Comparative RNA-seq analysis of resistant and susceptible banana genotypes reveals molecular mechanisms in response to banana bunchy top virus (BBTV) infection.

Bananas hold significant economic importance as an agricultural commodity, serving as a primary livelihood source, a favorite fruit, and a staple crop in various regions across the world. However, Banana bunchy top disease (BBTD), which is caused by banana bunchy top virus (BBTV), poses a considerable threat to banana cultivation. To understand the resistance mechanism and the interplay of host suitability factors in the presence of BBTV, we conducted RNA-seq-based comparative transcriptomics analysis on mock-inoculated and BBTV-inoculated samples from resistant (wild Musa balbisiana) and susceptible (Musa acuminata 'Lakatan') genotypes. We observed common patterns of expression for 62 differentially expressed genes (DEGs) in both genotypes, which represent the typical defense response of bananas to BBTV. Furthermore, we identified 99 DEGs exclusive to the 'Lakatan' banana cultivar, offering insights into the host factors and susceptibility mechanisms that facilitate successful BBTV infection. In parallel, we identified 151 DEGs unique to the wild M. balbisiana, shedding light on the multifaceted mechanisms of BBTV resistance, involving processes such as secondary metabolite biosynthesis, cell wall modification, and pathogen perception. Notably, our validation efforts via RT-qPCR confirmed the up-regulation of the glucuronoxylan 4-O-methyltransferase gene (14.28 fold-change increase), implicated in xylan modification and degradation. Furthermore, our experiments highlighted the potential recruitment of host's substrate adaptor ADO (30.31 fold-change increase) by BBTV, which may play a role in enhancing banana susceptibility to the viral pathogen. The DEGs identified in this work can be used as basis in designing associated gene markers for the precise integration of resistance genes in marker-assisted breeding programs. Furthermore, the findings can be applied to develop genome-edited banana cultivars targeting the resistance and susceptibility genes, thus developing novel cultivars that are resilient to important diseases.

A novel SNP panel developed for targeted genotyping-by-sequencing (GBS) reveals genetic diversity and population structure of Musa spp. germplasm collection.

The Philippines is situated in the geographic region regarded as the center of diversity of banana and its wild relatives (Musa spp.). It holds the most extensive collection of B-genome germplasm in the world along with A-genome groups and several natural hybrids with A- and B-genome combinations. Management of this germplasm resource has relied immensely on identification using local names and morphological characters, and the extent of genetic diversity of the collection has not been achieved with molecular markers. A high-throughput and reliable genotyping method for banana and its relatives will facilitate germplasm management and support breeding initiatives toward a marker-based approach. Here, we developed a 1 K SNP genotyping panel based on filtering of high-quality genome-wide SNPs from the Musa Germplasm Information System and used it to assess the genetic diversity and population structure of 183 accessions from a Musa spp. germplasm collection containing Philippine and foreign accessions. Targeted GBS using SeqSNP™ technology generated 70,376,284 next-generation sequencing (NGS) reads with an average effective target SNP coverage of 340 × . Bioinformatics pipeline revealed 971 polymorphic SNPs containing 76.9% homozygous calls, 23.1% heterozygous calls and 4% with missing data. A final set of 952 SNPs detected 2,092 alleles. Pairwise genetic distance varied from 0.0021 to 0.3325 with most pairs of accessions distinguished with 250 to 300 loci. The SNP panel was able to detect seven (k = 7) genetically differentiated groups and its composition through Principal Component Analysis (PCA) with k-means clustering algorithm and Discriminant Analysis of Principal Components (DAPC). Accession-specific SNPs were also identified. The 1 K SNP panel effectively distinguishes between genomic groups and provides relatively good resolution of genome-wide nucleotide diversity of Musa spp. This panel is recommended for low-density genotyping for application in marker-assisted breeding and germplasm management, and could be further enhanced to increase marker density for other applications like genetic association and genomic selection in bananas.

Reference-aided full-length transcript assembly, cDNA cloning, and molecular characterization of coronatine-insensitive 1b (COI1b) gene in coconut (Cocos nucifera L.).

BACKGROUND: In the Philippines, 26% of the total agricultural land is devoted to coconut production making coconut one of the most valuable industrial crop in the country. However, the country's multimillion-dollar coconut industry is threatened by the outbreak of coconut scale insect (CSI) and other re-emerging insect pests promoting national research institutes to work jointly on developing new tolerant coconut varieties. Here, we report the cloning and characterization of coronatine-insensitive 1 (COI1) gene, one of the candidate insect defense genes, using 'Catigan Green Dwarf' (CATD) genome sequence assembly as reference. METHODS AND RESULTS: Two (2) splicing variants were identified and annotated-CnCOI1b-1 and CnCOI1b-2. The full-length cDNA of CnCOI1b-1 was 7919 bp with an ORF of 1176 bp encoding for a deduced protein of 391 amino acids while CnCOI1b-2 has 2360 bp full-length cDNA with an ORF of 1743 bp encoding a deduced protein of 580 amino acids. The 3D structural model for the two (2) isoforms were generated through homology modelling. Functional analysis revealed that both isoforms are involved in various physiological and developmental plant processes including defense response of plants to insects and pathogens. Phylogenetic analysis confirms high degree of COI1 protein conservation during evolution, especially among monocot species. Differential gene expression via qRT-PCR analysis revealed a seven-fold increase of COI1 gene expression in coconut post introduction of CSI relative to base levels. CONCLUSION: This study provided the groundwork for further research on the actual role of COI1 in coconut in response to insect damage. The findings of this study are also vital to facilitate the development of improved insect-resistant coconut varieties for vibrant coconut industry.

Mining and validation of novel simple sequence repeat (SSR) markers derived from coconut (Cocos nucifera L.) genome assembly.

BACKGROUND: In the past, simple sequence repeat (SSR) marker development in coconut is achieved through microsatellite probing in bacterial artificial chromosome (BAC) clones or using previously developed SSR markers from closely related genomes. These coconut SSRs are publicly available in published literatures and online databases; however, the number is quite limited. Here, we used a locally established, coconut genome-wide SSR prediction bioinformatics pipeline to generate a vast amount of coconut SSR markers. RESULTS: A total of 7139 novel SSR markers were derived from the genome assembly of coconut 'Catigan Green Dwarf' (CATD). A subset of the markers, amounting to 131, were selected for synthesis based on motif filtering, contig distribution, product size exclusion, and success of in silico PCR in the CATD genome assembly. The OligoAnalyzer tool was also employed using the following desired parameters: %GC, 40-60%; minimum ΔG value for hairpin loop, -0.3 kcal/mol; minimum ΔG value for self-dimer, -0.9 kcal/mol; and minimum ΔG value for heterodimer, -0.9 kcal/mol. We have successfully synthesized, optimized, and amplified 131 novel SSR markers in coconut using 'Catigan Green Dwarf' (CATD), 'Laguna Tall' (LAGT), 'West African Tall' (WAT), and SYNVAR (LAGT × WAT) genotypes. Of the 131 SSR markers, 113 were polymorphic among the analyzed coconut genotypes. CONCLUSION: The development of novel SSR markers for coconut will serve as a valuable resource for mapping of quantitative trait loci (QTLs), assessment of genetic diversity and population structure, hybridity testing, and other marker-assisted plant breeding applications.