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Blood First Edition Paper, prepublished online June 5, 2014; DOI 10.1182/blood-2014-04-512756
A Tour Through the Transcriptional Landscape of Platelets
Sebastian Schubert1, Andrew S. Weyrich1,2 , and Jesse W. Rowley1,2
The Molecular Medicine Program1 and the Department of Internal Medicine2, University
of Utah School of Medicine, Salt Lake City, UT, USA
Running Title: mRNA in Platelets
Corresponding Author: Jesse W. Rowley, PhD
Department of Internal Medicine
University of Utah School of Medicine
Eccles Institute of Human Genetics
Building 533, Room 4260
15 North 2030 East
Salt Lake City, Utah 84112
[email protected]
Phone: 801-585-0706
Fax: 801-585-0701
1
Copyright © 2014 American Society of Hematology
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Abstract
The RNA code found within a platelet, and alterations of that code, continue to
shed light onto the mechanistic underpinnings of platelet function and dysfunction. It is
now known that features of messenger RNA (mRNA) in platelets mirror those of
nucleated cells. This review serves as a tour guide for readers interested in developing
a greater understanding of platelet mRNA. The tour provides an in-depth and
interactive examination of platelet mRNA, especially in the context of next-generation
RNA-sequencing (RNA-seq). At the end of the expedition the reader will have a better
grasp of the topography of platelet mRNA and how it impacts platelet function in health
and disease.
Introduction
Despite their small size and anucleate status, platelets have a rich repertoire of
RNAs including messenger RNAs (precursor-mRNA [pre-mRNA] and mature mRNA),
structural and catalytic RNAs (ribosomal RNA [rRNA], transferRNA [tRNA], and small
nucleolar RNA [snRNA]), and regulatory RNAs (microRNA [miRNA], long intergenic
non-coding RNA [lincRNA], pseudogenes, and anti-sense RNA). Examination of RNA
expression patterns in platelets have been used to identify biomarkers of disease,
explain genetically or environmentally induced alterations in platelet function, and
determine if genes are conserved between humans and mice1–13. The last decade has
also revealed that platelets can translate mRNA into protein or transfer RNA to recipient
cells where it regulates functional processes14–17. Because platelets are anucleate,
transcriptional production of RNA (with the notable exception of mitochondria derived
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transcripts) presumably occurs exclusively in the megakaryocyte. Therefore, the RNA
profile in platelets also provides an accessible window into the transcriptional status of
megakaryocytes, and the bone-marrow signals involved, at the time of platelet
production.
Several recent reviews have elegantly discussed the predictive value and functions
of platelet RNAs2,18,19. This review explores the landscape of platelet RNA as mapped
by next generation RNA sequencing (RNA-seq). Specifically, we examine what a
platelet mRNA looks like at the molecular level and how specific features of the mRNA
impact biological responses. Along the way, we provide a series of web links and
access instructions (see Table I) for readers to follow as they explore key landmarks of
platelet mRNA. Each stage of the tour (Figure 1) is discussed with reference to platelet
function and in a broader biological context. While screenshots are shown in figure
format, this review is meant to be interactive and dynamic, with the goal of providing the
tools necessary for researchers to explore the innumerable transcript features in
platelets in the context of their own research interests.
I.
How to navigate the landscape: an RNA-seq overview
To appreciate key features of platelet mRNA, one first has to understand the
fundamentals of next-generation RNA-sequencing (RNA-seq). RNA-seq has
revolutionized medical research and, as shown in Table II, several groups have recently
used this powerful technique to profile platelet mRNA1,20–22.
RNA-seq libraries are prepared from either total RNA or a sub-fraction of RNA (i.e.
poly-adenylated, ribosomal RNA depleted, 5 cap containing, small RNA, etc.) by
′
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random shearing of RNA into 100-400 base pair fragments. Barcodes and sequencing
primer binding sites are then added, and the RNA is briefly amplified, followed by size
selection of 100-400 bp fragments (also called inserts). This size selection captures
most protein coding RNAs and long non-coding RNAs. For small RNA sequencing,
which captures mature microRNAs, piwi-interacting RNAs, and other small RNAs,
fragmentation is skipped and inserts < 40 bp are selected. Following insert selection,
around 30 bp up to >150 bp of one end (single end read) or both ends (paired-end
reads) of each fragment are sequenced. Sequences (reads) are then computationally
mapped by finding unique (usually) matches between the annotated
genome/transcriptome and the sequenced end of the RNA fragment. If paired-ends are
sequenced, then each end is matched with the expected distance constrained to the
approximate expected fragment size. If libraries are stranded, then the read can be
mapped directionally to either the positive or negative strand of DNA. RNA-seq
methodology is reviewed in more detail elsewhere23–26.
Four long RNA sequencing datasets (i.e. protein coding and long non-coding RNA)
from human platelets and one from mouse platelets have been published1,20–22. Other
small RNA sequencing datasets (i.e. mature microRNAs) have also been published but
are not discussed here21,27. As shown in Table II, each RNA-seq dataset offers some
complementary features not provided by the other. For example, in our experience
shorter (36 bp) reads map better to poorly annotated transcripts or novel small exons
whereas longer reads (101 bp) aid in isoform discrimination and map better to lower
complexity regions. Paired reads are useful for determining splicing and exon exclusion
patterns. Stranded reads discriminate between genes overlapping in the genome, but
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transcribed from opposite strands. Oligo-dT enriches for protein coding transcripts,
whereas ribosomal depletion of total RNA will capture more non-coding transcripts. In
one of their samples, Kissoupoulou et. al.20 use a 5 end tagging strategy that marks the
′
extreme 5 terminus of transcripts. The aligned reads from three of these datasets are
′
available for rapid navigation via web links. Instructions to access and visualize the
aligned data in UCSC genome browser28 are given in Table I.
II.
A panoramic view of a platelet transcript as depicted by RNA-seq
RNA-seq creates snapshots of mRNAs that provide a wealth of information, from
gene expression to regional features and detailed sequence23. Before exploring
subdomains of platelet mRNA, it is helpful to understand the topography of snapshots
that are used to represent RNA-seq results. Figure 2A represents a visualization of
RNA-seq alignments to glycoprotein IX (GP9). In this example, GP9 is annotated as it
appears in platelets – a mature message that does not contain intronic sequences. Note
that individual paired reads, which in this case are ~36 bp in length, map across the
entire GP9 annotated transcript. The number of reads that cover each nucleotide along
the transcript are summed, and the count (y-axis) is displayed in the histogram-like
graph (coverage or read depth graph) above the reads. Various methods, ranging in
robustness, for calculating expression exist, and are reviewed in29,30. One simple and
commonly used index of abundance is the RPKM or FPKM (Read (or Fragment) Per
Kilobase normalized to a Million)26. This is calculated from the sum of all the reads
across a transcript, normalized to the transcript length and to the total number of reads
mapping to the entire transcriptome. Variability in fragmentation, sequencing, and
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mapping efficiency for sub-sequences within the transcript affect the read depth at each
loci, hence the “hills” and “valleys” of expression across the transcript30. Thus, RNA-seq
can quantify per-base, per-exon, per annotated region, or per-transcript expression on a
genome-wide scale without prior knowledge of the transcript23.
Unlike the depiction in Figure 2A, RNA-seq data is typically visualized in genomic
space using a genome browser such as the UCSC genome browser28. Figure 2B
depicts the same transcript, GP9, in a genome browser. Note now that the exons are
separated by the introns encoded in the genome, and like canyons between peaks of
expression, the introns are devoid of aligned reads. This is because GP9 is completely
spliced. Reads connected by thin lines represent either connected paired or split reads.
Although the intron spanned by paired reads is much longer than the expected 200 bp
fragment size, when the transcript is spliced the paired reads map at the expected
distance.
III.
Hidden structures: the 5′ cap and poly-A tail
Although anucleate, platelet mRNAs mirror mRNAs found in nucleated cells.
Eukaryotic RNAs are prepared for nuclear export and translation by the addition of a 5
′
7-methylguanosine cap, the addition of a 3 poly-A tail of variable length, and the
′
splicing out of introns31. The 5 cap facilitates ribosome loading for protein translation
′
via recognition and recruitment by eukaryotic initiation factor 4E (eIF4E)32,33. The polyA tail helps circularize mRNAs through interaction with the scaffold protein eIF4G, which
initiates cap recognition by eIF4E32. Both ends protect mRNA from degradation32. The
poly-A tail can be extended or shortened over the life of the RNA, but gradual poly-A
shortening followed by decapping is the eventual fate of most RNAs34,35. In other
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mammalian cells, typically only 5 capped, 3 polyadenylated, spliced mRNA will be
′
′
exported from the nucleus, and used for translation31. The majority of unspliced
cytoplasmic message is rapidly degraded by non-sense mediated decay (NMD)36.
The customary use of oligo-dT to prime reverse transcription leaves little doubt that
the majority of platelet transcripts are polyadenylated, consistent with descriptions of
poly-A+ RNA in platelets reported by Roth and colleagues in 198937. Platelets contain
poly-A binding proteins such as APP-138 that may support poly-A stimulated protein
translation. Evidence that a subset of transcripts are 5 capped comes from reports that
′
protein translation can be initiated in platelets39. The poly-A tail is not genomically
encoded and stretches of A’s cannot be effectively aligned from RNA-seq reads.
Therefore, poly-A reads are hidden in typical RNA-seq workflows. The 5’ cap is also not
detected by normal RNA sequencing reactions.
To characterize capping and polyadenylation in platelets, we have performed RNAseq on transcripts captured with oligo-dT conjugated beads or by a hyper-affinity mutant
eIF4E protein that enriches for transcripts with a 7-methylguanosine cap40. Figure 3A
offers a pictorial of this strategy. When comparing RNA-seq of poly-A isolated mRNA
with cap-captured RNA, poly-A isolation captures the majority of annotated protein
coding transcripts, while some transcripts with a very short or no poly-A tail are missed.
Such is the case for many histone coding transcripts with characteristically short or no
poly-A tails41 which are missed by poly-A capture, yet readily observed when isolated by
the 5 cap-based strategy (Figure 3B). Conversely, a handful of mRNAs detected by
′
poly-A based methods are not captured by 5 -based methods (not shown). Aside from
′
these exceptions, the bulk of annotated platelet transcripts that are captured by Oligo7
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dT are also captured by cap binding strategies (unpublished data). A prime example is
PAR1, whose mRNA was captured by both 3 and 5 based methods (Figure 3B). Other
′
′
transcripts, in particular non-coding RNA, are undoubtedly present in platelets that
contain neither a 5’ cap nor a 3’ poly-A tail21.
The beginning of the poly-A tail can be inferred from the reads by a sharp decline
in read depth at the end of the 3 UTR accompanied by 3 end mapped reads containing
′
′
strings of A’s20,42. This type of analysis was used by Osman and coworkers to identify
two poly-adenylation start sites for transcript PPBP20. On the other hand, the length of
poly-A tails in platelets and whether tail shortening occurs over time is not known. This
is in part because new and old populations of platelets are difficult to separate in vivo.
In vitro studies suggest that some platelet mRNAs are not stable over time.
Reticulated platelet counts potentially decrease after platelet storage43,44. mRNA for Pselectin and GAPDH disappear in stored platelets at 22oC degrees, but decline only
40% over 5 days when stored at 4oC degrees45. Sult2B1b mRNA, which affects platelet
functional responses via catalysis of the sulfonation of cholesterol46, also diminishes
rapidly at 37oC47. Sult2B1b mRNA levels decrease by 40% within just 30 minutes at
37oC, but are stable at 4oC. Interestingly, addition of high density lipoprotein to the
platelets slowed the decay. Of note, Sult2B1b protein levels and its product level of
cholesterol sulfate tracked with mRNA levels suggesting continued protein translation
and functional implications of mRNA stability in platelets. Whether the poly-A tail and
cap are involved in the stability and translation of these and other transcripts in platelets
merits further investigation.
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IV.
Peaks and canyons: pre-mRNAs, splicing, and intron retention
Surprisingly platelets contain a subset of precursor mRNAs that have escaped the
nucleus and NMD without splicing48. Platelet activation induces splicing and enables
protein production from the newly formed mRNA. Unspliced pre-mRNA can be
observed in RNA-seq data visually or bioinformatically by examining the ratio of intronic
to exonic reads. Interleukin-1β (IL-1β), a prototypical unspliced mRNA in platelets48,
contains intronic reads in relatively high proportion to exonic reads2. According to our
own analysis of platelet RNA-seq datasets, transcripts like IL-1β, with a large fraction of
completely unspliced pre-mRNA, are infrequent. Despite this, additional examples of
pre-mRNA splicing followed by protein production in platelets have surfaced in the
literature49,50. In addition to these, using RNA-seq we have bioinformatically identified a
handful of putatively unspliced transcripts in platelets that retain one or more introns.
One example shown in Figure 3C is FOSB. FOSB is a transcription factor that
regulates a wide array of cellular functions51. Whether FOSB can be spliced within
platelets remains to be determined.
V.
Regulatory regions: 5′ and 3′ UTRs
Serial analysis of gene expression (SAGE) in platelets previously demonstrated
that the set of transcripts in platelets have longer untranslated regions (UTRs) than the
set of transcripts found in other cells52. UTRs can also be assessed at the individual
level. Figure 4A and 4B are examples of transcripts in platelets where the actual UTR
does not match the predicted UTR. The 5 and 3 UTRs regulate RNA stability and
′
′
protein translation53–55. Within the 5 UTR, secondary structure and primary sequence
′
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motifs direct ribosome scanning and initiation of translation56. A kozak sequence within
the 5 UTR that contains the AUG start codon flags the ribosome to start translation of
′
the open reading frame (ORF). Premature upstream ORFs (uORFs) regulate the
efficiency of initiation at the coding ORF and secondary structures in 5 UTR’s further
′
affect ribosome loading and scanning efficiency57. Proteins and small RNAs (i.e.
microRNAs) bind to sequence motifs within the 5 UTR and 3 UTR to regulate
′
′
translation or degradation53,54,56,58. In platelets for example, the 5 UTR of B cell
′
lymphoma 3 (BCL3) controls translation of the mRNA in an mTOR (mammalian Target
of Rapamycin) dependent manner59. Given the many sequence dependent effects of
the UTR on RNA stability and translation, the predicted biological relevance of an
annotated UTR sequence versus the actual UTR expressed in platelets could be
profoundly different.
GFI1B controls megakaryocyte development60, and GFI1B mutants can cause
diseases of abnormal platelet formation and function like Gray Platelet Syndrome
(GPS)61. In Figure 4A, the annotated 5 UTR of GFI1B covers approximately 350 bp of
′
exons one and two. RNA-seq reads in platelets suggests that transcription begins an
additional 300 base pairs upstream from the annotated transcripts (Figure 4A, upper
panel). The preponderance of 5 terminally tagged reads (Figure 4A, lower panel) that
′
map nearly exclusively to the start of the extended region suggests a dedicated
transcription start site rather than aberrant run-on transcription. The additional 5
′
sequence contains 3 short upstream open reading frames.
miRNAs regulate RNA stability and protein translation by binding nearly
complementary target sequences in the 3 UTR of transcripts62. In platelets miRNAs are
′
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abundant and stable63,64. They have been reported as biomarkers and as functional
modifiers of platelets18,19,65. Interestingly, platelets can transfer miRNAs to regulate
targets in other cells15–17. miRNA studies in platelets are increasingly popular and are
the subject of several reviews18,19,66,67.
miRNA studies incorporate target prediction algorithms based on 3 UTR
′
annotations to identify potential microRNA-mRNA pairs68. Differences in 3 -UTR
′
annotations may affect the outcome of such studies. The P2Y purogenic receptor 1
(P2RY1) accounts for approximately 1/3 of adenosine diphosphate (ADP) receptors on
platelets69. Stimulation of P2RY1 by ADP induces intracellular calcium fluxes and
platelet aggregation70,71. P2RY1 is a potential target of anti-thrombotics69,71 and
differences in P2RY1 expression alter bleeding times and thrombosis in mice72. In
Figure 4B the annotated 3 UTR of P2RY1 is nearly 1 kb, whereas the actual 3 UTR in
′
′
platelets is considerably longer. According to miRNA target prediction algorithms
(miRanda73), this additional 2.5 kb harbors additional miRNA binding sites (Figure 4B).
This indicates that actual transcript data, not predicted annotation data, should be used
for miRNA target site predictions in platelets. Sequences in view can be extracted
directly in UCSC genome browser by selecting “DNA” in the “view” menu.
VI.
Changing landscapes: alternative splice variants
SNP association studies74 and mechanistic studies of platelet endothelial
aggregation receptor 1 (PEAR1) link it strongly to an aggregatory role in platelets75. As
illustrated in Figure 5A, RNA-seq distinguishes between multiple annotations for
PEAR1. Alternative splicing affects nearly every multi-exon eukaryotic transcript76.
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Exon skipping, intron retention, alternate exon usage, and addition or truncation of
exons are examples of alternative splicing76,77.
Many alternative splice variants lead to NMD36. Other splice variants regulate
protein synthesis or code for alternate protein products. Splice variants of the same
gene can effect opposing functions, and some variants may alter physiologic responses
to drugs78. Proteomics detects many novel protein isoforms in platelets. In one
proteomics study79, a number of proteins with novel exon skipping events were
identified. Three of these, ITGA2 (integrin, α2), FH (fumarate hydratase), and NPEPPS
(aminopeptidase puromycin sensitive) were further validated by PCR at the RNA level79.
Interestingly, activation altered the number of exon skipping events79.
We have seen how alternate initiation and termination can affect regulatory
elements within UTRs. Alternative splicing is another way to alter the 5 and 3 UTR. As
′
′
shown in Figure 5B, RNA-seq can distinguish tissue factor pathway inhibitor (TFPI)
splice variants in platelets. TFPI inhibits tissue factor activity and thereby limits
excessive coagulation80. Multiple splice variants have been characterized for TFPI.
Megakaryocytes and platelets express TFPI alpha, one of 2 major well characterized
isoforms80 produced by alternative splicing of the 5 and 3 end of the mRNA. Part of the
′
′
5 UTR in exon 2, which can be removed by alternative splicing80,81, represses TFPI
′
translation. On the other hand, the 3 UTR of the TFPI alpha isoform in platelets relieves
′
exon 2 mediated repression81. The proteins produced by TFPI splice variants are also
different. The C-termini of the TFPI isoforms differ in tissue factor inhibitory activity82.
Furthermore TPFI alpha is soluble and not linked to the cell surface like TFPI beta82.
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The production of a soluble versus membrane bound form of proteins is a recurring
theme of alternative splicing in platelets. Soluble P-selectin is shed from platelets after
activation, mediated in part by extra-cellular cleavage83. Alternative production of
soluble P-selectin may also be involved. cDNA sequencing predicted the presence of a
soluble splice variant of P-selectin84–86. Correspondingly, soluble P-selectin is found in
the non-membrane fraction of platelets, totaling up to 10% of total P-selectin levels87.
Splice variants of integrin beta388 code for a truncated protein, with the transmembrane
and cytoplasmic domains missing. Protein assays have confirmed the expression of
truncated ITGB3 (integrin, beta 3) protein in platelets88. Platelets contain multiple forms
of the low affinity immunoglobulin Fc region receptor IIa (FCGRIIa) mRNA. Variants with
and without the transmembrane exon are detected at approximately equal proportions
in platelets89.
Several other notable splice variants with relevance to platelet function have been
reported. A variant of cyclooxygenase 2a (COX2a) is increased 200-fold after coronary
artery bypass grafting90. Variants of PLC-beta (phospholipase C, beta 2) code for two
different proteins in platelets91. An angiopoietin-1 splice variant that opposes its parent
gene is found in platelet alpha granules92. These examples only scratch the surface of
the extensive collection of splice variants found in platelets. Some platelet specific
splice variants, including novel exons, have yet to be annotated within major databases.
Such is the case with growth factor independent IB (GFI1B, Figure 5C).
VII. Switchbacks: antisense and non-coding RNA
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Non-coding RNAs regulate the production of target mRNAs, the translation of
target mRNAs into protein, or directly modulate the target proteins themselves. Platelets
contain all major classes of non-coding RNAs including miRNAs (see above), lincRNAs
pseudogenes, and anti-sense RNA. Of these, platelets are particularly enriched in
miRNAs, pseudogenes, and anti-sense RNAs21.
In eukaryotic cells, a major fraction of expressed genes are accompanied by an
antisense93. Antisense transcripts regulate their complementary counterpart in
numerous ways. Antisense RNAs mask miRNA binding sites, destabilize transcripts,
repress translation, and promote or repress transcription93,94. They mediate both
cytoplasmic and nuclear RNA-RNA and RNA-DNA interactions93. Figure 6 depicts an
example antisense RNA found at the 5 end of cluster of differentiation (CD109). CD109
′
is a highly expressed human platelet alloantigen95. In this example, the antisense to
CD109 begins at the 5 end of CD109.
′
VIII. A sequence level view: SNPs, insertions, and deletions
By zooming to sequence level, individual SNPs (single nucleotide polymorphisms),
insertions, and deletions that are within the expressed transcript can be identified by
individual RNA-seq reads. Searching for “platelet” within the “clinical synopsis” field
within Online Mendelian Inheritance in Man (OMIM, http://omim.org/, April 4, 2014
update) retrieves 79 different inherited platelet disorders, corresponding to 59 different
defective genes. The inherited platelet disorders include well characterized platelet
gene/phenotypes such us ITGA2B/Glanzmann Thrombasthenia (GT)96 or NBEAL2/
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GPS97–99. Clicking on the genome coordinates (GRCh37) listed within any of these
disorders, while the RNA-seq platelet data UCSC genome browser window is open, will
bring you directly to the location listed overlaid with read sequence information. Of
note, visualization of mutations in RNA-seq read sequences from a patient with GPS
helped characterize a splicing defect in their NBEAL2 transcripts97.
Genome Wide Association Studies (GWAS) studies have identified SNPs near
more than 40 different genes associated with platelet size, count, or function100–104.
Meta-analyses have extended this number to around 80 (for a convenient list, see101). In
GWAS, a trait associated SNP serves as a regional marker of allelic association without
necessarily being the cause of the trait105,106. Figure 7 depicts a reported GWAS
common variant associated with platelet counts, RS6065100,107, that is found within the
RNA-seq read sequences of GP1BA compared to another individual without the variant.
As we understand more about the meaning of GWAS SNPs, the ability to link RNA
transcripts with marker SNPs and the causative mutations of trait variation may become
important.
Perspective
After completing this tour, it should be obvious that platelets have a complex
mRNA signature that reflects and affects their function. Indeed, mRNA expression
profiling was recently used to identify the molecular basis for differences in platelet
reactivity between blacks and whites7. The study found by microarray that PCTP mRNA
is four-fold higher in blacks than in whites. This correlated with PCTP protein levels and
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platelet reactivity to PAR4. Another RNA, miR-376c, inversely correlated with platelet
reactivity and regulated PCTP expression. This study is mentioned to underscore how
platelet mRNA can be leveraged to identify and understand the molecular mechanisms
that control human disease. Of note, the RNA-seq reads from 5 black and 5 white
individuals platelets were subsequently published22. In this dataset (which is site 4 of
Table I in this review), consistent with their microarray findings, PCTP mRNA is seven
fold higher in blacks than in whites22.
The intrinsic functional capacity and variability of platelets are determined by
environmental signals and genetic factors seen by the megakaryocyte. As has been
done for other cells108, integrating platelet and megakarycoyte RNA-seq with whole
genome sequencing or genome wide microarray genotyping will provide valuable
information regarding the relationship between the environment and genetics of altered
gene expression within platelets. In addition to expression, RNA-seq analysis provides a
tool for examining transcripts, and their plausible roles in platelet function and disease,
at several other layers within the platelet terrain including the 5 cap and poly-A tail, the
′
5 and 3 UTRs, intron retention, alternative splicing, strand, and single nucleotide
′
′
sequences (summarized in Figure 8). In conjunction with published reports, we predict
that forthcoming RNA-seq datasets will provide unprecedented opportunities to assess
both “what’s in a platelet” and “what’s different about platelets in disease”.
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Authorship
Contribution: S.S. and J.W.R. drafted and prepared the manuscript; A.S.W. reviewed
and edited the manuscript.
Conflict-of-interest disclosure: The authors declare no competing financial interests.
Acknowledgements
We are grateful for the contributions of Diana Lim in preparation of the figures. Work in
this report was supported by NIH awards 1U54 HL112311-01, 1K01 GM103806-01, and
2R01 HL066277-11 as well as by the German Research Foundation SCHU 2561/1-1.
17
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Site
Ref.
Descripton: Web Link > Access instructions
Coverage track: https://hci-bioapp.hci.utah.edu/gnomex/gnomexFlex.jsp?dataTrackNumber=DT1837 >
select “UCSC Browser” **
Bam (individual reads and sequences) track: https://hci-bio2
Rowley
app.hci.utah.edu/gnomex/gnomexFlex.jsp?dataTrackNumber=DT11785 >
et. al.1
select “UCSC Browser” **
3
Bray et. Coverage tracks and individual reads tracks:
https://cm.jefferson.edu/data-tools-downloads/platelets-2012/
al.21
> select “A” to enter UCSC Browser**
Coverage tracks and individual reads tracks:
4
Londin
et. al.22
https://cm.jefferson.edu/data-tools-downloads/platelets-2014/
> select “A” to enter UCSC Browser**
**Tips for UCSC Genome Browser:
1
•
•
•
•
•
•
•
•
Rowley
et. al.1
After entering UCSC’s website, select “Genome Browser”.
Search for a gene (“GP9”), location (“chr3:128,779,645-128,781,253”), or term of interest (“rs6065”).
Scroll down to see the available tracks. The platelet tracks can be found under the header “Custom
Tracks” for sites 1 and 2 or under the header “Mapping and Sequencing Tracks” for sites 3 and 4.
Using the toggle boxes, set the visibility settings to “Full” for the platelet data track(s) of interest (i.e.
“B2_Pltlts_...”). Set visibility to “Full” for one of the Gene Predictions tracks (i.e. RefSeq).
“Hide” other tracks and “zoom” to view the data as presented in the figures.
Select “Refresh” after making any changes to update the viewer.
Other datasets, including mouse datasets are accessible from within Gnomex by searching for
“platelet” in the search box at the top. Alternatively, the .Bam and .Bai index files can be
downloaded from Gnomex and opened in a genome browser such as IGV109. If the links don’t
initially work, try a different browser.
For more information on using UCSC genome browser, see:
https://genome.ucsc.edu/training.html.
Table I: Links for viewing platelet RNA-seq datasets in UCSC Genome Browser
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Reference
Rowley et. al.1
Bray e.t al.21
Londin et. al.22
Kissopoulou et. al.20
Platform
Illumina
ABI/LT SOLiD™
ABI/LT SOLiD™
Illumina
# Samples
Species
Gender
Race
Leukocyte
depletion
Reads
Sample
prep
4
2 human/2 mouse
1 M/1 F of each
species
ND
CD45 (Miltenyi)
36 bp,
paired end,
unstranded,
Poly-A+ RNA
4
10
4
human
human
human
ND
10 M
3 M/1 F
ND
5 black, 5 white
ND
CD45 (Miltenyi)
CD45 (Miltenyi)
Pan-Leuko (Dynabeads)
50 bp,
single end,
stranded
(30 bp stranded
small RNA reads
also available)
Total RNA and
ribosomal RNA
depleted RNA
50 bp,
single end,
stranded
Total RNA
Table II: Published platelet RNA-seq (long-RNA) datasets
19
100 bp,
single end,
unstranded
100 bp,
paired end,
stranded
Ribosomal RNA depleted
and poly-A+/5’ end
tagged RNA
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References
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Figure 1. A tour through the transcriptional landscape of a platelet. This tour
through the transcriptional landscape in platelets is divided into 8 segments, which are
listed in this figure.
Figure 2. General features of an mRNA transcript as visualized by RNA-seq. This
figure shows RNA-seq read coverage plots and individual reads for platelet
Glycoprotein 9 (GP9) mRNA as it appears (i.e. with introns removed) in the platelet (A)
or as viewed along genomic coordinates in a genome browser (B). The blue
annotations along the bottom are the consensus RefSeq annotations of the gene
structure. Individual exons are represented by the blue bars, and introns (only in B) by
the connecting blue lines. The thicker regions of the bars distinguish the coding region
from the 5 and 3 UTRs. Arrows indicate the strand orientation (forward [+] or reverse [′
′
]) of the annotation. For GP9, there are 3 exons and 2 introns. In GP9, the 5 UTR ends
′
just beyond the start of the 3rd exon (at the ATG start codon of the coding region), and
the 3 UTR starts near the end of the 3rd exon (after the TAA stop codon). Above the
′
annotation are the individual reads that align to the transcript. For simplicity, not all
reads are shown here. For visualization purposes, the individual reads are colored
according to which exon they align to. Above the reads is the read coverage map. This
is generated by plotting the sums of mapped reads that overlap (cover) each nucleotide
position along the genomic coordinates (x-axis). The y-axis represents the number of
reads. The examples are as illustrated in Integrated Genome Viewer (IGV)109. See it
yourself: To see GP9 in platelets via UCSC genome browser, follow the Web Link >
29
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Access instructions for Site 1 (see Table I). For individual reads, follow the Web Link >
Access instructions for Site 2 (see Table I). Search for GP9 in the browser
Figure 3. Platelet transcripts are capped, tailed, and spliced. A. depicts the
techniques we have used to capture and concentrate poly-A mRNA or 5 capped mRNA
′
from platelets. The captured RNAs are concentrated by immunoprecipitation
techniques. For poly-A pull down, bead conjugated oligo-dT sequence binds to the poly
A tail. For the 5 cap pull down, a bead conjugated hyper-affinity mutant enzyme, binds
′
to the 7-methyl guanosine cap (7mG) cap at the 5 end of the mRNA. B. Bar graph of
′
the RNA-seq expression estimates (RPKM) of a histone transcript HIST1H4B and the
transcript coding for the platelet thrombin receptor PAR1 (F2R) following mRNA
isolation by a 5 cap or poly-A tail pull-down. C. RNA-seq coverage graph of FOSB,
′
which appears mostly unspliced in platelets. Note that reads align to both exons and
introns. Compare this to GP9 in Figure 2 which has very few reads mapping to introns.
Figure 4. Platelets transcripts have variable 5′ or 3′ UTRs. A. Shown are coverage
graphs of the 5 end of GFI1b in platelets. The top panel represents coverage of all
′
reads mapping to the 5 end of GFI1b. The red box is drawn around the region that
′
maps beyond the known transcript annotation (blue). The bottom panels show only
those reads that contain the sequence tag ligated onto the 5 end at time of sample
′
preparation (for details see Osman et. al.). The 5 tagged reads are only the extreme 5
′
′
terminal reads, and therefore mark the transcription start site. B. Coverage graph of the
30
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3 end of P2RY1 in platelets. The red box is drawn around the region that maps beyond
′
the known 3 UTR annotation. microRNA target analysis of this additional sequence
′
identified several microRNA binding sites. A representative subset of the target sites
are marked underneath the graph. See it yourself: To see GFI1b and P2RY1 in
platelets via UCSC genome browser, follow the Web Link > Access instructions for Site
1 (see Table I). For individual reads, follow the Web Link > Access instructions for Site
2 (see Table I). Search for GFI1b or P2RY1 in the browser. Note that the 5 terminal
′
reads in the bottom panels of A were processed from the dataset published by Osman
et. al.20 and are therefore not yet available in a browser friendly format.
Figure 5. Platelets express mRNA splice variants. Shown are coverage graphs of
PEAR1 (A) TFPI (B) GFI1B (C) in platelets. A. Note that there are four different
Ensembl annotations listed underneath PEAR1. The red box is drawn around the
second exon which distinguishes the second annotation from the other three
annotations. B. The annotations for two major isoforms of TFPI, alpha (iso A) and beta
(iso B), are distinguished by their 3 ends. Note that platelets are rich in isoform alpha
′
but not isoform beta. Exon 2 (exon 2 is very small, so it is difficult to see the annotation
here), which can also be removed by alternative splicing, is also present in the platelet.
C. The arrow points to a putative novel exon of GFI1b. Annotations for an exon at this
region are not present in any of the Ensembl, UCSC, or RefSeq databases. The red box
outlines examples of paired reads that map from the unannotated novel exon to the
known second exon suggesting it is part of the GFI1B transcript. See it yourself: To
see PEAR1, TFPI, and GFI1B in platelets via UCSC genome browser, follow the Web
31
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Link > Access instructions for Site 1 (see Table I). For individual reads, follow the Web
Link > Access instructions for Site 2 (see Table I). Search for PEAR1, TFPI, or GFI1B in
the browser.
Figure 6. Antisense transcripts in platelets. Shown are positive and negative strand
coverage graphs of the 5 end of CD109 and of AK124950, an antisense transcript in
′
platelets. Positive strand reads are on the top panel in blue, and negative strand reads
are in the bottom panel in red. Both the expression of CD109, and the expression of an
antisense transcript (AK124950, surrounded by the red box) transcribed from the
opposite strand of DNA at the 5 end of CD109 can be inferred when the read
′
orientation is taken into account. See it yourself: To visualize CD109 and its antisense
transcript in platelets via UCSC genome browser, follow the Web Link > Access
instructions for Site 3 or 4 (see Table I). Search for CD109 or AK124950 in the browser.
Note that only stranded data (only sites 3 or 4 of Table I) can distinguish antisense from
sense transcripts.
Figure 7. Single nucleotide resolution of GP1BA in platelets. Individual reads from
platelets from two different individuals are shown. The arrow points to the location of the
SNP rs6065, which associates with platelet counts in GWAS studies. Looking at the
individual nucleotide sequences compared to the reference sequence indicates that the
reads from the bottom individual match the reference cyotosine (reference matches are
grayed out), whereas approximately half of the reads from the top individual contain the
32
From www.bloodjournal.org by guest on June 18, 2017. For personal use only.
C to T polymorphism. See it yourself: To visualize rs6065 in GP1BA expressed in
platelets via UCSC genome browser, follow the Web Link > Access instructions for Site
2 (see Table I). Search for rs6065 and zoom into the center of the region, or
alternatively type chr17:4,836,371-4,836,392 in the search box.
Figure 8. RNA-seq facilitates discovery in platelets. The table summarizes the
various transcript features, and their respective functions, that are identifiable through
RNA-seq in platelets. Each feature in platelets can be analyzed to provide mechanistic
insights into platelet function (1), as biomarkers or modulators of disease (2), or for their
role in megakaryocyte development and function (3). As an example, the vessel in 1
depicts platelets that express two different splice variants: a predominant three exon
variant (purple-green-red) and a minor two exon variant (purple-red). In vessel 2, which
is atherosclerotic, the predominant isoform in platelets is shifted to the two exon isoform
that may serve as a biomarker for atherosclerosis and may additionally contribute to
disease pathogenesis. As depicted in (3), a combination of genetics and environmental
signals reaching the bone marrow megakaryocyte dictate the expression of the RNAs,
and the features of RNA, that are captured as the platelets are formed.
33
A Tour Through the Transcriptional Landscape of a Platelet
I. How to navigate the landscape: an RNA-seq overview
II. A panoramic view of a platelet transcript as depicted by RNA-seq
III. Hidden structures: the 5΄ cap and poly-A tail
IV. Peaks and canyons: pre-mRNAs, splicing, and intron retention
V. Regulatory regions: 5΄ and 3΄ UTRs
VI. Changing landscapes: alternative splice variants
VII. Switchbacks: antisense and non-coding RNA
VIII. A sequence level view: SNPs, insertions, and deletions
Figure 1
A
GP9 mRNA
Per base coverage
(visual level of
expression)
Individual
reads
Known transcript
annotation
Exon 1 Exon 2
Coding region
5´ UTR
B
Exon 3
3´ UTR
Chromosomal
coordinate
Per base coverage
(visual level of
expression)
Individual
reads
Known gene
annotation
GP9 mRNA
viewed in genome browser
Intron
Intron
Exon 2
Exon 1
5´ UTR
ATG
Exon 3
Coding region
TAA
3´ UTR
Figure 2
B
Oligo-dT Pull-down
7mG
TTTTTTTTTT
AAAAAAAAAAAAA
Poly(A) tail
5´ Cap Pull Down
80
Capped
Tailed
60
RPKM
A
40
20
7mG
AAAAAAAAAAAAA
0
HIST1H4B
elF4EK119A
(High affinity mutant*)
F2R (PAR1)
C
FOSB
Figure 3
GFI1b
A
Extended 5´ UTR
GFI1b
P2RY1
B
P2RY1
Extended 3´ UTR
miRNA20a-5p
miRNA93-5p
miRNA105-3p
miRNA150-3p
miRNA412-3p
miRNA552-3p
Figure 4
PEAR1
A
B
Exon 2
TFPI
iso A
iso B
GFI1B
C
Paired reads
Unannotated
exon
Figure 5
Positive strand reads ►►►
CD109
◄◄◄ Negative strand reads
Antisense transcript
Figure 6
GP1BA
T
T
T
T
T
T
PLT count
Associated GWAS SNP
(rs6065)
Figure 7
1. Insights into platelet function and phenotype
Feature identified
by RNA-seq
Molecular function
5´ cap and poly-A tail
Regulation of translation
and RNA stability
Unspliced pre-mRNA
and intron retention
Regulation of translation
Extended and
alternate 5´ and 3´
UTR
Alternative regulatory motifs:
uORFs, RNA binding proteins,
and miRNA binding sites
Known and novel
alternative splice
variants
Regulation of translation, alternate
functional protein isoforms,
truncated proteins (i.e. secreted
vs. membrane bound)
Non-coding RNA
and antisense
transcripts
Regulation of transcription and
translation
Insertions, deletions,
single nucleotide
mutations
Altered functional
proteins, altered protein
expression
2. Biomarkers and regulators of disease
3. Insights into megakaryocyte development
and function
Bone
marrow
signals
Figure 8
From www.bloodjournal.org by guest on June 18, 2017. For personal use only.
Prepublished online June 5, 2014;
doi:10.1182/blood-2014-04-512756
A tour through the transcriptional landscape of platelets
Sebastian Schubert, Andrew S. Weyrich and Jesse W. Rowley
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