In S. cerevisiae, spindle orientation is linked to the inheritance of the `old' spindle pole by the bud. A player in this asymmetric commitment, Bud6p, promotes cortical capture of astral microtubules. Additionally, Bud6p stimulates actin cable formation though the formin Bni1p. A relationship with the second formin, Bnr1p, is unclear. Another player is Kar9p, a protein that guides microtubules along actin cables organised by formins. Here, we ask whether formins mediate Bud6p-dependent microtubule capture beyond any links to Kar9p and actin. We found that both formins control Bud6p localisation. bni1 mutations advanced recruitment of Bud6p at the bud neck, ahead of spindle assembly, whereas bnr1Δ reduced Bud6p association with the bud neck. Accordingly, bni1 or bnr1 mutations redirected microtubule capture to or away from the bud neck, respectively. Furthermore, a Bni1p truncation that can form actin cables independently of Bud6p could not bypass a bud6Δ for microtubule capture. Conversely, Bud61-565p, a truncation insufficient for correct actin organisation via formins, supported microtubule capture. Finally, Bud6p or Bud61-565p associated with microtubules in vitro. Thus, surprisingly, Bud6p may promote microtubule capture independently of its links to actin organisation, whereas formins would contribute to the program of Bud6p-dependent microtubule-cortex interactions by controlling Bud6p localisation.
In addition to protein coding genes a substantial proportion of mammalian genomes are transcribed. However, most transcriptome studies investigate steady-state mRNA levels, ignoring a considerable fraction of the transcribed genome. In addition, steady-state mRNA levels are influenced by both transcriptional and posttranscriptional mechanisms, and thus do not provide a clear picture of transcriptional output. Here, using deep sequencing of nuclear RNAs (nucRNA-Seq) in parallel with chromatin immunoprecipitation sequencing (ChIP-Seq) of active RNA polymerase II, we compared the nuclear transcriptome of mouse anemic spleen erythroid cells with polymerase occupancy on a genome-wide scale. We demonstrate that unspliced transcripts quantified by nucRNA-seq correlate with primary transcript frequencies measured by RNA FISH, but differ from steady-state mRNA levels measured by poly(A)-enriched RNA-seq. Highly expressed protein coding genes showed good correlation between RNAPII occupancy and transcriptional output; however, genome-wide we observed a poor correlation between transcriptional output and RNAPII association. This poor correlation is due to intergenic regions associated with RNAPII which correspond with transcription factor bound regulatory regions and a group of stable, nuclear-retained long non-coding transcripts. In conclusion, sequencing the nuclear transcriptome provides an opportunity to investigate the transcriptional landscape in a given cell type through quantification of unspliced primary transcripts and the identification of nuclear-retained long non-coding RNAs.
DNA microarrays and RNA sequencing (RNA-seq) are major technologies for performing high-throughput analysis of transcript abundance. Recently, concerns have been raised regarding the concordance of data derived from the two techniques. Using cDNA libraries derived from normal human foreskin fibroblasts, we measured changes in transcript abundance as cells transitioned from proliferative growth to quiescence using both DNA microarrays and RNA-seq. The internal reproducibility of the RNA-seq data was greater than that of the microarray data. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarray values were moderate. The two technologies had good agreement when considering probes with the largest (both positive and negative) fold change (FC) values. An independent technique, quantitative reverse-transcription PCR (qRT-PCR), was used to measure the FC of 76 genes between proliferative and quiescent samples, and a higher correlation was observed between the qRT-PCR data and the RNA-seq data than between the qRT-PCR data and the microarray data.
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