Back

Plant miRNAs Anti-Staphylococcus aureus: Therapeutic Perspective

Gharsallah, C.

2024-06-15 bioinformatics
10.1101/2024.06.13.598803 bioRxiv
Show abstract

Staphylococcus aureus is a pathogen that has developed resistance to each new antibiotic introduced for half a century especially through the acquisition of the mecA gene. This bacterial resistance to antibiotics represents a major public health problem. New revolutions are underway, in particular the design of drugs and vaccines targeting the system for detecting the regulatory quorum of the accessory gene (agr). It has been shown that the pathogenicity and resistance of S. aureus can be modulated through the intervention of this system. For this reason, we propose in this present work, a new therapeutic design based on an in silico study to identify plant miRNAs that could target this system as well as the mecA gene based on recent studies showing the inter-realm regulation of human transcripts by plant miRNAs. Out of a total of 20643 miRNAs from mature plants, we identified 29 miRNAs, obtained by the selection criteria MFE [≥] -25 kcal / mol, which could potentially target selected genes of S. aureus. Fifteen of them were selected on the basis of their thermodynamic stability. Interestingly, The seeds of ptc - miR171g and ptc - miR171h (Populus trichocarpa) both of which almost similar mature miRNA sequence was found to target UCCC region of RNAIII gene. miRNAs from plants targeting S. aureus have the potential to be developed as an alternative therapy of the future. To our knowledge, it is the first therapeutic alternative of the future via the in silico identification of plant miRNAs targeting pathogenic bacteria. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/598803v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@45daeaorg.highwire.dtl.DTLVardef@274b40org.highwire.dtl.DTLVardef@1bd8b25org.highwire.dtl.DTLVardef@fa3964_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.