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Deriving pathway-response signatures for 11 pathways. a Reasoning about pathway activation. Most pathway approaches make use of either the set (top panel) or infer or incorporate structure (middle panel) of signaling molecules to make statements about a possible activation, while signature-based approaches such as PROGENy consider the genes affected by perturbing the pathway. b Workflow of the data curation and model building. (1) Finding and curation of 208 publicly available experiment series in the ArrayExpress database, (2) Extracting 556 perturbation experiments from series’ raw data, (3) Performing QC metrics and discarding failures, (4) Computing z-scores per experiment, (5) Using a linear regression model to fit genes responsive to all pathways simultaneously obtaining the z-coefficients matrix, (6) Assigning pathway scores using the coefficients matrix and basal expression data. See methods section for details. c Size of the data set compared to an individual gene expression signature experiment. The amount of experiments that comprise each pathway is shown to scale and indicated. Figure 1b (2) created by Guillaime Paumier is published under a CC-BY-SA license, sourced from https://commons.wikimedia.org/wiki/File:DNA_microarray.svg. Figure 1b (4) is an adaptation (by Chen-Pan Liao) of the original work of User:Jhguch at en.wikipedia, published under a CC-BY-SA license, sourced from https://commons.wikimedia.org/wiki/File:Boxplot_vs_PDF.svg. Figure 1b (6) is an adaptation (by User:Ogrebot) of the original work of User:Bilou at en.wikipedia, published under a CC-BY-SA license, sourced from https://commons.wikimedia.org/wiki/File:Matrix_multiplication_diagram_2.svg

Deriving pathway-response signatures for 11 pathways. a Reasoning about pathway activation. Most pathway approaches make use of either the set (top panel) or infer or incorporate structure (middle panel) of signaling molecules to make statements about a possible activation, while signature-based approaches such as PROGENy consider the genes affected by perturbing the pathway. b Workflow of the data curation and model building. (1) Finding and curation of 208 publicly available experiment series in the ArrayExpress database, (2) Extracting 556 perturbation experiments from series’ raw data, (3) Performing QC metrics and discarding failures, (4) Computing z-scores per experiment, (5) Using a linear regression model to fit genes responsive to all pathways simultaneously obtaining the z-coefficients matrix, (6) Assigning pathway scores using the coefficients matrix and basal expression data. See methods section for details. c Size of the data set compared to an individual gene expression signature experiment. The amount of experiments that comprise each pathway is shown to scale and indicated. Figure 1b (2) created by Guillaime Paumier is published under a CC-BY-SA license, sourced from https://commons.wikimedia.org/wiki/File:DNA_microarray.svg. Figure 1b (4) is an adaptation (by Chen-Pan Liao) of the original work of User:Jhguch at en.wikipedia, published under a CC-BY-SA license, sourced from https://commons.wikimedia.org/wiki/File:Boxplot_vs_PDF.svg. Figure 1b (6) is an adaptation (by User:Ogrebot) of the original work of User:Bilou at en.wikipedia, published under a CC-BY-SA license, sourced from https://commons.wikimedia.org/wiki/File:Matrix_multiplication_diagram_2.svg

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Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific expe...

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... The psych and irr packages were used to calculate intraclass correlations. Pathway activity estimation from expression data was performed with PROGENy [43]. ...
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... ; https://doi.org/10.1101/2022.06.18.496114 doi: bioRxiv preprint publicly available VISIUM tumor datasets from seven tissues (Fig. 1A) were downloaded from a recent publication [13] (three liver leading-edge tumor samples; HCC-1L, cHC-1L, ICC-1L) and Transcription factor and pathway activity assessment MYC transcription factor activity was assessed using DoRothEA and Viper R packages after filtering for high confidence regulons [23,24]. TGFB pathway activity was assessed using PROGENy [25]. ...
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... To identify tumor-cell based TME imprinting characteristics, we next analyzed differentially enriched pathways (Schubert et al., 2018) in the tumor cells of each of the four immune phenotypes. The immune deserted subtype showed downregulation (FDR < 0.1) of the immune mediating pathways NFkB and TNF, as well as androgen, EGFR, and MAPK pathways ( Figure 2B). ...
... We performed pathway, transcription factor, and cytokine signaling analysis on primary tumor samples with PROGENy (Holland et al., 2020;Schubert et al., 2018), DoROthEA (Garcia-Alonso et al., 2019; ...
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... Active transcription factors within cluster 16 included Klf13, Hnf4a, Thap11, Grhl2, and Mxi1. Additionally, we ran a Pathway RespOnsive GENes (PROGENy) analysis (42) to determine key pathways that were enriched in each fibroblast-like cell cluster. As expected, Esr1 hi HAFs in cluster 3 had high enrichment for estrogen signaling ( Figure 4F). ...
... Pathway enrichment analysis was performed on differentially expressed genes using MetaCore (Clarivate Analytics). Pathway inference analysis on fibroblast clusters was performed using the PROGENy v1.12.0 R package (42,98,99). Pseudotime inference analysis of the fibroblast clusters was performed using Slingshot v1.8.0 Bioconductor R package, setting cluster 3 as the original cluster (40). ...
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... The importances of the features used as predictors in each view are consistent with biological processes. In the intraview (Fig. 7C), we recovered, among others, associations between NFkB and TNFa, and P53 and MAPK that have been reported previously [39]. These results capture pathway crosstalks within a spot as illustrated in the spatial distribution of pathway activities shown in Fig. 7D. ...
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... We analysed gene expression data from two recent studies (GSE147507 [13] and GSE148729 [14]), where lung epithelial cancer cell lines (Calu-3 and A549) were infected with SARS-CoV-2. To identify infection-induced pathway and transcription factor (TF) changes, we used the PROGENy [15,16] and DoRothEA [17,18] tools, respectively (more details in Methods). ...
... From previously calculated SARS-CoV-2 infection and effective drug-induced signatures, we inferred pathway activities using PROGENy (R package progeny [15,16]) and transcription factor activities using DoRothEA (R package dorothea [18]). ...
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Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity—and not inverse similarity—between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect.
... Finally, we analyzed cell types by clustering PROGENy's activity-score across signaling pathways and clusters (Schubert et al., 2018). We found that stressed cells form an outgroup and are marked by the upregulation of Hypoxia and VEGF pathways, and the downregulation of the PI3K pathway, highlighting oxygen deficiency and quiescence (Fig 3B, methods). ...
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... Using an additive model accounting for differences in histological subtypes (but not oxygen growth conditions), we next investigated differentially expressed genes in cell lines with high vs. low CA, and high vs. low MN frequencies (Fig. 4.20, showing volcano plots of differentially expressed genes). q q q qq q q q q q q q q q q q q q qqq q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q qq q qq q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q To further investigate and validate some of these findings, we next estimated PROGENy pathway activities (Holland et al., 2020;Schubert et al., 2018) for each cell line (Fig. 4.22). ...
... The PROGENy approach is based on the identification of a core of response genes associated with 14 oncogenic signalling pathways by leveraging publicly available perturbation data, and has been shown to infer pathway signalling activities from gene expression with higher accuracy than more conventional methods (Holland et al., 2020;Schubert et al., 2018). Consistent with previous results, we found a significant correlation between CA frequencies and NFκB and TNFα PROGENy pathway activities (Spearman's R = 0.28, p = q q q q q q q q q q q q q q q q q q qq q q q q q q qq q q q q q q qq q q q q q q q q q q qq q q q q qq q q q q q q qq q q q q qq q q q q q q q q q q qq q q qq qq q q q q q q q q q q qq q q qq q q q q q q q q qq q q q q qq qq q q qq qq qq qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q qq q q q q qq qq qq qq q q qq q q qq qq q q q q q q q q q q qq qq q q q q q q qq q q q q qq q q q q q q qq q q q q q q q q qq qq qq q q q q q q q q qq q q qq qq q q qq qq q q q q q q q q q q q q qq qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q qq q q q q q q q q qq q q q q qq q q q q q q qq q q q q q q qq qq q q q q q q q q qq qq q q q q q q qq q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q qq q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q qq qq q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q qq q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q qq q q q q q q q q q q qq qq q q q q qq q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q qq q q qq q q q q qq q q qq q q q q q q q q q q qq qq q q qq q q q q q q q q q q q q q q q q q q q q qq q q q q q q qq q q qq qq q q qq q q q q q q qq q q qq q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q TGFβ pathway activity (Fig. 4.23). ...
... PROGENy pathway activity scores were estimated as previously described (Holland et al., 2020;Schubert et al., 2018) using the progeny R package v1. 16.0 (https://saezlab. ...
Thesis
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... The addition of solvent and chemical already resembles a change in the environmental conditions. This change induced shifts in the global microbial metabolome, as microbial metabolism can alter quickly to adapt to environmental changes or perturbations (Schimel et al., 2007;Schubert et al., 2018). Nevertheless, the global metabolome of all cultures exposed to 354.0 µg/mL BPX and 28.3 µg/mL BPA for E. coli and the fecal microbiota changed significantly. ...
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Oral uptake is the primary route of human bisphenol exposure, resulting in an exposure of the intestinal microbiota and intestine-associated immune cells. Therefore, we compared the impact of bisphenol A (BPA), bisphenol F (BPF) and bisphenol S (BPS) on (i) intestinal microbiota, (ii) microbiota-mediated immunomodulatory effects and (iii) direct effects on mucosal-associated invariant T (MAIT) cells in vitro. We acutely exposed human fecal microbiota, Bacteroides thetaiotaomicron and Escherichia coli to BPA and its analogues BPF and BPS referring to the European tolerable daily intake (TDI), i.e. 2.3 µg/mL, 28.3 µg/mL and 354.0 µg/mL. Growth and viability of E. coli was most susceptible to BPF, whereas B. thetaiotaomicron and fecal microbiota were affected by BPA > BPF > BPS. At 354.0 µg/mL bisphenols altered microbial diversity in compound-specific manner and modulated microbial metabolism, with BPA already acting on metabolism at 28.3 µg/mL. Microbiota-mediated effects on MAIT cells were observed for the individual bacteria at 354.0 µg/mL only. However, BPA and BPF directly modulated MAIT cell responses at low concentrations, whereby bisphenols at concentrations equivalent for the current TDI had no modulatory effects for microbiota or for MAIT cells. Our findings indicate that acute bisphenol exposure may alter microbial metabolism and impact directly on immune cells.
... To understand whether signaling pathways are activated in ALS and how this relates to A1 and protective astrocytes, we next performed a pathway responsive genes (PROGENy) analysis (Schubert et al. 2018). In ALS astrocytes, the most substantial increase was noted in the profibrotic TGFB signaling pathway, followed by hypoxia, Wnt, and VEGF pathways (Fig. 3G). ...
... Overlap between two lists of genes was tested using the Fisher's exact test, whereas overlap between three lists was tested using the listOverlaps function that uses the ANOVA test. PROGENy was used to estimate pathway deregulation (Schubert et al. 2018). PROGENy scores were calculated using the PROGENy weights from the differential expression results, and scores were scaled to their respective null distribution to obtain a normalized pathway score. ...
Article
Astrocytes contribute to motor neuron death in amyotrophic lateral sclerosis (ALS), but whether they adopt deleterious features consistent with inflammatory reactive states remains incompletely resolved. To identify inflammatory reactive features in ALS human induced pluripotent stem cell (hiPSC)–derived astrocytes, we examined transcriptomics, proteomics, and glutamate uptake in VCP -mutant astrocytes. We complemented this by examining other ALS mutations and models using a systematic meta-analysis of all publicly-available ALS astrocyte sequencing data, which included hiPSC-derived astrocytes carrying SOD1 , C9orf72 , and FUS gene mutations as well as mouse ALS astrocyte models with SOD1 G93A mutation, Tardbp deletion, and Tmem259 (also known as membralin) deletion. ALS astrocytes were characterized by up-regulation of genes involved in the extracellular matrix, endoplasmic reticulum stress, and the immune response and down-regulation of synaptic integrity, glutamate uptake, and other neuronal support processes. We identify activation of the TGFB, Wnt, and hypoxia signaling pathways in both hiPSC and mouse ALS astrocytes. ALS changes positively correlate with TNF, IL1A, and complement pathway component C1q-treated inflammatory reactive astrocytes, with significant overlap of differentially expressed genes. By contrasting ALS changes with models of protective reactive astrocytes, including middle cerebral artery occlusion and spinal cord injury, we uncover a cluster of genes changing in opposing directions, which may represent down-regulated homeostatic genes and up-regulated deleterious genes in ALS astrocytes. These observations indicate that ALS astrocytes augment inflammatory processes while concomitantly suppressing neuronal supporting mechanisms, thus resembling inflammatory reactive states and offering potential therapeutic targets.