TERribly Difficult: Searching for Telomerase RNAs in Saccharomycetes

Abstract

The telomerase RNA in yeasts is large, usually >1000 nt, and contains functional elements that have been extensively studied experimentally in several disparate species. Nevertheless, they are very difficult to detect by homology-based methods and so far have escaped annotation in the majority of the genomes of Saccharomycotina. This is a consequence of sequences that evolve rapidly at nucleotide level, are subject to large variations in size, and are highly plastic with respect to their secondary structures. Here, we report on a survey that was aimed at closing this gap in RNA annotation. Despite considerable efforts and the combination of a variety of different methods, it was only partially successful. While 27 new telomerase RNAs were identified, we had to restrict our efforts to the subgroup Saccharomycetacea because even this narrow subgroup was diverse enough to require different search models for different phylogenetic subgroups. More distant branches of the Saccharomycotina remain without annotated telomerase RNA.

1. Introduction

The linear chromosomes of eukaryotes require a specialized mechanism for completing duplication. Most commonly this is achieved by a special reverse transcriptase, telomerase, that carries a specific RNA that includes the template with telomeric sequence [1]. Most likely, this constitutes the ancestral state in eukaryotes. Despite its crucial function, telomerase has been lost several times in both animals (in particular insects) and possibly also in some plants [2]. In some cases, the ancestral telomere structure has been replaced by tandem arrays of DNA sequences that look much like heterochromatin and can be elongated by gene conversion. Specialized telomere-specific retrotransposons are at work in Drosophila [3].

The telomerase (holo)enzyme consists of two main components, a specialized reverse transcriptase (TERT) and a RNA component (TER) that provides the template sequence. In addition, there are usually multiple clade-specific accessory protein components [4,5]. Four conserved regions in TER (Figure 1) are essential for telomerase activity: a core-enclosing helixig (CEH), template boundary element (TBE), the template sequence and a pseudoknot, are, in this order along the RNA, part of the the catalytic core [6]. The trans activating domain is involved in binding of TERT [7]. An Area of Required Connectivity (ARC) has been identified as the fifth essential element in Saccharomyces cerevisiae [6]. It is to some extent conserved in human telomerase RNA [8]. The ARC is not a localized sequence-structure feature but rather constitutes a combination of CEH, TBE, and some interactions between them. Hence, it is at least difficult to utilize the ARC for homology search. The three-way junction (TWJ) structure of this region region is widely conserved at least between animal and fungal telomerase RNAs (TER) [9]. It has been shown that, despite its conservation, the TWJ is not critical for TER function in S. cerevisiae [6,10,11]. The precisely defined template within TER is processively copied by TERT and regenerated, releasing a single-stranded DNA product [12].

Telomerase RNA is highly divergent. The TER in ciliates [14], human [15], and budding yeast [16,17,18] have a length of about 150 nt, 438 nt, and 1160 nt, respectively. A TER more than 2 kb in length has been reported for Candida glabrata [19], which, interestingly, seems to lack a TWJ. TERs in other kingdoms of eukaryotes have been discovered only quite recently in plants [20,21], excavates [22,23] and alveolates [24,25].

Despite their deeply conserved primary function and architectural similarities that seem to extend across eukaryotic kingdoms, TERs have turned out be very difficult to find by homology searches even within phylogenetically relatively narrow groups. Within the animal kingdom, even surveys of vertebrates turned out to be non-trivial [26]. Echinoderm TERs were found by deep sequencing of Strongylocentrotus purpuratus RNA pulled down with the TERT protein [27] after homology based searches remained unsuccessful. This opened the door to identifying TERs from other sea urchins, brittle stars, and a crinoid [28]. However, no TER from a protostome is known.

Within Fungi, the situation is similar: Thus far, TERs have been reported only for Ascomycota, while no candidates are known in Basidiomycota and any of the basal divisions. The TERs of Pezizomycotina and Taphrinomycotina share core features of vertebrate TERs. In particular, they have a fairly well-conserved secondary structure of the pseudoknot and the TWJ, and at least in these regions the sequence is sufficiently conserved for successful homology-based identification of TERs within these clades [29,30,31]. The TERs known for Saccharomycetes, the relatives of budding yeast, on the other hand, are sometimes remarkably large and present little similarity in sequence and secondary structure to vertebrate or ciliate TERs.

To-date, yeast TERs have been reported for three phylogenetically narrow subgroups (Saccharomyces spp. [16,17], Kluyveromyces spp. [9,32,33], and Candida spp. [34,35]), as well as some individual species such as Candida glabrata [19] and Hansenula polymorpha [36]. These sequences are already too diverse for reliable sequence alignments. It is not surprising, therefore, that simple sequence-based homology searches have not been successful in identifying TER in the majority of the saccharomycete genome sequences to-date. Even protein binding sites that are functionally important in budding yeast [37,38] are not widely conserved. For instance, Ku or Sm binding sites seem to be absent in the TERs of filamentous fungi [4,29].

The obvious alternative is to increase the set of known TERs by finding homologs that are sufficiently similar to one of known yeast TERs, to allow the construction of multiple alignments of phylogenetically narrow subgroup. From these alignments, conserved elements can be extracted, which in turn form the basis for searches with tools such as fragrep [39] or infernal [40]. This strategy has been successful in previous searches for TER genes in both animals [26] and fungi [29], but thus far has not been successfully applied to Saccharomycetes. Here, sequences are highly divergent, so that plausible multiple sequence alignments and secondary structure models derived from them can be obtained only for quite narrow phylogenetic groups. The best studied case is Saccharomyces sensu stricto [17,41].

Until very recently, a phylogenetically local approach to homology search was also hampered by the lack of a trustworthy phylogeny of the Saccharomycotina. Recent updates in the International Code of Nomenclature for algae, fungi and plants [42,43] have substantially restructured the classification of fungi in general and of Saccharomycotina in particular. With large-scale efforts to sequence fungal genomes underway, first phylogenomic studies provide a trustworthy backbone of Saccharomycotina phylogeny [44], which we largely confirmed with an independent analysis.

2. Materials and Methods

2.1. Phylogenomics of Ascomycotes

Annotated protein sequences for 72 yeast species were downloaded from the National Center for Biotechnology Information (NCBI) refseq database (https://www.ncbi.nlm.nih.gov/refseq/). Initially, ProteinOrtho [45,46] was used to identify an initial set of 21,289 ortholog groups. Only 193 of these contained representatives of all 72 species. We therefore included all 1666 ortholog groups that covered at least 67 species. We used OMA (2.2.1, with default settings) [47,48] to decompose the ProteinOrtho groups further into clusters of 1-1 orthologs. This resulted in 6295 groups, of which 841 contained at least 67 species. This conservatively filtered dataset was then processed with Gblocks [49] (version 0.91b, with default parameters) to remove uninformative and potentially error-prone parts of the alignment, resulting in a dataset comprising 72 species and 248,581 characters. Phylogenetic trees were estimated with RAxML [50].

2.2. Ascomycote Telomerase RNAs

Telomerase RNA regions have been published for several Saccharomyces [16,17], Kluyveromyces [9,32,33], and Candida [19,34,35] species. Most of these published TER regions are collected in the telomerase database [51], which therefore provided a good starting point for our research. These sequences, however, are too diverse to construct multiple sequence alignments beyond the three genera individually. This effectively prohibits the automated discovery of novel TERs beyond close relatives with the help of either BLAST [52] (using sequence information alone) or infernal (relying on a combination of sequence and secondary structure information).

Therefore, we explored different strategies to overcome the limitations imposed by the extremely poor sequence conservation of saccaromycete telomerase RNAs. The basic idea is to use common features of the TERs to extract candidates from the genomes that can be analyzed and then inspected further using different techniques.

First, we attempted to learn TER-specific sequence patterns using MEME/GLAM2 [53], and also several machine learning techniques using k-mer distributions within sequence windows of the size of the known TERs. All attempts to learn from a training set covering the Saccharomycetaceae or all Saccharomycotina species failed.

There are several possible reasons. Machine learning methods crucially depend on training and test sets, both positive and negative. In our case, we have few positive samples, these have poorly defined features, and are very diverse as far as their sequences are concerned. It is unclear in this setting how a negative training set should be properly designed. The obvious choice of picking genomic sequence at random may be confounded by unintended strong signals, such as coding potential or repetitive sequence elements. It would appear that at the very least a more a careful construction of the positive and negative sets, and an appropriate normalization or scaling of the feature data will be required to make progress in this direction. Restricting the training phase to a more narrow phylogenetic range to reduce the inherent diversity of the training data, on the other hand, is infeasible due to the small number of known TER sequences.

The EDeN motif finder [54] was applied to 24 known TERs as positive set and 48 shuffled sequences as negative data. Only trivial sequence motifs such a poly-U stretch, presumably corresponding to part of the U-rich pseudoknot region, were found. Unsupervised clustering also remained unsuccessful.

2.3. Synteny-Based Homology Search

As an alternative strategy, we established a semi-automated workflow that aims at first extracting partially conserved RNA sequence-structure elements, which are then used to identify candidate loci. In response to the negative results of a direct pattern-based approach, we systematically used synteny to narrow down the search space in the initial phase. Starting from a whole genome alignment of phylogenetically related species, we used the positions of protein coding genes whose homologs are known to be adjacent in a closely related species to delimit the syntenic regions that are likely to contain a TER gene. These candidate regions were then analyzed in detail by means of pairwise or multiple sequence alignments. Whenever a global alignment of the entire candidate syntenic region did not yield a plausible alignment, we attempted to identify conserved motifs inside the syntenic region (usually the SM binding site and/or the template region, which is sometimes conserved between close relatives). Typically, these motifs were also sufficient to determine the correct reading direction of the TER candidate.

To identify known features in the candidate TER regions, we first constructed infernal [40] covariance models restricted to subgroups of Saccharomycetaceae covering only substructures, such as the Ku hairpin, Est1 binding site, and TWJ in the Saccharomyces and Kluyveromyces species. The alignments underlying the infernal models were constructed with the help of many software tools, including locARNA [55], MAFFT [56], mauve [57], MEME [53] and fragrep [39], as well as manual curation. Default settings were used for all tools. The consensus structure for the Ku binding motive corresponds to a recent crystal structure [58]. These models were then analyzed with CMCws [59] and used for precise localization of conserved TER elements in species that were: (a) taxonomically closely related, but not/only partially annotated in the literature (Saccharomyces uvarum, Saccharomyces sp. “boulardii”, Saccharomyces sp. M14, and Saccharomyces eubayanus); or (b) phylogenetically located in the subtree spanned by the Saccharomyces and Kluyveromyces species (Figure 2). Both the ViennaNGS [60] suite and custom Perl/Python scripts were used for handling and conversion of genomic annotation data.

We then extracted a sequence corresponding to the most closely related TER sequence as initial estimate of the full-length TER gene. We used MAFFT [56] to produce initial sequence-based alignments of candidate regions, which were then realigned with locARNA [55] to obtain RNA structural alignments. The latter was used with its free-end-gaps option, in particular in those cases where MAFFT was not sensitive enough to reliably estimate the TER boundaries. Conversely, MAFFT was able to identify and correctly align highly conserved subsequences, providing reliable anchors for the more divergent sequence regions. While locARNA is good at finding locally conserved structures in the whole alignment, we expected only parts of the TER sequences to be structurally conserved. Typically, multiple iterations of refinement of the TER boundaries were required to obtain the final TER candidate sequence.

Following this approach, we could localize TER regions for several members of the Saccharomycetacea clade. Subsequent alignment of candidate regions with known TERs allowed for exact localization of TERs.

2.4. Search for Candidates Using Telomere Template Sequences

The scope of the synteny-based approach is limited because fungal genomes are subject to frequent genome rearrangements at the time-scales of interest. We therefore attempted to identify candidate regions containing the template sequence for the telomere repeats (see [61] for a comprehensive review of the characteristics of different telomeric repeats). In genomes for which these sequences have not been reported, we searched chromosome ends for telomeric repeats. Unfortunately, most genome assemblies are not on chromosome level or do not include the telomere regions, hence we only succeeded to newly identify the template region of Ashbya aceri and Eremothecium cymbalariae this way. For the latter species, the pertinent information is available in [62], although the telomeric repeat is not explicitly reported. In addition, we used the published telomere sequences from the telomerase database [51].

We used the concatenation of two copies of telomeric repeat sequence as query for a BLAST [52] search against the whole genome (in case of longer, complex repeats) or against the syntenic region for shorter repeats. Other template regions were identified with by aligning them to known sequences and/or BLAST searches of known template regions in closely related species. A typical feature of the template region, which helped us to verify our hits, is the fact that it usually contains a few nucleotides repeated at both the beginning and the end of the template region [19].

2.5. BLAST Pipeline

BLAST [52] is by far the most commonly used tool for homology search. While it has been reported to have limited sensitivity for telomerase RNAs in previous studies [26,27,39], it has contributed significantly to the identification of the TER sequences in other ascomycete clades [29,31]. Here, we used a set of known TER regions as BLAST queries that comprises all Saccharomycetales TER regions that we found in literature, as well as all TERs newly identified in the contribution. As targets for BLASTn (with default parameters) we used the full genomes of species that are featured at the NCBI refseq database within the Saccharomycetales group (Taxonomy ID: 4892). The resulting BLAST hits were then filtered for E-values ($E<0.1$), a minimum alignment length of 25 nt and a minimum identity of 60%. In addition, all hits on known telomeric regions were excluded. From the hits in genomes with known TERs, we computed the empirical false positive rate and found that the alignment length proved to be the most informative parameter. It has therefore been used to evaluate the reliability of hits, given their score.

The BLAST pipeline also contributed to the identification of the TER boundaries in some of the unannotated genomes. In cases where we initially chose the boundaries of our queries too generously and included neighboring coding regions or regulatory elements, the BLAST pipeline returned “false positive” hits. Thus, whenever multiple false positive hits in the beginning or the end of the query sequence occurred, we rechecked and, if necessary, improved the boundaries of the TER region.

3. Results

3.1. Phylogenomics of Saccharomycotina

The phylogenetic trees obtained of our phylogenomic analysis of the Saccharomycetales is essentially congruent with the one reported by Shen et al. [44] (see Figure A1 for more details). For consistency, we adopted the phylogenetic tree from the same publication as the basis for presenting our results.

3.2. Survey of Telomerase RNA Genes in Saccharomycotina

We initially screened 52 ascomycote genomes. Predominantly sequence-based methods (BLAST, but also MEME, GLAM2, and infernal) only contributed TERs from close relatives of baker’s yeast. The BLAST pipeline was applied to all 185 NCBI genomes Saccharomycetales, the subclade containing all known Saccharomycotina genomes. With the exception of the TER in Ogataea parapolymorpha, a very close relative of the known Ogataea polymorpha TER [36] all new sequences we found within the Saccharomycetaceae. We therefore restricted a more detailed analysis to this clade.

We found credible TER sequences in 46 of the 53 Saccharomycetaceae. Most of these TER sequences could be detected only after a short candidate region had been identified based on synteny. To our knowledge, at least 27 of these have not been reported previously.

3.3. Features of Telomerase RNA in Saccharomycetacea

To better understand the TER and its evolutionary constraints, at least within the Saccharomycetacea, we performed a detailed analysis of their structural features.

Table 1. Overview of conserved TER substructures in Saccharomycetacea, as identified by the combined synteny/covariance model pipeline. The 3${}^{\prime }$ end is defined as 10 nt downstream of the SM binding site. The 5${}^{\prime }$ end is approximate. Citations refer to publication in which the sequence and/or the coordinates of respective features are reported explicitly. These annotations form the basis of Figure 2.

Species	Accession	Strand	TER Coordinates	Ku Binding Site	Template Region	Est1 Binding Site	TWJ	SM1
K. aestuarii	AEAS01000245.1	neg	16,338–17,322 [32]		16,940–16,966	16,794–16,862 [32]	16,378–16,485 [9]	16,350–16,359
K. wickerhamii	AEAV01000432.1	pos	250–1327 [32]		662–693	765–858 [32]	1183–1290 [9]	1307–1316
K. marxianus	NC_036029.1	pos	506,443–507,711 [32]		506,855–506,888	506,967–507,049 [32]	507,518–50,7671 [9]	507,691–507,700
K. dobzhanskii	CCBQ010000012.1	pos	461,805–463,090 [32]		462,224–462,257	462,337–462,499 [32]	462,905–463,051 [9]	463,070–463,079
K. lactis	NC_006038.1	pos	611,456–612,727 [33]	absent [63]	611,890–611,919 [64]	612,006–612,090 [32]	612,532–612,687 [9]	612,708–612,716 [64]
E. coryli	AZAH01000001.1	neg	269,038–270,368		269,938–269,968
E. cymbalariae	NC_016454.1	pos	54,147–54,960		54,451–54,480
E. gossypii	NC_005782.2	neg	677,871–679,048 [65]		678,276–678,305 [35]
Ashbya aceri	CP006020.1	neg	693,543–694,708		693,942–693,973
L. kluyveri	CM000690.1	pos	348,600–349,844	348,876–348,930	348,957–348,982 [66]	349,129–349,208		349,825–349,833
L. lanzarotensis	NW_019212880.1	pos	854,162–855,236	854,389–854,444		854,754–854,820		855,217–855,225
L. waltii	AADM01000270.1	neg	134,961–136,000	135,698–135,756 [19]	135,613–135,636	135,409–135,470		134,973–134,981
L. thermotolerans	NC_013079.1	pos	702,500–703,549	702,730–702,791 [19]	702,853–702,876	703,022–703,083		703,530–703,538
L. dasiensis	LT598456.1	pos	682,034–682,916	682,124–682,181	682,261–682,283			682,900–682,905
L. sp. CBS 6924	LT598470.1	neg	441,802–442,700	442,582–442,638		442,229–442,292		441,811–441,820
L. fermentati	LT598488.1	neg	306,329–307,150	307,076–307,129		306,786–306,850		306,339–306,348
L. meyersii	LT598477.1	pos	575,851–576,676	575,886–575,941		576,233–576,294		576,657–576,666
L. mirantina	LT598468.1	pos	690,800–691,797			691,218–691,282		691,777–691,786
L. nothofagi	LT598449.1	pos	388,401–389,382	388,567–388,624		388,937–389,004		389,362–389,371
T. delbrueckii	NC_016504.1	pos	709,007–709,780	709,057–709,086		709,267–709,336		709,761–709,770
T. microellipsoides	FYBL01000005.1	neg	426,211–427,050	427,000–427,028		426,726–426,817		426,221–426,229
Z. bailii	HG316456.1	neg	712,655–713,400			712,902–712,974		712,665–712,673
Z. rouxii	NC_012990.1	pos	297,087–297,883			297,527–297,616		297,865–297,873
Z. parabailii	CP019499.1	pos	455,564–455,975 [67]			455,656–455,728		455,957–455,965
T. blattae	NC_020193.1	neg	404,150–405,050	405,003–405,033		404,650–404,733		404,165–404,173
N. castellii	NC_016499.1	pos	381,827–383,194 [64]	382,404–382,432 [19]	382,506–382,519 [64]	382,647–382,710 [64]	382,994–383,155 [64]	383,176–383,184 [64]
N. dairenensis	NC_016479.1	neg	1,519,837–1,521,377	1,520,648–1,520,678	1,520,550–1,520,562	1,520,303–1,520,369	1,519,864–1,520,027	1,519,849–1,519,857
C. castellii	CAPW01000002.1	neg	272,769–274,000		273,158–273,179	272,992–273,085		272,781–272,789
N. bacillisporus	CAPX01000073.1	pos	1230–2215					2197–2204
C. glabrata	NC_006032.2	neg	419,194–421,150 [19]	421,007–421,081 [19]	420,914–420,932 [19]	420,657–420,852 [19]		419,206–419,214 [19]
C. bracarensis	CAPU01000044.1	pos	2586–4361		2836–2854			4342–4350
N. delphensis	CAPT01000167.1	neg	254,761–256,469		256,151–256,169			254,773–254,781
C. nivariensis	CAPV01000033.1	pos	87,530–89,215		87,780–87,798			89,196–89,204
S. uvarum	NOWY01000011.1	pos	45,720–46,940	45,996–46,050	46,193–46,203	46,301–46,377	46,703–46,848	46,921–46,929
S. eubayanus	NC_030979.1	pos	476,134–47,7336	476,392–476,446	476,588–476,598	476,694–476,770	477,100–477,240	477,317–477,325
S. arboricola	NC_026172.1	pos	287,410–288,645	287,705–287,739	287,888–287,898	288,019–288,096	288,417–288,558	288,626–288,634
S. kudriavzevii	AY639012.1	pos	1–1215 [17]	284–320 [17]	424–434	585–662	981–1128 [9]	1201–1209
S. mikatae	AABZ01000048.1	neg	18,591–19,809 [17]	19,497–19,532 [17]	19,349–19,356	19,156–19,232	18,687–18,833 [9]	18,603–18,611
S. paradoxus	CP020294.1	pos	307,733–308,897 [17]	308,010–308,045 [17]	308,154–308,161	308,281–308,353	308,660–308,803 [9]	308,878–308,886
S. cerevisiae	NC_001134.8	pos	307,597–308,757 [17,18]	307,880–307,914 [68]	308,057–308,064 [64]	308,185–308,256 [32]	308,563–308,682 [9]	308,737–308,746 [64]
S. pastorianus	AZCJ01000004.1	neg	478,773–479,970 [17]	479,664–479,718 [17]	479,512–479,520	479,340–479,417	478,866–479,012 [9]	478,785–478,793
S. cer. x S. kud.	AGVY01000004.1	pos	284,183–285,344	284,465–284,501	284,645–284,655	284,772–284,843	285,150–285,269	285,325–285,333
S. bayanus	AACG02000058.1	pos	58,142–59,362 [17]	58,418–58,472 [17]	58,613–58,620	58,723–58,799	59,125–59,270 [9]	59,343–59,351
S. sp. ‘boulardii’	CM003558.1	pos	287,536–288,696	287,818–287,854	287,998–288,008	288,124–288,195	288,502–288,621	288,677–288,685
S. sp. M14	MVPU01000005.1	neg	473,800–474,997	474,691–474,745	474,537–474,547	474,368–474,444	473,894–474,038	473,812–473,820
S. cariocanus	AY639010.1	pos	1–1163 [17]	278–313 [17]	424–434	549–621	928–1072 [9]	1147–1155

summarizes the results of the homology search and the functional features of the candidate TER genes. A graphical overview is given in Figure 2.

The exact genomic positions marking the 3${}^{\prime }$ and 5${}^{\prime }$ ends of the TER RNA are difficult to determine without additional experimental evidence. The 5${}^{\prime }$ ends are therefore approximate. The 3${}^{\prime }$ end of the mature TER is produced by splicing in most Ascomycota [31,69,70]. This mechanism, however, was lost at some point during the evolution of the Saccharomycotina. It has been reported in the Candida group and for Ogataea angusta (previously H. polymorpha), but it is missing in Saccharomyces and Kluyveromyces [31]; hence, we expect that the splicing-based 3${}^{\prime }$-end processing was lost prior to the divergence of Saccharomycetacea. Indeed, no indication of a splice site was found for any of the TER sequences included in Table 1. We therefore used a position 10 nt downstream of the SM binding motif as approximation of the 3${}^{\prime }$ end in Table 1.

Several of the features listed in Table 1 have been discussed in some detail in the literature. Not all of them were found in all the candidates reported here. This may, in some cases, be explained by sequences that are too divergent to be detected. In other cases, most likely the function is not preserved. Unfortunately, many studies report neither complete sequences nor coordinates, making it effectively impossible to accurately compare their results with the annotation reported here. References are included in Table 1 if sufficient information was included to locate the features unambiguously.

No Ku binding hairpin was recovered in Kluyveromyces or the Eremothecium species. This is not unexpected since there is experimental evidence that neither the Ku binding hairpin nor its function is present in Kluyveromyces lactis [63]. The putative Ku binding hairpin reported for C. glabrata in [19] lacks experimental support and contains long insertions that made it impossible to include it in our covariance model. Furthermore, this region of the TER sequence is very poorly conserved in the closest relatives of C. glabrata. While the TER of C. glabrata is among the longest known members of this gene family [19], its close relative Candida castellii features a TER that has been shortened drastically in its 3${}^{\prime }$ half, with only ∼200 nt separating the EST1 and SM1 binding sites. Furthermore, the sequence GCUA, which is conserved in most known Ku binding sites, is not present within 600 nt upstream of the template region. The most likely explanation is that the TER of C. castellii (which, similar to C. glabrata, does not belong to the monophylogenetic genus Candida) (Appendix A) does not bind Ku. Of course, we cannot rule out without further experimental data that the motif has diverged beyond our ability to recognize it.

In a few species, we failed to identify the template region. In these cases (Lachancea, Zygosaccharomyces and Torulaspora species and Nakaseomyces bacillisporus), the telomeric repeat sequence is not known and seems to be very different from both the fungal consensus sequence TTAGGG [29] and the telomeric sequences found in closely related species.

The EST1 binding site forms a hairpin that is similar to the P3 domain of the RNase P and RNAse MRP RNAs [71]. We were not able to find an EST1 binding site in Eremothecium species, Lachancea dasiensis and in the C. glabrata group, even though it has been published for C. glabrata. While an EST1 binding site is present even in the more distantly related genus Candida [35], this motif is intrinsically too variable to be unambiguously recognizable in distant relatives. This pertains to both its sequence and the its base-pairing patterns.

Consistent with [19], we found no plausible secondary structure for the TWJ in C. glabrata, although the respective region of the sequence contains the highly conserved sequence AATA. It is worth noting in this context that the telomerase of the ciliate Tetrahymena has a stem-loop structure in place of the threeway junction [72]. TERs of the C. glabrata group thus may also have a functional trans-activation domain, albeit with an aberrant structure. Our TWJ covariance model, which was constructed from Kluyveromyces and Saccharomyces sequences only, also failed to detect a TWJ in Eremothecium and Lachancea. It remains an open question whether TERs of these species have a TWJ with a diverged structure that is just beyond our ability to detect, or whether trans-activation is achieved by different means. This is not implausible, given that the TWJ has been shown to be dispensable for TER function [6,10,11].

The sequence of the SM binding motif AATTTTTGG is perfectly conserved throughout much of the Saccharomycetaceae, with the notable exception of K. lactis [64] and additional small variations in other Kluyoveromyces species (Figure 3). We could not find this motif in species of the genus Eremothecium and the highly related species Ashba acerii.

4. Discussion

Although we succeeded in detecting 27 previously unknown TER sequences in Saccharomycetaceae, the main take-home message of this contribution is that homology search can be a terribly difficult problem. Although yeast TERs are quite long and fulfill a well-conserved function, their sequences are very poorly conserved. In this respect, yeast TER behaves similar to the the majority of long non-coding RNAs, which are also poorly conserved in sequence but often are evolutionary quite well conserved as functional entitied (see [73] for a recent review).

The “BLAST graph” in Figure 4 highlights the practical problem. Sequence comparison methods identify homology only in closely related species. A comparison of Figure 4 and a corresponding graph based on the previously published TER sequences only (see Supplementary Materials) shows that the larger set of queries identifies many additional connections and thus improves the situation at least within the Saccharomycetacea. Even within the clade, however, we have been unable to confirm the candidate hits in Kasachstania. The tree in Figure A1 indicates longer branch lengths leading to Kasachstania; it appears that the accelerated evolution of these genomes is already sufficient to hide the TER genes from our homology search methods.

While the direct sequence-based search against complete genomes was not very successful, we observed that the synteny-based approach worked remarkably well. This is not entirely unexpected, since the restriction to the interval between a pair of coding genes effectively reduces the size of the target from several million nucleotides to a few thousand. Unfortunately, the applicability of synteny-based methods is limited to relatively narrow phylogenetic scales. On longer time-scales, genome rearrangements are likely to disrupt syntenic conservation. A systematic exploitation of synteny similar to the work described here for Saccharomycetacea would most likely be successful in a survey for TER in the Candida group. In fact, synteny has been employed to find some of the known TERs in this clade [17,35].

The study presented here was largely conducted using publicly available tools complemented by some custom scripting. It also highlights the need for customized tools to conduct difficult homology searches. In particular, specific alignment tools and viewers to efficiently evaluate the synteny-based candidates relative to known template sequences and alignments of the better conserved regions would facilitate the manual curation efforts, which we found to be indispensable.

Finally, it remains on open question whether direct machine learning methods can be adapted as homology search tools, and, if so, whether such a strategy can be more effective than sequence comparison methods. It is likely that such efforts failed so far because of the difficulties inherent in the construction of a suitable negative training set that is not confounded by frequent genomic features such as coding sequence. Furthermore, the small number of positive samples was presumably insufficient to capture the full variability of TER sequences.

Complementarily, a phylogenetically dense sample of TERs that are sufficiently similar to support global sequence alignments might help to better understand the rapid divergence of TER sequences. This might be helpful not only to identify informative features for machine learning applications, but also to design modified sequence comparison algorithms that better reflect the peculiarities of rapidly evolving long non-coding RNAs. In this contribution, we have provided such a set of TERs for the Saccharomycetaceae.