Identification of Taxonomically Restricted Transcripts from Illumina RNA Sequencing Data.

In order to perform a well-balanced comparative transcriptomic analysis, the reference genome and annotations for all species included in the comparison must be of a similar quality and completeness. Frequently, comparative transcriptomic analyses include non-model organisms whose annotations are not as well curated; this inequality can lead to biases.To avoid potential biases stemming from incomplete annotations, a comparative transcriptomic analysis can incorporate de novo transcriptome assemblies for each species, which reduces this disparity. This chapter covers all of the steps which are necessary to run a comparative transcriptomic analysis with de novo transcriptome assemblies, from the first step of the experimental design to the sequencing, and ultimately the bioinformatic analysis.

Native RNA sequencing in fission yeast reveals frequent alternative splicing isoforms.

The unicellular yeast Schizosaccharomyces pombe (fission yeast) retains many of the splicing features observed in humans and is thus an excellent model to study the basic mechanisms of splicing. Nearly half the genes contain introns, but the impact of alternative splicing in gene regulation and proteome diversification remains largely unexplored. Here we leverage Oxford Nanopore Technologies native RNA sequencing (dRNA), as well as ribosome profiling data, to uncover the full range of polyadenylated transcripts and translated open reading frames. We identify 332 alternative isoforms affecting the coding sequences of 262 different genes, 97 of which occur at frequencies higher than 20%, indicating that functional alternative splicing in S. pombe is more prevalent than previously suspected. Intron retention events make about 80% of the cases; these events may be involved in the regulation of gene expression and, in some cases, generate novel protein isoforms, as supported by ribosome profiling data in 18 of the intron retention isoforms. One example is the rpl22 gene, in which intron retention is associated with the translation of a protein of only 13 amino acids. We also find that lowly expressed transcripts tend to have longer poly(A) tails than highly expressed transcripts, highlighting an interdependence between poly(A) tail length and transcript expression level. Finally, we discover 214 novel transcripts that are not annotated, including 158 antisense transcripts, some of which also show translation evidence. The methodologies described in this work open new opportunities to study the regulation of splicing in a simple eukaryotic model.

Uncovering de novo gene birth in yeast using deep transcriptomics.

De novo gene origination has been recently established as an important mechanism for the formation of new genes. In organisms with a large genome, intergenic and intronic regions provide plenty of raw material for new transcriptional events to occur, but little is know about how de novo transcripts originate in more densely-packed genomes. Here, we identify 213 de novo originated transcripts in Saccharomyces cerevisiae using deep transcriptomics and genomic synteny information from multiple yeast species grown in two different conditions. We find that about half of the de novo transcripts are expressed from regions which already harbor other genes in the opposite orientation; these transcripts show similar expression changes in response to stress as their overlapping counterparts, and some appear to translate small proteins. Thus, a large fraction of de novo genes in yeast are likely to co-evolve with already existing genes.

Extensive post-transcriptional buffering of gene expression in the response to severe oxidative stress in baker's yeast.

Cells responds to diverse stimuli by changing the levels of specific effector proteins. These changes are usually examined using high throughput RNA sequencing data (RNA-Seq); transcriptional regulation is generally assumed to directly influence protein abundances. However, the correlation between RNA-Seq and proteomics data is in general quite limited owing to differences in protein stability and translational regulation. Here we perform RNA-Seq, ribosome profiling and proteomics analyses in baker's yeast cells grown in rich media and oxidative stress conditions to examine gene expression regulation at various levels. With the exception of a small set of genes involved in the maintenance of the redox state, which are regulated at the transcriptional level, modulation of protein expression is largely driven by changes in the relative ribosome density across conditions. The majority of shifts in mRNA abundance are compensated by changes in the opposite direction in the number of translating ribosomes and are predicted to result in no net change at the protein level. We also identify a subset of mRNAs which is likely to undergo specific translational repression during stress and which includes cell cycle control genes. The study suggests that post-transcriptional buffering of gene expression may be more common than previously anticipated.

Transcriptomics data of 11 species of yeast identically grown in rich media and oxidative stress conditions.

OBJECTIVE: The objective of this experiment was to identify transcripts in baker's yeast (Saccharomyces cerevisiae) that could have originated from previously non-coding genomic regions, or de novo. We generated this data to be able to compare the transcriptomes of different species of Ascomycota. DATA DESCRIPTION: We generated high-depth RNA sequencing data for 11 species of yeast: Saccharomyces cerevisiae, Saccharomyces paradoxus, Saccharomyces mikatae, Saccharomyces kudriavzevii, Saccharomyces bayanus, Naumovia castelii, Kluyveromyces lactis, Lachancea waltii, Lachancea thermotolerans, Lachancea kluyveri, and Schizosaccharomyces pombe. Using RNA-Seq from yeast grown in rich and oxidative conditions we created genome-guided de novo assemblies of the transcriptomes for each species. We included synthetic spike-in transcripts in each sample to determine the lower limit of detection of the sequencing platform as well as the reliability of our de novo transcriptome assembly pipeline. We subsequently compared the de novo transcripts assemblies to the reference gene annotations and generated assemblies that comprised both annotated and novel transcripts.

Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage.

Isogenic cells show a large degree of variability in growth rate, even when cultured in the same environment. Such cell-to-cell variability in growth can alter sensitivity to antibiotics, chemotherapy and environmental stress. To characterize transcriptional differences associated with this variability, we have developed a method--FitFlow--that enables the sorting of subpopulations by growth rate. The slow-growing subpopulation shows a transcriptional stress response, but, more surprisingly, these cells have reduced RNA polymerase fidelity and exhibit a DNA damage response. As DNA damage is often caused by oxidative stress, we test the addition of an antioxidant, and find that it reduces the size of the slow-growing population. More generally, we find a significantly altered transcriptome in the slow-growing subpopulation that only partially resembles that of cells growing slowly due to environmental and culture conditions. Slow-growing cells upregulate transposons and express more chromosomal, viral and plasmid-borne transcripts, and thus explore a larger genotypic--and so phenotypic--space.