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Set1 and MLL1/2 Target Distinct Sets of Functionally Different Genomic Loci In Vivo.

ABSTRACT: Histone H3 lysine 4 trimethylation (H3K4me3) is known to correlate with both active and poised genomic loci, yet many questions remain regarding its functional roles in vivo. We identify functional genomic targets of two H3K4 methyltransferases, Set1 and MLL1/2, in both the stem cells and differentiated tissue of the planarian flatworm Schmidtea mediterranea. We show that, despite their common substrate, these enzymes target distinct genomic loci in vivo, which are distinguishable by the pattern each enzyme leaves on the chromatin template, i.e., the breadth of the H3K4me3 peak. Whereas Set1 targets are largely associated with the maintenance of the stem cell population, MLL1/2 targets are specifically enriched for genes involved in ciliogenesis. These data not only confirm that chromatin regulation is fundamental to planarian stem cell function but also provide evidence for post-embryonic functional specificity of H3K4me3 methyltransferases in vivo.

SUBMITTER: Duncan EM

PROVIDER: S-EPMC4707048 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Set1 and MLL1/2 Target Distinct Sets of Functionally Different Genomic Loci In Vivo.

Duncan Elizabeth M EM Chitsazan Alex D AD Seidel Chris W CW Sánchez Alvarado Alejandro A

Cell reports 20151217 12

Histone H3 lysine 4 trimethylation (H3K4me3) is known to correlate with both active and poised genomic loci, yet many questions remain regarding its functional roles in vivo. We identify functional genomic targets of two H3K4 methyltransferases, Set1 and MLL1/2, in both the stem cells and differentiated tissue of the planarian flatworm Schmidtea mediterranea. We show that, despite their common substrate, these enzymes target distinct genomic loci in vivo, which are distinguishable by the pattern ...[more]

PMID: 26711341

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Project description:BackgroundHigh-throughput sequencing can identify numerous potential genomic targets for microbial strain typing, but identification of the most informative combinations requires the use of computational screening tools. This paper describes novel software-- Automated Selection of Typing Target Subsets (AuSeTTS)--that allows intelligent selection of optimal targets for pathogen strain typing. The objective of this software is to maximise both discriminatory power, using Simpson's index of diversity (D), and concordance with existing typing methods, using the adjusted Wallace coefficient (AW). The program interrogates molecular typing results for panels of isolates, based on large target sets, and iteratively examines each target, one-by-one, to determine the most informative subset.ResultsAuSeTTS was evaluated using three target sets: 51 binary targets (13 toxin genes, 16 phage-related loci and 22 SCCmec elements), used for multilocus typing of 153 methicillin-resistant Staphylococcus aureus (MRSA) isolates; 17 MLVA loci in 502 Streptococcus pneumoniae isolates from the MLVA database (http://www.mlva.eu) and 12 MLST loci for 98 Cryptococcus spp. isolates.The maximum D for MRSA, 0.984, was achieved with a subset of 20 targets and a D value of 0.954 with 7 targets. Twelve targets predicted MLST with a maximum AW of 0.9994. All 17 S. pneumoniae MLVA targets were required to achieve maximum D of 0.997, but 4 targets reached D of 0.990. Twelve targets predicted pneumococcal serotype with a maximum AW of 0.899 and 9 predicted MLST with maximum AW of 0.963. Eight of the 12 MLST loci were sufficient to achieve the maximum D of 0.963 for Cryptococcus spp.ConclusionsComputerised analysis with AuSeTTS allows rapid selection of the most discriminatory targets for incorporation into typing schemes. Output of the program is presented in both tabular and graphical formats and the software is available for free download from http://www.cidmpublichealth.org/pages/ausetts.html.

Project description:BackgroundCDK9 is the catalytic subunit of the Positive Transcription Elongation Factor b (P-TEFb), which phosphorylates the CTD of RNAPII and negative elongation factors enabling for productive elongation after initiation. CDK9 associates with T-type cyclins and cyclin K and its activity is tightly regulated in cells at different levels. CDK9 is also the catalytic subunit of TAK (Tat activating Kinase), essential for HIV1 replication. Because of CDK9's potential as a therapeutic target in AIDS, cancer, inflammation, and cardiomyophathy it is important to understand the consequences of CDK9 inhibition. A previous gene expression profiling study performed with human glioblastoma T98G cells in which CDK9 activity was inhibited either with a dominant negative mutant form of CDK9 (dnCDK9) or the pharmacological inhibitor Flavopiridol unveiled striking differences in gene expression effects. In the present report we extended these studies by (1) using both immortalized normal human fibroblasts and primary human astrocytes, (2) eliminating potential experimental variability due to transduction methodology and (3) also modulating CDK9 activity with siRNA.FindingsStriking differences in the effects on gene expression resulting from the strategy used to inhibit CDK9 activity (dnCDK9 or FVP) remain even when potential variability due to viral transduction is eliminated. siRNA mediated CDK9 knockdown in human fibroblasts and astrocytes efficiently reduced CDK9 expression and led to potent changes in gene expression that exhibit little correlation with the effects of dnCDK9 or FVP. Interestingly, HEXIM1 a validated CDK9 target gene, was found to be potently downregulated by dnCDK9, FVP and siCDK9, but the cluster of genes with expression profiles similar to HEXIM1 was small. Finally, cluster analysis of all treatments revealed higher correlation between treatments than cell type origin.ConclusionThe nature of the strategy used to inhibit CDK9 profoundly affects the patterns of gene expression resulting from CDK9 inhibition. These results suggest multiple variables that affect outcome, including kinetics of inhibition, potency, off-target effects, and selectivity issues. This is particularly important when considering CDK9 as a potential target for therapeutic intervention.

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Set1 and MLL1/2 Target Distinct Sets of Functionally Different Genomic Loci In Vivo.

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Set1 and MLL1/2 Target Distinct Sets of Functionally Different Genomic Loci In Vivo.

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