In this investigation, the study focused on the RNAseq data generated in response to Fusarium oxysporum f.sp. cubense (Foc) race1 (Cavendish infecting strain VCG 0124), targeting both resistant (cv. Rose, AA) and susceptible cultivars (Namarai, AA), and Tropical Race 4 (TR4, strain VCG 01213/16), involving resistant (cv. Rose, AA) and susceptible cultivars (Matti, AA). The respective contrasting cultivars were independently challenged with Foc race1 and TR4, and the root and corm samples were collected in two replications at varying time intervals [0th (control), 2nd, 4th, 6th, and 8th days] in duplicates. The RNA samples underwent stringent quality checks, with all 80 samples meeting the primary parameters, including a satisfactory RNA integrity number (>7). Subsequent library preparation and secondary quality control steps were executed successfully for all samples, paving the way for the sequencing phase. Sequencing generated an extensive amount of data, yielding a range of 10 to 31 million paired-end raw reads per sample, resulting in a cumulative raw data size of 11-50 GB. These raw reads were aligned against the reference genome of Musa acuminata ssp. malaccensis version 2 (DH Pahang), as well as the pathogen genomes of Foc race 1 and Foc TR4, using the HISAT2 alignment tool. The focal point of this study was the investigation of differential gene expression patterns of Musa spp. upon Foc infection. In Foc race1 resistant and susceptible root samples across the designated day intervals, a significant number of genes displayed up-regulation (ranging from 1 to 228) and down-regulation (ranging from 1 to 274). In corm samples, the up-regulated genes ranged from 1 to 149, while down-regulated genes spanned from 3 to 845. For Foc TR4 resistant and susceptible root samples, the expression profiles exhibited a notable up-regulation of genes (ranging from 31 to 964), along with a down-regulation range of 316-1315. In corm samples, up-regulated genes ranged from 57 to 929, while down-regulated genes were observed in the range of 40-936. In addition to the primary analysis, a comprehensive secondary analysis was conducted, including Gene Ontology (GO), euKaryotic Orthologous Groups (KOG) classification, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and investigations into Simple Sequence Repeats (SSRs), Single Nucleotide Polymorphisms (SNPs), and microRNA (miRNA). The complete dataset was carefully curated and housed at ICAR-NRCB, Trichy, ensuring its accuracy and accessibility for the duration of the study. Further, the raw transcriptome read datasets have been successfully submitted to the National Center for Biotechnology Information - Sequence Read Archive (NCBI-SRA) database, ensuring the accessibility and reproducibility of this valuable dataset for further research endeavors.
Antibiotics have revolutionised medicine in the last century and enabled the prevention of bacterial infections that were previously deemed untreatable. However, in parallel, bacteria have increasingly developed resistance to antibiotics through various mechanisms. When resistant bacteria find their way into terrestrial and aquatic environments, animal and human exposures increase, e.g., via polluted soil, food, and water, and health risks multiply. Understanding the fate and transport of antibiotic resistant bacteria (ARB) and the transfer mechanisms of antibiotic resistance genes (ARGs) in aquatic environments is critical for evaluating and mitigating the risks of resistant-induced infections. The conceptual understanding of sources and pathways of antibiotics, ARB, and ARGs from society to the water environments is essential for setting the scene and developing an appropriate framework for modelling. Various factors and processes associated with hydrology, ecology, and climate change can significantly affect the fate and transport of ARB and ARGs in natural environments. This article reviews current knowledge, research gaps, and priorities for developing water quality models to assess the fate and transport of ARB and ARGs. The paper also provides inputs on future research needs, especially the need for new predictive models to guide risk assessment on AR transmission and spread in aquatic environments.