Daughter-Specific Transcription Factors Regulate Cell Size Control in Budding Yeast

The asymmetric localization of cell fate determinants results in asymmetric cell cycle control in budding yeast

Repression of Meiotic Genes by Antisense Transcription and by Fkh2 Transcription Factor in Schizosaccharomyces pombe

In S. pombe, about 5% of genes are meiosis-specific and accumulate little or no mRNA during vegetative growth. Here we use Affymetrix tiling arrays to characterize transcripts in vegetative and meiotic cells. In vegetative cells, many meiotic genes, especially those induced in mid-meiosis, have abundant antisense transcripts. Disruption of the antisense transcription of three of these mid-meiotic genes allowed vegetative sense transcription. These results suggest that antisense transcription represses sense transcription of meiotic genes in vegetative cells. Although the mechanism(s) of antisense mediated transcription repression need to be further explored, our data indicates that RNAi machinery is not required for repression. Previously, we and others used non-strand specific methods to study splicing regulation of meiotic genes and concluded that 28 mid-meiotic genes are spliced only in meiosis. We now demonstrate that the “unspliced” signal in vegetative cells comes from the antisense RNA, not from unspliced sense RNA, and we argue against the idea that splicing regulates these mid-meiotic genes. Most of these mid-meiotic genes are induced in mid-meiosis by the forkhead transcription factor Mei4. Interestingly, deletion of a different forkhead transcription factor, Fkh2, allows low levels of sense expression of some mid-meiotic genes in vegetative cells. We propose that vegetative expression of mid-meiotic genes is repressed at least two independent ways: antisense transcription and Fkh2 repression

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Functional genetic discovery of enzymes using full-scan mass spectrometry metabolomics

Our understanding of metabolic networks is incomplete, and new enzymatic activities await discovery in well studied organisms. Mass spectrometric methods for measuring cellular metabolism reveal compounds inside cells that are unexplained by existing maps of metabolic reactions. Current computational models are unable to account for all activities and contents observed within cells. Additional large-scale genetic and biochemical approaches are required to elucidate metabolic gene function. We have used full-scan mass spectrometry metabolomics to examine deletions of candidate enzymes in the model budding yeast Saccharomyces cerevisiae and report the identification of twenty-five candidates that alter metabolite levels. Triumphs and pitfalls of metabolic phenotyping screens are discussed, including estimates of the frequency of uncharacterized eukaryotic genes affecting metabolism and key issues to consider when searching for new enzymatic functions in other organisms.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

New antisense RNAs appear during meiosis.

<p>Genes that have antisense RNA in meiosis are colored green, meiotic genes are colored red and predicted forkhead binding sites are shown as a red box. Red arrows illustrate transcripts that are induced in meiosis (6 hr). The consensus forkhead-binding motif (GTAAAYA) was used to predict forkhead-binding sites. All three examples shown here are new antisense RNAs associated with a predicted forkhead-binding site. (A) Antisense RNA from bi-directional transcription of a Mei4-responsive gene. (B) Discrete antisense RNA that may be induced by a nearby forkhead binding site. (C) Antisense RNA from the 3′UTR of a meiotic gene.</p

Most “splicing regulated” genes have abundant antisense RNA in vegetative cells.

<p>(A) Left: strand-specific splicing assay. Right: standard (non-strand specific) splicing assay. Same RNA samples were used in both assays. Three middle meiotic genes, <i>rem1, crp79</i> and <i>meu31</i>, were analyzed. The <i>dpb3⁺</i> control (+RT) indicates equal loading and the –RT control (minus reverse transcriptase) indicates that samples were not contaminated with genomic DNA. The standard splicing assay shows the unspliced or antisense transcript for all three genes in vegetative or early meiotic cells (2 and 4 hr), while there was no unspliced transcript detected in the same RNA samples using the strand specific splicing assay that only detects sense transcript. More examples can be found at <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029917#pone.0029917-Rhind1" target="_blank">[9]</a>. (B) All genes that were identified as having meiosis-specific splicing are shown here (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029917#pone.0029917-Kishida1" target="_blank">[16]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029917#pone.0029917-McPheeters1" target="_blank">[19]</a> and our unpublished data). Genes were separated into three groups, early, middle and late, according to their expression time <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029917#pone.0029917-Mata1" target="_blank">[26]</a>. Each gene had two values, one for sense RNA (blue bar) and one for antisense RNA (red bar). The values were calculated using average probe intensity on vegetative data. Most middle meiotic genes had much higher antisense RNA level than sense RNA level in vegetative cells. For these genes, the splicing results acquired from the non-strand specific splicing assay were significantly influenced by the presence of antisense RNAs.</p

Internal bi-directional transcription.

<p>7 kb window views are shown for three meiotic genes: (A) <i>bgs2⁺,</i> (B) <i>aah2⁺</i> and (C) <i>SPAC1039.11c</i>. For each gene, transcription initiates from an internal promoter that generates a 5′ truncated sense RNA and a divergent non-coding antisense RNA in vegetative cells. The motif (ACGCTC) that might drive the bi-directional transcription is labeled as intP, <u>int</u>ernal <u>p</u>romoter. During meiosis the <u>mei</u>otic <u>p</u>romoters, marked as meiP for <u>mei</u>otic <u>p</u>romoter, are activated and full-length sense RNAs are made. (B) The meiotic promoter of <i>aah2⁺</i> seems to induce bi-directional transcription that generates sense transcription of two meiotic genes, <i>aah2⁺</i> and <i>mok11⁺</i>. (C) Similarly, the meiotic promoter of SPAC1039.11c seems to induce bidirectional transcription and generates a new non-coding RNA (underlined by a green line) during meiosis.</p