DOI: 10.5061/dryad.pvmcvdntj
title: Data and code for: Dihydrothiazolo ring-fused 2-pyridone antimicrobial compounds effectively treat Streptococcus pyogenes skin and soft tissue infection

description: We have developed GmPcides from a peptidomimetic dihydrothiazolo
 ring-fused 2-pyridone scaffold that have antimicrobial activities against
 a broad-spectrum of Gram-positive pathogens. Here we examine the treatment
 efficacy of GmPcides using skin and soft tissue infection (SSTI) and
 biofilm formation models by Streptococcus pyogenes. Screening our compound
 library for minimal inhibitory (MIC) and minimal bactericidal (MBC)
 concentrations identified GmPcide PS757 as highly active against S.
 pyogenes . Treatment of S. pyogenes biofilm with PS757 revealed robust
 efficacy against all phases of biofilm formation by preventing initial
 biofilm development, ceasing biofilm maturation and eradicating mature
 biofilm. In a murine model of S. pyogenes SSTI, subcutaneous delivery of
 PS757 resulted in reduced levels of tissue damage, decreased bacterial
 burdens and accelerated rates of wound-healing, which were associated with
 down-regulation of key virulence factors, including M protein and the SpeB
 cysteine protease. These data demonstrate that GmPcides show considerable
 promise for treating S. pyogenes infections.
description: <strong>RNA Sequencing. </strong>Microplate (96-well)
 culture in C medium was conducted as described above with the addition of
 0.4 µM PS757 or vehicle (DMSO). At 24 hrs, multiple wells were harvested
 and pooled for further processing, with the experiment repeated in
 triplicate. Extraction of RNA utilized the Direct-zol RNA Miniprep Plus
 Kit (Zymo Research, R2072) with the quality of the purified RNA determined
 by spectroscopy (NanoDrop 2000, Thermo Fisher). Libraries for Illumina
 sequencing were prepared using the FastSelect RNA kit (Qiagen, 334222),
 according to the manufacture’s protocol and sequences determined using an
 Illumina NovaSeq 6000. Basecalls and demultiplexing were performed with
 Illumina’s bcl2fastq software and a custom python demultiplexing program
 with a maximum of one mismatch in the indexing read. RNA-seq reads were
 then aligned to the Ensembl release 101 primary assembly with STAR version
 2.7.9a (<em>1</em>). Gene counts were derived from the number
 of uniquely aligned unambiguous reads by Subread:featureCount version
 2.0.3 (<em>2</em>). Isoform expression of known Ensembl
 transcripts were quantified with Salmon version 1.5.2
 (<em>3</em>) and assessed for the total number of aligned
 reads, total number of uniquely aligned reads, and features detected. The
 ribosomal fraction, known junction saturation, and read distribution over
 known gene models were quantified with RSeQC version 4.0
 (<em>4</em>). <strong>Comparative
 Transcriptomic Analysis. </strong>All gene counts obtained from
 RNA-seq were then imported into the R/Bioconductor package EdgeR
 (<em>5</em>) and TMM normalization size factors calculated to
 adjust for differences in library size. Ribosomal genes and genes not
 expressed in the smallest group size minus one sample greater than one
 count-per-million were excluded from further analysis. The TMM size
 factors and the matrix of counts were then imported into the
 R/Bioconductor package Limma (<em>6</em>). Weighted
 likelihoods based on the observed mean-variance relationship of every gene
 and sample were calculated for all samples and the count matrix
 transformed to moderated log<sub>2</sub>-counts-per-million
 with Limma’s voomWithQualityWeights (<em>7</em>). The
 performance of all genes was assessed with plots of the residual standard
 deviation of every gene to their average log-count with a robustly fitted
 trend line of the residuals. Differential expression analysis was then
 performed to analyze for differences between conditions with results
 filtered for only those genes with Benjamini-Hochberg false-discovery rate
 adjusted p-values less than or equal to 0.05. A principal component
 analysis (PCA) was performed on differential expression data to
 distinguish differences between conditions (<em>8</em>). To
 find the significantly regulated genes, the Limma voomWithQualityWeights
 transformed log<sub>2</sub>-counts-per-million expression data
 was then analyzed via weighted gene correlation network analysis with the
 R/Bioconductor package WGCNA (<em>9</em>). Briefly, all genes
 were correlated across each other by Pearson correlations and clustered by
 expression similarity into unsigned modules using a power threshold
 empirically determined from the data. An eigengene was then created for
 each de novo cluster and its expression profile was then correlated across
 all coefficients of the model matrix. Because these clusters of genes were
 created by expression profile rather than known functional similarity, the
 clustered modules were given the names of random colors where grey is the
 only module that has any pre-existing definition of containing genes that
 do not cluster well with others. The information for all clustered genes
 for each module were then combined with their respective statistical
 significance results from Limma to determine whether or not those features
 were also found to be significantly differentially expressed.
 <strong>References</strong>
 1.        A. Dobin, C. A. Davis, F. Schlesinger, J. Drenkow, C.
 Zaleski, S. Jha, P. Batut, M. Chaisson, T. R. Gingeras, STAR: ultrafast
 universal RNA-seq aligner. <em>Bioinformatics</em>
 <strong>29</strong>, 15-21 (2013).
 2.        Y. Liao, G. K. Smyth, W. Shi, featureCounts: an
 efficient general purpose program for assigning sequence reads to genomic
 features. <em>Bioinformatics</em>
 <strong>30</strong>, 923-930 (2014).
 3.        R. Patro, G. Duggal, M. I. Love, R. A. Irizarry, C.
 Kingsford, Salmon provides fast and bias-aware quantification of
 transcript expression. <em>Nat Methods</em>
 <strong>14</strong>, 417-419 (2017).
 4.        L. Wang, S. Wang, W. Li, RSeQC: quality control of
 RNA-seq experiments. <em>Bioinformatics</em>
 <strong>28</strong>, 2184-2185 (2012).
 5.        M. D. Robinson, D. J. McCarthy, G. K. Smyth, edgeR: a
 Bioconductor package for differential expression analysis of digital gene
 expression data. <em>Bioinformatics</em>
 <strong>26</strong>, 139-140 (2010).
 6.        M. E. Ritchie, B. Phipson, D. Wu, Y. Hu, C. W. Law, W.
 Shi, G. K. Smyth, limma powers differential expression analyses for
 RNA-sequencing and microarray studies. <em>Nucleic Acids
 Res</em> <strong>43</strong>, e47 (2015).
 7.        R. Liu, A. Z. Holik, S. Su, N. Jansz, K. Chen, H. S.
 Leong, M. E. Blewitt, M. L. Asselin-Labat, G. K. Smyth, M. E. Ritchie, Why
 weight? Modelling sample and observational level variability improves
 power in RNA-seq analyses. <em>Nucleic Acids Res</em>
 <strong>43</strong>, e97 (2015). 8.       
 Z. Zou, R. F. Potter, W. H. t. McCoy, J. A. Wildenthal, G. L. Katumba, P.
 J. Mucha, G. Dantas, J. P. Henderson, E. coli catheter-associated urinary
 tract infections are associated with distinctive virulence and biofilm
 gene determinants. <em>JCI Insight</em>
 <strong>8</strong>,  (2023). 9.        P.
 Langfelder, S. Horvath, WGCNA: an R package for weighted correlation
 network analysis. <em>BMC Bioinformatics</em>
 <strong>9</strong>, 559 (2008).
description: **Title of Dataset:** Data and code for: Dihydrothiazolo ring-fused
 2-pyridone antimicrobial compounds effectively treat *Streptococcus
 pyogenes* skin and soft tissue infection We have submitted our raw data
 (Differential_Expression_PCA) and R script (RNASeq_PCA). Access this data
 on Dryad (10.5061/dryad.pvmcvdntj). **Descriptions** ·       *Sample*: two
 group of samples. (1) GP757: RNA-seq analysis on *Streptococcus pyogenes*
 cells under the treatment of GmPcide PS757. This is the experimental
 group. (2) DMSO: RNA-seq analysis on *Streptococcus pyogenes* cells under
 the treatment of DMSO. This the control group.   ·       *Row 1*: gene
 names. (1) Genes with annotated protein functions, e.g., *aroE_1*. (2)
 Genes annotated as hypothetical protein, e.g., NA. ·       *Row 2~7*:
 Values representing the differential expression levels of *Streptococcus
 pyogenes* genes under two different treatment conditions, GP757 and DMSO.
 **Key information sources** Differential expression levels of
 *Streptococcus pyogenes* genes under two different treatment conditions
 were derived from the NCBI data base under the project accession
 no. PRJNA1040846. **Code/Software** R is required to run *RNASeq_PCA*; the
 script was created using version 4.1.1. Annotations are provided
 throughout the script through 1) library loading, 2) dataset loading and
 cleaning, 3) analyses, and 4) figure creation.