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Additional file 2: Script 1. Script limma_sva.r.

Additional file 1: Fig S1. Validation ofvideo-based exposure to a zebrafish predator. Fig S2. Principal component analysis (PCA) of RNA-seq performed in7 dpf larvae. Fig S3. RNA-seq vsqPCR correlation values.

Additional file 3: Script 2. Script fgsea.r.

Additional file 10. Dynamic visualization of Prunus persica response to PpPep1 from RNA-Seq experimental data on DiNAR application, P. persica Pep network (PEPN). Only differentially expressed genes are visualized (adj. p < 0.01, |log2FC| >1.0). Dynamic changes in gene expression after 1, 24 and 48 hours vs. non-treated samples are shown. Node colors correspond to gene regulation (red, upregulated and blue downregulated). Node sizes correspond to absolute log2FC values and are related to the maximum value in each time condition. Time points scale is at the bottom.

Additional file 2: Supplementary Table S1. Monoclonal antibodies (mAbs). Supplementary Table S7. Highlighted dysregulated genes in T CLL cells at progression. Supplementary Figure S1. Longitudinal analysis of the CCF of CNVs from paired B-CLL cells at diagnosis and progression before treatment. Comparison of the CCF with 95% CI for each CNV detected per patient (n = 10) between diagnosis and progression. Significantly increased (red lines) and stable CCF (grey lines) are shown. Recurrent CNVs in CLL (del(13q), del(11q), del(17p) and +[12]) are plotted with bold lines and labeled with CNV name: stable CCF (bold black) is shown. Supplementary Figure S2. CD8+ T cell differentiation subsets and PD1 expression in CD8+ T cells from progressing and non-progressing CLL patients. a CD4/CD8 ratio in progressing (n = 19) and non-progressing patients (n = 10) at diagnosis and progression or non-progression. b Absolute numbers of CD8+ T cell differentiation subsets (naive: CCR7+CD45RA+; central memory, CM: CCR7+CD45RA−; effector memory, EM: CCR7−CD45RA− and EM CD45RA+, EMRA: CCR7−CD45RA+) in progressing (n = 19) and non-progressing patients (n = 10) at diagnosis and progression or non-progression. c Absolute numbers of PD1+CD8+ T cells in progressing (left, n = 19) and non-progressing patients (middle, n = 10) at diagnosis and progression or non-progression. Fold change of PD1+CD8+ T cells between time points comparing progressing and non-progressing patients (right). d Percentage of PD1+CD244+ CD8+ T cells in progressing (left, n = 12) and non-progressing patients (middle, n = 9) at diagnosis and progression or non-progression. Fold change of PD1+CD244+CD8+ T cells between time points comparing progressing and non-progressing patients (right). e Percentage of PD1+ 160+ CD8+ T cells in progressing (left, n = 12) and non-progressing patients (middle, n = 9) at diagnosis and progression or non-progression. Fold change of PD1+CD160+CD8+ T cells between time points comparing progressing and non-progressing patients (right). f Density plots of PD1, CD160 and CD244 coexpression in CD8+ T cells in representative patients at diagnosis and progression and at diagnosis and non-progression. Graphs show mean ± SEM or paired values (*P

Figure S8. Changes in mitochondria-related gene expression is PD-L1 dose-dependent. (PDF 166 kb)

Table S1. List of primers used for RT-qPCR analyses. (PDF 44 kb)

Table S3. Pathways significantly enriched in the 578 genes selected. (PDF 56 kb)

Figure S1. Dose-dependent inhibition of T cell activation by PD-L1. (PDF 161 kb)

Figure S3. Scheme showing the metabolic pathways altered in PD-1-stimulated cells. (PDF 2970 kb)

Figure S4. GO enrichment analysis for molecular function terms. (PDF 776 kb)

Figure S5. GO enrichment analysis for biological processes terms. (PDF 777 kb)

Table S2. Top 20 genes with the most divergent expression in the LRT model. (PDF 89 kb)

Additional file 1. Supplementary Figures and Tables.