In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus.

Sugarcane bacilliform virus (SCBV) is considered one of the most economically damaging pathogens for sugarcane production worldwide. Three open reading frames (ORFs) are characterized in the circular, ds-DNA genome of the SCBV; these encode for a hypothetical protein (ORF1), a DNA binding protein (ORF2), and a polyprotein (ORF3). A comprehensive evaluation of sugarcane (Saccharum officinarum L.) miRNAs for the silencing of the SCBV genome using in silico algorithms were carried out in the present study using mature sugarcane miRNAs. miRNAs of sugarcane are retrieved from the miRBase database and assessed in terms of hybridization with the SCBV genome. A total of 14 potential candidate miRNAs from sugarcane were screened out by all used algorithms used for the silencing of SCBV. The consensus of three algorithms predicted the hybridization site of sof-miR159e at common locus 5534. miRNA-mRNA interactions were estimated by computing the free-energy of the miRNA-mRNA duplex using the RNAcofold algorithm. A regulatory network of predicted candidate miRNAs of sugarcane with SCBV-ORFs, generated using Circos-is used to identify novel targets. The predicted data provide useful information for the development of SCBV-resistant sugarcane plants.

1. Introduction

Sugarcane bacilliform viruses (SCBVs) are classified into the Badnavirus genus of the Caulimoviridae family. These viruses are composed of monopartite, circular, non-enveloped bacilliforms that are (30 × 120–150 nm) in size, with a double-stranded DNA (ds-DNA)- genome of approximately 7.2–9.2 Kbp in size [1]. The genome of SCBV constitutes three major open reading frames (ORFs) that are located on the ‘plus DNA strand’ with a single discontinuity [2]. ORF1 encodes a small hypothetical protein. ORF2 encodes a virion-associated DNA-binding protein. ORF3 encodes the largest polyprotein, represented as P3 here, and is composed of multiple functional sub-units. The polyprotein (P3) is cleaved by a viral aspartic protease to give rise to multiple functional small proteins, thereby forming intracellular movement, capsids, aspartic proteases, reverse transcriptase (RT), and ribonuclease H (RNase H) [1-6]. The RT-RNaseH-coding region is considered to be the most common taxonomic marker for the identification of badnaviral genomic components. This coding region is a standard source to compare the sequence diversity of the badnaviral genomes [7].

The first report of SCBV infection was observed in the Cuban sugarcane cultivar B34104 in 1985 [8]. These viruses have been disseminated worldwide and have reduced crop production significantly because of the accessibility and exchange of biological materials globally. SCBV is a source of infection for several bioenergy crop sugarcane cultivars, varieties, and species. The broad host range of the SCBV includes diverse and economically important members of the Poaceae (sugarcane, and rice) and Musaceae (banana) families. Natural transmission of SCBV is disseminated by sap-feeding mealybug species via vegetative cutting [9]. SCBV disease symptoms include chlorosis and leaf freckling. Infected sugarcane plants have also been monitored and feature no symptoms. In recent years in China, SCBV-infection in sugarcane plants has resulted in a reduced sucrose content, juice, stalk weight, purity, and gravity [6].

RNA silencing is an evolutionary conserved homolog-dependent regulatory mechanism of gene expression in all eukaryotes and is triggered by small RNA molecules (sRNA). dsRNA is the ultimate trigger of the RNAi complex, which works as a replication intermediate created by viral RNA-dependent RNA polymerases (RDRs) [10]. The RNAi mechanism works with cleavage of the precursor dsRNA into short 21–24 nt siRNA or miRNA duplexes using an RNaseIII-like enzyme called Dicer (DCL) [11-13].

The RNAi-mediated response of plants against invading viruses is especially significant during the infection period [14]. The RNAi mechanism inhibits protein translation at the mRNA level via a highly sequence-specific strategy [15]. Sugarcane has inherited an active immunity, consisting of small non-coding microRNAs (miRNAs) to control viral diseases. miRNA-mediated gene silencing is considered to validate the activity of positive or negative immune-based regulation; it is also considered a key activator of immune defense in plants [16, 17]. RNA silencing in the form of miRNAs within the host plant is a source of natural immunity. Such a mechanism provides resistance to the host plant after infection via foreign genetic elements, including plant viruses [18-20].

Artificial microRNA (amiRNA)-mediated RNAi produces a single 21-nucleotide amiRNA (analogous to a single siRNA) that only recognizes a target sequence with less than five mismatches. This feature not only ensures a higher silencing specificity for amiRNAs than hairpin RNAs but also offers unique advantages [21, 22]. amiRNA-mediated silencing of invading viruses in plants was first reported by Niu [23]. This amiRNA-based silencing strategy has been applied to with many plants in order to combat plant viruses, such as cotton leaf curl Kokhran virus (CLCuKoV) [24], cucumber mosaic virus (CMV) [25], cymbidium mosaic virus (CymMV), and odontoglossum ringspot virus (ORSV) [26].

In this study, we performed a comprehensive bioinformatics analysis to identify sugarcane miRNAs predicted to target the SCBV genome. Computational methods can determine how miRNAs target a desirable mRNA. A large number of computational algorithms are publicly available for miRNA target prediction. It is highly advantageous to acquire several computational tools with different features. Researchers are challenged with an important choice regarding selecting suitable tools for prediction [27]. The current study implements miRNA prediction algorithms and identifies potential targets of sugarcane-derived miRNAs against SCBV as a precedent for creating resistance in sugarcane cultivars using RNAi technology. Potential sugarcane miRNAs are also screened for understanding sugarcane–Badnavirus interactions. The novel computational approach here supports the idea of generating SCBV- resistant sugarcane plants through genetic engineering.

2. Materials and methods

2.1. Retrieval of sugarcane MicroRNAs

Mature sugarcane microRNAs (miRNAs) and stem-loop hairpin precursor sequences were retrieved from the miRNA biological sequence database miRBase (v22) (http://mirbase.org/). miRBase serves as primary public repository and standard online reference resource for all published miRNA sequences, along with providing textual annotations and gene nomenclature [28-30]. In this study, 16 S.officinarum (MI0001756-MI0001769) and 19 Saccharum spp. (MI0018180- MI18197) miRNA sequences were downloaded (S1 Table in S1 File).

2.2. SCBV genome retrieval and annotation

The full-length transcript of the SCBV-BRU genome was isolated from the S. officinarum cultivar and then published, and available via accession no. JN377537 [31]. The expected sizes and abundances of the ORFs along nucleotide distributions of the above mentioned NCBI retrieved SCBV-BRU genome were estimated using the pDRAW32 DNA analysis software (version 1.1.129) (AcaClone software). The SCBV-BRU genome annotation represents ORFs of varying lengths.

2.3. Target prediction in SCBV genome

Target prediction is considered a key feature towards the identification of credible miRNA–mRNA interaction hybridization. At present, many target prediction algorithms have been designed to predict and identify the best miRNA target candidate. Each tool uses specific criteria and methods for miRNA target prediction. We used four target prediction algorithms cited in the literature (miRanda, RNA22, RNAhybrid and psRNATarget) to find the most relevant sugarcane miRNAs for silencing of the SCBV genome (Table 1). These computational tools compute the complementarity-based attachment of miRNA-mRNA. This attachment is divided into seed and mid regions. The mismatch in the seed region is more damaging than that of a mismatch in the middle region of miRNA-mRNA attachment. This provides the basis for over-sensitivity for the computation. We can set higher penalty of a mismatch in seed region which will make the prediction more sensitive. We designed an effective computational approach to analyze miRNA targets at three different prediction levels namely the individual, union, and intersection levels. A detailed workflow pipeline is presented in (Fig 1) below.

Table 1

Comparison of distinctive parameters used in the common target prediction tools.

Tools	Algorithms	Seed pairing	Target site accessibility	Multiple sites	Translation Inhibition	Availability
miRanda	Local alignment	+	+	+	+	Web server and source code
RNA22	FASTA	_	+	+	_	Only web server
RNAhybrid	Interamolecular hybridization	+	+	+	+	Web server and source code
psRNATarget	Smith-Waterman	_	+	+	+	Only web server
Tapirhybrid	FASTA	+	+	+	_	Web server and source code
Targetfinder	FASTA	+	_	_	_	Only source code
Target-align	Smith-Waterman	_	_	+	_	Web server and source code
Targetscan	Custom made	+	_	+	+	Only source code

‘+’ Represents a feature was used, ‘-‘indicates that a feature was not used.

Fig 1

The methodology of host or sugarcane miRNA target prediction in the SCBV genome.

A flowchart designed for predicting candidate miRNAs of host that could potentially target SCBV genome. The biological data are composed of sugarcane miRNAs retrieved from the miRBase database and SCBV genome from NCBI GenBank database. The algorithmic framework consists of three kinds of tools used for identification of sugarcane-encoded miRNA targets, prediction of secondary structures and visualization of mi RNA–target interaction. The R language was used to create plots and select data using in-house scripts/codes.

2.4. miRanda

miRanda is considered to be a standard miRNA–target predictor scanning algorithm. It was implemented for the first time in 2003 [32] and has been updated into a web-based tool for miRNA analysis [33]. The latest version of the miRanda software was accessed using the online source website (http://www.microrna.org/).

2.5. RNA22

RNA22 is a user-friendly, web-based (http://cm.jefferson.edu/rna22v1.0/) novel pattern-recognition algorithm that is used for predicting target sites with corresponding hetero-duplexes. Non-seed- based interaction, pattern recognition, site complementarity, and folding energy are the key parameters of the RNA22 algorithm [34]. Final scoring removes the need to use a cross-species conservation sequence filter [35].

2.6. RNAhybrid

RNAhybrid is an easy-to-use, fast, flexible, web-based (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid) intermolecular hybridization algorithm that is used to estimate mi RNA–mRNA interaction as well perform target prediction based on MFE hybridization. A p-value is assigned to assess RNA–RNA interaction-based hybridization sites in the 3′ UTR sequence [36]. RNAhybrid is widely used to estimate the MFE of the consensual mi RNA–target pair and the mode of target inhibition as suggested [37].

2.7. psRNATarget

psRNATarget is a new web server (http://plantgrn.noble.org/psRNATarget/) that is used to identify the target genes of plant miRNAs based on a complementary matching scoring schema. It has been used to discover validated mi RNA–mRNA interactions [38]. The plant psRNATarget was designed to integrate a key function for miRNA target prediction using complementarity scoring and secondary structure prediction [39]. Target site accessibility was evaluated by estimating the unpaired energy (UPE) to unfold a secondary structure [37].

2.8. Mapping of mi RNA–target interaction

An interaction map was created between sugarcane miRNAs and SCBV ORFs using the Circos algorithm [40].

2.9. RNAfold

RNAfold is a new web-based algorithm and was applied for the prediction of the stable secondary structures of pre-miRNAs based on the MFEs [41].

2.10. Free energy (ΔG) estimation of duplex binding

RNAcofold is a novel web-based server (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAcofold.cgi) that is used for estimating free energy (ΔG) associated with miRNA–mRNA interactions [42]. The free energy of miRNA–miRNA duplexes is considered a key predictor for miRNA targeting during hybridization.

2.10.1. In Silico sugarcane miRNA expression profiling

Plant miRNA Expression Atlas (PmiRExAt) is a web-based resource comprising a miRNA expression profile and searching tool for 1,859 wheat, 2,330 rice, and 283 maize miRNAs [43]. PmiRExAt can be accessed at http://pmirexat.nabi.res.in/. The sequences of mature microRNAs from sugarcane were blasted in PmiRExAt and the expression patterns of the homologous microRNAs were searched in wheat, rice, and maize.

2.10.2. Graphical representation

All the computational data were processed into graphical representations using R version 3.1.1 [44].

3. Results

3.1. Genome Organization of SCBV

SCBV is a plant pararetrovirus that is, classified in the genus Badnavirus of the family Caulimoviridae. The genomic ds-DNA molecule of SCBV is comprised of three ORFs, separated by an intergenic region (IR). ORF1 is composed of 557 nucleotides (618–1175 nt), encoding a hypothetical protein (P1) with 185 amino acids (aa),while ORF2 is composed of 370 nucleotides (1176–1546 nt) codes for a virion-associated DNA binding protein (P2) with 123 aa. The precise functional capabilities of these proteins (encoded by ORF1 and ORF2) have not been explored. A large polyprotein (1977 amino acids) is encoded by ORF3 (1547–7479 nt) to cleave by a viral aspartic protease. The resulting proteins obtained are named as movement, capsid protein, aspartyl proteinase, reverse transcriptase and ribonuclease H. The IR is composed of 1022 nucleotides (7479–618) and is located between 3’-ORF3 to 5’-ORF1. The intergenic region (IR) works as a promoter and controls the transcription and regulation of the SCBV genome. The genome organization of the SCBV with three ORFs is shown in (Fig 2).

Fig 2

Genomic organization of the sugarcane bacilliform virus.

The predicted ORFs denoted with arrows are composed of dsDNA that is 7884 bp in size.

3.2. ORF1-encoding hypothetical protein

The hypothetical protein of the SCBV genome that is encoded by ORF1 had an unknown function [2]. In miRanda that only predicted two target sites for sugarcane miRNAs sof-miR156 and sof-miR168 at nucleotide positions 818–837 and 617–638 to target ORF1 (Fig 3A). RNA22 predicted the binding sites of miRNAs sof-miR156 and sof-miR168a at the two different locus positions of 817 and 834, respectively (Fig 3B). The RNAhybrid algorithm predicted multiple potential binding sites of sugarcane miRNAs sof-miR168 (a, b), ssp-mi827, and ssp-miR1128 at nucleotide positions 612–632, 1170–1192, and 1137–1157 respectively (Fig 3C). In addition, psRNATarget identified potential hybridization sites of sof-miR159 (c, e) at locus positions 1003 and 820 respectively (Fig 3D).

Fig 3

Target prediction of sugarcane miRNAs in the SCBV genome.

Computational prediction of candidate miRNA targets in the genome of the SCBV. (A) miRNA targets obtained from miRanda. (B) RNA22 predicted potential hybridization sites. (C) Target sites of sugarcane miRNAs as identified by RNAhybrid. (D) Prediction results of target sites of sugarcane miRNAs as obtained by psRNATarget.

3.3. ORF2 encoding DNA binding protein

A nucleic acid (DNA)-binding protein of the SCBV genome is encoded by ORF2 [6, 45]. RNAhybrid and miRanda predicted potential target binding site of ssp-miR166 at locus position 1449–1470 (Fig 3A and 3C). Suitable candidate miRNAs from sugarcane (ssp-miR444 (a, b, 3p) were observed to target ORF2 at a single loci nucleotide position (1301–1326) as determined by the miRanda algorithm (Fig 3A). No sugarcane miRNAs were predicted to target the ORF2 gene with the RNA22 tool (Fig 3B). Similarly, RNAhybrid predicted the binding of ssp-miR166 at locus 1450 (Fig 3C). The miRNA prediction results revealed that no candidate miRNA was identified to have a potential genome binding site in the ORF2 region, as predicted by psRNATarget (Fig 3D).

3.4. ORF3 encoding polyprotein (CP, AP, RT, and RNase H)

The poly proteins constitute the largest portion of the SCBV genome encoded by ORF3 [2, 6]. Potential candidate miRNAs from sugarcane were identified by the miRanda algorithm to target ORF3, including sof-miR159 (a, b, c, d, and e) at common locus 5534, sof-miR167 (a, b) at locus 2273, sof-miR168b at locus 4588, sof-miR408 (a, b, c, d, and e) at the two common loci of 4595 and 6695, ssp-miR166 at locus 1986, ssp-miR827 at locus 2816, and ssp-miR444 (a, b, and c-3p) at common locus 6184. Multiple loci interactions were predicted for the sof-miR159, sof-miR408, and ssp-miR444 families at nucleotide positions (5534–5552, 5576–5596), (4595–4615, 6695–6715) and (1679–1701, 3293–3313) of ORF3, respectively (Fig 3A).

Potential target binding sites were determined for ORF3 of the SCBV genome by the RNA22 algorithm. These included sof-miR168a at locus 3263, sof-miR168b at nucleotide positions 1693 and 3263, sof-miR396 at locus 2050, and ssp-827 at locus 2796 (Fig 3B). Multiple loci interactions were also identified for the sof-miR159, sof-miR408, and ssp-miR444 families at nucleotide positions (5532, 6536), (5645, 6695), and (5246, 6793) respectively (Fig 3B). Suitable miRNAs that potentially targeted ORF3 were hybridized in order to understand the miRNA—mRNA interaction via RNAhybrid. As a result of, sof-miR159 (a, b, d and e) was detected at common locus 5535, along with sof-miR159c at locus 6518, sof-miR167 (a, b) at locus 2826, sof-miR169 at locus 7362, ssp-miR473 (a, b, c) at common locus 6438, ssp-miR444 (a, b) at locus 6796, ssp-miR444 c-3p at locus 2899 and ssp-miR1432 at locus 7314 (Fig 3C). ORF3 was targeted by several candidate miRNAs, includingsof-miR159e at locus 2647, sof-miR396 at locus 5363, ssp-miR166 at locus 1986, ssp-miR437 (a, c) at locus 2647, ssp-miR827 at locus 7337, and ssp-miR444 (a, b and c-3p) at locus 6797, as identified by psRNATarget. Multiple loci interactions were observed for the sof-miR408 and ssp-miR444 families at the nucleotide positions of (1766–1786, 3669–3689, 5683–5702) and (4466–4486, 6797–6816, 6865–6885, 7079–7099), respectively (Fig 3D). The union plot indicates entire genome binding sites identified by the candidate miRNAs using target prediction tools (Fig 4, and S2 Table in S1 File).

Fig 4

Union plot representing all the predicted sugarcane miRNA targets in the SCBV genome.

miRNA target candidate prediction is represented as a union from all the algorithms used in this study.

3.5. Visualization and analysis of miRNA-target interaction network

Initially, the Circos plotting tool was designed to analyze mutations with comparative metagenomics and transcriptomic biological data [46]. To study a comprehensive visualization of host–virus interaction, we created a Circos plot to integrate biological data from sugarcane miRNAs and their predicted SCBV genomic target genes (ORFs) (Fig 5). In order to reduce visual graphical complexity and permit improved readability, we only used selected sugarcane miRNAs and their SCBV targets obtained from miRanda analysis. The miRanda algorithm considers seed-based interactions and the conservation level [47, 48]. The results suggest that biological data visualization of candidate miRNAs from sugarcane, with SCBV-encoded ORFs determines credible information of desirable preferred targets of SCBV ORFs using consensual miRNAs. We have combined sugarcane miRNA data and their predicted SCBV targets simultaneously in this manner.

Fig 5

Circos plot representing miRNA-target interaction.

Circos plot of genomic regulatory network interaction as predicted to be targeted by the sugarcane miRNAs. The red, green, and blue colored lines represent SCBV genome components (ORFs). The synergetic counterparts of sugarcane miRNAs and their target genes (ORFs) of the SCBV genome are interconnected with colored lines.

3.6. Predicting common sugarcane miRNAs

Based on predicted targeting miRNAs from sugarcane to silence the SCBV genome, fourteen miRNAs (sof-miR156, sof-miR159c, sof-miR159e, sof-miR168a, sof-miR396, sof-miR408a, sof-miR408b, sof-miR408c, sof-miR408d, sof-miR408e, ssp-miR827, ssp-miR444a, ssp-miR444b and sof-miR444c-3p) were detected by union of consensus between the multiple algorithms (miRanda, RNA22, RNAhybrid and psRNATarget) used in this study (Fig 6). Moreover, SCBV genomic components (ORF1, ORF2, ORF3, and the large intergenic region (LIR)) were observed to be targeted by a total of eleven sugarcane miRNAs which were hybridized at unique positions within ORF1(sof-miR156 (locus 818) and sof-miR168 (a, b) (locus 617)) ORF2 (ssp-miR166 (locus 1450), ORF3 (sof-miR159c (locus 5534) and sof-miR408 (a, b, c, d and e) (locus 6695), and the LIR sof-miR396 (locus 79)) according to intersection between two consensual algorithms (Table 2, and S3 Table in S1 File).

Fig 6

Venn diagram plot of SCBV genome targeted by sugarcane miRNAs.

Venn diagram plot of the SCBV genome targeted by sugarcane miRNAs. In total, 28 loci are targeted by sugarcane miRNAs as predicted from four unique algorithms.

Table 2

Sugarcane miRNAs and their target positions in SCBV as identified by various algorithms.

Sugarcane miRNAs	Position miRanda	Position RNA22	Position RNAhybrid	Position psRNATarget	MFE* miRanda	MFE** RNA22	MFE RNAhybrid	Expectation psRNATarget
sof-miR156	818	817	7608	7609	-17.23	-12.7	-23.9	8
sof-miR159a	5534	5532	5535		-21.45	-19.9	-26.7
sof-miR159a(1)	5576	6536			-17.54	-12.5
sof-miR159b	5534	5532	5535		-21.45	-19.9	-26.7
sof-miR159b(1)	5576	6536			-17.54	-12.5
sof-miR159c	5534	5532	6518	1003	-20.02		-28	6
sof-miR159c(1)		6533				-12.1
sof-miR159d	5534	5532	5535		-21.45	-19.9	-26.7
sof-miR159d(1)	5576	6536			-17.54	-12.5
sof-miR159e	5534	5532	5535		-21.45	-19.9	-26.7
sof-miR159e(1)	3633	6536			-16	-12.1
sof-miR167a	2273		2826		-15.24		-27
sof-miR167b	2273		2826		-15.24		-27
sof-miR168a	617	834	612	4046	-19.53	-12.8	-29.9	8.5
sof-miR168a(1)		3263				-13.6
sof-miR168b	617	1693	612		-19	-26.7	-28.7
sof-miR168b(1)	4588	4907			-15.49	-12.8
sof-miR396	79	2050	79	5563	-20.44	-13.3	-25.1	8.25
sof-miR408a	6695	6695	174	3669	-19.19	-16.1	-27.3	8
sof-miR408a(1)	4595	5645		1766	-21.35	-13.6		8
sof-miR408b	6695	6695	174	3669	-19.19	-16.1	-27.3	8
sof-miR408b(1)	4595	5645		1766	-21.35	-13.6		8
sof-miR408c	6695	6695	174	3669	-19.19	-16.1	-27.3	8
sof-miR408c(1)	4595	5645		1766	-21.35	-13.6		8
sof-miR408d	6695	6695	174	3669	-19.19	-16.1	-27.3	8
sof-miR408d(1)	4595	5645		1766	-21.35	-13.6		8
sof-miR408e	6695	6695	174	5683	-19.19	-16.1	-29.9	7.5
sof-miR408e (1)	4595	5645		1766	-19.53	-13.6		8
sof-miR408e (2)	242				-17.01
ssp-miR166	1449		1450	7750	-27.85		-28.7	6
ssp-miR166(1)	1986			1986	-20.95			7
ssp-miR169	7748		7362		-17.38		-27.7
ssp-miR437a			6438	2646			-20.1	7
ssp-miR437b			6438				-20.1
ssp-miR437c			6437	2647			-21.8	7.5
ssp-miR437c(1)				2974				6
ssp-miR528			7619				-26.2
ssp-miR827	2816	2796	1170	7337	-19.73	-13.6	-23.7	7.5
ssp-miR444a	6184	6793	6796	6797	-18.04	-13.2	-26.9	6
ssp-miR444a(1)	3293	5246		6865	-15.23	-15.6		6.5
ssp-miR444a(2)	1301				-17.44
ssp-miR444b	6184	6793	6796	6797	-18.04	-13.2	-26.9	6
ssp-miR444b(1)	3293	5246		7079	-15.23	-15.6		6
ssp-miR444b(2)	1301			4466	-17.44			7.5
ssp-miR444b(3)	1676				-16.25
ssp-miR444c-3p	6184	5246	2899	6797	-18.95	-17.1	-27.9	6
ssp-miR444c-3p(1)	328			6865		-16.8		7
ssp-miR444c-3p(2)	1301					-18.59
ssp-miR444c-3p(3)	1680					-16.96
ssp-miR1128			1137				-23
ssp-miR1432			7314				-23.6

*MFE: Minimum free energy measured in /Kcal/mol where *MFE represents minimum folding energy measured in Kcal/mol.

3.7. Predicting consensual sugarcane miRNAs for silencing the SCBV genome

Out of 28 sugarcane miRNAs, only six sugarcane miRNA (sof-miR159 (a, b, d and e) at common locus position 5535 and ssp-miR444 (a, b) at locus 6797) were predicted at the common locus by at least three of the algorithms used (Fig 7 and Table 2). Out of 14 consensual miRNAs, only one miRNA of S. officinarum (sof-miR159e at locus 5535), with a MFE of -26.7 Kcal/mol, was considered as the top effective candidate in terms of support more efficient silencing of the SCBV genome. The efficacy of the sof-miR159e target against SCBV was validated by the suppression of RNAi-mediated viral combat through the cleavage of viral mRNA or translational inhibition [43]. Multiple loci interactions were observed for sof-miR159e at nucleotide positions 5534–5552 (consensus of three algorithms, namely, miRanda, RNA22, and RNAhybrid) and 2647 (psRNATarget) of ORF3.

Fig 7

Intersection plot of sugarcane miRNAs predicted from at least three algorithms.

The intersection plot was created with the miRNAs predicted from at least three algorithms (miRanda, RNA22 and RNAhybrid). Color codes given within the figure.

3.8. Prediction of consensus secondary structures

The validation of consensual sugarcane miRNAs was confirmed by the prediction of their stable secondary structures using the RNAfold algorithm. Precursors of mature sugarcane miRNAs were manually curated. The MFE is the key factor to determine the stable secondary structures of precursors. All the predicted consensual sugarcane miRNA precursors were observed to possess lower MFE values (ranging from −57.70 to −114.70 kcal/mol) (Table 3).

Table 3

The salient parameters of precursor miRNAs were determined along with the estimation of free energy.

miRNA ID	Length miRNA	Length precursor	MFE1 (Kcal/mol)	AMFE2	MFEI3	(G+C)%	ΔG4 (Kcal/mol)
sof-miR156	20	137	-66.20	-48.32	-0.96	50.00	-14.30
sof-miR159a	21	265	-110.30	-41.62	-0.87	47.60	-20.10
sof-miR159b	21	266	-110.30	-41.46	-0.87	47.60	-20.10
sof-miR159c	21	238	-110.60	-46.47	-0.88	52.38	-19.70
sof-miR159d	21	265	-105.80	-39.92	-0.83	47.60	-20.10
sof-miR159e	21	264	-107.50	-40.71	-1.06	38.09	-20.40
sof-miR168a	21	104	-66.20	-63.65	-1.02	61.90	-18.20
sof-miR396	21	134	-67.40	-50.29	-1.17	42.85	-19.60
sof-miR408a	21	283	-114.70	-40.53	-0.60	66.66	-16.00
sof-miR408b	21	286	-113.20	-39.58	-0.59	66.66	-16.00
sof-miR408c	21	286	-115.80	-40.48	-0.60	66.66	-16.00
sof-miR408d	21	215	-79.00	-36.76	-0.55	66.66	-16.00
sof-miR408e	21	283	-99.00	-34.98	-0.56	61.90	-16.00
ssp-miR827	21	130	-64.00	-49.23	-1.29	38.09	-17.90
ssp-miR444a	21	105	-57.70	-54.95	-1.15	47.62	-14.50
ssp-miR444b	21	106	-63.70	-60.09	-1.26	47.62	-14.50
ssp-miR444c	21	108	-61.80	-57.22	-1.33	42.85	-15.30

1MFE is minimum free energy.

2AMFE represents adjusted minimum free energy.

3MFEI defines as minimum free energy index.

4ΔG represents minimum free energy of duplex formation.

The predicted secondary structures of six precursors of pre-miRNAs are shown in (Fig 8), as predicted by the intersection of three consensual algorithms at the same locus. The top stable secondary structure of the sof-MIR159e precursor was predicted with standard features (MFE: 107.50 Kcal/mol, MFEI: 1.06 Kcal/mol). The predicted secondary structures of 14 consensual sugarcane miRNAs passed the aforementioned standard criteria. We have determined the salient characteristics of six consensus precursor miRNAs in this study, such as the MFE, AMFE, MFEI, length precursor, and GC contents. In our studies, the length precursor ranges from 105–266 nucleotides, along with a MFE of -57.70 to −110.70 kcal/mol, AMFE of -39.92 to 60.09, GC content of 38–47%, and MFEI from −0.83 to −1.26.

Fig 8

Prediction of secondary structures of stem-loop sequences of sugarcane miRNAs.

Six pre-miRNA secondary structures (precursors of sugarcane miRNAs) were identified in this study by consensus between three algorithms. The sugarcane mature miRNA name IDs, accession IDs, MFEs and MFEIs are given as follows: (A) sof-MIR159a (MI0001756), -110.30 kcal/mol, -0.87 B) sof-MIR159b (MI0001757), -110.30 kcal/mol, -0.87; (C) sof-MIR159d (MI0001758), -105.80 kcal/mol, -0.83; (D) sof-MIR159e (MI0001759), -107.50 kcal/mol, -1.06; (E) ssp-MIR444a (MI0018185), -57.70 kcal/mol, -1.15; (F) ssp-MIR444b (MI0018186), -63.70kcal/mol, -1.26.

3.9. Assessment of free energy (ΔG) of miRNA-mRNA interaction

The predicted consensual sugarcane miRNAs were validated by estimating the free energies of miRNA/target duplexes (Table 3). The free energies (ΔG) of six consensual sugarcane miRNAs were estimated as follows: sof-miR159 (a, b, d) (ΔG: -20.10 kcal/mol), sof-miR15e (ΔG: -20.40 kcal/mol), and ssp-miR444 (a, b) (ΔG: -14.50 kcal/mol).

3.10. Tissue preferential expression analysis of sugarcane miRNAs

We used the “PmiRExAt” database to search for the expression analysis of the predicted sugarcane miRNAs. Homologous miRNAs were present in all three plant species, i.e., maize, rice, and wheat (S1–S3 Figs). The expression of these microRNAs was identified in all tissue types in each species. Therefore, the expression of sugarcane miRNAs was confirmed in other plant species, i.e., maize, rice, and wheat. Evidence of the existence of the same miRNAs in sugarcane is also provided. Most of the stated miRNAs have also been confirmed, in multiple studies, for their expression and roles in plant cellular pathways [49, 50].

4. Discussion

For the filtering of false positive results, we studied the effectiveness of the computational algorithms considered here to validate the miRNA target prediction data. We designed an effective approach for the validation of miRNA target prediction results at individual, union, and intersection levels. Computational prediction algorithms offer rapid methods to identify potential host-derived miRNA targets in virus genomes. Default parameters represent optimized specifications for each miRNA to its respective target site in the viral genome. This varies with respect to each algorithm/tool and can be modified for fine-tuning the settings or increasing the level of sensitivity for predicted sites. Default parameters are effective for screening out false-positive attachment sites for miRNAs using multiple prediction tools. miRanda is a widely used algorithm that includes the main aspects of miRNA–target prediction, such as the conservation level and miRNA 3’UTR site [51]. The RNA22 algorithm is a novel alternative option for exploring new miRNA–mRNA interactions because of its unique capabilities—although it has a high likelihood of generating false-positive results [47]. We calculated the MFE and determined the target inhibition as recommended by Broderson by using RNAhybrid [37].

Several potential sugarcane miRNA targets and miRNA–mRNA interactions could be consensually predicted by all of the algorithms (Fig 7). Plant miRNAs are responsible for inducing the degradation of the target genes using perfect or imperfect complementarity base pairing [52]. The current study demonstrates that SCBV genome components (ORF1, ORF2, and ORF3) are susceptible to targeting by a set of consensual sugarcane miRNAs. In addition, sof-miR159 (a, b, d, and e) was found to target ORF3 at a consensual hybridization site by at least three algorithms (Fig 8). Free energy assessment is a dynamic feature of miRNA and target binding. Previous studies have revealed a significant correlation of free energy between the translational repression and the hybridization binding of the seed region [53]. The thermodynamic stability of the miRNA–mRNA duplex was estimated by the assessment of free energy to monitor site accessibility for the determination of the secondary structure duplex [27]. In order to validate miRNA–mRNA interaction, the free energy of a duplex was assessed (Table 2). Our prediction results show high stability for the sugarcane-encoded miRNA–SCBV-mRNA duplex at a low free energy level (Table 3 and Fig 8). The RNA duplex is considered to be more stable due to the stronger binding of miRNA to mRNA [54, 55].

We used union and intersection approaches to reduce false positive prediction. Union approaches rely on combining more than one target prediction tool when finding true and false targets. The sensitivity level for a predicted target increases due to a decrease in specificity. An intersection approach is entirely different and depends upon the combination of two or more computational tools and enhances the specificity level of predicted targets due to a decrease in sensitivity [56]. Our target prediction results revealed that both computational approaches achieved the best outcomes with maximum performance for predicting and estimating the best targets (Figs 6 and 7). Previous studies have also reported the silencing of plant viruses using host-derived miRNAs when applying a set of computational algorithms. The identification and evaluation of best-fit candidate miRNA targets for different plants has been concluded successfully with potato virus Y (PVY) [57], maize chlorotic mottle virus (MCMV) [58], CLCuKoV-Bu [59], rice yellow mottle virus (RYMV), [60] and SCBGAV to find miRNA–target interaction [61]. We have designed an equal novel bioinformatics approach for target prediction in the SCBV genome to control the emerging presence of Badnavirus in sugarcane cultivars.

In our previous study, we identified the most ideal consensual sugarcane miRNA (sof-miR396) to target ORF3 of the SCBGAV genome using multiple computational algorithms [61]. The quantity of false positive miRNA–target interaction estimated by multiple algorithms depends upon the mode of miRNA–target recognition. MFE is also another important factor that affects miRNA–target interaction in result validation [62]. To set a lower MFE value will give rise to a higher probability of miRNA–target complex formation [63]. In the current study, for miRanda analysis, a stringent cut-off point of −15 kcal/mol was set for narrowing down the miRNA candidates. Similarly, to validate host–virus interaction, a MFE cut-off point of -20 kcal/mol applied for RNAhybrid analysis [32].

Although MFE has a considerable role for development of miRNA–mRNA complexes, it does not certify that interactions will lead to functional changes. In the current study, we have identified six potential miRNA hybridization binding sites that have exhibited low MFEs and free energy for duplex formation. These predicted miRNAs not only have potential targets for the SCBV genome at the transgenic level but also have a stronger probability to develop miRNA–viral mRNA complex formation. These miRNAs also have chance to participate in a SCBV replication mechanism, where a consensus sugarcane miRNA (sof-miR396) has a binding site within the SCBV large intergenic region (LIR) at locus 79 as predicted by the miRanda and RNAhybrid algorithms. In the previous study, we predicted that sof-miRNA396 is an effective candidate to target the SCBGAV genome [61]. Notably, sof-miR159e was predicted by all the algorithms. Additionally, miR159 was explored and was found to present a strong role for silencing GAMYB to enable normal growth [64]. Phe-MIR159 involved in regulating the gene responsible for secondary thickening in Phyllostachys edulis [65]. It is important to assess the function of predicted potential consensual miRNAs for the identification of Badnavirus replication to demonstrate SCBV replication experimentally. A hypothetical model was designed to show that sugarcane-derived miRNAs can inhibit SCBV mRNA and sugarcane genes against SCBV virus (Fig 9). It facilitates plant-encoded miRNAs in the cleavage of SCBV miRNA.

Fig 9

Schematic model designed for miRNA-mediated gene silencing in plant-virus interaction.

SCBV can activate the production of endogenous sugarcane miRNAs post infection. Moreover, sugarcane miRNAs can target SCBV mRNA for degradation.

RNAi screening is a novel technology for discovering various cellular functions and identifying host-derived factors of viruses [66]. Here, we selected 28 experimentally validated sugarcane miRNAs with annotated targets that are part of SCBV. amiRNA-based silencing technology has been successfully validated in many crop plants for controlling emerging plant viruses [23, 24, 26]. In summary, our computational work for SCBV genome silencing could offer a new approach for the production of antiviral agents. Furthermore, we demonstrated a method to minimize the novel antiviral effects of host-derived miRNAs against SCBV.

5. Conclusions

SCBV has appeared as a major problem in China. SCBV diminishes quantitative yields in all sugarcane cultivars. In the current study, prior to cloning, we have applied computational tools to predict and comprehensively analzse candidate miRNA from sugarcane against SCBV. Among them, sof-miR159e was predicted as the top effective candidate that could target the vital gene (ORF3) of the SCBV genome. Our results demonstrate an alternative strategy to existing molecular approaches that could be repurposed to control badnaviral infections. The current findings provide in silico evidence of a novel scheme to construct miRNA-mediated gene silencing therapeutics to combat SCBV.

(RAR)

Click here for additional data file.

Tissue preferential expression heatmap of sugarcane miRNAs in maize.

(TIF)

Click here for additional data file.

Tissue preferential expression heatmap of sugarcane miRNAs in rice.

(TIF)

Click here for additional data file.

Tissue preferential expression heatmap of sugarcane miRNAs in wheat.

(TIF)

Click here for additional data file.

15 Jan 2021

PONE-D-20-35060

An Algorithmic framework for genome-wide identification of Sugarcane (Saccharum officinarum L.)-encoded microRNA targets against SCBV

PLOS ONE

Dear Dr. Asharf,

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PLOS ONE

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1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Ashraf et al., have attempted to contribute in addressing the situation caused by SCBV to sugarcanes by envisaging miRNAs that could be helpful in the hushing of SCBV. This study sounds quite useful to me in terms of the economical burden and plants’ health; however, I have come across quite a few major and minor concerns that must be addressed before the manuscript could be accepted for publication.

Major concerns

(1) The title of the manuscript doesn’t complement the content. It gives a feeling of some novel algorithmic framework design; however, the study doesn’t directly design or improve any algorithm but just entirely depends on the existing platforms. Please modify the title to avoid misguidance.

(2) There is obstruction in the flow of the Abstract portion, please rearrange the sentences and remove any redundant detail.

(2) Introduction part, the authors mention names like RNAi technology and amiRNA approach, however, I didn’t find enough detail about these approaches in the text. They should be explained for the reader with citations.

(3) Line 135 (Page-7), why the authors maintained the default parameters? What makes them sure that the default parameters are suitable in this particular case, because though the default settings are universally applied in most of the conditions, these algorithms could get tricky in situations and they need to be tuned for a particular case. Is there any citation?

(4) Line 138 (Page-7) RNA22, what does the author mean by highly sensitive? This word doesn’t make sense to me. What’s the difference between a sensitive and an insensitive server?

(5) Please provide the citations for the all the maintained or adjusted parameters in case of every server used.

(6) In case of mutated viruses, do the outcomes of this study remains the same? The authors should consider variations in the genome since they could greatly alter the genomic behavior.

(7) Why the algorithms fail to compute data in most of the cases as can be seen in Table 2? Did the authors try to tune the parameters for computing the data?

(8) Discussion portion line 1, authors have mentioned that the performance of the computational algorithms is studied which is contradictory to the methodological section where the algorithms are only used for computing the data, not for evaluating or setting any benchmarks for comparisons. Please correct your statement, and search for similar confusions in the manuscript.

(9) What are the future prospects of this study? How the results could be considered further in terms of the computational drug design.

(10) Please read the manuscript by a native or at least a proficient English user, as I have encountered multiple errors in the punctuation, indentation, and word choice. I have highlighted few of them in the minor concerns but look for other similar issue thoroughly.

Minor concerns

(a) Line 16 (P-1), expand the full form of ORFs.

(b) Line 28 (P-1), “Consequently……mutants faster”, the sentence is grammatically wrong.

(c) Line 42 (P-2), avoid extra spacing.

(d) Line 42 (P-2), that was composed OR that is composed?

(e) Line 61 (P-3), Plant employs, the word employ is not suitable here as plants use their internal mechanism of defense, instead change it to “possess” or “enriched with” …

(f) Citation for pDRAW32 is missing.

(e) Line 104 (Page-5) The sentence has unnecessary detail and causes confusion. Instead, just write that the transcript is published and available via accession no. X, and provide the citation. Words like cloning, submission to NCBI etc. are not needed.

(g) Line 180 (Page-9), R is not a language, it’s a statistical environment. Please correct.

(h) Discussion section, no detail is given about the future prospects of this work

(i) Language needs to be highly improved.

Reviewer #2: In this study, the authors predicted and comprehensively evaluated sugarcane miRNAs for the silencing of SCBV genome using in-silico algorithms. They used several tools to predict the miRNA targets and identified several potential candidate miRNAs which may be used for the silencing of SCBV. However, the authors did not provide the expression of these miRNAs and predict their targets in the host sugarcane genome, which should be considered when selecting siRNA.

**********

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Reviewer #1: No

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23 Mar 2021

Response to Reviewer’s Comments

Dear Editor,

Coauthors and I very much appreciated the encouraging, critical and constructive comments on this manuscript by the reviewers. The comments have been very thorough and useful in improving the manuscript. We strongly believe that the comments and suggestions have increased the scientific value of revised manuscript by many folds. We have taken them fully into account in revision. We are submitting the corrected manuscript with the suggestion incorporated the manuscript. The manuscript has been revised as per the comments given by the reviewers, and our responses to all the comments are as follows:

Reviewer #1:

Ashraf et al., have attempted to contribute in addressing the situation caused by SCBV to sugarcanes by envisaging miRNAs that could be helpful in the hushing of SCBV. This study sounds quite useful to me in terms of the economic burden and plants’ health; however, I have come across quite a few major and minor concerns that must be addressed before the manuscript could be accepted for publication.

(Major Concerns)

1. The title of the manuscript doesn’t complement the content. It gives a feeling of some novel algorithmic framework design; however, the study doesn’t directly design or improve any algorithm but just entirely depends on the existing platforms. Please modify the title to avoid misguidance.

.

Response- Thank you so much for your suggestion. As per reviewer suggestion, correction has been made in the revised manuscript and we have modified the title. We have removed words “An Algorithmic framework for” from the title and added “In silico”. Please see line 1.

2. There is obstruction in the flow of the Abstract portion, please rearrange the sentences and remove any redundant details. Introduction part, the authors mention names like RNAi technology and amiRNA approach, however, I didn’t find enough detail about these approaches in the text. They should be explained for the reader with citations.

Response- Thank you very much for your valuable suggestion. Correction has been made as per reviewer suggestion. Redundant details have been removed. Please see line 21-23. Correction has been made as per reviewer suggestion. The introduction section is greatly improved with new text. Please see 62-67 lines for RNAi and lines 78-82 for amiRNA.

3. Line 135 (Page-7), why the authors maintained the default parameters? What makes them sure that the default parameters are suitable in this particular case, because though the default settings are universally applied in most of the conditions, these algorithms could get tricky in situations and they need to be tuned for a particular case. Is there any citation?

Response- Thank you so much for your comment. As per reviewer suggestion, correction has been made in the revised manuscript. It was written wrongly. We have replaced it with ‘standard’ instead of ‘default’. Sorry it type error. Please see line 145 of the revised manuscript.

4. Line 138 (Page-7) RNA22, what does the author mean by highly sensitive? This word doesn’t make sense to me. What’s the difference between a sensitive and an insensitive server?

Response- Thank you so much for your suggestion. RNA22 does not rely upon cross-species conservation, is resilient to noise, and, unlike previous methods, it first finds putative microRNA binding sites in the sequence of interest, then identifies the targeting microRNA. As per reviewer suggestion, ‘highly sensitive’ is removed. Please see line 148.

5. Please provide the citations for all the maintained or adjusted parameters in case of every server used.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. We used the server as such.

6. In case of mutated viruses, do the outcomes of this study remain the same? The authors should consider variations in the genome since they could greatly alter the genomic behavior.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. After mutation in SCBV viruses, the results will greatly change but it depends upon the kind of mutation in the sequences.

7. Why the algorithms fail to compute data in most of the cases as can be seen in Table 2? Did the authors try to tune the parameters for computing the data?

Response- Thank you so much for your comment. We did not tune any parameters during computing the data. The results are as usual the same as predicted by the algorithms.

8. Discussion portion line 1, authors have mentioned that the performance of the computational algorithms is studied which is contradictory to the methodological section where the algorithms are only used for computing the data, not for evaluating or setting any benchmarks for comparisons. Please correct your statement, and search for similar confusions in the manuscript.

Response- Thank you so much for your comments. Correction has been made per reviewer suggestion. We have removed the word “performance” and placed it with ‘effectiveness’ Please see line 346.

9. What are the future prospects of this study? How the results could be considered further in terms of the computational drug design?

Response- Thank you so much for your comments. Correction has been made in the revised manuscript. We have already showed text in discussion for future work. Please see line 420-424, and 426-434 of the revised manuscript.

10. Please read the manuscript by a native or at least a proficient English user, as I have encountered multiple errors in the punctuation, indentation, and word choice. I have highlighted few of them in the minor concerns but look for other similar issue thoroughly?

Response- Thank you so much for your comment. As per reviewer suggestion, correction has been made in the revised manuscript. The manuscript has now been edited by acquiring English correction services from the Mdpi Experts.

(Minor Concerns)

a. Line 16 (P-1), expand the full form of ORFs.

Response- Thank you so much for your comment. Correction has been made as per reviewer suggestion. Please see line 15.

b. Line 28 (P-1), “Consequently……mutants faster”, the sentence is grammatically wrong.

Response- Thank you so much for your comment. Correction has been made as per reviewer suggestion. Please see lines 26-27.

c. Line 42 (P-2), avoid extra spacing?

Response- Thank you so much for your comments. Correction has been made as per reviewer suggestion.

d. Line 42 (P-2), that was composed OR that is composed?

Response- Thank you so much for your comment. Correction has been made as per reviewer suggestion. Please see line 41.

e. Line 61 (P-3), Plant employs, the word employ is not suitable here as plants use their internal mechanism of defense, instead change it to “possess” or “enriched with”. Line 104 (Page-5) the sentence has unnecessary detail and causes confusion. Instead, just write that the transcript is published and available via accession no. X, and provide the citation. Words like cloning, submission to NCBI etc. are not needed.

Response- Thank you so much for your comments. Correction has been made as per reviewer suggestion. Please see line 61 and 115.

f. Citation for pDRAW32 is missing.

Response- Thank you so much for your comments. As per reviewer suggestion, correction has been made in the revised manuscript. Please see line 118.

g. Line 180 (Page-9), R is not a language, it’s a statistical environment. Please correct.

Response- As per reviewer suggestion, correction has been made in the revised manuscript. Please see line 191.

h. Discussion section, no detail is given about the future prospects of this work.

Response- Thank you so much for your comments. As per reviewer suggestion, correction has been made. Please see lines 393-395, 410-415, 420-424 and 426-434.

i. Language needs to be highly improved.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. As per reviewer suggestion, correction has been made in the revised manuscript. The manuscript has now been edited by acquiring English correction services from the Mdpi Experts. Editorial Certificate is attached along with rebuttal letter. We have corrected all the grammatical mistakes in the revised manuscript.

Reviewer #2:

In this study, the authors predicted and comprehensively evaluated sugarcane miRNAs for the silencing of SCBV genome using in-silico algorithms. They used several tools to predict the miRNA targets and identified several potential candidate miRNAs which may be used for the silencing of SCBV.

(Minor Concerns)

However, the authors did not provide the expression of these miRNAs and predict their targets in the host sugarcane genome, which should be considered when selecting siRNA.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Yes, we have not provided the expression data in this revised manuscript. The expression of targeted miRNAs is in process.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

22 Jul 2021

PONE-D-20-35060R1

In silico genome-wide identification of Sugarcane (Saccharum officinarum L.)-encoded microRNA targets against SCBV

PLOS ONE

Dear Dr. Asharf,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Sep 05 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

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An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Eduardo Andrés-León

Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

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Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: "author Response- Thank you so much for your comments and compliment. We have taken

reviewer’s comment in full consideration and it will be well reflected by the revised

version of manuscript. Yes, we have not provided the expression data in this revised

manuscript. The expression of targeted miRNAs is in process".

I think the authors did not address my comments.

**********

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Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

29 Aug 2021

Response to Reviewer’s Comments

Dear Editor,

Coauthors and I very much appreciated the encouraging, critical and constructive comments on this manuscript by the reviewer. The comments have been very thorough and useful in improving the manuscript. We strongly believe that the comments and suggestions have increased the scientific value of revised manuscript by many folds. We have taken them fully into account in revision. We are submitting the corrected manuscript with the suggestion incorporated the manuscript. The manuscript has been revised as per the comments given by the reviewers, and our responses to all the comments are as follows:

Reviewer #2 (First Round of Revision):

In this study, the authors predicted and comprehensively evaluated sugarcane miRNAs for the silencing of SCBV genome using in-silico algorithms. They used several tools to predict the miRNA targets and identified several potential candidate miRNAs which may be used for the silencing of SCBV.

(Minor Concerns)

However, the authors did not provide the expression of these miRNAs and predict their targets in the host sugarcane genome, which should be considered when selecting siRNA.

Reviewer #2 (Second Round of Revision):

"Author Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised

version of manuscript. Yes, we have not provided the expression data in this revised

manuscript. The expression of targeted miRNAs is in process". I think the authors did not address my comments.

Respected Sir,

We have greatly improved the existing manuscript for your kind comment after revision. It has increased the values of the current manuscript many folds. We are all very thankful to your comments.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see line 192-197 in the methodology section. The results section is greatly improved after revision. Please see line 349-356. The supplementary figures have been added in the manuscript (Figure S1, S2 and S3). The references section is also revised with three new references related to expression. Please see reference no. 49, 53 and 54.

Submitted filename: Response to Reviewers.pdf

Click here for additional data file.

20 Sep 2021

PONE-D-20-35060R2In silico genome-wide identification of Sugarcane (Saccharum officinarum L.)-encoded microRNA targets against SCBVPLOS ONE

Dear Dr. Asharf,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Nov 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

S.V. Ramesh, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Although peer-reviewers see merit in your manuscript, there seems to be number of queries that remain unanswered as pointed out by one of the reviewer. Address all the comments appropriately and resubmit the manuscript for further consideration.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: (No Response)

Reviewer #3: The reviewers comments have been partly addressed. For example, abstract and introduction have been revised but still not up to mark. Kindly revise it. There is still no justification for the default values of parameters for prediction methods. The question "What’s the difference between a sensitive and an insensitive server?" is not answered adequately. In response to comment 8, what other confusing words have been replaced is not mentioned. Confusing/incorrect statements may be grammatically correct but technically incorrect. In the abstract the "effective badnaviral methods" is confusing, I think you mean "effective anti-badnaviral methods".

Another concern is the validation of the results, still remains.

One of the comment for "miRNAs targets in the host sugarcane genome" has not been answered too. The amiRNAs may target even the host genomes, so it is important to avoid targeting host targets which can be harmful to plants and include the ones useful to the plant in fighting the virus.

Though language corrections have been made, there is still lot of scope for further improvement.

Comments and suggestions for improvements:-

1. The revised title is grammatically incorrect. It should be “In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus”.

1. Why is RNAcofold used for the estimation of free energy (ΔG) associated with miRNA–mRNA interactions, while every target prediction software results give free energy value? Justify this in the manuscript.

2. In circos author used only selected sugarcane miRNAs and their SCBV targets obtained from miRanda analysis only. Please explain the criteria behind this selection of miRNAs and why only miRanda was used for this graph generation why not other tools?

3. In line number 305 it is stated that “The efficacy of the sof-miR159e target against SCBV was validated by the suppression of RNAi-mediated viral combat through the cleavage of viral mRNA or translational inhibition”. Neither any reference nor any methodology is provided regarding this validation.

4. Please justify the prediction of Consensus Secondary Structures in section 3.8 while these structures are not used anywhere to conclude anything.

5. In section 3.9, it is mentioned that table 3 comprises the free energies (ΔG) of sugarcane miRNAs and were estimated as sof-miR159 (a, b, d) (ΔG: -20.10 kcal/mol). But sof-miR159 (a, b, d) are missing from Table 3.

6. Why union plot and intersection plots are not discussed in length while main results are based on these two findings only?

7. In line 365 it is stated that “miRanda and psRNATarget are two powerful plant miRNA prediction algorithms for identifying hybridization sites in a viral genome.” The fact is that miRanda is primarily designed for the prediction of miRNA targets in animals. Please give reference in support of your assertion. Further, these algorithms are designed to predict miRNA targets rather than miRNAs.

8. In line 339 it is mentioned that the top effective candidate sof-miR159e has ΔG: -20.40 340 kcal/mol. This energy is very high for any significant hybridization of miRNA and mRNA in plants. Please justify with reference.

9. Why two highly different ΔG cut off (-15 and -20) for miRanda and RNAhybrid are used while the whole manuscript is based on the comparison.

10. How did a study of Aedes aegypti cellular miRNA and arboviruses become relevant to the present study?

11. It is mentioned that “ We have designed an amiRNA-based gene construct,”, while it is not mentioned anywhere in the method section.

12. Please include a reference for amiRNA’s mechanism of action.

13. The first-time usage of S. officinarum must be in expanded form at the first instance

14. The full-length transcript of the SCBV-BRU genome with accession no. JN377537 is used as per the manuscript, then the title of section 3.1. Genome Assembly of SCBV is misleading it should be “Genome Organization of SCBV”.

15. In line 305 it is stated that “The efficacy of the sof-miR159e target against SCBV was validated by the suppression of RNAi-mediated viral combat through the cleavage of viral mRNA or translational inhibition.” However, no reference is provided in this regard.

16. Description of tools is repetitive in the manuscript. Firstly in the method section and then in the discussion, the same lines seems redundant.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

19 Nov 2021

Response to Reviewer’s Comments

Dear Editor,

Coauthors and I very much appreciated the encouraging, critical and constructive comments on this manuscript by the reviewer. The comments have been very thorough and useful in improving the manuscript. We strongly believe that the comments and suggestions have increased the scientific value of revised manuscript by many folds. We have taken them fully into account in this third round of revision. We are submitting the corrected manuscript with the suggestion incorporated the manuscript. The manuscript has been revised as per the comments given by the reviewers 3 and our responses to all the comments are as follows:

Reviewer #3 (General Comments):

General Comment1: The reviewer’s comments have been partly addressed. For example, abstract and introduction have been revised but still not up to mark. Kindly revise it.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see lines 14-23, 28-30, 40, 48-51, 62-63, 71-73, 75-77, 80, 86, 89-90 and 92-99.

General Comment2: There is still no justification for the default values of parameters for prediction methods.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see lines 377-381.

General Comment3: The question "What’s the difference between a sensitive and an insensitive server?" is not answered adequately.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see lines 130-135.

General Comment4: In response to comment 8, what other confusing words have been replaced is not mentioned. Confusing/incorrect statements may be grammatically correct but technically incorrect. In the abstract the "effective badnaviral methods" is confusing; I think you mean “effective ant-badnaviral methods”

Response- Thank you very much your kind comment. Correction has been made a per reviewer suggestion. Please see line 30.

General Comment5: Another concern is the validation of the results, still remains.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. We agree on this concern but as this manuscript is only about screening the best possible microRNAs In silico to create virus resistance, the wet lab validation is not claimed by authors.

General Comment6: One of the comments for "miRNAs targets in the host sugarcane genome" has not been answered too. The amiRNAs may target even the host genomes, so it is important to avoid targeting host targets which can be harmful to plants and include the ones useful to the plant in fighting the virus.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. The host-delivered miRNAs are retrieved from the same plant and they are already expressing inside the host. These miRNAs are expected not make much damage to the plant. We are expected that the increased in expression in the cell will stop the virus to cause extensive damage.

General Comment7: Though language corrections have been made, there is still lot of scope for further improvement:-

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript.

Reviewer #3 (Minor Concerns): Comments and suggestions for improvements:-

Minor Comment: The revised title is grammatically incorrect. It should be “In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus”.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Revision has been made. Please see line: 1-2.

Comment 1: Why is RNAcofold used for the estimation of free energy (ΔG) associated with miRNA–mRNA interactions, while every target prediction software results give free energy value? Justify this in the manuscript.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Revision has been made. Please see lines 198-203.

Comment 2: In circos author used only selected sugarcane miRNAs and their SCBV targets obtained from miRanda analysis only. Please explain the criteria behind this selection of miRNAs and why only miRanda was used for this graph generation why not other tools?

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Revision has been made. Please see lines 289-290 in the revised manuscript.

Comment 3: In line number 305 it is stated that “The efficacy of the sof-miR159e target against SCBV was validated by the suppression of RNAi-mediated viral combat through the cleavage of viral mRNA or translational inhibition”. Neither any reference nor any methodology is provided regarding this validation.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Revision has been made. Please see lines 328. Reference number 43 has been added.

Comment 4: Please justify the prediction of Consensus Secondary Structures in section 3.8 while these structures are not used anywhere to conclude anything.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. The validation of consensual sugarcane miRNAs was confirmed by the prediction of their stable secondary structures using the RNAfold algorithm. Please see lines 335-336.

Comment 5: In section 3.9, it is mentioned that table 3 comprises the free energies (ΔG) of sugarcane miRNAs and were estimated as sof-miR159 (a, b, d) (ΔG: -20.10 kcal/mol). But sof-miR159 (a, b, d) are missing from Table 3.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Revision has been made. Please see table3 at line 370.

Comment 6: Why union plot and intersection plots are not discussed in length while main results are based on these two findings only?

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Please see lines 414-421 in the discussion section.

Comment 7: In line 365 it is stated that “miRanda and psRNATarget are two powerful plant miRNA prediction algorithms for identifying hybridization sites in a viral genome.” The fact is that miRanda is primarily designed for the prediction of miRNA targets in animals. Please give reference in support of your assertion. Further, these algorithms are designed to predict miRNA targets rather than miRNAs.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. These lines are removed. Please see lines 398-400 in the discussion section.

Comment 8: In line 339 it is mentioned that the top effective candidate sof-miR159e has ΔG: -20.40 kcal/mol. This energy is very high for any significant hybridization of miRNA and mRNA in plants. Please justify with reference.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. The top effective candidate sof-miR159e was selected on the basis of consensual locus position predicted by at three of the algorithms used in this study.

Comment 9: Why two highly different ΔG cut off (-15 and -20) for miRanda and RNAhybrid are used while the whole manuscript is based on the comparison.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Please see lines 382-386.

Comment 10: How did a study of Aedes aegypti cellular miRNA and arboviruses become relevant to the present study?

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. This information is removed from the discussion section. Please see lines 438-440 and reference 70.

Comment 11: It is mentioned that “We have designed an amiRNA-based gene construct,” while it is not mentioned anywhere in the method section.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see lines 460, 462 and 463.

Comment 12: Please include a reference for amiRNA’s mechanism of action.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see line 75 and reference 19.

Comment 13: The first-time usage of S. officinarum must be in expanded form at the first instance.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see line 19.

Comment 14: The full-length transcript of the SCBV-BRU genome with accession no. JN377537 is used as per the manuscript, then the title of section 3.1. Genome Assembly of SCBV is misleading it should be “Genome Organization of SCBV”.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see line 214.

Comment 15: In line 305 it is stated that “The efficacy of the sof-miR159e target against SCBV was validated by the suppression of RNAi-mediated viral combat through the cleavage of viral mRNA or translational inhibition.” However, no reference is provided in this regard.

Response- Thank you very much for your comment. Correction has been made as per reviewer suggestion. Reference has been added. Please see line 328 and reference no. 43.

Comment 16: Description of tools is repetitive in the manuscript. Firstly in the method section and then in the discussion, the same lines seems redundant.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Repetitive information and references regarding tool is removed. Please see line 379-382, 388-390 and 394-395.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

23 Nov 2021

PONE-D-20-35060R3In silico identification of Sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virusPLOS ONE

Dear Dr. Asharf,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I would suggest the authors to tone down the use of amiRNA strategies etc in the MS and language needs to be rechecked once again. Please submit your revised manuscript by Jan 07 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

S.V. Ramesh, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Major issues:

This study is an in silico analysis of potential sugarcane derived miRNAs targeting Sugarcane Bacilliform Virus (SCBV) infecting the crop. The major concerns are:

a) No experimental evidence (wet lab) to prove the expression status of select or putatively antiviral miRNAs during viral infection. Had the expression data of sugarcane miRNAs was used in the analysis it would have been still acceptable. However, that was not the case.

b) Experimental evidence for the expression of host miRNAs along with RACE experiments to prove the cleavage of viral mRNA is a must to claim the antiviral role of host miRNAs. In the absence of any evidence it is a mere prediction which may or may not work.

c) When the study predicts that a select list of host miRNAs could target the viral genome sequence (or ORFs) the need for artificial miRNA construct in conferring virus resistance has not explained properly. Quite interestingly authors claim they have modified the miRNA/miRNA* sof-miRNA159. However what is the necessity for this modification or kindly explain what has been modified adequately.

d) The last sentence of the abstract states the following [The efficacies of the predicted candidate miRNAs are evaluated here to test the survival rates of the in vitro amiRNA-mediated effective anti-badnaviral methods in terms of silencing and resistance in sugarcane cultivars] however there are no experimental evidence to prove the efficacy of miRNAs in vitro or in vivo in silencing badnavirus genome.

e) A greater portion of ms is wasted in explaining the features of miRNA target prediction algorithms. Specific web links to the algorithms could suffice for the reader to understand what these softwares do.

Minor concerns:

• Still manuscript writing is not satisfactory

• There exists no connectivity whatsoever between what is presented in lines 78-82 and the lines just before (71-78).

However having said these, the title unequivocally states that the work is an in silico analysis. Hence I would suggest the authors to tone down the use of amiRNA strategies etc in the ms and language needs to be rechecked once again.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

23 Nov 2021

Response to Reviewer’s Comments

Dear Editor,

We are grateful to the reviewers and respected editor for their insightful comments on my papers. Coauthors and I very much appreciated the encouraging, critical and constructive comments on this manuscript by the respected editor. The comments have been very thorough and useful in improving the manuscript. We strongly believe that the comments and suggestions have increased the scientific value of revised manuscript by many folds. We have taken them fully into account in this third round of revision. We are submitting the revised manuscript with the suggestion incorporated the manuscript. We have been able to incorporate changes to reflect most of the suggestions provided by the reviewers. We have highlighted the changes within the manuscript. Here is a point-by-point response to the reviewers' comments and concerns. The manuscript has been revised as per the comments given by the respected editor and our responses to all the comments are as follows:

Editor (Optional Comments): Major Concerns

General Comment1: No experimental evidence (wet lab) to prove the expression status of select or putatively antiviral miRNAs during viral infection. Had the expression data of sugarcane miRNAs was used in the analysis it would have been still acceptable. However, that was not the case.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Revision has been made previously and accepted by the reviewer 2. Please see lines: 193-198 and 351-358.

General Comment2: Experimental evidence for the expression of host miRNAs along with RACE experiments to prove the cleavage of viral mRNA is a must to claim the antiviral role of host miRNAs. In the absence of any evidence it is a mere prediction which may or may not work.

Response- Thank you very much for your valuable suggestion. Thank you for this suggestion. It would have been interesting to explore this aspect. However, in the case of our study, it seems slightly out of scope and is part of our research in future.

General Comment3: When the study predicts that a select list of host miRNAs could target the viral genome sequence (or ORFs) the need for artificial miRNA construct in conferring virus resistance has not explained properly. Quite interestingly authors claim they have modified the miRNA/miRNA* sof-miRNA159. However what is the necessity for this modification or kindly explain what has been modified adequately.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. There is no modification. We have removed the figure 10. Please see lines 437-440 and 449-453.

General Comment4: The last sentence of the abstract states the following [The efficacies of the predicted candidate miRNAs are evaluated here to test the survival rates of the in vitro amiRNA-mediated effective anti-badnaviral methods in terms of silencing and resistance in sugarcane cultivars] however there are no experimental evidence to prove the efficacy of miRNAs in vitro or in vivo in silencing badnavirus genome.

Response- Thank you very much for your valuable comment for the improvement of the manuscript. Correction has been made as per editor suggestion. Please see lines 28-31.

General Comment5: A greater portion of MS is wasted in explaining the features of miRNA target prediction algorithms. Specific web links to the algorithms could suffice for the reader to understand what these softwares do.

Response- Thank you very much for your valuable comment for the improvement of the manuscript. Thank you for this suggestion. It would have been interesting to explore this aspect. Appropriate changes were made and highlighted in the revised manuscript according to the suggestions of the respected editor. We agree this and have incorporated your suggestion throughout the manuscript. Please see lines 138-139, 141-143, 149-153, 157-158, 161-163,170-174, 182-183 and 185-192.

Editor Comments: Minor Concerns

General Comment1: Still manuscript writing is not satisfactory.

Response- Thank you very much for your comments. This manuscript was edited for proper English language, grammar, punctuation, spelling, and overall style by one or more of the highly qualified native English speaking editors at MDPI Journal Experts. The editorial certificate was uploaded with the revised manuscript at first revision stage. I hope the revised manuscript will meet the requirements of academic publishing in PLoS ONE Journal and I have revised my manuscript according to the suggestions of the professional editor. Thanks again for the reminder.

General Comment2: There exists no connectivity whatsoever between what is presented in lines 78-82 and the lines just before (71-78).

Response- The comments from the peer-reviewers are very useful for the improvement

of our manuscript. Correction has been made in the revised manuscript. Please see lines 80-84.

General Comment3: However having said these, the title unequivocally states that the work is an in silico analysis. Hence I would suggest the authors to tone down the use of amiRNA strategies etc in the MS.

Response- The comments from the peer-reviewers are very useful for the improvement

of our manuscript. Correction has been made in the revised manuscript. Please see lines 94-95, 445-447 and 461.

General Comment3: Language needs to be rechecked once again.

Response- The comments from the peer-reviewers are very useful for the improvement

of our manuscript. Thank you very much for your comments. This manuscript was edited for proper English language, grammar, punctuation, spelling, and overall style by one or more of the highly qualified native English speaking editors at MDPI Journal Experts. Thanks again for the reminder. The manuscript will be submitted again for English correction after final acceptance.

Submitted filename: Rebuttal Letter.docx

Click here for additional data file.

1 Dec 2021

PONE-D-20-35060R4In silico identification of Sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virusPLOS ONE

Dear Dr. Asharf,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Line NO 79 Experimental validation of potential miRNA targets is highly costly and laborious.

Editor’s comments: Kindly remove this sentence.

Fig. 1 Title states “The methodology of miRNA prediction from the SCBV genome” Kindly change in to “The methodology of host or sugarcane miRNA target prediction in the SCBV genome”

Also the legend says “A flowchart designed for predicting candidate miRNAs from the SCBV genome pipeline”. In fact it is candidate miRNAs of host that could potentially target SCBV Genome. Change this too.

Kindly consult native English speaker to rectify the errors in the MS.

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Shunmugiah Veluchamy Ramesh, PhD

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

Unfortunately, I still find some errors in the manuscript. Couple of them are listed below. However, I suggest authors to thoroughly check for English usage and message they would like to convey after consulting native English speaker.

Line NO 79 Experimental validation of potential miRNA targets is highly costly and laborious.

Editor’s comments: Kindly remove this sentence.

Fig. 1 Title states “The methodology of miRNA prediction from the SCBV genome” Kindly change in to “The methodology of host or sugarcane miRNA target prediction in the SCBV genome”

Also the legend says “A flowchart designed for predicting candidate miRNAs from the SCBV genome pipeline”. In fact it is candidate miRNAs of host that could potentially target SCBV Genome. Change this too.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

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6 Dec 2021

Response to Reviewer’s Comments

Dear Editor,

We are grateful to the reviewers and respected editor for their insightful comments on my papers. Coauthors and I very much appreciated the encouraging, critical and constructive comments on this manuscript by the respected editor. The comments have been very thorough and useful in improving the manuscript. We strongly believe that the comments and suggestions have increased the scientific value of revised manuscript by many folds. We have taken them fully into account in this fourth round of revision. We are submitting the revised manuscript with the suggestion incorporated the manuscript. We have been able to incorporate changes to reflect most of the suggestions provided by the reviewers. We have highlighted the changes within the manuscript. Here is a point-by-point response to the reviewers' comments and concerns. The manuscript has been revised as per the comments given by the respected editor and our responses to all the comments are as follows:

Editor (Optional Comments): Minor Concerns

General Comment1: Line NO 79 Experimental validation of potential miRNA targets is highly costly and laborious. Kindly remove it.

Response- Thank you so much for your comments and compliment. We have taken reviewer’s comment in full consideration and it will be well reflected by the revised version of manuscript. Revision has been made. Please see line: 79.

General Comment2: Fig. 1 Title states “The methodology of miRNA prediction from the SCBV genome” Kindly change in to “The methodology of host or sugarcane miRNA target prediction in the SCBV genome”

Response- Thank you very much for your valuable suggestion. Revision has been made. Please see lines: 120-121.

General Comment3: Also the legend says “A flowchart designed for predicting candidate miRNAs from the SCBV genome pipeline”. In fact it is candidate miRNAs of host that could potentially target SCBV Genome. Change this too.

Response- Thank you very much for your valuable suggestion. As per reviewer suggestion, correction has been made in the revised manuscript. Please see lines 121-122.

General Comment4: Kindly consult native English speaker to rectify the errors in the MS.

Response- Response- Thank you very much for your valuable comments for the improvement of our manuscript. This manuscript was edited for proper English language, grammar, punctuation, spelling, and overall style by one or more of the highly qualified native English speaking editors at MDPI Journal Experts two times: First round of revision and final round of revision. The editorial certificates were uploaded with the revised manuscript in the system. I hope the revised manuscript will meet the requirements of academic publishing in PLoS ONE Journal and I have revised my manuscript according to the suggestions of the professional editor. Appropriate changes were made and highlighted in the revised manuscript according to the suggestions of the respected English editor. We agree this and have incorporated your suggestion throughout the manuscript using service of English editor at MDPI experts. We hope the manuscript is now acceptable for publication.

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response: Thank you so much for your comments and compliment. We have checked and there is no error found and all references are correct.

Submitted filename: Rebuttal Letter.docx

Click here for additional data file.

13 Dec 2021

In silico identification of Sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus

PONE-D-20-35060R5

Dear Dr. Asharf,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

S.V. Ramesh, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

10 Jan 2022

PONE-D-20-35060R5

In silico identification of Sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus

Dear Dr. Asharf:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Shunmugiah Veluchamy Ramesh

Academic Editor

PLOS ONE