Nano-electrospray tandem mass spectrometric analysis of the acetylation state of histones H3 and H4 in stationary phase in Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>The involvement of histone acetylation in facilitating gene expression is well-established, particularly in the case of histones H3 and H4. It was previously shown in <it>Saccharomyces cerevisiae </it>that gene expression was significantly down-regulated and chromatin more condensed in stationary phase compared to exponential phase. We were therefore interested in establishing the acetylation state of histone H3 and H4 in stationary and in exponential phase, since the regulation of this modification could contribute to transcriptional shut-down and chromatin compaction during semi-quiescence.</p> <p>Results</p> <p>We made use of nano-spray tandem mass spectrometry to perform a precursor ion scan to detect an <it>m/z </it>126 immonium ion, diagnostic of an N^ε-acetylated lysine residue that allowed unambiguous identification of acetylated as opposed to tri-methylated lysine. The fragmentation spectra of peptides thus identified were searched with Mascot against the Swiss-Prot database, and the y-ion and b-ion fragmentation series subsequently analyzed for mass shifts compatible with acetylated lysine residues. We found that K9, K14 and K36 of histone H3 and K12 and K16 of histone H4 were acetylated in exponential phase (bulk histones), but could not detect these modifications in histones isolated from stationary phase cells at the sensitivity level of the mass spectrometer. The corresponding un-acetylated peptides were, however, observed. A significantly higher level of acetylation of these residues in exponential phase was confirmed by immuno-blotting.</p> <p>Conclusion</p> <p>H4K16 acetylation was previously shown to disrupt formation of condensed chromatin <it>in vitro</it>. We propose that de-acetylation of H4K16 allowed formation of condensed chromatin in stationary phase, and that acetylation of H3K9, H3K14, H3K36, and H4K12 reflected the active transcriptional state of the yeast genome in exponential phase.</p

Non-random clustering of stress-related genes during evolution of the S. cerevisiae genome

BACKGROUND: Coordinately regulated genes often physically cluster in eukaryotic genomes, for reasons that remain unclear. RESULTS: Here we provide evidence that many S. cerevisiae genes induced by starvation and other stresses reside in non-random clusters, where transcription of these genes is repressed in the absence of stress. Most genes essential for growth or for rapid, post-transcriptional responses to stress in cycling cells map between these gene clusters. Genes that are transcriptionally induced by stresses include a large fraction of rapidly evolving paralogues of duplicated genes that arose during an ancient whole genome duplication event. Many of these rapidly evolving paralogues have acquired new or more specialized functions that are less essential for growth. The slowly evolving paralogues of these genes are less likely to be transcriptionally repressed in the absence of stress, and are frequently essential for growth or for rapid stress responses that may require constitutive expression of these genes in cycling cells. CONCLUSION: Our findings suggest that a fundamental organizing principle during evolution of the S. cerevisiae genome has been clustering of starvation and other stress-induced genes in chromosome regions that are transcriptionally repressed in the absence of stress, from which most genes essential for growth or rapid stress responses have been excluded. Chromatin-mediated repression of many stress-induced genes may have evolved since the whole genome duplication in parallel with functions for proteins encoded by these genes that are incompatible with growth. These functions likely provide fitness effects that escape detection in assays of reproductive capacity routinely employed to assess evolutionary fitness, or to identify genes that confer stress-resistance in cycling cells

SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures

Abstract Background The relationship between the sequence of a protein, its structure, and the resulting connection between its structure and function, is a foundational principle in biological science. Only recently has the computational prediction of protein structure based only on protein sequence been addressed effectively by AlphaFold, a neural network approach that can predict the majority of protein structures with X-ray crystallographic accuracy. A question that is now of acute relevance is the “inverse protein folding problem”: predicting the sequence of a protein that folds into a specified structure. This will be of immense value in protein engineering and biotechnology, and will allow the design and expression of recombinant proteins that can, for instance, fold into specified structures as a scaffold for the attachment of recombinant antigens, or enzymes with modified or novel catalytic activities. Here we describe the development of SeqPredNN, a feed-forward neural network trained with X-ray crystallographic structures from the RCSB Protein Data Bank to predict the identity of amino acids in a protein structure using only the relative positions, orientations, and backbone dihedral angles of nearby residues. Results We predict the sequence of a protein expected to fold into a specified structure and assess the accuracy of the prediction using both AlphaFold and RoseTTAFold to computationally generate the fold of the derived sequence. We show that the sequences predicted by SeqPredNN fold into a structure with a median TM-score of 0.638 when compared to the crystal structure according to AlphaFold predictions, yet these sequences are unique and only 28.4% identical to the sequence of the crystallized protein. Conclusions We propose that SeqPredNN will be a valuable tool to generate proteins of defined structure for the design of novel biomaterials, pharmaceuticals, catalysts, and reporter systems. The low sequence identity of its predictions compared to the native sequence could prove useful for developing proteins with modified physical properties, such as water solubility and thermal stability. The speed and ease of use of SeqPredNN offers a significant advantage over physics-based protein design methods

Calculating the statistical significance of physical clusters of co-regulated genes in the genome: the role of chromatin in domain-wide gene regulation

Physical clusters of co-regulated, but apparently functionally unrelated, genes are present in many genomes. Despite the important implication that the genomic environment contributes appreciably to the regulation of gene expression, no simple statistical method has been described to identify physical clusters of co-regulated genes. Here we report the development of a model that allows the direct calculation of the significance of such clusters. We have implemented the derived statistical relation in a software program, Pyxis, and have analyzed a selection of Saccharomyces cerevisiae gene expression microarray data sets. We have identified many gene clusters where constituent genes exhibited a regulatory dependence on proteins previously implicated in chromatin structure. Specifically, we found that Tup1p-dependent gene domains were enriched close to telomeres, which suggested a new role for Tup1p in telomere silencing. In addition, we identified Sir2p-, Sir3p- and Sir4p-dependent clusters, which suggested the presence of Sir-mediated heterochromatin in previously unidentified regions of the yeast genome. We also showed the presence of Sir4p-dependent gene clusters bordering the HMRa heterothallic locus, which suggested leaky termination of the heterochromatin by the boundary elements. These results demonstrate the utility of Pyxis in identifying possible higher order genomic features that may contribute to gene regulation in extended domains

Research capacity. Enabling the genomic revolution in Africa.

no availabl