H2A.Z marks antisense promoters and has positive effects on antisense transcript levels in budding yeast
© Gu et al.; licensee BioMed Central. 2015
Received: 10 December 2014
Accepted: 15 January 2015
Published: 19 February 2015
The histone variant H2A.Z, which has been reported to have both activating and repressive effects on gene expression, is known to occupy nucleosomes at the 5' ends of protein-coding genes.
We now find that H2A.Z is also significantly enriched in gene coding regions and at the 3' ends of genes in budding yeast, where it co-localises with histone marks associated with active promoters. By comparing H2A.Z binding to global gene expression in budding yeast strains engineered so that normally unstable transcripts are abundant, we show that H2A.Z is required for normal levels of antisense transcripts as well as sense ones. High levels of H2A.Z at antisense promoters are associated with decreased antisense transcript levels when H2A.Z is deleted, indicating that H2A.Z has an activating effect on antisense transcripts. Decreases in antisense transcripts affected by H2A.Z are accompanied by increased levels of paired sense transcripts.
The effect of H2A.Z on protein coding gene expression is a reflection of its importance for normal levels of both sense and antisense transcripts.
Chromatin components are key regulators of global gene expression. H2A.Z is a highly conserved histone variant that replaces H2A in a subset of nucleosomes, most prominently those that flank the transcription start sites (TSSs) of protein-coding genes (reviewed in ). TSS-adjacent H2A.Z localization is suggestive of a role in transcriptional regulation and indeed H2A.Z has been implicated in gene regulation in multiple organisms [2-5]. As H2A.Z is essential for normal development [6-8] and its over-expression is associated with poor patient prognosis in human cancers [9,10], it is important to understand how H2A.Z contributes to the regulation of gene expression.
Previous studies linking H2A.Z to transcription have primarily focused on protein-coding genes. It has recently become clear that eukaryotic transcriptomes are a complex mixture of coding and non-coding transcripts, with many transcripts being rapidly turned over by RNA-processing machinery such as the exosome (reviewed in [11,12]). In S. cerevisiae, such cryptic unstable transcripts often originate from the 5′ nucleosome-depleted-region (NDR) of a downstream tandemly arranged gene and can be detected in the absence of the exosome catalytic component Rrp6 [13,14]. In this study, we used yeast strains lacking Rrp6 to compare H2A.Z occupancy to global transcription and found that H2A.Z occupies the 3′ ends of protein-coding genes in addition to its well-known enrichment at their 5′ ends. H2A.Z is co-localised with other active histone modifications at the 3′ end of genes, at sites that match the start sites of non-coding transcripts transcribed in the antisense orientation relative to the sense protein-coding genes. The deletion of H2A.Z results in the down-regulation of antisense transcripts that normally have H2A.Z in their promoters. This novel association between H2A.Z and antisense transcripts differs fundamentally from the previously described role of H2A.Z in supressing antisense transcripts in fission yeast . Our findings indicate that H2A.Z is a general marker of TSSs and suggest that some apparently indirect effects of H2A.Z deletion on the expression of protein-coding genes whose promoters do not contain H2A.Z are mediated through effects on an antisense transcript.
Results and discussion
H2A.Z is significantly enriched at the 3′ ends of genes
Peaks of H2A.Z at the 3′ ends of genes correlate with other active histone marks
H2A.Z at the 3′ end of genes marks the start of antisense transcripts
H2A.Z is important for antisense transcript levels
Antisense transcripts that depend on H2A.Z are primarily transcribed from tandemly arranged genes
Regulation of antisense transcripts by H2A.Z can affect sense transcript levels
It is challenging to dissect the dependencies of sense/antisense transcript levels due to their inter-connected nature but the fact that Htz1 is more enriched at down-regulated sense and antisense transcripts leads us to propose a model in which the effects of Htz1 on down-regulated transcripts are direct and that up-regulated transcript levels generally result from an effect on the corresponding sense/antisense. To test this, we divided genes into 4 groups based on their Htz1-enrichment patterns (Figure 6B). Group 1 genes have Htz1 at their 5′ ends only and, based on our observations of sense transcript levels (Additional file 3: Figure S3), we would predict that levels of group 1 transcripts should decrease slightly when HTZ1 is deleted. Although the median log (2) expression is lower than 0 in this group, it is not significantly lowered. The behaviour of Group 2 genes is unpredictable as they have Htz1 at both 5′ and 3′ ends, and changes at Group 3 genes (having no Htz1 at either 5′ or 3′ end) can be attributed to indirect effects. However, Group 4 genes (having Htz1 only at 3′ ends) are predicted to have up-regulated sense transcripts and indeed we find that Group 4 genes are significantly up-regulated (p = 7.5 x 10−4; Figure 5C). These findings are consistent with Htz1 having an effect on antisense transcripts that consequently affects sense transcript levels and may indicate that Htz1 is more important for the activation of antisense transcripts than sense transcripts.
We find in this study that a significant proportion of Htz1 is located at the 5′ ends of antisense transcripts, and that Htz1 is required for normal levels of these non-coding transcripts in addition to its known role in regulating protein-coding genes. Comparison of strand-specific RNA-seq and ChIP-seq data shows that Htz1 occupancy has a predominantly activating effect on the promoters of antisense and sense transcripts. Previous work has described both activating and repressing roles for H2A.Z at individual genes in both yeast and mammalian cells (reviewed in ). While some genes that are up-regulated in htz1Δ have Htz1 at their promoters, these are relatively rare and most promoters of up-regulated transcripts have low Htz1 occupancy, arguing against a direct repressive effect of Htz1 on most transcripts, as previously observed by Li et al.  for protein-coding genes. However, up-regulation of protein coding genes that lack Htz1 in their promoters is not due to completely indirect effects, at least in some cases, but is mediated by Htz1 affecting levels of antisense transcripts generated from the 3′ ends of these genes. Mechanistically, some changes of sense transcript levels in htz1Δ strains have previously been ascribed to the aberrant activity of the SWR-C when Htz1 is absent . We could not test whether Swr1 is responsible for the changes in AS levels in rrp6Δhtz1Δ because we were unable to generate strains triply mutated for rrp6Δhtz1Δ and swr1Δ.
The importance of H2A.Z for antisense transcription may explain some of its apparently conflicting effects on the expression of protein coding genes and highlights the need to study all transcripts derived from a locus in order to fully understand how genes are regulated.
Yeast genetics and molecular biology
Yeast strains were created using standard methods and are described in Additional file 5: Table S2. Cells for RNA extraction and ChIP were in harvested from log-phase cultures growing in SD medium. Total RNA was purified using the Ribopure Purification Kit (Life Technologies) and contaminating genomic DNA was removed by DNaseI digestion. Htz1 ChIP was performed essentially as described previously , using affinity purified custom αHtz1 (α660) antibodies  in a protocol optimised for maximal Htz1 recovery and extensive chromatin fragmentation by sonication. RNA and purified ChIP DNA were amplified for sequencing using the Illumina TruSeq stranded mRNA sample preparation kit or the Illumina TruSeq ChIP Sample Prep Kit. Libraries were sequenced on either an Illumina GAIIX or HiSeq 2000. ChIP-seq samples had an average of 12 million uniquely mapping reads, and RNA-seq samples had an average of 35 million reads (Additional file 5: Table S3). Data were generated from two independent biological replicates for each strain in each analysis (ChIP-seq/RNA-seq) and correlations between biological replicates were high in all cases (Additional file 5: Table S1 A, B).
Sequence reads were mapped to the S. cerevisiae genome assembly sacCer1 using Bowtie version 0.12.9 , allowing up to 2 mismatches and no ambiguously mapped reads. Genomic coordinates for protein-coding transcripts were obtained from Xu et al. .
ChIP-seq data processing
Background signal was set to 1.2 standard deviations above the mean of the ChIP:input ratios and then subtracted from the ChIP-seq signal. Where background was higher than the ChIP-seq signal, the value was set to zero. The final ChIP-signal at each base pair was normalized to the number of reads per million mapped reads.
Strand-specific RNA-seq data processing
RNA-seq mapped reads were segregated into + and – strands. Normalisation was performed such that the total amount of sense-strand RNA was adjusted to 108 arbitrary units and antisense RNA-seq levels were adjusted by the same factor. Transcript levels were then normalized per kilobase of transcript length.
Regions that are 150 bp downstream of the TSS and upstream of the TES were used to quantify the 5′ and 3′-end enrichment of Htz1 respectively. Htz1 peaks were associated with a transcript if they had downstream RNA signals >3-fold higher than upstream signals. The nature of the associated transcripts (i.e. sense or antisense) was determined by comparison to ORF-Ts . For differential gene expression, the number of RNA-seq reads were calculated for each sense and antisense transcript  and merged in to one file. Differentially expressed genes were identified by DESeq2 , using corrected p-value < 0.05. Convergent overlapping transcripts were excluded from the quantification of antisense transcript levels to avoid potential confusion between sense and antisense transcripts in these cases.
Availability of supporting data
The data sets supporting the results of this article are available in the GEO repository, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54105.
The authors thank the FLS genomics facility staff for library preparation and sequencing, and Andrew Sharrocks, Gino Poulin and Magnus Rattray for discussions or comments on this manuscript. This work was supported by the Wellcome Trust (WT082335MA, 097820/Z/11/B).
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