Abstract
The pathogen Mycobacterium leprae is the root cause of the dire Hansen’s disease, commonly known as Leprosy. Mycobacterium leprae is a distinct species within the genus as it causes peripheral nerve damage. The microRNAs (miRNAs) can effectively regulate the transcripts at the stages of transcription and translation. Although authenticating the existence and roles of microRNAs in bacteria necessitate intensive research, a few reports on bacterial microRNAs were published. In this in silico study, the genome wide investigation of Mycobacterium leprae led to the identification of 30 putative mature microRNAs in the bacterium. Several reports manifest the transmittance of bacterial small RNAs to host cytoplasm and they subsequently target host cellular mRNA molecules. The mature microRNAs predicted in M. leprae might possibly degrade/ inhibit 297 mRNAs in human. Further, the target human genes were subjected to gene network analysis and functional annotation. It was evident that the central mediators of the target gene network were involved in host defence responses to pathogen. A few target genes executed the pivotal processes of nervous system, morphogenesis, homeostatsis, transcription factor binding, cell cycle and cell death.
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References
Belter A, Gudanis D, Rolle K, Piwecka M, Gdaniec Z, Naskręt-Barciszewska MZ, Barciszewski J (2014) Mature miRNAs form secondary structure, which suggests their function beyond RISC. PLoS One 9:e113848. https://doi.org/10.1371/journal.pone.0113848
Benjak A, Avanzi C, Singh P, Loiseau C, Girma S, Busso P (2018) Phylogenomics and antimicrobial resistance of the leprosy bacillus Mycobacterium leprae. Nat Commun 9:352. https://doi.org/10.1038/s41467-017-02576-z
Cambau E, Saunderson P, Matsuoka M, Cole ST, Kai M, Suffys P, Rosa PS, Williams D, Gupta UD, Lavania M et al (2018) Antimicrobial resistance in leprosy: results of the first prospective open survey conducted by a WHO surveillance network for the period 2009-15. Clin Microbiol Infect 24:1305–1310. https://doi.org/10.1016/j.cmi.2018.02.022
Cardin SE, Borchert GM (2017) Viral MicroRNAs, Host MicroRNAs Regulating Viruses, and Bacterial MicroRNA-Like RNAs. Methods Mol Biol 1617:39–56. https://doi.org/10.1007/978-1-4939-7046-9_3
Catalanotto C, Cogoni C, Zardo G (2016) MicroRNA in control of gene expression: an overview of nuclear functions. Int J Mol Sci 17:1712. https://doi.org/10.3390/ijms17101712
Chakrabarty S, Kumar A, Raviprasad K, Mallya S, Satyamoorthy K, Chawla K (2019) Host and MTB genome encoded miRNA markers for diagnosis of tuberculosis. Tuberculosis 116:37–43. https://doi.org/10.1016/j.tube.2019.04.002
Chan H, Ho J, Liu X, Zhang L, Wong SH, Chan MT, Wu WK (2017) Potential and use of bacterial small RNAs to combat drug resistance: a systematic review. Infect Drug Resist 10:521. https://doi.org/10.2147/IDR.S148444
Chen Y, Wang X (2019) miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48:D127–D131. https://doi.org/10.1093/nar/gkz757
Choi J-W, Kwon T-Y, Hong S-H, Lee H-J (2018) Isolation and characterization of a microRNA-size secretable small RNA in Streptococcus sanguinis. Cell Biochem Biophys 76:293–301. https://doi.org/10.1007/s12013-016-0770-5
Cole S, Eiglmeier K, Parkhill J, James K, Thomson N, Wheeler P, Honore N, Garnier T, Churcher C, Harris D (2001) Massive gene decay in the leprosy bacillus. Nature 409:1007–1011. https://doi.org/10.1038/35059006
Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21:3674–3676. https://doi.org/10.1093/bioinformatics/bti610
Dang THY, Tyagi S, D’Cunha G, Bhave M, Crawford R, Ivanova EP (2019) Computational prediction of microRNAs in marine bacteria of the genus Thalassospira. PLoS One 14:e0212996. https://doi.org/10.1371/journal.pone.0212996
Foss S, Bottermann M, Jonsson A, Sandlie I, James LC, Andersen JT (2019) TRIM21—From intracellular immunity to therapy. Front Immunol 10. https://doi.org/10.3389/fimmu.2019.02049
Frodsham AJ, Hill AV (2004) Genetics of infectious diseases. Hum Mol Genet 13(Spec No 2):R187–R194. https://doi.org/10.1093/hmg/ddh225
Furuse Y, Finethy R, Saka HA, Xet-Mull AM, Sisk DM, Smith KLJ, Lee S, Coers J, Valdivia RH, Tobin DM (2014) Search for microRNAs expressed by intracellular bacterial pathogens in infected mammalian cells. PLoS One 9:e106434. https://doi.org/10.1371/journal.pone.0106434
Gkirtzou K, Tsamardinos I, Tsakalides P, Poirazi P (2010) MatureBayes: A probabilistic algorithm for identifying the mature miRNA within novel precursors. PLoS One 5:e11843. https://doi.org/10.1371/journal.pone.0011843
Gu H, Zhao C, Zhang T, Liang H, Wang X-M, Pan Y, Chen X, Zhao Q, Li D, Liu F (2017) Salmonella produce microRNA-like RNA fragment Sal-1 in the infected cells to facilitate intracellular survival. Sci Rep 7:1–12. https://doi.org/10.1038/s41598-017-02669-1
Haning K, Cho SH, Contreras LM (2014) Small RNAs in mycobacteria: an unfolding story. Front Cell Infect Microbiol 4:96. https://doi.org/10.3389/fcimb.2014.00096
Honap TP, Pfister L-A, Housman G, Mills S, Tarara RP, Suzuki K, Cuozzo FP, Sauther ML, Rosenberg MS, Stone AC (2018) Mycobacterium leprae genomes from naturally infected nonhuman primates. PLoS Negl Trop Dis 12:e0006190. https://doi.org/10.1371/journal.pntd.0006190
Kang S-M, Choi J-W, Lee Y, Hong S-H, Lee H-J (2013) Identification of microRNA-size, small RNAs in Escherichia coli. Curr Microbiol 67:609–613. https://doi.org/10.1007/s00284-013-0411-9
Lalaouna D, Simoneau-Roy M, Lafontaine D, Massé E (2013) Regulatory RNAs and target mRNA decay in prokaryotes. Biochim Biophys Acta (BBA)-Gene Regul Mech 1829:742–747. https://doi.org/10.1016/j.bbagrm.2013.02.013
Lee H-J, Hong S-H (2012) Analysis of microRNA-size, small RNAs in Streptococcus mutans by deep sequencing. FEMS Microbiol Lett 326:131–136. https://doi.org/10.1111/j.1574-6968.2011.02441.x
Liu W, Wang X (2019) Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol 20:18. https://doi.org/10.1186/s13059-019-1629-z
Manning JA, Kumar S (2018) Physiological functions of Nedd4-2: lessons from knockout mouse models. Trends Biochem Sci 43:635–647. https://doi.org/10.1016/j.tibs.2018.06.004
Martinez AN, Lahiri R, Pittman TL, Scollard D, Truman R, Moraes MO, Williams DL (2009) Molecular determination of Mycobacterium leprae viability by use of real-time PCR. J Clin Microbiol 47:2124–2130. doi:https://doi.org/10.1128/JCM.00512-09
McEwan WA, Tam JC, Watkinson RE, Bidgood SR, Mallery DL, James LC (2013) Intracellular antibody-bound pathogens stimulate immune signaling via the Fc receptor TRIM21. Nat Immunol 14:327. https://doi.org/10.1038/ni.2548
Mullany LE, Herrick JS, Wolff RK, Slattery ML (2016) MicroRNA seed region length impact on target messenger RNA expression and survival in colorectal cancer. PLoS One 11:e0154177. https://doi.org/10.1371/journal.pone.0154177
Pickrell AM, Youle RJ (2015) The roles of PINK1, parkin, and mitochondrial fidelity in Parkinson’s disease. Neuron 85:257–273. https://doi.org/10.1016/j.neuron.2014.12.007
Sandig H, McDonald J, Gilmour J, Arno M, Lee TH, Cousins DJ (2009) Fibronectin is a TH1-specific molecule in human subjects. J Allergy Clin Immunol 124(3):528-35, 535.e1-5. https://doi.org/10.1016/j.jaci.2009.04.036
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. DOI:https://doi.org/10.1101/gr.1239303
Shmaryahu A, Carrasco M, Valenzuela PD (2014) Prediction of bacterial microRNAs and possible targets in human cell transcriptome. J Microbiol 52:482–489. https://doi.org/10.1007/s12275-014-3658-3
Shu X, Zang X, Liu X, Yang J, Wang J (2018) Predicting MicroRNA mediated gene regulation between human and viruses. Cells 7:100. https://doi.org/10.3390/cells7080100
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P et al (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45:D362–D368. https://doi.org/10.1093/nar/gkw937
Taneja S, Dutta T (2019) On a stake-out: Mycobacterial small RNA identification and regulation. Non-coding RNA Res 4:86–95. https://doi.org/10.1016/j.ncrna.2019.05.001
Teng Y, Wang Y, Zhang X, Liu W, Fan H, Yao H, Lin B, Zhu P, Yuan W, Tong Y (2015) Systematic genome-wide screening and prediction of microRNAs in EBOV during the 2014 ebolavirus outbreak. Sci Rep 5:9912. https://doi.org/10.1038/srep09912
Tyagi S, Vaz C, Gupta V, Bhatia R, Maheshwari S, Srinivasan A, Bhattacharya A (2008) CID-miRNA: a web server for prediction of novel miRNA precursors in human genome. Biochem Biophys Res Commun 372:831–834. https://doi.org/10.1016/j.bbrc.2008.05.134
Wheat WH, Casali AL, Thomas V, Spencer JS, Lahiri R, Williams DL, McDonnell GE, Gonzalez-Juarrero M, Brennan PJ, Jackson M (2014) Long-term survival and virulence of Mycobacterium leprae in amoebal cysts. PLoS Negl Trop Dis 8:e3405. https://doi.org/10.1371/journal.pntd.0003405
Wong N, Wang X (2015) miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res 43:D146–D152. https://doi.org/10.1093/nar/gku1104
Zhu W, Liu S, Liu J, Zhou Y, Lin H (2018) High-throughput sequencing identification and characterization of potentially adhesion-related small RNAs in Streptococcus mutans. J Med Microbiol 67:641. https://doi.org/10.1099/jmm.0.000718
Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415. https://doi.org/10.1093/nar/gkg595
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The authors are grateful to Assam University, Silchar-788011, Assam, India for providing the necessary research facility.
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DN: Writing- Original Draft, Data curation, Analysis of data, Investigation, Interpretation, Validation, Methodology.
SC: Conceptualization, Project administration, Supervision, Methodology, Writing- Review and Editing.
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Key points
1. Genome wide analysis of Mycobacterium leprae predicted 30 mature putative microRNAs.
2. The candidate microRNAs of Mycobacterium leprae possibly targeted 297 human genes in 3’ UTRs.
3. Gene network analysis revealed top 10 hub genes among the targets.
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Nath, D., Chakraborty, S. Genome wide analysis of Mycobacterium leprae for identification of putative microRNAs and their possible targets in human. Biologia 76, 2437–2454 (2021). https://doi.org/10.1007/s11756-021-00778-x
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DOI: https://doi.org/10.1007/s11756-021-00778-x