Synonymous Sites for Accessibility around MicroRNA Binding Sites in Bacterial Spot and Speck Disease Resistance Genes of Tomato

Synonymous Sites for Accessibility around MicroRNA Binding Sites in Bacterial Spot and Speck Disease Resistance Genes of Tomato | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Synonymous Sites for Accessibility around MicroRNA Binding Sites in Bacterial Spot and Speck Disease Resistance Genes of Tomato Yengkhom Sophiarani, Supriyo Chakraborty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2196207/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The major causes of mass tomato infections in both covered and open ground are agents of bacterial spot and bacterial speck diseases. MicroRNAs (miRNAs) are 16–21 nucleotides in length, non-coding RNAs that inhibit translation and trigger mRNA degradation. MiRNAs play a significant part in plant resistance to abiotic and biotic stresses by mediating gene regulation via post-transcriptional RNA silencing. In this study, we analyzed a collection of bacterial resistance genes of tomato and their binding sites for tomato miRNAs and Pseudomonas syringe pv. tomato miRNAs. Our study found that two genes, bacterial spot disease resistance gene (Bs4) and bacterial speck disease resistance gene (Prf), have a 7mer-m8 perfect seed match with miRNAs. Bs4 was targeted by one tomato miRNA (sly-miR9470-3p) and three Pseudomonas syringe pv. tomato miRNAs (PSTJ4_3p_27246, PSTJ4_3p_27246 and PSTJ4_3p_27246). Again, Prf gene was found to be targeted by two tomato miRNAs viz ., sly-miR9469-5p and sly-miR9474-3p. The accessibility of the miRNA-target site and its flanking regions, as well as the relationship between relative synonymous codon usage (RSCU) and tRNAs were compared. Strong access to miRNA targeting regions and decreased rate of translations suggested that miRNAs might be efficient in binding to their particular targets. We also found the existence of rare codons, which suggests that it could enhance miRNA targeting even more. The codon usage pattern analysis of the two genes revealed that both were AT-rich (Bs4 = 63.2%; Prf = 60.8%). We found a low codon usage bias in both genes, suggesting that selective restriction might regulate them. The silencing property of miRNAs would allow researchers to discover the involvement of plant miRNAs in pathogen invasion. However, the efficient validation of direct targets of miRNAs is an urgent need that might be highly beneficial in enhancing plant resistance to multiple pathogenic diseases. Bacterial spot disease resistance gene Bacterial speck disease resistance gene MicroRNA Codon usage bias Site accessibility Figures Figure 1 Figure 2 Figure 3 Figure 4 Key Message Strong access to miRNA binding sites in bacterial spot and speck disease resistance genes of tomato and decreased rate of translations suggested that miRNAs might be efficient in binding to their particular targets. 1. Introduction Tomato ( Solanum lycopersicum ) has excellent nutritional and gastronomic value, making it the world's second most significant vegetable crop after potato (Gerszberg, et al., 2015). Tomatoes are infected by a variety of pathogens, including bacteria, viruses, and fungi. Bacterial speck and bacterial spot are the two most frequent bacteria-induced tomato diseases. Bacterial speck disease of tomato (caused by the bacterium Pseudomonas syringae pv. tomato) can be found everywhere tomatoes are cultivated (Preston, 2000 ). The disease severely damages the leaves early in the growing season, resulting in a drastically reduced yield. When symptoms occur on tomato fruit, the disease has the potential to have a significant impact on quality and market value for corporate tomato farmers. Bacterial spot is a major tomato disease all over the world. Different species of the genus Xanthomonas cause bacterial spot disease (but primarily by Xanthomonas perforans ) (Koenraadt, et al., 2007). It can infect only the green fruits and not the red fruits. This disease, like bacterial speck, may be a major tomato disease that is difficult to treat when the disease pressure is high under favourable climatic conditions. The necessity for protection against pathogen is thought to be a powerful evolutionary force that leads to diverse selection and significant levels of diversity in plant genes encoding essential defense-related proteins. Many studies have found that microRNAs (miRNAs) are extremely sensitive to various physiological processes such as abiotic or biotic stress. MicroRNAs are small (16–21 nucleotides) non-coding RNAs that form the miRNA-induced silencing complex (miRISC) with argonaute proteins to inhibit the translation process and induce mRNA degradation (Fabian and Sonenberg, 2012 ). The degree of translational inhibition and mRNA degradation for each target region of miRNA might be quite different (Béthune, et al., 2012 ; Djuranovic, et al., 2012 ; Nam, et al., 2014; Selbach, et al., 2008). Poor miRNA binding or RNA binding proteins (RBPs) influencing inhibition might explain the variation in inhibition for particular target regions (Kedde, et al., 2010; Kertesz, et al., 2007; Kundu, et al., 2012). Previous researches have revealed the mechanisms by which miRNAs detect their targets in the 5′ untranslated regions (UTR), coding sequences (CDS), and 3′UTR and assessed whether these regions might influence miRNA-mediated suppression (Cottrell, et al., 2017 ; Kertesz, et al., 2007). MiRNAs are known to control the expression of a variety of developmental and stress-related genes. In plants, miRNA target sites were identified in the CDS and 3′ UTRs of mRNA, however it was not confirmed whether the efficacy of regulation was associated to target site locations or not (Bartel, 2009 ; Jones-Rhoades and Bartel, 2004 ). Previous study has shown that protein coding sequences can encode regulatory information by selecting certain synonymous codons (Itzkovitz, et al., 2010 ). In this study, we attempted to evaluate whether selection on synonymous codons near miRNA target regions might occur at the gene level. Based on 7mer-m8 seed match, we identified the miRNA targets in the CDS of bacterial spot disease resistance gene (Bs4) and bacterial speck disease resistance gene (Prf) of tomato. It has been reported that the majority of miRNA targets in plants were found in protein-coding domains (Jones-Rhoades and Bartel, 2004 ). Plant miRNAs have nearly perfect target site matching, making computational prediction of miRNA targets much more effective (Dai, et al., 2011 ). As a result, we tested our hypothesis using the coding sequences of bacterial spot and bacterial speck disease resistance genes of tomato. To determine the parameters that could control miRNA binding to target mRNA molecules, we predicted the miRNA target genes and then evaluated the target site accessibility, translational efficiency of the miRNA target's upstream, downstream, and target regions, mRNA stability, codon usage bias, and nucleotide base compositions using bioinformatics tools. In addition, we employed the free energy calculation technique, compAI, and COSM, as well as a t-test to analyse the variation among the upstream, downstream and target regions. 2. Methodology 2.1. Coding sequences and microRNAs data The complete coding sequences ( CDS) of bacterial disease resistance genes of tomato were retrieved from the Plant Resistance Genes database (PRGdb 4.0; http://prgdb.org/prgdb4/ ) and National Center for Biotechnology (NCBI) GenBank database ( http://www.ncbi.nlm.nih.gov ). The mature miRNA sequences of tomato (147 in total) were also downloaded from miRBASE version 21 ( http://www.mirbase.org ). The miRNAs of Pseudomonas syringae pv. tomato ( Pst ) were collected from our previously reported data (Sophiarani and Chakraborty, 2021 ). The coding regions of bacterial disease resistance genes were searched for Pst and tomato miRNA targets. 2.2. MicroRNA target prediction in coding sequences The 7mer-m8 seed match was used to find the miRNA targets in the CDS of bacterial disease resistance genes of tomato. A 7mer-m8 miRNA seed is defined as a of region 2–8 nucleotides in the miRNA 5'-3' direction (Peterson, et al., 2014). Watson-Crick (WC) base pairing has been used by the miRNAs to bind to their targets. The miRNA non-seed region is positioned adjacent to the miRNA seed. In the present study up to 4 mismatches between miRNA non-seed and target mRNA were permitted. After carefully screening the outputs, we selected only the CDS with the highest number of miRNA targets for further investigation. To obtain a deeper understanding, the miRNA target region, including its upstream and downstream regions, were all cleaved and saved individually with codons in a frame. Based on the findings of (Kertesz, et al., 2007), we collected 18 nucleotides (6 codons in frame) from the miRNA target’s upstream and downstream sections separately. 2.3. MiRNA target free energy and site accessibility analysis Site accessibility describes how easily a miRNA-RISC complex may identify and hybridize with its target mRNA sequence (Axtell, et al., 2011 ; Gu, et al., 2012). The hybridization of miRNA with its target mRNA involves binding of miRNA to a specific accessible region of mRNA and unfolding of the mRNA once it completes binding to the target (Peterson, et al., 2014; Riolo, et al., 2020). In this study, we employed the SantaLucia formula (in a 7bp sliding window at 37°C) to evaluate the folding energy/free energy (in kcal/mol) of the target mRNA and its sequence flanks, as well as the accessibility of the miRNA-binding region (SantaLucia, 1998 ). In this case, the absolute value of folding energy was taken into account, and the larger value of folding energy was interpreted as significant mRNA folding. For each miRNA target, the folding energies of the upstream, downstream and target regions were computed. 2.4. Translational efficiency analysis The parameter compAI was used to estimate the translational efficiencies of the miRNA targets. It was used to compare the miRNA-binding sites and their flank regions. The value of compAI varies from 0 to 1, with 0 denoting the slowest and 1 denoting the fastest translation rate (Dilucca, et al., 2015). The translational efficiency assessed by compAI is unaffected by gene expression bias. It examines the competition of comparable tRNA species. 2.5. Cosine similarity metric for microRNA targets analysis The parameter cosine similarity metric (COSM) was used to measure the degree of parallel association between tRNA loops and relative synonymous codon usage (RSCU) (Sun, et al., 2016). COSM values vary from 0 to 1, with 0 signifying the highest similarity and 1 denoting the least similarity. 2.6. Nucleotide composition analysis We calculated the nucleotide composition of the CDS of bacterial spot and bacterial speck disease resistance genes of tomato for (i) overall nucleotide composition (A%, T%, G%, and C%), and its composition at the 3rd codon position (A3%, T3%, G3% and C3%); (ii) frequencies of nucleotides GC (total G and C nucleotides) present at the 1st (GC1%), 2nd (GC2%), and 3rd (GC3%) synonymous codon positions; and (iii) total GC and AT3% (total A and T nucleotides at the 3rd synonymous codon positions) of the genes. 2.7. Relative Synonymous Codon Usage (RSCU) analysis The relative synonymous codon usage (RSCU) values of different synonymous codons in the CDS of bacterial spot and bacterial speck disease resistance genes of tomato were determined using the formula: Where, g ij represents the frequency of the relative codon usage of the i th codon for the j th amino acid which is encoded by n i synonymous codons (Sharp, et al., 1993). A positive codon usage bias related to a given codon is indicated by an RSCU value larger than one, and the associated codon is called a favoured codon. However, RSCU less than 1 denotes a bias against the usage of codons, and the associated codon is regarded as less prevalent for the relevant amino acid (Sau, et al., 2006). When the RSCU score is 1, the codon is considered unbiased for the specific amino acid and is selected equally or randomly in the RNA transcript with other synonymous codons from the same family. Furthermore, the synonymous codons with RSCU value higher than 1.6 are considered as over-represented whereas those codons with value less than 0.6 are considered under-represented (Butt, et al., 2014 ). 2.8. Synonymous codon usage order (SCUO) analysis In the present study, we employed SCUO as a metric to quantify the codon usage as well as gene expression. It determines how frequently synonymous codons of an amino acid are being used in a non-random manner. SCUO has a value ranging from 0 (lowest) to 1 (highest) (Angellotti, et al., 2007). Genes with a strong preference for specific codons are highly expressed, whereas genes with little or no codon preference are often underexpressed (Wan, et al., 2003 ). 2.9. Statistical analysis The t-test statistical analysis was used to understand the nucleotide base compositional changes of target regions in relation to upstream and downstream regions individually. 3. Results 3.1. MicroRNA targets in the coding sequences of tomato bacterial spot and speck disease resistance genes The 7mer-m8 seed match was used to search for miRNA binding sites on the CDS of disease resistance genes of tomato. The collected data were filtered, and two genes [bacterial spot disease resistance gene (Bs4) and bacterial speck disease resistance gene (Prf)] were chosen for further investigation. In this study, it was found that three Pseudomonas syringae pv. tomato ( Pst ) miRNAs (PSTJ4_3p_27246, PSTJ4_3p_27246 and PSTJ4_3p_27246) and one tomato miRNA (sly-miR9470-3p) targeted the Bs4 gene (Table 1 ). Additionally, two tomato miRNAs, sly-miR9469-5p and sly-miR9474-3p were found to target the Prf gene (Table 2 ). However, we found no Pst miRNA targets in the Prf CDS. (Brodersen, et al., 2008) suggested that in the miRNA-mRNA target hybrids, imperfect pairing with central mismatches enhances translational repression because it prevents slicing. On the other hand, translational repression is prevented by miRNAs that have exact central matches to their target mRNA, which allows for splicing. In this study, we observed that all of the miRNAs have central mismatches with their target mRNAs (Fig. 1 ). As a result, we hypothesized that these miRNAs might suppress the expression of Bs4 and Prf genes by translational repression, which is comparable to the findings of (Brodersen, et al., 2008). Table 1 MicroRNAs that might target Bs4 gene Name of miRNA miRNA sequence (excluding 1st base at 5՝ end) Target gene sequence (SeqFlank|Seed) Target gene nucleotide position in the coding sequence Binding sites of target gene and miRNA Tomato miRNA sly-miR9470-3p CGAUUUUAGGUACUCGGUUU SEQ-5'-ACTTAGAGAAAC|TAAAATC-3' 620–639 seq-5'-ACTTAGAGAAAC|TAAAATC-3'/miR-3'-TTTGGCTCATGG|ATTTTAG-5' Pseudomonas syringae pv. tomato miRNA PSTJ4_3p_27246 GAGUUCAUCAAGGGCGCGC SEQ-5'-GACCACCAATG|ATGAACT-3' 2661–2679 seq-5'-GACCACCAATG|ATGAACT-3'/miR-3'-CGCGCGGGAAC|TACTTGA-5' PSTJ4_3p_27246 GAGUUCAUCAAGGGCGCGC SEQ-5'-GACCACCAATG|ATGAACT-3' 3036–3054 seq-5'-GACCACCAATG|ATGAACT-3'/miR-3'-CGCGCGGGAAC|TACTTGA-5' PSTJ4_3p_27246 GAGUUCAUCAAGGGCGCGC SEQ-5'-GACCACCAATG|ATGAACT-3' 3330–3348 seq-5'-GACCACCAATG|ATGAACT-3'/miR-3'-CGCGCGGGAAC|TACTTGA-5' Table 2 MicroRNAs that might target Prf gene Name of miRNA miRNA sequence (excluding 1st base at 5՝ end) Target gene sequence (SeqFlank|Seed) Target gene nucleotide position in the coding sequence Binding sites of target gene and miRNA Tomato miRNA sly-miR9469-5p CCACAUAAGAAGACCGAAUUC SEQ-5'-CAATACATCATTC|TTATGTG-3' 1524–1544 seq-5'-CAATACATCATTC|TTATGTG-3'/miR-3'-CTTAAGCCAGAAG|AATACAC-5' sly-miR9474-3p UGACAUCAUAGACGCUUGUUUU SEQ-5'-ACAACCCGCTTGAA|TGATGTC-3' 3685–3706 seq-5'-ACAACCCGCTTGAA|TGATGTC-3'/miR-3'-TTTTGTTCGCAGAT|ACTACAG-5' 3.2. Target site accessibility We first calculated the free energy/folding energy (kcal/mol) of mRNA secondary structure using SantaLucia method in a 7-bp sliding window at 37°C, moving upstream and downstream in 18-nucleotide steps from the real miRNA target region for each gene to understand the differences that might exist among these regions. Lastly, we calculated the mean free energy in each miRNA target's upstream, downstream and target sequences (Table 3 ). Here, we found that the free energy values in the upstream, downstream, and target regions varied from 2.8 to 4.13 kcal/mol, indicating weak folding of the target site and sequence flanks (Tuller, et al., 2010). It was suggested that a positive free energy value indicates selection for loose RNA secondary structure at miRNA target sites (Gu, et al., 2012). Therefore, we hypothesized that the miRNA binding domains (target and flank) on these genes might fold into loose secondary structures. Table 3 Free energy of upstream, downstream, and microRNA target regions Genes microRNA Statistics Free energy (Kcal/mol) Up stream Down stream Target Tomato miRNA Bs4 sly-miR9470-3p Mean 3.3 2.91 2.8 Prf sly-miR9469-5p Mean 4.13 3.03 2.96 Prf sly-miR9474-3p Mean 3.16 2.97 3.79 Pseudomonas syringae pv. tomato miRNA Bs4 PSTJ4_3p_27246 Mean 2.83 3.86 3.35 Bs4 PSTJ4_3p_27246 Mean 2.83 3.86 3.35 Bs4 PSTJ4_3p_27246 Mean 2.83 3.86 3.35 3.3. Rare codon usage and translational efficiency To determine the rate of translation in each gene segment, the CDS of the Bs4 and Prf were examined in their upstream, downstream and target regions of the miRNA targets. In the present study, we found that the mean values of compAI in the two genes' upstream, downstream, and target areas exhibited lower translational efficiency (Table 4 ). Many studies have revealed that miRNA binding in the CDS results in translational inhibition (Brümmer and Hausser, 2014 ; Hausser, et al., 2013). Furthermore, it was reported that the miRNA-mediated translational repression with a high translation rate was shown to be more strongly repressed (Cottrell, et al., 2017 ). As a result, the low translational efficiency of the three regions reported in this study might interfere with the suppression of miRNA-mediated gene expression process. We calculated the COSM values to determine if the RSCU values correspond to the number of tRNA species. In general, COSM value ranges from 0 to 1 and a COSM value of 1 suggests a close resemblance, whereas a value of 0 reveals total dissimilarity. Table 5 shows the COSM values of the two genes in their upstream, downstream, and target regions. We found that the mean values of COSM were nearly equal to 0 in the three regions of the two genes, suggesting a weak association between synonymous codons found in the three regions and the tRNA pool. As a result, we hypothesized that the codons accessible in the three regions of Bs4 and Prf genes might be nonoptimal, resulting in a low translation efficiency. This is comparable to the previous work on growth rate-optimized tRNA abundance and codon usage, which revealed that the more frequently used codons, recognized by numerous tRNAs, resulted in faster translation elongation and greater translation efficiency (Berg and Kurland, 1997 ; Gustafsson, et al., 2004 ). Table 4 Rate of translation in the three regions of microRNA target genes of bacterial resistance genes of tomato Gene Upstream Downstream Target Target regions for tomato miRNA Bs4 0.39 0.08 0.15 Prf 0.41 0.25 0.23 Prf 0.28 0.13 0.36 Target region for Pseudomonas syringae pv. tomato miRNA Bs4 0.16 0.29 0.28 Table 5 Cosine similarity metric in upstream, downstream and target regions of bacterial resistance genes of tomato Gene Upstream Downstream Target Target regions for tomato miRNA Bs4 0.16 0.37 0.18 Prf 0.11 0.16 0.21 Prf 0.21 0.23 0.34 Target region for Pseudomonas syringae pv. tomato miRNA Bs4 0.09 0.14 0.23 3.4. Nucleotide base composition analysis in the coding sequences of Bs4 and Prf genes The overall nucleotide composition and CUB indices of the CDSs of Bs4 and Prf genes which included frequencies of nucleotide bases (adenine, guanine, cytosine and thymine) at the 3rd codon positions (A3%, G3%, C3% and T3%), GC and AT contents at the 3rd codon position (GC3% and AT3%) were estimated to understand their effect on CUB (Table 6 ). The nucleotide composition analysis at the 3rd codon position revealed that the average percentage of T3 (37.5%) was found to be the highest followed by A3 (29.15%), G3 (17.45%) and C3 (15.95%) in the two genes analysed. GC and AT distributions over the two genes were shown in Fig. 2 . The CDS of Bs4 and Prf have an overall GC content lower than 50% (Bs4 = 36.8%; Prf = 39.1%). This suggested that the GC content of both genes was low. The AT distributions of the CDS of Bs4 and Prf were significantly distinct ( p ≤ 0.01). This revealed that the AT-ending codons are systematically preferred over the GC-ending ones. 3.5. Most favoured codons for bacterial spot and speck disease resistance genes of tomato The patterns of codon usage in the two genes were evaluated using relative synonymous codon usage (RSCU) analysis. The heatmap of RSCUs (Fig. 3 ) revealed that Bs4 and Prf originated to encode for amino acids using a limited number of relatively optimum (A/T-ending) codons and were selectively enriched in AT-ending codons. Among the 59 synonymous codons of Bs4 and Prf genes we found 6 highly preferred codons (RSCU > 1.6) [TCT (Ser), CCA (Pro), AGA (Arg), ACA (Thr), GCT (Ala) and GAT (Asp)] for Bs4 gene, and 8 highly preferred codons [TCA (Ser), TCT (Ser), CCT (Pro), CAT (His), AGA (Arg), ACT (Thr), GTT (Val) and GAT (Asp)] for Prf gene (Fig. 4 ). In the present study, all the favoured codons of Bs4 and Prf genes ended with A/T. Previous research had revealed that GC-poor codons were probably selected for the miRNA target region in plants to increase site accessibility and facilitate miRNA binding (Gu, et al., 2012). Our findings revealed that the Bs4 and Prf genes favored AT-rich codons across the miRNA-binding sites. These findings clearly show that evolution has resulted in significantly different codon and amino acid distributions in the Bs4 and Prf genes. 3.6. Relationship between codon usage and gene expression The Bs4 and Prf genes exhibited SCUO values close to 0 (Bs4: SCUO = 0.09; Prf: SCUO = 0.1), indicating a low codon usage pattern. Moreover, we conducted the correlation analysis between ENC (effective number of codons), which determines the degree of synonymous codon bias in a particular gene or genome (Wright, 1990 ) and SCUO values to assess if the codon usage pattern influenced gene expression in the two genes. Here, we found a significant negative correlation (r = -1.00, p < 0.01) between ENC and SCUO. Our results suggested that gene expression level might have been influenced by the codon usage pattern. 4. Discussion Many studies have shown that protein coding sequences can encode regulatory information by exploiting certain synonymous codons (Bollenbach, et al., 2007 ; Itzkovitz, et al., 2010 ). MiRNAs have gained prominence due to their vital role in gene regulation, which may lead to the development of numerous disorders (Liu and Wang, 2019 ; Ni and Leng, 2015 ). The regulatory process starts when miRNAs bind to their target sites in mRNAs (Brodersen and Voinnet, 2009 ). MiRNA binding, which is brought on by local mRNA secondary structure or upstream translation efficiency, might be impacted by the surrounding nucleotides of miRNA target sites (Lin and Ganem, 2011). Several studies have suggested that miRNAs trigger mRNA degradation by binding to 3ʹ untranslated regions (UTRs) (Grimson, et al., 2007; Guo, et al., 2010; Nielsen, et al., 2007), while miRNAs binding to the CDS mostly inhibit translation (Brümmer and Hausser, 2014 ; Larsson, et al., 2010 ). Since, our miRNAs all targeted in the 3ʹ UTRs regions of the CDS (Tables 1 and 2 ), wet lab validation is required to better describe the amplitude and time-scale of gene regulation at CDS sites. It was suggested that miRNA binding to 3ʹ UTRs leads to changes in protein abundance mostly through mRNA deadenylation, whereas binding to CDS sites primarily represses translation with a minimal influence on the polyA tail of the target mRNA (Brümmer and Hausser, 2014 ). In this study, we analyzed the local translation efficiency and site accessibility in the upstream, downstream, and target regions of all coding targets of tomato and Pst miRNAs in the CDS of bacterial spot and bacterial speck disease resistance genes of tomato. In a previous study it was revealed that site accessibility and the translational process were the two most important elements in miRNA binding to its targets (Gu, et al., 2013). MiRNA activity is also influenced by RNA secondary structure around miRNA target sites. We have found that the free energies calculated in each gene's three locations (upstream, downstream and target) were low, suggesting weak mRNA folding and easy access to miRNA. We also observed a weak association between the RSCU values of the synonymous codons found in the three regions and the tRNA pool, suggesting that the existence of rare codons might enhance miRNA targeting even more. (Elf, et al., 2003) revealed that in response to amino acid deficiency, the charge levels of different tRNAs recognizing synonymous codons varied dramatically in bacteria. As a result, while some synonymous tRNA pools remain completely charged, the charged fraction of others might fall to zero. Therefore, an efficient validation of direct targets of miRNAs is an urgent need that might be highly beneficial in enhancing plant resistance to multiple pathogenic diseases. We found that the synonymous codons around the target sites were GC-poor. This finding is comparable to the previous study that GC-poor codons were locally favoured around miRNA target sites for loose secondary structure (Gu, et al., 2012). As a result, we hypothesized that the miRNAs might have functioned in such a way as to reach the target locations easily. Since the coding segments were AT-rich, their complementary miRNAs were also AT-rich. Thus, miRNA binding to their target regions might be greater, resulting in effective repression of disease resistance genes. Again, lower free energy of miRNA targets was associated with lower stability. This is consistent with the findings of a previous study (Gu, et al., 2012), which found significantly lower mRNA stability at miRNA targets in Oryza sativa and Zea mays . In this study, we found no selection signal in the miRNA target regions (upstream, downstream and target). This is consistent with the observation of low purifying selection in A. thaliana with novel miRNA genes and its targets (Cuperus, et al., 2011 ; Fahlgren, et al., 2010). Previous studies revealed that in A. thaliana , synonymous codons were largely chosen for efficient and accurate mRNA translation (Morton and Wright, 2007 ). We found that translational rates were low in the three miRNA target regions (upstream, downstream and target) of Bs4 and Prf genes. Our result is comparable to the findings that selection for translation in A. thaliana inhibits the selective pressure for site accessibility (Gu, et al., 2012). Thus, we could come to the conclusion that the ineffective translation process might inhibit the miRNA-induced gene regulation. Furthermore, it was revealed that site accessibility is a local selection limit operating on the synonymous codons in the miRNA target area, as opposed to translation efficiency and accuracy, indicating that in A. thaliana , the selection signal of enhanced site accessibility at miRNA target sites is most likely messed up by strong signals caused by translational selection . We found a weak codon usage bias in the two genes studied. (Gu, et al., 2012) reported in their study that certain miRNA targeting the CDS with a higher codon usage bias preferred to use rare codons in the upstream region of their target sites. This observation was contrary to our result. Although we studied the miRNA targets in the protein coding sequences of Bs4 and Prf genes by comparing just to a few restricted previous findings (Gu, et al., 2009; Lin and Ganem, 2011), the conclusions based on a single or several miRNA targets may be biased. As a consequence, we revealed that several additional genomic properties, such as folding energy near miRNA targets or local mRNA secondary structure (Kertesz, et al., 2007; Long, et al., 2007), could be potential factors facilitating miRNA binding, which is similar to the findings of (Lin and Ganem, 2011). 5. Conclusion In conclusion, we hypothesize that synonymous codon usage in the upstream, downstream, and target regions of miRNA targets is related to local translation efficiency in Bs4 and Prf genes of tomato. The selective restrictions around most miRNA targets, however, were too weak to be observed. When miRNA targets are located in the CDS, additional genetic traits, in addition to local translation efficiency, might even be more relevant to miRNA activity. We provide a description of selective effects on synonymous codon usage in the three regions of miRNA targets for optimal miRNA activity by assessing the local translation efficiency of all miRNA targets in the CDS of the two genes. Declarations Conflicts of Interest Authors declare no conflicts of interest in this study. Acknowledgments We are thankful to Assam University, Assam, India for providing the necessary facilities in carrying out this work. Funding : Not funded Authorship contribution statement YS and SC conceptualized the research idea, investigated the problem, applied appropriate methodology, collected, curated and analyzed data and interpreted the results. YS wrote the original manuscript with tables and figures. Further, SC supervised the entire research work, reviewed and edited the final manuscript. 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Nat Struct Mol Biol 16(2):144–150 Gu W et al (2012) Selection on synonymous sites for increased accessibility around miRNA binding sites in plants. Mol Biol Evol 29(10):3037–3044 Gu W et al (2013) Biological basis of miRNA action when their targets are located in human protein coding region. PLoS ONE 8(5):e63403 Gu W et al (2012) Translation efficiency in upstream region of microRNA targets in Arabidopsis thaliana. Evolutionary Bioinf 8:EBO Guo H et al (2010) Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466(7308):835–840 Gustafsson C, Govindarajan S, Minshull J (2004) Codon bias and heterologous protein expression. Trends Biotechnol 22(7):346–353 Hausser J et al (2013) Analysis of CDS-located miRNA target sites suggests that they can effectively inhibit translation. Genome Res 23(4):604–615 Itzkovitz S, Hodis E, Segal E (2010) Overlapping codes within protein-coding sequences. Genome Res 20(11):1582–1589 Jones-Rhoades MW, Bartel DP (2004) Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 14(6):787–799 Kedde M et al (2010) A Pumilio-induced RNA structure switch in p27-3′ UTR controls miR-221 and miR-222 accessibility. Nat Cell Biol 12(10):1014–1020 Kertesz M et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39(10):1278–1284 Koenraadt H, et al (2007) Development of specific primers for the molecular detection of bacterial spot of pepper and tomato. In, II International Symposium on Tomato Diseases 808 . p. 99–102 Kundu P et al (2012) HuR protein attenuates miRNA-mediated repression by promoting miRISC dissociation from the target RNA. Nucleic Acids Res 40(11):5088–5100 Larsson E, Sander C, Marks D (2010) mRNA turnover rate limits siRNA and microRNA efficacy. Mol Syst Biol 6(1):433 Lin H-R, Ganem D Viral microRNA targlows insight into the role of translation in governing microRNA target accessibility. Proceedings of the National Academy of Sciences et al (2011) ;108(13):5148–5153 Liu W, Wang X (2019) Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol 20(1):1–10 Long D et al (2007) Potent effect of target structure on microRNA function. Nat Struct Mol Biol 14(4):287–294 Morton BR, Wright SI (2007) Selective constraints on codon usage of nuclear genes from Arabidopsis thaliana. Mol Biol Evol 24(1):122–129 Nam J-W et al (2014) Global analyses of the effect of different cellular contexts on microRNA targeting. Mol Cell 53(6):1031–1043 Ni W-J, Leng X-M (2015) Dynamic miRNA–mRNA paradigms: New faces of miRNAs. Biochem Biophys Rep 4:337–341 Nielsen CB, et al (2007) Determinants of targeting by endogenous and exogenous microRNAs and siRNAs. Rna ;13(11):1894–1910 Peterson SM et al (2014) Common features of microRNA target prediction tools. Front Genet 5:23 Preston GM (2000) Pseudomonas syringae pv. tomato: the right pathogen, of the right plant, at the right time. Mol Plant Pathol 1(5):263–275 Riolo G et al (2020) miRNA targets: from prediction tools to experimental validation. Methods and protocols 4(1):1 SantaLucia J (1998) A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proceedings of the National Academy of Sciences ;95(4):1460–1465 Sau K, et al (2006) Factors influencing synonymous codon and amino acid usage biases in Mimivirus. Biosystems ;85(2):107–113 Selbach M, et al (2008) Widespread changes in protein synthesis induced by microRNAs. nature ;455(7209):58–63 Sharp PM et al (1993) Codon usage: mutational bias, translational selection, or both? Biochem Soc Trans 21(4):835–841 Sophiarani Y, Chakraborty S (2021) Prediction of microRNAs in Pseudomonas syringae pv. tomato DC3000 and their potential target prediction in Solanum lycopersicum. Gene Rep 25:101360 Sun S et al (2016) Pangenome evidence for higher codon usage bias and stronger translational selection in core genes of Escherichia coli. Front Microbiol 7:1180 Tuller T, et al (2010) Translation efficiency is determined by both codon bias and folding energy. Proceedings of the national academy of sciences ;107(8):3645–3650 Wan X, Xu D, Zhou J (2003) A new informatics method for measuring synonymous codon usage bias.Intelligent engineering systems through artificial neural networks Volume; 13 Wright F (1990) The ‘effective number of codons’ used in a gene. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2196207","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":147767408,"identity":"9ffe33da-8fa8-4850-8518-8bbedcc22203","order_by":0,"name":"Yengkhom Sophiarani","email":"","orcid":"","institution":"Assam University - Dargakona Campus: Assam University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yengkhom","middleName":"","lastName":"Sophiarani","suffix":""},{"id":147767409,"identity":"b69f15f6-6389-49f1-987e-4bea69945987","order_by":1,"name":"Supriyo Chakraborty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYLCCBCA2YG9sOPChAshiZm4gUgvP4YMPZ5wBaWEkQgsIGEikJRvztoGYBLTIz8g99uFhm428OUOOmeTMebXR/O1ALT8qtuE2/EZe8ozEtjTDnQ1nzCQ+bjueO+MwYwNjz5nbeNyTY8yQ2HY4weBgD9CWbcdyG4BamBnbcGuRnwHTcpjHTJp3zrHc+YS0MNyAaTnGBvR+Q03uBkJaDM68S2ZIOJdmuOEMMzCQjx3I3QjUchCfX+Tbcw8z/iizkTe4/xAYlTV1ufPOHz744EcFHocx8AAjgg3OOwwmD+BRD9HC8AfOq8OveBSMglEwCkYkAADGdmKzzDC5zAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5680-5276","institution":"Assam University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Supriyo","middleName":"","lastName":"Chakraborty","suffix":""}],"badges":[],"createdAt":"2022-10-23 16:37:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2196207/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2196207/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28533165,"identity":"8792eec0-6bfb-4d3b-9fc1-7b47e2ffc4f5","added_by":"auto","created_at":"2022-11-01 20:01:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":276628,"visible":true,"origin":"","legend":"\u003cp\u003eAlignments of microRNA to the 3ʹ UTR of the target genes. (A) Bacterial spot disease resistance gene of tomato and (B) Bacterial speck disease resistance gene of tomato\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2196207/v1/07853a0408499ca0cb60b35a.png"},{"id":28533166,"identity":"1b353cc9-9129-41de-9d69-416d26acb320","added_by":"auto","created_at":"2022-11-01 20:01:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44590,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of overall GC% and AT% distributions over the two genes\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2196207/v1/18aca7511a0570e0d6579915.png"},{"id":28533163,"identity":"e26cab63-4e27-4d16-aaa0-fd4b2db8552b","added_by":"auto","created_at":"2022-11-01 20:01:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160762,"visible":true,"origin":"","legend":"\u003cp\u003eHeat maps of relative synonymous codon usage (RSCU) values of Bs4 and Prf. Blue to red color indicates low to high RSCU values of codons\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2196207/v1/406c5cb54ccc110c2eb57502.png"},{"id":28533164,"identity":"c0323333-e0cc-4297-bc2c-2a31267336be","added_by":"auto","created_at":"2022-11-01 20:01:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":288907,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the frequency of relative synonymous codon usage (RSCU) between the 59 codons of Bs4 and Prf genes. x-axis represents the RSCU values and y-axis represents the 59 synonymous codons. RSCU\u0026gt;1.6: TCT (Ser), CCA (Pro), AGA (Arg), ACA (Thr), GCT (Ala) and GAT (Asp) for Bs4 gene; RSCU\u0026gt;1.6: TCA (Ser), TCT (Ser), CCT (Pro), CAT (His), AGA (Arg), ACT (Thr), GTT (Val) and GAT (Asp) for Prf gene\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2196207/v1/458839cc5431cb6e505f79cc.png"},{"id":28657308,"identity":"a1a26dda-05d7-4f01-ae94-e4b721218d58","added_by":"auto","created_at":"2022-11-04 09:43:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1415568,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2196207/v1/cc92fb15-afe4-48e4-8bd2-80596103529b.pdf"}],"financialInterests":"","formattedTitle":"Synonymous Sites for Accessibility around MicroRNA Binding Sites in Bacterial Spot and Speck Disease Resistance Genes of Tomato","fulltext":[{"header":"Key Message","content":"\u003cp\u003eStrong access to miRNA binding sites in bacterial spot and speck disease resistance genes of tomato and decreased rate of translations suggested that miRNAs might be efficient in binding to their particular targets.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eTomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e) has excellent nutritional and gastronomic value, making it the world's second most significant vegetable crop after potato (Gerszberg, et al., 2015). Tomatoes are infected by a variety of pathogens, including bacteria, viruses, and fungi. Bacterial speck and bacterial spot are the two most frequent bacteria-induced tomato diseases. Bacterial speck disease of tomato (caused by the bacterium \u003cem\u003ePseudomonas syringae\u003c/em\u003e pv. tomato) can be found everywhere tomatoes are cultivated (Preston, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The disease severely damages the leaves early in the growing season, resulting in a drastically reduced yield. When symptoms occur on tomato fruit, the disease has the potential to have a significant impact on quality and market value for corporate tomato farmers. Bacterial spot is a major tomato disease all over the world. Different species of the genus \u003cem\u003eXanthomonas\u003c/em\u003e cause bacterial spot disease (but primarily by \u003cem\u003eXanthomonas perforans\u003c/em\u003e) (Koenraadt, et al., 2007). It can infect only the green fruits and not the red fruits. This disease, like bacterial speck, may be a major tomato disease that is difficult to treat when the disease pressure is high under favourable climatic conditions. The necessity for protection against pathogen is thought to be a powerful evolutionary force that leads to diverse selection and significant levels of diversity in plant genes encoding essential defense-related proteins. Many studies have found that microRNAs (miRNAs) are extremely sensitive to various physiological processes such as abiotic or biotic stress.\u003c/p\u003e \u003cp\u003eMicroRNAs are small (16\u0026ndash;21 nucleotides) non-coding RNAs that form the miRNA-induced silencing complex (miRISC) with argonaute proteins to inhibit the translation process and induce mRNA degradation (Fabian and Sonenberg, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The degree of translational inhibition and mRNA degradation for each target region of miRNA might be quite different (B\u0026eacute;thune, et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Djuranovic, et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nam, et al., 2014; Selbach, et al., 2008). Poor miRNA binding or RNA binding proteins (RBPs) influencing inhibition might explain the variation in inhibition for particular target regions (Kedde, et al., 2010; Kertesz, et al., 2007; Kundu, et al., 2012). Previous researches have revealed the mechanisms by which miRNAs detect their targets in the 5\u0026prime; untranslated regions (UTR), coding sequences (CDS), and 3\u0026prime;UTR and assessed whether these regions might influence miRNA-mediated suppression (Cottrell, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kertesz, et al., 2007). MiRNAs are known to control the expression of a variety of developmental and stress-related genes. In plants, miRNA target sites were identified in the CDS and 3\u0026prime; UTRs of mRNA, however it was not confirmed whether the efficacy of regulation was associated to target site locations or not (Bartel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jones-Rhoades and Bartel, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Previous study has shown that protein coding sequences can encode regulatory information by selecting certain synonymous codons (Itzkovitz, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we attempted to evaluate whether selection on synonymous codons near miRNA target regions might occur at the gene level. Based on 7mer-m8 seed match, we identified the miRNA targets in the CDS of bacterial spot disease resistance gene (Bs4) and bacterial speck disease resistance gene (Prf) of tomato. It has been reported that the majority of miRNA targets in plants were found in protein-coding domains (Jones-Rhoades and Bartel, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Plant miRNAs have nearly perfect target site matching, making computational prediction of miRNA targets much more effective (Dai, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As a result, we tested our hypothesis using the coding sequences of bacterial spot and bacterial speck disease resistance genes of tomato. To determine the parameters that could control miRNA binding to target mRNA molecules, we predicted the miRNA target genes and then evaluated the target site accessibility, translational efficiency of the miRNA target's upstream, downstream, and target regions, mRNA stability, codon usage bias, and nucleotide base compositions using bioinformatics tools. In addition, we employed the free energy calculation technique, compAI, and COSM, as well as a t-test to analyse the variation among the upstream, downstream and target regions.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Coding sequences and microRNAs data\u003c/h2\u003e \u003cp\u003eThe complete coding sequences \u003cb\u003e(\u003c/b\u003eCDS) of bacterial disease resistance genes of tomato were retrieved from the Plant Resistance Genes database (PRGdb 4.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://prgdb.org/prgdb4/\u003c/span\u003e\u003cspan address=\"http://prgdb.org/prgdb4/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and National Center for Biotechnology (NCBI) GenBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The mature miRNA sequences of tomato (147 in total) were also downloaded from miRBASE version 21 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mirbase.org\u003c/span\u003e\u003cspan address=\"http://www.mirbase.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The miRNAs of \u003cem\u003ePseudomonas syringae\u003c/em\u003e pv. tomato (\u003cem\u003ePst\u003c/em\u003e) were collected from our previously reported data (Sophiarani and Chakraborty, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The coding regions of bacterial disease resistance genes were searched for \u003cem\u003ePst\u003c/em\u003e and tomato miRNA targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. MicroRNA target prediction in coding sequences\u003c/h2\u003e \u003cp\u003eThe 7mer-m8 seed match was used to find the miRNA targets in the CDS of bacterial disease resistance genes of tomato. A 7mer-m8 miRNA seed is defined as a of region 2\u0026ndash;8 nucleotides in the miRNA 5'-3' direction (Peterson, et al., 2014). Watson-Crick (WC) base pairing has been used by the miRNAs to bind to their targets. The miRNA non-seed region is positioned adjacent to the miRNA seed. In the present study up to 4 mismatches between miRNA non-seed and target mRNA were permitted. After carefully screening the outputs, we selected only the CDS with the highest number of miRNA targets for further investigation. To obtain a deeper understanding, the miRNA target region, including its upstream and downstream regions, were all cleaved and saved individually with codons in a frame. Based on the findings of (Kertesz, et al., 2007), we collected 18 nucleotides (6 codons in frame) from the miRNA target\u0026rsquo;s upstream and downstream sections separately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. MiRNA target free energy and site accessibility analysis\u003c/h2\u003e \u003cp\u003eSite accessibility describes how easily a miRNA-RISC complex may identify and hybridize with its target mRNA sequence (Axtell, et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gu, et al., 2012). The hybridization of miRNA with its target mRNA involves binding of miRNA to a specific accessible region of mRNA and unfolding of the mRNA once it completes binding to the target (Peterson, et al., 2014; Riolo, et al., 2020). In this study, we employed the SantaLucia formula (in a 7bp sliding window at 37\u0026deg;C) to evaluate the folding energy/free energy (in kcal/mol) of the target mRNA and its sequence flanks, as well as the accessibility of the miRNA-binding region (SantaLucia, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In this case, the absolute value of folding energy was taken into account, and the larger value of folding energy was interpreted as significant mRNA folding. For each miRNA target, the folding energies of the upstream, downstream and target regions were computed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Translational efficiency analysis\u003c/h2\u003e \u003cp\u003eThe parameter compAI was used to estimate the translational efficiencies of the miRNA targets. It was used to compare the miRNA-binding sites and their flank regions. The value of compAI varies from 0 to 1, with 0 denoting the slowest and 1 denoting the fastest translation rate (Dilucca, et al., 2015). The translational efficiency assessed by compAI is unaffected by gene expression bias. It examines the competition of comparable tRNA species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Cosine similarity metric for microRNA targets analysis\u003c/h2\u003e \u003cp\u003eThe parameter cosine similarity metric (COSM) was used to measure the degree of parallel association between tRNA loops and relative synonymous codon usage (RSCU) (Sun, et al., 2016). COSM values vary from 0 to 1, with 0 signifying the highest similarity and 1 denoting the least similarity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Nucleotide composition analysis\u003c/h2\u003e \u003cp\u003eWe calculated the nucleotide composition of the CDS of bacterial spot and bacterial speck disease resistance genes of tomato for (i) overall nucleotide composition (A%, T%, G%, and C%), and its composition at the 3rd codon position (A3%, T3%, G3% and C3%); (ii) frequencies of nucleotides GC (total G and C nucleotides) present at the 1st (GC1%), 2nd (GC2%), and 3rd (GC3%) synonymous codon positions; and (iii) total GC and AT3% (total A and T nucleotides at the 3rd synonymous codon positions) of the genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Relative Synonymous Codon Usage (RSCU) analysis\u003c/h2\u003e \u003cp\u003eThe relative synonymous codon usage (RSCU) values of different synonymous codons in the CDS of bacterial spot and bacterial speck disease resistance genes of tomato were determined using the formula:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cem\u003eg\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e represents the frequency of the relative codon usage of the \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e codon for the \u003cem\u003ej\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e amino acid which is encoded by n\u003csub\u003ei\u003c/sub\u003e synonymous codons (Sharp, et al., 1993).\u003c/p\u003e \u003cp\u003eA positive codon usage bias related to a given codon is indicated by an RSCU value larger than one, and the associated codon is called a favoured codon. However, RSCU less than 1 denotes a bias against the usage of codons, and the associated codon is regarded as less prevalent for the relevant amino acid (Sau, et al., 2006). When the RSCU score is 1, the codon is considered unbiased for the specific amino acid and is selected equally or randomly in the RNA transcript with other synonymous codons from the same family. Furthermore, the synonymous codons with RSCU value higher than 1.6 are considered as over-represented whereas those codons with value less than 0.6 are considered under-represented (Butt, et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Synonymous codon usage order (SCUO) analysis\u003c/h2\u003e \u003cp\u003eIn the present study, we employed SCUO as a metric to quantify the codon usage as well as gene expression. It determines how frequently synonymous codons of an amino acid are being used in a non-random manner. SCUO has a value ranging from 0 (lowest) to 1 (highest) (Angellotti, et al., 2007). Genes with a strong preference for specific codons are highly expressed, whereas genes with little or no codon preference are often underexpressed (Wan, et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe t-test statistical analysis was used to understand the nucleotide base compositional changes of target regions in relation to upstream and downstream regions individually.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. MicroRNA targets in the coding sequences of tomato bacterial spot and speck disease resistance genes\u003c/h2\u003e \u003cp\u003eThe 7mer-m8 seed match was used to search for miRNA binding sites on the CDS of disease resistance genes of tomato. The collected data were filtered, and two genes [bacterial spot disease resistance gene (Bs4) and bacterial speck disease resistance gene (Prf)] were chosen for further investigation. In this study, it was found that three \u003cem\u003ePseudomonas syringae\u003c/em\u003e pv. tomato (\u003cem\u003ePst\u003c/em\u003e) miRNAs (PSTJ4_3p_27246, PSTJ4_3p_27246 and PSTJ4_3p_27246) and one tomato miRNA (sly-miR9470-3p) targeted the Bs4 gene (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, two tomato miRNAs, sly-miR9469-5p and sly-miR9474-3p were found to target the Prf gene (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, we found no \u003cem\u003ePst\u003c/em\u003e miRNA targets in the Prf CDS. (Brodersen, et al., 2008) suggested that in the miRNA-mRNA target hybrids, imperfect pairing with central mismatches enhances translational repression because it prevents slicing. On the other hand, translational repression is prevented by miRNAs that have exact central matches to their target mRNA, which allows for splicing. In this study, we observed that all of the miRNAs have central mismatches with their target mRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As a result, we hypothesized that these miRNAs might suppress the expression of Bs4 and Prf genes by translational repression, which is comparable to the findings of (Brodersen, et al., 2008).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMicroRNAs that might target Bs4 gene\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName of miRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiRNA sequence (excluding 1st base at 5՝ end)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget gene sequence (SeqFlank|Seed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget gene nucleotide position in the coding sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBinding sites of target gene and miRNA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esly-miR9470-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGAUUUUAGGUACUCGGUUU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSEQ-5'-ACTTAGAGAAAC|TAAAATC-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e620\u0026ndash;639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eseq-5'-ACTTAGAGAAAC|TAAAATC-3'/miR-3'-TTTGGCTCATGG|ATTTTAG-5'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePseudomonas syringae\u003c/span\u003e \u003cb\u003epv. tomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSTJ4_3p_27246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGUUCAUCAAGGGCGCGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSEQ-5'-GACCACCAATG|ATGAACT-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2661\u0026ndash;2679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eseq-5'-GACCACCAATG|ATGAACT-3'/miR-3'-CGCGCGGGAAC|TACTTGA-5'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSTJ4_3p_27246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGUUCAUCAAGGGCGCGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSEQ-5'-GACCACCAATG|ATGAACT-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3036\u0026ndash;3054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eseq-5'-GACCACCAATG|ATGAACT-3'/miR-3'-CGCGCGGGAAC|TACTTGA-5'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSTJ4_3p_27246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGUUCAUCAAGGGCGCGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSEQ-5'-GACCACCAATG|ATGAACT-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3330\u0026ndash;3348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eseq-5'-GACCACCAATG|ATGAACT-3'/miR-3'-CGCGCGGGAAC|TACTTGA-5'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMicroRNAs that might target Prf gene\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName of miRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emiRNA sequence (excluding 1st base at 5՝ end)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget gene sequence (SeqFlank|Seed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget gene nucleotide position in the coding sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBinding sites of target gene and miRNA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esly-miR9469-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCACAUAAGAAGACCGAAUUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSEQ-5'-CAATACATCATTC|TTATGTG-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1524\u0026ndash;1544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eseq-5'-CAATACATCATTC|TTATGTG-3'/miR-3'-CTTAAGCCAGAAG|AATACAC-5'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esly-miR9474-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUGACAUCAUAGACGCUUGUUUU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSEQ-5'-ACAACCCGCTTGAA|TGATGTC-3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3685\u0026ndash;3706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eseq-5'-ACAACCCGCTTGAA|TGATGTC-3'/miR-3'-TTTTGTTCGCAGAT|ACTACAG-5'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Target site accessibility\u003c/h2\u003e \u003cp\u003eWe first calculated the free energy/folding energy (kcal/mol) of mRNA secondary structure using SantaLucia method in a 7-bp sliding window at 37\u0026deg;C, moving upstream and downstream in 18-nucleotide steps from the real miRNA target region for each gene to understand the differences that might exist among these regions. Lastly, we calculated the mean free energy in each miRNA target's upstream, downstream and target sequences (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Here, we found that the free energy values in the upstream, downstream, and target regions varied from 2.8 to 4.13 kcal/mol, indicating weak folding of the target site and sequence flanks (Tuller, et al., 2010). It was suggested that a positive free energy value indicates selection for loose RNA secondary structure at miRNA target sites (Gu, et al., 2012). Therefore, we hypothesized that the miRNA binding domains (target and flank) on these genes might fold into loose secondary structures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFree energy of upstream, downstream, and microRNA target regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emicroRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eFree energy (Kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUp stream\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDown stream\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBs4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esly-miR9470-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esly-miR9469-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esly-miR9474-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePseudomonas syringae\u003c/span\u003e \u003cb\u003epv. tomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBs4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSTJ4_3p_27246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBs4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSTJ4_3p_27246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBs4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSTJ4_3p_27246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Rare codon usage and translational efficiency\u003c/h2\u003e \u003cp\u003eTo determine the rate of translation in each gene segment, the CDS of the Bs4 and Prf were examined in their upstream, downstream and target regions of the miRNA targets. In the present study, we found that the mean values of compAI in the two genes' upstream, downstream, and target areas exhibited lower translational efficiency (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Many studies have revealed that miRNA binding in the CDS results in translational inhibition (Br\u0026uuml;mmer and Hausser, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hausser, et al., 2013). Furthermore, it was reported that the miRNA-mediated translational repression with a high translation rate was shown to be more strongly repressed (Cottrell, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As a result, the low translational efficiency of the three regions reported in this study might interfere with the suppression of miRNA-mediated gene expression process. We calculated the COSM values to determine if the RSCU values correspond to the number of tRNA species. In general, COSM value ranges from 0 to 1 and a COSM value of 1 suggests a close resemblance, whereas a value of 0 reveals total dissimilarity. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the COSM values of the two genes in their upstream, downstream, and target regions. We found that the mean values of COSM were nearly equal to 0 in the three regions of the two genes, suggesting a weak association between synonymous codons found in the three regions and the tRNA pool. As a result, we hypothesized that the codons accessible in the three regions of Bs4 and Prf genes might be nonoptimal, resulting in a low translation efficiency. This is comparable to the previous work on growth rate-optimized tRNA abundance and codon usage, which revealed that the more frequently used codons, recognized by numerous tRNAs, resulted in faster translation elongation and greater translation efficiency (Berg and Kurland, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Gustafsson, et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRate of translation in the three regions of microRNA target genes of bacterial resistance genes of tomato\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpstream\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDownstream\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget regions for tomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBs4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget region for\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePseudomonas syringae\u003c/span\u003e \u003cb\u003epv. tomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBs4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCosine similarity metric in upstream, downstream and target regions of bacterial resistance genes of tomato\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpstream\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDownstream\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget regions for tomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBs4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget region for\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ePseudomonas syringae\u003c/span\u003e \u003cb\u003epv. tomato miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBs4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Nucleotide base composition analysis in the coding sequences of Bs4 and Prf genes\u003c/h2\u003e \u003cp\u003eThe overall nucleotide composition and CUB indices of the CDSs of Bs4 and Prf genes which included frequencies of nucleotide bases (adenine, guanine, cytosine and thymine) at the 3rd codon positions (A3%, G3%, C3% and T3%), GC and AT contents at the 3rd codon position (GC3% and AT3%) were estimated to understand their effect on CUB (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The nucleotide composition analysis at the 3rd codon position revealed that the average percentage of T3 (37.5%) was found to be the highest followed by A3 (29.15%), G3 (17.45%) and C3 (15.95%) in the two genes analysed. GC and AT distributions over the two genes were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The CDS of Bs4 and Prf have an overall GC content lower than 50% (Bs4\u0026thinsp;=\u0026thinsp;36.8%; Prf\u0026thinsp;=\u0026thinsp;39.1%). This suggested that the GC content of both genes was low. The AT distributions of the CDS of Bs4 and Prf were significantly distinct (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01). This revealed that the AT-ending codons are systematically preferred over the GC-ending ones.\u003c/p\u003e \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Most favoured codons for bacterial spot and speck disease resistance genes of tomato\u003c/h2\u003e \u003cp\u003eThe patterns of codon usage in the two genes were evaluated using relative synonymous codon usage (RSCU) analysis. The heatmap of RSCUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed that Bs4 and Prf originated to encode for amino acids using a limited number of relatively optimum (A/T-ending) codons and were selectively enriched in AT-ending codons. Among the 59 synonymous codons of Bs4 and Prf genes we found 6 highly preferred codons (RSCU\u0026thinsp;\u0026gt;\u0026thinsp;1.6) [TCT (Ser), CCA (Pro), AGA (Arg), ACA (Thr), GCT (Ala) and GAT (Asp)] for Bs4 gene, and 8 highly preferred codons [TCA (Ser), TCT (Ser), CCT (Pro), CAT (His), AGA (Arg), ACT (Thr), GTT (Val) and GAT (Asp)] for Prf gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the present study, all the favoured codons of Bs4 and Prf genes ended with A/T. Previous research had revealed that GC-poor codons were probably selected for the miRNA target region in plants to increase site accessibility and facilitate miRNA binding (Gu, et al., 2012). Our findings revealed that the Bs4 and Prf genes favored AT-rich codons across the miRNA-binding sites. These findings clearly show that evolution has resulted in significantly different codon and amino acid distributions in the Bs4 and Prf genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Relationship between codon usage and gene expression\u003c/h2\u003e \u003cp\u003eThe Bs4 and Prf genes exhibited SCUO values close to 0 (Bs4: SCUO\u0026thinsp;=\u0026thinsp;0.09; Prf: SCUO\u0026thinsp;=\u0026thinsp;0.1), indicating a low codon usage pattern. Moreover, we conducted the correlation analysis between ENC (effective number of codons), which determines the degree of synonymous codon bias in a particular gene or genome (Wright, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) and SCUO values to assess if the codon usage pattern influenced gene expression in the two genes. Here, we found a significant negative correlation (r = -1.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between ENC and SCUO. Our results suggested that gene expression level might have been influenced by the codon usage pattern.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMany studies have shown that protein coding sequences can encode regulatory information by exploiting certain synonymous codons (Bollenbach, et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Itzkovitz, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). MiRNAs have gained prominence due to their vital role in gene regulation, which may lead to the development of numerous disorders (Liu and Wang, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ni and Leng, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The regulatory process starts when miRNAs bind to their target sites in mRNAs (Brodersen and Voinnet, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). MiRNA binding, which is brought on by local mRNA secondary structure or upstream translation efficiency, might be impacted by the surrounding nucleotides of miRNA target sites (Lin and Ganem, 2011). Several studies have suggested that miRNAs trigger mRNA degradation by binding to 3ʹ untranslated regions (UTRs) (Grimson, et al., 2007; Guo, et al., 2010; Nielsen, et al., 2007), while miRNAs binding to the CDS mostly inhibit translation (Br\u0026uuml;mmer and Hausser, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Larsson, et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Since, our miRNAs all targeted in the 3ʹ UTRs regions of the CDS (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), wet lab validation is required to better describe the amplitude and time-scale of gene regulation at CDS sites. It was suggested that miRNA binding to 3ʹ UTRs leads to changes in protein abundance mostly through mRNA deadenylation, whereas binding to CDS sites primarily represses translation with a minimal influence on the polyA tail of the target mRNA (Br\u0026uuml;mmer and Hausser, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the local translation efficiency and site accessibility in the upstream, downstream, and target regions of all coding targets of tomato and \u003cem\u003ePst\u003c/em\u003e miRNAs in the CDS of bacterial spot and bacterial speck disease resistance genes of tomato. In a previous study it was revealed that site accessibility and the translational process were the two most important elements in miRNA binding to its targets (Gu, et al., 2013). MiRNA activity is also influenced by RNA secondary structure around miRNA target sites. We have found that the free energies calculated in each gene's three locations (upstream, downstream and target) were low, suggesting weak mRNA folding and easy access to miRNA. We also observed a weak association between the RSCU values of the synonymous codons found in the three regions and the tRNA pool, suggesting that the existence of rare codons might enhance miRNA targeting even more. (Elf, et al., 2003) revealed that in response to amino acid deficiency, the charge levels of different tRNAs recognizing synonymous codons varied dramatically in bacteria. As a result, while some synonymous tRNA pools remain completely charged, the charged fraction of others might fall to zero. Therefore, an efficient validation of direct targets of miRNAs is an urgent need that might be highly beneficial in enhancing plant resistance to multiple pathogenic diseases.\u003c/p\u003e \u003cp\u003eWe found that the synonymous codons around the target sites were GC-poor. This finding is comparable to the previous study that GC-poor codons were locally favoured around miRNA target sites for loose secondary structure (Gu, et al., 2012). As a result, we hypothesized that the miRNAs might have functioned in such a way as to reach the target locations easily. Since the coding segments were AT-rich, their complementary miRNAs were also AT-rich. Thus, miRNA binding to their target regions might be greater, resulting in effective repression of disease resistance genes. Again, lower free energy of miRNA targets was associated with lower stability. This is consistent with the findings of a previous study (Gu, et al., 2012), which found significantly lower mRNA stability at miRNA targets in \u003cem\u003eOryza sativa\u003c/em\u003e and \u003cem\u003eZea mays\u003c/em\u003e. In this study, we found no selection signal in the miRNA target regions (upstream, downstream and target). This is consistent with the observation of low purifying selection in \u003cem\u003eA. thaliana\u003c/em\u003e with novel miRNA genes and its targets (Cuperus, et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fahlgren, et al., 2010). Previous studies revealed that in \u003cem\u003eA. thaliana\u003c/em\u003e, synonymous codons were largely chosen for efficient and accurate mRNA translation (Morton and Wright, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). We found that translational rates were low in the three miRNA target regions (upstream, downstream and target) of Bs4 and Prf genes. Our result is comparable to the findings that selection for translation in \u003cem\u003eA. thaliana\u003c/em\u003e inhibits the selective pressure for site accessibility (Gu, et al., 2012). Thus, we could come to the conclusion that the ineffective translation process might inhibit the miRNA-induced gene regulation. Furthermore, it was revealed that site accessibility is a local selection limit operating on the synonymous codons in the miRNA target area, as opposed to translation efficiency and accuracy, indicating that in \u003cem\u003eA. thaliana\u003c/em\u003e, the selection signal of enhanced site accessibility at miRNA target sites is most likely messed up by strong signals caused by translational selection .\u003c/p\u003e \u003cp\u003eWe found a weak codon usage bias in the two genes studied. (Gu, et al., 2012) reported in their study that certain miRNA targeting the CDS with a higher codon usage bias preferred to use rare codons in the upstream region of their target sites. This observation was contrary to our result. Although we studied the miRNA targets in the protein coding sequences of Bs4 and Prf genes by comparing just to a few restricted previous findings (Gu, et al., 2009; Lin and Ganem, 2011), the conclusions based on a single or several miRNA targets may be biased. As a consequence, we revealed that several additional genomic properties, such as folding energy near miRNA targets or local mRNA secondary structure (Kertesz, et al., 2007; Long, et al., 2007), could be potential factors facilitating miRNA binding, which is similar to the findings of (Lin and Ganem, 2011).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, we hypothesize that synonymous codon usage in the upstream, downstream, and target regions of miRNA targets is related to local translation efficiency in Bs4 and Prf genes of tomato. The selective restrictions around most miRNA targets, however, were too weak to be observed. When miRNA targets are located in the CDS, additional genetic traits, in addition to local translation efficiency, might even be more relevant to miRNA activity. We provide a description of selective effects on synonymous codon usage in the three regions of miRNA targets for optimal miRNA activity by assessing the local translation efficiency of all miRNA targets in the CDS of the two genes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflicts of interest in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to Assam University, Assam, India for providing the necessary facilities in carrying out this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Not funded\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYS and SC conceptualized the research idea, investigated the problem, applied appropriate methodology, collected, curated and analyzed data and interpreted the results. YS wrote the original manuscript with tables and figures. Further, SC supervised the entire research work, reviewed and edited the final manuscript. YS and SC read and approved the final manuscript for publication.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAngellotti MC et al (2007) CodonO: codon usage bias analysis within and across genomes. Nucleic Acids Res 35(suppl2):W132\u0026ndash;W136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAxtell MJ, Westholm JO, Lai EC (2011) Vive la diff\u0026eacute;rence: biogenesis and evolution of microRNAs in plants and animals. 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Biochem Soc Trans 21(4):835\u0026ndash;841\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSophiarani Y, Chakraborty S (2021) Prediction of microRNAs in Pseudomonas syringae pv. tomato DC3000 and their potential target prediction in Solanum lycopersicum. Gene Rep 25:101360\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun S et al (2016) Pangenome evidence for higher codon usage bias and stronger translational selection in core genes of Escherichia coli. Front Microbiol 7:1180\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuller T, et al (2010) Translation efficiency is determined by both codon bias and folding energy. \u003cem\u003eProceedings of the national academy of sciences\u003c/em\u003e ;107(8):3645\u0026ndash;3650\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan X, Xu D, Zhou J (2003) A new informatics method for measuring synonymous codon usage bias.Intelligent engineering systems through artificial neural networks Volume; 13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright F (1990) The \u0026lsquo;effective number of codons\u0026rsquo; used in a gene. Gene 87(1):23\u0026ndash;29\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bacterial spot disease resistance gene, Bacterial speck disease resistance gene, MicroRNA, Codon usage bias, Site accessibility","lastPublishedDoi":"10.21203/rs.3.rs-2196207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2196207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe major causes of mass tomato infections in both covered and open ground are agents of bacterial spot and bacterial speck diseases. MicroRNAs (miRNAs) are 16\u0026ndash;21 nucleotides in length, non-coding RNAs that inhibit translation and trigger mRNA degradation. MiRNAs play a significant part in plant resistance to abiotic and biotic stresses by mediating gene regulation \u003cem\u003evia\u003c/em\u003e post-transcriptional RNA silencing. In this study, we analyzed a collection of bacterial resistance genes of tomato and their binding sites for tomato miRNAs and \u003cem\u003ePseudomonas syringe\u003c/em\u003e pv. tomato miRNAs. Our study found that two genes, bacterial spot disease resistance gene (Bs4) and bacterial speck disease resistance gene (Prf), have a 7mer-m8 perfect seed match with miRNAs. Bs4 was targeted by one tomato miRNA (sly-miR9470-3p) and three \u003cem\u003ePseudomonas syringe\u003c/em\u003e pv. tomato miRNAs (PSTJ4_3p_27246, PSTJ4_3p_27246 and PSTJ4_3p_27246). Again, Prf gene was found to be targeted by two tomato miRNAs \u003cem\u003eviz\u003c/em\u003e., sly-miR9469-5p and sly-miR9474-3p. The accessibility of the miRNA-target site and its flanking regions, as well as the relationship between relative synonymous codon usage (RSCU) and tRNAs were compared. Strong access to miRNA targeting regions and decreased rate of translations suggested that miRNAs might be efficient in binding to their particular targets. We also found the existence of rare codons, which suggests that it could enhance miRNA targeting even more. The codon usage pattern analysis of the two genes revealed that both were AT-rich (Bs4\u0026thinsp;=\u0026thinsp;63.2%; Prf\u0026thinsp;=\u0026thinsp;60.8%). We found a low codon usage bias in both genes, suggesting that selective restriction might regulate them. The silencing property of miRNAs would allow researchers to discover the involvement of plant miRNAs in pathogen invasion. However, the efficient validation of direct targets of miRNAs is an urgent need that might be highly beneficial in enhancing plant resistance to multiple pathogenic diseases.\u003c/p\u003e","manuscriptTitle":"Synonymous Sites for Accessibility around MicroRNA Binding Sites in Bacterial Spot and Speck Disease Resistance Genes of Tomato","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-01 20:01:54","doi":"10.21203/rs.3.rs-2196207/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"809e9cc8-dd98-4e89-bb87-c1f23b7b9dfa","owner":[],"postedDate":"November 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-04T09:43:35+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-01 20:01:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2196207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2196207","identity":"rs-2196207","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}