Comparative transcriptomics of leaves of five mulberry accessions and cataloguing structural and expression variants for future prospects

Abstract

Bombyx mori, a monophagous insect, prefers leaves of the certain species of Morus more than others. The preference has been attributed to morphological and anatomical features and biochemical compounds. In the present manuscript a comparison has been made among the transcriptome of leaves of the two preferred cultivated varieties and three wild types species. While assembling, high quality transcriptomes of five genotypes were constructed with a total of 100930, 151245, 89724, 181761 and 102908 transcripts from ML, MN, MS, K2 and V1 samples respectively. Further, to compare them, orthologs were identified from these assembled transcriptome. A total of 22462, 23413, 23685, 24371, 18362, 22326, 20058, 18049, 17567 and 20518 clusters of orthologs were found in one to one comparison in KvsN, KvsL, KvsS, KvsV, LvsN, LvsS, LvsV, NvsS, NvsV, and SvsV respectively. 4236 orthologs with algebraic connectivity of 1.0 were then used to compare and to find out differentially expressed transcripts from all the genotypes. A total of 1037 transcripts expressed that include some of the important morphology, anatomy and biochemical pathways regulating transcription factors (AP2/ERFs and C2H2 Zinc fingers) and signalling components were identified to express differentially. Further, these transcriptomes were used find out markers (SSR) and variants and a total of 1101013, 537245, 970877, 310437, 675772, 338400, 581189, 751477, 514999 and 257107 variants including SNP, MNP, Insertions and deletions were found in one to one comparisons. Taken together, our data could be highly useful for mulberry community worldwide as it could be utilized in mulberry breeding programs.

Fig 1. Figure showing the number of orthologs reported in assembled transcriptomes from different genotypes using Proteinortho.

Trinity assembled transcriptome was used with default parameters. Numbers represent the transcripts in one to one comparison when reference transcriptome was used as query as well as source. See legend for details.

Fig 2. Heatmap showing the relative expression pattern exhibited by the > = 10 fold change showing differentially expressed genes in five leaves samples of three genotypes and two cultivars from a fourth species.

Mean normalized FPKM values obtained were used to plot the heatmap after applying Ward’s correction and Manhattan clustering.

Fig 3. Bar graphs showing the relative expression pattern obtained in leaf samples of five genotypes studies.δδCt method was applied to calculate the relative fold changes.

X axis represents the fold change and Y axis represents the stages. Error bars represent the standard errors. Person correlation coefficient was also calculated for data obtained from RNA-seq and qPCR (See text for details).

Fig 4. Figure showing the results of variant finding and their effect on transcriptome.

A) Bar graph showing the number of variants from different categories i.e. SNP, MNP, Insertions and deletions. B) Horizontal bars showing the number of SNPs having effects like non-sense, miss-sense and silent mutations on a log scale. C) Pie charts showing the SNPs type into transition (blue shade) and transversion events (red shade). D) Polar graph with stacked columns showing distribution of SNPs into their strength of their effect on coding sequences. E) Polar stacked columns showing the Effect of variants. Coloured legends details are as 1. Codon Change, 2. Codon Change Plus Codon Deletion, 3. Codon Change Plus Codon Insertion, 4. Codon Deletion, 5. Codon Insertion, 6. Frame Shift, 7. Non Synonymous Coding, 8. Non Synonymous Start, 9. Non Synonymous Stop, 10. Start Gained, 11. Start Lost, 12. Stop Gained, 13. Stop Lost, 14. Synonymous Coding, 15. Synonymous Stop, 16. UTR 3’ and 17. UTR 5’.

Fig 5. Network of GO terms of biological process showing the enriched terms for the unique genes of K2 (MI) genotype.

Nodes represent the GO terms. Size of the nodes represents the number of gene and the colour represent significant value (See scale).

Fig 6. Similarity matrices of unique GO terms obtained for K2, V1 (Morus indica cultivars) and ML, MN and MS leaf transcripts.

Similarity matrices are represented using heat maps that represent the similarity among GO terms and cluster them using binary cluster method. (See legend for similarity levels).