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. Author manuscript; available in PMC: 2018 May 13.
Published in final edited form as: Cell Rep. 2018 Jan 16;22(3):611–623. doi: 10.1016/j.celrep.2017.12.079

INO80 Chromatin Remodeling Coordinates Metabolic Homeostasis with Cell Division

Graeme J Gowans 1, Alicia N Schep 2, Ka Man Wong 1, Devin A King 1, William J Greenleaf 2, Ashby J Morrison 1,3,*
PMCID: PMC5949282  NIHMSID: NIHMS959032  PMID: 29346761

SUMMARY

Adaptive survival requires the coordination of nutrient availability with expenditure of cellular resources. For example, in nutrient-limited environments, 50% of all S. cerevisiae genes synchronize and exhibit periodic bursts of expression in coordination with respiration and cell division in the yeast metabolic cycle (YMC). Despite the importance of metabolic and proliferative synchrony, the majority of YMC regulators are currently unknown. Here, we demonstrate that the INO80 chromatin-remodeling complex is required to coordinate respiration and cell division with periodic gene expression. Specifically, INO80 mutants have severe defects in oxygen consumption and promiscuous cell division that is no longer coupled with metabolic status. In mutant cells, chromatin accessibility of periodic genes, including TORC1-responsive genes, is relatively static, concomitant with severely attenuated gene expression. Collectively, these results reveal that the INO80 complex mediates metabolic signaling to chromatin to restrict proliferation to metabolically optimal states.

Graphical abstract

In Brief: Gowans et al. reveal that the INO80 complex is needed to coordinate cell division with nutrient availability in S. cerevisiae. In metabolically synchronized INO80 mutants, oscillations of metabolic gene expression and chromatin accessibility are severely attenuated compared with wild-type cells.

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INTRODUCTION

The coordination of cellular function with the environment is essential for adaptation and survival. Cells and organisms have a remarkable ability to sense diverse (i.e., nutrient-rich or -limiting) environments and reprogram their energy metabolism and proliferative capacity accordingly. Limiting nutrient environments are ubiquitous throughout nature and range from competitive microorganism growth environments to niches surrounding developing tissues in multicellular organisms. Failure to adapt can lead to cell death, developmental defects, and disease. Indeed, energy metabolism alterations are a major contributing factor to many pathologies, including cancer, cardiovascular disease, and diabetes, which together account for approximately half of all deaths in the United States (Centers for Disease Control and Prevention, 2015).

Adaptive cellular responses are often achieved by rapid inducible changes in gene expression (López-Maury et al., 2008). For example, metabolic adaptation in the budding yeast Saccharomyces cerevisiae is achieved, in part, by coordinated regulation of gene expression, cell division, and metabolic status in low-nutrient environments that mimic natural environments in the wild. Under such glucose-deprived conditions, yeasts quickly synchronize their metabolic processes and undergo coordinated and periodic bursts of respiration during a phenomenon known as the yeast metabolic cycle (YMC) (Klevecz et al., 2004;Tu et al., 2005).

In the YMC, over half of all transcripts undergo periodic expression ( Figure 1A; Table S1). For example, genes involved in protein synthesis are induced during the high oxygen consumption (HOC) or oxidative (OX) phase. Following the OX phase, during the low oxygen consumption (LOC) or reductive building (RB) phase, genes involved in DNA replication and mitochondrial biogenesis are upregulated. Subsequently, genes involved in glycolysis and the environmental stress response are upregulated during periods of LOC in the reductive charging (RC) phase. This temporal organization facilitates “just in time” coordination of cellular function, wherein genes are transcribed just prior to the utilization of their encoded proteins within a particular metabolic or cell cycle pathway (Kuang et al., 2014;McAdams and Shapiro, 2003;Wang et al., 2015;Zaslaver et al., 2004). Furthermore, periodically expressed genes in multiple species are among the most energetically expensive to transcribe and translate (Wagner, 2005;Wang et al., 2015). As such, coordinated periodic transcription, rather than constitutive expression, maximizes the efficiency of limited cellular resources.

Figure 1. The INO80 Complex Is Essential for Respiration Oscillations.

Figure 1

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(A) Organization of the yeast metabolic cycle (YMC). Top: respiration cycles are shown with the corresponding percentage of dissolved oxygen (dO2) in culture. Bottom: relative fold change gene expression of previously classified periodic genes (Tu et al., 2005). RC, reductive charging (1,508 periodic genes); OX, oxidative (1,016 periodic genes); RB, reductive building (975 periodic genes).

(B) dO2 traces in the wild-type and indicated mutant strains lacking subunits of the INO80 complex.

(C) Illustration of the INO80 complex with structural modules noted fromTosi et al. (2013). Colors denote the severity of YMC defects observed upon deletion of the indicated subunit.

See also Figure S1 and Table S1.

Importantly, periodic gene expression is an evolutionarily conserved process. These highly robust gene expression cycles are observed in both lab and wild prototrophic yeast strains and in single cells from asynchronous populations (Burnetti et al., 2016;Laxman et al., 2010;Silverman et al., 2010). Similarly, mammals undergo circadian rhythms, with oscillations in gene transcription and metabolic pathways coordinated with the day/night cycle (Panda, 2016). Analysis of the mouse transcriptome across multiple tissues revealed that 43% of protein-coding genes exhibit circadian rhythm-driven expression oscillations (Zhang et al., 2014).

Tight regulation of periodic gene expression not only promotes metabolic efficiency, as previously mentioned, but also optimizes cell growth and division. This is evident by the temporal constraint of cell division in both the circadian cycle and YMC (Burnetti et al., 2016;Klevecz et al., 2004;Matsuo et al., 2003;Nagoshi et al., 2004;Tu et al., 2005). In specific prototrophic strains, cell division is gated within the RB phase of the YMC, purportedly to shield replicating DNA from genotoxic reactive oxygen species (Chen et al., 2007). In addition, cell cycle start is coupled to the initiation of the OX phase in several yeast strains, likely to ensure that cell division occurs following the accumulation of sufficient energy reserves (Burnetti et al., 2016). Cell division frequency can be dynamically altered by nutrient availability and is absent without metabolic oscillations (Papagiannakis et al., 2017;Robertson et al., 2008). These studies highlight the critical importance of coordinating cell growth and division with metabolic environments.

Although the importance of synchronous metabolic and cell division oscillations is becoming increasingly clear, the identification of regulatory mechanisms for these processes is currently lacking. However, an emerging concept is that chromatin modification is an ideal mechanism to orchestrate gene expression in coordination with the metabolic environment (Gut and Verdin, 2013). This is primarily because many chromatin modifiers are abundant, regulate a large number of genes, and require intermediary metabolites as enzymatic cofactors. Thus, they can “communicate” metabolic status to the global chromatin environment. Moreover, epigenetic alterations can be rapidly and reversibly induced and are capable of dynamically directing gene expression in coordination with changing nutrient environments.

We have recently identified the evolutionarily conserved INO80 complex as a regulator of metabolic function (Yao et al., 2016). The INO80 chromatin-remodeling complex restructures and repositions nucleosomes in vitro (Gerhold and Gasser, 2014;Morrison and Shen, 2009). In vivo, INO80 is enriched at the +1 nucleosome that plays critical roles in defining chromatin accessibility at promoters (Yao et al., 2016;Yen et al., 2012). The S. cerevisiae INO80 chromatin remodeler is composed of 15 subunits (Shen et al., 2000) that constitute four structurally distinct subunit modules along the Ino80 ATPase (Tosi et al., 2013;Watanabe et al., 2015). We found that the Arp5-Ies6 module, which is needed for chromatin remodeling catalytic activity (Shen et al., 2003;Tosi et al., 2013;Watanabe et al., 2015;Yao et al., 2015), regulates the expression of genes in energy metabolism pathways. Specifically, arp5Δ mutants display an upregulation of genes involved in the oxidative phosphorylation pathway (Yao et al., 2016). Accordingly, mitochondrial potential and oxygen consumption are altered in arp5Δ, ies6Δ, and ino80Δ mutants.

To further investigate the relationship between INO80 and metabolic homeostasis, we examined the effect of INO80 deletion on metabolic and cell division oscillations in the YMC. In this report, we show that deletion of INO80 subunits severely disrupts metabolic cycling. Furthermore, we demonstrate that the INO80 complex is needed for robust TORC1-mediated gene expression, which is critical for YMC maintenance. Finally, we show that disruption of the INO80 complex decouples cell division from metabolic oscillations. Collectively, our results establish the INO80 complex as a key regulator of metabolism that integrates nutrient signaling with gene expression and cell growth.

RESULTS

The INO80 Complex Is Essential for Respiration Oscillations

To determine the role of INO80 in the YMC, we analyzed the effect of genetic disruption of each of the unique, non-essential subunits of the INO80 complex on YMC respiratory oscillations. We found that deletion of the Ino80 ATPase subunit had the most severe effect on YMC organization because the culture rapidly lost the ability to periodically respire but maintained a constitutively high rate of oxygen consumption ( Figure 1B). These results are consistent with previous observations that INO80 chromatin remodeling and nucleosome assembly factors are needed to repress genes in the oxidative phosphorylation pathway (Galdieri et al., 2016;Yao et al., 2016).

Deletion of ARP8 resulted in a metabolic cycle that prematurely entered the OX phase at the midpoint of RC. Although not as severe as the ino80Δ mutant, deletion of either ARP5 or IES6 resulted in rapid oscillations in oxygen consumption ( Figure 1B), with each cycle lasting approximately half the length of a wild-type cycle. The arp5Δ and ies6Δ mutants exhibited similar phenotypes in the YMC, consistent with their physical association as a subcomplex both within and outside of the INO80 complex (Tosi et al., 2013;Watanabe et al., 2015;Yao et al., 2015,2016). The difference in YMC length between arp5Δ and wild-type strains was largely contributed by a drastically reduced RC (low oxygen consumption) phase. A similar profile was observed for the ino80Δ mutant before the cycles ceased ( Figure 1B).

Deletion of IES5 or NHP10 also disrupted the periodicity of the YMC (Figure S1A). Ies5 is part of the Nhp10 module (Tosi et al., 2013), and purification of the INO80 complex from an ies5Δ strain showed a dramatic reduction in Ies1, Ies3, and Nhp10 (Figure S1B), corroborating recent results (Sardiu et al., 2017). Interestingly, deletion of NHP10 and IES5 do not result in fitness defects in rich media (Morrison et al., 2007) (A.J.M., unpublished data), whereas deletion of the catalytic subunits of the INO80 complex, such as ARP8, ARP5, and IES6, reduces fitness (Yao et al., 2016). Nevertheless, nhp10Δ and ies5Δ mutants both displayed defects in respiration cycles in the YMC. Thus, defects observed in the YMC do not always translate to fitness defects in unsynchronized cells.

Deletion of other subunits of the INO80 complex, including IES1, IES3, and IES4, had only minimal effects on the YMC. A summary of the phenotypes within each structural module is illustrated in Figure 1C. Purification of Ino80-FLAG throughout the YMC from wild-type cells demonstrated that the complex composition is not dramatically altered (i.e., no obvious loss or gain of subunits is observed) during the respiration oscillations (data not shown); thus, reconfiguration of the complex does not appear to regulate normal YMC kinetics. Notably, the YMC defects observed in mutants of the INO80 complex are not common to all remodelers because deletion of SWR1, a subunit of the SWR1 chromatin-remodeling complex and another chromatin remodeler in the INO80 subfamily (Mizuguchi et al., 2004), had no effect on the cycle (Figure S1C).

Disruption of the INO80 Complex Alters Global Transcription across the YMC

In asynchronous cultures, the INO80 complex regulates the expression of an abundance of genes enriched in metabolic processes (Yao et al., 2016). To assess differences in transcript levels across the YMC, we performed an RNA sequencing (RNA-seq) analysis of both wild-type and arp5Δ cultures at six different time points during the cycle. We selected the arp5Δ mutant because it has a severely disrupted profile while still maintaining sufficient cycles for sampling. To examine the relationship between samples, principal-component analysis (PCA) (Ringnér, 2008) was performed. Principal components 1 and 2 revealed that wild-type samples (triangles) are roughly arranged in a circle, mirroring the cyclical nature of the system ( Figure 2A). A similar pattern is observed for the arp5Δ data (circles), although the samples are noticeably clustered closer together, indicating that the differences between these samples are smaller than the differences observed between the wild-type samples.

Figure 2. Disruption of the INO80 Complex Alters Global Transcription across the YMC.

Figure 2

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(A) Left: YMC dO2 traces in the wild-type and arp5Δ mutant. Samples were taken at the indicated time points (coded by color) for RNA-seq and PCA. Right: principal components (PCs) 1 and 2 are shown. Technical replicates are displayed for each time point and separated by strain.

(B) Left: mean expression of periodic genes in wild-type (WT) and arp5Δ strains within each YMC phase. n = number of periodic genes in each phase previously classified (Kuang et al., 2014). Expression of each gene is shown as FPKM. Right: expression of each individual gene is shown as heatmaps (scaled by row).

(C) DAVID functional enrichment analysis of significantly differentially expressed (SDE) genes at comparable time points between the wild-type and arp5Δ mutant (refer to Experimental Procedures for more detail). Bar colors denote the wild-type phase with which periodic genes are associated.

Enrichment scores are −log10(p value). See also Table S2.

To determine whether specific metabolic pathways were misregulated in the arp5Δ mutant, we next compared the expression of previously characterized periodic genes (Kuang et al., 2014) within each phase of the YMC ( Figure 2B). These genes were identified using a periodicity algorithm on RNA-seq samples compiled at high resolution (16 time points) across the YMC (Kuang et al., 2014). We determined functional annotation enrichments for these periodic genes in wild-type cells (Table S1), which were used as a reference to identify the YMC pathways disrupted in arp5Δ mutants.

In the arp5Δ strain, although transcripts peaked during the expected phases, expression was markedly attenuated compared with the wild-type. Significant differentially expressed (SDE) gene analysis between the mutant and wild-type during each phase of the YMC supported the observation of “muted” cycles in the arp5Δ mutant ( Figure 2C; Table S1). For example, pathways with peak expression in the wild-type OX phase, such as ribosome biogenesis, are also expressed in arp5Δ mutant at the same time point. However, these transcripts are identified as significantly downregulated compared with wild-type cells because peak expression is dampened in mutant cells ( Figure 2C). Conversely, many of these OX phase pathways are comparatively upregulated in the arp5Δ mutant during the RB phase because they are not repressed to the same degree as in wild-type cells. These data demonstrate that, in the arp5Δ mutant, periodic genes are generally expressed in the corresponding phase. However, the amplitude of periodic expression is dramatically decreased, resulting in YMC phasing that is less defined.

Cell Division Is Disconnected from the YMC in the arp5D Mutant

Analysis was then performed to identify periodic genes in the arp5Δ mutant that do not exhibit expression in the expected YMC phase. Unsupervised clustering (k-means) analysis was performed on previously defined periodic genes (Kuang et al., 2014) after normalizing (z-score) by transcript, time point, and strain, adjusting the muted expression defects observed in arp5Δ cells. For both wild-type and arp5Δ cells, the vast majority of periodic genes (>3,800 genes) clustered into one of three distinct patterns that reflect RC, OX, and RB peak mean expression ( Figure 3A; Table S3). This supports the observation that, although gene expression is dramatically muted in arp5Δ mutants, periodic expression of many genes is still detectable. However, we identified 722 genes in arp5Δ mutants with peak expression and k-means clustering in a different phase than expected, representing genes with severe defects in periodic transcriptional regulation (Table S3). Functional annotation analysis demonstrates that these genes are significantly enriched in cell cycle and mitotic pathways ( Figure 3B). As previously discussed, cell division and DNA replication are coupled with the RB phase of the YMC in this prototrophic strain (CEN.PK) (Burnetti et al., 2016;Chen et al., 2007;Klevecz et al., 2004;Tu et al., 2005). Additionally, genes involved in mitosis and chromosomal segregation were significantly upregulated in the RC phase of arp5Δ cells ( Figure 2C). Thus, cell cycle gene expression is dramatically altered in the arp5Δ mutant YMC.

Figure 3. Cell Division Is Disconnected from the YMC in the arp5Δ Mutant.

Figure 3

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(A) k-means clustering of previously classified periodic genes (Kuang et al., 2014) following z-score normalization of RNA-seq data for each gene, time point, and strain.

(B) DAVID functional enrichment of genes in the arp5Δ mutant that do not cluster in the corresponding YMC phase. Annotations related to cell cycle are highlighted. APC, anaphase-promoting complex. Enrichment scores are −log10(p value).

(C) Samples were taken at indicated time points across wild-type and arp5Δ YMCs. Top: dO2 traces and ratio of G2 to G1 cells for each strain. Bottom: representative DNA content histograms from samples indicated with a thicker outline at the top.

(D) Number of budding cells, indicative of M phase entry, in samples across wild-type and arp5Δ YMCs.

See also Table S3.

To further investigate the disruption of cell cycle kinetics in arp5Δ cells, we monitored DNA content and cell budding across the YMC. As reported previously (Tu et al., 2005), the wild-type RB phase was tightly coupled to DNA replication ( Figure 3C) and cell division ( Figure 3D). However, in the arp5Δ mutant, DNA replication was uncoupled from the YMC because we observed a population of cells with 2N DNA content and budding cells at all phases ( Figures 3C and 3D). The cell density of the arp5Δ culture re actor with constant perfusion of media. Thus, these defects in the arp5Δ mutant are not due to cell cycle arrest or cell death. Conversely, these results suggest that disruption of the INO80 complex decouples cell division from the metabolic state of the cell.

Loss of INO80 Function Alters Global Chromatin Accessibility in the YMC

To assess the influence of the INO80 chromatin remodeling in the YMC, we utilized the assay for transposase-accessible chromatin using sequencing (ATAC-seq) (Buenrostro et al., 2015). ATAC-seq uses a hyperactive Tn5 transposase that preferentially inserts specific adaptors in regions of accessible chromatin. Samples were taken at the same time points as for RNA-seq ( Figure 2A). Similar to the RNA-seq results, PCA illustrates the cyclical relationship among samples ( Figure 4A) within PC2 and PC3, whereas PC1 largely partitioned wild-type and arp5Δ samples separately (Figure S2). The distribution of samples in the PCA plot is indicative of periodic fluctuations in chromatin accessibility across the YMC. However, the arp5Δ samples were clustered in the center of the plot, suggesting that the differences between these samples were much less pronounced than in the wild-type ( Figure 4A).

Figure 4. Loss of INO80 Function Alters Global Chromatin Accessibility in the YMC.

Figure 4

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(A) PCA plot of ATAC-seq data from samples taken at time points shown in Figure 2A. Replicates are displayed for each time point and strain (colored as in Figure 2A).

(B) Comparison of mean ATAC-seq scores for periodic genes from each phase of wild-type and arp5Δ YMC following Z score normalization for each gene and time point. The RC, OX, and RB phases are highlighted.

(C) Chromosomal plot of log2 mean wild-type and arp5Δ ATAC-seq and RNA-seq (FPKM) in 500 bp bins for the YMC time points described in Figure 2A. Scores in both wild-type and arp5Δ cells were normalized to mean wild-type. Red and blue boxes highlight static regions of increased and decreased accessibility, respectively, in arp5Δ cells. Gray boxes indicate the corresponding chromosomal regions in wild-type cells.

See also Figures S2 and S3.

To explore potential mechanisms of INO80-mediated transcription regulation, we then investigated ATAC accessibility across all periodic gene promoters, defined as −400 bp downstream and 100 bp upstream of the transcriptional start site (TSS). ATAC scores largely correlated with gene expression (correlation coefficient [r] = 0.86 for OX, 0.72 for RC, and 0.99 for RB), with increased accessibility corresponding to the phase of periodic expression ( Figure 4B). However, the accessibility of periodic gene promoters was more static in arp5Δ cells compared with the wild-type. When accessibility was viewed along individual chromosomes in 500-bp bins, oscillating accessibility was observed in wild-type cells, with relatively decreased accessibility during the RC-to-OX transition and increased accessibility during the RB phase ( Figure 4C; Figure S3). However, accessibility along chromosomes in arp5Δ cells was much less dynamic during the YMC. Moreover, large domains of relatively accessible (red boxes) or inaccessible (blue boxes) chromatin were observed in arp5Δ cells, with corresponding alterations in RNA abundance ( Figure 4C; Figure S3). These data reveal that disruption of INO80 chromatin remodeling results in global chromatin accessibility defects that reduce the plasticity of the chromatin template during metabolic oscillations.

Metabolic Gene Promoters Are Dependent on INO80-Facilitated Chromatin Accessibility

To identify individual transcription factors that are influenced by INO80 chromatin remodeling, chromatin accessibility was assessed for promoters of specific transcription factor (TF) motifs. The presence of each annotated transcription factor motif (n = 177) was assessed for all promoters, defined as −400 bp downstream and 100 bp upstream relative to the TSS. The results revealed that, similar to the data in Figure 4A, the wild-type data were much more dynamic than the mutant data ( Figure 5A). In particular, several motifs exhibited comparably large fluctuations in accessibility ( Figure 5B), suggesting a relatively large dependence on chromatin manipulation for regulation of those genes. Indeed, periodic expression of these highlighted TF-regulated genes is largely coordinated with accessibility in wild-type cells ( Figure 5C). However, in the arp5Δ mutant, these motifs lacked dynamic accessibility and exhibited significant differential accessibility between wild-type and arp5Δ cells (Benjamini-Hochberg-adjusted p < 0.01) ( Figure 5B). In addition, corresponding gene expression was deregulated ( Figure 5C).

Figure 5. Metabolic Gene Promoters Are Dependent on INO80-Regulated Chromatin Accessibility.

Figure 5

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(A) Promoter ATAC accessibility scores organized by all annotated transcription factor (TF) motifs are shown for both wild-type and arp5Δ YMCs.

(B) Magnification of indicated region in (A). The time points and YMC phase from Figure 2A are shown.

(C) Mean RNA-seq FPKM for genes that contain the indicated transcription factor motifs in their promoters.

(D) Left: illustration of the TORC1 pathway for the indicated transcription factors. Right: DAVID significant functional enrichment of genes with the indicated transcription factor motifs. SAM, S-adenosyl-methionine; TCA, tricarboxylic acid.

(E) ATAC accessibility is shown as the number of Tn5 insertions per million reads around the indicated transcription factor motif for wild-type (green) and arp5Δ (pink) samples.

(F) +1 and −1 nucleosome densities, relative to transcription start sites, containing Abf1 or Reb1 motifs. Wild-type (green) and arp5Δ (pink) samples across all time points of the YMC are shown. Red/blue dotted lines denote the median nucleosome position for the wild-type and arp5Δ, respectively. p = 0.002 for Abf1 and 0.158 for Reb1.

See also Figure S4.

Notably, many of these highlighted transcription factor motifs are components of the energy-responsive TORC1 pathway ( Figure 5D). For example, under nutrient-rich growth conditions, Msn2/4 are inhibited by TORC1 via phosphorylation, which results in their exclusion from the nucleus (Beck and Hall, 1999). Under nutrient-depleted growth conditions, these transcription factors translocate to the nucleus and express genes in the general stress response (Gasch et al., 2000;Martínez-Pastor et al., 1996;Schmitt and McEntee, 1996). Dot6, Tod6, and Stb3 are phosphorylated by Sch9 in a TORC1-dependent manner to promote expression of ribosomal protein (RP) and ribosome biogenesis (Ribi) gene expression (Huber et al., 2009;2011; Figure 5D). TORC1 also directly phosphorylates the transcriptional activator Sfp1 to facilitate its binding to RP gene promoters and subsequent expression (Lempiäinen et al., 2009;Marion et al., 2004).

Interestingly, Reb1 and Abf1, which are critical factors in controlling nucleosome positioning at promoters (Hartley and Madhani, 2009), are found in the promoters of ribosome biogenesis genes and genes involved in protein and mRNA transport ( Figure 5D). Consistent with our observations, a recent study identified Reb1 and Abf1 at ribosome biogenesis promoters using chromatin immunoprecipitation (ChIP) in vivo (Bosio et al., 2017). In arp5Δ mutant cells, Reb1 and Abf1 motifs were the most significantly statically inaccessible among all motifs and across all phases of the YMC (Benjamini-Hochberg-adjusted p < 0.001). Comparatively, in wild-type cells, the Reb1 and Abf1 motifs exhibited coordinated oscillations of accessibility and gene expression in the YMC ( Figures 5B and 5C).

We then examined accessibility at high resolution across the motifs of these transcription factors ( Figure 5E; Figure S4A). We observed that there were striking phase-dependent differences between wild-type and arp5Δ mutant cells. Specifically, when comparing these two strains, the Dot6, Sfp1, Reb1, and Abf1 motifs show the greatest difference in accessibility during the OX phase ( Figure 5E). In wild-type cells, this peak of accessibility correlates with the peak of expression of genes containing these motifs in their promoters ( Figures 5B and 5C) and likely stems from a shared translation ( Figure 5D). Conversely, regions containing Msn2/4 motifs have reduced accessibility during the OX phase, with comparably smaller differences between the wild-type and the arp5Δ mutant ( Figure 5B). However, at higher resolution, strain-dependent differences are observed within Msn2/4 motifs during the RB and RC phase ( Figure 5E). During these phases of the YMC, Msn2/4-dependent gene expression is induced in the wild-type ( Figure 5C) and enriched in energy metabolism pathways ( Figure 5D). Interestingly, these results suggest that Msn2/4 are not restricted to stress-related gene expression in the YMC. Furthermore, these analyses demonstrate that INO80 activity is necessary to create dynamically accessible chromatin at the motifs of transcription factors important for YMC regulation.

As previously stated, INO80 is a chromatin remodeler that is enriched at the +1 nucleosome (Yao et al., 2016;Yen et al., 2012). Thus, we also assessed promoter-proximal nucleosome positioning across the YMC in both wild-type and arp5Δ cells. However, our ability to confidently call nucleosomes was confounded in the arp5Δ mutants because of an overall decrease in predicted nucleosome-spanning fragments compared with wild-type samples. The precise origin of this technical limitation is not known. However, it may be a consequence of increased +1 nucleosome “fuzziness”; i.e., deviation in mean nucleosome positioning of individual +1 nucleosomes in a cell population, which was previously observed in ino80Δ and arp5Δ mutants (Yao et al., 2016). For +1 nucleosomes that could be confidently called, no statistically significant deviations were detected proximal to transcription factor motifs when comparing wild-type with arp5Δ mutants ( Figure 5F and data not shown).

However, we did observe a significant change in the −1 nucleosome position for genes containing either the Abf1 or Reb1 transcription factor motifs. We observed differences in the size and significance of the median shift in nucleosome position depending on the motif identification method used. Using the JASPAR database (Mathelier et al., 2016), we observed median shifts of 6 and 4.5 bp in 1 nucleosome position proximal to Abf1 (p = 0.026) and Reb1 (p = 0.158) motifs, respectively ( Figure 5F). Using a previously published ChIP analysis in the absence of chromatin crosslinking, occupied regions of genomes from affinity-purified naturally isolated chromatin (ORGANIC) (Kasinathan et al., 2014), we observed median shifts in −1 nucleosome position of 3.5 and 4 bp proximal to Abf1 (p = 0.012) and Reb1 (p = 0.004) motifs, respectively (Figure S4B). No other statistically significant deviations were observed for nucleosomes proximal to other transcription factor motifs (data not shown). These data support a previously unrecognized role for the INO80 complex in −1 nucleosome positioning at specific metabolic promoters.

For most transcription factors, these defects in accessibility, gene regulation, and nucleosome positioning do not appear to be due to general gene expression defects of the transcription factors themselves (Figures S4C and S4D). Although many of these transcription factors display periodic expression, which has been reported previously for Msn4 (Rao and Pellegrini, 2011), the pattern of expression does not always directly correlate with the expression of other genes that have the motif. However, notable exceptions are Tod6 and Sfp1, which have patterns of transcription factor expression that closely mirror the expression of genes with corresponding motifs in their promoters ( Figure 5C; Figure S4C). Thus, a portion of the defects in metabolic gene expression and chromatin architecture observed in arp5Δ cells may originate with alterations of transcription factor expression itself. However, INO80 chromatin remodeling also plays a major role in regulating the chromatin architecture at the recognition motifs of several metabolic transcription factors. The combined effect of INO80 loss of function is a dramatically unresponsive and static chromatin architecture that does not exhibit periodic regulation characteristics of the YMC.

The INO80 Complex Is a Key Regulator of TORC1-Responsive Gene Expression

Of all transcription factors assessed, Msn2 and Msn4 had the largest ATAC-seq variability across the YMC of wild-type cells, which is altered in the arp5Δ mutant ( Figure 5B). Because Msn2 and Msn4 are regulated by the TORC1 pathway, we sought to further determine the role of TORC1 in the YMC. We first examined Msn2-mediated transcription in greater detail by restricting the analysis to ChIP-validated Msn2-induced genes following acute glucose deprivation (n = 88) (Elfving et al., 2014). Again, we found that Msn2-dependent genes were markedly reduced in the OX phase of the wild-type YMC ( Figure 6A), when accessibility of Msn2 motif-containing genes is lowest ( Figure 5B). In the arp5Δ mutant, we observed lower expression of these Msn2-induced genes in the RC phase of the cycle ( Figure 6A), with generally static accessibility patterns across the entire YMC at corresponding motifs ( Figure 5B).

Figure 6. The INO80 Complex Is a Key Regulator of TORC1-Responsive Gene Expression.

Figure 6

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(A) Top: same as Figure 2A. YMC dO2 traces in the wild-type and arp5Δ mutant. RNA-seq samples were taken at the indicated time points. Mean expression is shown as FPKM of Msn2-induced genes (center, Msn2 genes) and rapamycin-sensitive genes (bottom, Rap genes) across the YMC. Right: gene expression heatmaps of rapamycin-sensitive genes scaled by row.

(B) Pearson’s correlation between the RNA transcript levels across the wild-type YMC and those in an asynchronous culture before and after rapamycin treatment (30 nM) for the indicated time in minutes.

(C) Western blot analysis of the indicated wild-type time points with an antibody that recognizes the phosphorylated TORC1 substrate, mammalian homolog ribosomal protein S6 (Rps6). Hexokinase 1 (Hxk1) is shown as a loading control.

(D) Samples were taken across a wild-type YMC at the indicated time points before and after treatment with rapamycin (50 nM), indicated by the dashed line. Also shown is a western blot analysis using antibodies that recognize pRps6, Hxk1, acetylated histone H3 lysine 9, (H3K9ac), and histone H3 (H3).

(E) Western blot analysis of the arp5Δ mutant YMC as in (C).

See also Figure S5 and Table S4.

We then examined the requirement for INO80 on all TORC1-regulated gene expression by analyzing the expression of rapamycin-sensitive genes in the YMC. We identified both significantly upregulated (n = 1177) and downregulated (n = 1249) genes from asynchronous cultures treated with rapamycin (Table S4). Analysis of these transcript levels showed that rapamycin-induced genes (TORC1-repressed) have reduced expression during the OX phase ( Figure 6A, bottom). The expression pattern of these TORC1-repressed genes closely mirrors the pattern of Msn2-regulatd genes. Conversely, rapamycin-repressed genes (TORC1-induced) have peak expression during the OX phase. In the arp5Δ mutant, this divergent pattern between rapamycin-induced and -repressed expression was much less distinguished, demonstrating that TORC1-regulated gene expression is periodic in the YMC and dependent on INO80-mediated chromatin remodeling.

Overall, gene expression in untreated asynchronous cultures had the strongest correlation with the OX phase of the YMC; however, rapamycin treatment greatly weakened this association ( Figure 6B). We then monitored TORC1 signaling by examining the phosphorylation of ribosomal protein S6 (Rps6) (González et al., 2015), a downstream substrate in the TORC1 pathway (Yerlikaya et al., 2016). Rps6 phosphorylation was nearly undetectable in the RC phase of the YMC and peaked during the OX phase of wild-type cells ( Figure 6C). These data reinforce the idea of the OX phase being the main TOR-regulated growth phase of the YMC, with the other phases similar to quiescent or stationary phases in asynchronous cultures.

Because of the importance of TORC1 signaling in the YMC, treatment with rapamycin resulted in disruption of the YMC at the point of maximal TOR activity ( Figure 6D), confirming previous findings that addition of rapamycin disrupted respiration cycles in the YMC (Murray et al., 2007). Rapamycin treatment prevented the increase in phospho-Rps6 and acetylation of a number of H3 lysine residues, which are known to fluctuate across the YMC (Cai et al., 2011; Figure 6D; data not shown). (Note that rapamycin was prepared in ethanol, which itself can be metabolized, accounting for the transient decrease in dissolved oxygen [dO2]; addition of ethanol alone had no effect on subsequent cycles; Figure S5A). Addition of acetate has been shown to induce premature entry into OX phase (Cai et al., 2011) and, as we observed, an increase in TOR activity and histone acetylation (Figure S5B). Last, genetic deletion of SCH9 or RIM15, downstream components of the TORC1 pathway (Urban et al., 2007;Wanke et al., 2008), also either prevented or disrupted metabolic cycles (Figure S5C).

As mentioned, TORC1-responsive gene expression is muted in arp5Δ cells ( Figure 6A). Interestingly, we observed that the induction of TORC1 signaling appeared normal in the arp5Δ strain, with a peak in the OX phase, despite the differences in YMC period ( Figure 6E). Thus, although upstream TORC1 signaling appears intact in the arp5Δ mutant, downstream TORC1-regulated gene expression is disrupted. These results demonstrate that the INO80 complex is a critical effector of TORC1-regulated gene expression that coordinates the quiescence (RC) to growth (OX) transition and maintains metabolic homeostasis.

DISCUSSION

The INO80 Chromatin Remodeler Is a Key Regulator of Metabolic Function

Our data show that the INO80 chromatin-remodeling complex plays a major role in maintaining metabolic homeostasis in yeast. The cumulative defect in arp5Δ mutant cells is a system-level disorganization of the metabolic cycle, characterized by severely diminished ability to organize robust YMC phasing and coordinate cell division with metabolic timing.

The resulting effect of INO80 disruption is likely to contribute to metabolic inefficiency, whereby transcripts are produced out of synchrony with the function of the encoded protein. For example, in arp5Δ mutants, ribosomal gene expression is both produced (i.e., derepressed) out of phase and not fully activated during the optimal OX phase. Gene expression related to ribosome biogenesis involves nearly 10% of the genome, with many exhibiting more than 40-fold amplitude changes in the YMC. It has been proposed that approximately 60% of total S. cerevisiae transcription is devoted to rRNA production and that 50% of RNA polymerase II (Pol II) transcription is involved in ribosomal protein expression (Warner, 1999). Thus, the process of translation creates a tremendous energy expenditure for the cell that must be tightly regulated to ensure efficiency. In the YMC, gene expression programs related to translation peak during the OX phase, coincident with the production of ATP (Machné and Murray, 2012) and acetyl-coenzyme A (CoA) (Cai et al., 2011), which can feed these energy-demanding processes. Because mutants of the INO80 complex do not optimize this energy-demanding process, all cellular processes that require coordination of function with energy availability are likely jeopardized.

Previous research demonstrates that INO80 maintains proper DNA ploidy during cell division (Chambers et al., 2012). In this study, we report unrestricted cell division in arp5Δ mutants. The coordination of energy metabolism and cell division is vital to survival in competitive nutrient environments. Moreover, excessive proliferation that is disconnected from nutrient availability is characteristic of diseases such as cancer. For example, the mTOR signaling pathway is often constitutively active in cancer, promoting growth signaling and proliferation irrespective of metabolic environments (Laplante and Sabatini, 2009). In this study, we find that the INO80 complex is a critical component for enacting TOR-responsive transcriptional programs. Interestingly, subunits of the evolutionarily conserved INO80 complex are commonly amplified in many cancers, including 51% of lung squamous cell carcinoma (Cancer Genome Atlas Research Network, 2012), 50% of pancreatic (Witkiewicz et al., 2015), and 45% of bladder cancers (Cancer Genome Atlas Research Network, 2014). The results from this study reveal the INO80 complex as a previously unrecognized regulator of proliferative capacity that restricts cell division to metabolically optimal states. Additional studies may further define the role of INO80 subunits in human metabolism and disease.

The INO80 Complex Regulates the Chromatin Architecture of Metabolic Genes

We observe that chromatin accessibility is dynamically altered in the YMC. Specifically, metabolic promoters exhibit large fluctuations in accessibility that correspond to gene expression. For example, we observed that chromatin accessibility surrounding TORC1-responsive promoters fluctuated across the YMC and was largely static in the arp5Δ mutant. Thus, the organization of the YMC is particularly dependent on TORC1-regulated gene expression, which, in turn, is influenced by INO80 chromatin remodeling.

Notably, we also found that Msn2/4-regulated gene expression and ATAC accessibility are dramatically altered in arp5Δ mutants. As previously mentioned, Msn2/4 transcription factors are well known to regulate stress responses in a TORC1-dependent manner (Gasch et al., 2000;Martínez-Pastor et al., 1996;Schmitt and McEntee, 1996). For example, in response to glucose deprivation, Msn2 induces energy metabolism genes, such as those involved in trehalose and glycogen metabolism and oxidation reduction (Elfving et al., 2014). Furthermore, Msn2/4 have recently been found to regulate carbohydrate metabolism and acetyl-CoA abundance in the YMC (Kuang et al., 2017). In this study, we found that Msn2/4-regulated genes are periodically expressed in the YMC and enriched in energy metabolism pathways. Loss of INO80 chromatin remodeling severely diminishes Msn2/4-regulated gene expression, concomitant with attenuated chromatin accessibility at Msn2/4 motifs. It should be noted that INO80 may directly influence TORC1-mediated signaling by altering chromatin at TORC1-responsive loci and/or indirectly by regulating the cellular energetic state.

We also observed dramatically reduced accessibility at Abf1 and Reb1 motif-containing promoters in the arp5Δ mutant. These promoters are found at ribosome biogenesis genes, and, in wild-type cells, are among those with the largest fluctuations in accessibility across the YMC. Abf1 and Reb1 play major roles in establishing nucleosomal array positioning at promoters (Hartley and Madhani, 2009). Abf1 motifs are enriched upstream of the TSS (Ganapathi et al., 2011), and Reb1 localizes to the −1 nucleosome (Koerber et al., 2009). We find that INO80 chromatin remodeling is needed to position the −1 nucleosome at promoters that contain Reb1 and Abf1 motifs. Interestingly, most of the −1 nucleosomes bound by Reb1 are found at divergently transcribed genes (Koerber et al., 2009), and disruption of Reb1 function causes a reduction in the expression of these genes (Wang and Donze, 2016). Additionally, loss of Abf1 caused decreased nucleosome occupancy upstream of the TSS (Ganapathi et al., 2011). Our data demonstrate that −1 nucleosome positioning by Abf1 and Reb1-mediated is dependent on INO80 chromatin remodeling, the disruption of which is detrimental for metabolic homeostasis.

It should be noted that some of the YMC defects observed in arp5Δ cells may be an indirect result of INO80 dysfunction. For example, ARP5 deletion may directly result in a shortened RC phase, which consequently results in a relative elongation of the subsequent OX phase. Acute disruption of INO80 function will be needed to resolve direct and indirect YMC effects.

The YMC Is a Powerful Tool for Studying Metabolic Signaling to Chromatin

Our results also demonstrate that chromatin modification is an excellent means to regulate gene expression in coordination with changing metabolic environments. Previous research also supports this model by demonstrating that histone acetylation is tightly coupled to carbon availability (Cai et al., 2011), which is converted to acetyl-CoA, an intermediary metabolite and cofactor for histone acetyltransferases.

It has been proposed that cellular ATP oscillations might influence the activity of ATP-dependent chromatin remodelers during the YMC (Amariei et al., 2014;Machné and Murray, 2012). Considering that there are several thousand chromatin-remodeling enzymes in the cell (Ghaemmaghami et al., 2003) and an order of magnitude more predicted nucleosome substrates, the energy demand of chromatin remodeling may consume a significant amount of cellular resources. OX phase genes exhibit the highest amplitude in gene expression throughout the YMC, which is coincident with the peak of ATP (Machné and Murray, 2012; A.J.M., unpublished data). It may be that this increase in ATP production provides a metabolically permissive environment to allow extensive chromatin remodeling. Alternatively, when intracellular ATP levels are low, it may limit the amount of chromatin remodeling that can occur across the genome. Indeed, previous research has linked dynamic nucleosome positioning and transcription to respiration oscillations in the YMC (Amariei et al., 2014;Machné and Murray, 2012;Nocetti and Whitehouse, 2016). Future research connecting chromatin modulation with YMC gene expression is likely to further uncover chromatin connections to metabolism.

EXPERIMENTAL PROCEDURES

Yeast Strains and Metabolic Treatment

Saccharomyces cerevisiae strains were constructed in the CEN. PK background using standard genetic techniques (Table S5). Metabolic cycling conditions were performed as described previously (Tu et al., 2005), except that starter cultures were grown in minimal media without sulfuric acid. For RNA-seq analysis of the rapamycin-sensitive transcriptome, the BY4741 strain was used.

Western Blotting

Protein was extracted using an NaOH/tricarboxylic acid (TCA) extraction method. The following antibodies were used in this study: Hxk1 (Novus Biologicals, NB120-20547), Arp5 (Abcam, ab12099), H3 (Active Motif, 39163), acetylated histone H3 lysine 9 (H3K9ac), (Millipore, 06-942), pRps6 (Cell Signaling Technology, 2211), FLAG (Sigma, F1804), and hemagglutinin (HA) (Sigma, 11867423001).

RNA-Seq Analysis

RNA was prepared from samples (1.5 optical density [OD]) using the MasterPure yeast RNA purification kit (Epicenter, MPY03100). The sequencing libraries were prepared from 0.8 μg of RNA/sample using the Illumina TruSeq stranded mRNA kit (Illumina, 15031047). A minimum of 10 million reads per time point were aligned, in duplicate, using Bowtie 2 and analyzed using the DESeq2 package (Love et al., 2014). Replicates were combined for final analysis. Fragments per kilobase of transcript per million mapped reads is abbreviated as FPKM. PCA plots were generated on log-transformed data using the DESeq2 package (Love et al., 2014).

For the transcriptional comparative analysis in Figure 2C, time points with the highest correlation between wild-type and mutant log2-transformed expression within each phase were chosen, which includes the following: wild-type sample 2 versus mutant sample 1 (RC), r = 0.95; wild-type sample 3 versus mutant sample 3 (OX), r = 0.91; and wild-type sample 6 versus mutant sample 5 (RB), r = 0.96. SDE genes (adjusted p < 0.05) were identified at each of these time points using the DESeq2 package (Love et al., 2014). Functional annotation analysis was performed on SDE genes using Database for Annotation, Visualization, and Integrated Discovery (DAVID) with default parameters (Huang et al., 2009a;2009b).

Genes expressed in the wild-type YMC were previously determined as “periodic” or “non-periodic” using a periodicity algorithm on RNA-seq compiled at high resolution (16 time points) across the YMC (Kuang et al., 2014; Table S1).

ATAC-Seq Analysis

The ATAC-seq assay and data processing were performed as described previously (Schep et al., 2015). The ATAC-seq signal in promoters was quantified as the number of fragments mapping to the window 400 bp upstream to 100 bp downstream of the TSS. For motif analysis, the Motifmatchr R package was used to identify motifs from the JASPAR 2016 database (Mathelier et al., 2016) using a p value threshold of 10−4. Accessibility scores for promoters and motif-containing promoters were determined using chromVAR (Schep et al., 2017). Nucleosome positions were determined using NucleoATAC (Schep et al., 2015), and the first nucleosome at least 50 bp upstream of the TSS was identified as the −1 nucleosome and the first nucleosome downstream of that position as the +1 nucleosome.

Analysis of DNA Content and Budding Index

Cells were fixed with 70% ethanol and treated with RNase A and proteinase K prior to staining with Sytox Green at 2 μM in 50 mM sodium citrate (pH 7.4). DNA content was analyzed using a flow cytometer. The budding percentage was calculated as number budded/total cells (minimum of 400 counted per time point).

DATA AND SOFTWARE AVAILABILITY

The accession number for the RNA-seq and ATAC-seq data reported in this paper is NCBI: GSE101290.

Supplementary Material

supplement
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b
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Highlights.

  • The INO80 complex controls cellular respiration in S. cerevisiae

  • Cell division is disconnected from energy metabolism in INO80 mutants

  • INO80 mutants exhibit globally altered gene expression and chromatin accessibility

  • INO80 regulates downstream TORC1-responsive gene expression

Acknowledgments

This work was funded by NIH R35GM119580 (to A.J.M.). We thank the Department of Genetics Bootcamp for technical assistance with ATAC-seq sample processing.

Footnotes

Data and Software Availability

GSE101290

AUTHOR CONTRIBUTIONS

Conceptualization and Methodology, A.J.M. and G.J.G.; Investigation, G.J.G.; Formal Analysis and Visualization, G.J.G., A.N.S., and K.M.W.; Resources, D.A.K.; Writing – Original Draft, A.J.M. and G.J.G.; Writing – Review & Editing, A.J.M., G.J.G., K.M.W., and D.A.K.; Supervision, A.J.M. and W.J.G.; Funding Acquisition, A.J.M.

SUPPLEMENTAL INFORMATION

Supplemental Information includes five figures and five tables and can be found with this article online at https://doi.org/10.1016/j.celrep.2017.12.079.

DECLARATION OF INTERESTS

The authors declare no competing interests.

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NIHMS959032-supplement.pdf (5.5MB, pdf)
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