Understanding Conditional Associations between ToxCast in Vitro Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods

Current in vitro models for hepatotoxicity commonly suffer from low detection rates due to incomplete coverage of bioactivity space. Additionally, in vivo exposure measures such as Cmax are used for hepatotoxicity screening which are unavailable early on. Here we propose a novel rule-based framework to extract interpretable and biologically meaningful multi-conditional associations to prioritize in vitro endpoints for hepatotoxicity and understand the associated physicochemical conditions. The data used in this study was derived for 673 compounds from 361 ToxCast bioactivity measurements and 29 calculated physicochemical properties against two lowest effective levels (LEL) of rodent hepatotoxicity from ToxRefDB, namely 15mg/kg/day and 500mg/kg/day. In order to achieve 80% coverage of toxic compounds, 35 rules with accuracies ranging from 96% to 73% using 39 unique ToxCast assays are needed at a threshold level of 500mg/kg/day, whereas to describe the same coverage at a threshold of 15mg/kg/day 20 rules with accuracies of between 98% and 81% were needed, comprising 24 unique assays. Despite the 33-fold difference in dose levels, we found relative consistency in the key mechanistic groups in rule clusters, namely i) activities against Cytochrome P, ii) immunological responses, and iii) nuclear receptor activities. Less specific effects, such as oxidative stress and cell cycle arrest, were used more by rules to describe toxicity at the level of 500mg/kg/day. Although the endocrine disruption through nuclear receptor activity formulated an essential cluster of rules, this bioactivity is not covered in four commercial assay setups for hepatotoxicity. Using an external set of 29 drugs with drug-induced liver injury (DILI) labels, we found promiscuity over important assays discriminates between compounds with different levels of liver injury. In vitro-in vivo associations were also improved by incorporating physicochemical properties especially for the potent, 15mg/kg/day toxicity level, as well for assays describing nuclear receptor activity and phenotypic changes. The most frequently used physicochemical properties, predictive for hepatotoxicity in combination with assay activities, are linked to bioavailability, which were the number of rotatable bonds (less than 7) at a of level of 15mg/kg/day, and the number of rings (of less than 3) at level of 500mg/kg/day. In summary, hepatotoxicity cannot very well be captured by single assay endpoints, but better by a combination of bioactivities in relevant assays, with the likelihood of hepatotoxicity increasing with assay promiscuity. Together these findings can be used to prioritize assay combinations which are appropriate to assess potential hepatotoxicity

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells

Extracellular vesicles produced by the human commensal gut bacterium Bacteroides thetaiotaomicron affect host immune pathways in a cell-type specific manner that are altered in inflammatory bowel disease

The gastrointestinal (GI) tract harbours a complex microbial community, which contributes to its homeostasis. A disrupted microbiome can cause GI-related diseases, including inflammatory bowel disease (IBD), therefore identifying host-microbe interactions is crucial for better understanding gut health. Bacterial extracellular vesicles (BEVs), released into the gut lumen, can cross the mucus layer and access underlying immune cells. To study BEV-host interactions, we examined the influence of BEVs generated by the gut commensal bacterium, Bacteroides thetaiotaomicron, on host immune cells. Single-cell RNA sequencing data and host-microbe protein-protein interaction networks were used to predict the effect of BEVs on dendritic cells, macrophages and monocytes focusing on the Toll-like receptor (TLR) pathway. We identified biological processes affected in each immune cell type and cell-type specific processes including myeloid cell differentiation. TLR pathway analysis highlighted that BEV targets differ among cells and between the same cells in healthy versus disease (ulcerative colitis) conditions. The in silico findings were validated in BEV-monocyte co-cultures demonstrating the requirement for TLR4 and Toll-interleukin-1 receptor domain-containing adaptor protein (TIRAP) in BEV-elicited NF-kB activation. This study demonstrates that both cell-type and health status influence BEV-host communication. The results and the pipeline could facilitate BEV-based therapies for the treatment of IBD

SARS-CoV-2 Causes a Different Cytokine Response Compared to Other Cytokine Storm-Causing Respiratory Viruses in Severely Ill Patients

Hyper-induction of pro-inflammatory cytokines, also known as a cytokine storm or cytokine release syndrome (CRS), is one of the key aspects of the currently ongoing SARS-CoV-2 pandemic. This process occurs when a large number of innate and adaptive immune cells activate and start producing pro-inflammatory cytokines, establishing an exacerbated feedback loop of inflammation. It is one of the factors contributing to the mortality observed with coronavirus 2019 (COVID-19) for a subgroup of patients. CRS is not unique to the SARS-CoV-2 infection; it was prevalent in most of the major human coronavirus and influenza A subtype outbreaks of the past two decades (H5N1, SARS-CoV, MERS-CoV, and H7N9). With a comprehensive literature search, we collected changing the cytokine levels from patients upon infection with the viral pathogens mentioned above. We analyzed published patient data to highlight the conserved and unique cytokine responses caused by these viruses. Our curation indicates that the cytokine response induced by SARS-CoV-2 is different compared to other CRS-causing respiratory viruses, as SARS-CoV-2 does not always induce specific cytokines like other coronaviruses or influenza do, such as IL-2, IL-10, IL-4, or IL-5. Comparing the collated cytokine responses caused by the analyzed viruses highlights a SARS-CoV-2-specific dysregulation of the type-I interferon (IFN) response and its downstream cytokine signatures. The map of responses gathered in this study could help specialists identify interventions that alleviate CRS in different diseases and evaluate whether they could be used in the COVID-19 cases.</p

SignaFish: A zebrafish-specific signaling pathway resource

Understanding living systems requires an in-depth knowledge of the signaling networks that drive cellular homeostasis, regulate intercellular communication, and contribute to cell fates during development. Several resources exist to provide high-throughput data sets or manually curated interaction information from human or invertebrate model organisms. We previously developed SignaLink, a uniformly curated, multi-layered signaling resource containing information for human and for the model organisms nematode Caenorhabditis elegans and fruit fly Drosophila melanogaster. Until now, the use of the SignaLink database for zebrafish pathway analysis was limited. To overcome this limitation, we created SignaFish ( http://signafish.org ), a fish-specific signaling resource, built using the concept of SignaLink. SignaFish contains more than 200 curation-based signaling interactions, 132 further interactions listed in other resources, and it also lists potential miRNA-based regulatory connections for seven major signaling pathways. From the SignaFish website, users can reach other web resources, such as ZFIN. SignaFish provides signaling or signaling-related interactions that can be examined for each gene or downloaded for each signaling pathway. We believe that the SignaFish resource will serve as a novel navigating point for experimental design and evaluation for the zebrafish community and for researchers focusing on nonmodel fish species, such as cyclids

P005 Cytokine mediated intercellular communication in inflammatory bowel disease

Abstract Background During inflammatory bowel disease the mucosal immune system is altered. The mucosal immune cells are communicating through the various cytokines. Single cell and small volume RNA-seq and proteomics approaches make the investigation of cytokine networks plausible However the lack of specific resources make such efforts hard. Methods To address this need in this project, we built a cell-cell communication map, CytokineLink, which collates cytokine mediated intercellular interactions. CytokineLink collects the cytokine-cytokine receptor interactions from the OmniPath, immuneXpresso and immunoGlobe databases. We demonstrate the applicability of CytokineLink by presenting how cytokine feedback loops are built and altered during Ulcerative Colitis. We mapped single-cell RNA-seq expression data from inflamed and uninflamed Ulcerative Colitis biopsies to the interactions between cytokines and cytokine receptors, and then we compared the specific cytokine-mediated cell-cell interactions. Results Using our approach, we were able to point out major differences in cell-cell communication between inflamed and uninflamed conditions, and identify key cytokine changes. For example, the generally anti-inflammatory cytokine IL-10 is produced by regulatory T-cells in both conditions. However the IL-10 receptor positive cells are altered between the inflamed and uninflamed condition: dendritic cells and innate lymphocytes did not express the receptor in the sufficient amount. It suggests that not the cytokine level directly but the receptor level alterations are involved in ulcerative colitis. Also the chemokine CXCL12 was expressed by the inflammatory fibroblasts. This cytokine promotes the T-cell recruitment and through that inflammation. Conclusion With CytokineLink, researchers are capable to pinpoint the most important interactions in the changing mucosal immune system and propose novel therapeutic approaches. We are currently developing a website and easy to follow workflows to make CytokineLink available. </jats:sec

DDREL: From drug-drug relationships to drug repurposing

Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs' anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups

P004 Critical paralog proteins has a cell-type specific rewiring role in Ulcerative Colitis associated signalling processes

Abstract Background Cell functions are regulated by signalling pathways that often cross-talk with each other. These cross-talks are usually cell-type specific and, as we showed earlier, often mediated by so called critical paralog proteins (proteins resulted due to gene duplication but then diverged both in terms of their regulation and their functions). As dysregulation of cell functions is a hallmark of chronic inflammatory diseases, including Ulcerative Colitis (UC), here we investigated the role of such critical paralog proteins in the regulation of some key functions, in UC-associated cell types. Methods In this study, we compared healthy and diseased (non-inflamed UC) conditions. Using network biology approaches combined with single-cell RNAseq data, we identified critical paralog groups in myofibroblasts, regulatory T cells and goblet cells that show cell and/or condition specificity in the process of autophagy, Notch and T-cell receptor signalling. Results We focused our analysis on the Notch pathway-related processes, in particular the EGLN paralogs (EGLN1-3). The EGLN enzymes are prolyl hydroxylases and EGLN1, EGLN3 can directly inhibit the HIF1A transcription factor, while HIF1A induces the expression of all the three EGLN gene upon hypoxia. This negative feedback loop tends to control the amount of EGLNs and the activation status of the HIF1A proteins, therefore maintaining an adequate hypoxia response. In the intestine, EGLN2 and EGLN3 are described as potential regulators of inflammation, and both are downregulated in UC patients. Importantly, we found that the expression of EGLN paralogs shows cell-type and condition specificity. While goblet cells express all three paralogs in both healthy and UC conditions, myofibroblasts express EGLN3 only in the healthy condition. As EGLN3 is responsible for tight junction integrity, and it can regulate hypoxia response, the lack of EGLN3 in UC could contribute to the disrupted epithelial barrier function and dysregulation of myofibroblasts. Accordingly, in mice, depletion of Egln3 causes an increased susceptibility to colitis. Conclusion We have developed a bioinformatic pipeline to reconstruct cell-type specific signalling networks to identify the key differences among critical regulators of the signalling flow (ie., critical paralog proteins) in UC-associated cell types and in comparison of healthy and non-inflamed UC conditions. We analysed the altered expression of paralog genes in signalling pathways in UC-associated cell-types, and demonstrated their role with the condition and cell-type specific expression of EGLN3. </jats:sec

P020 Mapping the changing intercellular communication and its downstream effect in Ulcerative Colitis

Abstract Background Intercellular communication is essential for growing and differentiating in multicellular organisms by transducing the signal from cell to cell. Despite its importance, the molecular background is less discovered due to the lack of data. This gap has started to be addressed with the appearance of single-cell omics approaches providing an insight among others into the gene expression of individual cells. Methods We have developed a method to predict and compare cell-cell signalling interactions using single-cell RNAseq data from colon biopsies. Transcriptomic data alone is not capable of connecting the cells, a reliable network resource is needed to mediate the signal via protein-protein interactions between the source and target cells. Here we used OmniPath - a resource providing not only intra- and intercellular interactions but also annotations of proteins involved in the interplay of cells - to reconstruct signalling networks. We examined intercellular communication among five cell-types (regulatory T cell, macrophage, dendritic cell, goblet cell and myofibroblast) in healthy colon and during Ulcerative Colitis. Results Our analysis shows that there are significant differences in the type of cell-cell communication (ligand-receptor connections, adherens junctions, etc.) between the healthy and Ulcerative Colitis (UC) conditions, and these differences lead to altered downstream effects in the signal receiving cell. In both conditions, the ligand-receptor and adhesion connections were overrepresented, however cell junctions were less abundant in UC. Regarding the communication among the five cell-types, in healthy condition, cells are tightly connected to dendritic cells while in diseased condition to regulatory T cells. Focusing on ligand-receptor interactions between myofibroblasts and regulatory T cells, our pipeline identified the MAPK, Toll-like receptor (TLR) 2/6 and TLR 7/8 pathways enriched downstream in healthy conditions. In contrast, TLR3 and TLR4 pathways were affected by the myofibroblast in Ulcerative Colitis. Conclusion We found key intercellular mechanisms leading to well-defined differential pathway activation profiles. We showed that in uninflamed UC condition myofibroblasts disrupt the anti-inflammatory effect of regulatory T cells. Our pipeline is able to predict and analyse cell-cell interactions and their downstream effects and to highlight the differences in healthy and diseased states. </jats:sec