SmithHunter: a workflow for the identification of candidate smithRNAs and their targets.
Epigenetic regulation
Nuclear–mitochondrial interactions
Small non coding RNAs
Small transcriptome
SmithRNAs
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
02 Sep 2024
02 Sep 2024
Historique:
received:
20
02
2024
accepted:
21
08
2024
medline:
3
9
2024
pubmed:
3
9
2024
entrez:
2
9
2024
Statut:
epublish
Résumé
SmithRNAs (Small MITochondrial Highly-transcribed RNAs) are a novel class of small RNA molecules that are encoded in the mitochondrial genome and regulate the expression of nuclear transcripts. Initial evidence for their existence came from the Manila clam Ruditapes philippinarum, where they have been described and whose activity has been biologically validated through RNA injection experiments. Current evidence on the existence of these RNAs in other species is based only on small RNA sequencing. As a preliminary step to characterize smithRNAs across different metazoan lineages, a dedicated, unified, analytical workflow is needed. We propose a novel workflow specifically designed for smithRNAs. Sequence data (from small RNA sequencing) uniquely mapping to the mitochondrial genome are clustered into putative smithRNAs and prefiltered based on their abundance, presence in replicate libraries and 5' and 3' transcription boundary conservation. The surviving sequences are subsequently compared to the untranslated regions of nuclear transcripts based on seed pairing, overall match and thermodynamic stability to identify possible targets. Ample collateral information and graphics are produced to help characterize these molecules in the species of choice and guide the operator through the analysis. The workflow was tested on the original Manila clam data. Under basic settings, the results of the original study are largely replicated. The effect of additional parameter customization (clustering threshold, stringency, minimum number of replicates, seed matching) was further evaluated. The study of smithRNAs is still in its infancy and no dedicated analytical workflow is currently available. At its core, the SmithHunter workflow builds over the bioinformatic procedure originally applied to identify candidate smithRNAs in the Manila clam. In fact, this is currently the only evidence for smithRNAs that has been biologically validated and, therefore, the elective starting point for characterizing smithRNAs in other species. The original analysis was readapted using current software implementations and some minor issues were solved. Moreover, the workflow was improved by allowing the customization of different analytical parameters, mostly focusing on stringency and the possibility of accounting for a minimal level of genetic differentiation among samples.
Sections du résumé
BACKGROUND
BACKGROUND
SmithRNAs (Small MITochondrial Highly-transcribed RNAs) are a novel class of small RNA molecules that are encoded in the mitochondrial genome and regulate the expression of nuclear transcripts. Initial evidence for their existence came from the Manila clam Ruditapes philippinarum, where they have been described and whose activity has been biologically validated through RNA injection experiments. Current evidence on the existence of these RNAs in other species is based only on small RNA sequencing. As a preliminary step to characterize smithRNAs across different metazoan lineages, a dedicated, unified, analytical workflow is needed.
RESULTS
RESULTS
We propose a novel workflow specifically designed for smithRNAs. Sequence data (from small RNA sequencing) uniquely mapping to the mitochondrial genome are clustered into putative smithRNAs and prefiltered based on their abundance, presence in replicate libraries and 5' and 3' transcription boundary conservation. The surviving sequences are subsequently compared to the untranslated regions of nuclear transcripts based on seed pairing, overall match and thermodynamic stability to identify possible targets. Ample collateral information and graphics are produced to help characterize these molecules in the species of choice and guide the operator through the analysis. The workflow was tested on the original Manila clam data. Under basic settings, the results of the original study are largely replicated. The effect of additional parameter customization (clustering threshold, stringency, minimum number of replicates, seed matching) was further evaluated.
CONCLUSIONS
CONCLUSIONS
The study of smithRNAs is still in its infancy and no dedicated analytical workflow is currently available. At its core, the SmithHunter workflow builds over the bioinformatic procedure originally applied to identify candidate smithRNAs in the Manila clam. In fact, this is currently the only evidence for smithRNAs that has been biologically validated and, therefore, the elective starting point for characterizing smithRNAs in other species. The original analysis was readapted using current software implementations and some minor issues were solved. Moreover, the workflow was improved by allowing the customization of different analytical parameters, mostly focusing on stringency and the possibility of accounting for a minimal level of genetic differentiation among samples.
Identifiants
pubmed: 39223476
doi: 10.1186/s12859-024-05909-0
pii: 10.1186/s12859-024-05909-0
doi:
Substances chimiques
RNA
63231-63-0
RNA, Mitochondrial
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
286Subventions
Organisme : Italian Ministry of University and Research
ID : 2020BE2BC3
Organisme : Italian Ministry of University and Research
ID : 2020BE2BC3
Informations de copyright
© 2024. The Author(s).
Références
Ghildiyal M, Zamore PD. Small silencing RNAs: an expanding universe. Nat Rev Genet. 2009;10:94–108.
pubmed: 19148191
pmcid: 2724769
doi: 10.1038/nrg2504
Formaggioni A, Cavalli G, Hamada M, Sakamoto T, Plazzi F, Passamonti M. The evolution and characterization of the RNA interference pathways in Lophotrochozoa. Genome Biol Evol. 2024. https://doi.org/10.1093/gbe/evae098 .
doi: 10.1093/gbe/evae098
pubmed: 38713108
pmcid: 11114477
Biswas K, Jolly MK, Ghosh A. First passage time properties of miRNA-mediated protein translation. J Theor Biol. 2021;529:110863.
pubmed: 34400149
doi: 10.1016/j.jtbi.2021.110863
Bartel DP. Metazoan microRNAs. Cell. 2018;173:20–51.
pubmed: 29570994
pmcid: 6091663
doi: 10.1016/j.cell.2018.03.006
Szczepanek J, Pareek CS, Tretyn A. The role of microRNAs in animal physiology and pathology. Transl Res Vet Sci. 2018;1:13–33.
Moran Y, Agron M, Praher D, Technau U. The evolutionary origin of plant and animal microRNAs. Nat Ecol Evol. 2017;1:27.
pubmed: 28529980
doi: 10.1038/s41559-016-0027
Bofill-De Ros X, Yang A, Gu S. IsomiRs: expanding the miRNA repression toolbox beyond the seed. Biochim Biophys Acta Gene Regul Mech. 2020;1863:194373.
pubmed: 30953728
doi: 10.1016/j.bbagrm.2019.03.005
Shabalina SA, Koonin EV. Origins and evolution of eukaryotic RNA interference. Trends Ecol Evol. 2008;23:P578–87.
doi: 10.1016/j.tree.2008.06.005
Kim SS, Lee S-JV. Non-coding RNAs in Caenorhabditis elegans aging. Mol Cells. 2019;42:379–85.
pubmed: 31094164
pmcid: 6537654
Riggs CL, Summers A, Warren DE, Nilsson GE, Lefevre S, Dowd WW, Milton S, Podrabsky JE. Small non-coding RNA expression and vertebrate anoxia tolerance. Front Genet. 2018;9:230.
pubmed: 30042786
pmcid: 6048248
doi: 10.3389/fgene.2018.00230
Wang M, Jiang S, Wu W, Yu F, Chang W, Li P, Wang K. Non-coding RNAs function as immune regulators in teleost fish. Front Immunol. 2018;9:2801.
pubmed: 30546368
pmcid: 6279911
doi: 10.3389/fimmu.2018.02801
Larriba E, del Mazo J. Role of non-coding RNAs in the transgenerational epigenetic transmission of the effects of reprotoxicants. Int J Mol Sci. 2016;17:452.
pubmed: 27023531
pmcid: 4848908
doi: 10.3390/ijms17040452
Jiao Y, Zheng Z, Du X, Wang Q, Huang R, Deng Y, Shi S, Zhao X. Identification and characterization of microRNAs in Pearl Oyster Pinctada martensii by Solexa deep sequencing. Mar Biotechnol. 2014;16:54–62.
doi: 10.1007/s10126-013-9528-x
Li P, Jiao J, Gao G, Prabhakar BS. Control of mitochondrial activity by miRNAs. J Cell Biochem. 2012;113:1104–10.
pubmed: 22135235
pmcid: 3325319
doi: 10.1002/jcb.24004
Paramasivam A, Vijayashee PJ. MitomiRs: new emerging microRNAs in mitochondrial dysfunction and cardiovascular disease. Hypertens Res. 2020;43:851–3.
pubmed: 32152483
doi: 10.1038/s41440-020-0423-3
Fan S, Tian T, Chen W, Lv X, Lei X, Zhang H, Sun S, Cai L, Pan G, He L, Ou Z, Lin X, Wang X, Perez MF, Tu Z, Ferrone S, Tannous BA, Li J. Mitochondrial miRNA determines chemoresistance by reprogramming metabolism and regulating mitochondrial transcription. Cancer Res. 2019;79:1069–84.
pubmed: 30659020
pmcid: 8631248
doi: 10.1158/0008-5472.CAN-18-2505
Ro S, Ma HY, Park C, Ortogero N, Song R, Hennig GW, Zheng H, Lin YM, Moro L, Hsieh JT, et al. The mitochondrial genome encodes abundant small noncoding RNAs. Cell Res. 2013;23:759–74.
pubmed: 23478297
pmcid: 3674384
doi: 10.1038/cr.2013.37
Mercer TR, Neph S, Dinger ME, Crawford J, Smith MA, Shearwood AM, Haugen E, Bracken CP, Rackham O, Stamatoyannopoulos JA, et al. The human mitochondrial transcriptome. Cell. 2011;146:645–58.
pubmed: 21854988
pmcid: 3160626
doi: 10.1016/j.cell.2011.06.051
Pozzi A, Plazzi F, Milani L, Ghiselli F, Passamonti M. SmithRNAs: could mitochondria “bend” nuclear regulation? Mol Biol Evol. 2017;34:1960–73.
pubmed: 28444389
pmcid: 5850712
doi: 10.1093/molbev/msx140
Boore JL. Animal mitochondrial genomes. Nucleic Acids Res. 1999;27:1767–80.
pubmed: 10101183
pmcid: 148383
doi: 10.1093/nar/27.8.1767
Formaggioni A, Luchetti A, Plazzi F. Mitochondrial genomic landscape: a portrait of the mitochondrial genome 40 years after the first complete sequence. Life (Basel). 2021;11:663.
pubmed: 34357035
Ghiselli F, Gomes-Dos-Santos A, Adema CM, Lopes-Lima M, Sharbrough J, Boore JL. Molluscan mitochondrial genomes break the rules. Philos Trans R Soc Lond B Biol Sci. 2021;376:20200159.
pubmed: 33813887
pmcid: 8059616
doi: 10.1098/rstb.2020.0159
Passamonti M, Plazzi F. Doubly uniparental inheritance and beyond: the contribution of the Manila clam Ruditapes philippinarum. J Zool Syst Evol Res. 2020;58:529–40.
doi: 10.1111/jzs.12371
Zouros E, Rodakis GC. Doubly uniparental inheritance of mtDNA: an unappreciated defiance of a general rule. Adv Anat Embryol Cell Biol. 2019;231:25–49.
pubmed: 30637482
doi: 10.1007/102_2018_4
D’Souza AR, Minczuk M. Mitochondrial transcription and translation: overview. Essays Biochem. 2018;62:309–20.
pubmed: 30030363
pmcid: 6056719
doi: 10.1042/EBC20170102
Plazzi F, Le Cras Y, Formaggioni A, Passamonti M. Mitochondrially mediated RNA interference, a retrograde signaling system affecting nuclear gene expression. Heredity. 2023. https://doi.org/10.1038/s41437-023-00650-5 .
doi: 10.1038/s41437-023-00650-5
pubmed: 37714959
Passamonti M, Scali V. Gender-associated mitochondrial DNA heteroplasmy in the venerid clam Tapes philippinarum (Mollusca Bivalvia). Curr Genet. 2001;39:117–24.
pubmed: 11405096
doi: 10.1007/s002940100188
Milani L, Ghiselli F, Passamonti M. Mitochondrial selfish elements and the evolution of biological novelties. Curr Zool. 2016;62:687–97.
pubmed: 29491956
pmcid: 5804245
doi: 10.1093/cz/zow044
Plazzi F, Puccio G, Passamonti M. Comparative large-scale mitogenomics evidences clade-specific evolutionary trends in mitochondrial DNAs of Bivalvia. Genome Biol Evol. 2016;8:2544–64.
pubmed: 27503296
pmcid: 5010914
doi: 10.1093/gbe/evw187
Breton S, Milani L, Ghiselli F, Guerra D, Stewart DT, Passamonti M. A resourceful genome: updating the functional repertoire and evolutionary role of animal mitochondrial DNAs. Trends Genet. 2014;30:555–64.
pubmed: 25263762
doi: 10.1016/j.tig.2014.09.002
Passamonti M, Calderone M, Delpero M, Plazzi F. Clues of in vivo nuclear gene regulation by mitochondrial short non-coding RNAs. Sci Rep. 2020;10:8219.
pubmed: 32427953
pmcid: 7237437
doi: 10.1038/s41598-020-65084-z
Shaukat A-N, Kaliatsi EG, Stamatopoulou V, Stathopoulos C. Mitochondrial tRNA-derived fragments and their contribution to gene expression regulation. Front Physiol. 2021;12:729452.
pubmed: 34539450
pmcid: 8446549
doi: 10.3389/fphys.2021.729452
Mesguer S. MicroRNAs and tRNA-derived small fragments: key messenger in nuclear-mitochondrial communication. Front Mol Biosci. 2021;8:643575.
doi: 10.3389/fmolb.2021.643575
Chen Z, Sun Y, Yang X, Wu Z, Guo K, Niu X, Wang Q, Ruan J, Bu W, Gao S. Two featured series of rRNA-derived RNA fragments (rRFs) constitute a novel class of small RNAs. PLoS ONE. 2017;12:e0176458.
pubmed: 28441451
pmcid: 5404876
doi: 10.1371/journal.pone.0176458
Xu X, Ji H, Jin X, Cheng Z, Yao X, Liu Y, Zhao Q, Zhang T, Ruan J, Bu W, Chen Z, Gao S. Using pan RNA-seq analysis to reveal the ubiquitous existence of 5’ and 3’ end small RNAs. Front Genet. 2019;10:1–11.
pubmed: 30804975
pmcid: 6370629
doi: 10.3389/fgene.2019.00105
Jun X, Cheng Z, Wang B, Yau T, Chen Z, Barker SC, Chen D, Bu W, Sun D, Gao S. Precise annotation of human, chimpanzee, rhesus macaque and mouse mitochondrial genomes leads to insight into mitochondrial transcription in mammals. RNA Biol. 2020;17:359–402.
Smith CH, Mejia-Trujillo R, Breton S, Pinto BJ, Kirkpatrick M, Havird JC. Mitonuclear sex determination? Empirical evidence from bivalves. Mol Biol Evol. 2023;40:msad240.
pubmed: 37935058
pmcid: 10653589
doi: 10.1093/molbev/msad240
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023.
Anaconda Software Distribution. Anaconda documentation. Anaconda Inc.; 2020. https://docs.anaconda.com/ .
Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39:1278–84.
pubmed: 17893677
doi: 10.1038/ng2135
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011. https://doi.org/10.14806/ej.17.1.200 .
doi: 10.14806/ej.17.1.200
Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90.
pubmed: 30423086
pmcid: 6129281
doi: 10.1093/bioinformatics/bty560
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
pubmed: 22388286
pmcid: 3322381
doi: 10.1038/nmeth.1923
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. 1000 Genome project data processing subgroup. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.
pubmed: 19505943
pmcid: 2723002
doi: 10.1093/bioinformatics/btp352
Bensasson D, Zhang D, Hartl DL, Hewitt GM. Mitochondrial pseudogenes: evolution’s misplaced witnesses. Trends Ecol Evol. 2001;16:314–21.
pubmed: 11369110
doi: 10.1016/S0169-5347(01)02151-6
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.
pubmed: 20110278
pmcid: 2832824
doi: 10.1093/bioinformatics/btq033
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.
pubmed: 27781170
pmcid: 5075697
doi: 10.7717/peerj.2584
Rice P, Longden I, Bleasby A. EMBOSS: the European molecular biology open software suite. Trends Genet. 2000;16:276–7.
pubmed: 10827456
doi: 10.1016/S0168-9525(00)02024-2
Wu S, Manber U. Agrep—a fast approximate pattern-matching tool. In: 1992 Winter USENIX Conference. San Francisco, California. CiteSeer
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.
pubmed: 2231712
doi: 10.1016/S0022-2836(05)80360-2
Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB. Prediction of mammalian microRNA targets. Cell. 2003;115:787–98.
pubmed: 14697198
doi: 10.1016/S0092-8674(03)01018-3
Tan GC, Chan E, Molnar A, Sarkar R, Alexieva D, Isa IM, Robinson S, Zhang S, Ellis P, Langford CF, et al. 5′ isomiR variation is of functional and evolutionary importance. Nucleic Acids Res. 2014;42:9424–35.
pubmed: 25056318
pmcid: 4132760
doi: 10.1093/nar/gku656
Shin C, Nam J-W, Farh KK-H, Chiang HR, Shkumatava A, Bartel DP. Expanding the microRNA targeting code: functional sites with centered pairing. Mol Cell. 2010;38:789–802.
pubmed: 20620952
pmcid: 2942757
doi: 10.1016/j.molcel.2010.06.005
Krüger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34:W451–4.
pubmed: 16845047
pmcid: 1538877
doi: 10.1093/nar/gkl243
Lorenz R, Bernhart SH, Siederdissen CHZ, Tafer H, Flamm C, Stadler PF, Hofacker IL. ViennaRNA package 2.0. Algorithms Mol Biol. 2011;6:26.
pubmed: 22115189
pmcid: 3319429
doi: 10.1186/1748-7188-6-26
Ghiselli F, Milani L, Chang PL, Hedgecock D, Davis JP, Nuzhdin SV, Passamonti M. De Novo assembly of the Manila clam Ruditapes philippinarum transcriptome provides new insights into expression bias, mitochondrial doubly uniparental inheritance and sex determination. Mol Biol Evol. 2012;29:771–86.
pubmed: 21976711
doi: 10.1093/molbev/msr248
Ghiselli F, Iannello M. A transcriptome annotation pipeline for non-model organisms. 2023. Osfhome. https://doi.org/10.17605/OSF.IO/CDKB9 .
doi: 10.17605/OSF.IO/CDKB9
Ahyong S, Boyko CB, Baylli N, Bernot J, Bieler R Brandao SN, et al. Word register of marine species. 2023. https://www.marinespecies.org . Accessed 21 Dec 2023.
Pozzi A, Dowling DK. The genomics origins of small mitochondrial RNAs: are they transcribed by the mitochondrial DNA or by mitochondrial pseudogenes within the nucleus (NUMTs)? Genome Biol Evol. 2019;11:1883–96.
pubmed: 31218347
pmcid: 6619488
doi: 10.1093/gbe/evz132
Ghiselli F, Milani L, Guerra D, Chang PL, Breton S, Nuzhdin SV, Passamonti M. Structure, transcription, and variability of metazoan mitochondrial genome: perspectives from an unusual mitochondrial inheritance system. Genome Biol Evol. 2013;5:1535–54.
pubmed: 23882128
pmcid: 3762199
doi: 10.1093/gbe/evt112
Pozzi A, Dowling DK. New insights into mitochondrial-nuclear interactions revealed through analysis of small RNAs. Genome Biol Evol. 2022;14:evac023.
pubmed: 35143645
pmcid: 8883506
doi: 10.1093/gbe/evac023