Identification of potential antimicrobials against <i>Salmonella typhimurium</i> and <i>Listeria monocytogenes</i> using Quantitative Structure-Activity Relation modeling

<div><p>The shelf-life of fresh carcasses and produce depends on the chemical and physical properties of antimicrobials currently used for treatment. For many years the gold standard of these antimicrobials has been Cetylpyridinium Chloride (CPC) a quaternary ammonium compound (QAC). CPC is very effective at removing bacterial pathogens from the surface of chicken but has not been approved for other products due to a toxic residue left behind after treatment. Currently there is also a rising trend in QAC resistant bacteria. In order to find new compounds that can combat both antimicrobial resistance and the toxic residue we have developed two Quantitative Structure-Activity Relationship (QSAR) models for <i>Salmonella typhimurium</i> and <i>Listeria monocytogenes</i>. These models have been shown to be accurate and reliable through multiple internal and external validation techniques. In processing these models we have also identified important descriptors and structures that may be key in producing a viable compound. With these models, development and testing of new compounds should be greatly simplified.</p></div

Descriptor distribution for top QSAR models.

<p>Distribution of chemical descriptors (black) and their respective average absolute coefficient magnitudes (black), in top models for <i>S</i>. <i>typhimurium</i> (A) and <i>L</i>. <i>monocytogenes</i> (B). Descriptor distribution normalized to the total number of models observed for each bacteria. (C) Descriptor distribution (black) and average absolute coefficient magnitudes (grey) based on previously created models against <i>E</i>. <i>coli</i>.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189580#pone.0189580.ref033" target="_blank">33</a>] Most descriptors are in canonical SMILES, using single letters to represent atoms in a molecule, “*” denote any atom, “‘“ represent potential atoms, “()” represent branches in a molecule, numbers represent joining points in ring structures, “=“ represent double bonds, and lower case letters are atoms involved in aromatic strucutres. (D) Distribution of descriptor types across all top models. Descriptors that were pertinent to multiple bins were included in all potential bins.</p

Linear regression for top model against <i>L</i>. <i>monocytogenes</i>.

<p>Regression of experimentally validated and model predicted log(MIC) values for the top QSAR model (model 90) for <i>L</i>. <i>monocytogenes</i> selected through internal and external validations. The yellow dots represent the training set while the blue dots represent the test set.</p

Internal and external validation data for top S. typhimurium and L. monocytogenes Models.

<p>Internal and external validation data for top S. typhimurium and L. monocytogenes Models.</p

Substructure amongst top compounds predicted by QSAR modelling.

<p>Major substructure detected by SIMCOMP2 software for the top similar structures from their prediction sets.</p

Pairwise similarity comparisons between top 50 predicted compounds for three bacterial species.

<p>Pairwise similarity comparisons between top 50 predicted compounds for three bacterial species.</p

Top 5 compounds according to average log(MIC) from all 3 predictive models.

<p>Top 5 compounds according to average log(MIC) from all 3 predictive models.</p

Top 10 compounds against S. typhimurium in terms of log(MIC).

<p>Top 10 compounds against S. typhimurium in terms of log(MIC).</p

Additional file 3: Table S3. of Whole blood transcriptional profiling comparison between different milk yield of Chinese Holstein cows using RNA-seq data

Primer sequences used for qRT-PCR. (DOCX 14 kb

Additional file 1: Table S1. of Whole blood transcriptional profiling comparison between different milk yield of Chinese Holstein cows using RNA-seq data

The total transcripts analyzed in investigated samples. (XLSX 2461 kb