Understanding non-coding DNA regions in yeast - Semantic Scholar

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Understanding non-coding DNA regions in yeast Margarita Schlackow* and Monika Gullerova†1 *Mathematical Institute, University of Oxford, Oxford OX1 3LB, U.K., and †Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, U.K.

Abstract Non-coding transcripts play an important role in gene expression regulation in all species, including budding and fission yeast. Such regulatory transcripts include intergenic ncRNA (non-coding RNA), 5 and 3 UTRs, introns and antisense transcripts. In the present review, we discuss advantages and limitations of recently developed sequencing techniques, such as ESTs, DNA microarrays, RNA-Seq (RNA sequencing), DRS (direct RNA sequencing) and TIF-Seq (transcript isoform sequencing). We provide an overview of methods applied in yeast and how each of them has contributed to our knowledge of gene expression regulation and transcription.

Introduction Three decades ago, studies of gene expression regulation were based mostly on biological and biochemical analyses of a specific gene. Technological advances of experimental strategies enabled a rapid development of the genome-wide approaches involving quantitative transcriptome sequencing and subsequent bioinformatic and statistical analyses. These offer a much deeper and general understanding of transcriptional regulation. Regulatory elements relate to transcribed, but not translated, nucleic acid regions such as intergenic ncRNA (non-coding RNA), 5 and 3 UTRs of a gene, introns and antisense transcripts. In the present review, we focus on RNA polymerase IItranscribed regulatory elements in the fission yeast Schizosaccharomyces pombe and the budding yeast Saccharomyces cerevisiae. These two commonly used model organisms have contributed substantially to the comprehension of eukaryotic genome function. Investigative techniques of the yeast genome have undergone a dramatic evolution. Starting from the analysis of individual genes [1], followed by DNA microarrays [2], ESTs [3], RNA-Seq (RNA sequencing) [4,5], DRS (direct RNA sequencing) [6] and TIF-Seq (transcript isoform sequencing) [7], they are providing more and more advances in the technical aspect of experiments and consequently result in larger and more detailed datasets.

Experimental approaches employed to determine transcribed genomic regions Expressed sequence tags ESTs are partial reads of cDNA sequences. They provide the possibility of evaluating gene expression dependent on Key words: budding yeast, fission yeast, polyadenylation, sequencing, untranslated region (UTR). Abbreviations used: CUT, cryptic unstable transcript; DRS, direct RNA sequencing; ncRNA, non-coding RNA; NUE, near-upstream element; PAS, polyadenylation signal; priRNA, primary small RNA; RNA-Seq, RNA sequencing; SAGE, serial analysis of gene expression; snoRNA, small nucleolar RNA; SUT, stable unannotated transcript; TIF-Seq, transcript isoform sequencing; TSS, transcription start site; uORF, upstream ORF; VT, virtual terminator. 1 To whom correspondence should be addressed (email [email protected]).

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the cell cycle stage and cell type [8]. Isolated eukaryotic mRNA is reverse-transcribed into cDNA, which is inserted into a suitable vector (Figure 1A). Inserted fragments are sequenced by priming their ends in randomly selected clones. Bioinformatic analysis removes low sequence reads and contaminating vector sequence. Individual EST reads can vary greatly in length, up to 800 nt [8]. Although ESTs have proved to be useful in studying gene expression, several disadvantages have pushed them to the sideline of currently used techniques. In particular, low numbers of full-length ESTs, chimaeric sequences due to cDNA template switching [9], internal cDNA priming events and low-quality sequences at the EST ends [10] (reviewed in [11]) led to the development of further approaches to study gene expression.

DNA microarrays DNA microarrays became one of the most popular tools to measure transcriptional activity and genotype in multiple genomic regions. Fluorescently labelled cDNA or cRNA (complementary RNA) can hybridize to microscopic spots on a surface, each containing one specific fluorescent DNA probe (Figure 1B). A laser-scanning microscope detects the fluorescent signal corresponding to sample–probe hybridization [12,13]. Microarrays enable parallelization of data acquisition, allowing gene comparison. A major challenge of these microarrays is the elimination of crosshybridization. Additionally, microarrays depend on specific probes to allow the detection of particular gene expression. ESTs, in this case, are helpful for probe design. The more recent tiling arrays are independent of gene annotation and can probe for any genomic sequence. This allows the representation of non-repetitive DNA at various sequence resolutions, thus enabling the discovery of novel transcripts and regulatory elements [14]. High-density oligonucleotide tiling arrays allowed the re-annotation of gene boundaries and the estimation of levels of coding and non-coding transcripts in S. cerevisiae [15] and S. pombe [16]. Biochem. Soc. Trans. (2013) 41, 1654–1659; doi:10.1042/BST20130144

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Figure 1 Schematic illustration of experimental techniques RNA is extracted from a population of cells. (A) ESTs. The extracted RNA is converted into cDNA and inserted into a plasmid. Random clones are sequenced by 5 and 3 end priming. (B) DNA microarray. The RNA from a population of interest and a control population is extracted and reverse-transcribed into cDNA. Each population is differently fluorescently labelled. The samples are spotted on to a microarray slide, whose spots contain different probes to which the samples can hybridize by complementarity. Different levels and colours of fluorescence imply where and how strong the probed region is expressed. (C) RNA-Seq. After reverse transcription and shearing of the cDNA, the fragments undergo adapter ligation. Size-selected fragments are added to a flow cell, which is covered with oligonucleotides binding to the adapters. Bound sequences are locally amplified and sequenced via addition of reversibly chemically blocked nucleotides. Strand-specific and non-strand-specific RNA-Seq protocols are employable. (D) DRS. Poly(dT) oligonucleotides cover a surface which binds the polyadenylated RNA. A ‘fill and lock’ step fills in the overhanging poly(A) tail and locks the first non-A nucleotide with a complementary fluorescently labelled VT–nucleotide. The dye–nucleotide is then chemically cleaved and the next VT–nucleotide can be incorporated and imaged. This step is then repeated. (E) TIF-Seq. The 5 end is capped. Full-length cDNA is generated and the 5 end is biotin-tagged. The molecules are then circularized and sheared. The sequence is obtained by paired end sequencing.

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RNA-Seq DNA tiling arrays could fail to identify short exons and precise UTR boundaries. They may give high falsepositive rates of transcribed regions and cannot detect posttranscriptionally modified sequences [17]. RNA-Seq is a more recent quantitative method allowing estimation of gene expression levels, where cDNA reads are mapped back to genomic regions [18]. Unique DNA adapters are attached to both ends of sheared DNA fragments (60% of budding yeast genes [20]. Additionally, co-ordination between sense and antisense polyadenylated transcript levels plays a role in gene expression regulation. Genes that are expressed at low levels in budding yeast show a positive correlation between sense and antisense transcripts, whereas highly expressed genes demonstrate a negative correlation [20]. In contrast, in fission yeast, tiling array analysis suggests that most antisense transcripts are not polyadenylated [16]. This method also detected a likely propensity of highly expressed genes and histone-depleted regions to show elevated antisense transcription [16]. The antisense transcripts generally occur more frequently in the 3 UTR than in the 5 UTRs, which implies that they may also arise from overlapping 3 UTRs, as shown by RNA-Seq [17]. However, the identification of antisense transcription using any method that involves reverse transcription of RNA to cDNA requires caution, as this can cause secondary mispriming. Generally, it seems that antisense transcription is lower than sense transcription [15,19]. Also, some antisense transcripts are part of lncRNAs (long ncRNAs). These long antisense transcripts, as well as some long intergenic transcripts are present at less than one copy number per cell, which could reflect their tight repression at transcriptional, post-transcriptional or chromatin levels [5].  C The

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An important difference between S. cerevisiae and S. pombe is the lack of an RNAi pathway in budding yeast. The RNAi pathway in fission yeast and in higher eukaryotes is induced by dsRNA formation. dsRNA is processed into short RNA molecules (siRNA), which targets nascent mRNA via complementarity or induce heterochromatin formation and consequent gene silencing at the transcriptional level. Multiple protein complexes, including Dicer [35], are involved in the RNAi pathway. Small RNA libraries are prepared by size-selecting molecules, followed by RNA-Seq, which determines sequence content [36]. It is apparent that RNAi in S. pombe acts jointly with the exosome to repress developmentally regulated genes and retrotransposons. Furthermore, similar analysis in Dicer-knockout cells [37] revealed Dicer-independent priRNAs (primary small RNAs). It appears that priRNAs originate from degradation of abundant transcripts, and they target antisense transcripts arising from bidirectional transcription of DNA repeats.

Conclusion Over the years, increasingly sophisticated experimental and analytical tools have given scientists the opportunity to gain a deeper insight into gene regulation elements. These may range from the production of regulatory ncRNA, which cause a gradient of genetic repression and even gene silencing, up to coding transcript variation. Transcript diversity and functionality may depend on the protein sequence [17,19], translational activity [32] or deletion/retention of binding sites for RNA-binding proteins [7]. Sequencing resources have not yet reached their limits. Their depth and precision grow with newly developing techniques. In the present review, we have summarized a few significant experimental techniques and regulatory factors in several regions on the genome. Moreover, we have provided a simple overview of RNA polymerase II-transcribed regulatory elements. Many other regulatory elements such as rRNA, tRNA, spliceosomal RNA, snoRNA, telomerase RNA, signal recognition particle RNA, RNA components of RNase P and RNase MRP and mitochondrial RNA [38] can be also detected by the techniques described. To date, many effects in gene expression are not fully classified, and possibly only vaguely predicted. We anticipate that the rapid development and reduction in the costs of high-throughput sequencing, which dramatically increases the available genomic and transcriptome sequence resources in many organisms, will lead to the discovery of more regulatory RNAs. These resources, together with the powerful tools of comparative genomics have potential to bring new insights into the functional dynamics of gene expression regulation.

Acknowledgement We thank Eleanor White for valuable comments on our paper.

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Funding This work was supported by the Engineering and Physical Sciences Research Council (to M.S.), a L’Oreal/UNESCO Woman in Science UK and Ireland award (to M.G.) and a Medical Research Council Career Development Award (to M.G.).

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Received 8 July 2013 doi:10.1042/BST20130144

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