Genome-Wide View and Characterization of Natural Antisense Transcripts in Cannabis Sativa L.

doi:10.21203/rs.3.rs-2441787/v1

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Genome-Wide View and Characterization of Natural Antisense Transcripts in Cannabis Sativa L.

https://doi.org/10.21203/rs.3.rs-2441787/v1

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Natural Antisense Transcripts (NATs) are a kind of complex regulatory RNAs that play vitriol roles in gene expression and regulation. In recent years, due to the tremendous economic and medicinal values of cannabinoids, the genome of Cannabis Sativa L. was sequenced and the the biosynthetic pathway of cannabinoids was deciphered. Moreover, the regulation of non-coding RNAs, including microRNAs and long non-coding RNAs involved in the biosynthesis of cannabinoids were predicted. However, the NATs in C. sativa remain unknown. In this study, we predicted C. sativa NATs genome-wide by a computational pipeline using strand-specific RNA sequencing (ssRNA-Seq) data. Then, we performed a comprehensive analysis and validated the expression profiles by strand-specific quantitative reverse transcription PCR (ssRT-qPCR). As a result, 260 NATs were predicted in C. sativa, including 92 cis- and 168 trans-NATs. The primary type of cis-NATs was sense transcripts (STs) containing NATs. The expression profiles of 92% of transcripts of ssRNA-Seq were consistent with those of the ssRT-qPCR. Functional enrichment analysis demonstrated that the C. sativa NATs potentially participated in growth and development, stress resistance, and the biosynthesis of compounds. Finally, 12 cis- and 278 trans- NAT-ST pairs were predicted to produce 476 cis- and 2342 trans- small interfering RNA (nat-siRNAs), respectively. These nat-siRNAs were potentially involved in the biosynthesis of cannabinoids, fatty acids, and cellulose. All these results will shed light on the regulation of NATs and nat-siRNAs in C. sativa.

Cannabis sativa L.

Natural Antisense Transcripts

Nat-siRNAs

Natural Antisense Transcripts (NATs) refer to endogenous RNAs complementary to their corresponding sense transcripts (STs), which usually result in forming double-stranded RNAs (dsRNAs) in vivo (Yuan et al., 2015). Based on the origin of the genomic locus, NATs can be grouped into cis- and trans-NATs. Cis-NATs transcribe from the same genomic locus relative to their corresponding STs and exhibit perfect sequence complementarity. While trans-NATs transcribe from the different genomic locus close to the STs and display partial complementarity (Lapidot and Pilpel, 2006). Moreover, according to the mapping region between cis-NATs and their corresponding STs, the cis-NAT-ST pairs can be further divided into four subtypes: divergent (5'-5' overlap), convergent (3'-3'overlap), S > N (ST containing NAT), and N > S (NAT containing ST) (Chen et al., 2012).

Previous studies revealed that NATs played important roles through diverse mechanisms, including transcriptional interference at the transcriptional level (Prescott and Proudfoot, 2002), RNA masking (Lapidot and Pilpel, 2006), dsRNA-dependent small interfering (siRNA) mediated gene silencing (Borsani et al., 2005), and epigenetic modifications at the posttranscriptional level (Yuan et al., 2015). For example, in Saccharomyces cerevisiae, GAL10 and GAL7 formed convergent cis-NAT-ST pairs. When these two transcripts are overlapped, elongation is restricted, resulting in the negatively correlated expression of these two transcripts (Prescott and Proudfoot, 2002). In mice, miR-34a targeted Sirt1 in the overlap region between the Sirt1 and Sirt1-AS. Sirt1-AS enhanced the expression level of Sirt1(Wang et al., 2016). In Arabidopsis thaliana, the open reading frame (ORF) of Δ¹-Pyrroline-5-carboxylate dehydrogenase (P5CDH) was complementary to the 3′UTR of an unknown gene designated as SRO5, leading to producing both 24-nucleotides and 21-nucleotides siRNAs under high salt condition (Borsani et al., 2005). In maize, NAT gene regions exhibit higher trimethylation levels at histone 3' lysine 4/36 and higher acetylation levels at histone 3' lysine 9 under drought stress (Xu et al., 2017).

NATs are ubiquitous in bacteria, prokaryotes, and eukaryotes (Barrell et al., 1976; Tomizawa et al., 1981; Zhang et al., 2012). With the progress of RNA sequencing and computer approaches, genome-wide NATs were viewed and characterized gradually in the medicinal fungus and plants, such as Ganoderma lucidum (Shao et al., 2017)_and Salvia miltiorrhiza (Jiang et al., 2021), as well as in the model plants or crops, such as Arabidopsis (Yuan et al., 2015), Oryza sativa (Osato et al., 2003) and maize (Mao et al., 2021).

As an important economic and medicinal plant, Cannabis sativa L. is rich in cannabinoids (CBDs), fatty acid (FA), and cellulose. In recent years, due to the tremendous economic and medicinal values of cannabinoids, the genome of C. sativa was sequenced (Gao et al., 2020; Grassa et al., 2021; Laverty et al., 2019; van Bakel et al., 2011). The biosynthetic pathway of cannabinoids was deciphered (Laverty et al., 2019; van Bakel et al., 2011). Moreover, the regulation of non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) involved in the biosynthesis of cannabinoids in C. sativa, were preliminarily predicted (Wu et al., 2021). However, the NATs in C. sativa remain unknown.

In this study, nine strand-specific RNA Sequencing (ssRNA-Seq) libraries of C. sativa were constructed, and a bioinformatics pipeline was established to predict NATs using the ssRNA-Seq data. Then, the characterization and expression profiles of NAT-ST pairs were analyzed. Function enrichment revealed that the C. sativa NATs might be involved in the growth and development, biological stress resistance, and the production of free fatty acids. Finally, NAT-derived small interfering RNA (siRNAs) were predicted, and these nat-siRNAs are potentially involved in many mechanisms. All these results will shed light on the regulation of NATs and nat-siRNAs in C. sativa.

Plant Materials and RNA Extraction

Cannabis Sativa L. plants were grown in the Guangxi Pharmaceutical Botanical Garden (N22°51'34 ", E108°22'10'). Leaves, stems, and roots samples from three female C. sativa plants growing in the natural environment were collected, respectively. Total RNAs of leaves, stems, and roots were extracted using RNAprep Pure plant kit (DP441, TIANGEN, Beijing, China) following its protocol. Then, all the collected samples wrapped up with aluminum foil paper were frozen in liquid nitrogen immediately and transferred to − 80°C refrigerators.

SsRNA-seq library construction and analysis

The libraries of ssRNA-seq were constructed by the Beijing Allwegene company using the Nebnext® strand-specific Library kit (NEB, USA) following its protocol, which is similar to our description previously (Jiang et al., 2021). Except, mRNA was isolated from every sample by Epicentre Ribo-zero™ rRNA Removal kit (RNR07250, Epicentre, USA) to remove rRNA. Finally, the library containing the suitable size cDNA fragments was sequenced using a HiSeq 4000 platform.

Computational Prediction of NATs in C. sativa

After sequencing, low-quality sequence reads were screened to obtain clean data according to the following thresholds: the percentage of "N" exceeds 10% of the reads, and the ratio of low-quality alkali (Q ≤ 20) exceeds 50% of the length of the reads. Then, python scripts and analysis software were used to systematically predict candidate NATs and optimize a NATs prediction process, similar to those described previously (Jiang et al., 2021). The pipeline can be divided into three steps.

In step 1, the clean data of each sample were aligned with the C. sativa “CBDRx” genome using Bowtie2(v2.3.4)(Langmead and Salzberg, 2012). Then, the mapped reads were sorted by Samtools༈v2.1.1༉(Li et al., 2009) and were further assembled and merged by StringTie into the longest transcripts༈v2.1.1༉(Pertea et al., 2015).

In step 2, we compared the identified longest transcripts with the previously identified protein-coding genes (PCGs) (Grassa et al., 2021) using the BLASTN (v2.7.1) with an E-value cutoff of 1e-5 (Altschul et al., 1997). We called the PCGs sense transcript (ST). The transcripts similar to the ST in the reverse complementary orientation were called potential NATs. Each pair of ST and the potential NAT were called a NAT-ST pair or SAT pair. Then the transcripts meeting the following criteria were selected as candidate NATs: (1) the length of the complementary region is longer than 50% of either transcript of the SAT pair; (2) the consecutive complementary region of the SAT pair ≥ 100 bp. We judged the transcripts complementary to PCGs in the same locus as cis-NATs. By contract, we deemed the transcripts partially complementary to PCGs and originated from different loci as potential trans-NATs. Then, RNA hybridization of trans-NATs was predicted by RNAplex (v2.4.14) (Tafer and Hofacker, 2008). We further evaluated the results of BLASTN and RNAplex using NATpipe (Yu et al., 2016). The NATs satisfying the following two criteria were considered candidate trans-NATs. Firstly, the annealed region is consistent with the complementary region and exceeds 80% of the complementary region. Secondly, the length of any bubbles is shorter than 10% of the annealed region.

In step 3, according to the protein-coding capabilities of NATs and STs, NAT and ST pairs were divided into four categories: coding-coding, coding-noncoding, noncoding-coding, and noncoding-noncoding. Only noncoding-coding SAT pairs were analyzed further in this study. We removed the transcripts with a length shorter than 150 bp and those with the potential to encode proteins. We considered the transcripts meeting one of the following three conditions to have the protein-encoding potential: (1) the transcripts could code a protein sequence that is more than 100 aa, and (2) the transcripts were similar to protein sequences from the Swissprot or Pfam databases, and (3) transcripts with Coding Potential Calculator score more than 0 (Kong et al., 2007). The transcripts with an amino acid length greater than 100, corresponding to the ORFs longer than 300 nucleotides, were identified using the getorf program in EMBOSS package (v6.5.7.0). The corresponding parameters were “-minsize 300 -fnd 1 -reverse N” (Rice et al., 2000). We searched these ORFs against the Swissprot database using the BLASTN (v2.7.1) with an e-value cutoff of 1e-5. Next, we searched the ORFs against the Pfam database using the PfamScan.pl (v1.6) with an e-value cutoff of 1e-5.

Functional enrichment analysis of STs

The sequences of STs having cis-NATs were extracted using the gffread module in the Tuxedo package (v2.1.1) (Trapnell et al., 2012). We further mapped the annotated genes to the Gene Ontology (GO) Terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Enrichment analyses were conducted using the DAVID web server (Sherman et al., 2022). We considered the terms or pathways with a q value < 0.05, a minimum count of 3, and an enrichment factor ≥ 1.5 as significantly enriched. The q value was the multi-test adjusted p-value calculated using the Benjamini–Hochberg method (Ferreira, 2007). The enrichment factor was the ratio between the observed counts and the counts expected by chance.

Differentially expressed NATs

The tissue expression profiles of NATs were calculated by using the Cufdiff in the Tuxedo packages (v2.2.1) (Trapnell et al., 2012). The NATs with |log2(fold change) | ≥ 1 and q ≤ 0.05 were considered as significantly differentially expressed NATs (DENATs).

Validation of the expression profiles of NATs by strand-specific quantitative real-time RT-PCR

To validate the expression profiles of NATs and their corresponding STs, we used the strand-specific quantitative reverse transcription PCR (ssRT-qPCR). As described previously, the housekeeping gene CsActin was used as a reference gene for normalization (Adal et al., 2021). First, the sequences of NATs or STs were extracted using the gffread module in Tuxedo packages (v2.1.1) (Trapnell et al., 2012). Then, the primer sequences were designed using DNAMAN. To distinguish the expression of NATs and STs, we added nucleotide tags to the reverse transcription primers as in our previous paper (Jiang et al., 2021). The primers with a GC content of about 50% and the product length appropriate for qPCR were used for reverse transcription. The reverse transcription and qPCR primer sequences are listed in Table S1, respectively. In particular, RNA used for ssRNA-seq library construction was reversely transcribed to cDNA using strand-specific primers for NATs and STs. And reference genes were reverse transcribed using random primers by TransScript First-Strand cDNA Synthesis SuperMix kit (TransGen Biotech, China). Then, qPCR was carried out for each cDNA sample with three technical replicates using the qPCR kit (Takara, Japan). The reaction contained approximately 1 µl cDNA, 1ul 10 µM each of the forward and reverse primer, 0.4 µl passive reference dye (50 ×) polymerase, 10 µl 2 × SuperReal PreMix Plus, and 4.8 µl ddH₂O. The qPCR was performed using a Real-time PCR instrument (StepOnePlus, ABI, USA) under the following conditions: 95°C for 10 min, 40 cycles of 94°C for 10 s, and 60°C for 32 s.

Co-expression analysis of NATs and STs

We analyzed the correlation of their expression profiles to identify potential interactions between the NATs and STs. For ssRNA-Seq data, we calculated the Pearson correlation coefficient (r) of expression levels, represented as FPKM values, between NATs and their corresponding STs (Zhao et al., 2021). Then we used T.TEST in Excel to test the significance of the R values. Finally, the obtained p values were corrected using the fdrtool package (v3.60) in R language to get the q values (Strimmer, 2008). The q-value significance threshold was set to 0.05. Those expression profiles having q values ≤ 0.05 were considered significantly correlated.

Subsequently, we determined the expression levels of the NATs and STs using ssRT-qPCR. We used the 2^−△△CT method (Livak and Schmittgen, 2001) to calculate the relative expression of each nat and the corresponding STs based on the Ct value of ssRT-qPCR. The actin gene was used as the reference (Adal et al., 2021). Then we calculated the correlation of the expression levels obtained from ssRT-qPCR and ssRNA-seq. The false discovery rate (q value) was calculated using R’s fdrtool package (v3.60) (Strimmer, 2008).

Prediction of miRNA Targets

To decipher the regulation of miRNAs in NATs and STs in C. sativa, we downloaded all the released plant mature miRNA sequences (V 22.1) from miRbase (Kozomara et al., 2019). Then we collected the published miRNA sequences of C. sativa from our previous report (Wu et al., 2021). Then, these miRNAs' targeted NATs and STs were predicted using the psRNATarget server with a penalty score of ≤ 2.5 (Dai et al., 2018).

Prediction of nat-siRNAs and their targets

Previously, we sequenced the small RNA (sRNA) library and predicted the miRNA in C. sativa (Wu et al., 2021). In this study, to distinguish the sRNAs derived from the paring of cis-NAT or trans-NAT and their ST, the clean reads of these sRNAs were mapped to NATs and STs, respectively, using BLAST 2.7.1 + with the following parameters: “-outfmt 6 -num_threads 8 -evalue 1e-5 -word_size 7”. The sRNA having reads ≥ 2 and mapped to the overlapping region of at least one SAT pair was considered as a nat-siRNA (Yuan et al., 2015).

Prediction of Targets of nat-siRNAs and validation of the cleaved target by degradome

Targets of nat-siRNAs were predicted using psRNATarget Server with a penalty score of ≤ 2.5 (Dai et al. 2018). Then, the cleaved sites and the targets of nat-siRNAs were analyzed using our previous degradome data with reads ≥ 2 (Wu et al., 2021) using psRobot (v1.2) with the default parameter.

Genome-wide identification of NATs in C. sativa

We sequenced a total of nine samples from three different tissue types using an Illumina Hiseq 4000 platform. In total, 120.31 Gb raw data were produced. The raw data size of each sample ranged from 78.43 Gb to 99.97 Gb, respectively (Table S2). After filtering, a total of 118.39 Gb clean reads were obtained, ranging from 76.89 Gb to 98.47 Gb for each sample.

Then, we optimized a pipeline to predict NATs in C. sativa (Fig. 1), which included three modules. Firstly, in step 1, the clean reads of nine ssRNA-seq libraries were mapped to the C. sativa reference genome using Bowtie2 and StringTie. The mapping rate ranged from 41.4–68.6%. Then, these sequences were assembled into 26,446 to 34,536 transcripts, respectively. Lastly, we merged the transcripts identified from the nine libraries, resulting in 64,272 transcripts.

In step 2, 295 cis- and 1,431 trans- candidate NATs were identified from the merged 64,272 transcripts.

In step 3, these candidate NATs were further evaluated by the following criteria to identify non-coding RNAs from these candidate NATs. These criteria included: the sequence length > 150 bp, length of encoded peptide fragments < 100 amino acids, and the protein-coding potential < 0. A total of 92 cis-NATs and 168 trans-NATs were obtained after this step.

Characterization of NATs in C. Sativa

We found the NATs and their corresponding STs exhibited multiple mapping ratios. In other words, one NAT could match one or more STs and vice versa. In total, 897 SAT pairs were obtained. 92 cis-NATs and 86 STs formed 92 cis-NAT/ST pairs, and 168 trans-NATs and 588 STs formed 805 trans-NAT-ST pairs (Table S3).

We further analyzed the length distribution of cis-NATs and trans-NATs. As shown in Fig. 2A, the length of 92 cis-NATs ranged from191 to 5,069 bp, with an average length of 1,245 bp. In contrast, the length of 168 trans-NATs ranged from 201 to 3,482 bp, with an average length of 999 bp. The most abundant group of cis-NATs and trans-NATs had lengths ranging from 200 to 300 bp, followed by those with more than 1000 bp long.

Next, we analyzed the ratio of the length for overlap regions between the NATs and STs to the total length of NATs and STs. As shown in Fig. 2B, more than half of the NAT sequences were in the overlapping area, and the average ratio of the total length of NATs in the overlapping area sequence was 51.07%. The average ratio of the total length of the STs is 45.37%, indicating the high homology between sequences of the NATs and the STs.

According to the mapping region between cis-NATs and STs, the cis-SAT pairs can be divided into four subtypes (Fig. 2C): divergent, convergent, S > N (ST contains NAT), and N > S (NAT contains ST). Results showed that S > N accounted for the main type, including 35 pairs, which was in accord with previous studies in Arabidopsis thaliana [1], Oryza sativa [13], and Salvia miltiorrhiza [12]. The second most abundant type was divergent, having 32 pairs. For the rest, there were 21 pairs of the convergent and four pairs of N > S, respectively.

Based on expression profiles of NATs in leaves, roots, and stems, the distributions of NATs among different tissues were also analyzed (Fig. 2D). Results showed that 364 (40.6%) NATs were common in the three tissues; 406 (45.3%) NATs, (0.9%) NATs only existed in the roots and stems, respectively.

Functional enrichment analysis of C. sativa NATs

It is generally believed that NATs could play roles by regulating their corresponding STs. To decipher the potential biological functions of the NATs C. sativa, we carried out GO and KEGG functional enrichment analysis for the STs related to the 260 NATs. Results showed that these STs were annotated with GO terms of three categories: biological processes, cell composition, and molecular functions, of which 17 Go terms were significantly enriched (q ≤ 0.05) (Table 1). GO Terms of STs were significantly enriched in the growth and developmental processes for multiple plants, including the auxin metabolic process (GO: 0009850), positive regulation of post-embryonic development (GO: 0048582), lysine biosynthetic process via diaminopimelate (GO: 0009089), the heterocycle catabolic process (GO: 0046700) and defense response to a bacterium (GO: 0042742). It should be noted that GO: 0042742 included 7 STs, ST048, ST072, ST084, ST266, ST345, ST370, and ST598. In addition, STs were also significantly enriched in enzyme activity-related GO Terms, including transferase activity (GO: 0016740), diaminopimelate decarboxylase activity (GO: 0008836), and phosphatidylinositol N-acetylglucosaminyltransferase activity (GO: 0017176), indicating the regulatory effect on the activity of enzymes and the involvement in the biosynthesis of compounds in C. sativa.

Table 1

Functional enrichment analysis of STs corresponding to NATs in *C. sativa*
Category ID	Subcategory	Term	Number of STs in the category	q value
GO:0009850	Biological Processes	auxin metabolic process	3	7.21E-3
GO:0042742	Biological Processes	defense response to bacterium	7	9.53E-3
GO:0048582	Biological Processes	positive regulation of post-embryonic development	3	0.014
GO:0009089	Biological Processes	lysine biosynthetic process via diaminopimelate	3	0.030
GO:0046700	Biological Processes	heterocycle catabolic process	4	0.036
GO:0006888	Biological Processes	ER to Golgi vesicle-mediated transport	3	0.040
GO:0006417	Biological Processes	regulation of translation	3	0.048
GO:0016021	Cellular Components	integral component of membrane	9	2.78E-3
GO:0005730	Cellular Components	nucleolus	7	0.034
GO:0005737	Cellular Components	cytoplasm	34	0.045
GO:0003964	Molecular Functions	RNA-directed DNA polymerase activity	8	8.0E-11
GO:0004521	Molecular Functions	endoribonuclease activity	9	2.58E-8
GO:0003676	Molecular Functions	nucleic acid binding	13	1.6E-4
GO:0016740	Molecular Functions	transferase activity	8	9.4E-4
GO:0008836	Molecular Functions	diaminopimelate decarboxylase activity	2	0.011
GO:0017176	Molecular Functions	phosphatidylinositol N-acetylglucosaminyltransferase activity	2	0.027
GO:0015145	Molecular Functions	monosaccharide transmembrane transporter activity	2	0.038
ath00300	KEGG_Pathway	Lysine biosynthesis	3	0.040
ath00563	KEGG_Pathway	The glycosylphosphatidylinositol (GPI)-anchor biosynthesis	2	0.049

KEGG enrichment analysis showed that STs were significantly enriched in the pathways: Lysine biosynthesis (ath00300) and Glycosylphosphatidylinositol (GPI)-anchor biosynthesis (ath00563) (Table 1). As an important signaling molecule, lysine plays a central role in regulating plant growth and responses to the environment (Galili et al., 2001). The glycosylphosphatidylinositol (GPI) is a category of sugar-lipid compounds that enable proteins to be attached to cell membranes. GPI plays an essential role in the fertility of the male and female gametophytes (Desnoyer and Palanivelu, 2020). These results indicated that C. sativa NATs participated in the regulation of biosynthetic pathways of multiple compounds.

Validation of the expression profiles of cis-SAT pairs

To verify the expression profiles of cis-SAT pairs in C. sativa, we use the ssRT-qPCR approach (Kawakami et al., 2011), which could distinguish NATs from STs by adding sequence labels for reverse transcription (Kawakami et al., 2011; Tercero et al., 2019). The 25 cis-SATpairs were selected for verification based on the two following criteria. Firstly, the ST sequence was annotated by Go Terms or enriched in the KEGG pathway. Secondly, the correlation of ssRNA-seq expression profiles between NATs and their corresponding STs was significantly positively related (r ≥ 0.8). Results showed that 46 of 50 (92%) ssRT-qPCR profiles were positively related to ssRNA-Seq (r ≥ 0.8). In particular, 27 of 50 (54%) transcripts had r ≥ 0.9 (Fig. 3, 4). These results showed that the NATs found in this study were reliable and can be used for further analysis.

Significantly differentially expressed NATs among tissues

To explore the potential regulatory roles of NATs, we analyzed the expression levels of NATs in different tissues. In total, 17 DENATs were detected using the threshold of |log₂(fold change) | ≥ 1 and q ≤ 0.05 (Table S4). Among them, NAT066, NAT089, NAT139, NAT154, NAT375, NAT573, NAT791, NTA842, and NAT843 expressed higher in the roots than in the leaves. The expression level of NAT897 in the root was higher than in the stem. In contrast, the expression levels of NAT623 and NAT673 in the stems were higher than in the roots. And NAT327, NAT375, NAT578, NAT724, NAT743, NAT783, and NAT893 had higher expression levels in the stems than in the leaves.

Co-expression between NATs and their corresponding STs

To explore whether NATs have a regulatory effect on STs, we further analyzed and verified the correlation between NATs and their corresponding STs expression based on the ssRT-qPCR experiments (Fig. 5). Results showed that in the 25 SAT pairs verified. The expression profiles of two NATs were significantly negatively correlated with the corresponding STs (r≤-0.9). In contrast,13 NATs were significantly positively correlated with the corresponding STs (r ≥ 0.9). The r of 5 NAT-ST pairs ranged from 0 to 0.9, indicating the positive regulation of these NATs.

Identification of miRNA targeting the overlapping regions in the SAT pairs

Previous studies showed that NATs could stabilize the STs by forming an RNA duplex to reduce the degradation of STs by miRNA (Wang et al., 2016). To explore the crosstalk between miRNA, NATs, and their corresponding STs in C. sativa, we predicted the target sites of known miRNAs in the overlapping regions of the SAT pairs were predicted using the psRNATarget server. The overlapping regions of seven SAT pairs were identified to be targeted by miRNAs (Table 2). Moreover, the relative expression levels of these seven NATs, including NAT002, NAT006, NAT020, NAT024, NAT033, NAT037, NAT061, NAT076, and their corresponding STs were significantly positively correlated in the three tissues with r ≥ 0.9. Taking it together, we proposed that these NATs might interact with their STs to form RNA duplex, inhibit miRNAs to cleavage the STs, and enhance the stability of STs.

Table 2

miRNA targeting the overlapping regions of SAT pairs
ST ID	MiRNA	Start	End	Aligned MiRNA Fragment	Alignment ^a	Aligned ST Fragment	Inhibition
ST0002	gra-miR8639a	24	52	UAAACAUAAGUAGAAUUAAACAAG	.::: :: : :.::.::::::::	UUUGAUUUACUUUAUUUAUGUUUU	Cleavage
ST0006	CPA-miR8154	1	22	CAGAGGAGGAGAUGAAGAGGGA	:.:.:::::.:::.:: .:.::	UUCUUCUUCGUCUUCUGUUUUG	Cleavage
ST0020	hme-miR-14	1	22	UCAGUCUUUUUCUCUCUCCUAU	::::::::.:.::. :::	GAGAGAGAGAGAGAGAGGGUGA	Cleavage
ST0024	gma-miR1526	1	22	CCGGAAGAGGAAAAUUAAGCAA	.::::::::..:.::..::	UAAUUUAAUUUUUUUUUUUUGG	Cleavage
ST0033	gra-miR7504i	1	21	AUAUGAAACUGAGAUACCAUG	: : ::::.::: ::::::::	CCUCGUAUUUCACUUUCAUAU	Cleavage
ST0037	osa-miR2919	1	19	AAGGGGGGGGGGGGAAAGA	:::::: .:.::.:.:.::	UCUUUCGUCUCCUCUCUUU	Cleavage
ST0061	mtr-miR2597	1	21	UUUGGUACUUCGUCGAUUUGA	::::::::.::.:::: :.:	ACAAAUCGAUGAGGUACAAGA	Cleavage

Conservation of C. sativa NATs

To reveal the conservation of C. sativa NATs, the 260 NATs were compared with the plant lncRNA database (Jin et al., 2013), the Green Non-Coding Database (Di Marsico et al., 2022), and lncRNAs reported in hemp (Wu et al., 2021) using BLAST with e-value 1e-5 and alignment length > 50% of the total length. Results showed that one NAT (TCONS_00048050) was homologous to Tdicoccoides_TRIDC3AG050040.2 and Tdicoccoides_TRIDC3AG050040.2 in the public database (Table S5). Moreover, 15 NATs were aligned with previous reported 24 lncRNAs in C. sativa variety ‘Yunma No.1’ (Wu et al., 2021), which indicated these NATs might play similar roles among these two cultivars of C. sativa (Table S5).

Identification of nat-siRNAs

SAT pairs usually form dsRNA in vivo, which could lead to dsRNA-dependent siRNA-mediated gene silencing in some cases (Zhang et al., 2012). In this study, we found that 12 cis- and 278 trans- SAT pairs could produce siRNAs, respectively. In total, 476 cis- and 2342 trans- nat-siRNAs were identified (Table S6). We then determined the functions of these siRNA-producing STs. In total, 233 STs were annotated as being potentially involved in metabolites transfer or transport, growth and development, DNA repair, regulating gene expression, disease resistance, signal transduction, flavanone biosynthesis, etc. (Table S6). For example, some STs (RNA-XM_030625573.1) encode 2-oxoglutarate-dependent dioxygenase (DAO), which functions in the flavonoid pathway by catalyzing hydroxylation and desaturation reactions (Wang et al., 2021). Some other STs (RNA-XM_030622242.1, RNA-XM_030631242.1, RNA-XM_030646986.1) encode FAR1-related sequence proteins and play crucial roles in the growth and development of Arabidopsis (Ma and Li, 2018).

Prediction and validation of nat-siRNAs targets

Target prediction showed that a total of 9,653 transcripts were found potentially to be targeted by 2,422 nat-siRNAs (Table S7). KEGG pathway analysis exhibited that these targets were involved in multiple metabolisms, including cysteine and methionine metabolism (ko00270), purine metabolism (ko00230), and pyruvate metabolism (ko00620); genetic information processing, including ribosome (03010), spliceosome (ko03040), and protein processing in the endoplasmic reticulum (ko04141); signaling and cellular processes including MAPK signaling pathway - plant (ko04016), plant hormone signal transduction (ko04075), and plant-pathogen interaction (ko04626) (Table S8). Next, we are concerned with the CBD-, FA- and cellulose- related Nat-siRNAs. 15 Nat-siRNAs targeted genes encoding the acyl-activating enzyme, 1-deoxy-D-xylulose-5-phosphate synthase, dehydrodolichyl diphosphate synthase, or geranylgeranyl diphosphate reductase. These enzymes are involved in CBD biosynthesis. Two Nat-siRNAs targeted four acetyl-CoA carboxylase genes of the Fatty Acids biosynthetic pathway. 120 Nat-siRNAs targeted cellulose biosynthesis-related genes, including cellulose synthase A, cellulose synthase-like protein, kinesin-like protein, mitogen-activated protein kinase kinase kinase, inositol polyphosphate 5-phosphatase or UDP-galactose transporter (Table S9).

It is known that miRNA/siRNA usually cleaves target mRNA at the 9-11th nucleotide from its 5′ ends, occasionally in other site (Wu et al., 2021. Degradome sequencing, a modified approach of 5’-Rapid Amplification of cDNA Ends using high-throughput sequencing technology, is a practical approach to detecting cleaved miRNA/siRNA targets (Addo-Quaye et al., 2009). In this study, a total of 718 targets were found to be targeted by 543 nat-siRNAs based on the degradome sequencing data (Table S10).

In this study, a computational pipeline was optimized to identify the NATs in C. sativa for the first time. Using the pipeline, we analyzed ssRNA-Seq data from samples of C. sativa leaves, roots, and stems. Finally, 260 NATs were finally predicted. The primary type of cis-NATs was S > N, and most of the NATs were expressed in a tissue-specific manner. C. sativa NATs might function in plant growth and development, resistance, and the biosynthesis of active compounds.

We observed both negative and positive correlated expression profiles of NATs and STs from the SAT pairs, suggesting that their mechanisms of action might be diverse. Among the validated 25 SAT pairs, the expression profiles of 13 NATs were significantly positively correlated with those of the corresponding STS (r ≥ 0.9). Previous studies showed that NATs could positively regulate the expression of STs mainly through two mechanisms. On the one hand, NATs could increase the transcriptional level of STs by histone modifications (Zhao et al., 2018). On the other hand, as a stabilizer, NATs can interact with ST to form a double-stranded structure, hindering the cleavage of ST by miRNA targeted to increase the stability of STs [6]. In this study, miRNA target prediction was performed on the overlapping regions of SAT pairs, and it was found that eight pairs of validated NATs and their corresponding STs were potential miRNA targets in the overlapping regions. We proposed that these eight NATs might regulate the expression of STs by acting as stabilizers for their corresponding STs.

SAT pairs usually form dsRNA, leading to the produce of nat-siRNAs, and are incorporated into RNA-induced silencing complex to guide gene silencing (Borsani et al., 2005). Nat-siRNAs were found in various animals and plant fungi (Thody et al., 2020; Zhang et al., 2012). This study found that 12 cis SAT pairs and 278 trans SAT pairs could produce 476 cis- and 2342 trans- nat-siRNAs, respectively, for the first time. Some homologous STs producing siRNAs were reported in another organism. For example, in Arabidopsis, many siRNAs are derived from 5S ribosomal RNA (5S rRNA), while the producing and action mechanism remain to be illustrated (Szymanski and Karlowski, 2016). Taking the pentatricopeptide repeat-containing gene (PPR) for another example, in this study, we found PPR (RNA-XM_030636992.1) with high homologous to At3g49730 producing nat-siRNAs. In contrast, it is different from the PPRs (AT1G62590, AT1G62670, AT1G62910, AT1G6291, AT1G6332, AT1G63330, AT1G63400) in Arabidopsis that produce trans-acting small interfering RNAs (tasiRNAs). Previously, tasiRNAs from some PPRs in Arabidopsis are initiated by cleavage of miR161, miR400, and miR173 (Vargas-Asencio and Perry, 2019; Xia et al., 2013) or in peach by miR7122 (Xia et al., 2013). On the other hand, some STs producing siRNAs were not reported in other species, which indicated the possible C. sativa-specific mechanism. For example, STs (RNA-XR_004007625.1, RNA-XR_004009122.1, RNA-XR_004011855.1, and RNA-XR_004009143.1) were annotated to encode mannosyltransferase family protein, which was paired with NAT (TCONS_00004037), and produced siRNAs.

Unfortunately, only 260 NATs were found in our study, less than those reported in other species. For example, previous research predicted that about 40.7, 40.9 and 25.6% of genes form NATs in humans, mice, and flies, respectively (Zhang et al., 2006). Another study reported that 7,962 (23.9%) genes in Arabidopsis could form SAT pairs, including 4,080 cis-SAT pairs and 2,491 trans-SAT pairs (Yuan et al., 2015). The small number of detected NATs might lie in two aspects. Firstly, a potent CBD-type cultivar “CBDRx" genome, was used for mapping and NAT identification (Grassa et al., 2021), which is different from our C. sativa cultivar. Four genome sequences were released (Gao et al., 2020; Grassa et al., 2021; Laverty et al., 2019; van Bakel et al., 2011). Significant diversity has been found among these genomes. The other C. sativa genomes might be used for transcriptome assembly and NATs identification in C. sativa. Secondly, we focused on noncoding-NAT versus PCT pairs, which overlooked different types of NATs, such as protein-protein cis-NATs. For example, in Arabidopsis, the ORF of P5CDH was complementary to the 3′UTR of SRO5 (Borsani et al., 2005). All these laid the foundation for studying the function of NATs and nat-siRNAs in C. sativa.

In this study, we optimized a computer pipeline to predict NATs in a genome-wide manner and carried out a comprehensive analysis. 260 NATs were predicted in C. sativa, including 92 cis- and 168 trans-NATs. The primary type of cis-NATs was STs containing NATs. The expression profiles of 25 SAT pairs were validated by ssRT-qPCR. C. sativa NATs were involved in growth and development, stress resistance, and the biosynthesis of compounds. Finally, 2818 nat-siRNAs were predicted. These siRNAs were potentially involved in the biosynthesis of CBDs, FA, and cellulose. All these results suggest that NATs and nat-siRNAs might play complex regulatory roles in C. sativa.

Funding: This work was supported by the Chinese Academy of Medical Sciences Innovation Funds for Medical Sciences (CIFMS) [2021-I2M-1-022], the National Science Foundation of China [81872966], and the National Science &Technology Fundamental Resources Investigation Program of China [2018FY100705]. The funders were not involved in the study design, data collection, analysis, decision to be published, or manuscript preparation.

Data Availability Statement:

The RNA-Seq raw data of leaves, roots, and stems have been deposited into the SRA database with the accession numbers SRR17817906, SRR17817907, and SRR17817908, respectively.

Conflicts of Interest: The authors declare no conflict of interest.

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TableS19.xlsx
Table S1 The primers used in this study. Table S2 Statistics of ssRNA-Seq data. Table S3 897 SAT pairs identified in C. sativa. Table S4 Differentially expressed NATs among tissues in C. sativa. Table S5 Conservation of C. sativa NATs. Table S6 Nat-siRNAs identified in C. sativa. Table S7 Predicted targets of Nat-siRNAs in C. sativa. Table S8 KEGG pathway analysis of targets of nat-siRNAs in C. sativa. Table S9 Prediction of CBD-, FA- and cellulose- related nat-siRNAs.
TableS10.txt
Table S10 Analysis of cleaved targets of nat-siRNAs by degradome.

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You are reading this latest preprint version

Genome-Wide View and Characterization of Natural Antisense Transcripts in Cannabis Sativa L.

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Abstract

Figures

Introduction

Materials And Methods

Plant Materials and RNA Extraction

SsRNA-seq library construction and analysis

Computational Prediction of NATs in C. sativa

Functional enrichment analysis of STs

Differentially expressed NATs

Validation of the expression profiles of NATs by strand-specific quantitative real-time RT-PCR

Co-expression analysis of NATs and STs

Prediction of miRNA Targets

Prediction of nat-siRNAs and their targets

Prediction of Targets of nat-siRNAs and validation of the cleaved target by degradome

Results

Genome-wide identification of NATs in C. sativa

Characterization of NATs in C. Sativa

Functional enrichment analysis of C. sativa NATs

Validation of the expression profiles of cis-SAT pairs

Significantly differentially expressed NATs among tissues

Co-expression between NATs and their corresponding STs

Identification of miRNA targeting the overlapping regions in the SAT pairs

Conservation of C. sativa NATs

Identification of nat-siRNAs

Prediction and validation of nat-siRNAs targets

Discussion

Conclusion

Declarations

References

Supplementary Files

Status:

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