Ontology highlight
ABSTRACT:
PROVIDER: PRJNA495857 | ENA |
REPOSITORIES: ENA
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Project description:Teixobactin is the first novel antimicrobial to be discovered in decades and represents a new class of antimicrobials. Teixobactin shows great promise with proven efficacy against multi-drug resistant organisms such as methicillin-resistant S. aureus (MRSA), vancomycin-resistant Enterococci (VRE), and Mycobacterium tuberculosis. VRE infections are notoriously difficult to treat with complex and adaptable cell wall stress response systems, which confer intrinsic resistance to a wide variety of antimicrobials. The aim of this study was to isolate the teixobactin-induced transcriptional response by challenging lab strain Enterococcus faecalis JH2-2 with sub-MIC levels of teixobactin using RNA sequencing. Two cultures of E. faecalis were grown to an OD600 of 0.2 and subsequently split into three to form a total of six cultures (two samples, with three technical replicates each), and grown to an OD600 of 0.5. One set of three cultures were treated with 0.5 ug/ml (0.25 x MIC), and all six cultures were grown for a further 1h. Cells were harvested by centrifugation and stored at -80 degrees C. RNA was extracted using TRIzol-chloroform and RNA samples were run through the RNAeasy Minikit (Qiagen). The Agilent RNA 6000 Nano kit and the Agilent 2100 Bioanalyzer (RIN >8), was used to verify RNA quality as per the manufacturer’s instructions, and RNA concentration was determined using a NanoDrop ND-100 spectrophotometer. Ribosomal RNA was removed from total RNA using Ribo-Zero and cDNA libraries were created using the Illumina TruSeq™ stranded total RNA library prep kit. Sequencing was completed using Illumina MiSeq_v3 generating 150 bp single end reads. Adapter sequences were removed from raw fastq files using Flexbar and reads shorter than 50bp were discarded. Sequence reads from each sample were mapped against the E. faecalis JH2-2 genome (NZ_KI518257.1) using Bowtie to produce a table of raw read counts for JH2-2 genes for each of the replicates. Statistical and principal component analysis were performed using the Bioconductor DESeq package.
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Model Type: Predictive machine learning model.
Model Relevance: The model predicts the probability of growth inhibition.
Model Encoded by: Sarima Chiorlu (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos18ie
2024-05-08 | MODEL2405080002 | BioModels