Comparative genomics profiling revealed multi-stress responsive roles of the CC-NBS-LRR genes in three mango cultivars

Abstract

The nucleotide-binding site-leucine-rich repeat (NBS–LRR) gene family is the largest group of disease resistance (R) genes in plants and is active in response to viruses, bacteria, and fungi usually involved in effector-triggered immunity (ETI). Pangenome-wide studies allow researchers to analyze the genetic diversity of multiple species or their members simultaneously, providing a comprehensive understanding of the evolutionary relationships and diversity present among them. The draft pan-genome of three Mangifera indica cultivars (Alphonso, Hong Xiang Ya, and Tommy atkins) was constructed and Presence/absence variants (PAVs) were filtered through the ppsPCP pipeline. As a result, 2823 genes and 5907 PAVs from H. Xiang Ya, and 1266 genes and 2098 PAVs from T. atkins were added to the reference genome. For the identification of CC-NBS-LRR (CNL) genes in these mango cultivars, this draft pan-genome study has successfully identified 47, 27, and 36 members in Alphonso, H. Xiang Ya, and T. atkins respectively. The phylogenetic analysis divided MiCNL proteins into four distinct subgroups. All MiCNL genes are unevenly distributed on chromosomes. Both tandem and segmental duplication events played a significant role in the expansion of the CNL gene family. These genes contain cis-elements related to light, stress, hormone, and development. The analysis of protein-protein interactions (PPI) revealed that MiCNL proteins interacted with other defense-responsive proteins. Gene Ontology (GO) analysis indicated that MiCNL genes play a role in defense mechanisms within the organism. The expression level of the identified genes in fruit peel was observed under disease and cold stress which showed that Mi_A_CNL13 and 14 were up-regulated while Mi_A_CNL15, 25, 30, 31, and 40 were down-regulated in disease stress. On the other hand, Mi_A_CNL2, 14, 41, and 45 were up-regulated and Mi_A_CNL47 is down-regulated in cold stress. Subsequently, the Random Forest (RF) classifier was used to assess the multi-stress response of MiCNLs. It was found that Mi_A_CNL14 is a gene that responds to multiple stress conditions. The CNLs have similar protein structures which show that they are involved in the same function. The above findings provide a foundation for a deeper understanding of the functional characteristics of the mango CNL gene family.

Comparative genomics analysis of pheophorbide a oxygenase (PAO) genes in eight pyrus genomes and their regulatory role in multiple stress responses in Chinese pear (Pyrus bretschneideri)

“…The chromosomal positions of each Pyrus PAOs were extracted from GFF/GFF3 files and mapped to chromosomes using TBtools-II v2.067: Gene Location Visualize advanced (C. Chen et al, 2018 ). By assessing whether the shorter gene’s length covered 70% of the longer gene and if the alignment similarity between the two genes was 70% or higher, instances of PAOs gene duplication were identified ( ul Qamar et al, 2023 ). The duplication pattern, whether segmental or tandem, was also examined.…”

Comparative genomic profiling of transport inhibitor Response1/Auxin signaling F-box (TIR1/AFB) genes in eight Pyrus genomes revealed the intraspecies diversity and stress responsiveness patterns

“…The chromosomal positions for each Pyrus TIR1/AFB were retrieved from the GFF/GFF3 files and mapped to chromosomes by using the gene location visualization tool of the TBtools software (Chen et al, 2018). Depending on whether the shorter gene's length covered 70% of the longer gene and whether the two aligned genes' similarity was equal to or greater than 70%, TIR1/ AFB gene duplication occurrences were identified (Tahir ul Qamar et al, 2023). The genes were also checked to determine whether the duplication pattern was segmental or tandem.…”

Comparative genomics and bioinformatics approaches revealed the role of CC-NBS-LRR genes under multiple stresses in passion fruit

“… Liu et al, 2021 ) , 361 in Solanum tuberosum ( Jupe et al, 2012 ) , 78 in Solanum pimpinellifolium ( Wei et al, 2020 ) , 14 in Lagenaria siceraria, 146 in Triticum urartu ( Qian et al, 2021 ) , 166 in Discorea rotundata, 103 in Glycine max ( Afzal et al, 2022 ), 467 in Hordeum vulgare ( Liu et al, 2017 ), 54 in Broussonetia papyrifera (X. Zhang et al, 2023 ), 95 in Elaeis guineensis ( Rosli et al, 2018 ) , 10 in Citrus sinensis ( Yin et al, 2023 ), 47 in Alphonso , 27 in Hong Xiang Ya , and 36 in Tommy atkins ( ul Qamar et al, 2023 ). Machine learning approaches have also been applied in studies reported previously for the elucidation of candidate genes implicated in multi-stress responsiveness in Oryza sativa and Sorghum bicolor ( Woldesemayat et al, 2018 ; Ramkumar et al, 2022 ).…”