The Complete Mitochondrial Genome of Paeonia lactiflora Pall. (Saxifragales: Paeoniaceae): Evidence of Gene Transfer from Chloroplast to Mitochondrial Genome

Paeonia lactiflora (P. lactiflora), a perennial plant renowned for its medicinal roots, provides a unique case for studying the phylogenetic relationships of species based on organelle genomes, as well as the transference of DNA across organelle genomes. In order to investigate this matter, we sequenced and characterized the mitochondrial genome (mitogenome) of P. lactiflora. Similar to the chloroplast genome (cpgenome), the mitogenome of P. lactiflora extends across 181,688 base pairs (bp). Its unique quadripartite structure results from a pair of extensive inverted repeats, each measuring 25,680 bp in length. The annotated mitogenome includes 27 protein-coding genes, 37 tRNAs, 8 rRNAs, and two pseudogenes (rpl5, rpl16). Phylogenetic analysis was performed to identify phylogenetic trees consistent with Paeonia species phylogeny in the APG Ⅳ system. Moreover, a total of 12 MTPT events were identified and 32 RNA editing sites were detected during mitogenome analysis of P. lactiflora. Our research successfully compiled and annotated the mitogenome of P. lactiflora. The study provides valuable insights regarding the taxonomic classification and molecular evolution within the Paeoniaceae family.

Rapid AMR prediction in Pseudomonas aeruginosa combining MALDI-TOF MS with DNN model

BACKGROUND

Pseudomonas aeruginosa is a significant clinical pathogen that poses a substantial threat due to its extensive drug resistance. The rapid and precise identification of this resistance is crucial for effective clinical treatment. Although matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been used for antibiotic susceptibility differentiation of some bacteria in recent years, the genetic diversity of P. aeruginosa complicates population analysis. Rapid identification of antimicrobial resistance (AMR) in P. aeruginosa based on a large amount of MALDI-TOF-MS data has not yet been reported. In this study, we employed publicly available datasets for P. aeruginosa, which contain data on bacterial resistance and MALDI-TOF-MS spectra. We introduced a deep neural network model, synergized with a strategic sampling approach (SMOTEENN) to construct a predictive framework for AMR of three widely used antibiotics.

RESULTS

The framework achieved area under the curve values of 90%, 85%, and 77% for Tobramycin, Cefepime, and Meropenem, respectively, surpassing conventional classifiers. Notably, random forest algorithm was used to assess the significance of features and post-hoc analysis was conducted on the top 10 features using Cohen's d. This analysis revealed moderate effect sizes (d = 0.5-0.8) in Tobramycin and Cefepime models. Finally, putative AMR biomarkers were identified in this study.

CONCLUSIONS

This work presented an AMR prediction tool specifically designed for P. aeruginosa, which offers a hopeful pathway for clinical decision-making.