Article

A comprehensive synthetic genetic interaction network governing yeast histone acetylation and deacetylation

High-Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Genes & Development (Impact Factor: 12.64). 09/2008; 22(15):2062-74. DOI: 10.1101/gad.1679508
Source: PubMed

ABSTRACT Histone acetylation and deacetylation are among the principal mechanisms by which chromatin is regulated during transcription, DNA silencing, and DNA repair. We analyzed patterns of genetic interactions uncovered during comprehensive genome-wide analyses in yeast to probe how histone acetyltransferase (HAT) and histone deacetylase (HDAC) protein complexes interact. The genetic interaction data unveil an underappreciated role of HDACs in maintaining cellular viability, and led us to show that deacetylation of the histone variant Htz1p at Lys 14 is mediated by Hda1p. Studies of the essential nucleosome acetyltransferase of H4 (NuA4) revealed acetylation-dependent protein stabilization of Yng2p, a potential nonhistone substrate of NuA4 and Rpd3C, and led to a new functional organization model for this critical complex. We also found that DNA double-stranded breaks (DSBs) result in local recruitment of the NuA4 complex, followed by an elaborate NuA4 remodeling process concomitant with Rpd3p recruitment and histone deacetylation. These new characterizations of the HDA and NuA4 complexes demonstrate how systematic analyses of genetic interactions may help illuminate the mechanisms of intricate cellular processes.

0 Followers
 · 
104 Views
 · 
9 Downloads
  • Source
    • "See also Table S3. both Rad57 and Rad5 are required for SCR (Figure 4B), in agreement with previous reports (Mozlin et al., 2008; Zhang and Lawrence, 2005). Interestingly , SCR rates were not decreased in the hst1Dhos2Dsir2D mutant but rather were slightly increased (Figure 4B). "
    [Show abstract] [Hide abstract]
    ABSTRACT: CAG/CTG trinucleotide repeats are unstable, fragile sequences that strongly position nucleosomes, but little is known about chromatin modifications required to prevent genomic instability at these or other structure-forming sequences. We discovered that regulated histone H4 acetylation is required to maintain CAG repeat stability and promote gap-induced sister chromatid recombination. CAG expansions in the absence of H4 HATs NuA4 and Hat1 and HDACs Sir2, Hos2, and Hst1 depended on Rad52, Rad57, and Rad5 and were therefore arising through homology-mediated postreplication repair (PRR) events. H4K12 and H4K16 acetylation were required to prevent Rad5-dependent CAG repeat expansions, and H4K16 acetylation was enriched at CAG repeats during S phase. Genetic experiments placed the RSC chromatin remodeler in the same PRR pathway, and Rsc2 recruitment was coincident with H4K16 acetylation. Here we have utilized a repetitive DNA sequence that induces endogenous DNA damage to identify histone modifications that regulate recombination efficiency and fidelity during postreplication gap repair.
    Molecular Cell 08/2014; 55(6). DOI:10.1016/j.molcel.2014.07.007 · 14.46 Impact Factor
  • Source
    • "Acetylated sites with high stoichiometry are likely to be important for protein function. Many previously known regulatory acetylation sites were AcP‐insensitive, including Smc3 lysines 112 and 113 (Zhang et al, 2008), Sas2 (MYST homolog) lysine 168 (Yuan et al, 2012), Yng2 lysine 170 (Lin et al, 2008), RTT109 lysine 290 (Albaugh et al, 2011b), SNF2 lysines 1494 and 1498 (Kim et al, 2010), histone H2AZ (Htz1) lysines 4, 9, 11, and 15 (Babiarz et al, 2006; Millar et al, 2006; Lin et al, 2008), histone H3 (Hht1) lysines 19, 24, and 57 (Zhang et al, 1998; Suka et al, 2001; Hyland et al, 2005), histone H4 (Hhf1) lysines 6, 9, 13, and 17 (Suka et al, 2001) and histone H2B (Htb2) lysines 16 and 17. These 20 sites constitute 18% of the 111 AcP‐insensitive sites that we identified, indicating that AcP‐insensitivity is a good predictor of functionally important acetylation sites. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Lysine acetylation is a frequently occurring posttranslational modification; however, little is known about the origin and regulation of most sites. Here we used quantitative mass spectrometry to analyze acetylation dynamics and stoichiometry in Saccharomyces cerevisiae. We found that acetylation accumulated in growth-arrested cells in a manner that depended on acetyl-CoA generation in distinct subcellular compartments. Mitochondrial acetylation levels correlated with acetyl-CoA concentration in vivo and acetyl-CoA acetylated lysine residues nonenzymatically in vitro. We developed a method to estimate acetylation stoichiometry and found that the vast majority of mitochondrial and cytoplasmic acetylation had a very low stoichiometry. However, mitochondrial acetylation occurred at a significantly higher basal level than cytoplasmic acetylation, consistent with the distinct acetylation dynamics and higher acetyl-CoA concentration in mitochondria. High stoichiometry acetylation occurred mostly on histones, proteins present in histone acetyltransferase and deacetylase complexes, and on transcription factors. These data show that a majority of acetylation occurs at very low levels in exponentially growing yeast and is uniformly affected by exposure to acetyl-CoA.
    Molecular Systems Biology 01/2014; 10(1):716. DOI:10.1002/msb.134766 · 14.10 Impact Factor
  • Source
    • "E-MAP was also used to map GIs in different yeast species such as Schizosaccharomyces pombe (Ryan et al., 2012). Among the other high-throughput methods to discover GIs in yeast, diploid-based synthetic lethality analysis with microarrays (dSLAM), uses a library of barcoded mutants and barcode microarrays to measure the relative abundance of each barcoded double mutants in pooled populations to identify digenic SSL interactions (Pan et al., 2006; Lin et al., 2008). Optical density measurements (St Onge et al., 2007), biomass quantification analysis termed flux balance analysis (FBA) (Segre et al., 2005), quantitative phenotype (Drees et al., 2005) and gene expression data (Van Driessche et al., 2005) have also been employed to map GIs in specific biological processes. "
    [Show abstract] [Hide abstract]
    ABSTRACT: A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.
    Frontiers in Genetics 12/2013; 4:290. DOI:10.3389/fgene.2013.00290
Show more