source code for reproducing all the analyses is available in Supplementary File S1.
3. Results
3.1. Global Consequences of m6A Depletion on T Cells RNA Dynamics
In order to characterize the impact of m6A depletion on RNA dynamics, we used INSPEcT to
quantify the rates of RNA synthesis, processing, and degradation in both Mettl3-KO and WT T cells
(Figure 1A). We then compared their distributions for 4527 transcripts expressed with adequate levels
in both their mature and premature forms. In agreement with the original study [
19
], the rates of
degradation were significantly reduced following m6A decrease (Figure 1B). Indeed, in KO naive T
cells the transcripts showed a global increase in stability. Interestingly, synthesis and processing rates
were also reduced in KO cells (Figure 1B). Taking advantage of the m6A profiling within untreated WT
Genes 2019,10, 28 5 of 9
cells, we confirmed these trends on the subset of m6A+ transcripts. Noteworthy, the same trend could
be observed also for m6Atranscripts, while with a lower significance (Figure 1B).
We used functional enrichment analysis to characterize the transcripts with the largest changes in
RNA kinetic rates (Figure 1C). Transcripts with lower degradation rates in KO cells were not enriched
for specific terms, suggesting that the overall reduction in degradation was a broad consequence
of m6A depletion and did not preferentially affect any specific class of transcripts. On the contrary,
transcripts with the opposite pattern, i.e., higher degradation rates in KO cells were involved in protein
and nucleic acid metabolism (metabolic process, p= 7
×
10
9
, and nitrogen compound metabolic
process, p= 1.0
×
10
4
). Transcripts with higher processing rates in KO cells were enriched in terms
related to energy production (mitochondrion, p= 2
×
10
9
, and Genes involved in Respiratory electron
transportation, p= 4
×
10
5
), while those with lower processing rates were related to RNA translation,
processing, and metabolism (translation, p= 5
×
10
8
“nucleic acid metabolic process, p= 3
×
10
11
and spliceosomal complex, p= 1
×
10
6
). Noteworthy, although m6A depletion did not globally affect
the rates of synthesis, transcripts with increased synthesis rates in KO cells were enriched in immune
system and cytokine response (response to cytokine stimulus, p= 2
×
10
5
, and Genes involved in
Immune System, p= 2 ×105).
Altogether these analyses indicate that, in unstimulated T cells, m6A exerts a global control on
the stability of transcripts. They also suggest that m6A plays a role in determining synthesis and
processing dynamics, specifically for classes of transcripts involving co- and post-transcriptional
regulation. It remains to be confirmed whether these effects are direct or indirect consequences of m6A.
3.2. Impact of m6A Depletion on T Cells Treated with IL-7
We characterized the effect of m6A depletion on T cells by comparing RNA kinetic rates in WT
and Mettl3-KO T cells, at 15 and 60 min after induction with IL-7 (further details on why these specific
time-points were selected can be found in Materials and Methods—Dataset description). In WT cells,
all kinetic rates became affected after 15 min of IL-7 treatment (Figure 2A).
Genes 2019, 10, x FOR PEER REVIEW 5 of 10
the contrary, transcripts with the opposite pattern, i.e., higher degradation rates in KO cells were
involved in protein and nucleic acid metabolism (metabolic process, p = 7 × 10−9, and nitrogen
compound metabolic process, p = 1.0 × 10−4). Transcripts with higher processing rates in KO cells were
enriched in terms related to energy production (mitochondrion, p = 2 × 109, and Genes involved in
Respiratory electron transportation, p = 4 × 105), while those with lower processing rates were related
to RNA translation, processing, and metabolism (translation, p = 5 × 108 “nucleic acid metabolic
process, p = 3 × 1011 and spliceosomal complex, p = 1 × 106). Noteworthy, although m6A depletion
did not globally affect the rates of synthesis, transcripts with increased synthesis rates in KO cells
were enriched in immune system and cytokine response (response to cytokine stimulus, p = 2 × 105,
and Genes involved in Immune System, p = 2 × 105).
Altogether these analyses indicate that, in unstimulated T cells, m6A exerts a global control on
the stability of transcripts. They also suggest that m6A plays a role in determining synthesis and
processing dynamics, specifically for classes of transcripts involving co- and post-transcriptional
regulation. It remains to be confirmed whether these effects are direct or indirect consequences of
m6A.
3.2. Impact of m6A Depletion on T Cells Treated with IL-7
We characterized the effect of m6A depletion on T cells by comparing RNA kinetic rates in WT
and Mettl3-KO T cells, at 15 and 60 min after induction with IL-7 (further details on why these specific
time-points were selected can be found in Materials and MethodsDataset description). In WT cells,
all kinetic rates became affected after 15 min of IL-7 treatment (Figure 2A).
After 60 min, these effects stayed unchanged in the case of synthesis and processing rates, while
increased with regard to degradation rates. Only synthesis and degradation rates were globally
affected in KO cells, while processing rates were mostly unaffected (Figure 2B). Notably, while
synthesis rates increased in both WT and KO cells, their induction was delayed in KO cells.
Additionally, while degradation rates increased in both WT and KO cells, th is effect was partially
reversed following 60 min of IL-7 induction in KO cells. These observations suggest an m6A
dependent response to IL-7 induction in terms of: (i) timing in the induction of synthesis rates, (ii)
modulation in the processing rates, and (iii) sustained up-regulation of degradation rates.
time[min] time[min] time[min]
MW: p = 1 x 10
-32
KS: p < 1 x 10
-165
p = 7 x 10
-1
p = 2 x 10
-1
MW: p = 9 x 10
-25
KS: p < 1 x 10
-165
p = 5 x 10
-1
p = 4 x 10
-7
MW: p = 5 x 10
-165
KS: p < 1 x 10
-165
p = 5 x 10
-22
p < 1 x 10
-165
MW: p = 1 x 10
-6
KS: p = 4 x 10
-4
p = 1 x 10
-46
p < 1 x 10
-165
MW: p = 7 x 10
-1
KS: p = 6 x 10
-2
p = 2 x 10
-1
p = 1 x 10
-3
MW: p = 2 x 10
-112
KS: p < 1 x 10
-165
p = 9 x 10
-20
p < 1 x 10
-165
Figure 2.
Distributions of synthesis, processing, and degradation rates in WT and KO cells, at: 0,
15, and 60 min after IL-7 induction. The figure shows p-values resulting from the application of the
Kolmogorov–Smirnov (KS) and of the Mann–Whitney (MW) tests on each pair of distributions tested.
Genes 2019,10, 28 6 of 9
After 60 min, these effects stayed unchanged in the case of synthesis and processing rates, while
increased with regard to degradation rates. Only synthesis and degradation rates were globally affected
in KO cells, while processing rates were mostly unaffected (Figure 2B). Notably, while synthesis rates
increased in both WT and KO cells, their induction was delayed in KO cells. Additionally, while
degradation rates increased in both WT and KO cells, this effect was partially reversed following
60 min of IL-7 induction in KO cells. These observations suggest an m6A
dependent response to IL-7
induction in terms of: (i) timing in the induction of synthesis rates, (ii) modulation in the processing
rates, and (iii) sustained up-regulation of degradation rates.
3.3. Clusters Analysis
Cluster analysis was used to identify groups of genes with coordinated response to IL-7 in terms
of RNA species abundance and RNA kinetic rates (Figure 3).
Genes 2019, 10, x FOR PEER REVIEW 7 of 10
Figure 3. Heatmaps showing the 8 largest clusters emerging from our analysis of Z-scores and Log2
Fold Changes. For each cluster and each quantity among: total RNA, premature RNA, synthesis,
processing and degradation rates, the figure reports: the average Z-score between WT and KO, the
Log2 Fold Change at 60 min for WT and the Log2 fold change at 60 min for KO. Enriched GO terms
are indicated for each cluster.
Similar to cluster 3, transcripts in cluster 5 (composed of 410 genes) were highly expressed and
synthesized. The corresponding genes were involved in hematologic diseases (hematologic cancer, p
= 4 × 10−12, and hematopoietic system disease, p = 2 × 10−11). Differently from cluster 3, cluster 5
transcripts were stable. IL-7 induction resulted in an increase in their degradation, leading to their
down-regulation. Cluster 7 (composed of 53 genes) included highly expressed genes that were
quickly synthesized and slowly degraded, and which differed from cluster 5 transcripts for their
extremely high processing rates. In WT cells, IL-7 induction caused a remarkable decrease in the
processing rates of the genes in cluster 7, together with a strong decrease in their stability. Mettl-3 KO
cells showed a similar trend but with responses of noticeably smaller magnitudes, highlighting then
how the regulation of the processing and degradation kinetics depends on m6A. The genes forming
cluster 7 were mainly involved in translation (p = 5 × 1035).
Finally, cluster 8 (composed of 251 genes) was characterized by the highest processing rates,
which showed a decrease in response to IL-7 dampened by m6A depletion. As expected, the
modulation of processing did not affect total RNA levels, while it originated the observed increase in
premature RNA abundance. Interestingly, genes were enriched for terms involving RNA processing
(RNA processing, p = 2 × 109, mRNA processing, p = 5 × 108, spliceosomal complex, p = 2 × 108, Genes
involved in Processing of Capped Intron-Containing Pre-mRNA, p = 2 × 109). This suggests a
feedback regulatory strategy, where transcripts involved in RNA processing modulate their own
processing dynamics.
1.5
1
0.5
0
0.5
1
1.5
total
RNA premature
RNA synthesis
rate processing
rate degradation
rate
0h (z-score)
1h IL7 (WT)
1h IL7 (KO)
0h (z-score)
1h IL7 (WT)
1h IL7 (KO)
0h (z-score)
1h IL7 (WT)
1h IL7 (KO)
0h (z-score)
1h IL7 (WT)
1h IL7 (KO)
0h (z-score)
1h IL7 (WT)
1h IL7 (KO)
0h (z-score)
1h IL7 (log2to 0h)
Genes #
1496 1853 93 296 410
Cluster #
1
5
6
7
8
35 53 251
p = 3 x 10-14
p = 6 x 10-12
p = 9 x 10-11
p = 6 x 10-39
p = 1 x 10-35
p = 3 x 10-21
p = 6 x 10-16
p = 4 x 10-12
p = 5 x 10-13
p = 3 x 10-11
p = 4 x 10-9
p = 2 x 10-9
p = 1 x 10-8
p = 5 x 10-9
p = 1 x 10-8
p = 1 x 10-21
p = 8 x 10-14
p = 4 x 10-12
p = 2 x 10-12
p = 2 x 10-11
p = 4 x 10-52
p = 1 x 10-50
p = 5 x 10-35
p = 7 x 10-34
p = 4 x 10-30
p = 1 x 10-12
p = 2 x 10-9
p = 5 x 10-8
p = 2 x 10-8
p = 1 x 10-8
Figure 3.
Heatmaps showing the 8 largest clusters emerging from our analysis of Z-scores and Log2
Fold Changes. For each cluster and each quantity among: total RNA, premature RNA, synthesis,
processing and degradation rates, the figure reports: the average Z-score between WT and KO, the
Log2 Fold Change at 60 min for WT and the Log2 fold change at 60 min for KO. Enriched GO terms are
indicated for each cluster.
Clusters 1 and 2 were the largest clusters (composed of 1496 and 1853 genes, respectively),
grouping genes spanning across various functional categories, including: RNA metabolic process,
protein metabolic process, chromatin modification, catalytic activity and binding (p< 1
×
10
10
).
These clusters showed similar inductions in terms of synthesis and premature RNA, but different
Genes 2019,10, 28 7 of 9
behaviors in terms of total RNA. In fact, following IL-7 treatment in both WT and KO cells, the total
RNA abundance decreased for cluster 1 genes while increased for cluster 2 genes. Moreover, these
two clusters showed very different patterns in terms of degradation rates. Cluster 1 was characterized
by stable transcripts of which half-lives were markedly reduced after IL-7 induction. On the contrary,
cluster 2 was characterized by less stable transcripts whose degradation rates partially increased.
Noteworthy, the increase in the degradation rates of cluster 2 transcripts was dampened in KO cells,
suggesting that m6A depletion partially impaired post-transcriptional regulation.
Cluster 3 (composed of 93 genes) included highly expressed genes characterized by fast kinetics
both in terms of synthesis and degradation rates. Following IL-7 induction, despite a reduction in
their rate of synthesis, the total RNA abundance of these transcripts increased due to an increase in
RNA stability, which seemed to be more homogeneous in m6A depleted cells. These transcripts were
maintained at high level of expression despite high degradation rates, denoting the investment of a
significant amount of energy. Due to their low stability, these transcripts were fast responders in case
of perturbations and could be expected to encode critical functions in T cells. Indeed, these genes were
involved in biological processes related to abnormal T cell differentiation (p= 5
×
10
13
), and to RNA
processing (RNA splicing, p= 4 ×109, and mRNA processing, p= 2 ×109).
Similar to cluster 3, transcripts in cluster 5 (composed of 410 genes) were highly expressed and
synthesized. The corresponding genes were involved in hematologic diseases (hematologic cancer,
p= 4 ×1012
, and hematopoietic system disease, p= 2
×
10
11
). Differently from cluster 3, cluster 5
transcripts were stable. IL-7 induction resulted in an increase in their degradation, leading to their
down-regulation. Cluster 7 (composed of 53 genes) included highly expressed genes that were quickly
synthesized and slowly degraded, and which differed from cluster 5 transcripts for their extremely
high processing rates. In WT cells, IL-7 induction caused a remarkable decrease in the processing
rates of the genes in cluster 7, together with a strong decrease in their stability. Mettl-3 KO cells
showed a similar trend but with responses of noticeably smaller magnitudes, highlighting then how
the regulation of the processing and degradation kinetics depends on m6A. The genes forming cluster
7 were mainly involved in translation (p= 5 ×1035).
Finally, cluster 8 (composed of 251 genes) was characterized by the highest processing rates, which
showed a decrease in response to IL-7 dampened by m6A depletion. As expected, the modulation of
processing did not affect total RNA levels, while it originated the observed increase in premature RNA
abundance. Interestingly, genes were enriched for terms involving RNA processing (RNA processing,
p= 2
×
10
9
, mRNA processing, p= 5
×
10
8
, spliceosomal complex, p= 2
×
10
8
, Genes involved in
Processing of Capped Intron-Containing Pre-mRNA, p= 2
×
10
9
). This suggests a feedback regulatory
strategy, where transcripts involved in RNA processing modulate their own processing dynamics.
Altogether, the characterization of the effects of m6A depletion on the IL-7 response reinforces
the patterns described for the untreated condition (Figure 1), suggesting a role for m6A not only in
the regulation of degradation dynamics but also in the control of synthesis and processing dynamics.
Cluster analyses illustrate how the combined modulation of various RNA kinetic rates closely explain
and re-capitulate the observed transcriptional modulation for both premature and mature RNA species.
4. Discussion
Similar to other RNA modifications, dynamic m6A marks have the potential to impact various
stages of an RNA life-cycle. Nonetheless, the involvement of m6A has been mostly elucidated in
relation to the degradation and translation of transcripts. Indeed, the impact of m6A on transcriptional
and co-transcriptional regulation, including RNA synthesis and processing, is far from being
established. In particular, the impact of m6A on the dynamics of these fundamental cellular processes
is mostly unknown.
A study was recently published that investigated the impact of m6A depletion on the dynamics
of premature and mature RNA species in T cells [
19
]. In that study, the authors focused on
m6A-dependent RNA degradation and showed its importance in the case of a few genes that are
Genes 2019,10, 28 8 of 9
crucial to T cells differentiation. Our genome-wide analysis re-capitulate and broaden the impact of
m6A on RNA degradation in T cells. In addition, we suggest that m6A marks affect other stages of the
RNA life-cycle. In the context of T cells homeostasis, m6A depletion causes a global slowdown for
all the RNA kinetic rates. Moreover, it impacts on all RNA kinetic rates during T cells differentiation,
by: (i) delaying the induction of synthesis rates, and impairing both (ii) the modulation of processing
rates, and (iii) the sustained up-regulation of degradation rates. Noteworthily, these effects might be
direct or indirect consequences of m6A and its deregulation. Further experiments are necessary to
investigate the prevalence of these two alternative scenarios.
Overall, our analysis suggests a broader impact of m6A on the dynamics of the RNA life-cycle and
highlights the importance of studying the role of m6A marks in combination with experiments able to
profile the full set of RNA kinetic rates. We anticipate that further studies in this direction will strongly
benefit from: (i) considering the context-dependent functional role of m6A, (ii) profiling m6A patterning
using both base-resolution (miCLIP [
25
]) and more quantitative methods (m6A-LAIC-seq [
26
]), (iii)
characterizing the impact of m6A on the life-cycle of RNA polymerase II complexes [
11
]. These
approaches will be critical to fully decipher the impact of this and other RNA modifications on the fate
of coding and noncoding RNA species.
Supplementary Materials:
The following files are available online at http://www.mdpi.com/2073- 4425/10/1/
28/s1, Figure S1: Gene expression correlations between samples, Figure S2: Steady state Z scores and temporal
Fold Changes distributions, Figure S3: Clustering metrics, Figure S4: Complete heatmap, File S1: contains the R
code to reproduce the analyses.
Author Contributions:
E.G. and M.P. conceived the study; M.F., S.d.P. and E.G. performed the analyses; all
authors contributed to write the manuscript.
Funding:
This work is supported by the “Departments of Excellence” Grant awarded by the Italian Ministry of
Education, University and Research (MIUR) (L.232/2016), and by the EPITRAN COST Action (CA16120).
Conflicts of Interest: The authors declare no conflict of interest.
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2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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By using a cell fraction technique that separates chromatin associated nascent RNA, newly completed nucleoplasmic mRNA and cytoplasmic mRNA, we have shown that residues in exons are methylated (m6A) in nascent pre-mRNA and remain methylated in the same exonic residues in nucleoplasmic and cytoplasmic mRNA. Thus, there is no evidence of a substantial degree of demethylation in mRNA exons that would correspond to so-called "epigenetic" demethylation. The turnover rate of mRNA molecules is faster depending on m6A content in HeLa cell mRNA suggesting specification of mRNA stability may be the major role of m6A exon modification. In mouse embryonic stem cells (mESCs) lacking Mettl3, the major mRNA methylase, the cells continue to grow, making the same mRNAs with unchanged splicing profiles in the absence (>90%) of m6A in mRNA suggesting no common obligatory role of m6A in splicing. All these data argue strongly against a commonly used "reversible dynamic methylation/demethylation" of mRNA, calling into question the concept of "RNA epigenetics" that parallels the well-established role of dynamic DNA epigenetics.
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N(6)-methyladenosine (m(6)A) has been identified as the most abundant modification on eukaryote messenger RNA (mRNA). Although the rapid development of high-throughput sequencing technologies has enabled insight into the biological functions of m(6)A modification, the function of m(6)A during vertebrate embryogenesis remains poorly understood. Here we show that m(6)A determines cell fate during the endothelial-to-haematopoietic transition (EHT) to specify the earliest haematopoietic stem/progenitor cells (HSPCs) during zebrafish embryogenesis. m(6)A-specific methylated RNA immunoprecipitation combined with high-throughput sequencing (MeRIP-seq) and m(6)A individual-nucleotide-resolution cross-linking and immunoprecipitation with sequencing (miCLIP-seq) analyses reveal conserved features on zebrafish m(6)A methylome and preferential distribution of m(6)A peaks near the stop codon with a consensus RRACH motif. In mettl3-deficient embryos, levels of m(6)A are significantly decreased and emergence of HSPCs is blocked. Mechanistically, we identify that the delayed YTHDF2-mediated mRNA decay of the arterial endothelial genes notch1a and rhoca contributes to this deleterious effect. The continuous activation of Notch signalling in arterial endothelial cells of mettl3-deficient embryos blocks EHT, thereby repressing the generation of the earliest HSPCs. Furthermore, knockdown of Mettl3 in mice confers a similar phenotype. Collectively, our findings demonstrate the critical function of m(6)A modification in the fate determination of HSPCs during vertebrate embryogenesis.
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Over 100 types of chemical modifications have been identified in cellular RNAs. While the 5′ cap modification and the poly(A) tail of eukaryotic mRNA play key roles in regulation, internal modifications are gaining attention for their roles in mRNA metabolism. The most abundant internal mRNA modification is N⁶-methyladenosine (m⁶A), and identification of proteins that install, recognize, and remove this and other marks have revealed roles for mRNA modification in nearly every aspect of the mRNA life cycle, as well as in various cellular, developmental, and disease processes. Abundant noncoding RNAs such as tRNAs, rRNAs, and spliceosomal RNAs are also heavily modified and depend on the modifications for their biogenesis and function. Our understanding of the biological contributions of these different chemical modifications is beginning to take shape, but it's clear that in both coding and noncoding RNAs, dynamic modifications represent a new layer of control of genetic information.
Article
Modifications in mRNA constitute ancient mechanisms to regulate gene expression post-transcriptionally. N(6)-methyladenosine (m(6)A) is the most prominent mRNA modification, and is installed by a large methyltransferase complex (the m(6)A 'writer'), not only specifically bound by RNA-binding proteins (the m(6)A 'readers'), but also removed by demethylases (the m(6)A 'erasers'). m(6)A mRNA modifications have been linked to regulation at multiple steps in mRNA processing. In analogy to the regulation of gene expression by miRNAs, we propose that the main function of m(6)A is post-transcriptional fine-tuning of gene expression. In contrast to miRNA regulation, which mostly reduces gene expression, we argue that m(6)A provides a fast mean to post-transcriptionally maximize gene expression. Additionally, m(6)A appears to have a second function during developmental transitions by targeting m(6)A-marked transcripts for degradation.
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Messenger RNA (mRNA) modification provides an additional layer of gene regulation in cells. In this issue of Cancer Cell, Zhang et al. report that ALKBH5, a demethylase of the mRNA modification N⁶-methyladenosine, regulates proliferation and self-renewal of glioblastoma stem-like cells by modulating pre-mRNA stability and expression of the FOXM1 gene.