Comparative de novo transcriptome analysis identifies salinity stress responsive genes and metabolic pathways in sugarcane and its wild relative Erianthus arundinaceus [Retzius] Jeswiet

Comparative genome-wide characterization of salt responsive micro RNA and their targets through integrated small RNA and de novo transcriptome profiling in sugarcane and its wild relative Erianthus arundinaceus

Soil salinity and saline irrigation water are major constraints in sugarcane affecting the production of cane and sugar yield. To understand the salinity induced responses and to identify novel genomic resources, integrated de novo transcriptome and small RNA sequencing in sugarcane wild relative, Erianthus arundinaceus salt tolerant accession IND 99-907 and salt-sensitive sugarcane genotype Co 97010 were performed. A total of 362 known miRNAs belonging to 62 families and 353 miRNAs belonging to 63 families were abundant in IND 99-907 and Co 97010 respectively. The miRNA families such as miR156, miR160, miR166, miR167, miR169, miR171, miR395, miR399, miR437 and miR5568 were the most abundant with more than ten members in both genotypes. The differential expression analysis of miRNA reveals that 221 known miRNAs belonging to 48 families and 130 known miRNAs belonging to 42 families were differentially expressed in IND 99-907 and Co 97010 respectively. A total of 12,693 and 7982 miRNA targets against the monoploid mosaic genome and a total of 15,031 and 12,152 miRNA targets against the de novo transcriptome were identified for differentially expressed known miRNAs of IND 99-907 and Co 97010 respectively. The gene ontology (GO) enrichment analysis of the miRNA targets revealed that 24, 12 and 14 enriched GO terms (FDR < 0.05) for biological process, molecular function and cellular component respectively. These miRNAs have many targets that associated in regulation of biotic and abiotic stresses. Thus, the genomic resources generated through this study are useful for sugarcane crop improvement through biotechnological and advanced breeding approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03867-7.

Comparative transcriptome profiling to unravel the key molecular signalling pathways and drought adaptive plasticity in shoot borne root system of sugarcane

Sugarcane root system comprises of superficial sett roots as well as deeply-penetrating shoot borne roots (SBR) with latter being the permanent root system. In sugarcane, the healthy SBR contributes to a better crop yield and it also helps to produce multiple ratoon crops after the harvest. There is a dearth of in-depth knowledge on SBR system architecture and its functional role in modern day commercial hybrids. A comprehensive phenotypic, anatomical and whole transcriptome profiling, conducted between the commercial sugarcane hybrids and a wild germplasm Erianthus, found a developmental delay in both initiation and establishment of the SBR in commercial hybrid compared to Erianthus. The SBR system in Erianthus proved to be an extensive drought-adaptive root system architecture that significantly contributes to drought tolerance. On the other hand, SBRs in the commercial hybrids showed an irreversible collapse and damage of the root cells under drought stress. The outcomes from the comparative analysis of the transcriptome data showed a significant upregulation of the genes that regulate important stress signalling pathways viz., sugar, calcium, hormone signalling and phenylpropanoid biosynthesis in the SBRs of Erianthus. It was found that through these key signalling pathways, Erianthus SBRs triggered the downstream signalling cascade to impart physiological responses like osmoprotection, modification of the cell walls, detoxification of reactive oxygen species, expression of drought responsive transcription factors, maintenance of cell stability and lateral root development. The current study forms a basis for further exploration of the Shoot Borne Root system as a valuable breeding target to develop drought tolerant sugarcane genotypes.

bestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data

Background

Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it.

Methods

We developed a novel web application called "bestDEG" to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project.

Results

The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.