Fusarium wilt is caused by the fungus Fusarium oxysporum f. sp. cubense (Foc) and is the most serious disease affecting bananas (Musa spp.). The fungus is classified into Foc race 1 (R1), Foc race 2, and Foc race 4 based on host specificity. As the rate of spread and the ranges of the devastation of the Foc races exceed the centre of the banana’s origin, even in non-targeted cultivars, there is a possibility of variation in virulence-associated genes. Therefore, the present study investigates the genome assembly of Foc races that infect the Cavendish (AAA) banana group in India, specifically those of the vegetative compatibility group (VCG) 0124 (race 1), 0120 (subtropical race 4), and 01213/16 (tropical race 4). While comparing the general features of the genome sequences (e.g., RNAs, GO, SNPs, and InDels), the study also looked at transposable elements, phylogenetic relationships, and virulence-associated effector genes, and sought insights into race-specific molecular mechanisms of infection based on the presence of unique genes. The results of the analyses revealed variations in the organisation of genome assembly and virulence-associated genes, specifically secreted in xylem (SIX) genes, when compared to their respective reference genomes. The findings contributed to a better understanding of Indian Foc genomes, which will aid in the development of effective Fusarium wilt management techniques for various Foc VCGs in India and beyond.
Banana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.
Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels for thousands of genes in a single experiment. It is important to consider finding differentially expressed genes in a dataset of microarray experiments as differentially expressed genes are often referred as clinical markers. A number of statistical methods have been suggested for the identification of differentially expressed genes using different statistical methods and algorithms. In the present paper, an experimental investigation of four different algorithms for tracking differentially expressed genes using four publicly and freely available programs namely MeV (t-test), SAM (Significance Analysis of Microarray), EDGE (Optimal Discovery method) and iArray (Mann-Whitney test) is reported. To assess the performance of each program, 50 artificial microarray datasets with known differentially expressed genes were used for comparative study. Performance and evaluation of these programs from a biologist's perspective has been studied and reported in this paper.
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