SARS-CoV-2 is the causative agent for coronavirus disease-19 (COVID-19) and belongs to the family Coronaviridae that causes sickness varying from the common cold to more severe illnesses such as severe acute respiratory syndrome, sudden stroke, neurological complications (Neuro-COVID), multiple organ failure, and mortality in some patients. The gene expression profiles of COVID-19 infection models can be used to decipher potential therapeutics for COVID-19 and related pathologies, such as Neuro-COVID. Here, we used the raw RNA-seq reads (Single-End) in quadruplicates derived using Illumina Next Seq 500 from SARS-CoV-infected primary human bronchial epithelium (NHBE) and mock-treated NHBE cells obtained from the Gene Expression Omnibus (GEO) (GSE147507), and the quality control (QC) was evaluated using the CLC Genomics Workbench 20.0 (Qiagen, United States) before the RNA-seq analysis using BioJupies web tool and iPathwayGuide for gene ontologies (GO), pathways, upstream regulator genes, small molecules, and natural products. Additionally, single-cell transcriptomics data (GSE163005) of meta clusters of immune cells from the cerebrospinal fluid (CSF), such as T-cells/natural killer cells (NK) (TcMeta), dendritic cells (DCMeta), and monocytes/granulocyte (monoMeta) cell types for comparison, namely, Neuro-COVID versus idiopathic intracranial hypertension (IIH), were analyzed using iPathwayGuide. L1000 fireworks display (L1000FWD) and L1000 characteristic direction signature search engine (L1000 CDS2) web tools were used to uncover the small molecules that could potentially reverse the COVID-19 and Neuro-COVID-associated gene signatures. We uncovered small molecules such as camptothecin, importazole, and withaferin A, which can potentially reverse COVID-19 associated gene signatures. In addition, withaferin A, trichostatin A, narciclasine, camptothecin, and JQ1 have the potential to reverse Neuro-COVID gene signatures. Furthermore, the gene set enrichment analysis (GSEA) preranked method and Metascape web tool were used to decipher and annotate the gene signatures that were potentially reversed by these small molecules. In conclusion, our study unravels a rapid approach for applying next-generation knowledge discovery (NGKD) platforms to discover small molecules with therapeutic potential against COVID-19 and its related disease pathologies.

Mito and ETC genes with GSEA analysis: Clustered heatmap showing all genes associated with the "Mitochondrion" GO term in young and old, mouse CMs as observed by the GSEA pre-ranked list enrichment analysis (normalised enrichment score: -1.70; FDR q-value < 0.05). Alongside this is a column clustered heatmap displaying a list of genes from the electron transport chain (ETC) GO ontology. In both instances, genes are by column and samples by row with the colour intensity representing column Z-score, where red indicates highly and blue lowly expressed.. List of tagged entities: multiple components, , fluorescence intensity (bao:BAO_0000363),gene expression assay (bao:BAO_0002785),GSEA-P enrichment analysis (bao:BAO_0002356)

Antigen-induced arthritis (AIA) was induced in C57BL6 mice by immunization with methylated bovine serum albumin (mBSA) and subsequent intra-articular injection of mBSA. Non-immunized (NI) mice were injected with phosphate buffered saline at all timepoints. Ten days after intra-articular injection knee joints were harvested and synovial cells were released by collagenase type IV injection into joint cavitied, and fluorescence-activated cell sorting (FACS) was used to sort 200-500 TER119–CD31–CD45–CD51+CD200+CD105– cells from NI mice (NI 200+, Sample 1-4) and mice with AIA (AIA 200+, Sample 5-9) and, TER119–CD31–CD45–CD51+CD200–CD105+ cells from mice with AIA (AIA 105+, Sample 11-14) using BD FASCAria IIu. For each sample, 200-500 live (DAPI–) cells were sorted directly into cell lysis buffer from Smartseq v4 Ultra® Low Input RNA Kit for Sequencing (TakaRa, Kyoto, Japan). ERCC RNA Spike-In Mix (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) were added to lysed cell samples. cDNA amplicons were created using SmartSeq v4 Ultra® Low Input RNA Kit for Sequencing (TakaRa) and libraries were prepared using Nextera XT DNA Library Preparation Kit (Illumina, San Diego, CA, USA). Libraries were sequenced using NextSeq 500 (Illumina) instrument and raw files are available at GSE148130. After quality control of raw reads, reads were trimmed, aligned, assembled and quantified using cutadapt (Martin, EMBnet Journal, 2011), HISAT2 (Kim, Nat Methods, 2015) and StringTie (Pertea, Nat Biotecnol, 2015), and normalized and filtered in egdeR package (Robinson, Bioinformatics, 2010). Gene set enrichment analysis (GSEA) was conducted by ClusterProfiler package using gseGO function (Yu, OMICS, 2012) on GO gene sets from biological processes (BP) cell component (CC) and molecular function (MF) categories. Genes were preranked according to signed logarithm (log10) of Benjamini-Hochberg adjusted p value, with positive or negative sign given to genes with positive or negative fold change, respectively. Gene sets with Benjamini-Hochberg adjusted p value < 0.05 were considered significantly enriched and are listed in the tables, together with their enrichment score, normalized enrichment score (NES), p value, adjusted p value (Benjamini-Hochberg) and q value, rank at which the maximum enrichment score occurred, leading edge analysis and core enriched genes for each gene set. The table also includes gene set ID, ontology category (BP, CC or MF), description and size of gene sets. Table 1 contains analysis of comparison of NI 200+ and AIA 200+, Table 2 NI 200+ and AIA 105+ and Table 3 AIA 200+ and AIA 105+.

The mechanisms underlying ETS-driven prostate cancer initiation and progression remain poorly understood due to a lack of model systems that recapitulate this phenotype. We generated a genetically engineered mouse with prostate-specific expression of the ETS factor, ETV4, at lower and higher protein dosages through mutation of its degron. Lower-level expression of ETV4 caused mild luminal cell expansion without histologic abnormalities and higher-level expression of stabilized ETV4 caused prostatic intraepithelial neoplasia (mPIN) with 100% penetrance within 1 week. Tumor progression was limited by p53-mediated senescence and Trp53 deletion cooperated with stabilized ETV4. The neoplastic cells expressed differentiation markers such as Nkx3.1 recapitulating luminal gene expression features of untreated human prostate cancer. Single-cell and bulk RNA-sequencing showed stabilized ETV4 induced a novel luminal-derived expression cluster with signatures of the cell cycle, senescence, and epithelial to mesenchymal transition. These data suggest that ETS overexpression alone, at sufficient dosage, can initiate prostate neoplasia. Methods Mouse prostate digestion: Intraperitoneal injection of tamoxifen was administered in 8-week-old mice. 2 weeks after tamoxifen treatment, the mouse prostate was digested 1 hour with Collagenase/Hyaluronidase (STEMCELL, #07912), and then 30 minutes with TrypLETM Express Enzyme (Thermo Fischer, # 12605028) at 37°C to isolate single prostate cells. The prostate cells were stained with PE/Cy7 conjugated anti-mouse CD326 (EpCAM) antibody (BioLegend, 118216) and then, CD326 and EYFP double positive cells were sorted out by flow cytometry, which are luminal cells mainly from the anterior prostate and dorsal prostate. The mRNA or genomic DNA were extracted from these double-positive cells and then were used for ATAC-sequencing and RNA-sequencing analysis. ATAC-seq and primary data processing: ATAC-seq was performed as previously described. Primary data processing and peak calling were performed using ENCODE ATAC-seq pipeline (https://github.com/kundajelab/atac_dnase_pipelines). Briefly, paired-end reads were trimmed, filtered, and aligned against mm9 using Bowtie2. PCR duplicates and reads mapped to mitochondrial chromosome or repeated regions were removed. Mapped reads were shifted +4/-5 to correct for the Tn5 transposase insertion. Peak calling was performed using MACS2, with p-value < 0.01 as the cutoff. Reproducible peaks from two biological replicates were defined as peaks that overlapped by more than 50%. On average 25 million uniquely mapped pairs of reads were remained after filtering. The distribution of inserted fragment length shows a typical nucleosome banding pattern, and the TSS enrichment score (reads that are enriched around TSS against background) ranges between 28 and 33, suggesting the libraries have high quality and were able to capture the majority of regions of interest. Differential peak accessibility: Reads aligned to peak regions were counted using R package GenomicAlignments_v1.12.2. Read count normalization and differential accessible peaks were called with DESeq2_v1.16.1 in R 3.4.1. Differential peaks were defined as peaks with adjusted p-value < 0.01 and |log2(FC)| > 2. For visualization, coverage bigwig files were generated using bamCoverage command from deepTools2, normalizing using the size factor generated by DESeq2. The differential ATAC-seq peak density plot was generated with deepTools2, using regions that were significantly more or less accessible in ETV4AAA samples relative to EYFP samples. Motif analysis: Enriched motif was performed using MEME-ChIP 5.0.0 with differentially accessible regions in ETV4AAA relative to EYFP. ATAC-seq footprinting was performed using TOBIAS. First, ACACCorrect was run to correct Tn5 bias, followed by ScoreBigwig to calculate footprint score, and finally BindDetect to generate differential footprint across regions. RNA-seq analysis: The extracted RNA was processed for RNA-sequencing by the Integrated Genomics Core Facility at MSKCC. The libraries were sequenced on an Illumina HiSeq-2500 platform with 51 bp paired-end reads to obtain a minimum yield of 40 million reads per sample. The sequenced data were aligned using STAR v2.3 with GRCm38.p6 as annotation. DESeq2_v1.16.1 was subsequently applied on read counts for normalization and the identification of differentially expressed genes between ETV4AAA and EYFP groups, with an adjusted p-value < 0.05 as the threshold. Genes were ranked by sign(log2(FC)) * (-log(p-value)) as input for GSEA analysis using ‘Run GSEA Pre-ranked’ with 1000 permutations (48). The custom gene sets used in GSEA analysis are shown in Table S2. Unsupervised hierarchical clustering: To get an overall sample clustering as part of QC, hierarchical clustering was performed using pheatmap_v1.0.10 package in R on normalized ATAC-seq or RNA-seq data. It was done using all peaks or all genes, with Spearman or Pearson correlation as the distance metric. To have an overview of the differential gene expression from the RNA-seq data, unsupervised clustering was also performed on a matrix with all samples as columns and scaled normalized read counts of differentially expressed genes between ETV4AAA and EYFP as rows. Integrative analysis of ATAC-seq, RNA-seq, and ChIP-seq data: ERG ChIP-seq peaks were called using MACS 2.1, with an FDR cutoff of q < 10-3 and the removal of peaks mapped to blacklist regions. Reproducible peaks between two biological replicates were identified as ETV4AAA ATAC-seq peaks. ERG ChIP-seq peaks and ETV4AAA ATAC-seq peaks were considered as overlap if peak summits were within 250bp. To determine whether the overlap was significant, enrichment analysis was done using regioneR_v1.8.1 in R, which counted the number of overlapped peaks between a set of randomly selected regions in the genome (excluding blacklist regions) and the ERG-ChIP seq peaks or ETV4AAA ATAC-seq peaks. A null distribution was formed using 1000 permutation tests to compute the p-value and z-score of the original evaluation. To assign ATAC-seq peaks to genes, ChIPseeker_v1.12.1 in R was used. Each peak was unambiguously assigned to one gene with a TSS or 3’ end closest to that peak. Differential gene expression between ETV4AAA and EYFP was evaluated using log2(FC) calculated by DESeq2. p-values were estimated with Wilcoxon rank t-test and Student t-test. scRNA-sequencing: Tmprss2-CreERT2, EYFP; Tmprss2-CreERT2, ETV4WT; Tmprss2-CreERT2, ETV4AAA; and Tmprss2-CreERT2, ETV4AAA; Trp53L/L mice were euthanized 2 weeks or 4 months after tamoxifen treatment (n=3 mice for each genotype and time point). After euthanasia, the prostates were dissected out and minced with scalpel, and then processed for 1h digestion with collagenase/hyaluronidase (#07912, STEMCELL Technologies) and 30min digestion with TrypLE (#12605010, Gibco). Live single prostate cells were sorted out by flow cytometry as DAPI-. For each mouse, 5,000 cells were directly processed with 10X genomics Chromium Single Cell 3’ GEM, Library & Gel Bead Kit v3 according to manufacturer’s specifications. For each sample, 200 million reads were acquired on NovaSeq platform S4 flow cell. Reads obtained from the 10x Genomics scRNAseq platform were mapped to mouse genome (mm9) using the Cell Ranger package (10X Genomics). True cells are distinguished from empty droplets using scCB2 package. The levels of mitochondrial reads and numbers of unique molecular identifiers (UMIs) were similar among the samples, which indicates that there were no systematic biases in the libraries from mice with different genotypes. Cells were removed if they expressed fewer than 600 unique genes, less than 1,500 total counts, more than 50,000 total counts, or greater than 20% mitochondrial reads. Genes detected in less than 10 cells and all mitochondrial genes were removed for subsequent analyses. Putative doublets were removed using the Doublet Detection package. The average gene detection in each cell type was similar among the samples. Combining samples in the entire cohort yielded a filtered count matrix of 48,926 cells by 19,854 genes, with a median of 6,944 counts and a median of 1,973 genes per cell, and a median of 2,039 cells per sample. The count matrix was then normalized to CPM (counts per million), and log2(X+1) transformed for analysis of the combined dataset. The top 1000 highly variable genes were found using SCANPY (version 1.6.1) (77). Principal Component Analysis (PCA) was performed on the 1,000 most variable genes with the top 50 principal components (PCs) retained with 29% variance explained. To visualize single cells of the global atlas, we used UMAP projections (https://arxiv.org/abs/1802.03426). We then performed Leiden clustering. Marker genes for each cluster were found with scanpy.tl.rank_genes_groups. Cell types were determined using the SCSA package, an automatic tool, based on a score annotation model combining differentially expressed genes (DEGs) and confidence levels of cell markers from both known and user-defined information. Heat-map were performed for single cells based on log-normalized and scaled expression values of marker genes curated from literature or identified as highly differentially expressed. Differentially expressed genes between different clusters were found using MAST package, which were shown in heat-map. The logFC of MAST output was used for the ranked gene list in GSEA analysis (48). The custom gene sets used in GSEA analysis are shown in Table S2. Gene imputation was performed using MAGIC (Markov affinity-based graph imputation of cells) package, and imputated gene expression were used in the heatmap. Analysis of public human gene expression datasets: To analyze TP53 RNA expression in human prostate cancer samples, we obtained normalized RNA-seq data from prostate cancer TCGA (www.firebrowse.org) (3). To assess the role of TP53 loss on

The three dataset files here are:

var_ccbr1144_Metadata.rds

Sample metadata table describing samples, group assignments, batch information, and plot labels.

var_msigdb_v6_2_with_orthologs.rds

MSigDB v6 database, with orthologs from Mouse and Macaca used in GSEA Preranked analyses.

var_counts_matrix.rds

Raw counts (RSEM) for RNAseq analysis.

These data can be used with the docker and code found at this GitHub link:

https://github.com/NIDAP-Community/Entinostat-and-NHS-IL12-in-aPD1-aPDL1-refractory-models

Additional file 3: Supplementary Table 1A. Signalment and clinical features of dogs in this study. Supplementary Table 1B. Flow cytometric features of canine PTCLs. Supplementary Table 1C. Flow cytometric features of normal control nodal and circulating lymphocytes. Supplementary Table 1D. Flow cytometric features of normal control canine thymocytes. Supplementary Table 2A. Top 500 differentially expressed genes in canine CD4+ PTCL compared to control canine CD4+ lymphocytes. Supplementary Table 2B. Normalized RNA-seq read counts. Supplementary Table 3A. GSEA results for MSigDB gene sets using the Broad Institute GSEAPreranked tool. Supplementary Table 3B. GSEA results for curated gene sets using the Broad Institute GSEAPreranked tool. Supplementary Table 4A. GSEA results for MSigDB gene sets using clusterProfiler. Supplementary Table 4B. GSEA results for curated gene sets using clusterProfiler. Supplementary Table 5. GSVA scores. Supplementary Table 6A. Novogene RNA-seq QA/QC report. Supplementary Table 6B. MultiQC RNA-seq QA/QC report. Supplementary Table 7. Software programs and packages. Supplementary Table 8. Correlation of protein expression by flow cytometry and gene expression by RNA-seq.

Gene enrichment analysis for the up- and down-regulated genes was done using GSEA Pre Ranked Tool. The selection criteria were set at Benjamin-Hochberg P = 0.05. Only statistically significant pathways showing a permuted P-value = 0.05 and a positive (enrichment) z-score >2 were selected.

Genes commonly modulated by chlorophyllin and ellagic acid in the enriched pathways (z-score >2) are listed. Gene enrichment analysis was done using GSEA Pre Ranked Tool. P value correction was done using Benjamini and Hochberg method.

Additional file 1. Gene set enrichment analysis. The Excel file (.xls) shows the output of gene set enrichment analysis (GSEA) [49, 50] of pre-ranked gene lists from the rat LSEC and KC RNA-seq datasets, associated with Gene Ontology (GO) biological processes (BP). The genes were pre-ranked based on expression. We have used the C5 collection of annotated gene sets in the Molecular Signatures Database (release 6.2; BP) [53] which consists of gene sets derived from GO [51, 52]. Name of worksheets: “GSEA_plot”, “GSEA_RNAseq_LSEC_BP”, and “GSEA_RNAseq_KC_BP”. The worksheet named “GSEA_Plot” contains the selected enriched BPs shown in Fig. 4.

Although radiotherapy is given to more than 50% of cancer patients, little progress has been made in identifying optimal radiotherapy - drug combinations to improve treatment efficacy. Using molecular data from The Cancer Genome Atlas (TCGA), we extracted a total of 1016 cancer patients that received radiotherapy. The patients were diagnosed with head-and-neck (HNSC - 294 patients), cervical (CESC - 166 patients) and breast (BRCA - 549 patients) cancer. We analyzed mRNA expression patterns of 50 hallmark gene sets of the MSigDB collection, which we divided in eight categories based on a shared biological or functional process. Tumor samples were split into upregulated, neutral or downregulated mRNA expression for all gene sets using a gene set analysis (GSEA) pre-ranked analysis and assessed for their clinical relevance. We found a prognostic association between three of the eight gene set categories (Radiobiological, Metabolism and Proliferation) and overall survival in all three cancer types. Furthermore, multiple single associations were revealed in the other categories considered. To the best of our knowledge, our study is the first report suggesting clinical relevance of molecular characterization based on hallmark gene sets to refine radiation strategies.

Additional file 1: Table S1. Differential expression analysis by DESeq2 between the DNAmAge-ACC and DNAmAge-DEC groups; Table S2. GO analysis based on differentially expressed genes; Table S3. KEGG GSEA analysis based on the pre-ranked gene list derived from DESeq2 result; Table S4. GO GSEA analysis based on the pre-ranked gene list derived from DESeq2 result; Table S5. Independent test between frequently mutated genes (> 10%) and DNAm age groups; Table S6. Differentially methylated probes in CpG islands identified by ChAMP between two DNAm age groups; Table S7. Demographic and clinicopathological characteristic comparison between samples with reversed methylation level in two DNAmAge groups; Table S8. Demographic and clinicopathological characteristic comparison between the two new DNAmAge groups; Table S9. Independent test between frequently mutated genes (> 10%) and new DNAm age groups; Table S10. Differentially methylated probes in CpG islands identified by ChAMP between two new DNAm age groups.

Sup.Table.S6: Gene sets enriched in B16F10 tumors treated with anti-PD-L1 + swainsonine vs swainsonine + IgG. Pre-ranked gene set enrichment analysis (GSEA) was used to perform the analysis

Sup.Table.S4: Gene sets enriched in Man2a1-null B16F10 tumors vs control tumors treated with IgG isotype control. Pre-ranked gene set enrichment analysis (GSEA) was used to perform the analysis.

Tabular ranked gene list file (viewable in Excel) pertaining to differential gene expression testing comparing siRNA-mediated knockdown of β-catenin versus scrambled siRNA control in HSC-3 cells used for GSEA. Fields indicate gene symbol and t test statistic of differential expression, respectively, used as input to the pre-ranked GSEA tool. (TXT 289 kb)

Sup.Table.S7: Gene sets enriched in B16F10 tumors treated with anti-PD-L1 + swainsonine vs IgG +PBS. Pre-ranked gene set enrichment analysis (GSEA) was used to perform the analysis.

Sup.Table.S5: Gene sets enriched in control tumors treated with anti-PD-L1 vs IgG isotype control. Pre-ranked gene set enrichment analysis (GSEA) was used to perform the analysis.

Sup.Table.S3: Gene sets enriched in Man2a1-null B16F10 tumors vs control tumors treated with anti-PD-L1. Pre-ranked gene set enrichment analysis (GSEA) was used to perform the analysis.

Pre-ranked gene set enrichment analysis of differential mRNA expression between all aneuploid clones (SS6, SS119, SS51, SS111) and the pseudo-diploid clone (SS48)

Acute ischemic stroke (AIS) is a primary cause of mortality and morbidity worldwide. Currently, no clinically approved immune intervention is available for AIS treatment, partly due to the lack of relevant patient classification based on the peripheral immunity status of patients with AIS. In this study, we adopted the consensus clustering approach to classify patients with AIS into molecular subgroups based on the transcriptomic profiles of peripheral blood, and we identified three distinct AIS molecular subgroups and 8 modules in each subgroup by the weighted gene co-expression network analysis. Remarkably, the pre-ranked gene set enrichment analysis revealed that the co-expression modules with subgroup I-specific signature genes significantly overlapped with the differentially expressed genes in AIS patients with hemorrhagic transformation (HT). With respect to subgroup II, exclusively male patients with decreased proteasome activity were identified. Intriguingly, the majority of subgroup III was composed of female patients who showed a comparatively lower level of AIS-induced immunosuppression (AIIS). In addition, we discovered a non-linear relationship between female age and subgroup-specific gene expression, suggesting a gender- and age-dependent alteration of peripheral immunity. Taken together, our novel AIS classification approach could facilitate immunomodulatory therapies, including the administration of gender-specific therapeutics, and attenuation of the risk of HT and AIIS after ischemic stroke.

Table S1. TCGA samples included in study. Table S2. Gene weights of MYC signatures. Table S3. Pre-ranked Gene Set Enrichment Analysis results of cancer type-specific MYC activity signatures. Table S4. MYC AUC characteristics. Table S5. Recurrent MYC mutations. Table S6. Multivariate Cox regression model assessing the relationship between patient prognosis and MYC activity scores, immune infiltration and expression of E2F family members in SKCM. Table S7. Prognostic results of individual datasets from GEO and PRECOG. Table S8. GEO and PRECOG datasets.