Dicer and Hsp104 Function in a Negative Feedback Loop to Confer Robustness to Environmental Stress

SummaryEpigenetic mechanisms can be influenced by environmental cues and thus evoke phenotypic variation. This plasticity can be advantageous for adaptation but also detrimental if not tightly controlled. Although having attracted considerable interest, it remains largely unknown if and how environmental cues such as temperature trigger epigenetic alterations. Using fission yeast, we demonstrate that environmentally induced discontinuous phenotypic variation is buffered by a negative feedback loop that involves the RNase Dicer and the protein disaggregase Hsp104. In the absence of Hsp104, Dicer accumulates in cytoplasmic inclusions and heterochromatin becomes unstable at elevated temperatures, an epigenetic state inherited for many cell divisions after the heat stress. Loss of Dicer leads to toxic aggregation of an exogenous prionogenic protein. Our results highlight the importance of feedback regulation in building epigenetic memory and uncover Hsp104 and Dicer as homeostatic controllers that buffer environmentally induced stochastic epigenetic variation and toxic aggregation of prionogenic proteins

The highly dynamic heterochromatin protein Swi6 mediates degradation of heterochromatic transcripts

The aim of my thesis was to investigate the mechanism of heterochromatin repression mediated by heterochromatin protein Swi6 in S. pombe. Research over the years challenged the view of heterochromatin as a static and transcriptionally inert structure. Especially in fission yeast it has become clear that heterochromatin silencing requires not only the action of chromatin modifying factors, but also transcriptional activity and RNA degradation processes. Moreover, heterochromatin protein Swi6 was shown to be highly dynamic, unlike what was expected for a protein that is perceived as a major structural component of heterochromatin. Yet, the prevailing model of heterochromatin establishment and spreading is thought to occur by iterative HP1 binding to methylated H3K9 and recruitment of histone methylation activity. Driven by recent findings in our lab that described a new role for Swi6 in repression and provided a possible explanation for its dynamic behavior, I set out to investigate the mechanism in vivo by studying Swi6 dynamics. Therefore, a major focus of my PhD was to establish a suitable, robust microscopy-based method that allowed me to follow rapid dynamics and produce reliable data. The work I have done challenged the role of Swi6 in heterochromatin maintenance and spreading, but coincides with a clear involvement in sustaining tight repression. While H3K9me levels remained high in the absence of Swi6 and even spread into neighboring regions, heterochromatic transcript levels increased. These observations revealed unanticipated functions for Swi6 and made us reconsider the mechanism of Swi6-mediated silencing. As previously proposed, Swi6 could function as a co-transcriptional checkpoint that mediates RNA degradation (Keller et al., 2012). In this model RNA binds to Swi6 and gets primed for destruction as it is handed over to the RNA decay machinery, involving Cid14 and the exosome or the RNAi machinery. The target specificity depends on the epigenetic make-up of the locus, meaning the recognition of H3K9me by the CD of Swi6, while RNA binding occurs in a sequence independent manner (Keller et al., 2012). Therefore, additional processes that confer specificity, like siRNAs, are needed to ensure correct targeting of H3K9me marks to trigger Swi6-mediated turnover of unwanted RNA transcripts and not of any other random region in the genome

The human EKC/KEOPS complex is recruited to Cullin2 ubiquitin ligases by the human tumour antigen PRAME.

Contains fulltext : 103825.pdf (publisher's version ) (Open Access

HP1(Swi6) mediates the recognition and destruction of heterochromatic RNA transcripts

HP1 proteins are major components of heterochromatin, which is generally perceived to be an inert and transcriptionally inactive chromatin structure. Yet, HP1 binding to chromatin is highly dynamic and robust silencing of heterochromatic genes can involve RNA processing. Here, we demonstrate by a combination of in vivo and in vitro experiments that the fission yeast HP1(Swi6) protein guarantees tight repression of heterochromatic genes through RNA sequestration and degradation. Stimulated by positively charged residues in the hinge region, RNA competes with methylated histone H3K9 for binding to the chromodomain of HP1(Swi6). Hence, HP1(Swi6) binding to RNA is incompatible with stable heterochromatin association. We propose a model in which an ensemble of HP1(Swi6) proteins functions as a heterochromatin-specific checkpoint, capturing and priming heterochromatic RNAs for the RNA degradation machinery. Sustaining a functional checkpoint requires continuous exchange of HP1(Swi6) within heterochromatin, which explains the dynamic localization of HP1 proteins on heterochromatin

Decoding a signature-based model of transcription cofactor recruitment dictated by cardinal cis-regulatory elements in proximal promoter regions.

Genome-wide maps of DNase I hypersensitive sites (DHSs) reveal that most human promoters contain perpetually active cis-regulatory elements between -150 bp and +50 bp (-150/+50 bp) relative to the transcription start site (TSS). Transcription factors (TFs) recruit cofactors (chromatin remodelers, histone/protein-modifying enzymes, and scaffold proteins) to these elements in order to organize the local chromatin structure and coordinate the balance of post-translational modifications nearby, contributing to the overall regulation of transcription. However, the rules of TF-mediated cofactor recruitment to the -150/+50 bp promoter regions remain poorly understood. Here, we provide evidence for a general model in which a series of cis-regulatory elements (here termed 'cardinal' motifs) prefer acting individually, rather than in fixed combinations, within the -150/+50 bp regions to recruit TFs that dictate cofactor signatures distinctive of specific promoter subsets. Subsequently, human promoters can be subclassified based on the presence of cardinal elements and their associated cofactor signatures. In this study, furthermore, we have focused on promoters containing the nuclear respiratory factor 1 (NRF1) motif as the cardinal cis-regulatory element and have identified the pervasive association of NRF1 with the cofactor lysine-specific demethylase 1 (LSD1/KDM1A). This signature might be distinctive of promoters regulating nuclear-encoded mitochondrial and other particular genes in at least some cells. Together, we propose that decoding a signature-based, expanded model of control at proximal promoter regions should lead to a better understanding of coordinated regulation of gene transcription

Decoding a Signature-Based Model of Transcription Cofactor Recruitment Dictated by Cardinal Cis-Regulatory Elements in Proximal Promoter Regions

<div><p>Genome-wide maps of DNase I hypersensitive sites (DHSs) reveal that most human promoters contain perpetually active cis-regulatory elements between −150 bp and +50 bp (−150/+50 bp) relative to the transcription start site (TSS). Transcription factors (TFs) recruit cofactors (chromatin remodelers, histone/protein-modifying enzymes, and scaffold proteins) to these elements in order to organize the local chromatin structure and coordinate the balance of post-translational modifications nearby, contributing to the overall regulation of transcription. However, the rules of TF-mediated cofactor recruitment to the −150/+50 bp promoter regions remain poorly understood. Here, we provide evidence for a general model in which a series of cis-regulatory elements (here termed ‘cardinal’ motifs) prefer acting individually, rather than in fixed combinations, within the −150/+50 bp regions to recruit TFs that dictate cofactor signatures distinctive of specific promoter subsets. Subsequently, human promoters can be subclassified based on the presence of cardinal elements and their associated cofactor signatures. In this study, furthermore, we have focused on promoters containing the nuclear respiratory factor 1 (NRF1) motif as the cardinal cis-regulatory element and have identified the pervasive association of NRF1 with the cofactor lysine-specific demethylase 1 (LSD1/KDM1A). This signature might be distinctive of promoters regulating nuclear-encoded mitochondrial and other particular genes in at least some cells. Together, we propose that decoding a signature-based, expanded model of control at proximal promoter regions should lead to a better understanding of coordinated regulation of gene transcription.</p></div

Diversity of functional outcomes associated with NRF1/LSD1 recruitment at −150/+50 bp regions.

<p><i>(</i><b><i>A</i></b><i>)</i> Left: Western blot analysis of NRF1 and LSD1 in whole cell extracts obtained from MCF7 cells in which NRF1 or LSD1 (indicated on top) were depleted by siRNA. Actin is shown as loading control. Scrambled (CTL) siRNA is included as control of transfection. Panels: RT-qPCR analysis of genes that have been identified in this study as having promoters co-occupied by NRF1 and LSD1 (NRF1⁺ and LSD1⁺ targets). Gene names are indicated on top of each panel. Treatments are indicated at the bottom. Scrambled (CTL) siRNA was used as control. The y-axis refers to normalized expression to levels of <i>ACTB</i> mRNA. <i>(</i><b><i>B</i></b><i>)</i> Top: Western blot analysis as shown in <b><i>A</i></b> (left panel) but in U2OS cells. Bottom: Venn diagram depicting the overlap of genes affected by <i>NRF1</i> (light red circle) and <i>LSD1</i> (orange circle) siRNA treatments with respect to control (CTL) siRNA in U2OS cells, based on microarray. The number of total and category-wise genes affected by the treatments are indicated, as well as the statistical significance of the overlap. <i>(</i><b><i>C</i></b><i>)</i> Matrix of motif enrichment of cardinal motifs in −150/+50 bp regions of genes identified by microarray as affected by <i>NRF1</i> (left) or <i>LSD1</i> (right) knockdown in U2OS cells. Enrichment levels were determined with respect to background frequencies of the same motifs in −150/+50 bp regions. Motif enrichments higher than background are shown as a gradient of blue, while motif enrichments lower than background are shown as a gradient of red. No motif enrichment is shown as white. The number of promoters analyzed is also indicated. <i>(</i><b><i>D</i></b><i>)</i> Venn diagram depicting the overlap of microarray-identified genes classified based on their type of response to <i>NRF1</i> or <i>LSD1</i> siRNA treatments. The numbers of genes in the overlaps are indicated, as well as the numbers of genes that did not overlap and the total numbers. <i>(</i><b><i>E</i></b><i>)</i> Combined analysis of microarray and ChIP-based data. We combined microarray results identifying <i>NRF1</i> and/or <i>LSD1</i> siRNA-mediated effects and ChIP-DSL data identifying <i>NRF1</i> and/or <i>LSD1</i> occupied promoters. Gene classes based on (<i>D</i>). The y-axis refers to the relative enrichment over background in the number of genes that were affected by both <i>NRF1</i> and <i>LSD1</i> siRNA treatments and that had promoters occupied by LSD1 (orange) and/or NRF1 (red; see text for more details). A ratio of ‘fold over background’ higher or lower than 1 (>1 or <1, respectively) distinguishes when a gene class contains a higher or lower frequency of either LSD1- or NRF1-occupied promoters over the frequency observed in genes not affected by <i>LSD1</i> or <i>NRF1</i> siRNA treatments (which we defined as ‘background’). <i>(</i><b><i>F</i></b><i>)</i> As in <b><i>E</i></b>, but for genes that were affected only by either <i>NRF1</i> or <i>LSD1</i> siRNA treatments.</p

Signature of cofactors associated with the enrichment of a specific cardinal motif.

<p><i>(</i><b><i>A</i></b><i>)</i> Heatmap analysis of relative enrichment of cardinal motifs in proximal promoter regions occupied by different proteins, based on >60 ChIP-seq datasets: n = 8 TF ChIP-seq datasets (labeled in red) and n = 59 cofactors/others ChIP-seq datasets (labeled in black). Sources of ChIP-seq experiments: ENCODE (accession number and/or laboratory are included in parenthesis); and NRF1, NFYB, and LSD1 datasets were generated in this study. The vector of motif enrichment for each experiment was normalized and centered on the mean value to reveal the preferences of each experiment for cardinal motifs. The analysis shows negative log of the hypergeometric <i>p</i>-value. <i>(</i><b><i>B</i></b><i>)</i> List of cofactors associated with each cardinal motif, based on (<i>A</i>). <i>(</i><b><i>C</i></b><i>)</i> Model of signatures of cofactors associated with the presence of a specific cardinal motif (see text for details).</p

A series of cis-regulatory elements (here termed ‘cardinal’ motifs) are highly enriched at −150/+50 bp relative to TSS (+1) and may define different subsets of human promoters.

<p>(<b><i>A</i></b>) Most human promoters contain ‘open’ chromatin regions at −150/+50 bp relative to +1, or TSS. These regions are surrounded by heavily modified nucleosomes containing H3K4me2/3 (depicted in red in the vignette). We have identified the most enriched cis-regulatory elements in these particular regions by <i>de novo</i> motif discovery analysis of n = 21,000 human promoters. The panel shows rank of element enrichment, fraction of promoters containing these elements, consensus sequence, and cognate TF when known (e.g. NRF1 or NFY) or when proposed (e.g. Clus1). We refer to these elements as ‘cardinal’ motifs, and to the TFs that recognize them as ‘cardinal’ TFs. (<b><i>B</i></b>) Analysis of motif co-occurrences among cardinal motifs. <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003906#s2" target="_blank">Results</a> are shown as a matrix of co-occurrences based on the analysis of n = 21,000 human promoters (−150/+50 bp). Co-occurrence log₂<i>p</i>-values are shown as a gradient of blue-to-red for positive-to-negative co-occurrences, and as white in the absence of significant co-occurrence. (<b><i>C</i></b>) Positional binding analysis of cardinal TFs NRF1 (red) and NFYB (blue) with respect to −150/+50 bp genomic regions in MCF7 cells, based on ChIP-seq data. The x-axis refers to genomic distances with respect to −150/+50 bp (center of the panel). Genomic windows span: 200 bp (between −150/+50 bp and ±2 kb), 1 kb (between ±2 kb and ±10 kb), and the rest of distances together (beyond ±10 kb). The y-axis refers to percentage of the total of NRF1 and NFYB peaks in each genomic range. The total number of peaks (n) and the specific number of peaks within −150/+50 bp regions (n) are also indicated in the panel. (<b><i>D</i></b>) Meta-analysis of sequencing read density based on DNaseI-seq (top) and H3K4me2 MNase-seq (bottom) around NRF1 (red) and NFYB (blue) ChIP-seq peaks (both at the center of the panel). (<b><i>E</i></b>) Venn diagram depicting the overlap of RNA PolII (grey circle), NRF1 (red circle), and NFYB (blue circle) ChIP-seq peaks in MCF7 cells. We considered as ‘overlap’ the coincidence of NRF1 and NFYB peaks in the same −150/+50 bp region. Also, we considered as ‘overlap’ the coincidence of RNA PolII peaks within ±1 kb of a TSS containing NRF1 or NFYB peaks at −150/+50 bp. <i>(</i><b><i>F</i></b><i>)</i> Functional (gene ontology, or GO) analysis of genes with NRF1 (top) or NFYB (bottom) ChIP-seq peaks in their −150/+50 bp regions. <i>P</i>-values (log scale) are shown in the <i>x</i>-axis. GO terms are indicated in the y-axis.</p