MicroRNA from Moringa oleifera: Identification by High Throughput Sequencing and Their Potential Contribution to Plant Medicinal Value

Abstract

Moringa oleifera is a widespread plant with substantial nutritional and medicinal value. We postulated that microRNAs (miRNAs), which are endogenous, noncoding small RNAs regulating gene expression at the post-transcriptional level, might contribute to the medicinal properties of plants of this species after ingestion into human body, regulating human gene expression. However, the knowledge is scarce about miRNA in Moringa. Furthermore, in order to test the hypothesis on the pharmacological potential properties of miRNA, we conducted a high-throughput sequencing analysis using the Illumina platform. A total of 31,290,964 raw reads were produced from a library of small RNA isolated from M. oleifera seeds. We identified 94 conserved and two novel miRNAs that were validated by qRT-PCR assays. Results from qRT-PCR trials conducted on the expression of 20 Moringa miRNA showed that are conserved across multiple plant species as determined by their detection in tissue of other common crop plants. In silico analyses predicted target genes for the conserved miRNA that in turn allowed to relate the miRNAs to the regulation of physiological processes. Some of the predicted plant miRNAs have functional homology to their mammalian counterparts and regulated human genes when they were transfected into cell lines. To our knowledge, this is the first report of discovering M. oleifera miRNAs based on high-throughput sequencing and bioinformatics analysis and we provided new insight into a potential cross-species control of human gene expression. The widespread cultivation and consumption of M. oleifera, for nutritional and medicinal purposes, brings humans into close contact with products and extracts of this plant species. The potential for miRNA transfer should be evaluated as one possible mechanism of action to account for beneficial properties of this valuable species.

Introduction

Moringa oleifera Lam, a naturalized species from the monogenus family Moringaceae, is one of the best known, most widely distributed and most useful nutritional and medicinal plants [1–3]. Several organs of the Moringa tree are edible (e.g., pods, seeds, flowers and leaves) and are used in many countries (including many regions of Africa) for their high nutritional value [1–2]. Almost all tissues of this plant can be used in the treatment of inflammation or infectious diseases along with cardiovascular, gastrointestinal, haematological and neoplastic diseases [2–4]. The leaves are a source of natural antioxidants [5], vitamins A, B and C, minerals, proteins and essential amino acids [1–2]. The Italian Ministry of Health in compliance with the European Pharmaceutical Plant legislation has included Moringa seeds in the “List of Plant and Vegetal Integrators”.

In 2014, Jung IL reported a tumor suppressor activity in in mammalian cells treated with cold water-soluble extract of M. oleifera leaves [6]. The author found an abnormal ribosomal RNA (rRNA) pattern and down-regulation of many genes and proteins involved in cell transformation and proliferation in mammalian cells treated with this extract. He concluded that the cold water-soluble extract of M. oleifera induced rRNA degradation. In 2015, Tian and coworkers reported a high-quality draft genome sequence of M. oleifera and compared the genome to related woody plant genomes in order to clarify the derivation of this species [7].

Plant miRNAs are a class of 18–24 nucleotide (nt) small, non-coding RNA that negatively regulate specific messenger RNA (mRNA). MiRNAs operate in a sequence-specific manner and silence specific protein-coding genes at the post-transcriptional level by targeting the 3’ untranslated region (3’UTR) of mRNA [8]. This process causes mRNA cleavage and decreases protein translation [9–11]. In general, miRNAs are key regulators of development, stress response, growth and other important physiological processes [12–13].

In 2012, Zhang and collaborators demonstrated that osa-miR-168a and other exogenous miRNAs that are abundant in rice plants can be acquired by mice through food intake, as evidenced by their presence in sera or tissues of the mammalian. In vitro and in vivo functional studies showed that these exogenous miRNAs are able to inhibit mammalian gene expression in the liver, demonstrating the first case of cross-kingdom regulation [14]. More recently, oral administered cocktails of endogenous tumor suppressor miRNAs, exhibiting characteristics of plant miRNAs, reduced tumor burden in a mouse model of colon cancer [15]. These observations show that plant miRNAs are absorbed in the mammalian digestive tract and can target mammalian genes. Moreover, they suggest the hypothesis that engineered edible plants producing mammalian tumor suppressor miRNAs might be a new treatment modality for cancer. Such treatment might be an effective, nontoxic, and inexpensive chemo-preventive strategy for human.

Recently, Shu and collaborators presented an integrative study where comparative analysis and computational prediction have been applied to assess the cross-species transportation of miRNAs, particularly focusing on inferring the likelihood of exogenous miRNA in human circulation [16].

This work demonstrated the data-driven computational analysis is highly promising approach to study novel molecular characteristics of deliverable miRNAs allowing to bypass the complex mechanistic details.

Several miRNAs discovery methods including computational prediction, cloning strategies and others have been used [17–19], even though these methods demand an increased rigor in miRNA annotations [20]. High-throughput sequencing technologies have contributed markedly to the expansion of knowledge about the miRNA universe in eukaryotic cells. These technologies have revealed a number of newly evolved and species-specific miRNAs that were previously unknown [21–22].

With the effort to define medicinal factors found in this interesting plant species, we searched for understanding better genome expression and gene regulation by micro RNA (miRNA) in M. oleifera Lam. We recognize that seeds contain all information about the tissues that will develop in the adult plant; therefore, we have programmed to analyze the miRNA populations obtained from seeds. In this work, we have identified novel miRNAs and their potential target genes from M. oleifera seeds using Illumina platform technologies. We found 94 conserved miRNAs and two novel ones, and some of them were validated by qRT-PCR. Target genes were predicted by in silico and results indicate that mol-miRNAs are putatively involved in many physiological process. A selected number of miRNAs were compared to other crop species plants, resulting to be conserved across multiple species. By taking advantage of a recently developed web-application based on an algorithm that compares plant and mammalian miRNAs (http://160.80.35.140/MirCompare), we have identified a few M. oleifera miRNAs with functional homologies to mammalian ones. We conducted a preliminary analysis to investigate potential human gene regulation by the plant miRNA mimics. The reported analysis increases the information on plant miRNAs currently available and improves the knowledge on to the molecular mechanisms associated to nutritional and medicinal activities of this plant species.

Materials and Methods

Plant materials, sample collection and RNA extraction

Seeds collected from mature pods, before they split open and fall to the ground, harvested from M. oleifera trees grown in the District of Dschang in West Cameroon, without use of chemical fertilizer, by the Cooperative of Medical Plant Producers SOCOPOMO. Seeds were stored at -80°C until used. Seeds were germinated in a greenhouse at the Department of Biology, University of “Tor Vergata”, Rome, by placing them on paper soaked in sterile water. Leaves, stems and roots were collected at one month from the beginning of germination, immediately frozen in liquid nitrogen and subsequently stored at -80°C until used. Tissues from other species (Solanum tuberosa, Olea europaea and Medicago sativa) grown under natural field conditions, were also stored at -80°C until used. Total RNA was extracted from plant tissues using the mirVana kit (Ambion, USA) according to the manufacturer’s protocol. The assessment of RNA quality and quantity were evaluated by Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbroon, Germany) and by spectrophotometry (SmartSpec Plus, Bio-Rad, USA), respectively.

Constructing and sequencing small RNA libraries

Quality control for next generation sequencing experiments was performed by Genomix4life S.r.l. (Baronissi, Salerno, Italy). Individual indexed libraries were prepared from 1 μg of purified RNA using the TruSeq SmallRNA Sample Prep Kit (Illumina, USA) according to the manufacturer’s instructions. Libraries were quantified using the Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbroon, Germany) and pooled so that each index-tagged sample was present in equimolar amounts with final concentrations of the pooled samples adjusted to 2 nM. The pooled samples were subject to cluster generation and sequencing using an Illumina HiSeq 2500 System (Illumina, USA) in a 1x50 single read format at a final concentration of 10 pmol. The raw sequence files generated underwent quality control analyses using FastQC [23]. This method offers a powerful means for quantitative and qualitative profiling small RNA populations in different plant species, for which limited genome information is available, such as M. oleifera.

Sequence data analysis

Several bioinformatics tools have been developed to identify conserved miRNAs and discover new ones, starting from high-throughput sequencing [24–27]. The biggest limitation of these algorithms is that genome sequencing is required to execute the analysis. The only published algorithm able to discover novel miRNAs in species without sequenced genome is miReader [28]. Jha and coworkers also presented an approach for identifying novel miRNAs in Miscanthus giganteus, whose genome has not been sequenced yet [28]. Although the M. oleifera genome was already sequenced in 2015 [7], the raw data are not yet available, therefore we proceeded with a genome-independent strategy based on Jha’s work.

S1 Fig summarizes the analysis workflow performed with small RNA raw data. This analysis was performed to identify a pool of RNA fragments with lengths between 18 and 24 nucleotides that would be added to the subsets of known or uncharacterized miRNAs. These data have been deposited in NCBI/GEO public database under accession number GSE70423.

Raw data filtering

Starting from a total of 31,290,964 reads the Illumina small-RNA adapter was first clipped using FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit). Sequences shorter than 15nt after trimming were discarded and we obtained a total of 22,737,895 reads. Sequencing artifacts were removed and the remaining reads were trimmed on the 3p and 5p ends in order to remove low-quality bases. At the end of this procedure BLASTn [29] v 2.2.30 with parameters “-task megablast, -perc_identity 100” was used to compare the remaining reads with the Rfam database (Rfam 11.0) [30]. This stringent step removed non-coding RNA (rRNA, tRNA, snRNA, snoRNA) and degraded fragments of mRNA. Reads with lengths less than 15 nucleotides were discarded. After the alignment process, a total of 10,415,180 reads were identified as possible miRNA sequences.

Identification of known and putative novel miRNAs

The 10,415,180 reads highlighted as possible miRNAs were aligned against a specific-plant miRBASE (Release 21) with a low-redundancy rate (http://www.mirbase.org/) using BLASTn v 2.2.30 [29] with parameters “-task blastn-short, -perc_identity 100”. Sequences with homology rate equal to 100% were considered conserved M. oleifera miRNAs. In the alignment process all reads with abundance values lower than 10 copies were discarded resulting in a total of 303,872 reads.

In order to identify uncharacterized putative novel miRNAs, all the discarded reads, generated from the alignment with the wide libraries of miRNAs present in the repertory of miRBase bank, have been selected by a homemade custom script and were considered as candidate novel miRNAs.

Subsequently, the miReader algorithm [28] has been used to identify a strict number of uncharacterized miRNAs without the support of a reference genome. Although, this algorithm is less qualitatively informative (i.e. prediction of secondary structures) than others algorithms, it can be considered the most effective quantitative analysis with unknown genome, as is the case of M. oleifera.

Quality controls

Quality controls were performed using FastQC software [23], before (S1 File) and after (S1 File) removing the sequencing adapter. Figures A and B in S1 File show the quality scores across all bases and sequences in more detail, highlighting the validity of sequencing and analysis processes. The Illumina small-RNA adapter inside the sequenced sample is clearly visible. (Figure C in S1 File). In Figures A and B in S2 File is visible how sequencing quality was preserved through the filtering process, the complete removal of the small-RNA adapter (Figure C in S2 File) and the removal of all ncRNAs that do not belong to miRNAs.

Predicting conserved miRNAs across other plant species

In order to evaluate the conservation rate for M. oleifera miRNAs across all others plant species, we developed a custom script, which retrieves the sequence information for plant organisms stored inside MirBase repository and evaluates the overlapping rate in terms of sequence homology between M. oleifera and all other plant miRNAs.

Target prediction

Predicting mRNA targets in plants for both known and uncharacterized miRNAs

S2 Fig shows the process to predict gene target for known and novel-putative miRNA. Starting from the list of 74 known, psRNATarget [31] was used to predict target genes in the plant. To reduce the false-positive rate we set the maximum expectation value to 2.0 and forced the system to select only the best five predictions for each miRNA. After the target prediction analysis, the three gene sets were submitted to PlantGSEA [32] to obtain a smaller number of gene clusters that are related to plant molecular processes.

Predicting known miRNA with putative roles in human gene regulation

Several Moringa miRNAs were further analyzed to investigate their possible roles in human gene regulation. MirCompare (http://160.80.35.140/MirCompare) is a web-application based on an algorithm that compares libraries of miRNAs belonging to organisms from plant and animal kingdoms in order to find cross-kingdom regulation. The main settings for MirCompare analysis are: 1) r-value: the best comparison rate between each miRNA couple; 2) seed-region stringency: the minimum number of matches, related to the seed region.

MirCompare was used with r-value = 0.55 and seed-region threshold = 5 to identify possible Moringa miRNAs with functional homologies to mammalian miRNAs. The combinatorial miRNA target prediction (COMIR) web tool [33], which combines four popular scoring schemes (miRanda, PITA, TargetScan and mirSVR), was used to compute the potential of a gene to be targeted by a set of miRNAs. Therefore, COMIR tool generated a list of genes that might be regulated by M. oleifera miRNAs. Subsequently, the identified genes were submitted to Gene Ontology enrichment analysis to understand the pathways, in which miRNAs might putatively play a regulating role.

Transfection

We studied biological activity of the synthetic mol-miR168a (UCGCUUGGUGCAGGUCGGGAC) that was transfected into the hepatocellular carcinoma cell line (HEP-G2) by the lipofectamin method (Hi-Fect, Qiagen, USA) according with manufacture’s instruction (MiRNA mimic and inhibitor experiments protocols®, Qiagen, USA). Synthetic mol-miR168a tagged with fluorescein isothiocyanate (FITC) was used also as control of transfection. Cells were harvested 72h after transfection and characterized for the efficiency of miRNA uptake by EVOS FLoid cell imaging station (LifeTechnologies, USA) analysis, and for the effect of miRNAs on specific target genes by Western Blot assay.

Western blot analysis

Aliquots of 3x10⁶ transfected cells were suspended in buffers for cytoplasmic or nuclear protein extraction, and further processed for western blot analysis as already described [34]. Primary antibodies included the rabbit monoclonal antibody SIRT1 and the goat monoclonal antibody human beta-actin (Santa Cruz biotechnology, CA, USA). Secondary antibodies included anti-goat and anti-rabbit IgG chain-specific antibodies that were conjugated to peroxidase (Calbiochem, Merck Millipore, Darmstadt, Germany). Western blot analysis for each sample was quantified by densitometry analysis (TINA software)

Data analysis was performed using GraphPad Prism version 6.00 (GraphPad Software, La Jolla, CA, USA). Statistical probabilities were expressed as p <0.05 (*) or p < 0.01 (**). Comparison of means of SIRT-1 protein expression in response to mol-miR168a transfection was carried out using t-Test analysis.

Experimental validation of conserved miRNAs by Quantitative Real-Time PCR (qRT-PCR)

The expression level of different miRNAs was validated using poly(A)-tailed qRT-PCR method. According to the manufacturer’s protocols (Exiqon A/S, Vedbaek, Denmark), a poly-A tail was added to the mature miRNA templates (20 ng). cDNA was synthesized using a poly-T primer with a 3’ degenerate anchor and a 5’ universal tag at 42°C for 60 min followed by heat-inactivated for 5 min at 95°C. To provide a control for quality of the cDNA synthesis reaction and the PCR, RNA spike-in (UniSp6) was added to the sample prior to cDNA synthesis. The cDNA template was then amplified using miRNA-specific and LNA™-enhanced forward and reverse primers. SYBR®Green was used for detection. The reactions were carried out in a Rotor-Gene®Q 72-Well Rotor (Qiagen, USA) with the following amplification conditions: activation/denaturation at 95°C for 10 min followed by 40 cycles of denaturation at 95°C for 10s, annealing and extension together at 60°C for 60 sec. Finally, melting analyses were performed to confirm the absence of false-positive peaks. qRT-PCR was performed only for conserved miRNAs. All reactions were performed in triplicate for each sample. Two controls (no template control and no Reverse Transcription control) were included in all reactions. Relative expression levels of miRNAs were quantified by using the 2^{-Δ ΔCt} method and miR159 was used as the internal control miRNA. To determine significant differences among samples or miRNAs we applied a One Way ANOVA analysis using GraphPad Prism version 6.00.

Results

Analyzing small RNA populations

sRNAs from M. oleifera seeds with 5’-phosphate and 3’-hydroxyl groups were identified by high-throughput Illumina TruSeq smallRNA sequencing (Illumina HiSeq 1500). cDNA libraries were constructed from seeds of M. oleifera plants after removing the 5 bp adapter sequence and filtering out low quality ‘‘n” sequences. One small RNA library was constructed with about 30 million reads. Reads cleaned from adapters, ranging from 14 to 51 nts in length, were filtered with the Rfam database [30]. Looking at the sequence distribution after the filtering process, the majority of reads were 18 to 25 nts in length. The main size groups were 21 nt and 24 nt, respectively (Fig 1). These results were consistent with previous studies in other plant species where 24 nt small RNAs were the most abundant [19]. The amounts of 21 nt and 24 nt small RNAs were approximately 18.15% and 16.06%, respectively.

Fig 1. Length distribution of small RNA populations.

Sequence length distributions of sRNAs in M. oleifera seeds filtered by RFAM database.

Identifying conserved M. oleifera-specific miRNAs

Conserved families of miRNAs are present in many plant species due to their important regulatory role played. The highly conserved nature gives the opportunity to identify miRNAs in plant species for which genome sequence information is partially or fully available, or previously undefined. To identify the conserved miRNAs in M. oleifera seed plants, the massive dataset was compared to known plant mature miRNAs against miRBASE (Release 21), using BLASTn. The sequence analysis revealed the presence of 94 miRNAs belonging to 40 conserved families (Table 1). Deeper investigations conducted by miRBASE also identify secondary structures for most of the known M. oleifera miRNA sequences (S1 Table).

Table 1. Known microRNAs in M. oleifera seeds.

miRNA family	miRNA members	miRNA sequence	miRNA* sequence	Read Counts
				miR	miR*
mol-miR156	mol-miR156	CUGACAGAAGAGAGUGAGCAC		2459
	mol-miR156d		GCUCUCUAUGCUUCUGUCAUCA		23
	mol-miR156f	CUGACAGAAGAGAGUGAGCA	CUCACUUCUCUUUCUGUCAAUC	86	34
	mol-miR156g	CGACAGAAGAGAGUGAGCAC		77
	mol-miR156h	UGACAGAAGAAAGAGAGCAC	GCUCUCUUUCCUUCUGCCACC	33	NA
	mol-miR156j	GUUGACAGAAGAGAGUGAGCAC		2546
	mol-miR156q	UGACAGAAGAGAGUGAGCACU		2476
	mol-miR156t	UUGACAGAAGAGAGAGAGCAC		111
mol-miR157	mol-miR157a	UUGACAGAAGAUAGAGAGCAC	GCUCUCUAGCCUUCUGUCAUCA	316	NA
	mol-miR157d	UGACAGAAGAUAGAGAGCAC	GCUCUCUAUGCUUCUGUCAUC	NA	23
mol-miR159	mol-miR159	AGCUCCCUUCGAUCCAAUC	CUUGGAUUGAAGGGAGCUCU	NA	48
	mol-miR159a	UUUGGAUUGAAGGGAGCUCUA		11908
	mol-miR159b		UUUGGAUUGAAGGGAGCUCUU		2555
	mol-miR159b.1		UUUGGAUUGAAGGGAGCUCUG		2492
	mol-miR159c		UUUGGAUUGAAGGGAGCUCCU		1917
	mol-miR159d	AUUGGAUUGAAGGGAGCUCCG		21
	mol-miR159f	CUUGGAUUGAAGGGAGCUCUA		442
mol-miR160	mol-miR160h	UGCCUGGCUCCCUGUAUGCCAUU		25
mol-miR162	mol-miR162		UCGAUAAACCUCUGCAUCCAG		158
	mol-miR162a	UCGAUAAACCUCUGCAUCCA		16
mol-miR164	mol-miR164a	UGGAGAAGCAGGGCACGUGAA		13
	mol-miR164c	UGGAGAAGCAGGGCACGUGCG		13
	mol-miR164d	UGGAGAAGCAGGGCACGUGCA		373
mol-miR165	mol-miR165a	UCGGACCAGGCUUCAUCCCCC		368
mol-miR166	mol-miR166	CCGGACCAGGCUUCAUCCCAG		14
	mol-miR166b	UCGGACCAGGCUUCAUUCCCUU		2367
	mol-miR166e	GGACCAGGCUUCAUUCCCC		5541
	mol-miR166h	UCGGACCAGGCUUCAUUCCCGU		2923
	mol-miR166i	UCGGACCAGGCUUCAUUCCCCC		65243
	mol-miR166j	UCCGGACCAGGCUUCAUUCCC		546
	mol-miR166k	GGAUUGUUGUCUGGCUCGGUG	UCGGACCAGGCUUCAAUCCCU	NA	240
	mol-miR166u	UCUCGGACCAGGCUUCAUUC		3738
mol-miR167	mol-miR167	UCAAGCUGCCAGCAUGAUCUA	AGAUCAUGUGGCAGUUUCACC	27	514
	mol-miR167a	UGAAGCUGCCAGCAUGAUCUC		3881
	mol-miR167b	UGAAGCUGCCAGCAUGAUCUA		3834
	mol-miR167c	UGAAGCUGCCAGCAUGAUCUGG		3908
	mol-miR167c	UAAGCUGCCAGCAUGAUCUUG	UAGGUCAUGCUGGUAGUUUCACC	271	NA
	mol-miR167d	UGAAGCUGCCAGCAUGAUCUGA		4007
	mol-miR167h	UGAAGCUGCCAGCAUGAUCUUA	AGAUCAUGUGGCAGUUUCACC	18563	NA
	mol-miR167i	UCAUGCUGGCAGCUUCAACUGGU		586
mol-miR168	mol-miR168	AUUCAGUUGAUGCAAGGCGGGAUC		91
	mol-miR168a	UCGCUUGGUGCAGGUCGGGAC		165
	mol-miR168c	UCGCUUGGUGCAGGUCGGGAC	CCCGCCUUGCAUCAACUGAAU	NA	91
	mol-miR168d	UCGCUUGGUGCAGGUCGGGAA	CCCGCCUUGCAUCAACUGAAU	517	NA
mol-miR169	mol-miR169d	UAGCCAAGGAUGACUUGCCU		22
mol-miR170	mol-miR170	UAUUGGCCUGGUUCACUCAGA	UGAUUGAGCCGUGUCAAUAUC	NA	77
mol-miR171	mol-miR171a	UGAUUGAGCCGUGCCAAUAU		423
	mol-miR171c	UAUUGACGCGGUUCAAUUCGA	UGAUUGAGCCGUGCCAAUAUC	NA	503
	mol-miR171d		UUGAGCCGUGCCAAUAUCACG		410
mol-miR172	mol-miR172m	GGAGCAUCAUCAAGAUUCACA	AGAAUCUUGAUGAUGCUGCAG	NA	68
mol-miR319	mol-miR319	UUGGACUGAAGGGAGCUCCC		1204
	mol-miR319e	UUUGGACUGAAGGGAGCUCCU		2843
	mol-miR319g	UUGGACUGAAGGGAGCUCCUUC		1240
mol-miR390	mol-miR390a	AAGCUCAGGAGGGAUAGCGCC	CGCUAUCCAUCCUGAGUUUCA	1128	82
	mol-miR390d	AAGCUCAGGAGGGAUAGCGCC	CGCUAUCCAUCCUGAGUUUUA	NA	15
	mol-miR390e	AGCUCAGGAGGGAUAGCGCC	CGCUAUCUAUCCUGAGCUCCA	203	NA
mol-miR393	mol-miR393a	CAUCCAAAGGGAUCGCAUUGA		591
	mol-miR393b	UCCAAAGGGAUCGCAUUGAUC		1730
	mol-miR393c	UCCAAAGGGAUCGCAUUGAUCU	AUCAGUGCAAUCCCUUUGGAAU	11813	NA
	mol-miR393h	UUCCAAAGGGAUCGCAUUGAUC		9974
mol-miR394	mol-miR394b	UUGGCAUUCUGUCCACCUCC	CUGUUGGUCUCUCUUUGUAA	1147	NA
mol-miR395	mol-miR395a	CUGAAGUGUUUGGGGGAACUC		84
	mol-miR395d	UGAAGUGUUUGGGGGAACUUU		26
	mol-miR395g	UUGAAGUGUUUGGGGGAACUC		43
	mol-miR395h	AUGAAGUGUUUGGGGGAACUU		26
mol-miR396	mol-miR396a	UUCCACAGCUUUCUUGAACGU		265
	mol-miR396c	UUCCACAGCUUUCUUGAACUU		7917
	mol-miR396e	UUCCACAGGCUUUCUUGAACUG		188
	mol-miR396g	UCCCACAGCUUUAUUGAACUG	GUUCAAGAAAGCUGUGGAAGA	12	265
	mol-miR396h	UCCACAGCUUUCUUGAACUG		419
mol-miR397	mol-miR397a	UCAUUGAGUGCAGCGUUGAUG		529
mol-miR398	mol-miR398a	GGGUUGAUUUGAGAACAUAUG	UAUGUUCUCAGGUCGCCCCUG	NA	23
	mol-miR398c	UGUGUUCUCAGGUCGCCCCUG		3285
	mol-miR398f	GGUGUUCUCAGGUCGCCCCUG		115
mol-miR399	mol-miR399a	CGCCAAAGGAGAGUUGCCCUU		119
	mol-miR399d	UGCCAAAGGAGAGUUGCCCUU		76
mol-miR403	mol-miR403	UUAGAUUCACGCACAAACUCG		5277
	mol-miR403a	UUAGAUUCACGCACAAACUUG		324
mol-miR408	mol-miR408	CAGGGAUGAGGCAGAGCAUGG	CUGCACUGCCUCUUCCCUGGC	NA	27
mol-miR530	mol-miR530	UCUGCAUUUGCACCUGCACCU		950
mol-miR535	mol-miR535a	UGACAACGAGAGAGAGCACGC		347
mol-miR827	mol-miR827	UUUGUUGAUUGACAUCUAUAC	UUAGAUGACCAUCAACAAACG	22	NA
mol-miR858	mol-miR858b	UUCGUUGUCUGUUCGACCUUG		34
mol-miR894	mol-miR894	CGUUUCACGUCGGGUUCACC		396
mol-miR1310	mol-miR1310	AGGCAUCGGGGGCGCAACGCCC		1170
mol-miR1511	mol-miR1511	CGUGGUAUCAGAGUCAUGUUA	ACCUGGCUCUGAUACCAUAAC	1279	NA
mol-miR1515	mol-miR1515	UCAUUUUUGCGUGCAAUGAUCC		61
mol-miR3711	mol-miR3711	UGGCGCUAGAAGGAGGGCCU		271
mol-miR5139	mol-miR5139	AAACCUGGCUCUGAUACCA		1612
mol-miR5559	mol-miR5559	UACUUGGUGAAUUGUUGGAUC		322
mol-miR6300	mol-miR6300	GUCGUUGUAGUAUAGUGG		10523
mol-miR6478	mol-miR6478	CCGACCUUAGCUCAGUUGGUG		8520
mol-miR7751	mol-miR7751	AUCUUCCUCGUGGACAAGCGGUAG	UUUGGUGCACCCGGCUGGAGAUGG	NA	1683
mol-miR8155	mol-miR8155	UAACCUGGCUCUGAUACCA		1612

The most abundant miRNA families were miR166, miR156 and miR167 with eight members each. Among the other families, in miR159, seven members, and in miR396 five members were present. The remaining miRNA families had less than five members. As reported in Fig 2, the sequence length distribution had two major peaks at 21 and 22nts with high levels of reads from 20 to 22 nucleotides. Low levels of 24 nucleotide fragments could be due to the high stringent filters applied.

Fig 2. Length distribution of miRNA populations.

Sequence length distributions of known and unknown miRNAs in M. oleifera seeds.

Predicting uncharacterized M. oleifera miRNAs

All data set was analysed by BLASTn and 21,065 sequences were identified as available for novel miRNA identification with miReader software. To avoid spurious contamination of mRNA fragments or sequencing artifacts, this filtering process was forced to discard all reads shorter than 18nts nucleotides. The length distribution of the resulted sequences is shown in Fig 2. The distribution had a peak at 18 nts decreasing as the sequence length increases. The processed set of sequences was sub-sequentially provided to miReader and, after the analysis, only two uncharacterized miRNA duplexes were predicted as novel uncharacterized miRNAs (Table 2). This result was not surprising since only the non-conserved miRNAs are normally expressed at low level, only in specific cell-types or under specific organ development and growth conditions [35].

Table 2. Putative novel miRNAs list.

miRNA members	miRNA sequence	miRNA* sequence	Read Counts
			miR	miR*
mol-miR1p	CCGUCUCGCCCGGACCCUG	CGACGCGGAUCGCGACGG	12	99
mol-miR2p	CUAUACCCGGCCGUUGGGGC	ACCGCAUAGCGCAGUGGAU	129	53

Identifying known miRNA conservation rates among plant species

In order to evaluate the conservation rate for the M. oleifera known miRNAs within the plant kingdom an in-house ruby script was used. In Table 3, the top 20 conserved miRNAs together with their conservation rates and their abundance in M. oleifera seeds are reported.

Table 3. Most highly conserved miRNAs among selected plants.

miRNA name	Sequence	Conservation Rate	Read Counts
mol-miR166i	UCGGACCAGGCUUCAUUCCCCC	147	65,243
mol-miR156	CUGACAGAAGAGAGUGAGCAC	136	2,459
mol-miR160h	UGCCUGGCUCCCUGUAUGCCAUU	91	25
mol-miR395a	CUGAAGUGUUUGGGGGAACUC	90	84
mol-miR171c-3p	UGAUUGAGCCGUGCCAAUAUC	77	503
mol-miR164d	UGGAGAAGCAGGGCACGUGCA	73	373
mol-miR167b	UGAAGCUGCCAGCAUGAUCUA	66	3,834
mol-miR157a-5p	UUGACAGAAGAUAGAGAGCAC	63	316
mol-miR390a-5p	AAGCUCAGGAGGGAUAGCGCC	54	1,128
mol-miR169d	UAGCCAAGGAUGACUUGCCU	51	22
mol-miR394b-5p	UUGGCAUUCUGUCCACCUCC	44	1,147
mol-miR396c	UUCCACAGCUUUCUUGAACUU	40	7,917
mol-miR167d	UGAAGCUGCCAGCAUGAUCUGA	38	4.007
mol-miR162	UCGAUAAACCUCUGCAUCCAG	34	158
mol-miR319e	UUUGGACUGAAGGGAGCUCCU	33	2,843
mol-miR403	UUAGAUUCACGCACAAACUCG	31	5,277
mol-miR171d	UUGAGCCGUGCCAAUAUCACG	30	410
mol-miR168d-5p	UCGCUUGGUGCAGGUCGGGAA	25	517
mol-miR393b	UCCAAAGGGAUCGCAUUGAUC	24	1,730
mol-miR159a	UUUGGAUUGAAGGGAGCUCUA	23	11,908

Moreover, we drew a plot for better understanding the relationship between miRNA conservation rate and abundance in the seed tissues of Moringa. A logarithmic scale was used to obtain a linear plot since the range of abundance values was too high. As shown in Fig 3, mol-miR166i was the most conserved miRNA throughout plant species and the most abundant miRNA in Moringa seed tissues.

Fig 3. Correlation analysis.

Correlation analysis between the conservation rate through plants and abundance in M. oleifera samples.

Validating expression of conserved miRNAs among the organ of M. oleifera and crop plants species

To validate the representative expression patterns of the miRNAs, we performed qRT-PCR analysis of eight conserved miRNAs on different organ tissues of M. oleifera and on three crop plant species. The data collected demonstrated that the expression patterns were similar between the two analytical tools (Illumina sequencing and qRT-PCR) for six of the eight miRNAs, whereas mol-miR156f-5p showed different expression pattern as detected between the two molecular tools (Fig 4).

Fig 4. Quantitative RT-PCR analysis of mature miRNAs in M. oleifera seeds.

A quantitative analysis of plant miRNAs in M. oleifera seeds by qRT-PCR. The levels of 8 plant miRNAs detected by Illumina sequencing and confirmed by qRT-PCR. All qRT-PCR reactions were prepared in triplicate for each sample.

As illustrated in Fig 5, mol-miR168d-5p and mol-miR156f-5p were more abundant than other miRNAs in seed, leaf and root tissues. Vice versa, mol-miR164c showed higher expression levels in stem tissues. However, apart from mol-miR164c other miRNAs detected by qRT-PCR had higher expression in seeds compared to the other organ tissues. Similar results were obtained by assessing conserved mol-miR168d-5p, mol-miR166j and mol-miR156t in multiple plant tissues. As shown (Fig 6A) mol-miR168d-5p was the most abundant among well-expressed miRNAs, except for M. sativa sprout and O. europaea leaf. Moreover, when comparing mol-miR156t expression levels in different tissues, including zygotic embryo, and in seeds treated in different way, we found that it was mainly expressed in dry seed tissues rather than other tissues (Fig 6B).

Fig 5. Quantitative RT-PCR analysis of mature miRNAs in M. oleifera different tissues.

The relative levels of 6 plant miRNAs detected by qRT-PCR in different M. oleifera tissues. The expression level of miR390d-3p was set as control and taken as 1, and expression level in all other miRNAs was quantified relative to it. MiR159 was used as an endogenous control. Each value represents the mean of three different determination. Significant differences at P<0.05 (One Way ANOVA) between miRNAs are indicated with different letters.

Fig 6. Quantitative RT-PCR analysis of mature miRNAs in different edible plants and in different M. oleifera tissues.

(A) Quantitative analysis of plant mol-miR168d-5p, mol-miR166j and mol-miR156t in different edible plants. (B) Quantitative analysis of mol-miR156t in different tissue and in different seed conditions. Data are reported as the mean of three different experiments. Significant differences at P<0.05 (One Way ANOVA) between samples are indicated with asterisks or line.

Predicting target genes for conserved and uncharacterized miRNAs

The system of prediction analysis was forced to select the five best predictions for each miRNA, and from the resulting data, 48 putative target genes were identified as potentially regulated by known miRNAs (Table 4).

Table 4. List of target genes for known M. oleifera miRNAs.

miRNA name	Target Gene	Entrez ID	Gene Aligned Sequence	Mechanism of regulation
mol-miR393c-5p	AFB3	837838	AACAAUGCGAUCCCUUUGGA	Cleavage
mol-miR393c-5p	AFB2	822296	AACAAUGCGAUCCCUUUGGA	Cleavage
mol-miR156	SPL3	817948	UGCUUACUCUCUUCUGUCAG	Cleavage
mol-miR156q	SPL10	839626	AGUGCUCUCUCUCUUCUGUCA	Cleavage
mol-miR156j	SPL11	839625	GUGCUCUCUCUCUUCUGUCAAC	Cleavage
mol-miR156j	SPL10	839626	GUGCUCUCUCUCUUCUGUCAAC	Cleavage
mol-miR159a	MYB101	817807	UAGAGCUUCCUUCAAACCAAA	Cleavage
mol-miR159a	DUO1	825217	UGGAGCUCCAUUCGAUCCAAA	Cleavage
mol-miR159a	AtMYB104	817236	UGGAGCUCCCUUCAUUCCAAG	Cleavage
mol-miR397a	LAC2	817462	AUCAAUGCUGCACUCAAUGA	Cleavage
mol-miR397a	IRX12	818386	GUCAACGCUGCACUUAAUGA	Cleavage
mol-miR397a	LAC17	836124	AUCAAUGCUGCACUUAAUGA	Cleavage
mol-miR166j	PHV	839928	GGGAUGAAGCCUGGUCCGGA	Cleavage
mol-miR393a	AFB3	837838	CAAUGCGAUCCCUUUGGAUG	Cleavage
mol-miR393a	TIR1	825473	CAAUGCGAUCCCUUUGGAUG	Cleavage
mol-miR393a	AFB2	822296	CAAUGCGAUCCCUUUGGAUG	Cleavage
mol-miR393a	GRH1	828045	CCAUGCGAUCCCUUUGGAUG	Cleavage
mol-miR171a	HAM1	836582	AUAUUGGCGCGGCUCAAUCA	Cleavage
mol-miR171a	HAM2	830834	AUAUUGGCGCGGCUCAAUCA	Cleavage
mol-miR171a	HAM3	828208	AUAUUGGCGCGGCUCAAUCA	Cleavage
mol-miR156t	SPL10	839626	GUGCUCUCUCUCUUCUGUCAA	Cleavage
mol-miR156t	SPL2	834345	GUGCUCUCUCUCUUCUGUCAA	Cleavage
mol-miR396e	AtGRF4	824457	CCGUUCAAGAAAGCCUGUGGAA	Cleavage
mol-miR396e	AtGRF3	818213	CCGUUCAAGAAAGCCUGUGGAA	Cleavage
mol-miR396e	AtGRF1	816815	GUUCAAGAAAGCCUGUGGAA	Cleavage
mol-miR396e	AtGRF2	829930	GUUCAAGAAAGCCUGUGGAA	Cleavage
mol-miR396e	AtGRF9	819156	GUUCAAGAAAGCUUGUGGAA	Cleavage
mol-miR172m-3p	AP2	829845	CUGCAGCAUCAUCAGGAUUCU	Cleavage
mol-miR172m-3p	TOE2	836134	UGCAGCAUCAUCAGGAUUCU	Cleavage
mol-miR156h-5p	SPL10	839626	GUGCUCUCUCUCUUCUGUCA	Translation
mol-miR156h-5p	SPL13A	21393429	GUGCUCUCUCUCUUCUGUCA	Translation
mol-miR156h-5p	SPL2	834345	GUGCUCUCUCUCUUCUGUCA	Translation
mol-miR156f-5p	SPL3	817948	UGCUUACUCUCUUCUGUCAG	Cleavage
mol-miR160h	ARF17	844120	UGGCAUGCAGGGAGCCAGGCA	Cleavage
mol-miR160h	ARF10	817382	GGAAUACAGGGAGCCAGGCA	Cleavage
mol-miR827-5p	NLA	839559	UGUUUGUUGAUGGUCAUCUAA	Cleavage
mol-miR395g	AST68	830882	AGUUCUCCCAAACACUUCAA	Cleavage
mol-miR395h	APS4	834400	GAGUUCCUCCAAACACUUCAU	Cleavage
mol-miR395d	APS4	834400	AGAGUUCCUCCAAACACUUCA	Cleavage
mol-miR395d	AST68	830882	AAGUUCUCCCAAACACUUCA	Cleavage
mol-miR166	PHV	839928	UUGGGAUGAAGCCUGGUCCGG	Cleavage
mol-miR166	PHB	818036	UUGGGAUGAAGCCUGGUCCGG	Cleavage
mol-miR157a-5p	SPL10	839626	GUGCUCUCUCUCUUCUGUCA	Translation
mol-miR157a-5p	SPL13A	21393429	GUGCUCUCUCUCUUCUGUCA	Translation
mol-miR157a-5p	SPL2	834345	GUGCUCUCUCUCUUCUGUCA	Translation
mol-miR164c	CUC1	820748	GCACGUGUCCUGUUUCUCCA	Cleavage
mol-miR164c	CUC2	835478	GCACGUGUCCUGUUUCUCCA	Cleavage
mol-miR164a	ANAC080	830661	UUUACGUGCCCUGCUUCUCCA	Cleavage

Despite research of targets for all putative novel miRNA, only 3 target genes were selected (Fig 7) that involve mol-miR2p-5p, the most abundant uncharacterized miRNA highlighted by the miReader algorithm.

Fig 7. List of target genes for putative novel M. oleifera miRNAs.

Enrichment analysis with gene ontology terms on both target gene sets highlighted the relevance of these genes for plant organisms. As shown in Fig 8, most genes regulated by known miRNAs are involved in biological pathways localized to root, meristem and seed. Furthermore, these miRNAs participate in fundamental processes such as the development of anatomical (root, leaf) and reproductive structure, maintenance (flower, meristem, pollen and stamen), embryonic, and post-embryonic development. Genes putatively regulated by unknown miRNAs are involved in cellular metabolism of macromolecules, in particular macromolecules affecting protein transport and biosynthesis.

Fig 8. Known miRNA target functions.

The miRNA target genes were classified according to the biological process.

Bioinformatics prediction of human gene targets for M. oleifera miRNAs

To investigate whether M. oleifera miRNAs might regulate human gene expression we used MirCompare software to search homologous human miRNAs. The most and conserved mol-miR166i resulted functional homologies with hsa-miR6503-3p (Table 5) that is involved in regulating inflammation [36], and mol-miR393c was homologous to hsa-miR548ah-5p that is involved in immune tolerance [37]. Further, mol-mir168a showed sequence homology with hsa-miR579, a human miRNA that normally regulates TNFα expression during endotoxin tolerance [38].

Table 5. Comparing M. oleifera miRNAs with their human counterparts using MirCompare software.

Human miRNA name	M. oleifera miRNA name	r-value	Sequence alignment details
hsa-miR6503-3p	mol-miR166i	0.67	-GGAC-AGG-U-CA--CC-CC
hsa-miR548ah-5p	mol-miR393c	0.65	-AAAG-GAU-GCA-UG-U--
hsa-miR3940-5p	mol-miR159a	0.65	-UGG-UUG--G-G-GCUCU-
hsa-miR579	mol-miR168a	0.57	UC--UUGGU--A---CG-GA-
hsa-miR4534	mol-miR159a	0.71	GGAU-GA-G-G-G-UCU
hsa-miR1306-3p	mol-miR6478	0.78	AC-UU-GCUC-G-UGGUG
hsa-miR4703-3p	mol-miR6300	0.72	GU-GUUGUA-U-UA-UG-
hsa-miR5008-5p	mol-miR6300	0.67	G-C-UUG--G-A-AGUGG
hsa-miR4273	mol-miR398c	0.67	GUGUUCUC-G-U-G-C--

COMIR software [33] predicted human genes that could be regulated by M. oleifera miRNAs. A subset of genes relevant to leukemia and acute myeloid (Table 6), which have key roles in the regulating cellular pathways such as apoptosis, cell cycle and protein degradation, have been found. Based on these results we identified mol-miR168a homologous to hsa-miR579, a human miRNA with many target genes including SIRT1. The complete report of predicted genes ranked by COMIR score (always upper than 0.9) is given in S2 Table.

Table 6. Predicting gene target in humans using COMIR software.

miRNA name	Ensemble Gene ID	Entrez ID	Target Gene	COMIR Score
mol-miR166i	ENSG00000082701	2932	GSK3B	0.9019
	ENSG00000064393	28996	HIPK2	0.9076
	ENSG00000156113	3778	KCNMA1	0.9038
	ENSG00000263162	8924 100653292	HERC2	0.9074
	ENSG00000169213	5865	RAB3B	0.9075
	ENSG00000171105	3643	INSR	0.9015
	ENSG00000078142	5289	PIK3C3	0.9044
	ENSG00000263162	8924 100653292	HERC2	0.9074
	ENSG00000119547	9480	ONECUT2	0.9075
mol-miR393c	ENSG00000178662	80034	CSRNP3	0.9075
	ENSG00000102908	10725	NFAT5	0.9074
	ENSG00000128585	4289	MKLN1	0.9074
	ENSG00000145907	10146	G3BP1	0.9074
	ENSG00000009413	5980	REV3L	0.9072
	ENSG00000010244	7756	ZNF207	0.9072
	ENSG00000100354	23112	TNRC6B	0.9072
	ENSG00000143190	5451	pou2f1	0.9072
	ENSG00000173611	286205	Scai	0.9072
	ENSG00000100731	22990	pcnx	0.9071
mol-miR159a	ENSG00000171435	283455	KSR2	0.9076
	ENSG00000119547	9480	ONECUT2	0.9076
	ENSG00000153721	154043	CNKSR3	0.9074
	ENSG00000261115	1.01E+08	TMEM178B	0.9074
	ENSG00000158445	3745	KCNB1	0.9072
	ENSG00000077157	4660	PPP1R12B	0.907
	ENSG00000196090	11122	PTPRT	0.907
	ENSG00000055609	58508	KMT2C	0.9068
	ENSG00000132549	157680	VPS13B	0.9065
	ENSG00000148019	84131	CEP78	0.9062
mol-miR168a	ENSG00000096717	23411	SIRT1	0.9087
	ENSG00000178562	940	CD28	0.9099
	ENSG00000134352	3572	IL6ST	0.9237
	ENSG00000118689	2309	FOXO3	0.9190
	ENSG00000106799	7046	TGFBR1	0.9115
	ENSG00000169967	10746	MAP3K2	0.9118
	ENSG00000175595	2072	ERCC4	0.9110
	ENSG00000149311	472	ATM	0.9187
	ENSG00000149948	8091	HMGA2	0.9119
	ENSG00000007372	5080	PAX6	0.9189
mol-miR6478	ENSG00000134313	57498	KIDINS220	0.9061
	ENSG00000106261	7586	ZKSCAN1	0.9058
	ENSG00000134909	9743	ARHGAP32	0.9055
	ENSG00000136709	55339 84826	WDR33	0.9055
	ENSG00000112706	3617	IMPG1	0.9051
	ENSG00000107331	20	ABCA2	0.9049
	ENSG00000088808	23368	PPP1R13B	0.9029
	ENSG00000167654	85300	ATCAY	0.9028
	ENSG00000189339	728661	SLC35E2B	0.9026
	ENSG00000180370	5062	PAK2	0.9025
mol-miR6300	ENSG00000178567	9852	EPM2AIP1	0.9076
	ENSG00000213699	54978	SLC35F6	0.9075
	ENSG00000197818	23315	SLC9A8	0.9072
	ENSG00000183751	10607	TBL3	0.9071
	ENSG00000206190	57194	ATP10A	0.9071
	ENSG00000166206	2562	GABRB3	0.9071
	ENSG00000198000	55035	NOL8	0.907
	ENSG00000172380	55970	GNG12	0.907
	ENSG00000152443	284309	ZNF776	0.9067
	ENSG00000133703	3845	KRAS	0.9066
mol-miR398c	ENSG00000055609	58508	KMT2C	0.9076
	ENSG00000164684	619279	ZNF704	0.9076
	ENSG00000145012	4026	LPP	0.9076
	ENSG00000151914	667	DST	0.9075
	ENSG00000158258	64084	CLSTN2	0.9074
	ENSG00000110436	6506	SLC1A2	0.9073
	ENSG00000064393	28996	HIPK2	0.9073
	ENSG00000118482	23469	PHF3	0.907
	ENSG00000143970	55252	ASXL2	0.907
	ENSG00000135968	9648	GCC2	0.907

Synthetic mol-miR168a transfection and protein modulation

In these preliminary studies, we tested whether an exogenous miRNA derived from M. oleifera might be functional in human cell line and we verified its ability to inhibit post transcriptional mRNA expression. In particular, transfecting Hepatoma cell line G2 with the synthetic FITC mol-miR168a was run. By 72 hours after transfection with FITC (Fig 9A, green cells, panel b), synthetic mol-miR168a increased the number of fluorescent cells by EVOS FLoid cell imaging station analysis (about 60–70% of HEPG2 cells resulted significantly and positively transfected). The transfection of synthetic mol-miR168a, a plant miRNA that show sequences homology with hsa-miR579, determined a significant decrease of SIRT1 protein level in comparison with HF and control samples (Fig 9B and 9C).

Fig 9. Experimental Validation of miRNA-mRNA human gene regulation.

Histograms represent the mean values and ± the SD values. A) Presence of green cells in HepG2 cell lines (panel b) 72hrs after transfection with the synthetic FITC mol-miR168a (EVOS FLoid cell imaging station, LifeTechnologies). Left panel: HepG2 cells transfected only with the lipofectamin (HF); right panel: HepG2 cells transfected with lipofectamin (HF) and synthetic FITC mol-miR168a. B) One representative experiment (out of three) of Western Blot analysis of SIRT-1 expression in HepG2 cells after synthetic mol-miR168a transfection; β-actin expression, run on the same gel, indicates that an equal amount of protein was loaded for each sample. C) Western blot analysis for each sample was quantified by densitometry analysis and values were expressed as OD-Bkg/mm²*10³. Histograms represent mean values ± S.D. from three independent experiments performed on HepG2 cells. Statistical comparison of means using t-Test provided the following results: control 72h versus HF, not significant (NS); synthetic mol-miR168a versus control and versus HF, p<0.001.

Discussion

Medicinal plants are studied to identify new therapeutic agents and understand their mechanisms of action against a variety of human diseases. Medicinal plant compounds are known to have high biocompatibility, low toxicity, and potential biological activity [39]. In particular, M. oleifera is commonly known and used for its health benefits [2]. For centuries and in many cultures around the world, M. oleifera has been used to treat human diseases [1] and is an example of traditional medicine that is increasingly popular among African countries, due in part to their poor economic conditions. A recent collaboration between universities in Italy and Cameroon has the objective of investigating the anti-oxidant and anti-tumor properties of M. oleifera [4]. The incredible Moringa’s usage as medicinal, claimed by real-life experience, is now slowly being confirmed by scientific experiences.

Here we address a new aspect of plant biology that may have substantial impact on understanding of medicinal activity. The miRNA expressed in plants may comprise an independent category of medicinal agents capable of providing beneficial effects for human consumers of valuable plants.

Recently, Shahzad and collaborators [3] reported a study of DNA markers for genetic diversity and population structure in worldwide collections of M. oleifera. However, no study on the breadth of miRNA has yet been reported for M. oleifera, therefore, the knowledge about molecular mechanisms for compounds produced by M. oleifera is still limited. Here, we describe the identification and characterization of conserved and possibly novel miRNAs from seed of M. oleifera plants grown in Cameroon. We have used bioinformatics tools to understand their potential roles in diverse biological processes and their possible role in human gene regulation.

Since the discovery of sRNAs (miRNAs and siRNAs) as regulators of gene expression in C. elegans [40] and, more recently, the discovery of plant miRNAs [41–42], the study on miRNAs has become an important and integral topic in functional genomic research.

In plants, miRNAs regulate gene expression at the post-transcriptional level by degrading or repressing translation of target mRNAs [43]. A high number of experimental and computational studies have indicated that mature miRNAs are evolutionarily conserved in plants. miRNA-mediated gene regulation has an ancient phylogenetic origin and plays an important regulatory role in physiological processes [43], many aspects of plant growth, development and environmental adaptability.

High-throughput sequencing based on Illumina technology has become a good approach for identification and expression analysis of miRNAs in several plant species, like Arabidopsis [44] and other plant species [45–46], although the presence of biases introduced during the construction of sRNA libraries, primarily derived from the adaptor ligation steps [47], requires a carefully attention.

In this study, we identified 94 conserved miRNAs belonging to 40 families, two uncharacterized miRNA duplexes with 48 and 3 targets gene for conserved and uncharacterized miRNAs respectively. As in earlier studies [45–46], the majority of highly conserved miRNAs from Moringa-seed that were predicted by our analysis resulted to be evolutionarily conserved across plant species and to have high levels of expression. For example, miR166, miR393, miR167, miR396, miR159 and miR156 families are well conserved among other plant species [48] and have a fundamental role in plant biology. Other miRNA families, such as miR408 or miR1515 were present at lower abundance. mol-miR166 families had the highest number of reads (80,612); in particular, mol-miR166i and mol-miR166 were the most and the least abundant miRNAs in these families, respectively. Results indicate that different members of the same miRNA family have differing levels of expression.

The most and least conserved miRNA families may have evolved to play different roles in plant biology. For example, miR156, miR159, miR166 and miR160 target SPL, MYB, PHV and ARF genes respectively. These transcription factors are important to plant growth and development. The miR159 was reported to target MYB101 and MYB33 transcription factors, which are positive regulators of ABA signaling during Arabidopsis seed germination. Indeed, miR159 may play a role in seed germination [49]. MiR156 plays crucial role in the control of juvenile-to-adult transition in plants by targeting the SQUAMOSA PROMOTER BINDING PROTEIN LIKE (SPL) plant-specific transcription factors. SPLs affect processes like leaf development, shoot maturation, phase change and flowering in plants [50–52]. A recent study showed that miR156 regulates shoot regenerative capacity and a gradual increase in miR156 leads to a decline in shoot regenerative capacity for old plants [53]. Auxin response factors were found as a predicted target of miR160, while research studies assessed that this miRNA targets ARF10, ARF16 and ARF17 and regulates various aspects of plant development in Arabidopsis [54]. MiR166/165 is an example of well-studied plant miRNAs implicated in various aspects of plant development. The miR166/165 negatively regulates its targets Class III Homeodomain Leucine-Zipper (HD-ZIPIII) transcription factors that in turn regulate the polarity establishment in leaves and vasculature and radial patterning of root. The majority of HD-ZIPIII gene family members consisting of PHABULOSA (PHB), PHAVOLUTA (PHV), REVOLUTA (REV), ATHB8, and ATHB15, are conserved in several land plants including bryophytes, lycopods and seed plants [55–58]. MiR393 is a conserved miRNA family discovered in many plants [41]. In A. thaliana, four F-box genes TIR1 (TRANSPORT INHIBITOR RESPONSE PROTEIN1), AFB1, AFB2 and AFB3 (AUXIN SIGNALING F-BOX) were validated as miR393 targets [59]. In rice, overexpression of miR393 negatively regulates mRNAs of TIR1 and AFB2 [60]. TIR1 and AFB2 interact with IAA (INDOLE-3-ACETIC ACID) proteins, probably releasing the activities of ARFs (AUXIN RESPONSE FACTORS) and increasing resistance to auxin. The change in auxin response consequently affects diverse aspects of plant growth and development, such as flag leaf inclination, primary root growth, crown root initiation and seed development.

In this study, three possible novel miRNA targets were found: mol-miR2p-5p could affect target genes involved in macromolecule metabolism, in particular cellular protein transport and biosynthesis. In Arabidopsis thaliana, the ATPTR1 gene seems to be involved in long-distance transport of di- and tri-peptides during seed germination [61] while DUF579 affects xylan biosynthesis and modulates cell wall biosynthesis [62]. Further research on possible targets in other plants may provide important evidence to facilitate the understanding of these novel miRNA functions. Additional studies into novel M. oleifera miRNAs may shed light on their roles in M. oleifera biological processes.

The final phase of our bioinformatics analysis focused on the potential for human gene regulation by the most conserved M. oleifera miRNAs. The possibility of identifying plant miRNAs able to regulate human genome expression may be highly important in future studies on the nutritional value and medical usage of food. The combined use of MirCompare and COMIR software on the massive collection of data has identified a small number of human genes that might be regulated by M. oleifera miRNAs. These gene targets include cell-cycle regulation and signaling through the p53 pathway; genes related to some classes of cancers including leukemia, acute myeloid and lipoma. For instance, Sirtuins have important roles in cell cycle, apoptosis, metabolic regulation and inflammation. The human genome encodes seven Sirtuin isoforms SIRT1-SIRT7 with varying intracellular distribution; a number of studies reported evidence for their roles in a spectrum of disease like cancer, diabetes, obesity and neurodegenerative diseases [63]. Recent evidence suggests that genomic stability requires cooperation of p53 and SIRT1 [64]. Our transfection experiments showed that mol-miR168a identified by MirCompare-COMIR software inhibited translation of SIRT1 mRNA in cancer cells. We focused our attention on mol-miR168a because this is the first plant miRNA involved in cross-kingdom activity (osa-miR168a shown high sequence homology with mol-miR168a) [14] and it has functional homology with hsa-miR579, a putative regulator of the SIRT1 gene.

In conclusion, we have identified a population of Moringa-specific miRNAs that could help our understanding of the regulatory role of miRNAs in this plant. Our results demonstrate that the differentially expressed miRNAs and predictions for their target genes provides a basis for further understanding M. oleifera seed miRNAs and biological processes in which they are involved. Further studies are necessary to search for more miRNAs that are novel and to validate their targets by expression analysis during seed development stage.

Supporting Information

S1 Fig. Sequence analysis workflow.

Schematization of the process for the identification of known and novel miRNAs.

(TIF)

S2 Fig. Workflow of miRNA target prediction.

Plant targets were predicted using PsRNA Target. Enrichment analysis for all the predicted targets were conducted using Plant GSEA. MirCompare was used to predict cross-kingdom interaction targets in human.

(TIF)

S1 File. Quality control analysis before the removal of the sequencing adapter.

Quality scores across all bases (Figure A in S1 File). Quality score distribution over all sequences (Figure B in S1 FIle). Percentage of adapter sequence (Figure C in S1 File).

(DOCX)

S2 File. Quality control analysis after the removal of the sequencing adapter.

Quality scores across all bases (Figure A in S1 File). Quality score distribution over all sequences (Figure B in S1 File). Percentage of adapter sequence (Figure C in S1 File).

(DOCX)

S1 Table. Known microRNAs in M. oleifera juvenile seed, with additional detailed information.

(DOCX)

S2 Table. Complete prediction report of gene target in humans, using COMIR software.

(DOCX)

Data Availability

Data has been deposited in NCBI/GEO public database under accession number GSE70423.

Funding Statement

The authors received no specific funding for this work. Mir-Nat s. r. l. provided support in the form of salaries for authors SP, VC and AG and provided support in the form of research materials, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.