Identification of Small Non-Coding Rnas from Rhizobium Etli By
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Rajendran et al. ExRNA (2020) 2:14 https://doi.org/10.1186/s41544-020-00054-1 ExRNA RESEARCH Open Access Identification of small non-coding RNAs from Rhizobium etli by integrated genome- wide and transcriptome-based methods Kasthuri Rajendran1, Vikram Kumar2, Ilamathi Raja1, Manoharan Kumariah1 and Jebasingh Tennyson2* Abstract Background: Small non-coding RNAs (sRNAs) are regulatory molecules, present in all forms of life, known to regulate various biological processes in response to the different environmental signals. In recent years, deep sequencing and various other computational prediction methods have been employed to identify and analyze sRNAs. Results: In the present study, we have applied an improved sRNA scanner method to predict sRNAs from the genome of Rhizobium etli, based on PWM matrix of conditional sigma factor 32. sRNAs predicted from the genome are integrated with the available stress specific transcriptome data to predict putative conditional specific sRNAs. A total of 271 sRNAs from the genome and 173 sRNAs from the transcriptome are computationally predicted. Of these, 25 sRNAs are found in both genome and transcriptome data. Putative targets for these sRNAs are predicted using TargetRNA2 and these targets are involved in a wide array of cellular functions such as cell division, transport and metabolism of amino acids, carbohydrates, energy production and conversion, translation, cell wall/membrane biogenesis, post- translation modification, protein turnover and chaperones. Predicted targets are functionally classified based on COG analysis and GO annotations. Conclusion: sRNAs predicted from the genome, using PWM matrices for conditional sigma factor 32 could be a better method to identify the conditional specific sRNAs which expand the list of putative sRNAs from the intergenic regions (IgRs) of R. etli and closely related α-proteobacteria. sRNAs identified in this study would be helpful to explore their regulatory role in biological cellular process during the stress. Keywords: sRNA, Rhizobium etli, Sigma factor 32, Genome, Transcriptome Background mRNA translation and stability [1–3]. The regulation of Small non-coding RNAs are bacterial regulatory mole- sRNAs are mediated with the help of chaperone Hfq, en- cules, 50-500 nt (bp) in length and contain several stem hance RNA-RNA interaction, through the preferential loops. sRNAs are often located in the intergenic regions, binding at single-stranded AU-rich regions of the non- transcribed from their own promoter or promoters of coding RNAs and their target mRNAs [4]. nearby genes and contain rho-independent terminator. Several sRNAs have been identified by genome-wide pro- sRNAs regulate the gene expression by perfect or imper- filing and transcriptome-based methods. To date, many com- fect base pairing with complementary sequence putational techniques and experimental methods have been stretches, generally located in 5′-UTR regions of trans- used to predict sRNAs in both gram-negative and gram- encoded target mRNAs, resulting in altered target positive bacteria [5–11]. sRNAs regulate diverse cellular pro- cesses and conditionally expressed during oxidative stress, * Correspondence: [email protected] iron uptake, quorum sensing, virulence and heat shock [5, 2Department of Plant Sciences, School of Biological Sciences, Madurai 12–14]. Sigma factors are transcription initiation factors that Kamaraj University, Madurai, Tamil Nadu 625 021, India enable specific binding of RNA polymerase (RNAP) to gene Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Rajendran et al. ExRNA (2020) 2:14 Page 2 of 11 promoters. Bacteria contain different sigma factors, each cap- based on the PWM matrices of conditional sigma factor able of directing the core polymerase to transcribe a specific 32 (Heat shock sigma factor) using improved sRNA set of genes, depending on the environmental or develop- Scanner program [16]. sRNA scanner demarks the tran- mental signals [15]. However reports on screening the condi- scription units (TUs) using consensus sequences of tional sRNAs are not yet available for the group of α- sigma factor binding sites (− 35 and − 10 (Supplementary proteobacteria, except sRNAs predicted from RNA sequence file 3)) and rho-independent transcription terminator se- analysis under heat shock and saline shock; and sRNAs pre- quences. An earlier version of sRNA Scanner uses PWM dicted from the genome are integrated with the virulence matrix; only for housekeeping sigma factor 70 and rho- specific transcriptome data [14, 16]. In our previous work, independent transcription termination in which limited conditional sigma factor based-sRNAs were predicted from numbers of training sequences were used. The total the genome of Agrobacterium using an improved sRNA number of sRNAs predicted from each replicons of Rhi- scanner. This method was used to identify the sRNAs that zobium etli is graphically represented in Fig. 1. are regulated by several conditional sigma factors, such as, The majority of the sRNA candidates identified varied 24, 32 and 54. sRNA scanner identified the sRNAs resided in in length between 50 and 500 nt. GC content for most of intergenic regions of the genome, based on the transcrip- the sRNAs of Rhizobium found to have 50 to 70%. A total tional signals. This bioinformatic tool uses PWM matrices of of 247 sRNAs were predicted from the genome of R. etli sRNA promoter and rho-independent terminators signals, known to be conditionally regulated by sigma factor 32. through sliding window-based genome scans, using consen- To find the novel putative sRNAs, predicted sRNAs were sus sequences of sigma factor promoter binding sites − 35 searched against Rfam database and BSRD database to and − 10 and rho-independent transcription terminator se- eliminate the conserved homologs (Table 1). Seventeen quences [16]. and four sRNA candidates have shown homology with Rhizobium etli is a gram-negative bacterium that be- already identified sRNAs in Rfam and BSRD database, re- longs to Rhizobials of α-proteobacteria, interacts symbi- spectively (Table 1). The sRNAs predicted from the gen- otically with the common beans Phaseolus vulgaris to ome were compared with previously reported sRNAs. form nitrogen-fixing root nodules. Inside the nodules, Eight sRNA candidates were conserved with the earlier re- bacteria differentiate into bacteroids that are capable of ported sRNAs by Vercruysse et al. 2010 [9] and one sRNA fixing the atmospheric N2 into NH3. The genome of R. with the López-Leal et al. 2015 [14]. etli consists of one circular chromosome (6,530,228 bp) and six plasmids: p42a (194,229 bp), p42b (184,338 bp), Transcriptome based sRNAs prediction p42c (250,948 bp), p42d (371,254 bp), p42e (505,334 bp) The high-quality RNAseq reads of R. etli under control, and p42f (642,517 bp) with 6034 protein-coding genes heat and saline shock were aligned to the genome of R. etli [17]. Two earlier studies were reported on the identifica- using Rockhopper. After alignment, transcripts from the tion of sRNA candidates in R.etli. Using tiling micro- intergenic regions and antisense regions from the comple- array analysis, 66 novel sRNA candidates comprising 17 mentary strand of the protein-coding genes were identi- putative sRNAs and 49 putative cis-regulatory ncRNAs fied. The intergenic sRNAs having a length of 50-500 nt were computationally predicted and 4 of these were con- were taken for further analysis. A total of 68 trans- firmed subsequently by wet-lab experiments [9]. Yet an- encoded sRNAs under the heat shock and 105 under the other study, identified 13 differentially expressed saline shock were identified. A relatively larger number of ncRNAs under heat shock and 9 under saline shock con- sRNAs were found to be expressed from the chromo- ditions in R.etli [14]. However, there is scanty informa- somes, has a length of 50 to 150 nt (Fig. 1). Further, pre- tion on stress conditional specific sRNAs in Rhizobium. dicted sRNA candidates were searched against Rfam and In the present study, we report the sRNAs predicted BSRD databases. To find the novel putative sRNA candi- from the genome and transcriptome of Rhizobium etli. dates, the above-screened sRNAs were compared with Further, sRNAs predicted from the genome are inte- previously reported sRNAs (supplementary file 4). Eight grated with the stress-specific transcriptome to identify sRNAs from heat shock and fourteen sRNAs from saline putative conditional specific sRNAs. The mRNA targets shock showed homology with already reported sRNAs by for these sRNAs were identified and data are presented Vercruysse et al. 2010 [9]; five sRNAs from heat shock on the functional categorization and regulatory network and two sRNAs from saline shock with sRNAs reported analysis for the predicted mRNA targets. by López-Leal et al. 2015 [14]. Results sRNA conservation and comparative analysis Genome-wide sRNA prediction by improved sRNAscanner sRNAs are known to be conserved in nature, in order to Prediction of sRNAs from the nitrogen-fixing Rhizobium study the sRNA conservation in the present study, the was performed by genome-wide computational analysis, sRNA conservation analysis was performed between the Rajendran et al. ExRNA (2020) 2:14 Page 3 of 11 Fig.