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. 2019 Mar 12;14(3):e0212996. doi: 10.1371/journal.pone.0212996

Computational prediction of microRNAs in marine bacteria of the genus Thalassospira

Thi Hoang Yen Dang 1, Sonika Tyagi 2,3, Glenn D’Cunha 2, Mrinal Bhave 1, Russell Crawford 4, Elena P Ivanova 1,4,5,*
Editor: Patrick Jon Biggs6
PMCID: PMC6413936  PMID: 30861013

Abstract

MicroRNAs (miRNAs) are key players in regulation of gene expression at post-transcription level in eukaryotic cells. MiRNAs have been intensively studied in plants, animals and viruses. The investigations of bacterial miRNAs have gained less attention, except for the recent studies on miRNAs derived from Streptococcus mutans ATCC 25175 and Escherichia coli DH10B. In this study, high-throughput sequencing approach was employed to investigate the miRNA population in bacteria of the genus Thalassospira using both the miRDeep2 and CID-miRNA methods. A total of 984 putative miRNAs were identified in 9 species of the genus Thalassospira using both miRDeep and CID-miRNA analyses. Fifty seven conserved putative miRNAs were found in different species of the genus Thalassospira, and up to 6 miRNAs were found to be present at different locations in the T. alkalitolerans JCM 18968T, T. lucentensis QMT2T and T. xianhensis P-4T. None of the putative miRNAs was found to share sequence to the reported miRNAs in E. coli DH10B and S. mutans ATCC 25175. The findings provide a comprehensive list of computationally identified miRNAs in 9 bacterial species of the genus Thalassospira and addressed the existing knowledge gap on the presence of miRNAs in the Thalassospira genomes.

Introduction

MicroRNA (miRNA) is a class of small, non-coding RNA molecules containing 19–22 nucleotides. Since the discovery of miRNA in Caenorhabditis elegans [1], a large number of predicted miRNA molecules has been reported in animals, plants and viruses as key players in regulation of gene expression networks [2–5]. In bacteria, the small non-coding RNAs (sRNAs) have been demonstrated to have a similar function to eukaryotic miRNA, in modulating the target mRNAs in various ways at a post-transcriptional level [6]. A number of sRNAs have been identified in bacteria, some of which were identified in marine bacteria such as Vibrio spp. and Synechococcus spp. that are functional analogues to plant miRNAs in response to environmental changes [7, 8]. The investigations of bacterial miRNAs, however, have gained little attention, except for the recent studies on miRNAs derived from Streptococcus mutans ATCC 25175 and Escherichia coli DH10B [9, 10].

The genus Thalassospira includes Gram-negative, aerobic and halophilic bacteria dwelling in a marine environment [11]. Bacteria of this genus are involved in the biodegradation of a variety of hydrocarbons [12–14]. For example, T. tepidiphila 1-1BT and T. povalilytica Zumi 95T have the ability to degrade polycyclic aromatic hydrocarbons and polyvinyl alcohol [12, 13]; and T. australica NP 3b2T is able to utilise poly (ethylene) terephthalate (PET) plastic as a carbon source [14]. Currently the genus Thalassospira is comprised of 10 validly named species, of which the whole genome sequences of four species (T. australica NP 3b2T, T. lucentensis QMT2T, T. profundimaris WP0211T and T. xiamenensis M-5T) have been assembled and deposited in public databases [15–17]. The presence of miRNAs in bacteria of this genus has, however, not been demonstrated. Here, the investigation of miRNA populations was carried out using 9 validly named Thalassospira species, i.e., T. alkalitolerans JCM 18968T, T. lucentensis QMT2T, T. mesophila JCM 18969T, T. povalilytica Zumi 95T, T. profundimaris WP0211T, T. tepidiphila 1-1BT, T. xiamenensis M-5T, T. xianhensis P-4T and T. australica NP 3b2T. Since Thalassospira lohafexi 139Z-12T was not validly published until 2015 [11], this species was not included in this study. A deep sequencing approach has been used to identify miRNAs in bacteria in other studies [10]. Thus, the aim of this work was to identify the potential miRNAs in bacteria of the genus Thalassospira using computational approaches from small RNA sequence dataset generated by high-throughput sequencing technology.

Materials and methods

Bacterial strains and growth conditions

Nine type strains of Thalassospira species were obtained from various culture collections and used in this study. T. australica JCM 31222T, T. alkalitolerans JCM 18968T, T. mesophila JCM 18969T, T. povalilytica JCM 18746T, T. tepidiphila JCM 14578T and T. xianhensis JCM 14850T were obtained from RIKEN BRC-Japan Collection of Microorganisms (JCM) and T. lucentensis DSM 14000T, T. profundimaris DSM 17430T and T. xiamenensis DSM 17429T were obtained from German Collection of Microorganisms and Cell Cultures (DSMZ). Bacteria were aerobically grown at their stationary phase in marine broth 2216 (BD, U.S.A.) at 22 oC for 48 h [18, 19]. Bacterial cells were collected by centrifugation and used for RNA extraction or stored at -80°C in marine broth 2216 (BD, USA) supplemented with 20% (v/v) glycerol.

RNA isolation and construction of small RNA libraries

Total RNAs were extracted from bacterial cells of Thalassospira species using TRIsure reagent (Bioline, Australia) according to the manufacturer’s instructions. The RNA concentration and purity were determined by measuring the absorbance at 260 nm and A260/A280 ratio using Nanodrop (ThermoFisher, Australia). The integrity of extracted RNAs was examined by a Bioanalyzer (Agilent Technology, USA). A RIN value ≥ 5 was applied for selection of good RNA quality with RIN (RNA integrity number) generated for each sample based on the ratio of ribosomal bands and the presence or absence of degradation products on the electrophoretic image. Small RNA-Seq libraries were prepared using the TruSeq Small RNA library preparation kit for Illumina (New England Biolabs, USA). Briefly, rRNA was depleted with Ribo Zero. RNAs were fragmented by heat and divalent cations and the 1st strand cDNA was synthesised with SuperScript II Reverse Transcriptase (Invitrogen). The DNA fragments were 3’adenylated, ligated with 3’ and 5’ adapters and amplified via PCR (13 cycles). The amplified products were loaded on 6% polyacrylamide for small RNA (18–35 nucleotides) selection. The amplicons of small RNA were purified and sequenced using Illumina Hiseq2500 sequencing platform with 50 bp single end chemistry. The raw sequencing data generated from this study was submitted to the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) under accession number PRJNA505357.

Bioinformatics analysis of bacterial miRNAs

The raw reads (18–35 nucleotides) generated from the Illumina sequencing were first filtered by removing any contaminations including rDNA, Illumina small RNA adapter sequences or low-quality reads with < 18 nt length using Cutadapt [20]. The remaining reads after filtering were then aligned against the Thalassospira genome sequences available in the NCBI database using Bowtie (version 1.1.2) with parameters setting (-n 0 -e 80 -l 18 -a -m 5—best–strata) [21]. The whole genome sequences used as references for these 9 Thalassospira species were listed in S1 Table. The mapped sequences to genomes were further processed to remove redundancy and normalised expression was computed as CPM (Reads counts per Million) [22]. After normalisation, these mapped sequences were used to predict miRNAs. Two different miRNA prediction algorithms, miRDeep2 and CID-miRNA were used to detect miRNA in this study. The miRDeep2 software (version 2.0.0.7, http://www.mdc-berlin.de/rajewsky/miRDeep) was utilised to predict miRNAs based on an investigation of the secondary structure of the miRNA precursor sequences and integration of miRNA precursors with Dicer, providing the precursors and mature miRNA sequences [23]. CID-miRNA [24] is a method to identify miRNA precursors based on secondary structure filter and an algorithm of stochastic context free grammar (SCFG) [24]. This method only predicts the miRNA precursors. Therefore, MatureBayes (http://mirna.imbb.forth.gr/MatureBayes.html) was employed to identify mature miRNAs based on the sequence and structure of the miRNA precursors provided by CID-miRNA [25]. The small sequences mapped to the genome were processed through miRDeep2 ver2.0.0.7 as well as CID-miRNA and MatureBayes for identifying mature miRNAs with default parameters. The detected mature miRNAs were further investigated by searching any shared miRNA sequences generated in both methods using BLAST algorithm (version 4) with default setting. The potential precursor sequences predicted by both methods were also BLAST-searched to find any shared precursor sequences. Any shared miRNA sequences were then confirmed using ClustalW2 (https://www.ebi.ac.uk/Tools/msa/clustalw2/) with default setting. In order to find any conserved miRNA across the genus, the predicted mature miRNAs were aligned to those reported in Escherichia coli DH10B and Streptococcus mutans ATCC 25175 [9, 10]. The candidates were also aligned to miRNAs predicted within the genus Thalassospira to identify any conserved miRNAs across all bacteria within the genus.

Phylogenetic analysis and genome comparison

The 16S rRNA gene sequences of validly described Thalassospira species were collected from NCBI (https://www.ncbi.nlm.nih.gov/) and compared using the ClustalW2 (https://www.ebi.ac.uk/Tools/msa/clustalw2/) with default setting. Evolutionary phylogenetic trees were then generated using the neighbour-joining (NJ) algorithm [26]. Genetic distances for the NJ tree were calculated using Kimura’s two-parameter model [27], with the robustness of 1,000 replications, using MEGA 7 software [28].

Results and discussion

Generation of small RNA libraries and data evaluation

Small RNA-seq libraries were constructed for nine Thalassospira species, including T. alkalitolerans JCM 18968T, T. lucentensis QMT2T, T. mesophila JCM 18969T, T. povalilytica Zumi 95T, T. profundimaris WP0211T, T. tepidiphila 1-1BT, T. xiamenensis M-5T, T. xianhensis P-4T and T. australica NP 3b2T. Next-generation small RNA-seq was used for detection of the presence of small RNAs. The results yielded 242,204,737 sequence reads of 18–33 nucleotides (nt) length, ranging from 22.99 to 31.43 million reads for 9 libraries (Table 1). The reads were curated for any contamination or artefacts such as rDNA or Illumina small RNA adaptor sequences using Cutadapt [20]. The cleaned reads were then aligned to the Thalassospira genome sequences available in the NCBI database using Bowtie software [21].

Table 1. Bacterial small RNA-Seq data yield from Hiseq2500 sequencing.

Samples T. australica NP 3b2T T. alkalitolerans JCM 18968T T. lucentensis QMT2T T. mesophila JCM 18969T T. povalilytica Zumi 95T T. profundimaris WP0211T T. tepidiphila 1-1BT T. xiamenensis M-5T T. xianhensis P-4T
Original number of readsa 22,991,595
 31,431,409
 28,737,289
 27,213,115
 28,225,762
 25,272,618
 25,641,708
 28,144,650
 24,546,591
Reads after filter and collapseb 3,177,403 5,068,260 4,606,730 2,954,668 3,761,719 5,327,201 4,368,445 1,712,391 3,300,999
Reads aligning to reference genomec 2,285,671 160,990 2,511,917 102,497 95,972 2,046,567 1,230,299 1,143,173 631,902
Aligned reads, % 71.94 3.18 54.53 3.47 2.55 38.42 28.16 66.76 19.14
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a: total reads obtained from different bacterial species

b: reads obtained after filtering

c: reads were aligned to the Thalassopsira genome sequences using the Bowtie algorithm. The reads obtained from this experiment were set from 18–33 nt using an Illumina genome analyser with a low error rate (0.005 error per base).

Since the whole genome sequences of T. australica NP 3b2T, T. lucentensis QMT2T, T. profundimaris WP0211T and T. xiamenensis M-5T were available in the NCBI database, the small RNA sequence reads were aligned to these genome sequences. The remaining 5 Thalassospira species that did not have the whole genome sequence were aligned to these references based on their phylogenetically closest species [14] (S1 Table, S1 Fig). By aligning 34.23 million collapsed reads from nine species of the genus Thalassospira to the genome sequences available in NCBI, it was found that a proportion of cleaned reads was mappable to the reference genomes, ranging from 38.42% to 71.94% for 4 species with available genome sequences and from 2.55% to 28.16% for the remaining 5 species without whole genome sequenced (Table 1). The aligned reads were further analysed for miRNA identification.

Identification of miRNAs using the mirDeep2 method

Identification of bacterial miRNAs is in early stages in comparison to that in animals and plants. Therefore, no specific method has been developed for investigation of miRNAs in bacteria. miRDeep2 software (http://www.mdc-berlin.de/rajewsky/miRDeep) was first presented as an algorithm to predict miRNA in human and animals [23]. This software was later used to successfully identify miRNAs in plant [29]. Based on the applicability in different organisms, miRDeep2 software was, therefore, employed to detect putative bacterial miRNAs from the millions of short sequences generated from the dataset. The miRDeep2 software was designed to detect putative miRNAs based on an investigation of the secondary structure of the miRNA precursor sequences. The potential miRNA precursors were then integrated with a model for miRNA precursor processing using Dicer, releasing the mature miRNA sequences, star sequences (also called miRNA5p and miRNA3p sequences) and the loop [23]. By using miRDeep2, 86 potential miRNA precursor sequences could be identified from the dataset. The mature miRNA, star sequence and the loop were also identified within each precursor (Fig 1).

Fig 1. An example of miRDeep output for T.prof_5p_16873.

Fig 1

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(A) Predicted secondary structure of miRNA precursor with the mature, star and loop sequences (highlighted in red, yellow and purple, respectively). The scores associated to the miRNA, the read counts for mature, star and loop sequences and the total read count are also given. (B) Sequences of the predicted miRNA precursors mapped to the mature, star and loop sequences on the genome (obs line) and the experimental sequence reported in miRBase (exp line). The frequency (reads column) and mismatches of the read with the genomic sequence (mm column) are also given with the mismatches shown in capital letters.

The identified 86 putative mature miRNAs were located in either the 5’ arm or the 3’ arm of the precursors, with 11 sequences from T. australica NP 3b2T, 11 from T. lucentensis QMT2T, 14 from T. profundimaris WP0211T, 6 from T. xiamenensis M-5T, 11 from T. alkalitolerans JCM 18968T, 5 from T. mesophila JCM 18969T, 4 from T. povalilytica Zumi 95T, 9 from T. tepidiphila 1-1BT and 15 from T. xianhensis P-4T (Table 2) being obtained.

Table 2. miRNAs of bacteria of the genus Thalassospira identified using miRDeep2.

miRNA name* Location Sequence (5’-3’) Length (nucleotides) Number of reads
T. australica NP 3b2T
T.aust_3p_10018 JRJE01000008.1_scaffold_30_10018 TAACGTCTGTCCTTCGGATT 20 2252
T.aust_5p_26441 JRJE01000032.1_scaffold_0_26441 CTTGGCAGGCTGGGCGCTCC 20 845
T.aust_3p_11556 JRJE01000009.1_scaffold_3_11556 TTTTGACTGGATCGGCAACCGTGAT 25 151
T.aust_5p_26002 JRJE01000032.1_scaffold_0_26002 TTTGGCGGGGTCGGGAACC 19 88
T.aust_5p_15617 JRJE01000022.1_scaffold_18_15617 ATCCTCTCCCCGCAACCA 18 2888
T.aust_5p_9704 JRJE01000008.1_scaffold_30_9704 ATTGGCGTCACAGATCAGGGGCAT 24 15
T.aust_3p_23062 JRJE01000031.1_scaffold_1_23062 GAAATCCCTGATCGCGCAG 19 13
T.aust_5p_10770 JRJE01000009.1_scaffold_3_10770 ACAAATCTCGGCAAGGCC 18 229
T.aust_5p_13346 JRJE01000019.1_scaffold_20_13346 CGAACTCTGCACCAAGGC 18 12
T.aust_5p_22895 JRJE01000031.1_scaffold_1_22895 CGAATCTCTCATCACCCACCA 21 4199
T.aust_5p _5988 JRJE01000005.1_scaffold_5_5988 AAACCGGATCCTGCAGCC 18 9
T. alkalitolerans JCM 18968T
T.alka_5p_5662 ATWN01000006.1_5662 TCACCGGTTGGGAAGGCGCTGA 22 15
T.alka_3p_6962 ATWN01000007.1_6962 CTTCCCGCCCCATGGCCGA 19 14
T.alka_3p_6323 ATWN01000007.1_6323 AATTAATGGGTCCTGACC 18 336
T.alka_5p_4136 ATWN01000004.1_4136 TTTCGGGTGGGCAGCGCC 18 1
T.alka_3p_1438 ATWN01000001.1_1438 CGCATGGGCGGAGCTTTTCGTTAG 24 13
T.alka_3p_6324 ATWN01000007.1_6324 AATTAATGGGTCCTGACC 18 336
T.alka_3p_2121 ATWN01000002.1_2121 ATTGATTGCGGCCATCCG 18 11
T.alka_5p_4462 ATWN01000005.1_4462 CAAGAACCGCCATCTGCATGCC 22 9
T.alka_5p_1889 ATWN01000002.1_1889 ATGCTTTTTGGCCGCATT 18 1
T.alka_3p_75 ATWN01000001.1_75 CGAGGTCGAACATGATGAA 19 25
T.alka_5p_3621 ATWN01000003.1_3621 ATGTTGCCGGTGCGGCGGCGGGC 23 7
T. lucentensis QMT2T
T.luce_5p_27853 ATWN01000011.1_27853 CCGAGGTCCGGTATCGCCTGACT 23 13160
T.luce_5p_31831 ATWN01000015.1_31831 ATCGTGGCCGCACTGGAGCC 20 907
T.luce_3p_14305 ATWN01000004.1_14305 CGCGCAGGCGGGGATCTCGAGC 22 1860
T.luce_5p_11956 ATWN01000003.1_11956 ATCGCTGCGGGCAATAAAAGACC 23 60
T.luce_5p _2660 ATWN01000001.1_2660 TTACCCGTGAGGTCGGCTGTGCGAT 25 163
T.luce_5p_10500 ATWN01000003.1_10500 ATAATGACGTCCGTTGCGAC 20 922
T.luce_3p_13875 ATWN01000004.1_13875 AAACGGGGTCGGGGGGCTG 19 3395
T.luce_3p_12006 ATWN01000003.1_12006 ACCACAGGTGCGGGCATGGGCATG 24 158
T.luce_5p_14218 ATWN01000004.1_14218 AAAGCCCCGGCGCGATTGTCC 21 158
T.luce_3p_14713 ATWN01000004.1_14713 TTTGCGCGATGGGTCCCTGAT 21 17
T.luce_3p_22524 ATWN01000007.1_22524 TCACAGTCGAGACGCTCTCTCACC 24 50057
T. mesophila JCM 18969T
T.meso_5p_3464 ATWN01000007.1_3464 ATAAGGAGTAGGCGAATGAGC 21 69
T.meso_3p_3562 ATWN01000007.1_3562 TCACAGTCGAGACGCTCTCTCACC 24 10
T.meso_3p_3026 ATWN01000006.1_3026 CTTGGCGTCGAAGGCATGA 19 2
T.meso_5p_2868 ATWN01000006.1_2868 TTTGGCAAGGCACAGCGCGCAG 22 9
T.meso_3p_3086 ATWN01000006.1_3086 CTGCGCGCTGTGCCTTGCC 19 9
T. povalilytica Zumi 95T
T.pova_5p_4964 AMRN01000014.1_4964 ATCTTTCGATGGTCGTGGCA 20 251
T.pova_5p_4720 AMRN01000012.1_4720 CCAAGCGCGGTGCGGACCG 19 21
T.pova_5p_661 AMRN01000001.1_661 ATGGGCATCCTGACCGAAGGCACG 24 8
T.pova_5p_390 AMRN01000001.1_390 CTTGAAGACCTGCATCAGCGTTC 23 7
T. profundimaris WP0211T
T.prof_5p_16873 AMRN01000003.1_16873 ACGAATAGCATGTCGATGGC 20 50145
T.prof_5p_35057 AMRN01000009.1_35057 ATCGCCTGAACGCGCGCCTGACCG 24 1316
T.prof_5p_36012 AMRN01000010.1_36012 GTCCGGTGGTCTGGGCACCATG 22 1679
T.prof_5p_14768 AMRN01000003.1_14768 ATCCTGCCCCCGCAACCA 18 721
T.prof_5p_660 AMRN01000001.1_660 CTATGCAGACACCCCGGAC 19 1702
T.prof_5p_26017 AMRN01000006.1_26017 ATCACGTTGAGCCAAAAGAAAAGC 24 18
T.prof_5p_15266 AMRN01000003.1_15266 ATACAACTGATGTCGCCTGC 20 693
T.prof_3p_2147 AMRN01000001.1_2147 ATCCTCGGAATAGGTATAGGCTTCC 25 57
T.prof_3p_20338 AMRN01000004.1_20338 ATCAATCGCCGGGATCATGATCCC 24 76
T.prof_5p_12543 AMRN01000002.1_12543 ATATACGGCCTGGCATAATC 20 2
T.prof_5p_2228 AMRN01000001.1_2228 TTTGCGGAATGCCACCCGGCAACG 24 10
T.prof_5p_19418 5’ AMRN01000004.1_19418 GTGTTCTTTTGGTCGCGCATGCCG 24 11
T.prof_3p_6436 AMRN01000001.1_6436 AAAAGACCGTCCTGCCACCG 20 6
T.prof_3p_49 AMRN01000001.1_49 CTCCTGAGCCGGGCCAAT 18 9
T. tepidiphila 1-1BT
T.tepi_5p_8152 AMRN01000002.1_8152 ATAATGACGTCCGTTGCGA 19 90
T.tepi_5p_16607 AMRN01000006.1_16607 TTCAAGTCTGATGCCCGCGCC 21 9
T.tepi_5p_22622 AMRN01000010.1_22622 GTCCGGTGGTCTGGGCACCATG 22 252
T.tepi_5p_2762 AMRN01000001.1_2762 GCAGTGGCTTGGCGGGATCGGGAT 24 148
T.tepi_5p_19992 AMRN01000008.1_19992 GGGCCGAGATCGAAAGCAACACG 23 17
T.tepi_5p_8570 AMRN01000003.1_8570 ATCCTGCCCCCGCAACCA 18 1135
T.tepi_5p_11638 AMRN01000004.1_11638 TTTGTCGTTCTGGGCTGGCA 20 12
T.tepi_5p_21088 AMRN01000009.1_21088 ATTGATATCGCATCGGTTACCGA 23 65
T.tepi_5p_23416 AMRN01000011.1_23416 GTATATTGCCAATTTTGT 18 88
T. xiamenensis M-5T
T.xiam_3p_12910 CP004388.1_12910 CTTGCCGCCGGTATGCTCGCATC 23 2582
T.xiam_5p_18209 CP004388.1_18209 ATGCAGATCGGTTTGCGCACC 21 47
T.xiam_5p_1950 CP004388.1_1950 ACGGTTTGCGTCGGTCACGCTGGC 24 331
T.xiam_5p_12633 CP004388.1_12633 ATGGACTCCCGCTTTCGC 18 12
T.xiam_5p_12105 CP004388.1_12105 ATTTGCATGCCCGTCTGGC 19 13
T.xiam_3p_3097 CP004388.1_3097 AGCATTCAAGCATCGGCGGGAT 22 13
T. xianhensis P-4T
T.xian_5p_4477 CP004388.1_4477 TAGGCGGGAGTCCACCGGGC 20 5
T.xian_3p_19710 CP004388.1_19710 GGATCAGCTGGGTAACATC 19 11
T.xian_3p_20546 CP004388.1_20546 CTTGCACCGGGCCGCTTTCGGATG 24 14
T.xian_3p_19849 CP004388.1_19849 ACGCGACCGCGGCAAGGAAA 20 24
T.xian_5p_25207 CP004388.1_25207 AAAGCAGGAAGAATACGAACAGA 23 27
T.xian_5p_31617 CP004388.1_31617 ATGCACCCGGACCGAAACCC 20 558
T.xian_3p_3269 CP004388.1_3269 AAAGCGCGCCCCCTTGCTCCC 21 43
T.xian_5p_9480 CP004388.1_9480 ATTCAGGAATCTGTTCTGACGCAGC 25 26
T.xian_5p_19360 CP004388.1_19360 ATTTTAGTCCGCGTCGCAAC 20 13
T.xian_3p_38616 CP004388.1_38616 CAAACAGCTGAAGGCCTCCC 20 166
T.xian_3p_35228 CP004388.1_35228 ATTCCGATGATCTGGTGATTG 21 11
T.xian_3p_13589 CP004388.1_13589 TTGCCGATCATCGCCCTTGCCCTG 24 32
T.xian_5p_34659 CP004388.1_34659 CTTCAGTTCCTCGACCTT 18 13
T.xian_3p_4479 CP004388.1_4479 CTGACTGGATTCCCGCGT 18 9
T.xian_3p_32022 CP004388.1_32022 ATCATGCCGGGCAGATCA 18 8
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*: The identified miRNAs were named as <name of bacteria>_<location of miRNA in the precursor>_<position of miRNA in the genome>.

The identified putative miRNAs are 18–25 nucleotides in length (Table 2), which is in the range previously reported for animal and plant miRNAs [30]. These results are also consistent with the length distribution of miRNAs recently detected in E. coli DH10B and Streptococcus mutans ATCC 25175 [8, 9]. Based on the number of read counts, these putative miRNAs appear to have various expression levels among nine libraries, ranging from one to thousands of reads in each library, under the normal growth condition. Among the 86 detected putative miRNAs, the highest expression was found in T.prof_5p_16873, with 50145 reads, while the lowest expression was shared between T.alka_5p_4136 and T.alka_5p_1889 with only one read each being found in the libraries. Of the 86 detected putative miRNAs, thirteen were in high abundance, with over a thousand reads [10] with three miRNAs from T. australica NP 3b2T, four from T. lucentensis QMT2T, four from T. profundimaris WP0211T and one each from T. tepidiphila 1-1BT and T. xiamenensis M-5T being identified. The high degree of expression of these putative miRNAs suggested that they may play specific roles involving in the growth and development processes of these bacteria (Fig 2).

Fig 2. Expression of miRNAs detected in bacteria of the genus Thalassospira.

Fig 2

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Based on number of reads, the high degree of expression of miRNAs was found in T. australica NP 3b2T, T. lucentensis QMT2T, T. profundimaris WP0211T, T. tepidiphila 1-1BT and T. xiamenensis M-5T.

Identification of miRNAs using CID-miRNA method

The 86 putative miRNAs detected in nine Thalassospira species using miRDeep method appeared to be rather low in number compared to 400 miRNAs identified in E. coli DH10B [10] and 900 miRNAs in Streptococcus mutans ATCC 25175 [9]. Therefore, an alternative method, CID-miRNA[24] was employed in the expectation of identifying further potential miRNAs from the dataset. CID-miRNA is a web-server developed for the identification of the miRNA precursors based on the secondary structure filter and an algorithm of stochastic context free grammar (SCFG) [24]. The server was first used to predict the potential miRNA precursors in human genome [24] which was later applied in animals such as mouse, zebrafish and sea squirt [31]. Using this method, 449 potential miRNA precursors were identified from over 242 million reads of T. australica NP 3b2T (77 potential miRNA precursors), T. lucentensis QMT2T (161), T. profundimaris WP0211T (78), T. xiamenensis M-5T (16), T. alkalitolerans JCM 18968T (21), T. mesophila JCM 18969T (1), T. povalilytica Zumi 95T (2), T. tepidiphila 1-1BT (61) and T. xianhensis P-4T (32).

In order to identify the mature miRNAs, these precursor sequences were further analysed by employing another web tool, MatureBayes (http://mirna.imbb.forth.gr/MatureBayes.html). MatureBayes is designed to incorporate to the Naïve Bayes classifier for identifying the putative mature miRNA molecules based on the sequence and structure of the miRNA precursors [25]. This tool provided putative mature miRNA positions in the 5’ and 3’ direction of the miRNA precursors, resulting in 898 putative mature miRNAs in the 9 Thalassospira species, comprised of T. australica NP 3b2T (154 miRNAs), T. lucentensis QMT2T (322 miRNAs), T. profundimaris WP0211T (156 miRNAs), T. xiamenensis M-5T (32 miRNAs), T. alkalitolerans JCM 18968T (42 miRNAs), T. mesophila JCM 18969T (2 miRNAs), T. povalilytica Zumi 95T (4 miRNAs), T. tepidiphila 1-1BT (122 miRNAs) and T. xianhensis P-4T (64 miRNAs) (S2 Table). These identified putative miRNAs were used for further analysis of miRNA conservation.

Comparison of bacterial miRNAs identified using miRDeep2 and CID-miRNA

In order to determine whether any miRNAs were detected by both methods, miRNA data generated from miRDeep were BLAST-searched to those produced by CID-miRNA. The results showed that there were no shared putative mature miRNA sequences. miRDeep2 predicts putative miRNAs based on distribution of minimum free energy (MFE) and stability of secondary structure established for nematode, C. elegans, while CID-miRNA utilised secondary structure filter and stochastic context free grammar trained on human miRNAs [23, 24]. The differences of parameter setting may cause no overlap of putative mature miRNA sequences detected in both methods. Thakur et al [32] also pointed out that application of new parameters improved the accuracy of plant miRNA prediction using miRDeep in compared to the default setting for animals.

Study on miRNA biogenesis showed that one or more mature miRNAs can be produced from one pre-miRNA molecule [33]. These identified putative miRNAs, therefore, were aligned to the potential precursor miRNA sequences generated by both methods. BLAST-search between putative mature miRNA sequences identified from miRDeep2 and the potential precursor miRNAs detected by CID-miRNA was carried out and vice versa. 5 putative mature miRNA sequences identified by CID-miRNA (T.luce_5p_228121, T.luce_3p_228121, T.xian_5p_2738, T.xian_5p_2740 and T.xian_3p_2740) were found to locate in 3 potential precursor miRNAs producing T.luce_5p_11956, T.xian_5p_4477 and T.xian_3p_4479 from miRDeep2. The precursor miRNA sequences producing 5 putative mature miRNAs from CID-miRNA were then aligned to 3 potential precursor miRNAs from miRDeep2 using ClustalW2. The result showed the overlap of 3 precursor miRNAs predicted in both methods in which the putative mature miRNAs identified by each method located at different positions within the precursor sequences (Fig 3).

Fig 3. Sharing sequences of potential miRNA precursors identified in both miRDeep2 and CID-miRNA methods.

Fig 3

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(A) T.luce_228121 precursor from CID-miRNA and T.luce_5p_11956 precursor from miRDeep2. (B) T.xian_2738 precursor from CID-miRNA and T.xian_5p_4477 precursor from miRDeep2. (C) T.xian_2740 precursor from CID-miRNA and T.xian_3p_4479 precursor from miRDeep2. The asterisk (*) indicate that the nucleotides are identical to the top sequence. The putative mature miRNAs are highlighted in grey. (CID-miRNA) indicated the potential precursor detected by CID-miRNA method. (miRDeep2) indicated the potential precursor detected by miRDeep2 method.

The shared sequences from the same Thalassospira species proved the probability of putative miRNAs finding in both methods. Thus, a total of 984 putative miRNA candidates were preliminarily obtained from both methods with default setting together for 9 species (Table 3). Further work, such as real time PCR or Northern blot analysis, is needed to verify these putative miRNAs.

Table 3. Identification of miRNAs using miRDeep2 and CID-miRNA methods.

Organism miRDeep2 analysis CID-miRNA analysis Total
T. alkalitolerans JCM 18968T 11 42 53
T. australica NP 3b2T 11 154 165
T. lucentensis QMT2T 11 322 333
T. mesophila JCM 18969T 5 2 7
T. povalilytica Zumi 95T 4 4 8
T. profundimaris WP0211T 14 156 170
T. tepidiphila 1-1BT 9 122 131
T. xianhensis P-4T 15 64 79
T. xiamenensis M-5T 6 32 38
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Conservation of miRNAs in the genus Thalassospira and miRNAs previously described in Streptococcus mutans ATCC 25175 and E. coli DH10B

Previous studies showed the conservation of some miRNAs across animals or the plant kingdom [34, 35]; however, novel sequences can only be found in a particular species. The conserved and novel miRNAs in bacteria still remain largely unknown, in comparison to intensive studies of miRNAs in eukaryotic organisms and viruses. In this study, 984 putative miRNA candidates of the genus Thalassospira were BLAST-searched against those reported for E. coli DH10B (400 miRNAs) and S. mutans ATCC 25175 (900 miRNAs) [9, 10] in order to identify any conserved miRNA. However, no conserved sequences could be found without any mismatch or with three and fewer nucleotide substitution. E. coli is an enteric bacterium commonly found in low intestine of humans and animals [36], S. mutans is an oral pathogen that causes human dental caries [37], while bacteria of the Thalassospira genus are environmental bacteria [14]. The differences in their characteristics may influence the low degree of conservation of miRNAs among these bacteria. The results obtained in this study is in agreement with previous studies that reported the lack of significant sequence similarity in non-coding RNA homologues of different bacterial species [38, 39].

In order to identify any conserved miRNAs among species of the genus Thalassospira, the putative miRNA sequences obtained were compared. It appears that the nine species shared 57 common putative miRNAs. Among these, T. profundimaris WP0211T and T. tepidiphila 1-1BT had the highest number of conserved miRNAs, with 45 sequences presenting in both species. Five putative miRNAs in T. alkalitolerans JCM 18968T were also found in the T. lucentensis QMT2T, while T. xianhensis P-4T and T. xiamenensis M-5T shared 4 conserved sequences. One sequence was shared between T. australica NP 3b2T and T. tepidiphila 1-1BT, T. lucentensis QMT2T and T. tepidiphila 1-1BT and T. lucentensis QMT2T and T. mesophila JCM 18969T (Table 4). As seen from the data, T. lucentensis QMT2T has identical miRNA sequences as T. alkalitolerans JCM 18968T (5 sequences), T. tepidiphila 1-1BT (1) and T. mesophila JCM 18969T (1), while T. tepidiphila 1-1BT also shared sequences with T. profundimaris WP0211T (45) and T. australica NP 3b2T (1).

Table 4. Conserved miRNAs in different species of bacteria of the genus Thalassospira.

miRNA sequence T. alkalitolerans JCM 18968T T. australica NP 3b2T T. lucentensis QMT2T T. mesophila JCM 18969T T. profundimaris WP0211T T. tepidiphila 1-1BT T. xianhensis P-4T T. xiamenensis M-5T
TTAATCCGGACCCATTAATTAT T. alka_5p_4942 T. luce_5p_520914
CATAATTAATGTGTTCGGAACT T. alka_3p_4942 T. luce_3p_520914
ATCAGGTCGAAGCCATGACCAT T. alka_5p_1819 T. luce_5p_218949
TCTGGCATCGGCGTTTCTATCG T. alka_5p_4684 T. luce_5p_498538
CGTCGTCTGATCCGCTTTGCCA T. alka_3p_4684 T. luce_3p_498538
AGCGACAACGCCGGTGGGATCA T. profu_5p_37885 T. tepi_5p_24769
TGCCACCGGCGTTGTTGTCTTC T. profu_3p_37885 T. tepi_3p_24769
AAGAAGCAGCGTCGGCCAGCCA T. profu_5p_14495 T. tepi_5p_9431
GCCGGCGCTGCCTCAACTCGTT T. profu_3p_14495 T. tepi_3p_9431
ATCAAAAAGGCGGAGCTGATTT T. profu_5p_9602 T. tepi_5p_6592
CTCCGCCTTTTTTTTGTTCGAG T. profu_3p_9602 T. tepi_3p_6592
AATCATCGATCCGTTGATCTTC T. profu_5p_49438 T. tepi_5p_33046
AACTTTGTGCAGCTGTTTGGTC T. profu_3p_49438 T. tepi_3p_33046
CGCGGCGGTGGCGTTGCCGAAC T. profu_5p_45214 T. tepi_5p_29936
AACGTGATGGCGTCATGCACCG T. profu_5p_45214 T. tepi_3p_29936
CGGTTGCAATTGCGACCACCAC T. profu_5p_29989 T. tepi_5p_19433
CCTTATAGCGGAATGCGCCCTG T. profu_3p_29989 T. tepi_3p_19433
AACAAAACCCGCAAGGCCAATG T. profu_5p_41148 T. tepi_5p_26936
TTGCGGGTTTTGCTGTGATGTT T. profu_3p_41148 T. tepi_3p_26936
GGGGGGAAAAGTTCCCTTGCCG T. profu_5p_54785 T. tepi_5p_36487
TTGCCGAACGGCTGAAAGAGCT T. profu_3p_54785 T. tepi_3p_36487
CAATCTGTTGCAGTGCCTGATC T. profu_5p_30109 T. tepi_5p_19555
CTGATCTGCTTCGTTACGGATA T. profu_3p_30109 T. tepi_3p_19555
TGCCTATCGCGTCGACGAGGTG T. profu_5p_7879 T. tepi_5p_5471
TGTCGAGGCGGCTGGTCTGCGT T. profu_3p_7879 T. tepi_3p_5471
CACCGATGTCGAAAGATCTTCG T. profu_5p_778 T. tepi_5p_572
TTCCATATCGTGTCGTTATTCA T. profu_3p_778 T. tepi_3p_572
AATTGTATGTGCAATAATGCGA T. profu_5p_32260 T. tepi_5p_20938
GCGTTCGGAGGATTGCACATGC T. profu_3p_32260 T. tepi_3p_20938
CTCATGAGTAATGTGTTCGGAA T. profu_3p_3800 T. tepi_3p_2570
TGTGATGGTTTCTTCTATCGCA T. profu_5p_19048 T. tepi_5p_11782
GTCGGTGGCGGTAACACCGCGG T. profu_3p_19048 T. tepi_3p_11782
TTTCAACAACGCCCGTTGATTG T. profu_5p_38661 T. tepi_5p_25251
ATTGAAATCCCCCGCCTAAACC T. profu_3p_38661 T. tepi_3p_25251
ATTTTGTACCTGATGAAACGGC T. profu_5p_42425 T. tepi_5p_27927
CGTTTTGTTAGGTGTTAACCTG T. profu_3p_42425 T. tepi_3p_27927
TATGCCAACAATCCGACCGGGT T. profu_5p_24235 T. tepi_5p_15757
GCGGTCTGGATGTTGGCCTGCC T. profu_3p_24235 T. tepi_3p_15757
AGCAAAAGCTGCCTAATTAAGG T. profu_5p_21694 T. tepi_5p_13698
CTTCTGCTTTACAGACAGAATT T. profu_3p_21694 T. tepi_3p_13698
TGTCTTTTTCTGACGTTTTTTC T. profu_5p_49945 T. tepi_5p_33271
CGTTTTTTCTCAAAAAAGGGTT T. profu_3p_49945 T. tepi_3p_33271
TTGTCTGTCAAACAGGCAAGGA T. profu_5p_17170 T. tepi_5p_11264
AAGGATTGCGGTCGGCCTTACT T. profu_3p_17170 T. tepi_3p_11264
TGACGCAGAGGCTTTCTCTCAT T. profu_5p_51572 T. tepi_5p_34376
AGGTGGCCTTTGGATCACCCGG T. profu_3p_51572 T. tepi_3p_34376
GTCGGCGTTGTCGCGCTGTTCA T. profu_5p_12216 T. tepi_5p_8405
TTCAAGGAGCCGCTGCATGTTG T. profu_3p_12216 T. tepi_3p_8405
AGACGTGACCTTCGGGTCGCGT T. xian_5p_16668 T. xiam_5p_26826
CGTCTTTTTTATTGTCTGGTGG T. xian_3p_16668 T. xiam_3p_26826
CAATTAAAAACCCCCTCAGGCG T. xian_5p_6822 T. xiam_5p_11702
AGGGGTTTTTTAATTGGTAGCC T. xian_3p_6822 T. xiam_3p_11702
CAATTTAAACATTGTCACCATG T. aust_3p_49513 T. tepi_3p_22906
ATAATGACGTCCGTTGCGAC T.luce_5p_10500 T.tepi_5p_8152
TCACAGTCGAGACGCTCTCTCACC T.luce_3p_22524 T.meso_3p_3562
GTCCGGTGGTCTGGGCACCATG T.prof_5p_36012 T.tepi_5p_22622
ATCCTGCCCCCGCAACCA T.prof_5p_14768 T.tepi_5p_8570
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A comparative analysis of 16S rRNA sequence similarities revealed that the highest number of common miRNAs shared by the phylogenetically close species; e.g., T. tepidiphila 1-1BT were found to be phylogenetically closely related to T. profundimaris WP0211T (99.3% sequence similarity); T. xianhensis P-4T and T. xiamenensis M-5T also shared 99.3% 16S rRNA similarity, while T. mesophila JCM 18969T and T. alkalitolerans JCM 18968T shared 95.3% and 94.9% of 16S rRNA sequence similarity, respectively, with T. lucentensis QMT2T [14]. Conserved miRNA sequences found in these species may indicate a close genetic relationship and that these miRNAs have a similar role in the regulation of the growth and development of bacteria. It is interesting to note that two species, T. australica NP 3b2T and T. tepidiphila 1-1BT, which are capable of hydrocarbon degradation [12, 14], shared one conserved miRNAs and the potential targets of this putative miRNA needs to be investigated for any role related to this process. In addition, T. alkalitolerans JCM 18968T, T. lucentensis QMT2T and T. xianhensis P-4T were also found to have the same sequence presented in different genomic locations (Table 5). These miRNAs can have an influence on their expression and function at different genomic locations [40]. It will be of great interest to identify the target mRNA of these miRNAs and investigate their roles and the mechanisms of gene regulation in the physiology of these unique environmental bacteria.

Table 5. miRNAs present in different locations of the same Thalassospira species.

miRNA sequence T. alkalitolerans JCM 18968T T. lucentensis QMT2T T. xianhensis P-4T
AATTAATGGGTCCTGACC T.alka_3p_6323 T.alka_3p_6324
ATTCCCGCGTGCGCGGGAATGA T. xian_3p_2740 T. xian_3p_2738
TTCGGTGCTCACGTACTTTTAG T. luce_5p_528656 T. luce_5p_528636
TGCGCTCCGATGCGCGTGAACC T. luce_3p_528656 T. luce_3p_528636
TCGTGCGTCAGCTTGGCGTGAC T. luce_5p_389406 T. luce_5p_389408
TCACCCGACCTGACCATGGTCG T. luce_3p_389406 T. luce_3p_389408
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Conclusions

Over 242 million reads of 18 to 33 nucleotides length were generated from nine bacterial species using high-throughput sequencing technology. Using miRDeep2 and CID-miRNA analyses, a total of 984 putative miRNAs were eventually identified, with typical miRNA length of 19–25 nucleotides. Compared to other species, these detected putative miRNAs were not conserved to those reported in E. coli DH10B and S. mutans ATCC 25175. This study presents the first comprehensive list of computationally identified miRNAs in 9 bacterial species of the genus Thalassospira without experimental verification. The further work, however, is needed to validate these candidates experimentally. In addition, further identification of miRNA targets will provide insights into the fundamental functions of miRNAs in the physiology of these bacteria.

Supporting information

S1 Fig. Phylogenetic tree of valid species of the genus Thalassospira.

(DOCX)

Click here for additional data file. (34.7KB, docx)
S1 Table. Whole genome sequences of bacteria of the genus Thalassospira available in GenBank.

(DOCX)

Click here for additional data file. (12.5KB, docx)
S2 Table. miRNAs retrieved from bacteria of the genus Thalassospira as identified by CID-miRNA analysis.

(DOCX)

Click here for additional data file. (50.7KB, docx)

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

Mekong Project 1000 provided support in the form of scholarship for author THYD. Australian Genome Research Facility Ltd. provided support in the form of salaries to ST and GD. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Phylogenetic tree of valid species of the genus Thalassospira.

(DOCX)

Click here for additional data file. (34.7KB, docx)
S1 Table. Whole genome sequences of bacteria of the genus Thalassospira available in GenBank.

(DOCX)

Click here for additional data file. (12.5KB, docx)
S2 Table. miRNAs retrieved from bacteria of the genus Thalassospira as identified by CID-miRNA analysis.

(DOCX)

Click here for additional data file. (50.7KB, docx)

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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