The complex transcriptional landscape of the anucleate human platelet
Open accessAbstract
BACKGROUND: Human blood platelets are essential to maintaining normal hemostasis, and platelet dysfunction often causes bleeding or thrombosis. Estimates of genome-wide platelet RNA expression using microarrays have provided insights to the platelet transcriptome but were limited by the number of known transcripts. The goal of this effort was to deep-sequence RNA from leukocyte-depleted platelets to capture the complex profile of all expressed transcripts. RESULTS: From each of four healthy individuals we generated long RNA (≥40 nucleotides) profiles from total and ribosomal-RNA depleted RNA preparations, as well as short RNA (<40 nucleotides) profiles. Analysis of ~1 billion reads revealed that coding and non-coding platelet transcripts span a very wide dynamic range (≥16 PCR cycles beyond β-actin), a result we validated through qRT-PCR on many dozens of platelet messenger RNAs. Surprisingly, ribosomal-RNA depletion significantly and adversely affected estimates of the relative abundance of transcripts. Of the known protein-coding loci, ~9,500 are present in human platelets. We observed a strong correlation between mRNAs identified by RNA-seq and microarray for well-expressed mRNAs, but RNASeq identified many more transcripts of lower abundance and permitted discovery of novel transcripts. CONCLUSIONS: Our analyses revealed diverse classes of non-coding RNAs, including: pervasive antisense transcripts to protein-coding loci; numerous, previously unreported and abundant microRNAs; retrotransposons; and thousands of novel un-annotated long and short intronic transcripts, an intriguing finding considering the anucleate nature of platelets. The data are available through a local mirror of the UCSC genome browser and can be accessed at: http://cm.jefferson.edu/platelets_2012/.
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The CRBN E3 ligase and its associated E1 (UBA1) and E2 (SFT) enzymes are poorly expressed in human platelets.
“By analyzing previously published human platelet RNA sequencing data, we found that the CRBN E3 ligase and its associated E1 (UBA1) and E2 (SFT) enzymes are poorly expressed in human platelets34,35.”
From Using proteolysis-targeting chimera technology to reduce navitoclax platelet toxicity and improve its senolytic activity (2020) · cites this paper as [34]
PZ is a Bcl-xl specific PROTAC that targets Bcl-xl to the E3 ligase cereblon (CRBN), which is poorly expressed in platelets.
“Here, we report the use of PROTAC technology to reduce ABT263 on-target toxicity by converting ABT263 into PZ15227 (PZ), a Bcl-xl specific PROTAC (Bcl-xl-P), which targets Bcl-xl to the E3 ligase cereblon (CRBN) that is poorly expressed in platelets34,35.”
From Using proteolysis-targeting chimera technology to reduce navitoclax platelet toxicity and improve its senolytic activity (2020) · cites this paper as [34]
PZ selectively induced Bcl-xl degradation in non-senescent cells and senescent cells, but not platelets, because it targeted Bcl-xl to the CRBN E3 ligase that is poorly expressed in platelets but abundantly expressed in other cells.
“The resulting Bcl-xl-specific PROTAC lead compound, PZ, selectively induced Bcl-xl degradation in NCs and SCs, but not platelets, because it targeted Bcl-xl to the CRBN E3 ligase that is poorly expressed in platelets but abundantly expressed in other cells34,35.”
From Using proteolysis-targeting chimera technology to reduce navitoclax platelet toxicity and improve its senolytic activity (2020) · cites this paper as [34]
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Self-citationType 2 diabetes mellitus and cardiovascular disease: Genetic and epigenetic links
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Comparative Genomics of Two Sequential <i>Candida glabrata</i> Clinical Isolates
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- Authors
- Bray, P. F., McKenzie, S. E., Edelstein, L. C., Nagalla, S., Delgrosso, K., Ertel, A., Kupper, J., Jing, Y., Londin, E., Loher, P., Chen, H.-W., Fortina, P., & Rigoutsos, I.
- Affiliations
- Thomas Jefferson University (US)
- Corresponding author
- Bray, Paul F
- Rigoutsos, Isidore
- ORCID
- Venue
- BMC Genomics
- Published
- 2013
- Volume
- 14
- Issue
- 1
- Pages
- 1
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC3722126
- OpenAlex
- W2168990146
- Cited by
- 802
- References
- 78
- Citations by year
- 2026: 26 · 2025: 85 · 2024: 59 · 2023: 74 · 2022: 77 · 2021: 77 · 2020: 65 · 2019: 51 · 2018: 52 · 2017: 109 · 2016: 73 · 2015: 28 · 2014: 17 · 2013: 8
- FWCI
- 18.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The Medicine Forum (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- W. M. Keck Foundation
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL102482)
- National Cancer Institute (P30CA056036)
- National Institute of Allergy and Infectious Diseases (U19AI056363)
- National Institute on Minority Health and Health Disparities (R01MD007880)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Health Sciences › Medicine › Hematology › Platelet Disorders and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- platelet transcriptome, ribosomal RNA depletion, non-coding RNA, antisense transcripts, microRNA, retrotransposons, novel transcripts, platelet dysfunction, hemostasis
- MeSH terms
- Blood Platelets, Cell Nucleus, Humans, Introns, Pseudogenes, RNA, Messenger, RNA, Ribosomal, Transcription, Genetic, RNA, Antisense, Sequence Analysis, RNA, Reverse Transcriptase Polymerase Chain Reaction, Internet, Genomics, Data Mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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