Homo sapiens
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| DRR228495_1.fastq.gz | Fastqsanger.gz | |||
| DRR228495_2.fastq.gz | Fastqsanger.gz | |||
| DRR228496_1.fastq.gz | Fastqsanger.gz | |||
| DRR228496_2.fastq.gz | Fastqsanger.gz | |||
| DRR228497_1.fastq.gz | Fastqsanger.gz |
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Project description:Study to investigate the role of histone residues H3K4 and H3K36 for gene expression, histone localization and neuronal lineage specification by mutation of K4 and K36 in H3.3 to alanine. Histone variant H3.3 differs from the canonical H3.1/H3.2 by only 4 to 5 amino acids, which are necessary for nucleosome assembly independent of DNA replication, and is encoded by two gene copies. Complete loss of the two H3.3 genes (H3f3a and H3f3b) leads to embryonic lethality while single gene knockout yields viable mice. We used CRISPR-Cas9 to delete H3f3a and introduce homozygous point-mutations into H3f3b, thus ensuring that the entire pool of H3.3 protein carries the mutation of interest. We differentiated H3.3ctrl (H3f3a knock-out; H3f3b wild type), H3.3K4A mutant (H3f3a knock-out; H3f3b K4A) and H3.3K36A mutant (H3f3a knock-out; H3f3b K36A) ESCs into glutamatergic neurons. To assess the effect of the K4A mutation on Pol II activity, nascent RNA levels were mesaured by PRO-seq.
2020-01-28 | E-MTAB-8617 | biostudies-arrayexpress
Project description:Study to investigate the role of histone residues H3K4 and H3K36 for gene expression, histone localization and neuronal lineage specification by mutation of K4 and K36 in H3.3 to alanine. Histone variant H3.3 differs from the canonical H3.1/H3.2 by only 4 to 5 amino acids, which are necessary for nucleosome assembly independent of DNA replication, and is encoded by two gene copies. Complete loss of the two H3.3 genes (H3f3a and H3f3b) leads to embryonic lethality while single gene knockout yields viable mice. We used CRISPR-Cas9 to delete H3f3a and introduce homozygous point-mutations into H3f3b, thus ensuring that the entire pool of H3.3 protein carries the mutation of interest. We differentiated H3.3ctrl (H3f3a knock-out; H3f3b wild type), H3.3K4A mutant (H3f3a knock-out; H3f3b K4A) and H3.3K36A mutant (H3f3a knock-out; H3f3b K36A) ESCs into glutamatergic neurons. Gene expression profiles were measured by mRNA-Sequencing in undifferentiated ESCs (D0), neurodevelopment (D8) and differentiated neurons (D12) to assess the impact of the mutation on gene expression and development.
2020-01-28 | E-MTAB-6821 | biostudies-arrayexpress
Project description:Study to investigate the role of histone residues H3K4 and H3K36 for gene expression, histone localization and neuronal lineage specification by mutation of K4 and K36 in H3.3 to alanine. Histone variant H3.3 differs from the canonical H3.1/H3.2 by only 4 to 5 amino acids, which are necessary for nucleosome assembly independent of DNA replication, and is encoded by two gene copies. Complete loss of the two H3.3 genes (H3f3a and H3f3b) leads to embryonic lethality while single gene knockout yields viable mice. We used CRISPR-Cas9 to delete H3f3a and introduce homozygous point-mutations into H3f3b, thus ensuring that the entire pool of H3.3 protein carries the mutation of interest. We differentiated H3.3ctrl (H3f3a knock-out; H3f3b wild type), H3.3K4A mutant (H3f3a knock-out; H3f3b K4A) and H3.3K36A mutant (H3f3a knock-out; H3f3b K36A) ESCs into glutamatergic neurons. Genomic localization of H3.3 protein was determined by ChIP-Sequencing in ESCs (D0). Histone modifications patterns of H3K4me1, H3K4me3 and H3K27ac were measured by ChIP-Sequencing in ESCs (D0) to assess the impact of the H3.3K4A mutation on the epigenetic landscape. Levels of H3K36me3 were measured by ChIP-Sequencing in WT and H3.3K36A mutant ESCs (D0), NPCs (D8) and neurons (D12) to assess the impact of the H3.3K36A mutation on H3K36me3 levels in development.
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Project description:Study to investigate the role of histone residues H3K4 and H3K36 for gene expression, histone localization and neuronal lineage specification by mutation of K4 and K36 in H3.3 to alanine. Histone variant H3.3 differs from the canonical H3.1/H3.2 by only 4 to 5 amino acids, which are necessary for nucleosome assembly independent of DNA replication, and is encoded by two gene copies. Complete loss of the two H3.3 genes (H3f3a and H3f3b) leads to embryonic lethality while single gene knockout yields viable mice. We used CRISPR-Cas9 to delete H3f3a and introduce homozygous point-mutations into H3f3b, thus ensuring that the entire pool of H3.3 protein carries the mutation of interest. We differentiated H3.3ctrl (H3f3a knock-out; H3f3b wild type), H3.3K4A mutant (H3f3a knock-out; H3f3b K4A) and H3.3K36A mutant (H3f3a knock-out; H3f3b K36A) ESCs into glutamatergic neurons. Genomic localization of H3.3 protein was determined by ChIP-Sequencing in ESCs (D0). Distribution patterns of RNA Polymerase II Phosphorylated on Serine 5 (RNA Pol II Ser5P), of histone modification H3K27me3 and chromatin remodeler components Brg1/Smarca4 (Swi/Snf) and Chd4 (NuRD) were measured by ChIP-Sequencing in ESCs (D0) to assess the impact of the H3.3K4A mutation on the epigenetic landscape. Distribution patterns of H3.3 were assessed by ChIP-Sequencing in HEK293T cells after depletion of Brg1/Smarca4 (Swi/Snf) and Chd4 (NuRD).
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Project description:Stability Analysis of a Mathematical Model for Glioma-Immune Interaction under Optimal Therapy
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Abstract
We investigate a mathematical model using a system of coupled ordinary differential equations, which describes the interplay of malignant glioma cells, macrophages, glioma specific CD8+T cells and the immunotherapeutic drug Adoptive Cellular Immunotherapy (ACI). To better understand under what circumstances the glioma cells can be eliminated, we employ the theory of optimal control. We investigate the dynamics of the system by observing biologically feasible equilibrium points and their stability analysis before administration of the external therapy ACI. We solve an optimal control problem with an objective functional which minimizes the glioma cell burden as well as the side effects of the treatment. We characterize our optimal control in terms of the solutions to the optimality system, in which the state system coupled with the adjoint system. Our model simulation demonstrates that the strength of treatment u1(t) plays an important role to eliminate the glioma cells. Finally, we derive an optimal treatment strategy and then solve it numerically.
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