Additional file 1 of Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model
Creators: name: Hoch, Matti
name: Smita, Suchi
name: Cesnulevicius, Konstantin
name: Schultz, Myron
name: Lescheid, David
name: Wolkenhauer, Olaf
name: Gupta, Shailendra
Publisher: name: figshare
Year: 2024
Subjects: subject: Biophysics
subject: Genetics
subject: FOS: Biological sciences
subject: Pharmacology
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Hematology

description: Additional file 1: Figure S1. PCA analysis of RNA-seq samples for Tr14 treatment (A) and saline control (B) at 12h. Outliers (orange) were removed afterward. (C and D) Volcano plots showing the impact of outlier removal on the DeSeq2 differential analysis for Tr14 vs. control. (E) Venn diagram comparing the number of significant genes (adj. p-value &lt; 0.05) before and after the removal of outliers. Figure S2. PCA analysis of RNA-seq samples for Tr14 treatment (A) and saline control (B) at 72h. Outliers (orange) were removed afterward. (C and D) Volcano plots showing the impact of outlier removal on the DeSeq2 differential analysis for Tr14 vs. control. (E) Venn diagram comparing the number of significant genes (adj. p-value &lt; 0.05) before and after the removal of outliers. Figure S3. PCA analysis of RNA-seq samples for Tr14 treatment (A) and saline control (B) at 96h. Outliers (orange) were removed afterward. (C and D) Volcano plots showing the impact of outlier removal on the DeSeq2 differential analysis for Tr14 vs. control. (E) Venn diagram comparing the number of significant genes (adj. p-value &lt; 0.05) before and after the removal of outliers. Figure S4. PCA analysis of RNA-seq samples for Tr14 treatment (A) and saline control (B) at 120h. Outliers (orange) were removed afterward. (C and D) Volcano plots showing the impact of outlier removal on the DeSeq2 differential analysis for Tr14 vs. control. (E) Venn diagram comparing the number of significant genes (adj. p-value &lt; 0.05) before and after the removal of outliers. Figure S5. PCA analysis of RNA-seq samples for diclofenac treatment (A) and placebo control (B) at 96h. Outliers (orange) were removed afterward. (C and D) Volcano plots showing the impact of outlier removal on the DeSeq2 differential analysis for diclofenac vs. control. (E) Venn diagram comparing the number of significant genes (adj. p-value &lt; 0.05) before and after the removal of outliers. Figure S6. Core Regulatory Networks (CRNs) of the “apoptotic process” phenotype for Tr14 and Diclofenac treatment at 96 and 120 hours. Gene triplets in the molecular interaction map connected to the phenotype are ranked by log2 fold change values and network features. The highest ranked motifs were than selected and merged into the CRNs. Figure S7. Core Regulatory Networks (CRNs) of the “NETosis” phenotype for Tr14 and Diclofenac treatment at 96 and 120 hours. Gene triplets in the molecular interaction map connected to the phenotype are ranked by log2 fold change values and network features. The highest ranked motifs were than selected and merged into the CRNs. Figure S8. Core Regulatory Networks (CRNs) of the “apoptotic cell clearance” (efferocytosis) phenotype for Tr14 and Diclofenac treatment at 96 and 120 hours. Gene triplets in the molecular interaction map connected to the phenotype are ranked by log2 fold change values and network features. The highest ranked motifs were than selected and merged into the CRNs. Figure S9. Core Regulatory Networks (CRNs) of the “M2 phenotype and behavior” phenotype for Tr14 and Diclofenac treatment at 96 and 120 hours. Gene triplets in the molecular interaction map connected to the phenotype are ranked by log2 fold change values and network features. The highest ranked motifs were than selected and merged into the CRNs.