- Research article
- Open Access
Identification and characterisation of non-coding small RNAs in the pathogenic filamentous fungus Trichophyton rubrum
- Tao Liu†1,
- Xianwen Ren†1,
- Tengfei Xiao†2,
- Jian Yang1,
- Xingye Xu1,
- Jie Dong1,
- Lilian Sun1,
- Runsheng Chen2Email author and
- Qi Jin1Email author
© Liu et al.; licensee BioMed Central Ltd. 2013
- Received: 8 September 2013
- Accepted: 20 December 2013
- Published: 30 December 2013
Accumulating evidence demonstrates that non-coding RNAs (ncRNAs) are indispensable components of many organisms and play important roles in cellular events, regulation, and development.
Here, we analysed the small non-coding RNA (ncRNA) transcriptome of Trichophyton rubrum by constructing and sequencing a cDNA library from conidia and mycelia. We identified 352 ncRNAs and their corresponding genomic loci. These ncRNA candidates included 198 entirely novel ncRNAs and 154 known ncRNAs classified as snRNAs, snoRNAs and other known ncRNAs. Further bioinformatic analysis detected 96 snoRNAs, including 56 snoRNAs that had been annotated in other organisms and 40 novel snoRNAs. All snoRNAs belonged to two major classes—C/D box snoRNAs and H/ACA snoRNAs—and their potential target sites in rRNAs and snRNAs were predicted. To analyse the evolutionary conservation of the ncRNAs in T. rubrum, we aligned all 352 ncRNAs to the genomes of six dermatophytes and to the NCBI non-redundant nucleotide database (NT). The results showed that most of the identified snRNAs were conserved in dermatophytes. Of the 352 ncRNAs, 102 also had genomic loci in other dermatophytes, and 27 were dermatophyte-specific.
Our systematic analysis may provide important clues to the function and evolution of ncRNAs in T. rubrum. These results also provide important information to complement the current annotation of the T. rubrum genome, which primarily comprises protein-coding genes.
- Trichophyton Rubrum
- snoRNA Gene
- Small Nuclear RNAs
- Potential Target Site
- ncRNA Candidate
Numerous studies have demonstrated that non-coding RNAs (ncRNAs) are widely expressed in both prokaryotes and eukaryotes [1–4]. Furthermore, the number of ncRNAs substantially increases with the complexity of the organism, whereas the number of protein-coding genes remains relatively static. In bacteria, unicellular eukaryotes, and invertebrates, the coding sequences constitute approximately 95, 30, and 20% of the genomic DNA, respectively. In mammals, open-reading frames only account for approximately 1–2% of the genomes [5–9].
NcRNAs include highly abundant and functionally important RNAs, such as transfer RNA (tRNA) and ribosomal RNA (rRNA), as well as other small, stable RNAs, such as small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), RNase P and mitochondrial RNA processing (MRP) RNA, signal recognition particle (SRP) RNA, and telomerase RNA. These RNAs have been characterised and are involved in splicing, ribosome biogenesis, translation, and chromosome replication [10, 11]. Recent transcriptomic and bioinformatic studies have also identified an increasing number of new ncRNAs whose function has not been validated [12–16]. Hence, the discovery and analysis of ncRNAs has become an important step in our understanding of genomic structure and will expand our knowledge of the function and the regulatory roles of ncRNAs in the cell cycle and development.
In recent years, ncRNAs have been identified using experimental methods and computational predictions in several fungi [3, 4, 17–22]. A large number of non-coding RNA genes, including 33 box C/D snoRNA genes, have been predicted in the genome of Schizosaccharomyces pombe. Functional analyses of 20 Box H/ACA snoRNAs indicated that the snoRNAs evolved in coordination with rRNAs to preserve post-transcriptional modification sites among distant eukaryotes [3, 4, 20]. A comparative genomics analysis of seven different yeast species identified a substantial number of evolutionarily conserved, structured ncRNAs, suggesting their roles in post-transcriptional regulation . NcRNAs that participate in the cleavage and processing of tRNAs were observed in Aspergillus fumigatus. An extensive analysis of snoRNA genes from Neurospora crassa indicated a high diversity of post-transcriptional modification guided by snoRNAs in the fungus kingdom . Thus far, the ncRNAs of dermatophytes have not been studied.
Trichophyton rubrum is the most common dermatophyte that can infect human keratinised tissue (skin, nails, and, rarely, hair) [23–25]. T. rubrum has a 22.5-Mbp haploid nuclear genome consisting of five chromosomes that range in size from 3.0–5.8 Mbp and a 27-kbp circular mitochondrial genome . The Broad Institute has sequenced the T. rubrum genome and predicted more than 8,700 protein-coding genes. However, apart from rRNAs and tRNAs, no other ncRNAs have been annotated and characterised within the T. rubrum genome . In the present study, we constructed an ncRNA library (ranging from 70–500 nt) and identified ncRNAs in T. rubrum using an RNA-Seq method. A total of 352 ncRNA candidates were characterised, including 198 entirely novel ncRNAs and 154 known ncRNAs. We also analysed the sequence conservation, and genomic location of these ncRNAs in six other dermatophytes. Our results may guide further studies of the important roles of ncRNA in T. rubrum and provide important complementary information to the annotation of the T. rubrum genome.
Identification of ncRNA candidates inT. rubrum
Characteristics of ncRNA candidates
The spliceosome contains five essential small nuclear RNAs (snRNAs)—U1, U2, U4, U5, and U6—that are essential components for assembling the spliceosome and accomplishing the intricate task of intron removal from newly synthesised eukaryotic RNAs [17, 18, 27]. Here, we identified the genomic loci of snRNAs U1, U2, U5, and U6, each of which exhibited a unique genomic location. U5 and U6 were the most abundant snRNAs among our data, found in 15,583 and 9,034 reads, respectively. The expression of U2 and U4 was lower than the other snRNA candidates; we found only 163 reads of U2 and 146 reads of U4. These results are in agreement with those of the small ncRNA transcriptome analysis of another filamentous fungus, A. fumigatus[21, 28]. U4 was not initially identified in our data. To find the U4 genomic locus in T. rubrum, we downloaded the U4 sequences of A. fumigatus, A. oryzae, and A. niger from Rfam to use as query sequences to search for homologues in the T. rubrum genome using BLASTn. One genomic locus was identified. Corresponding reads assigned to the same locus had been sequenced and clustered in our data but had been eliminated because the percentage of ORF in the cluster was greater than 80%.
Conservation level of snRNAs in T. rubrum and related dermatophytes
Conserved in dermatophytes (% sequence identity)
M. gypseum (98%), M. canis (98%), A. benhamiae (100%)
T. tonsurans (98%), T. equinum (98%), M. gypseum (97%), T. verrucosum (99%), M. canis (96%), A. benhamiae (99%)
T. tonsurans (100%), T. equinum (100%), M. gypseum (99%), A. benhamiae (100%)
T. tonsurans (92%), T. equinum (92%), M. gypseum (95%), T. verrucosum (93%), M. canis (91%), A. benhamiae (100%)
T. tonsurans (100%), M. gypseum (100%), M. canis (99%), A. benhamiae (100%)
In eukaryotic cells, two major classes of small nucleolar ncRNA (snoRNA) have been identified: C/D box snoRNAs, which are involved in the 20-O-methylation of ribosomal, spliceosomal, and transfer RNAs (the latter in Archaea only), and H/ACA snoRNAs, which guide pseudouridylation in these RNA species [29, 30].
C/D box snoRNA candidates identified in T. rubrum
25S: Am651, Gm654; 18S: Am1159
25S: Am2268, Am3277,Cm964,Cm961;U5: Cm103; 18S: Am1540
25S: Cm964, Cm961;18S: Um604; U5: Cm103
18S: Cm673, Gm234
25S: Um2301; Um769
18S: Am1105; 25S: Am499, Am1453
18S: Am350, Gm698, Cm701;25S: Gm215, Cm3127
18S: Um50, Cm379;25S: Cm2352
18S: Cm534; 25S: Cm1583, Cm1196, Cm3233
25S: Cm2324, Um2867; U2: Um43
18S: Am721; 25S: Gm2780; Am2243
25S: Cm1856,Cm1673; 18S: Am833; U2: Am155
18S: Um565, Am564
18S: Cm1674;25S: Cm2184, Am2266, Cm3294, Cm1758
SNORD27, U27, snR74
18S: Um525, Gm527
18S: Um418; 25S: Cm1363, Cm1633, Cm1983, Cm3165; U1: Cm45
18S: Cm1301,25S: Cm880
H/ACA box snoRNA candidates identified in T. rubrum
Other types of ncRNA inT. rubrum
We also identified 51 other ncRNA genomic loci, such as pri-miRNAs or pre-miRNAs, RNAse MRP, and telomerase RNA. miRNAs related transcriptional loci were the most widely distributed ncRNAs in the T. rubrum genome; for example, the mir-598 miRNA family had 13 transcriptional regions and mir-533 had eight. In our data, these miRNA homologies of ncRNAs, which varied from 70–270 bp, were much longer than the lengths of mature miRNAs (18–25 bp), they may be pri- or pre-miRNAs candidates.
Evolutionary conservation of the ncRNAs inT. rubrum
The ncRNA candidates specifically expressed in T. rubrum
The ncRNA candidates specifically expressed in dermatophytes
RNA is emerging as a central player in cellular regulation, with active roles in multiple regulatory layers, including transcription, RNA maturation, RNA modification, and translational regulation . Recent studies have revealed an unexpected complexity of regulatory RNAs, even in bacteria [2, 33]. In the present study, we first used an RNA-Seq method to analyse the ncRNAs in the genome of the dermatophyte fungus T. rubrum. We identified 352 sncRNA candidates, including snRNAs, snoRNAs, miRNAs, and other types of ncRNAs; 196 novel ncRNAs were predicted. We further confirmed the genomic loci of these ncRNAs in T. rubrum. This work provides an important complement to the current annotation of the T. rubrum genome, which is currently comprised primarily of protein-coding genes.
Five types of snRNAs (U1, U2, U4, U5, and U6) were identified, and their secondary structures were predicted by RNAfold . We found these snRNAs to be highly conserved among dermatophytes. We also detected 96 snoRNAs, including 55 that were annotated in other organisms and 41 that were novel snoRNAs. Using the Snoscan and snoGPS programs, we bioinformatically identified their potential target sites on rRNAs and snRNAs. miRNAs have been previously reported in some fungi, such as S. pombe, but have not been found in A. fumigatus[21, 34]. In our data, we detected 68 genomic loci corresponding to 12 miRNA families; the lengths of these ncRNAs varied from 80–270 bp, suggesting that they were pri-miRNAs or pre-miRNAs . To analyse the evolutionary conservation of ncRNAs, we aligned the 352 snRNAs to six other dermatophyte genomes and the NT database; we found 27 dermatophyte-specific ncRNAs and 11 T. rubrum-specific ncRNAs.
In this study, sequences for ncRNAs were obtained in T.rubrum and characterized by sequence comparison to know ncRNAs in other organisms, some of which were presumably functionally characterized in other work. This will prove to be a valuable resource but real understanding of regulatory mechanisms will come from followon work from this strong beginning.
Strain and culture conditions
The T. rubrum strain BMU01672 was grown on potato glucose agar (Difco) at 28°C for ten days to produce conidia. The conidia were isolated as previously reported, introduced into YPD medium (2% dextrose, 2% Bacto-Peptone, and 1% yeast extract), and incubated at 28°C with constant shaking at 200 rpm (Innova 4230 Refrigerated Incubator Shaker; New Brunswick Scientific, Edison NJ) . After culture, the mycelia were harvested and ground to a powder in liquid nitrogen for RNA extraction.
RNA extraction and cDNA library construction
Total RNA was extracted from conidia and mycelia using the RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Same amount of total RNA from conidia and mycelia was mixed and pooled on a denaturing 8% polyacrylamide gel [7 M urea and 1× TBE buffer (90 mM Tris, 64.6 mM boric acid, 2.5 mM EDTA, pH 8.3)]. We collected gel bands containing RNAs of 70–500 bp, excluding the 5.8S rRNA band. RNAs were passively eluted and then ethanol-precipitated. RNA size and concentration were quantified with the Agilent 2100 Bioanalyser and the Agilent RNA 6000 Pico Kit according to the manufacturer’s protocols. The fractionated RNA was dephosphorylated with FastAP (Fermentas) and ligated to the 3′-adaptor oligonucleotide (UUUUGACCACGGTACCCAG, RNA is underlined) by T4 RNA ligase (Promega). Subsequently, the RNA was reverse transcribed using oligo 3RT (CTGGGTACCGTGGTCAAA) and converted into double-stranded cDNA with a SuperScript Double-Stranded cDNA Synthesis Kit (Invitrogen). The ds-cDNA was purified using the MinElute Reaction Cleanup Kit (Qiagen) according to the manufacturer’s protocol.
454/Roche sequencing and data bioinformatic analysis
For 454/Roche sequencing, approximately 5 μg of the size-fractionated cDNA sample (70–500 bp) was blunted. The pieces were then ligated with short adaptors prior to amplification and sequencing. The sequencing run was performed using the method of Margulies et al..
After 454 sequencing, the 5′ and 3′ adaptors were removed from the reads. Genome data for T. rubrum and six related dermatophytes (Trichophyton equinum, Trichophyton tonsurans, Trichophyton verrucosum, Arthroderma benhamiae, Microsporum gypseum, and Microsporum canis) were downloaded from the Broad Institute web site (http://www.broadinstitute.org/annotation/genome/dermatophyte_comparative/MultiDownloads.html).
The high-quality reads were mapped to the genome using BLAST (version 2.2.22) (Eval < 1e − 5). Then, reads that were 80% mapped to the genome were clustered according to their genomic position and assembled into contigs according to the genomic sequence at the corresponding loci. The ORFs in the contigs were predicted using getorf in the EMBOSS program (version 6.3.1). Contigs with less than 80% ORF were aligned to TrED EST sequences and the NCBI non-redundant protein sequence database (NR) [38, 39]. The clusters with no hits in the TrED EST sequences and NR were used for the following steps: (1) alignment to non-coding RNA sequences with rRNA sequences downloaded from Rfam and GenBank , (2) identification of tRNAs with tRNAscan-SE (version 1.1) , and (3) alignment of clusters to Rfam sequences using HMMER (version 3.0)  and INFERNAL (version 1.0.2). The criteria for identification of known ncRNAs were as follows: (1) percentage of ORF less than 80%, (2) no hits in NR, (3) not mRNA, and (4) with homologues in Rfam [Eval (HMMER and INFERNAL) < 0.01]. For new ncRNA identification, the criteria were as follows: (1) percentage of ORF less than 80%, (2) no hits in NR, (3) not mRNA, (4) not rRNA, (5) not tRNA, and (6) no hits in Rfam (Eval > 0.01).
Analysis of snRNAs folding and predication of snoRNAs putative targets
T. rubrum snRNAs are compared with the homologs in other fungi using the multiple sequence alignment software ClustalW2. The secondary structures of aligned sequences are predicted by RNAalifold . The putative targets of snoRNAs were predicted by Snoscan and SnoGPS programs [17, 18]. The potential target sequences as the 5.8S, 18S, and 25S rRNAs of T. rubrum were downloaded from GenBank under the accession number JX431933.
To predict the two classes of snoRNAs and their putative targets in our data, we used the Snoscan and SnoGPS programs, defining the potential target sequences as the 5.8S, 18S, and 25S rRNAs of T. rubrum and all snRNAs identified in our data [17, 18].
Northern blot analysis
For the northern blot analysis, 10 μg of total RNA was separated by electrophoresis on an 8% polyacrylamide gel containing 7 M urea and then electrotransferred onto a nylon membrane (Hybond-N+; Amersham) using a semi-dry blotting apparatus (BioRad). A total of 24–30 mer DNA oligonucleotides antisense to snRNAs and 15 randomly selected ncRNA candidates were end-labelled with (γ32P)-ATP and hybridised at 45°C for 16 hr. After stringency washes, the blots were exposed to phosphor storage screens, which were then scanned with a Typhoon 9200 imager (GE Healthcare).
Nucleotide sequence accession numbers
The 352 ncRNAs sequences of T. rubrum were submitted to GenBank under the following accession numbers: KC352999 – KC353350.
This work was supported by the National Nature Science Foundation of China (Grant No. 30870104), the National High Technology Research and Development Program of China (Grant No. 2012AA020303), the National Science and Technology Major Project of China (Grant No. 2013ZX10004-601), and an intramural grant from the Institute of Pathogen Biology, Chinese Academy of Medical Sciences (Grant No. 2006IPB008).
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