Complete Genome Sequencing and Comparative Analysis of the Clinically-Derived Apiotrichum mycotoxinivorans Strain GMU1709

Abstract

Over the past decade, Apiotrichum mycotoxinivorans has been recognized globally as a source of opportunistic infections. It is a yeast-like fungus, and its association as an uncommon pulmonary pathogen with cystic fibrosis patients has been previously reported. Immunocompromised patients are at the highest risk of A. mycotoxinivorans infections. Therefore, to investigate the genetic basis for the pathogenicity of A. mycotoxinivorans, we performed whole-genome sequencing and comparative genomic analysis of A. mycotoxinivorans GMU1709 that was isolated from sputum specimens of a pneumonia patient receiving cardiac repair surgery. The assembly of Oxford Nanopore reads from the GMU1709 strain and its subsequent correction using Illumina paired-end reads yielded a high-quality complete genome with a genome size of 30.5 Mb in length, which comprised six chromosomes and one mitochondrion. Subsequently, 8,066 protein-coding genes were predicted based on multiple pieces of evidence, including transcriptomes. Phylogenomic analysis indicated that A. mycotoxinivorans exhibited the closest evolutionary affinity to A. veenhuisii, and both the A. mycotoxinivorans strains and the formerly Trichosporon cutaneum ACCC 20271 strain occupied the same phylogenetic position. Further comparative analysis supported that the ACCC 20271 strain belonged to A. mycotoxinivorans. Comparisons of three A. mycotoxinivorans strains indicated that the differences between clinical and non-clinical strains in pathogenicity and drug resistance may be little or none. Based on the comparisons with strains of other species in the Trichosporonaceae family, we identified potential key genetic factors associated with A. mycotoxinivorans infection or pathogenicity. In addition, we also deduced that A. mycotoxinivorans had great potential to inactivate some antibiotics (e.g., tetracycline), which may affect the efficacy of these drugs in co-infection. In general, our analyses provide a better understanding of the classification and phylogeny of the Trichosporonaceae family, uncover the underlying genetic basis of A. mycotoxinivorans infections and associated drug resistance, and provide clues into potential targets for further research and the therapeutic intervention of infections.

Machine learning screening for molecular subtyping of Alzheimer's disease based on pyroptosis-related genes

“…We collected 52 pyroptosis-related related genes (PRGs) through Pubmed database 12 , selected GSE132903 as the Train set, referenced limma to obtain the expression level of PRGs, and then calculated the difference in PRGs expression between AD group and control group using the wilcox function to obtain the differentially expressed PRGs (DE-PRGs). Finally, To display the data, we utilised the R packages pheatmap, ggpubr, & Rcircos.P < 0.05 was regarded as statistically meaningful (13,14) .…”