Identification of novel mirnas from drought tolerant rice variety nagina 22

ABSTRACT MicroRNAs regulate a spectrum of developmental and biochemical processes in plants and animals. Thus, knowledge of the entire miRNome is essential to understand the complete

regulatory schema of any organism. The current study attempts to unravel yet undiscovered miRNA genes in rice. Analysis of small RNA libraries from various tissues of drought-tolerant

_‘aus’_ rice variety Nagina 22 (N22) identified 71 novel miRNAs. These were validated based on precursor hairpin structure, small RNA mapping pattern, ‘star’ sequence, conservation and

identification of targets based on degradome data. While some novel miRNAs were conserved in other monocots and dicots, most appear to be lineage-specific. They were segregated into two

different classes based on the closeness to the classical miRNA definition. Interestingly, evidence of a miRNA-like cleavage was found even for miRNAs that lie beyond the classical

definition. Several novel miRNAs displayed tissue-enriched and/or drought responsive expression. Generation and analysis of the degradome data from N22 along with publicly available

degradome identified several high confidence targets implicated in regulation of fundamental processes such as flowering and stress response. Thus, discovery of these novel miRNAs

considerably expands the dimension of the miRNA-mediated regulation in rice. SIMILAR CONTENT BEING VIEWED BY OTHERS A CONSERVED SNP VARIATION IN THE PRE-_MIR396C_ FLANKING REGION IN _ORYZA

SATIVA INDICA_ LANDRACES CORRELATES WITH MATURE MIRNA ABUNDANCE Article Open access 07 February 2023 IDENTIFICATION OF KEY SEQUENCE FEATURES REQUIRED FOR MICRORNA BIOGENESIS IN PLANTS

Article Open access 21 October 2020 GENOME-WIDE IDENTIFICATION OF MICRORNAS INVOLVED IN THE REGULATION OF FRUIT RIPENING AND CLIMACTERIC STAGES IN MELON (_CUCUMIS MELO_) Article Open access

01 July 2020 INTRODUCTION MicroRNAs are one of the major components of molecular networks that regulate several plant developmental processes such as leaf development, flowering time, organ

polarity1 as well as biotic and abiotic stress responses2,3. Thus, discovery of novel miRNA genes that have not been reported in any organism would have a significant impact on our

understanding of the complex molecular regulatory networks. Since discovery of miRNA genes is primarily based on detection of their expression in the small RNA (sRNA) NGS libraries, no

single data set is sufficient to identify miRNA genes. Consequently, diverse tissues, growth conditions and varieties need to be explored in order to identify and characterize miRNA genes.

Response to abiotic stress is perhaps one of the most widely researched topics due to its impact on the productivity of major agro-economically important crops such as rice, a model

monocotyledon. Drought imposes a major threat to its productivity. Water stress during reproductive stage severely affects panicle exsertion and anther dehiscence leading to crop failure4.

India has a rich collection of rice germplasm wherein many cultivars/varieties have a natural tolerance to various stress conditions. In general, upland rice cultivars, like Nagina 22 (N22),

have a better tolerance to drought as compared to rainfed lowland cultivars like Pusa Basmati 1 (PB1). N22 is a deep-rooted, drought- and heat-tolerant5,6,7 rice cultivar. Detailed

molecular characterization of such drought-tolerant cultivars would provide valuable information about the nature of adaptive diversity evolved in these cultivars8,9. N22 offers unique

opportunity as a model for studying stress adaptive diversity as it harbors traits, at both physiological6 and molecular level5,10, to combat drought and heat stress. Nevertheless, only a

couple of studies have characterized and explored the dynamism of miRNA population in N228,9. Currently, 592 rice miRNA precursor entries are available in the miRBase database release 21.

Study with sRNA deep sequenced data in N22 rice further identified a few novel miRNAs9, indicating that several miRNA genes remain undiscovered. In general, deep sequenced sRNA populations

have been widely used for identification of novel miRNAs in different plant species11,12,13,14,15,16. A major challenge in miRNA gene mining is the presence of small interfering RNAs

(siRNAs), which are similar to miRNAs and can only be differentiated by their biogenesis17. siRNAs originate from double stranded (ds) precursors formed by intermolecular hybridization of

two complementary RNA strands while miRNAs are derived from RNA Pol II transcribed single stranded (ss) precursors possessing a hairpin structure due to intramolecular

self-complementarity17. The hairpin precursors are processed by Dicer-Like 1 or 4 (DCL1 or DCL4)18,19 to produce a dsRNA duplex of ~21–24 bp. HASTY transports this duplex out of the

nucleus20, which is then methylated by HUA enhancer (HEN1) at 3′ end21,22. The guide or mature strand is incorporated into the RNA induced silencing complex (RISC) whereby the effector

protein Argonaute (AGO)23 mediates target mRNA identification and cleavage or translational arrest. The other strand of the duplex called the miRNA* (star) is usually degraded. However,

there are reports on the functionality of miRNA* as well, under certain conditions24,25. In order to classify sRNA as a bonafide miRNA, certain criteria based on its secondary structure,

expression, biogenesis and conservation need to be considered26. Along with the primary criteria of secondary structure, the mapping pattern of the sRNA tags on the genome helps in the

identification of the probable miRNA gene locus. A tightly stacked mapping of sRNA tags at the locus is indicative of miRNA processing whereas a dispersed mapping all over the locus is more

suggestive of a siRNA-like miRNA27. The main objective of the current study was to identify miRNA genes that have not been reported so far in rice or any other organism. Deep sequencing of

the sRNA populations from different tissues such as flag leaf, spikelets ‘heading stage’ and spikelets ‘anthesis stage’ and mature root of N22 plants grown under control and drought

conditions was done. Subsequent rigorous analysis, based on well-defined criterion26,28, identified several putative novel miRNA genes loci. To the best of our knowledge, except an isolated

study9, sRNA population from these tissues has not been utilized for identification of novel miRNA genes. qRT-PCR analysis was done to validate their expression wherein several of these

novel miRNAs were found to have drought responsive as well as tissue preferential expression patterns. While some novel miRNAs were found to be conserved in other plants, most were

lineage-specific as they were primarily detected in multiple rice sRNA libraries. The biological activity of these novel miRNAs was confirmed by identifying their target genes and the

cut-site with the help of degradome data. Thus, multiple lines of evidences were gathered to support the existence and activity of these novel miRNA genes. RESULTS IDENTIFICATION OF NOVEL

MIRNAS IN N22 Small RNA data is a major resource for identification of novel miRNA genes. Thus, in order to uncover yet unidentified novel MIR gene loci in rice, five sRNA libraries from

adult tissues (flag leaf control & stress, heading spikelet control & stress and anthesis spikelet control) of the rice cultivar, N22 were deep sequenced to generate >19 million

sRNA tags. This data was used for identification of any unreported miRNA genes as well as to study modulation of miRNome (already known miRNAs) under environmental and temporal cues. While

the data on miRNome modulation is presented elsewhere (Balyan S. _et al._, under-review), the current study summarizes the discovery of novel miRNA genes from the sRNA data. The detailed

analysis pipeline has been described in the ‘materials and methods’ section. In summary, all the sRNA tags were mapped to the N22 genome and genomic loci having a non-phasic mapping of sRNA

tags were identified. Subsequently, genomic sequences (~200 bp) adjoining these sites were extracted and considered as the putative novel pre-miRNA sequence. These putative novel pre-miRNA

sequences were then analyzed for their ability to form a miRNA-like hairpin structure. A total of 2,010 intergenic and 542 intronic putative miRNA loci were initially selected based on

hairpin structures. A second round of screening was done to ensure that the sRNA tags within the putative miRNA precursor region clustered at a particular location i.e. mature sequence

region27. Ultimately, we could identify 71 putative novel miRNA gene loci in N22, details of which are summarized in Supplementary Table S1. Further analysis of these putative novel mature

miRNA sequences identified 18 novel miRNAs that had at least 17 bp sequence similarity to miRNAs reported in published literature (Supplementary Table S2) on rice but not available at

miRBase database. Thirteen of them (n-032, n-042, n-054, n-064, n-069, n-098, n-102, n-107, n-120, n-128, n-131, n-149 and n-184) did not have significant similarity at the precursor

sequence level, while 5 of the putative novel miRNAs (n-020, n-055, n-169, n-050 and n-213) shared appreciable similarity at the precursor level. Only one putative novel miRNA i.e., n-020

was found to be identical with miR2175–0.229 or miR301830, which had been reported separately. In summary, 17 of the putative novel molecules identified in this study appear to be family

members of miRNAs reported in the literature while one was found to be the same molecule. CHARACTERIZATION AND CLASSIFICATION OF NOVEL MIRNAS The putative novel miRNAs were further analyzed

from several aspects in order to validate and characterize them. One of the foremost criteria for defining a miRNA gene is that the precursor transcript should be able to fold back into a

typical hairpin structure that has ≤4 symmetrical mismatches within the duplex and/or only 1–2 asymmetric bulge(s) of up to 2 bases26. Close scrutiny of the secondary structures indicated

that hairpin structures of all the 71 putative novel miRNA candidates conform to the established norms. The secondary structures of the 71 candidates are provided in Supplementary Fig. S1

while their precursor sequences are given in Supplementary Table S1. Further, presence of ‘star’ sequence is another important criteria for identification of miRNA genes. However, due to

unstable nature of the sequence it is difficult to detect the ‘star’ sequence for all the miRNA genes. We were able to detect the ‘star’ sequence for 36 novel miRNA genes (Supplementary

Table S1). Another important characteristic feature of a miRNA gene locus is the manner in which sRNA tags map on the precursor sequence. Since the generation of the mature miRNA population

from the precursor sequence is through a near precise cut made by DCL1 and associated proteins, majority of the mature miRNA tags share almost a common 5′ end (with ±3 bases variation)31.

The 3′ end, however is quite variable. Thus, when sRNA tags are mapped on the precursor sequences, most of them should map as a tight cluster in the region that is proposed to give rise to

the mature miRNA sequence. Scrutiny of the mapping pattern of sRNA reads in the entire precursor sequence indicated that majority of tags mapped in the proposed mature miRNA region. However,

in some cases the mapping of tags was slightly dispersive in nature. Based on the mapping pattern, the putative novel miRNAs were divided into two classes. While both classes had a good

hairpin secondary structure, class-I loci had a consistent mapping pattern (≥40% of total reads mapped to the mature sequence region) while class-II loci had slightly dispersive mapping

pattern (<40% of total reads mapped to the mature sequence region). The percentage of sRNA reads abundance at mature position as compared to the total reads mapped to the precursor is

given in Supplementary Table S1. Examples of each class based on mapping pattern and secondary structure is given in Fig. 1a,b. In summary, there are 40 class-I and 31 class-II novel miRNA

candidates. The putative novel miRNAs were also characterized by assessing their abundance in multiple rice sRNA libraries as well as by detecting the mature miRNA sequence with the help of

qRT-PCR. As expected, all the novel miRNAs were detected in multiple sRNA libraries of N22 and comparison of their normalized tag abundance in these libraries indicated that most of them

express at a moderate level (Fig. 2a). The qRT-PCR based gel profiles of the amplified mature novel miRNA sequences were also generated in order to detect their expression as well as to

confirm their size (Supplementary Fig. S2). The amplified product includes the adapter (~100 nt) as well as the mature miRNA (~20 nt) thereby giving the size of the amplified product in the

range of ~120 bp. Based on mature sequence length, 49 out of 71 are of canonical length (20–23 bp) while 22 are long molecules (24–25 bp). They are further sub-classified as either

intergenic (39) or intronic (32) based on their genomic location. Chromosomal distribution of the 71 novel miRNAs indicates highest abundance at chromosome (chr) 3 followed by chr 1 (Fig.

2b), while size distribution indicates that 22–24 bp novel miRNAs were of highest abundance (Fig. 2c). Further, association of the miRNAs with the RISC complex is an indication of their

functionality, since only functional miRNAs tend to get loaded onto the RISC complex. Thus, publicly available AGO pulldown sRNA libraries published earlier32,33 were analyzed wherein a

significant number (67) of novel miRNAs were found to be associated with the RISC complex (Supplementary Table S3). Tags of 36 out of 67 were found in various AGO1 pulldown libraries while

tags of all the 67 novel miRNAs were present in various AGO4 pulldown libraries (Supplementary Table S3). Moreover, sRNAs with different 5′ starting base of the mature sequence have

differential AGO loading32,33,34,35. The distribution of 5′ starting base in novel and known miRNAs was quite comparative wherein novel miRNAs and known miRNAs (miRBase) had 35.21% and

45.58% with 5′U, 23.94% and 15.01% with 5′G, 33.80% and 11.50% with 5′A and 7.04% and 27.91% with 5′C, respectively (Fig. 2d). Finally, the copy number of all the 71 novel MIR genes was also

checked to ascertain if they originated from multiple loci or discrete genomic locus. The study was done by analyzing the occurrence of precursor as well as the mature novel miRNA sequences

on the rice genome sequence. Based on the analysis of the precursor sequences, almost all the novel MIRs was found to originate from a single genomic locus except n-170, which was present

in 2 copies on the genome. On the other hand, analysis with the mature sequence indicated that, most of the novel miRNAs had <20 genome hits, while 20 of them showed multiple hits

(>20) to the genome (Supplementary Table S4). Similar analysis with known rice miRNAs from published literature revealed that many of them had >20 genome hits as well (Supplementary

Table S4). Further, since recent studies have also indicated that miRNAs may also originate from within transposable elements (TE)36, we explored the rice repeat sequences (RGAP database;

www.rice.plantbiology.msu.edu) for the presence of any novel miRNA precursors. Consequently, four novel miRNA candidates, all belonging to class-II i.e., n-002, n-123, n-128 and n-195 were

found to have 100% sequence similarity with MITE-adh, type D-like repeats ORSgTEMT01700916, ORSgTEMT01700334, ORSgTEMT01701756 and ORSgTEMT01700523, respectively. The secondary structures of

these repeat sequences are shown in Fig. 3a–d. NOVEL MIRNA FAMILY MEMBERS The mature sequences of the novel miRNAs were further analyzed to ascertain if they can be clubbed as miRNA family

members. Based on sequence similarity, 10 novel MIR families containing 27 novel miRNAs were identified. In general, family members share mature sequence similarity and often target related

genes. The 10 novel miRNA families thus identified are MIRn-130, MIRn-050, MIRn-059, MIRn-098, MIRn-099, MIRn-102, MIRn-129, MIRn-134, MIRn-160 and MIRn-195. The multiple alignments of their

mature sequences are shown in Supplementary Fig. S3a–j. Family of MIRn-160 had the highest number of miRNA members namely, n-160, n-100, n-225, n-169 and n-213. Additionally, analysis on

genomic arrangement of novel miRNAs _vis-à-vis_ known miRNAs in N22 genome revealed an interesting fact about a novel miRNA locus. Although present on different chromosomal loci, the

precursor of n-026 (chr 5) is very similar (>80%) to that of osa-miR5788 (chr 3). However, their mature sequences have only 5 bp overlap and thus they cannot be classified as family

members. Nevertheless, there appears to be some kind of evolutionary relationship between the two miRNAs since their precursor sequences share significant similarity. IDENTIFICATION OF

UNREPORTED PUTATIVE MIRNA ORTHOLOGS IN N22 The phylogenetic conservation of miRNA sequences (mature and precursor) as well as the secondary structure facilitates identification of putative

orthologous miRNAs26. Thus, in addition to the aforementioned novel miRNA genes which were not reported in rice or any other organism, analysis was also done to identify rice orthologs of

miRNA genes known in other plant species. Accordingly, as depicted in the analysis pipeline (Supplementary Fig. S4), 22 non-rice known MIR precursors could be aligned with ≥50% sequence

similarity to the N22 genome. Of these, 16 had ≤3 mismatches in the mature region. The pairwise alignments of the parent MIR precursors (miRBase) to that of identified rice putative ortholog

precursors are shown in Supplementary Fig. S5. Subsequently, secondary structure analysis indicated that rice ortholog of 6 i.e. MIR5056, MIR5368, MIR6187, MIR6196, MIR6221 and MIR9774 had

a good hairpin structure while others (MIR6199, MIR5141, MIR9780, MIR6485, MIR6207, MIR6206, MIR6183, MIR2916, MIR7767 and MIR6194) had inconsistent structures, despite a significant

conservation at the precursor sequence level. The details of 16 putative orthologs and their secondary structures are summarized in Table 1 and Supplementary Fig. S6a–p, respectively. NOVEL

MIRNAS ARE LINEAGE-SPECIFIC In order to check the extent of conservation/divergence of novel miRNAs, their abundance was analyzed in other publicly available sRNA libraries of rice and other

plants. A total of 14 sRNA libraries from rice (3), maize (1), wheat (2), barley (3), _Arabidopsis_ (3) and soybean (2) available at NCBI ‘SRA’ database were utilized (Supplementary Table

S5). Tag abundance of 71 miRNAs could be detected in all the 3 rice sRNA libraries, indicating the reliability for their existence and expression in other varieties/stage/tissue of rice.

Further, sRNA tags corresponding to 14 novel miRNAs were detected in other monocots, which include 13 in wheat, 10 in barley and 3 in maize. Heat map of the 71 novel miRNAs in sRNA libraries

of rice, maize, wheat, barley, _Arabidopsis_ and soybean is shown in Fig. 4a. Similarly, sRNA tags for 6 novel miRNAs were detected in dicots including 4 in _Arabidopsis_ and 2 in soybean.

While most of them were detected at low levels, some had >5 tags (Supplementary Table S5). Analysis of the genomic loci in the respective plants revealed that the precursors of 3 novel

miRNAs in barley (n-006, n-107 and n-118) and 3 in wheat (n-024, n-032 and n-107) had acceptable miRNA-like hairpin structures (Supplementary Fig. S7a–f). Similar analysis for known miRNAs

to compare the extent of conservation/divergence indicated that 23.7%, 31.6%, 27.8%, 21.7% and 19.9% of known miRNAs were detected in the sRNA libraries of maize, wheat, barley,

_Arabidopsis_ and soybean, respectively (Fig. 4b, Supplementary Table S5). In summary, most of these novel miRNAs are considerably conserved in rice whereas only few could be detected in

other plants indicating a lineage-specific nature17. IDENTIFICATION OF NOVEL MIRNA TARGETS To ascertain the functionality of these novel miRNAs, ‘degradome sequencing’37 or ‘PARE’ (Parallel

Analysis of RNA Ends)38 was performed to identify and validate target mRNA cleavage. Sequencing of three degradome libraries from anthesis spikelets (ASp), heading spikelets (HSp) and flag

leaf (FL) of N22 generated more than six million reads from each library (Supplementary Table S6). Subsequent analysis of the degradome data by CleaveLand analysis pipeline39 identified

40:105, 20:52 and 30:91 novel miRNA:target pairs in the PARE libraries from ASp, HSp and FL, respectively. CleaveLand analysis was further extended by including five more publicly available

rice PARE libraries (SRR032098, SRR039716-039720, SRR521269, SRR032097 and SRR034102) (Supplementary Table S7). In summary, analyses of data from 8 PARE libraries indicated putative multiple

targets of the novel miRNAs (including low confidence targets). Distribution of miRNA:target pairs based on different confidence parameters (degradome read number, category and P-value) of

CleaveLand pipeline is shown in Supplementary Table S8. Analysis of the top ten targets for each novel miRNAs reveals diverse targets including transcription factors, metabolic enzymes,

signaling components (kinases and phosphatases) that have been implicated in important cellular processes such as plant development and stress (biotic and abiotic) response (Supplementary

Table S9). Of all the putative targets, essentially 33 miRNA:target pairs (25 genes targeted by 19 novel miRNAs) are of high confidence (degradome reads ≥10, category ≤2 and P-value ≤0.05)

(Table 2). A cut-off based on simply the alignment score was not considered as it has been reported that miRNAs could cleave targets even with higher mismatches40. T-plots (target plots) for

selected high confidence and important targets of novel miRNAs are shown in Fig. 5a–i and discussed in subsequent text. Of these 19 novel miRNAs having high confidence target genes, 11 were

of class-I while 8 of class-II. Further, novel miRNAs with multiple loci also had high confidence targets based on the degradome data indicating their biological activity. For example,

novel miRNA n-121 with 397 genomic hits (at mature level) had a target identified with 36 degradome reads (category ‘0’). Similarly, n-118 having 177 genome hits had a target supported by

101 degradome reads (category ‘0’). Subsequently, targets of orthologous miRNAs were also identified from the 8 PARE libraries (Supplementary Table S10). Targeting of 38 genes by 10

orthologous miRNAs are supported by ≥10 degradome reads (Supplementary Fig. S8a) while 11 pairs (4 miRNA:11 target genes) are of ≥10 degradome reads and category ‘0’. Interestingly, two

(osa-miR6485 and osa-miR6207) of the miRNA:target pairs with ‘0’ category do not have a consistent hairpin structures. The targeting of LOC_Os11g15240 (retrotransposon) by osa-miR9774 could

be supported with highest confidence. Representative ‘t-plots’ for some of the orthologous miRNA targets are shown in Supplementary Fig. S8b–m. The degradome libraries were also used to

identify targets of known miRNAs. Consequently, 266:895, 266:925 and 350:3054 miRNA:target pairs were obtained from N22 ASp, HSp and FL, degradome libraries, respectively (Supplementary

Table S6). On further inclusion of 5 more PARE libraries, 551:16,381 miRNA:target pairs were identified (no filters applied on degradome criteria). Out of this, 102:126 miRNA:target pairs

are of high confidence (≥10, category ≤2 and P-value ≤0.05) (Supplementary Table S7). The ratio of high confidence targets among all the targets is quite comparable for both novel and known

miRNAs (Supplementary Tables S9 and S10). EXPRESSIONS OF NOVEL MIRNAS IN RESPONSE TO DEVELOPMENTAL AND ENVIRONMENTAL CUES Expressions of miRNAs are guided by environmental or developmental

cues. Thus, sRNA deep sequenced data from various tissues of N22 (FL, HSp, ASp and MR, i.e. mature root) grown under control and drought conditions was analyzed to assess the tissue-biased

and drought responsive nature of the novel miRNAs. Analysis of the sRNA tags from different tissues of plants grown under control conditions identified tissue preferential expression of

several novel miRNA genes. In summary, 47, 51, 46 and 49 novel miRNAs were found to have ≥10 normalized tag density (RPM) in FL, HSp, ASp and MR of N22. Further, 5 novel miRNA (n-024, n-039,

n-063, n-148 and n-153) were significantly up-regulated (fold change ≥4, P-value ≤0.05) in the FL as compared to both HSp and MR whereas 8 novel miRNAs (n-001, n-019, n-028, n-046, n-050,

n-130, n-140 and n-173) were up-regulated in HSp as compared to both FL and MR (Supplementary Table S11). Incidentally, we did not find any novel miRNA that was significantly up-regulated in

MR as compared to both FL and HSp. Comparison of the spikelet from the two stages of inflorescence development i.e. heading and anthesis identified 28 novel miRNAs that were differentially

regulated even in these closely spaced developmental stages (Supplementary Table 11). While 19 novel miRNAs were up-regulated, 9 were down-regulated in the spikelets during transition from

heading to anthesis. An interesting member of this group is n-001 which is significantly down-regulated (42 folds) in ASp and targets a Casein Kinase-1 protein, “Hd16” (_HEADING DATE 16_) or

“El1” (Early flowering 1)41,42. Further, comparison of the sRNA libraries from control and drought treated N22 plants revealed several drought responsive novel miRNAs. In total, 48 novel

miRNAs were found to be significantly drought stress responsive in the four different tissues of N22. Heat map showing the fold change expression of 71 novel miRNAs in FL, HSp, ASp and MR in

drought stress treated libraries as compared to control libraries are shown in Fig. 6a. Most of the novel miRNAs were down-regulated while only 8, 1, 8 and 1 novel miRNAs were up-regulated

in FL, HSp, ASp and MR, respectively (Supplementary Table S12). Interestingly, four novel miRNAs _viz_., n-016 and n-129 in ASp and n-046 and n-170 in FL had ≤1 (normalized) tag under

control conditions but ≥30 tags (normalized) during drought. The drought-regulated expression of several novel miRNAs which target important genes was validated by qRT-PCR (Fig. 6b–d). Novel

miRNAs n-025, n-032, n-098, n107 and n-200 were found to be down-regulated (Fig. 6b) whereas n-001, n-009, n-016, n-019 and n-130 were up-regulated in the FL during drought (Fig. 6c).

Similarly, except for n-192, miRNAs n-002, n-006, n-024, n-025, n-032, n-063, n-098, n-107, n-137, n-195 and n-200 were down-regulated in ASp during drought (Fig. 6c). Novel miRNAs n-025,

n-032, n-098, n-107 and n-200 were commonly down-regulated in both FL and ASp during drought. We further verified drought responsiveness of the target genes of these drought responsive novel

miRNAs. Indeed, analysis of the transcriptome data (Indica Rice Database; www.genomeindia.org.in/irdb) revealed that targets of many of the novel miRNAs showed anti-correlation in

expression during drought stress (Supplementary Table S13). The sRNA libraries were also used to assess the expression of the putative orthologous miRNAs. The expression profile of 17 mature

putative orthologous miRNAs is depicted by heat map generated from transformed values of sRNA abundance in 8 different N22 sRNA libraries (Supplementary Fig. S8n). Five of them

(osa-miR5368, osa-miR6485, osa-miR6196, osa-miR2916 and osa-miR9774) were constitutively expressed in most of the N22 libraries while expression of osa-miR6187, osa-miR6199, osa-miR6206,

osa-miR6221-5p, osa-miR6221-3p and osa-miR9780 could not be detected in our libraries. DISCUSSION MicroRNAs regulate various metabolic and developmental processes43,44 and thus knowledge of

the complete repertoire of the miRNA genes is critical to understand the complicated regulatory mechanisms in both plants and animals. Discovery of miRNA genes has been revolutionized by

next generation sequencing technologies. In rice, over 700 mature miRNAs (miRBase release 21) have been identified, nevertheless, the list is not saturating. Like any other gene, expression

of miRNAs is under tight control of spatial, temporal and environmental cues. Since identification of miRNA is primarily based on the detection of its expression (e.g. RNA seq), low

expressing miRNAs or those having restricted expression may be missed if appropriate sRNA libraries are not available. In the current study, we have explored sRNA libraries from flag leaves,

spikelets and roots tissues of a drought tolerant _‘aus’_ rice variety Nagina 22 and after rigorous multi-tiered analysis were able to identify 71 novel miRNA genes that have not been

reported earlier in rice or any other organism. Interestingly, 39 of them were intergenic while 32 were localized within the introns of protein coding genes. Four of the novel miRNAs were

found to originate from within the repeat sequences in rice. Earlier studies have also reported miRNAs originating from repetitive regions of genome36. MITEs (Miniature Inverted Transposable

Elements) loci in _Arabidopsis_ and rice have been reported to generate sRNAs45. Moreover, some of these novel genes are probably family members of already known miRNA genes in rice while

others could be grouped as members of novel miRNA families. Family members represent similar/same mature sequence but with distinct MIR precursor genes46. They probably have a common

evolutionary origin and often cleave same/related target genes. In order to confirm the existence of these novel miRNAs, multiple lines of evidences were investigated. These included

acceptable hairpin structure27,28, mapping pattern of sRNA tags on the precursor, qRT-PCR based expression, detection of ‘star’ sequence, conservation in sRNA libraries from rice and other

plants as well as identification of target genes based on degradome data. In general, all the novel miRNAs had hairpin structure similar to a typical miRNA gene and could be detected by both

sRNA NGS data as well as qRT-PCR analysis. Thus, there is a high level of confidence in these novel miRNA genes. Detection of these novel miRNA genes in the AGO pulldown sRNA libraries

further supports their existence and functionality. In-depth characterization of these novel miRNAs indicated that while all had a typical miRNA hairpin structure, the mapping pattern of

sRNA tags on some novel miRNA precursors was not very stacked, indicating an imprecise processing. Thus, we segregated the novel miRNAs based on the mapping patterns wherein class-I genes

can be referred to as typical miRNA genes whereas the class-II genes are more of a siRNA-like miRNA genes27. Several earlier studies have also reported existence of such siRNA-like miRNA

loci27,47. Interestingly, it was possible to identify high confidence targets (with typical miRNA like precise target cleavage) for several class-II novel miRNA genes indicating

functionality of the siRNA-like miRNA genes. Thus, even though class-II genes undergo an imprecise processing, they are able to cleave the target in a typical miRNA like manner. Similarly,

it was possible to identify high confidence targets even for novel miRNA genes that have multiple genome hits (>20). Incidentally, some known miRNA genes (deposited in the miRBase) are

also known to have more than 20 genome hits. For example, miR818 has 472 hits, miR815 has 87 hits in rice genome. Apparently, although the mature sequence may hit the genome multiple times,

however, their corresponding precursor sequences may not be able to fold into a typical miRNA like hairpin. Another important feature of miRNA genes is that most loci are conserved across

organisms. However, non-conserved miRNAs that are weakly expressed and have limited phylogenetic conservation are a universal feature of land plants48. While, almost all the novel miRNAs

could be detected in the publicly available rice (mostly _japonica_) sRNA libraries, only few were conserved in other monocots such as wheat, maize and barley. A couple of novel miRNAs were

even conserved in dicots (_Arabidopsis_ and soybean) as well. Thus, apparently most of the novel miRNAs were lineage-specific (rice) in nature and may have recently evolved17. An interesting

feature of the miRNAs, in general, is the distribution of the first 5′ base of the mature miRNA sequence. In plants, miRNAs and 21 bp siRNAs with 5′ U are primarily directed to AGO1, 24 bp

siRNAs with 5′ A onto AGO4, sRNAs with 5′ A on AGO2 and those with 5′ C are primarily loaded onto AGO532. The distribution of the 5′ base of the novel miRNAs identified in the study was

quite similar to known miRNAs. Further, number of AGO4 associated novel miRNAs was comparatively higher than AGO1 associated miRNAs. Indeed, many of these miRNAs could be defined as long

miRNAs having length ≥24 bp. Studies on these miRNAs would yield interesting results since long miRNAs are known to be involved in regulating DNA methylation33. Besides basic identification

and characterization of the novel miRNA genes, the evidence of the cleavage of the target transcript is one of the most convincing indications of a miRNA activity. Thus, analysis of the

degraded transcripts is essential in order to validate the existence of the novel miRNA genes as well as to assess the biological impact of the novel miRNA activity. Consequently, analysis

of in-house generated degradome libraries as well as several publicly available degradome libraries of rice32,49,50,51 revealed appreciable number (≥10) of degradome tags for more than 50%

of the novel miRNAs. Many of these targets are of high confidence and thus reinforce the biological functionality of these novel miRNA genes. A global overview of all the targets of these

novel miRNAs indicates a wide range of biological activities ranging from gene regulation to metabolic pathways. Many of these targets are components of biochemical pathways (RiceCyc,

http://pathway.gramene.org/gramene/ricecyc.shtml). For example, _GLUTATHIONE S-TRANSFERASE_ targeted by n-118 is involved in ROS metabolism whereas _ARGINYL-TRNA SYNTHETASE_ targeted by

n-042 (class-II) is involved in ‘tRNA charging’ (Metabolic cluster pathway) wherein it catalyses the formation of L-arginyl tRNA from L-arginine with the use of an ATP molecule. Similarly,

_POLYGALACTURONASE_ targeted by n-173 (class-I) is involved in ‘homogalacturonan degradation pathway’_. CYCLOPROPANE-FATTY-ACYL-PHOSPHOLIPID SYNTHASE_ targeted by n-108 (class-II) is

involved in ‘Fatty acids and lipids biosynthesis pathway,’ specifically in cyclopropane and cyclopropene fatty acid biosynthesis. Other important targets include _PROTEIN PHOSPHATASE 2C_,

_SKIP/SNW_ protein, plant viral response protein, _F-BOX_ domain containing protein, _TYROSINE PROTEIN KINASE_ targeted by n-139, n-156, n-140, n-102, and n-098, respectively. The study

further explores the expression dynamic of these miRNAs during the reproductive phase (‘heading’ and ‘anthesis’) of the rice plant development and includes tissue such as flag leaf,

spikelets and roots, which have not been explored extensively in terms of miRNA expression dynamics. Flag leaf, which bears the panicle, is the major source of photosynthates to the

developing panicle and thus a very important tissue52,53. Novel miRNA n-024 and n-063, which putatively target a ‘_GLUTATHIONE S-TRANSFERASE’_ and an _‘EARLY FLOWERING PROTEIN’_, were found

to be significantly enriched in the flag leaf as compared to other tissues. Similarly, roots are important as they are the first to experience change in the soil moisture content. Studies on

comparative proteome studies in the roots of wild type and _DREB1A_ over-expression lines under drought stress conditions indicated that many stress and defense related proteins were

up-regulated in both plants but a novel protein R40C1 was up-regulated only in transgenic roots54. Root-specific over-expression of _OsNAC10_ increases grain yield significantly under field

drought conditions by enlarging roots and enhancing drought tolerance55. More than 70% of all the novel miRNA genes expressed appreciably in the mature roots. Under drought conditions almost

all novel miRNAs were found to be down-regulated in the mature roots, except n-170 that gets up-regulated and putatively targets an ‘_AUXIN RESPONSE FACTOR (ARFs)’._ Other known miRNAs such

as miR160 and miR167, which also target ARFs are known to be involved in the regulation of root development56,57. Another interesting developmentally regulated novel miRNA is n-001 that

targets “Hd16”. Hd16 is known to phosphorylate rice DELLA protein SLR1 and Ghd7, a CO-like protein which suppresses flowering by inhibiting the expression of Ehd1 (a floral activator)41,42.

Hd16/EL1 is a flowering repressor identified from a cross between Nipponbare and Koshihikari. It is postulated that Hd16 might act as a mediator between floral transition and other

developmental processes such as gibberellin signaling and tillering. Similarly, the drought responsive novel miRNAs target several important genes such as _GLUTATHIONE S-TRANSFERASE_

(n-024), _GLUTAREDOXIN 2_ (n-137), _EARLY FLOWERING PROTEIN_ (n-063)_, SKP1-LIKE PROTEIN 1B_ (n-192), _GPI-ANCHORED PROTEIN_ (n-002), _OsFBX213_ (n-006) and _CYCLIN-DEPENDENT KINASE

INHIBITOR_ (n-006) among others. Thus, expression profiling in the adult tissues of rice plant provided useful insight into the probable functionality of these novel miRNA genes. In summary,

rapid advances in deep sequencing technology has greatly assisted discovery of miRNA genes in rice14,15,16,58,59,60. Identification of 71 novel miRNAs and unreported orthologous miRNAs in

rice is significant as it expands the number of biological activities that are under the regulation of miRNAs. Moreover, it also implies that there could be several other miRNAs that remain

undiscovered. Thus, further studies should still be carried out on diverse tissue and growth conditions to identify novel miRNA genes in rice. Previous findings have also indicated that

there must be a large number of miRNAs that may be unique to the rice genome, and possibly many more miRNAs remain unidentified60. Moreover, while further investigations would need to be

done, nevertheless, it appears that there could be functional miRNA genes which do not fall within the confines of a classical miRNA gene definition. It would be very interesting to analyze

the activity (biogenesis and targeting) of such loci as it may help better understand the diversity of miRNA genes. METHODS PLANT GROWTH CONDITIONS AND STRESS TREATMENT Seeds of _Oryza

sativa_ L. ssp. _aus_ cultivar N22 were surface sterilized with 0.1% HgCl2 and Teepol, rinsed and then soaked in R.O. water overnight. Seeds were grown on muslin cloth tied over a tray

containing Yoshida rice growth medium {40 mg/L NH4NO3, 10 mg/L NaH2PO4.2H2O, 40 mg/L K2SO4, 40 mg/L CaCl2, 40 mg/L MgSO4.7H2O, 0.5 mg/L MnCl2.4H2O, 0.05 mg/L 0.2 mg/L H3BO3, 0.01 mg/L

ZnSO4.7H2O, 0.01 mg/L CuSO4.5H2O and 2 mg/L FeCl3.6H2O (in monohydrate citric acid) with pH adjusted to 5} for two weeks in culture room with 28 ± 2 °C for 16/8 h photoperiod and then

transplanted to field. Drought treatment was given to field grown mature plants by withholding water supply 10–15 days before the expected date for ‘heading’. Soil moisture was monitored

using Hydra Probe Soil Moisture Sensor. Tissue was collected when soil moisture content reached below 15% and plants showed distinct leaf rolling phenotype. Tissues (flag leaves, spikelets

and roots) were collected only from plants that were at an appropriate stage of panicle development (heading or anthesis). Drought induction was checked by estimating the transcript levels

of drought stress marker genes _Rubisco small subunit_ (_RBCS_)61 and _OsbZIP23_62 in the collected tissue. _IN-SILICO_ ANALYSIS TO IDENTIFY NOVEL MIRNAS The sRNA tags (19,555,985) from 5

sRNA libraries were mapped to N22 genome sequence (Indica Rice Database; www.genomeindia.org.in/irdb) with the help of ‘maq’ software. Simultaneously, the tags were also mapped to miRBase

and Rfam (excluding miRNAs) databases and reads that matched with ≥80% similarity with miRBase (http://www.mirbase.org/) and ≥90% with Rfam database (http://rfam.sanger.ac.uk/) were excluded

from downstream analysis. The mapping of 15,933,373 remaining tags was further analyzed to identify loci where significant numbers of tags were mapped within a window size of 5 bases with

respect to the 5′ end-mapping coordinate. 2,610,147 such clusters were identified, out of which 145,028 clusters had ≥10 reads per cluster. Further analysis was done on these clusters to

identify both intergenic and intronic putative novel miRNA genes. For intergenic novel miRNAs, clusters falling within the CDS, intron and UTR regions of rice genes (RGAP,

http://rice.plantbiology.msu.edu/) were removed to represent only the intergenic regions of genome. Flanking 100 bp genomic sequence on either side of such clusters (120,456) was extracted

from the N22 genome (Indica Rice Database or IRDB; www.genomeindia.org/irdb). These sequences (~200 bp) were later trimmed down to adequate length capable of forming the hairpin. The

secondary structures were generated using UNAFold and mfold softwares63. The resultant structure files (.ct) were initially screened with the help of in-house developed perl scripts for a

suitable hairpin structure. For finding intronic miRNAs, clusters falling within the introns were retained (57,688) and subjected to similar analysis as described above. It was followed by

checking the structures and mapping pattern of the sRNA reads on the precursor sequences to identify the region of high reads accumulation in a non-phasic manner so as to represent the

mature sequence. Screening criteria64 was followed as (i) read abundance of ≥10 from multiple independent experiments (ii) ability of flanking sequences to form a miRNA precursor-like

hairpin with miRNA:miRNA* duplex (iii) mapped reads should originate from non-coding regions (iv) reads annotated as mature miRNA should have a consistent 5′ end while the 3′-end may be more

variable (v) preferably presence of tags corresponding to both miRNA and miRNA*. The sample details of all the libraries used in this study are given in Supplementary Table S14.

IDENTIFICATION OF PUTATIVE ORTHOLOGOUS MIRNA GENES In order to identify orthologous miRNAs in N22, ‘miFam.dat’ file was downloaded from miRBase 21. Out of 7,057 plant specific precursors,

those specific to rice are 592 while the rest 6,465 belong to plants other than rice. 5,099 precursors are distributed among 525 different families while the rest 1,958 are not assigned to

any family. The distribution of 525 families is such that 35 are rice specific, 38 are common among rice and other plants while 452 families are not found in rice. With these remaining 452

families (comprising of 3,204 precursors), downstream analysis for ortholog identification was carried out. 3,204 precursor sequences of miRBase were mapped on N22 genome by performing

blastn. Twenty-two had ≥50% precursor sequence similarity, which were checked for the conservation of mature sequence region. 16 out of 22 had ≤3 mismatches in the mature region. Secondary

structures were generated using RNAfold65 (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi). PHYLOGENETIC CONSERVATION AND DIVERGENCE OF NOVEL MIRNAS Small RNA libraries were downloaded from

NCBI SRA database (http://www.ncbi.nlm.nih.gov/sra/) for two dicots, _viz. Arabidopsis thaliana_ (SRX058635, SRX058636 and SRX058638) and _Glycine max_ (SRX031157 and SRX131086); and four

monocots: _Oryza sativa_ (ERX014997, SRX017641 and SRX024857), _Triticum aestivum_ (SRX131958 and SRX131964), _Zea mays_ (SRX015774) and _Hordeum vulgare_ (SRR513546, SRR513547 and

SRR513548). Trimmed sRNA reads were aligned to the mature novel miRNA sequences. EXPRESSION PROFILING ON TAG DENSITY The expression profile of the novel miRNAs was determined by mapping the

sRNA libraries from flag leaf, spikelets (heading and anthesis) and mature roots from control and drought treated N22 plants (Supplementary Tables S11 and S12). The number of tags mapping to

each novel miRNA was normalized and the expression values were represented as reads per million (RPM) of the total genome-mapped sRNA tags in the library. The statistical significance of

the fold change was calculated as reported earlier66,67. QUANTITATIVE RT-PCR Total RNA was extracted from harvested tissues using TRI Reagent (Sigma) followed by DNaseI treatment

(Fermentas). Small RNA samples enriched using 4M LiCl were polyadenylated using Poly(A) Tailing kit (Ambion) and 2 μg of each sample was reversed transcribed with miR_oligodT_RTQ primer for

first strand cDNA synthesis using SuperScript II Reverse Transcriptase (Invitrogen). To analyze the expression of the mature miRNAs, qRT-PCR was done using TaqMan Fast Universal PCR Master

Mix (ABI) with RTQ universal reverse primer, miRNA specific forward primer and fluorogenic probe68 in StepOnePlus Real-Time PCR System (ABI) according to the manufacturer’s protocol. The

expression level of miRNA was normalized using 5S rRNA’s expression as an endogenous control. ∆∆Ct method was employed to calculate relative fold change (2−∆∆Ct) in expression and standard

error was calculated. The detail of primers used for qRT-PCR expression analysis is given in Supplementary Table S15. DEGRADOME LIBRARY PREPARATION Degradome libraries were prepared38 from

three different tissues of N22, _viz_. heading spikelet, anthesis spikelet and flag leaf at heading stage. Briefly, polyA enriched mRNAs obtained by using Oligotex kit, (QIAGEN) were ligated

with 5′-RNA adapter having an _Mme_I site using T4 RNA Ligase (Ambion) and reversed transcribed with an extended oligo(dT) having a 3′ adapter sequence using Superscript II RT (Invitrogen).

A short PCR of 15 cycles was performed to amplify the cDNA and the PCR product was digested with _Mme_I (NEB) to generate equal-sized fragments that was recovered by 12% PAGE. Then a

double-stranded 3′-DNA adapter was ligated to the _Mme_I digested products. The resulting product was PCR-amplified (21 cycles), gel-purified, cloned in PUC19 to check the quality of library

prepared and then given for high-throughput sequencing by Illumina HiSeq 2000 platform. Details of all the primer and adapters used in degradome library preparation are given in

Supplementary Table S15. The degradome data was then analyzed with the help of CleaveLand (ver. 3.1.1) pipeline to identify target genes and generate t-plots, utilizing the mature sequences

of sRNAs and cDNA transcripts of RGAP (TIGR 7). As per the analysis, the targets are identified in 5 categories (category ‘0’ to category ‘4’)39. Category ‘0’ is the best while category ‘4’

is the least significant. Category ‘0’ indicates the best possible condition in which the cleaved site has the maximum number of degradome reads (>1) and there is only one position in the

transcript for this maximum value. Category ‘1’ means >1 read which is equal to the maximum number of reads on the transcript when there is >1 position at maximum value. Category ‘2’

means >1 read which is >average depth but not the maximum on the transcript. Category ‘3’ means >1 read but ≤average depth on transcript while category ‘4’ means a single read at

that position. P-value is the indication of statistical significance and is based on the number and distribution of the degradome reads on the target transcript with reference to the

miRNA-binding site. Alignment score is based on miRNA-mRNA alignments and is calculated on position-weighted scoring matrix39. ADDITIONAL INFORMATION HOW TO CITE THIS ARTICLE: Mutum, R. D.

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was supported by the Department of Biotechnology (DBT), Government of India (Grant no. BT/PR10071/AGR/36/31/2007 and BT/PR628/AGR/36/674/2011). R.D.M. acknowledges fellowships from UGC

(University Grants Commission) and University of Delhi, S. Kumar from DBT, S.B. from University of Delhi, S. Kansal from CSIR (Council of Scientific and Industrial Research), India. AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Department of Plant Molecular Biology, University of Delhi South Campus, Benito Juarez Road, New Delhi, 110021, India Roseeta Devi Mutum, Santosh

Kumar, Sonia Balyan, Shivani Kansal & Saurabh Raghuvanshi * National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India Saloni Mathur Authors * Roseeta

Devi Mutum View author publications You can also search for this author inPubMed Google Scholar * Santosh Kumar View author publications You can also search for this author inPubMed Google

Scholar * Sonia Balyan View author publications You can also search for this author inPubMed Google Scholar * Shivani Kansal View author publications You can also search for this author

inPubMed Google Scholar * Saloni Mathur View author publications You can also search for this author inPubMed Google Scholar * Saurabh Raghuvanshi View author publications You can also

search for this author inPubMed Google Scholar CONTRIBUTIONS R.D.M did the analysis, identification and expression profiling of novel miRNAs and orthologous miRNAs in N22, prepared and

analyzed degradome libraries and compiled the manuscript. S. Kumar assisted in screening of molecules and performing of real time analysis. S.B. provided small RNA libraries, S. Kansal

helped in initial screening. S.M. generated the genome sequence of N22 while S.R. conceptualized and supervised the study. CORRESPONDING AUTHOR Correspondence to Saurabh Raghuvanshi. ETHICS

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_et al._ Identification of novel miRNAs from drought tolerant rice variety Nagina 22. _Sci Rep_ 6, 30786 (2016). https://doi.org/10.1038/srep30786 Download citation * Received: 11 February

2016 * Accepted: 11 July 2016 * Published: 08 August 2016 * DOI: https://doi.org/10.1038/srep30786 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

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