Differential Contribution of Steady-State RNA and Active Transcription in Chromatin Organization
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ABSTRACT:
PROVIDER: PRJNA470985 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7151830_1.fastq.gz | Fastqsanger.gz | |||
| SRR7151830_2.fastq.gz | Fastqsanger.gz | |||
| SRR7151831_1.fastq.gz | Fastqsanger.gz | |||
| SRR7151831_2.fastq.gz | Fastqsanger.gz | |||
| SRR7151832_1.fastq.gz | Fastqsanger.gz |
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Project description:Nuclear RNA and the act of transcription have been implicated in nuclear organization. However, their global contribution to shaping fundamental features of higher-order genome structure such as topologically associated domains (TADs) and genomic compartments remains unclear. To investigate these questions, we perform Hi-C genome-wide chromatin conformation capture in the presence and absence of RNase before and after crosslinking, or a transcriptional inhibitor. TAD boundaries are largely unaffected by RNase treatment, whereas transcriptional inhibition leads to higher inter-TAD interactions. Collectively, our findings demonstrate differences in the relative contribution of RNA and transcription to the formation of TAD boundaries detected by the widely used Hi-C methodology.
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Project description:Nuclear RNA and the act of transcription have been implicated in nuclear organization. However, their global contribution to shaping fundamental features of higher-order chromatin organization such as topologically associated domains (TADs) and genomic compartments remains unclear. To investigate these questions, we perform genome-wide chromatin conformation capture (Hi-C) analysis in the presence and absence of RNase before and after crosslinking, or a transcriptional inhibitor. TAD boundaries are largely unaffected by RNase treatment, although a subtle disruption of compartmental interactions is observed. In contrast, transcriptional inhibition leads to weaker TAD boundary scores. Collectively, our findings demonstrate differences in the relative contribution of RNA and transcription to the formation of TAD boundaries detected by the widely used Hi-C methodology.
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Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA. Strand specific expression profiling by high throughput sequencing.
2012-09-13 | E-GEOD-34473 | biostudies-arrayexpress
Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA. H3K36me3 ChIP followed by deep sequencing // time-series of mRNA measurement on Affymetrix microarray platform following actinomycinD treatment. MmES Samples: mRNA measurement on Affymetrix microarray platform following thioU labeling and separation of fractions according to labeled (newly synthetised) and unlabeled (pre-existing) RNA.
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Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA.
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Project description:Messenger RNA levels in eukaryotes are balanced by two consecutive regulatory layers. Primary, transcriptional regulation at the level of chromatin and secondary, post-transcriptional regulation of the initial transcript in the cytoplasm. Each layer is individually studied in mechanistic detail, while integration of both processes is required to quantify the individual contribution to steady-state RNA levels. Here we show that chromatin features are sufficient to model transcription rate but with different sensitivities in dividing versus post mitotic cells. In both cases chromatin derived transcript levels explains over 80% of variance in measured RNA level enabling to separate transcription from different post-transcriptional processes. By further inclusion of measurements of mRNA half-life and micro RNA expression data we identify a low quantitative contribution of RNA decay by either micro RNA or general differential turnover to final mRNA levels. Together this establishes a chromatin based quantitative model for the contribution of transcriptional and posttranscriptional processes to steady-state levels of messenger RNA.
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Project description:Macrophages populate the steady-state myocardium. Previously, all macrophages were thought to arise from monocytes; however, it emerged that, in several organs, tissue-resident macrophages may self-maintain through local proliferation.Our aim was to study the contribution of monocytes to cardiac-resident macrophages in steady state, after macrophage depletion in CD11b(DTR/+) mice and in myocardial infarction.Using in vivo fate mapping and flow cytometry, we estimated that during steady state the heart macrophage population turns over in ?1 month. To explore the source of cardiac-resident macrophages, we joined the circulation of mice using parabiosis. After 6 weeks, we observed blood monocyte chimerism of 35.3±3.4%, whereas heart macrophages showed a much lower chimerism of 2.7±0.5% (P<0.01). Macrophages self-renewed locally through proliferation: 2.1±0.3% incorporated bromodeoxyuridine 2 hours after a single injection, and 13.7±1.4% heart macrophages stained positive for the cell cycle marker Ki-67. The cells likely participate in defense against infection, because we found them to ingest fluorescently labeled bacteria. In ischemic myocardium, we observed that tissue-resident macrophages died locally, whereas some also migrated to hematopoietic organs. If the steady state was perturbed by coronary ligation or diphtheria toxin-induced macrophage depletion in CD11b(DTR/+) mice, blood monocytes replenished heart macrophages. However, in the chronic phase after myocardial infarction, macrophages residing in the infarct were again independent from the blood monocyte pool, returning to the steady-state situation.In this study, we show differential contribution of monocytes to heart macrophages during steady state, after macrophage depletion or in the acute and chronic phase after myocardial infarction. We found that macrophages participate in the immunosurveillance of myocardial tissue. These data correspond with previous studies on tissue-resident macrophages and raise important questions on the fate and function of macrophages during the development of heart failure.
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Project description:Characterization of hematopoietic stem cells (HSCs) has advanced largely owing to transplantation assays, in which the developmental potential of HSCs is assessed generally in nonhomeostatic conditions. These studies established that adult HSCs extensively contribute to multilineage hematopoietic regeneration upon transplantation. On the contrary, recent studies performing lineage tracing of HSCs under homeostatic conditions have shown that adult HSCs may contribute far less to steady-state hematopoiesis than would be anticipated based on transplantation assays. Here, we used 2 independent HSC-lineage-tracing models to examine the contribution of adult HSCs to steady-state hematopoiesis. We show that adult HSCs contribute robustly to steady-state hematopoiesis, exhibiting faster efflux toward the myeloid lineages compared with lymphoid lineages. Platelets were robustly labeled by HSCs, reaching the same level of labeling as HSCs by 1 year of chase. Our results support the view that adult HSCs contribute to the continuous influx of blood cells during steady-state hematopoiesis.
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Project description:The â??small perturbationâ?? approach is critical in studying the â??steady stateâ?? of a biological system. In our experiments, small perturbations were generated by applying a series of repeating intermittent small doses of ultraviolet radiation to a human keratinocyte cell line, HaCaT. The biological response was assessed by monitoring the gene expression profiles using a high reliability and high resolution cDNA microarray system. Following intermittent 10 J/m2 UVB small perturbations, two opposite classes of genes, down-regulated and up-regulated, exhibited an immediate response followed by relaxation between each small perturbation, but were prolonged down- or up-regulated without relaxation while larger doses (233 or 582.5 J/m2) of UVB were applied. A repeated cycle pattern of gene expression following small perturbations is an indication of the existence of steady states. This cycle pattern is suppressed when large perturbations are applied. We believe that this is a universal phenomenon. In our experiments, the functions of up-regulated genes were mainly associated with anti-proliferation, anti-mitogenesis, and apoptosis. On the other hand, down-regulated genes were mainly related to proliferation, mitogenesis, and anti-apoptosis. In conclusion, this study experimentally proves the concept of steady state at the transcription level and demonstrates the feasibility of using small perturbation approaches for investigating steady states. This study could also set a foundation of computational systems biology, which has implicitly used the concept of steady state. Keywords: time course Three UVB exposures are indicated by UV at time points of 0, 8 and 16 hours. T1, T2, T3, T4, T5, and T6 denote the sampling time points. T1, T3 and T5 are allocated 30 minutes after the corresponding UVB irradiation. T2, T4 and T6 are allocated 8 hours after each UVB irradiation. At each sampling time point, two samples (control and UV-irradiated) are collected. See supplementary file Loop_design.pdf for further explanation.
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