Chapter 5: Conclusion
5.1 Research Implications
This research generated a large amount of data including the full salt-related expression of mRNA and miRNA in I. aquatica and I. pes-caprae leaf and root tissue.
This resulted in the identification of thousands of differentially expressed genes and miRNAs in both plants. The plethora of information generated by this study can be utilized in future research into salt-related genes and miRNAs. The lncRNAs of both plants are being analyzed as well, which will only increase the knowledge of genetic mechanisms of salt-tolerance in plants. Analyzing the vast network of interactions between mRNAs and Nc-RNAs is crucial as it will allow for a more fine-tuned approach in I. aquatica genetic engineering. Comparative genomic analysis of both plants can also lead to identification of differences in regulatory regions
(like promoters) or gene structures than can further enhance the knowledge of salt- tolerance mechanisms.
The results of this study will undoubtedly lead to the identification of more genes than can be used in genetic engineering studies to enhance the salt-tolerance of crops. The genes discussed in this study (HKT and NAC) can be used to genetically engineer I. aquatica to increase its salt tolerance levels. These genes can be used to improve I. aquatica, as well as other Ipomoea species of great value like sweet potato, which also has great economic value. Further studies into other Ipomoea species that build on the information generated from this research project are therefore necessary.
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Appendix
Table 1: Measurements of mineral content in I. aquatica (IA) samples and I. pes- caprae (IB). Sample labeled C for control, E for Early, and L for Late. Samples were labeled L for leaf and R for Root (IA CL1 is I. aquatica control leaf triplicate 1)
Lab Ref Sample Labels
mg/Kg (ppm) Percent (%)
Ca K Mg
Na Chloride
1 IA CL 1 6645.32 20973.1 1852.3 3862.1 1.79
2 IA CL 2 7203.35 16580.2 1817.2 4807.1 1.52
3 IA CL 3 7866.98 16254.5 1946.2 4471.4 1.81
4 IA EL 1 5301.92 22781.0 1979.7 11004.8 2.31
5 IA EL 2 4849.47 21173.3 1986.4 8539.4 2.37
6 IA EL 3 6012.21 21509.6 2088.5 11720.6 2.41
7 IA LL 1 4143.75 19458.8 1663.0 25293.7 6.22
8 IA LL 2 4779.86 19522.7 1470.3 30459.4 7.18
9 IA LL 3 4752.33 25402.3 1761.9 23083.6 4.55
10 IA CR 1 4009.36 12007.4 1125.9 12856.3 1.41
11 IA CR 2 3787.99 10770.9 901.7 13543.6 1.60
12 IA CR 3 3809.08 11771.0 1044.4 12741.6 1.56
13 IA ER 1 2491.95 18270.6 746.6 16774.0 1.94
14 IA ER 2 2360.35 19574.8 747.5 16496.2 2.33
15 IA ER 3 2383.13 19533.1 790.4 16699.3 2.09
16 IA LR 1 3212.66 15940.9 585.6 17568.4 2.87
17 IA LR 2 2609.1 18663.6 629.3 18827.3 2.99
18 IA LR 3 2818.38 16340.0 540.3 15085.8 2.75
19 RL 1 IB 1921.82 24815.5 985.1 25880.0 5.48
20 RL 2 IB 2243.28 24808.3 921.0 24697.4 5.53
21 RL 3 IB 2209.91 23737.3 994.0 24912.6 5.69
22 RC 1 IB 4308.56 26502.8 1103.5 1889.3 0.61
23 RC 2 IB 4644.04 25726.4 1316.9 1962.5 0.82
24 RC 3 IB 4436.22 25581.1 1176.5 1756.3 0.83
25 LL 1 IB 4646.11 16000.7 1029.7 14576.5 4.85
Table 1: Measurements of mineral content in I. aquatica (IA) samples and I. pes- caprae (IB). Sample labeled C for control, E for Early, and L for Late. Samples were labeled L for leaf and R for Root (IA CL1 is I. aquatica control leaf triplicate 1) – (Continued)
Lab Ref Sample Labels mg/Kg (ppm) Percent (%)
Ca K Mg Na Chloride
26 LL 2 IB 14112.6 41503.7 3812.5 33324.2 4.64 27 LL 3 IB 4861.89 25128.6 1859.2 20417.0 3.93
28 LC 1 IB 4936.27 22554.2 1571.6 1905.2 0.42
29 LC 2 IB 8299.65 24144.2 2157.1 2950.9 0.56
30 LC 3 IB 8738.31 28206.8 2332.2 3120.2 0.46
31 LE 1 IB 8840.3 25483.6 2017.3 6634.6 1.20
32 LE 2 IB 7827.18 24773.3 2001.5 5054.0 1.38
33 LE 3 IB 6871.63 23767.4 2062.3 3661.7 1.16
34 RE 1 IB 2017.42 28196.9 943.3 17675.6 4.08
35 RE 2 IB 2173.3 28827.1 1092.1 14142.9 3.25
36 RE 3 IB 2309.04 29112.5 1020.1 13491.7 4.24
Table 2: Results of I. aquatica bioinformatics analysis representing the percentage of reads remaining after trimming as well as the percentage of reads that were aligned to the Reference genome from each sample. The last column shows the number of reads assembled into non-redundant reads.
Trimmomatic HISAT2 StringTie2
Sample Reads Retained (%)
Reads Aligned (%)
Reads Assembled
Control_Leaf_1 97.06 97.23% 27852
Control_Leaf_2 97.33 96.94% 27852
Control_Leaf_3 96.76 97.04% 27852
Control_Root_1 97.18 89.16% 40410
Control_Root_2 96.90 89.53% 40410
Control_Root_3 97.01 88.43% 40410
Early_Leaf_1 97.12 95.89% 27852
Early_Leaf_2 94.98 95.61% 27852
Early_Leaf_3 97.25 96.06% 27852
Early_Root_1 97.41 88.69% 40410
Early_Root_2 97.48 90.95% 40410
Early_Root_3 97.10 89.79% 40410
Late_Leaf_1 97.32 96.16% 27852
Late_Leaf_2 97.30 95.87% 27852
Late_Leaf_3 96.91 96.83% 27852
Late_Root_1 96.96 87.57% 40410
Late_Root_2 97.70 89.41% 40410
Late_Root_3 97.32 88.28% 40410
Table 3: Results of I. pes-caprae bioinformatics analysis representing the percentage of reads remaining after trimming as well as the percentage of reads that were
aligned to the Reference genome from each sample. The last column shows the number of reads assembled into non-redundant reads.
Trimmomatic HISAT2 StringTie2
Sample Retained reads (%) Reads Aligned (%)
Reads Assembled
Control_Leaf_1 98.15 98.35 33119
Control_Leaf_2 98.14 98.38 33119
Control_Leaf_3 98.12 98.36 33119
Control_Root_1 97.46 90.49 45605
Control_Root_2 97.73 89.27 45605
Control_Root_3 97.55 89.18 45605
Early_Leaf_1 97.80 94.65 33119
Early_Leaf_2 95.14 92.70 33119
Early_Leaf_3 97.41 93.87 33119
Early_Root_1 97.58 82.13 45605
Early_Root_2 98.22 88.76 45605
Early_Root_3 97.49 81.9 45605
Late_Leaf_1 97.62 94.68 33119
Late_Leaf_2 98.22 97.93 33119
Late_Leaf_3 98.17 98.16 33119
Late_Root_1 98.20 92.00 45605
Late_Root_2 98.18 86.77 45605
Late_Root_3 98.11 84.74 45605
65 Table 4: Validation of selected genes using qRT-PCR
I. aquatica Early I. aquatica Late I. pes-caprae Early I. pes-caprae Late
RNA-seq qPCR RNA-seq qPCR RNA-seq qPCR RNA-seq qPCR
NAC 1.69511602 0.92756494 2.73475868 2.07444127 5.16274998 2.61562169 2.63998625 1.96914594 NAC3-like 1.9 1.27926763 0.9842655 0.88137086 4.26984861 5.58664725 1.84841597 2.07168554 HKT6 0.23863715 -1.2723455 -1.0691276 -2.6830629 3.89355905 14.4987353 0.34399464 5.62489842 HKT-2 0.60322479 -0.9497805 4.21963612 1.60374451 -1.5188326 0.64589123 0.71408732 1.25142173 Osmotin 0.1070466 -0.2128108 3.44088551 2.22739442 6.02758804 7.38484185 6.18228058 5.45065027 Catalase 0.44336878 -0.1423677 2.19361237 0.9762675 0.66359542 2.01668353 2.06475671 3.20536331 AP2/ERF 2.68976998 1.83973853 0.69472769 0.9750843 3.96721652 7.95241976 -1.7975338 1.35847707 CIPK6 -0.2918318 -0.0668186 -0.0456143 0.58152676 2.25552896 3.47852154 0.71883826 1.94122625 SUS3 -0.4476431 1.51454163 1.51544447 0.24311574 2.07586678 2.87647228 1.60309879 1.98267405 KTI2 -0.369762 3.25004832 -1.1114697 2.90570323 5.77987513 10.0456213 2.08842557 4.10232696
Figure 1: example of FastQC result showing the quality of the sequence along its axis
Figure 2: The numbers of known and novel miRNAs identified in I. aquatica samples.
0 200 400 600 800 1000 1200 1400 1600 1800
Control_Leaf_1 Control_Leaf_2 Control_Leaf_3 Control_Root_1 Control_Root_2 Control_Root_3 Early_Leaf_1 Early_Leaf_2 Early_Leaf_3 Early_Root_1 Early_Root_2 Early_Root_3 Late_Leaf_1 Late_Leaf_2 Late_Leaf_3 Late_Root_1 Late_Root_2 Late_Root_3
I. aquatica miRNA
Novel Known