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AIMS Bioengineering

2021, Volume 8, Issue 1: 36-51. doi: 10.3934/bioeng.2021005
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Research article

Evaluation of a radial basis function node refinement algorithm applied to bioheat transfer modeling

  • Department of Engineering and Management of Process and Systems, IETEC – Institute of Technological Education, Belo Horizonte, Minas Gerais, 30140-138, Brazil.
  • Received: 30 October 2020 Accepted: 17 December 2020 Published: 23 December 2020

  • Many bioheat transfer problems involve linear/non-linear equations with non-linear or time-dependent boundary conditions. For heat transfer problems, the presence of time and space-dependent functions under Neumann and Mixed type boundary conditions characterize trivial applications in bioengineering, such as thermotherapies, laser surgeries, and burn studies. This greatly increases the complexity of the numerical solution in several problems, requiring fast and accurate numerical solutions. This paper has a main objective evaluate an adaptive mesh refinement radial basis function method strategy for the classical Penne's bioheat transfer modeling. Our numerical results had errors of ~0.1% compared to analytical solutions. Thus, the proposed methodology is accurate and has a low computational cost. For step function heating, two RBF shape parameters were applied, again achieving excellent results. The distributions of the nodes in the solution domain show that the primary source of error in the numerical solutions came from the boundary conditions. This finding should arouse the interest of engineers and scientists in the development of new strategies for problems involving boundary conditions with periodic functions.

    Citation: Rafael Pinheiro Amantéa, Robspierre de Carvalho, Luiz Otávio Barbosa. Evaluation of a radial basis function node refinement algorithm applied to bioheat transfer modeling[J]. AIMS Bioengineering, 2021, 8(1): 36-51. doi: 10.3934/bioeng.2021005

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  • Many bioheat transfer problems involve linear/non-linear equations with non-linear or time-dependent boundary conditions. For heat transfer problems, the presence of time and space-dependent functions under Neumann and Mixed type boundary conditions characterize trivial applications in bioengineering, such as thermotherapies, laser surgeries, and burn studies. This greatly increases the complexity of the numerical solution in several problems, requiring fast and accurate numerical solutions. This paper has a main objective evaluate an adaptive mesh refinement radial basis function method strategy for the classical Penne's bioheat transfer modeling. Our numerical results had errors of ~0.1% compared to analytical solutions. Thus, the proposed methodology is accurate and has a low computational cost. For step function heating, two RBF shape parameters were applied, again achieving excellent results. The distributions of the nodes in the solution domain show that the primary source of error in the numerical solutions came from the boundary conditions. This finding should arouse the interest of engineers and scientists in the development of new strategies for problems involving boundary conditions with periodic functions.

    Nomenclature

    Symbol Description Unit
    θc Coarse node parameter -
    θr Refine node parameter -
    ωb Blood perfusion m3/s/m3
    Ø Radial basis function -
    Δt Numerical time step s
    c Specific heat of tissue J/(kg·K)
    cb Specific heat of blood J/(kg·K)
    h0 Heat convection coeficient W/(m2·K)
    k Thermal conductivity of tissue W/(m·K)
    L Distance between skin surface and body core m
    Qm Metabolic rate of tissue W/m3
    Qr Spatial heating W/m3
    r Euclidean distance -
    t Time s
    T Tissue temperature °C
    T0 Steady state temperature °C
    Ta Arterial temperature °C
    Tc Blood temperature °C
    Tf Fluid temperature °C
    x Spatial coordinate m
    α Thermal diffusivity m2/s
    ϵ Multiquadric shape parameter -
    λ Radial basis function interpolator -
    ρ Density of tissue Kg/m3
    ρb Density of blood Kg/m3

    1. Introduction

    MicroRNAs (miRNAs) are distinctive regulatory member of the small RNAs that regulate gene silencing at post-transcriptional level. Gene silencing by miRNAs is an important, advance and exciting area of present regulatory RNA research. They are endogenous, non-coding in nature and about 18 to 26 nucleotides (nt) in size. They are the negative regulator at post-transcriptional stage of gene regulation [1]. Initially, a self-folded stable hair-pin/stem-loop secondary structure termed as precursor-miRNAs (pre-miRNAs) is generate from long single strand RNA known as primary miRNA (pri-miRNA). Later the pre-miRNAs give rise a small sized (18–26nt) functional RNA known as mature miRNA. This mature miRNA is integrate into argonaute protein and advanced into the RNA induced silencing complex (RISC) [2,3]. The RISC complex having mature miRNA triggers post-transcriptional gene suppression of the messenger RNA (mRNA) either by inhibiting protein encoding or by activating mRNA degradation. This inhibition and degradation capability of the miRNA depends on the scale of complementarity between miRNA and its targeted mRNA [4]. In case of partial pairing between miRNAs and its mRNA target causes its inhibition. While, the complete pairing of miRNAs with it mRNA target causes the mRNAs degradation [1,5]. They participate as gene regulator in almost each and every life activity, such as growth and development, foreign genes suppression, signal transduction, environmental stresses and as a defense against the attacking microbes in various living organisms [1,6,7,8,9]. Majority of the miRNAs show conserved behavior among various plant species. Many researchers, based on this conserved nature, have identified a huge number of miRNAs using comparative genomic approaches in a wide range of plant species, including cowpea [10], Brassicanapus [11], Glycinemax [12], cotton species [13,14], Zeamays [15], tobacco [16], switch grass [17], Phaseolus [18], tomato [19], eggplant [20] and chilli [21]. These reports strongly suggest that comparative genomic strategies are valid, highly efficient, convenient, and economical-friendly methods to identify new miRNAs.

    Cowpea (Vigna unguiculata L.) is an important leguminous crop of Asia, Africa, Southern Europe and USA [22]. It is a good food due to the presence of carbohydrate and high protein contents. This makes it not only essential diet to the human, but also serve as fodder to livestock. Cowpea is also significant to grow under low soil fertility, heat and drought. It is a key constituent of low-input farming systems for farmers. Cowpea also play vital role in the nitrogen fixation which is necessary for the enhancement of soil productiveness [22,23]. Very little reports and data are available about the miRNAs in this important plant. According to the latest version of miRNA registry database (Version Rfam 21.0, released June, 2014) [24], only few miRNAs are available for cowpea. This situation demands to focus and profile new miRNAs and their targets in cowpea that will act as preliminary data to manage and understand the cowpea at molecular level.

    Consequently, a total of 46 new miRNAs belonging to 45 families in cowpea were identified. In this study, one miRNA gene was also found as pre-miRNA cluster (vun-mir4414). Furthermore, these newly identified miRNAs were also validated for their protein targets.

    2. Materials and methods

    2.1. Identification of raw sequences

    A similar methodology [15] with a little modification as described by Barozai MYK, et al. [13] was applied to profile the potential miRNAs from cowpea expressed sequence tags (ESTs). As reference miRNAs, a total of 4739 known plant miRNA sequences, both precursors and matures, were downloaded from the microRNA registry database (Version Rfam 21.0 released June, 2014) [24], and subjected to basic local alignment search tool (BLAST) for alignment against publicly available 187487 ESTs of cowpea from the dbEST (database of EST), release 130101 at http://blast.ncbi.nlm.nih.gov/Blast.cgi, using BLASTn program [25].

    2.2. Creation of single tone EST

    The repeated ESTs from the same gene were eliminated and a single tone EST per miRNA was produced by using BLASTn program against the cowpea EST database with default parameters [25].

    2.3. Elimination of coding sequences

    The initial potential miRNA sequences of cowpea, predicted by the mature source miRNAs, were checked for protein coding. The FASTA format of initial potential sequences were subjected against protein database at NCBI using BLASTX with default parameter [26] and the protein coding sequences were removed.

    2.4. Creation of hair-pen structures

    The initial potential candidate cowpea miRNA sequences, confirming as non-protein coding nature, having 0–4 mismatches with the reference miRNAs and representing single tone gene were subjected to generate hair-pen or secondary structures. Publicly available Zuker folding algorithm http://www.bioinfo.rpi.edu/applications/mfold/rna/form1.cgi, known as MFOLD (version 3.6) [27] was used to predict the secondary structures. The MFOLD parameters were adjusted same as published by various researchers for the identification of miRNAs in various plant and animal species [7,8,28]. For physical scrutinizing, the hair-pen structures either showing the lowest free energy ≤−18 kcal mol−1 or less than or equal to the lowest free energy of the reference miRNAs were preferred. The Ambros et al. [29] threshold values were applied as reference to finalize the potential miRNAs in cowpea. The stem regions of the stem-loop structures were checked and confirmed for the mature sequences with either at least 16 or equal to the reference miRNAs base pairing involved in Watson-Crick or G/U base pairing between the mature miRNA and the opposite strand (miRNA*).

    2.5. Convergence and phylogenetic analysis

    The convergence and phylogenetic analysis was carried out for the one of conserved cowpea miRNA (vun-mir398). Simply, the vun-mir398, for its conserved behavior in different plant species was checked for convergence and phylogenetic investigation. The vun-mir398 alignment was created with Glycine max (gma), Nicotiana tabacum (nta) and Cucumis melo (cme) by the publicly accessible web logo: a sequence logo generator and ClustalW to produce cladogram tree using neighbor joining clustering method respectively. The results were saved.

    2.6. Prediction of miRNAs targets

    Dual schemes were used to predict the potential targets for cowpea miRNAs. In the first scheme, the newly identified cowpea miRNAs were subjected to psRNATarget (http://bioinfo3.noble.org/psRNATarget), with default parameters [30]. The cowpea miRNAs that not produced potential targets through psRNATarget, were subjected to the second scheme as described by Barozai [31]. Briefly, the cowpea mature miRNA sequences were subjected as queries through BLASTn program. The parameters were adjusted as, database: reference mRNA sequences (refseq_rnat); organism: Vigna unguiculata (taxid:4072) and Program Selection: highly similar sequences (megablast). The mRNA sequences showing ≥75% query coverage were selected and further subjected to RNA hybrid—a miRNA target prediction tool [32]. Only targets, confirming stringent seed site located at either positions 2–7 and/or 8–13 from the 5′ end of the miRNAs along with the supplementary site and having minimum free energy (MFE) ≤−20 kcal mol−1 were selected. For more stringency, these targets were subjected to the NTNU microRNA target prediction tool available at http://tare.medisin.ntnu.no/mirna_target/search#results, to confirm the RNA hybrid results. These predicted targets were further analyzed through Gene Ontology (GO) on AmiGO website.

    3. Results and discussion

    3.1. The new cowpea miRNAs

    In order to identify and characterize the potential miRNAs in cowpea, a comparative genomic approach was applied using bioinformatics tools. This is in agreement with the previous reports [8,28,31] that the homology based search by applying comparative genomics is a valid and logical approach to find interesting findings in plants at genomic level. The current study resulted a total of 46 new conserved miRNAs from the analyses of 187487 cowpea ESTs using bioinformatics tools (Table 1). The 46 potential cowpea miRNAs belong to 45 families (vun-miR: 398,413,435,834,1512,1514,1525,1848,2095,2606,2609,2622,2630,2636,2657,2678,2950,3434,4351,4392,4408,4414 (cluster), 4992,4996,5012,5043,5215,5216,5219,5227,5241,5246,5255,5261,5280,5290,5298,5376,5561,5758,5770,6252,7696,8182,9748). The vun-miR4414 family is observed as cluster pre-miRNA. Available miRNAs literature revealed that all these 46 miRNAs are profiled for the first time in cowpea. In the light of the empirical formula for biogenesis and expression of the miRNAs suggested by Ambros et al. [29], these miRNAs are considered as a valid candidate after justifying the criteria B, C and D. According to Ambros et al. [29] only the criterion D is enough for homologous sequences to validate as potential miRNAs in other species. The present study is in agreement with the other research groups [21,33,34,35,36] where similarity based search by applying comparative genomics has produced novel and interesting findings in plants genomics.

    Table 1. The newly identified conserved cowpea miRNAs characterization. Cowpea miRNAs were characterized in terms of precursor miRNA length (PL), minimum free energy (MFE), mature sequence (MS), number of mismatches (NM) (represented in bold red and enlarged font size), mature sequence length (ML), source EST (SE), mature sequence arm (MSA), GC content percentage (GC%), SL = Strand Location and organ of expression (OE).
    vun miRNAs Ref. miRNAs PL MFE MS NM ML SE # MSA GC% SL OE
    vun-mir398 mtr-mir398a 131 −32.24 TGTGTTCTCAGGTCGCCCCTG 2 21 FF542932 5' 61.90 + leaves
    vun-mir413 ath-mir413 353 −88.55 TTAGTTTCTCTTGTTCTGCTT 2 21 FG940215 5' 33.33 + mixed
    vun-mir435 osa-mir435 347 −124.38 TTATGAGGCTTTGGAGTTGA 4 20 FG811172 3' 40.00 + mixed
    vun-mir834 ath-mir834 135 −52.95 TGGTAGCAGTGGCGGTGGTGG 3 21 FG822669 3' 66.66 − mixed
    vun-mir1512 gma-mir1512a 46 −10.60 CCTTTAAGAATTTCA-TTA-- 4 18 FG880488 3' 22.22 − mixed
    vun-mir1514 gma-mir1514 127 −31.70 TTCATTTCTAAAATAGGCATC 2 21 FF388166 5' 28.57 − root
    vun-mir1525 gma-mir1525 78 −14.10 GGGGTTAAATATGTTTTTAGT 3 21 FG845219 5' 28.57 + mixed
    vun-mir1848 osa-mir1848 77 −32.20 CGCTCGCCGGCGCGCGCGTCCA 2 22 FG920123 3' 86.36 + mixed
    vun-mir2095 osa-mir2095 57 −17.20 CTTCCATTTATGACATGTTT 3 20 FG838629 5' 30.00 − mixed
    vun-mir2606 mtr-mir2606a 69 −13.00 TTGAAGTGCTTGGTTCTCACT 4 21 FG931806 5' 42.85 + mixed
    vun-mir2609 mtr-mir2609a 70 −13.00 TTGAAGTGCTTGGTTCTCACT 4 21 FG931806 5' 42.85 + mixed
    vun-mir2622 mtr-mir2622 210 −36.85 CTTGTGTGCCATTGTGAGCTTA 3 22 FG900047 3' 42.85 − mixed
    vun-mir2630 mtr-mir2630a 114 −24.70 TGGTTTTGGTCTTTGGTTTTA 3 21 FF391380 5' 33.33 + root
    vun-mir2636 mtr-mit2636 191 −29.40 GGATGTTAGTGTGCTGAATAT 4 21 FG814033 5' 38.09 − mixed
    vun-mir2657 mtr-mir2657 156 −35.38 TTTTATTGTATTGATTTTGTTG 4 22 FG926034 5' 18.18 − mixed
    vun-mir2678 mtr-mir2678 136 −39.32 TAAAGTTGTTGCGCGTGTC 3 19 FF389500 3' 47.36 − root
    vun-mir2950 mes-mir2950 347 −83.20 TTCCATCTCTTGCAGACTGAA 2 21 FG872933 5' 42.85 − mixed
    vun-mir3434 ath-mir3434 78 −17.40 TGAGAGTATCAGCCATGAGA 2 20 FF392538 3' 45.00 − root
    vun-mir4351 gma-mir4351 148 −63.30 GTTAGGGTTCAGTTGGAGTTGG 3 22 FG936300 3' 50.00 − mixed
    vun-mir4392 gma-mir4392 306 −80.53 TCTGTGAGAACGTGATTTCGGA 3 22 FG857306 5' 45.45 + mixed
    vun-mir4408 gma-mir4408 66 −20.70 CAACAACATTGGATGAGTATAGGA 4 24 FG894682 3' 37.5 + mixed
    vun-mir4414a vun-mir4414b mtr-mir4414a 120 −42.20 AGCTGCTGACTCGTTGGTTCAATTCAACGATGCGGGAGCTGC 0 1 21 21 FF537171 5' 3' 52.38 57.14 + + leaves
    vun-mir4992 gma-mir4992 63 −21.20 CATCTAAGATGGTTTTTTTCAG 4 22 FG926352 3' 31.81 − mixed
    vun-mir4996 gma-mir4996 163 −49.83 TAGAAGTTACCCATGTTCTC 2 20 FF388735 3' 40.00 − root
    vun-mir5012 ath-mir5012 172 −43.44 TTTTGCTGCTCCGTGTGTTCC 3 21 FG809429 3' 52.38 + mixed
    vun-mir5043 gma-mir5043 125 −48.20 CTTCTCCTTCTCTGCACCACC 3 21 FG810406 5' 57.14 + mixed
    vun-mir5215 mtr-mir5215 181 −49.63 AGGAGGATGAGCTAGTTGATT 3 21 FG939979 5' 42.85 + mixed
    vun-mir5216 mtr-mir5216a 124 −27.58 TTGGGAGTGAAAAACAGTGGAA 2 22 FF399948 5' 40.90 + root
    vun-mir5219 mtr-mir5219 107 −25.23 TCATGGAATCTCAGCTGCAGCAG 1 23 FG850600 3' 52.17 − mixed
    vun-mir5227 mtr-mir5227 140 −18.04 AGAACAGAAGAAGATTGAAGAA 3 22 FG915684 5' 31.81 − mixed
    vun-mir5241 mtr-mir5241a 381 W#8722;119.80 TGGGTGAATGGAAGAGTGAAT 3 21 FG904590 3' 42.85 + mixed
    vun-mir5246 mtr-mir5246 68 −18.70 CACCAGAGAGCTTTGAAGGTT 4 21 FG856911 3' 47.61 + mixed
    vun-mir5255 mtr-mir5255 54 −10.40 TGACAGGATAGAGGACATGAC 4 21 FG910302 5' 47.61 − mixed
    vun-mir5261 mtr-mir5261 311 −71.81 CGATTGTAGATGGCTTTGGCT 3 21 FG838847 5' 47.61 − mixed
    vun-mir5280 mtr-mir5280 90 −20.22 TAAGTAGAAACGGGCCGAGATCGGGG 4 26 FG915361 5' 57.69 − mixed
    vun-mir5290 mtr-mir5290 217 −30.24 AAAGTAGAGAGAGAAAGACACATA 4 24 FG852502 5' 33.33 + mixed
    vun-mir5298 mtr-mir5298a 192 −36.58 TGGATTTCAAGATGAAGATGAAGAA 4 25 FF402284 3' 32.00 − root
    vun-mir5376 gma-mir5376 341 −132.02 TGGAGATTGTGAAGAATTTGAGA 3 23 FG872123 3' 34.78 + mixed
    vun-mir5561 mtr-mir5561 346 −69.34 ATCTCTCTCTCTCTAAATGTA 3 21 FF390124 5' 33.33 − root
    vun-mir5758 mtr-mir5758 91 −22.60 TAAGTTGGATCTATGTATTTG 3 21 FG893334 3' 28.57 + mixed
    vun-mir5770 gma-mir5770a 98 −30.40 TTAGGACTATGGTTTGGATGA 1 21 FG937135 3' 38.09 − mixed
    vun-mir6252 osa-mir6252 90 −20.90 ATGAGTTGTGTTGAGAGAGGGTT 4 23 FG841373 3' 43.47 − mixed
    vun-mir7696 mtr-mir7696a 173 −33.67 ACAAGTACTTA-AATTCAAAA 4 20 FG864277 3' 20.00 − mixed
    vun-mir8182 ath-mir8182 170 −31.80 TTGTGTTGCGTTTGTGATGACT 3 22 FG942892 5' 40.90 − mixed
    vun-mir9748 gma-mir9748 98 −32.45 GAAGGAAGTGTTGAGGGAGGAG 3 22 FG921211 5' 54.54 + mixed
     | Show Table
    DownLoad: CSV

    3.2. Characterization of cowpea miRNAs

    Characterization of newly identified candidate miRNAs is a set crucial step for their validation, as reported earlier [16,17,37]. The pre-miRNA length of the profiled cowpea miRNAs ranges from 46 to 381 nt with an average of 159 nt. The pre-miRNAs were further illustrated on the basis of their length (Figure 1). The minimum folding free energy (MFE) of pre-miRNA is a vital and valid term of characterization. The newly identified potential cowpea pre-miRNAs have shown MFEs in range from −10 to −132 kcal mol−1 with an average of −40 kcal mol−1 as shown in Figure 2. The numbers of mismatches of mature sequences with their reference sequences were observed in a range of 0–4 with an average of three mismatches as categorized in Figure 3. These values are matched with the previously reported values in different plants [21,37,38,39]. Mature miRNA sequences lengths were observed from 18 to 26 nt with an average of 21 nt as explained in Figure 4. These findings of mature sequences length are in agreement to prior published data in other plant species [16,17,18,36]. The 52% cowpea miRNAs sequences were found at 5′ arm, while 48% were at 3′ arm (Figure 5(A), 6). The GC content was found from 18 to 86% with an average of 42% as shown in Figure 7. Strand orientation is another important character for the generation of mature miRNAs transcripts. In this study, 24 mature miRNAs were found on minus strand while 22 were observed on plus strand of the transcripts (Figure 8). The same results for plus and minus strand orientation of mature miRNAs are in agreement with the earlier research work [40]. The identified conserved cowpea miRNAs were also characterized on the basis of their organ of expression as presented in Figure 9. These findings are similar with the earlier reports [37] and suggesting organ dependent expression pattern of miRNAs in cowpea. The miRNA organ specific expression would be utilized to manage the organogenesis in cowpea. The secondary self-folded stem-loop structures of the cowpea pre-miRNAs are observed with at least 17 nucleotides engaged in Watson-Crick or G/U base pairing between the mature miRNA and the opposite arms (miRNAs*) in the stem region (Figure 10). Except few where the reference miRNAs have also less base pairing and these precursors do not contain large internal loops or bulges. The mature miRNA sequences are observed in the double stranded stem region of the pre-miRNA secondary structures, as shown in Figure 5(A). Almost similar findings for various plant and animal species were reported by many researchers [16,17,20,37,41,42]. Furthermore, the newly identified cowpea miRNAs were also confirmed as non-protein coding nature by showing no significant similarity with known proteins. This validation strengthens the expressed nature for computationally identified miRNAs as non-coding RNAs. Similar results were observed in various research papers by many groups [16,43,44].

    Figure 1. Distribution of the newly identified cowpea pre-miRNAs on the basis of their length.
    DownLoad: Full-Size ImgPowerPoint
    Figure 2. Distribution and classification of newly identified cowpea miRNAs on the basis of their minimum free energies (MFEs).
    DownLoad: Full-Size ImgPowerPoint
    Figure 3. Distribution of the cowpea miRNAs mismatches (nt) with their reference miRNAs.
    DownLoad: Full-Size ImgPowerPoint
    Figure 4. Distribution of the cowpea mature miRNAs for their length.
    DownLoad: Full-Size ImgPowerPoint
    Figure 5. (A) The newly identified cowpea miRNAs' secondary structures. Cowpea pre-miRNAs secondary structures were developed through Mfold algorithm. These structures clearly showing the mature miRNAs in stem portion of the stem-loop structures. (B) Cowpea pre-miRNA cluster. Cowpea miRNA (vun-miR4414) was found as a pre-miRNA cluster with two mature miRNAs (miR4414a and miR4414b). The pre-miRNA cluster secondary structure was created by Mfold (version 3.6), showing mature sequences in green within the same pre-miRNA sequence.
    DownLoad: Full-Size ImgPowerPoint
    Figure 6. Distribution of mature miRNAs location on the either arms of hair-pen structures and numbers (frequency%) of miRNAs occurring.
    DownLoad: Full-Size ImgPowerPoint
    Figure 7. Percentage distribution of GC content and numbers (frequency%) of miRNAs occurring.
    DownLoad: Full-Size ImgPowerPoint
    Figure 8. Percentage distribution of strand orientation and numbers (frequency%) of miRNAs occurring.
    DownLoad: Full-Size ImgPowerPoint
    Figure 9. Percentage distribution of organ expression and numbers (frequency%) of miRNAs occurring.
    DownLoad: Full-Size ImgPowerPoint
    Figure 10. Percentage distribution of base pairing between the mature miRNA and the opposite arms (miRNAs*) in the stem region and numbers (frequency%) of miRNAs occurring.
    DownLoad: Full-Size ImgPowerPoint

    3.3. Cluster pre-miRNA gene in cowpea

    In animals, a large number of miRNAs have been found in clusters and have been predicted to have similar expression profiles and functions [45]. The miRNA clusters have rarely been detected in plants. They were first reported by Jones-Rhoades and Bartel [46]. In this study, we also identified one pre-miRNA (mir4414) as cluster in cowpea having two mature miRNAs within Figure 5(B). On the basis of current available literature, this miRNA family (miR4414) was found for the first time in cowpea as a cluster.

    3.4. Convergence and phylogenetic studies

    The newly characterized cowpea miRNA vun-mir398, due to its conserved nature, was investigated for convergence and phylogeny. Simply, the cowpea miRNA vun-mir398 alignment and cladogram tree, using neighbour joining clustering method, were created with Glycine max (gma), Nicotiana tabacum (nta) and Cucumis melo (cme) by the publicly available Web-Logo, a sequence logo generator [47] and ClustalW, a multiple sequence alignment tool [48]. The cowpea miRNA vun-mir398 is observed in convergence with Glycine max (gma), Nicotiana tabacum (nta) and Cucumis melo (cme) as shown in Figure 11(A). The Phylogenetic cladogram tree, as illustrated in Figure 11(B), clearly showed that on the basis of sharing a more recent common ancestor the cowpea miRNA is more closely related to Glycine max (gma) than Nicotiana tabacum (nta) and Cucumis melo (cme). Zeng et al. [49] have also reported conserved nature in Euphorbiaceous plants.

    Figure 11. (A) Cowpea miRNA's conservation studies. Alignment of V. unguiculata (vun) miRNA (vun-mir398) with G. max (gma), N. tabacum (nta) and C. melo (cme) was generated using Web logo: a sequence logo generator, showing conserved nature mature miRNA sequences. The mature sequences highlighted in a rectangle red box. (B) Cowpea miRNA's phylogenetic analysis. V. unguiculata (vun) miRNA (vun-mir398) with G. max (gma), N. tabacum (nta) and C. melo (cme) was done with the help of ClustalW and cladogram tree was generated using neighbor joining clustering method. The phylogenetic tree showed that the V. unguiculata (vun) is more closed to G. max (gma) than N. tabacum (nta) and C. melo (cme). The closed plant species highlighted in a rectangle red box.
    DownLoad: Full-Size ImgPowerPoint

    3.5. The potential cowpea miRNAs targeted genes

    Profiling the potential cowpea miRNAs targeted genes is a vital step for validation of the computationally identified miRNAs. A total of 138 targeted genes were predicted for the 46 potential cowpea miRNAs. The detail description is mentioned in Table 2. Different cowpea miRNAs targeting same proteins and vice versa were predicted here. This showed that one miRNA target more than one mRNAs and a single mRNA targets by many miRNAs [50]. The profiled targeted genes are categories as, 27% (37 of 138) are engaged in metabolism, 26% (36 of 138) are playing role as transcription factors, 11% (15 of 138) are involved in transport activities, 11% (15 of 138) are shown with stress related, and the rest are engaged in hypothetical protein, signal transduction, growth and development, structural proteins and diseases related. Almost all of these targets were already reported as miRNA targets in other plants [7,16,17].

    Table 2. Targets of cowpea miRNAs: As predicted by psRNAtarget and RNA hybrid in terms of miRNA family number, target acc., target description and function.
    miRNA Target Acc. Target Description Function Alignment
    vun-mir398 TC8412 Predicted protein Hypothetical protein miRNA 21 GUCCCCGCUGGACUCUUGUGU 1
    ::::::::.:::: ::::::
    Target 24 CAGGGACGAUCUGAUAACACA 44
    vun-mir413 TC18010 H/ACA ribonucleoprotein complex Transcription factor miRNA 21 UUCGUCUUGUUCUCUUUGAUU 1
    :::::::::::::::::::::
    Target 432 AAGCAGAACAAGAGAAACUAA 452
    vun-mir413 FF538223 Tropinone reductase Metabolism miRNA 21 UUCGUCUUGUUCUCUUUGAUU 1
    .:::::::.::.:.::::::
    Target 321 GAGCAGAAUAAUGGGAACUAA 341
    vun-mir413 TC16544 Valyl-tRNA synthetase Metabolism miRNA 21 UUCGUCUUGUUCUCUUUGAUU 1
    :.::::::::::.:::. :::
    Target 1013 AGGCAGAACAAGGGAAGAUAA 1033
    vun-mir413 TC9044 Uroporphyrinogen decarboxylase Metabolism miRNA 21 UUCGUCUUGUUCUCUUUGAUU 1
    .:: :::: :::::::.::.:
    Target 59 GAGAAGAAGAAGAGAAGCUGA 79
    vun-mir435 TC9534 Chromosome chr12 scaffold_238, Hypothetical protein miRNA 20 AGUUGAGGUUUCGGAGUAUU 1
    :::::::::: :..::::.:
    Target 242 UCAACUCCAAUGUUUCAUGA 261
    vun-mir435 FF387447 Chromosome chr9 scaffold_7, Hypothetical protein miRNA 20 AGUUGAGGUUUCGGAGUAUU 1
    ::::.:.:::.::::: :::
    Target 386 UCAAUUUCAAGGCCUCCUAA 405
    vun-mir435 TC16349 Ripening related protein Growth and development miRNA 20 AGUUGAGGUUUCGGAGUAUU 1
    :.::::::::: :.:.::.:
    Target 523 UUAACUCCAAAACUUUAUGA 542
    vun-mir435 FG810938 Protein kinase Signal transduction miRNA 20 AGUUGAGGUUUCGGAGUAUU 1
    ::: :::::: :::::.::.
    Target 474 UCACCUCCAAUGCCUCGUAG 493
    vun-mir834 TC4272 SCOF-1 Transcription factor miRNA 21 GGUGGUGGCGGUGACGAUGGU 1
    :::::.::::::: :.:::::
    Target 281 CCACCGCCGCCACCGUUACCA 301
    vun-mir834 TC8566 Cytochrome P450 monooxygenase CYP83E9 Metabolism miRNA 20 GUGGUGGCGGUGACGAUGGU 1
    ::::: : ::::::::::::
    Target 465 CACCAACACCACUGCUACCA 484
    vun-mir834 TC7191 DnaJ-like protein Stress related miRNA 21 GGUGGUGGCGGUGACGAUGGU 1
    ::.::::::::::::: :::.
    Target 173 CCGCCACCGCCACUGCAACCG 193
    vun-mir834 FG876294 Zinc finger-like protein Transcription factor miRNA 21 GGUGGUGGCGGUGACGAUGGU 1
    ::::::::::::: :: ::::
    Target 138 CCACCACCGCCACCGCCACCA 158
    vun-mir834 TC4023 GroEL-like chaperone, ATPase Stress related miRNA 21 GGUGGUGGCGGUGACGAUGGU 1
    :: ::.:::::.:::::.:::
    Target 78 CCUCCGCCGCCGCUGCUGCCA 98
    vun-mir834 TC7031 Oxophytodienoate reductase Metabolism miRNA 21 GGUGGUGGCGGUGACGAUGGU 1
    .::.::.:::::::::: :::
    Target 19 UCAUCAUCGCCACUGCUUCCA 39
    vun-mir834 TC15421 MYB Transcription factor miRNA 20 GUGGUGGCGGUGACGAUGGU 1
    ..:.::.::.::::::::::
    Target 955 UGCUACUGCUACUGCUACCA 974
    vun-mir834 GH622195 Ribosomal protein Structural protein miRNA 21 GGUGGUGGCGGUGACGAUGGU 1
    :::::.:::::::: :::::
    Target 110 CCACCGCCGCCACUUCUACCU 130
    vun-mir834 TC7768 Calcium-binding EF-hand) Transcription factor miRNA 21 GGUGGUGGCGGUGACGAUGGU 1
    ..::.::.:..::::.:::::
    Target 470 UUACUACUGUUACUGUUACCA 490
    vun-mir1512 XM_013230906 Biomphalaria glabrata dual oxidase Metabolism target 5' C U 3'
    AAUGAAAUUCUUAAAGG
    UUACUUUAAGAAUUUCC
    miRNA 3' A 5'
    vun-mir1512 XM_006957329 Nucleoside triphosphate hydrolase protein Transcription factor target 5' U A 3'
    UAAUGAAAUUCUUAAAG
    AUUACUUUAAGAAUUUC
    miRNA 3' C 5'
    vun-mir1512 KC463855 NB-LRR receptor (RSG3-301) Transcription factor target 5' C CCC GG U 3'
    AAUGA AA CUUGAAGG
    UUACU UU GAAUUUCC
    miRNA 3' A AA 5'
    vun-mir1512 EF076031 Phosphatidic acid phosphatase alpha (PAPa) Metabolism target 5' A AAGGGG G A 3'
    UGGUGAAA UC UAAAGG
    AUUACUUU AG AUUUCC
    miRNA 3' A A 5'
    vun-mir1512 AF413209 Dolichos biflorus chloroplast ribulose-1, 5-bisphosphate carboxylase Metabolism target 5' C G 3'
    UGGUGAAAU UAAAGG
    AUUACUUUA AUUUCC
    miRNA 3' AGA 5'
    vun-mir1514 FF388166 NAC domain-containing protein 78 Transcription factor miRNA 21 CUACGGAUAAAAUCUUUACUU 1
    :::::::::::::::::::::
    Target 687 GAUGCCUAUUUUAGAAAUGAA 707
    vun-mir1514 FF540114 Phosphate transporter family protein transporter miRNA 20 UACGGAUAAAAUCUUUACUU 1
    ::::::.::::.::::::::
    Target 461 AUGCCUGUUUUGGAAAUGAA 480
    vun-mir1514 TC15423 NAM-like protein Transcription factor miRNA 20 UACGGAUAAAAUCUUUACUU 1
    ::::::.::::.::::::::
    Target 589 AUGCCUGUUUUGGAAAUGAA 608
    vun-mir1514 TC869 ATP-binding cassette sub-family f member 2 Transporter miRNA 21 CUACGGAUAAAAUCUUUACUU 1
    :: ::.:::: ::::::::::
    Target 733 GAGGCUUAUUCUAGAAAUGAA 753
    vun-mir1514 FG830151 Starch branching enzyme Metabolism miRNA 20 UACGGAUAAAAUCUUUACUU 1
    ::::: ::::::::.::::
    Target 314 AUGCCAAUUUUAGAGAUGAU 333
    vun-mir1514 TC5197 Cytochrome c biogenesis protein-like Transporter miRNA 20 UACGGAUAAAAUCUUUACUU 1
    ::.::::::::::.::::
    Target 749 AUAUCUAUUUUAGAGAUGAU 768
    vun-mir1525 TC17248 Salt-tolerance protein Stress related miRNA 21 UGAUUUUUGUAUAAAUUGGGG 1
    ::::::::::::::::::.:.
    Target 306 ACUAAAAACAUAUUUAACUCU 326
    vun-mir1525 FG915097 UDP-N-acetylmuramoylalanine-D-glutamate ligase Transcription factor miRNA 21 UGAUUUUUGUAUAAAUUGGGG 1
    ::::::::.::::::.::::
    Target 468 ACUAAAAAUAUAUUUGACCCA 488
    vun-mir1525 TC14268 Non-specific lipid-transfer protein transporter miRNA 20 GAUUUUUGUAUAAAUUGGGG 1
    ::.:::...:::::::::.:
    Target 505 CUGAAAGUGUAUUUAACCUC 524
    vun-mir1525 TC18336 Heat shock protein Stress related miRNA 20 GAUUUUUGUAUAAAUUGGGG 1
    .:.:..:.::::::.:::::
    Target 166 UUGAGGAUAUAUUUGACCCC 185
    vun-mir1848 EG424245 Radical SAM domain protein Metabolism miRNA 20 CUGCGCGCGCGGCCGCUCGC 1
    :: ::: :::: ::::::::
    Target 110 GAAGCGAGCGCAGGCGAGCG 129
    vun-mir2095 FF402667 Resistance protein MG55 Stress related miRNA 20 UUUGUACAGUAUUUACCUUC 1
    .: :.::::::::::::::
    Target 592 GAUCGUGUCAUAAAUGGAAU 611
    vun-mir2095 TC2784 Vacuolar protein sorting-associated protein 26-like protein transporter miRNA 20 UUUGUACAGUAUUUACCUUC 1
    :::::::::::.: :::::
    Target 824 AAACAUGUCAUCGAAGGAAG 843
    vun-mir2606 TC406838 SNF1 related protein kinase Signal transduction miRNA 20 CACUCUUGGUUCGUGAAGUU 1
    : :::::. :::::::::::
    Target 1051 GAGAGAAUAAAGCACUUCAA 1070
    vun-mir2606 TC401737 ATP binding protein Transcription factor miRNA 20 CACUCUUGGUUCGUGAAGUU 1
    :::::::.::::.:::::
    Target 242 UCGAGAACCGAGCAUUUCAA 261
    vun-mir2606 NP305366 Hypothetical protein Hypothetical protein miRNA 21 UCACUCUUGGUUCGUGAAGUU 1
    : ::::::.:.::.::::::.
    Target 420 ACUGAGAAUCGAGUACUUCAG 440
    vun-mir2609 NP038997 Jasmonate induced protein Stress related miRNA 21 UCACUCUUGGUUCGUGAAGUU 1
    : ::.:: :::::::::::::
    Target 220 ACUGGGAUCCAAGCACUUCAA 240
    vun-mir2609 NP568563 SEC14-like protein Transcription factor miRNA 21 UCACUCUUGGUUCGUGAAGUU 1
    :: :::::::::: ::::.::
    Target 417 AGCGAGAACCAAGGACUUUAA 437
    vun-mir2609 TC406838 SNF1 related protein kinase-like protein Signal transduction miRNA 20 CACUCUUGGUUCGUGAAGUU 1
    : :::::. :::::::::::
    Target 1051 GAGAGAAUAAAGCACUUCAA 1070
    vun-mir2609 TC401737 ATP binding protein Signal transduction miRNA 20 CACUCUUGGUUCGUGAAGUU 1
    :::::::.::::.:::::
    Target 242 UCGAGAACCGAGCAUUUCAA 261
    vun-mir2622 TC9003 Alpha-expansin 2 Metabolism miRNA 22 AUUCGAGUGUUACCGUGUGUUC 1
    ::::::::::::::::::::::
    Target 64 UAAGCUCACAAUGGCACACAAG 85
    vun-mir2630 TC15462 Auxin influx transport protein Transporter miRNA 20 UUUUGGUUUCUGGUUUUGGU 1
    ::::::::: :::::::::
    Target 293 AAAACCAAAAACCAAAACCU 312
    vun-mir2630 FF390661 Serine/arginine repetitive matrix 1 Transcription factor miRNA 20 UUUUGGUUUCUGGUUUUGGU 1
    ::::: ::: ::::::::::
    Target 349 AAAACAAAAAACCAAAACCA 368
    vun-mir2630 FG865319 Monosaccharid transport protein Transporter miRNA 20 UUUUGGUUUCUGGUUUUGGU 1
    :::.:::::::.::.::::
    Target 109 UAAAUCAAAGACUAAGACCA 128
    vun-mir2630 TC4441 Ras-related protein RAB8-1 Transcription factor miRNA 20 UUUUGGUUUCUGGUUUUGGU 1
    ::::.:::: ::::::::::
    Target 75 AAAAUCAAA-ACCAAAACCA 93
    vun-mir2630 TC1550 Homeodomain leucine zipper protein HDZ3 Transcription factor miRNA 21 AUUUUGGUUUCUGGUUUUGGU 1
    :.::::..:. ::::::::::
    Target 1253 UGAAACUGAGAACCAAAACCA 1273
    vun-mir2630 FC457466 Pseudouridylate synthase Metabolism miRNA 21 AUUUUGGUUUCUGGUUUUGGU 1
    :::::. :..:::.:::::::
    Target 504 UAAAAUGAGGGACUAAAACCA 524
    vun-mir2630 TC6720 Ubiquitin carrier protein Transporter miRNA 20 UUUUGGUUUCUGGUUUUGGU 1
    :::::::::: :::::.::
    Target 685 AAAACCAAAGCCCAAAUUCA 704
    vun-mir2636 TC7750 NADH-ubiquinone oxidoreductase chain 2 Metabolism miRNA 21 UAUAAGUCGUGUGAUUGUAGG 1
    :::::.::::::::::.:.:
    Target 225 AUAUUUAGCACACUAAUAAUC 245
    vun-mir2636 FF537611 Na+/H+ antiporter Metabolism miRNA 20 AUAAGUCGUGUGAUUGUAGG 1
    : :::::::::::..:::..
    Target 25 UCUUCAGCACACUGGCAUUU 44
    vun-mir2636 TC1711 Beta-1, 3-glucanase-like protein Metabolism miRNA 19 UAAGUCGU-GUGAUUGUAGG 1
    : :::::: :::::::::::
    Target 1279 AAUCAGCAACACUAACAUCC 1298
    vun-mir2657 TC7897 Proteinase inhibitor 20 Metabolism miRNA 20 UGUUUUAGUUAUGUUAUUUU 1
    :.::::: ::::.:::::::
    Target 934 AUAAAAUAAAUAUAAUAAAA 953
    vun-mir2657 FG852576 Heat shock protein 70 cognate Stress related miRNA 22 GUUGUUUUAGUUAUGUUAUUUU 1
    :::.:.:::::::. :::.:::
    Target 77 CAAUAGAAUCAAUGAAAUGAAA 98
    vun-mir2657 TC5942 2, 4-D inducible glutathione S-transferase Metabolism miRNA 21 UUGUUUUAGUUAUGUUAUUUU 1
    ::.:::::. ::..:::::::
    Target 745 AAUAAAAUUUAUGUAAUAAAA 765
    vun-mir2678 EF472252 Bound starch synthase Metabolism target 5' U UG UG A 3'
    GGC G GCA GAC
    CUG C CGU UUG
    miRNA 3' UG G UG AAAU 5'
    vun-mir2678 D88122 CPRD46 protein Stress related target 5' U C G 3'
    GCGCGUA CAACUU
    UGCGCGU GUUGAA
    miRNA 3' CUG U AU 5'
    vun-mir2678 AY466858 Peroxisomal ascorbate peroxidase Metabolism target 5' U A C A 3'
    GGCACG UG CGGC ACUU
    CUGUGC GC GUUG UGAA
    miRNA 3' U AU 5'
    vun-mir2678 AB028025 YLD mRNA for regulatory protein Metabolism target 5' A CCA C G 3'
    GCGC GCG CGGCGAC
    UGUG CGC GUUGUUG
    miRNA 3' C AAAU 5'
    vun-mir2950 TC11773 F-box/Kelch-repeat protein Transcription factor miRNA 21 AAGUCAGACGUUCUCUACCUU 1
    :::::::::::::::::::::
    Target 614 UUCAGUCUGCAAGAGAUGGAA 634
    vun-mir2950 TC2831 Ethylene responsive protein Stress related miRNA 20 AGUCAGACGUUCUCUACCUU 1
    :..:: ::.::::::::::.
    Target 1700 UUGGUAUGUAAGAGAUGGAG 1719
    vun-mir3434 TC7167 Protein transport protein Sec24-like At3g07100 Transporter miRNA 20 AGAGUACCGACUAUGAGAGU 1
    :::.::::.:::: ::.:::
    Target 662 UCUUAUGGUUGAUUCUUUCA 681
    vun-mir4351 TC5899 Expressed protein Hypothetical protein miRNA 22 GGUUGAGGUUGACUUGGGAUUG 1
    ::::::::::::::::::::::
    Target 27 CCAACUCCAACUGAACCCUAAC 48
    vun-mir4351 FF391835 NADH-ubiquinone oxidoreductase chain 2 Metabolism miRNA 20 UUGAGGUUGACUUGGGAUUG 1
    ::: ::::.: ::::::::.
    Target 22 AACCCCAAUUAAACCCUAAU 41
    vun-mir4392 TC14606 AKIN beta1 Signal transduction miRNA 22 AGGCUUUAGUGCAAGAGUGUCU 1
    : : :::::::::.:::.:::
    Target 791 UGCUAAAUCACGUCUUCAUAGA 812
    vun-mir4392 TC9038 SNF1-related protein kinase regulatory beta subunit 1 Signal transduction miRNA 22 AGGCUUUAGUGCAAGAGUGUCU 1
    : : :::::::::.:::.:::
    Target 979 UGCUAAAUCACGUCUUCAUAGA 1000
    vun-mir4408 TC2049 Monooxygenase Metabolism miRNA 24 AGGAUAUGAGUAGGUUACAACAAC 1
    :: :::.::::: :: :::::::
    Target 369 UCAGAUAUUCAUCAAAAGUUGUUG 392
    vun-mir4992 FG809835 TfIIE Transcription factor miRNA 22 GACUUUUUUUGGUAGAAUCUAC 1
    ::::::::::::::::::::::
    Target 247 CUGAAAAAAACCAUCUUAGAUG 268
    vun-mir4992 TC11468 Uncharacterized protein At2g03890.2 Hypothetical protein miRNA 22 GACUUUUUUUGGUAGAAUCUAC 1
    :::::::: :::::.:::::::
    Target 836 CUGAAAAAUACCAUUUUAGAUG 857
    vun-mir4992 TC414 Zinc finger protein 7 Transcription factor miRNA 22 GACUUUUUUUGGUAGAAUCUAC 1
    .:::.:.:::::::.::.:::
    Target 739 UUGAGAGAAACCAUUUUGGAUC 760
    vun-mir4992 TC2268 Zinc finger protein 4 Transcription factor miRNA 22 GACUUUUUUUGGUAGAAUCUAC 1
    .:::.:.:::::::.::.:::
    Target 857 UUGAGAGAAACCAUUUUGGAUC 878
    vun-mir5012 TC1335 Ribosomal protein L30 Structural protein miRNA 21 CCUUGUGUGCCUCGUCGUUUU 1
    ::::.::. ::::::::::::
    Target 209 GGAAUACGAGGAGCAGCAAAA 229
    vun-mir5012 TC59 Acireductone dioxygenase Metabolism miRNA 21 CCUUGUGUGCC-UCGUCGUUUU 1
    ::::::::: : ::::::::::
    Target 19 GGAACACACUGUAGCAGCAAAA 40
    vun-mir5012 TC12731 Mn-specific cation diffusion facilitator transporter Transporter miRNA 20 CUUGUGUGCCUCGUCGUUUU 1
    ::.::::::::: :::::.
    Target 186 GAGCACACGGAGAAGCAAGU 205
    vun-mir5043 FF401363 Ran-specific GTPase-activating protein Transcription factor miRNA 21 CCACCACGUC-UCUUCCUCUUC 1
    : :::::::: :::.:::::::
    Target 444 GAUGGUGCAGGAGAGGGAGAAG 465
    vun-mir5215 FG909052 Ferredoxin Ⅰ precursor Metabolism miRNA 21 UUAGUUGAUCGAGUAGGAGGA 1
    :::::::::::::::::::::
    Target 179 AAUCAACUAGCUCAUCCUCCU 199
    vun-mir5215 GH620837 L-lactate dehydrogenase Metabolism miRNA 20 UAGUUGAUCGAGUAGGAGGA 1
    :::.:: :::::.:::::::
    Target 491 AUCGACGAGCUCGUCCUCCU 510
    vun-mir5215 TC8326 50S ribosomal protein L21 Structural protein miRNA 21 UUAGUUGAUCGAGUAGGAGGA 1
    :::.::.:.:::.::::::.:
    Target 943 AAUUAAUUGGCUUAUCCUCUU 963
    vun-mir5215 FG849457 Vancomycin resistance protein Stress related miRNA 20 UAGUUGAUCGAGUAGGAGGA 1
    ::::::.:::::::::.:
    Target 340 AUCAACAGGCUCAUCCUUCG 359
    vun-mir5215 TC6816 General substrate transporter Transporter miRNA 21 UUAGUUGAUCGAGUAGGAGGA 1
    ::::::::.:::: :.:::::
    Target 1035 AAUCAACUGGCUC-UUCUCCU 1054
    vun-mir5216 FG851044 Metal ion binding Transcription factor miRNA 22 AAGGUGACAAAAAGUGAGGGUU 1
    :.:::: :::::.:::.::::
    Target 227 UAUCACUUUUUUUUACUUCCAA 248
    vun-mir5216 FG841236 T5I8.13 Transcription factor miRNA 22 AAGGUGACAAAAAGUGAGGGUU 1
    :::::.: :: ::::.::::::
    Target 132 UUCCAUUCUUCUUCAUUCCCAA 153
    vun-mir5216 FG931306 Predicted protein Hypothetical protein miRNA 21 AGGUGACAAAAAGUGAGGGUU 1
    :.:::::::: ::..:.::::
    Target 2 UUCACUGUUUCUCGUUUCCAA 22
    vun-mir5219 TC16320 Tumor-related protein Growth and development miRNA 20 GACGUCGACUCUAAGGUACU 1
    ::::: :::::.:: :::::
    Target 141 CUGCACCUGAGGUUACAUGA 160
    vun-mir5227 TC9947 TINY-like protein Transcription factor miRNA 22 AAGAAGUUAGAAGAAGACAAGA 1
    ::.::::: ::::::.::::::
    Target 1075 UUUUUCAA-CUUCUUUUGUUCU 1095
    vun-mir5227 FG842691 HMG1/2-like protein Transcription factor miRNA 20 GAAGUUAGAAGAAGACAAGA 1
    :::::::.::.:::: ::.:
    Target 27 CUUCAAUUUUUUUCUAUUUU 46
    vun-mir5227 FG886406 Probable intracellular septation protein Growth & development miRNA 22 AAGAAGUUAGAAGAAGACAAGA 1
    :.::::: :::.::.::::.:
    Target 48 UGUUUCAACCUUUUUUUGUUUU 69
    vun-mir5227 TC17852 Glutathione S-transferase PM24 Metabolism miRNA 20 GAAGUUAGAAGAAGACAAGA 1
    :::::::.:::: ::::::
    Target 1044 CUUCAAUUUUCUCGUGUUCU 1063
    vun-mir5227 TC10272 DNA-directed RNA polymerase subunit Transcription factor miRNA 20 GAAGUUAGAAGAAGACAAGA 1
    :::::: ::.::.::::::
    Target 288 CUUCAAGAUUUUUUUGUUCU 307
    vun-mir5241 TC10790 VDAC-like porin Transporter miRNA 20 AAGUGAGAAGGUAAGUGGGU 1
    ::::::::::::::::..::
    Target 201 UUCACUCUUCCAUUCAUUCA 220
    vun-mir5241 TC18525 Peptidyl-prolyl cis-trans isomerase Metabolism miRNA 20 AAGUGAGAAGGUAAGUGGGU 1
    :::..::::::.::::::.:
    Target 58 UUCGUUCUUCCGUUCACCUA 77
    vun-mir5241 FG863193 Probable plastid-lipid-associated protein 13 Stress related miRNA 20 AAGUGAGAAGGUAAGUGGGU 1
    ::::.:: :.:::::::.::
    Target 158 UUCAUUCAUUCAUUCACUCA 177
    vun-mir5241 TC7362 Serine/threonine protein kinase Signal transduction miRNA 20 AAGUGAGAAGGUAAGUGGGU 1
    ::..::.:::.:::::..::
    Target 934 UUUGCUUUUCUAUUCAUUCA 953
    vun-mir5241 TC16629 Multidrug resistance protein Disease related miRNA 20 AAGUGAGAAGGUAAGUGGGU 1
    :::::::::::: :: :.::
    Target 915 UUCACUCUUCCAGUCUCUCA 934
    vun-mir5241 TC2781 Non-specific lipid-transfer protein Transporter miRNA 20 AAGUGAGAAGGUAAGUGGGU 1
    ::::::::::: ::: :.::
    Target 20 UUCACUCUUCCUUUCUCUCA 39
    vun-mir5241 TC212 Chaperone GrpE type 2 Stress related miRNA 20 AAGUGAGAAGGUAAGUGGGU 1
    ::::.:::.: ::::::::
    Target 207 UUCAUUCUCUCCUUCACCCA 226
    vun-mir5255 TC8912 Pyruvate kinase Signal transduction miRNA 20 AGUACAGGAGAUAGGACAGU 1
    :.:::::.:::.::.:::.:
    Target 71 UUAUGUCUUCUGUCUUGUUA 90
    vun-mir5255 TC18327 Cysteine protease Metabolism miRNA 20 AGUACAGGAGAUAGGACAGU 1
    ::: :::::. ::.::::::
    Target 605 UCAAGUCCUUGAUUCUGUCA 624
    vun-mir5261 FG838847 Chromosome undetermined scaffold_221 Hypothetical protein miRNA 21 UCGGUUUCGGUAGAUGUUAGC 1
    :::::::::::::::::::::
    Target 540 AGCCAAAGCCAUCUACAAUCG 560
    vun-mir5261 FF398912 TIR Stress related miRNA 21 UCGGUUUCGGUAGAUGUUAGC 1
    ::::::::.::::::::::::
    Target 413 AGCCAAAGUCAUCUACAAUCG 433
    vun-mir5290 TC3168 Hydroxyproline-rich glycoprotein Disease related miRNA 24 AUACACAGAAAGAGAGAGAUGAAA 1
    : : : :::::::::.::::.:::
    Target 82 UCUCUUUCUUUCUCUUUCUAUUUU 105
    vun-mir5290 FG844083 PAS sensor protein Signal transduction miRNA 24 AUACACAGAAAGAGAGAGAUGAAA 1
    : : : :::::::.:::.::.:::
    Target 99 UUUCUCUCUUUCUUUCUUUAUUUU 122
    vun-mir5290 FG871448 Eco57I restriction endonuclease Metabolism miRNA 20 ACAGAAAGAGAGAGAUGAAA 1
    : ::::::::::::: ::::
    Target 42 UCUCUUUCUCUCUCUCCUUU 61
    vun-mir5290 TC11392 Ribonuclease Ⅲ Transcription factor miRNA 24 AUACACAGAAAGAGAGAGAUGAAA 1
    ::: :: ::: ::::.:.::::::
    Target 841 UAUAUGACUUCCUCUUUUUACUUU 864
    vun-mir5290 TC12655 Calcium dependent protein kinase Signal transduction miRNA 20 ACAGAAAGAGAGAGAUGAAA 1
    ::::::.:.:::.:.::::
    Target 1254 GGUCUUUUUUUCUUUGCUUU 1273
    vun-mir5290 TC4908 ACC oxidase Growth & development miRNA 22 ACACAGAAAGAGAGAGAUGAAA 1
    : : ::::::::::::::. ::
    Target 1376 UCUCUCUUUCUCUCUCUAUCUU 1397
    vun-mir5290 FG874464 RNA-binding protein Transcription factor miRNA 20 ACAGAAAGAGAGAGAUGAAA 1
    : :::::::::::::.:::
    Target 14 UCUCUUUCUCUCUCUCUUUU 33
    vun-mir5298 TC16082 Translation initiation factor IF Transcription factor miRNA 25 AAGAAGUAGAAG-UAGAACUUUAGGU 1
    :.::::::::: : :::::::::::
    Target 34 UCUUUCAUCUUCGAACUUGAAAUCCA 59
    vun-mir5298 TC11481 Non-specific lipid-transfer protein Transporter miRNA 24 AGAAGUAGAAGUAGAACUUUAGGU 1
    :.: ::: ::.:::::::.::..:
    Target 614 UUUACAUGUUUAUCUUGAGAUUUA 637
    vun-mir5298 TC16211 (Iso) Flavonoid glycosyltransferase Metabolism miRNA 25 AAGAAGUAGAAGUAGAACUUUAGGU 1
    : ::.. :::: ::::::::::::
    Target 233 UCCUCUGCCUUCUUCUUGAAAUCCA 257
    vun-mir5376 TC18575 Zgc:158399 protein Hypothetical protein miRNA 23 AGAGUUUAAGAAGUGUUAGAGGU 1
    :::::::::::::::::::::::
    Target 517 UCUCAAAUUCUUCACAAUCUCCA 539
    vun-mir5376 TC16446 Predicted protein Hypothetical protein miRNA 23 AGAGUUUAAGAAGUGUUAGAGGU 1
    :::::::::::::: :::.: ::
    Target 687 UCUCAAAUUCUUCAGAAUUUACA 709
    vun-mir5376 FC457472 Chromosome chr1 scaffold_135 Hypothetical protein miRNA 20 GUUUAAGAAGUGUUAGAGGU 1
    .: ::::::::::::::.:
    Target 141 AGAUUUCUUCACAAUCUCUA 160
    vun-mir5561 TC1062 H+/Ca2+ exchanger 2 Transporter miRNA 20 UGUAAAUCUCUCUCUCUCUA 1
    : :::::::::::::::::
    Target 8 AGAUUUAGAGAGAGAGAGAG 27
    vun-mir5561 TC8162 GTPase Metabolism miRNA 20 UGUAAAUCUCUCUCUCUCUA 1
    :..: ::::::::::::::
    Target 102 AUGUAUAGAGAGAGAGAGAG 121
    vun-mir5561 TC11798 Cold shock domain Stress related miRNA 20 UGUAAAUCUCUCUCUCUCUA 1
    ::: : : ::::::::::::
    Target 2 ACAGUGACAGAGAGAGAGAU 21
    vun-mir5758 TC975 Chromosome chr11 scaffold_13 Hypothetical protein miRNA 21 GUUUAUGUAUCUAGGUUGAAU 1
    :::::::::::::::::::::
    Target 213 CAAAUACAUAGAUCCAACUUA 233
    vun-mir5758 TC5742 Pyrophosphate-dependent phosphofructo-1-kinase Signal transduction miRNA 21 GUUUAUGUAUCUAGGUUGAAU 1
    .:::::.::::::::::: ::
    Target 306 UAAAUAUAUAGAUCCAACCUA 326
    vun-mir5758 TC16939 Chromosome undetermined scaffold_310 Hypothetical protein miRNA 20 UUUAUGUAUCUAGGUUGAAU 1
    :::::::: :::::::: ::
    Target 509 AAAUACAUUGAUCCAACGUA 528
    vun-mir5770 TC1925 Amine oxidase Metabolism miRNA 21 AGUAGGUUUGGUAUCAGGAUU 1
    :::::::::::::::::::::
    Target 165 UCAUCCAAACCAUAGUCCUAA 185
    vun-mir5770 TC5168 Copper amine oxidase Metabolism miRNA 21 AGUAGGUUUGGUAUCAGGAUU 1
    :..::::::::::::::: ::
    Target 148 UUGUCCAAACCAUAGUCCAAA 168
    vun-mir5770 TC18480 Ribonuclease H Transcription factor miRNA 20 GUAGGUUUGGUAUCAGGAUU 1
    :::.:::.:.:::::..:::
    Target 613 CAUUCAAGCUAUAGUUUUAA 632
    vun-mir5770 TC1738 Allyl alcohol dehydrogenase Metabolism miRNA 20 GUAGGUUUGGUAUCAGGAUU 1
    ::::.::::. ::::.::.:
    Target 766 CAUCUAAACUUUAGUUCUGA 785
    vun-mir6252 FG841373 Nucleoporin-like protein Transcription factor miRNA 23 UUGGGAGAGAGUUGUGUUGAGUA 1
    :::::::::::::::::::::::
    Target 24 AACCCUCUCUCAACACAACUCAU 46
    vun-mir6252 FG857360 Membrane protein Transporters miRNA 21 GGGAGAGAGUUGUGUUGAGUA 1
    .::::::::::::: :::::
    Target 247 UCCUCUCUCAACACUCCUCAU 267
    vun-mir6252 TC15301 Homeobox domain, ZF-HD class Transcription factor miRNA 23 UUGGGAGAGAGUUGUGUUGAGUA 1
    : : :::::::::: :::::::
    Target 9 AUCACUCUCUCAACUCAACUCAA 31
    vun-mir7696 FG864277 BZIP transcription Transcription factor miRNA 20 AAAACUUAAAUUCAUGAACA 1
    ::::::::::::::::::::
    Target 17 UUUUGAAUUUAAGUACUUGU 36
    vun-mir7696 FF383199 Olfactory receptor Signal transduction miRNA 20 AAAACUUAAAUUCAUGAACA 1
    :::: : ::::::::::::
    Target 141 UUUUUAUUUUAAGUACUUGG 160
    vun-mir8182 TC3507 Pectin methylesterase Metabolism miRNA 21 CAGUAGUGUUUGCGUUGUGUU 1
    ::::::::::..:::::: :.
    Target 654 GUCAUCACAAGUGCAACAGAG 674
    vun-mir9748 TC16306 Lectin-like protein kinase Signal transduction miRNA 22 GAGGAGGGAGUUGUGAAGGAAG 1
    :.:::..:::::::::::::.
    Target 17 CGUCUCUUUCAACACUUCCUUU 38
    vun-mir9748 TC1064 Zinc finger, RING-type: Thioredoxin-related Transcription factor miRNA 22 GAGGAGGGAGUUGUGAAGGAAG 1
    .:::::.::::::.::.::::
    Target 16 UUCCUCUCUCAACUUUUUCUUC 37
    vun-mir9748 TC9843 Beta-xylosidase/alpha-L-arabinosidase Metabolism miRNA 20 GGAGGGAGUUGUGAAGGAAG 1
    :.::..:::::::::::::
    Target 478 CUUCUUUCAACACUUCCUUG 497
    vun-mir9748 TC15743 Heat shock protein Stress related miRNA 22 GAGGAGGGAGUUGUGAAGGAAG 1
    :::.:::::::::.::.::::
    Target 244 CUCUUCCCUCAACGCUCUCUUC 265
    vun-mir9748 TC15591 Transcription factor AHAP2 Transcription factor miRNA 22 GAGGAGGGAGUUGUGAAGGAAG 1
    .::.:::::::: :::::: ::
    Target 64 UUCUUCCCUCAAGACUUCCAUC 85
    vun-mir9748 TC298 Glutathione reductase Metabolism miRNA 20 GGAGGGAGUUGUGAAGGAAG 1
    .:::.:::::::::.::::
    Target 95 UCUCUCUCAACACUCUCUUC 114
    vun-mir9748 TC1040 Glycine-rich protein 2b Transcription factor miRNA 20 GGAGGGAGUUGUGAAGGAAG 1
    ::.::::.::::::::::
    Target 567 ACUUCCUCUGCACUUCCUUC 586
     | Show Table
    DownLoad: CSV

    Majority (27%) of the newly characterized cowpea miRNAs are observed to regulate the metabolic proteins. Such findings regarding metabolism related genes targeted by miRNAs are similar with the prior publications in plants and animals [28,43,44]. Pectin methylesterase (PME) is an important enzyme that acts on pectin, a major component of plant cell wall. PME catalyzes reactions according to the double-displacement mechanism [51]. In this study, the PME is predicted as a putative target for vun-miR1882. Thus the vun-miR1882 is a valuable resource to regulate cell wall. Another important enzyme ribulose-1, 5-bisphosphate carboxylase (Rubisco) is a key enzyme in photosynthesis and photorespiration, where it catalyzes the fixation of CO2 and O2, respectively. Due to its rate-limiting property in photosynthesis, it is the prime focus of improving the plant productivity [52]. The cowpea miRNA (vun-miR2657) is predicted to target this important enzyme which is the potential resource to modify Rubisco expression and ultimately plant productivity.

    The transcription factor myeloblastosis (MYB) is an important regulator of many developmental and physiological processes in plants. Ballester et al. [53], suggested that the MYB also plays a significant role in regulating the flavonoid pathway in plants. The newly identified cowpea miRNA family vun-834 is found to target the MYB transcription factors. Thus this miRNA is an important resource to fine tune the MYB regulation for the desirable traits in cowpea fruit. The transcription factor, zinc finger is believed to be involved in many biotic and abiotic stresses as responding gene to manage the plant under these stresses [54]. The same family of transcription factor is also reported to play a crucial role in plant development [55]. The newly identified cowpea miRNA families vun-miR834 and 4992 are found to target this zinc finger transcription factor family. These miRNAs are important resources to regulate the zinc finger family proteins for the betterment of cowpea under various biotic and abiotic stresses and fruit development.

    Similarly 12% targeted genes by cowpea miRNAs are engaged in transport activities. ATP-binding cassette transporters comprise a highly conserved family of ATP-binding proteins that are involved in transporting of various molecules across plasma membrane. Here vun-miR1514 is identified to target ATP-binding cassette transporters. Such findings are in agreement with the other workers in the miRNA field [37,43].

    Biotic and abiotic stresses like salinity, drought, temperature extremities, heavy metals, pathogen attacks, and pollution cause huge yield reductions in plants [56]. Naturally plants have various systems to protect themselves from these stresses that occur at various levels, i.e., at whole plant, tissue, cellular, sub-cellular, genetic and molecular levels [56,57,58,59,60]. Many studies suggest that plant miRNAs are involved in these stresses [9,17,61]. In this study identified miRNAs such as vun-miR1525,2657 and 9748 also targeted heat shock proteins that expressed in response of heat stress. This suggests the role of these miRNAs during the heat stressed condition of plants. Similar findings were reported in switch grass [17].

    Some miRNAs of cowpea were observed to target the protein functioning in the process of cell signal transduction. Almost similar findings were observed by many researchers in various organisms [42,43]. Protein kinases are key regulators of cell function and play crucial role in protein phosphorylation and dephosphorylation that are major signaling pathways induced by osmotic stress in higher plants. Similarly, SNF1 (sucrose non-fermenting-1) is an osmotic-stress-activated protein kinase in Arabidopsis thaliana that can significantly impact drought tolerance of Arabidopsis thaliana plants [62]. These two important proteins were targeted by cowpea miRNAs families, like vun-miR435,2606,2609 and 4392 respectively. Serine/threonine protein kinase (STPKs) is another protein kinase that is targeted by miRNA family (miR5241), act as sensors of environmental signals and regulate different developmental changes and also host pathogen interactions [63].

    In this study, newly profiled cowpea miRNAs were also observed to target hypothetical proteins, growth and development, structural proteins and disease related proteins. Such findings were also published earlier [19,21,37].

    4. Conclusion

    The current study is resulted 46 new miRNAs and their 138 targeted genes in an important commercial plant cowpea. All these miRNAs are profiled for the first time in cowpea. These findings will serve as resources to fine tune cowpea plant at micro-molecular level. This will help us to enhance the production ability of cowpea against biotic and abiotic stress tolerance. Furthermore these miRNAs and their targets are also powerful functional genomic resources in the Kingdom plantae.

    Conflict of Interest

    The authors declare that there is no conflict of interest regarding the publication of this article.



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