Genome-wide mapping of nucleosome positions in Saccharomyces cerevisiae in response to different nitrogen conditions

Chromatin Regulators Ahc1p and Eaf3p Positively Influence Nitrogen Metabolism in Saccharomyces cerevisiae

There is a complex regulatory network of nitrogen metabolism in Saccharomyces cerevisiae, and many details of this regulatory network have not been revealed. This study explored the global regulation of nitrogen metabolism in S. cerevisiae from an epigenetic perspective. Comparative transcriptome analysis of S. cerevisiae S288C treated with 30 nitrogen sources identified nine chromatin regulators (CRs) that responded significantly to different nitrogen sources. Functional analysis showed that among the CRs identified, Ahc1p and Eaf3p promoted the utilization of non-preferred nitrogen sources through global regulation of nitrogen metabolism. Ahc1p regulated nitrogen metabolism through amino acid transport, nitrogen catabolism repression (NCR), and the Ssy1p-Ptr3p-Ssy5p signaling sensor system. Eaf3p regulated nitrogen metabolism via amino acid transport and NCR. The regulatory mechanisms of the effects of Ahc1p and Eaf3p on nitrogen metabolism depended on the function of their histone acetyltransferase complex ADA and NuA4. These epigenetic findings provided new insights for a deeper understanding of the nitrogen metabolism regulatory network in S. cerevisiae.

Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach

Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.

Regulation of alternative polyadenylation in the yeast Saccharomyces cerevisiae by histone H3K4 and H3K36 methyltransferases

Adjusting DNA structure via epigenetic modifications, and altering polyadenylation (pA) sites at which precursor mRNA is cleaved and polyadenylated, allows cells to quickly respond to environmental stress. Since polyadenylation occurs co-transcriptionally, and specific patterns of nucleosome positioning and chromatin modifications correlate with pA site usage, epigenetic factors potentially affect alternative polyadenylation (APA). We report that the histone H3K4 methyltransferase Set1, and the histone H3K36 methyltransferase Set2, control choice of pA site in Saccharomyces cerevisiae, a powerful model for studying evolutionarily conserved eukaryotic processes. Deletion of SET1 or SET2 causes an increase in serine-2 phosphorylation within the C-terminal domain of RNA polymerase II (RNAP II) and in the recruitment of the cleavage/polyadenylation complex, both of which could cause the observed switch in pA site usage. Chemical inhibition of TOR signaling, which causes nutritional stress, results in Set1- and Set2-dependent APA. In addition, Set1 and Set2 decrease efficiency of using single pA sites, and control nucleosome occupancy around pA sites. Overall, our study suggests that the methyltransferases Set1 and Set2 regulate APA induced by nutritional stress, affect the RNAP II C-terminal domain phosphorylation at Ser2, and control recruitment of the 3′ end processing machinery to the vicinity of pA sites.

Regulation and metabolic engineering strategies for permeases of Saccharomyces cerevisiae

Microorganisms have evolved permeases to incorporate various essential nutrients and exclude harmful products, which assists in adaptation to different environmental conditions for survival. As permeases are directly involved in the utilization of and regulatory response to nutrient sources, metabolic engineering of microbial permeases can predictably influence nutrient metabolism and regulation. In this mini-review, we have summarized the mechanisms underlying the general regulation of permeases, and the current advancements and future prospects of metabolic engineering strategies targeting the permeases in Saccharomyces cerevisiae. The different types of permeases and their regulatory mechanisms have been discussed. Furthermore, methods for metabolic engineering of permeases have been highlighted. Understanding the mechanisms via which permeases are meticulously regulated and engineered will not only facilitate research on regulation of global nutrition and yeast metabolic engineering, but can also provide important insights for future studies on the synthesis of valuable products and elimination of harmful substances in S. cerevisiae.

Nucleotide-Resolution Genome-Wide Mapping of Oxidative DNA Damage by Click-Code-Seq

Single-nucleotide-resolution sequencing of DNA damage is required to decipher the complex causal link between the identity and location of DNA adducts and their biological impact. However, the low abundance and inability to specifically amplify DNA damage hinders single-nucleotide mapping of adducts within whole genomes. Despite the high biological relevance of guanine oxidation and seminal recent advances in sequencing bulky adducts, single-nucleotide-resolution whole genome mapping of oxidative damage is not yet realized. We coupled the specificity of repair enzymes with the efficiency of a click DNA ligation reaction to insert a biocompatible locator code, enabling high-throughput, nucleotide-resolution sequencing of oxidative DNA damage in a genome. We uncovered thousands of oxidation sites with distinct patterns related to transcription, chromatin architecture, and chemical oxidation potential. Click-code-seq overcomes barriers to DNA damage sequencing and provides a new approach for generating comprehensive, sequence-specific information about chemical modification patterns in whole genomes.

Regulation of Sensing, Transportation, and Catabolism of Nitrogen Sources in Saccharomyces cerevisiae

Nitrogen is one of the most important essential nutrient sources for biogenic activities. Regulation of nitrogen metabolism in microorganisms is complicated and elaborate. For this review, the yeast Saccharomyces cerevisiae was chosen to demonstrate the regulatory mechanism of nitrogen metabolism because of its relative clear genetic background. Current opinions on the regulation processes of nitrogen metabolism in S. cerevisiae, including nitrogen sensing, transport, and catabolism, are systematically reviewed. Two major upstream signaling pathways, the Ssy1-Ptr3-Ssy5 sensor system and the target of rapamycin pathway, which are responsible for sensing extracellular and intracellular nitrogen, respectively, are discussed. The ubiquitination of nitrogen transporters, which is the most general and efficient means for controlling nitrogen transport, is also summarized. The following metabolic step, nitrogen catabolism, is demonstrated at two levels: the transcriptional regulation process related to GATA transcriptional factors and the translational regulation process related to the general amino acid control pathway. The interplay between nitrogen regulation and carbon regulation is also discussed. As a model system, understanding the meticulous process by which nitrogen metabolism is regulated in S. cerevisiae not only could facilitate research on global regulation mechanisms and yeast metabolic engineering but also could provide important insights and inspiration for future studies of other common microorganisms and higher eukaryotic cells.