Genome-wide analysis of mRNA decay in resting and activated primary human T lymphocytes - PubMed (original) (raw)
Comparative Study
. 2002 Dec 15;30(24):5529-38.
doi: 10.1093/nar/gkf682.
Affiliations
- PMID: 12490721
- PMCID: PMC140061
- DOI: 10.1093/nar/gkf682
Comparative Study
Genome-wide analysis of mRNA decay in resting and activated primary human T lymphocytes
Arvind Raghavan et al. Nucleic Acids Res. 2002.
Abstract
We used microarray technology to measure mRNA decay rates in resting and activated T lymphocytes in order to better understand the role of mRNA decay in regulating gene expression. Purified human T lymphocytes were stimulated for 3 h with medium alone, with an anti-CD3 antibody, or with a combination of anti-CD3 and anti-CD28 antibodies. Actinomycin D was added to arrest transcription, and total cellular RNA was collected at discrete time points over a 2 h period. RNA from each point was analyzed using Affymetrix oligonucleotide arrays and a first order decay model was used to determine the half-lives of approximately 6000 expressed transcripts. We identified hundreds of short-lived transcripts encoding important regulatory proteins including cytokines, cell surface receptors, signal transduction regulators, transcription factors, cell cycle regulators and regulators of apoptosis. Approximately 100 of these short-lived transcripts contained ARE-like sequences. We also identified numerous transcripts that exhibited stimulus-dependent changes in mRNA decay. In particular, we identified hundreds of transcripts whose steady-state levels were repressed following T cell activation and were either unstable in the resting state or destabilized following cellular activation. Thus, rapid mRNA degradation appears to be an important mechanism for turning gene expression off in an activation-dependent manner.
Figures
Figure 1
Comparison of transcript half-lives determined by northern blot or microarrays. Purified human T lymphocytes were stimulated for 3 h with medium or αCD3+αCD28. Act D was added and total cellular RNA was then isolated at the 0, 45, 90 and 120 min time points. Expression of TNFSF14, MAD-3 and p27kip1 was evaluated by northern blot. Each plot was also probed for GAPDH expression. The blots were quantified using a phosphorimager and the intensity of each band was normalized to the intensity of the GAPDH band. mRNA decay curves were derived for each transcript and were used to calculate transcript half-lives. Transcript half-life values derived using microarrays are also shown. The Affymetrix probe IDs for TNFSF14, MAD-3 and p27kip1 are 31724_at, 1461_at and 33847_s_at, respectively.
Figure 2
Profile of T lymphocyte transcript half-lives. (A) Purified human T lymphocytes were stimulated for 3 h with medium, αCD3 or αCD3+αCD28. Act D was added and total cellular RNA was isolated at discrete time points over a 2 h period. This RNA was used to probe Affymetrix microarrays in order to calculate mRNA half-lives. Transcripts with an Affymetrix ‘present’ call in at least three of four experiments under each stimulation condition were categorized by their median half-life value into five intervals. The median half-life values were calculated based on data from four independent experiments. The data is shown as a percentage of transcripts expressed under each stimulation condition. (B) The subset of transcripts that exhibited 5-fold or greater induction upon stimulation with αCD3 and αCD3+αCD28 were profiled by median half-life values.
Figure 3
Short-lived transcripts whose steady-state levels were induced or repressed upon T cell activation. Purified human T lymphocytes were stimulated for 3 h with medium or αCD3+αCD28. Act D was added and total cellular RNA was isolated at the 0, 45, 90 and 120 min time points. This RNA was used to probe Affymetrix microarrays. The data shown is from an individual experiment and shows raw hybridization intensity (AD) data for 200 short-lived transcripts that were induced or repressed following αCD3+αCD28 stimulation. The intensity data is represented by a color scale, showing low intensity in green and high intensity in red.
Similar articles
- Patterns of coordinate down-regulation of ARE-containing transcripts following immune cell activation.
Raghavan A, Dhalla M, Bakheet T, Ogilvie RL, Vlasova IA, Khabar KS, Williams BR, Bohjanen PR. Raghavan A, et al. Genomics. 2004 Dec;84(6):1002-13. doi: 10.1016/j.ygeno.2004.08.007. Genomics. 2004. PMID: 15533717 - T-cell proliferation involving the CD28 pathway is associated with cyclosporine-resistant interleukin 2 gene expression.
June CH, Ledbetter JA, Gillespie MM, Lindsten T, Thompson CB. June CH, et al. Mol Cell Biol. 1987 Dec;7(12):4472-81. doi: 10.1128/mcb.7.12.4472-4481.1987. Mol Cell Biol. 1987. PMID: 2830495 Free PMC article. - Interleukin-15 differentially enhances the expression of interferon-gamma and interleukin-4 in activated human (CD4+) T lymphocytes.
Borger P, Kauffman HF, Postma DS, Esselink MT, Vellenga E. Borger P, et al. Immunology. 1999 Feb;96(2):207-14. doi: 10.1046/j.1365-2567.1999.00679.x. Immunology. 1999. PMID: 10233697 Free PMC article. - Inhibiting transcription in cultured metazoan cells with actinomycin D to monitor mRNA turnover.
Lai WS, Arvola RM, Goldstrohm AC, Blackshear PJ. Lai WS, et al. Methods. 2019 Feb 15;155:77-87. doi: 10.1016/j.ymeth.2019.01.003. Epub 2019 Jan 6. Methods. 2019. PMID: 30625384 Free PMC article. Review. - RNA Metabolism in T Lymphocytes.
Choi JO, Ham JH, Hwang SS. Choi JO, et al. Immune Netw. 2022 Oct 7;22(5):e39. doi: 10.4110/in.2022.22.e39. eCollection 2022 Oct. Immune Netw. 2022. PMID: 36381959 Free PMC article. Review.
Cited by
- Nuclear export is a limiting factor in eukaryotic mRNA metabolism.
Müller JM, Moos K, Baar T, Maier KC, Zumer K, Tresch A. Müller JM, et al. PLoS Comput Biol. 2024 May 16;20(5):e1012059. doi: 10.1371/journal.pcbi.1012059. eCollection 2024 May. PLoS Comput Biol. 2024. PMID: 38753883 Free PMC article. - GCLiPP: global crosslinking and protein purification method for constructing high-resolution occupancy maps for RNA binding proteins.
Zhu WS, Litterman AJ, Sekhon HS, Kageyama R, Arce MM, Taylor KE, Zhao W, Criswell LA, Zaitlen N, Erle DJ, Ansel KM. Zhu WS, et al. Genome Biol. 2023 Dec 7;24(1):281. doi: 10.1186/s13059-023-03125-2. Genome Biol. 2023. PMID: 38062486 Free PMC article. - Variability of the innate immune response is globally constrained by transcriptional bursting.
Alachkar N, Norton D, Wolkensdorfer Z, Muldoon M, Paszek P. Alachkar N, et al. Front Mol Biosci. 2023 Jun 27;10:1176107. doi: 10.3389/fmolb.2023.1176107. eCollection 2023. Front Mol Biosci. 2023. PMID: 37441161 Free PMC article. - Post-transcriptional checkpoints in autoimmunity.
Bechara R, Vagner S, Mariette X. Bechara R, et al. Nat Rev Rheumatol. 2023 Aug;19(8):486-502. doi: 10.1038/s41584-023-00980-y. Epub 2023 Jun 13. Nat Rev Rheumatol. 2023. PMID: 37311941 Review. - Pervasive effects of RNA degradation on Nanopore direct RNA sequencing.
Prawer YDJ, Gleeson J, De Paoli-Iseppi R, Clark MB. Prawer YDJ, et al. NAR Genom Bioinform. 2023 Jun 9;5(2):lqad060. doi: 10.1093/nargab/lqad060. eCollection 2023 Jun. NAR Genom Bioinform. 2023. PMID: 37305170 Free PMC article.
References
- Malter J.S. (1998) Posttranscriptional regulation of mRNAs important in T cell function. Adv. Immunol., 68, 1–49. - PubMed
- Pearson P.L. and Van der Luijt,R.B. (1998) The genetic analysis of cancer. J. Intern. Med., 243, 413–417. - PubMed
- Raymond V., Atwater,J.A. and Verma,I.M. (1989) Removal of an mRNA destabilizing element correlates with the increased oncogenicity of proto-oncogene fos. Oncogene Res., 5, 1–12. - PubMed
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources