Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies

Logical and experimental modeling of cytokine and eicosanoid signaling in psoriatic keratinocytes

Psoriasis is a chronic skin disease, in which immune cells and keratinocytes keep each other in a state of inflammation. It is believed that phospholipase A2 (PLA2)-dependent eicosanoid release plays a key role in this. T-helper (Th) 1-derived cytokines are established activators of phospholipases in keratinocytes, whereas Th17-derived cytokines have largely unknown effects. Logical model simulations describing the function of cytokine and eicosanoid signaling networks combined with experimental data suggest that Th17 cytokines stimulate proinflammatory cytokine expression in psoriatic keratinocytes via activation of cPLA2α-Prostaglandin E2-EP4 signaling, which could be suppressed using the anti-psoriatic calcipotriol. cPLA2α inhibition and calcipotriol distinctly regulate expression of key psoriatic genes, possibly offering therapeutic advantage when applied together. Model simulations additionally suggest EP4 and protein kinase cAMP-activated catalytic subunit alpha as drug targets that may restore a normal phenotype. Our work illustrates how the study of complex diseases can benefit from an integrated systems approach

A middle-out modeling strategy to extend a colon cancer logical model improves drug synergy predictions in epithelial-derived cancer cell lines

Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model’s predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic

Functional studies of CpSRP54 in diatoms show that the mechanism of thylakoid protein insertion differs from plants and green algae

International audienceThe chloroplast signal recognition particle 54 kDa (CpSRP54) protein is a member of the CpSRP pathway known to target proteins to thylakoid membranes in plants and green algae. Loss of CpSRP54 in the marine diatom Phaeodactylum tricornutum lowers the accumulation of a selection of chloroplast encoded subunits of photosynthetic complexes, indicating a role in the co-translational part of the CpSRP pathway. In contrast to plants/green algae, absence of CpSRP54 does not have a negative effect on the content of light-harvesting antenna complex proteins and pigments in P. tricornutum, indicating that the diatom CpSRP54 protein has not evolved to function in the post-translational part of the CpSRP pathway. Cpsrp54 knockout mutants display altered photophysiological responses, with a stronger induction of photoprotective mechanisms and lower growth rates compared to wild type when exposed to increased light intensities. Nonetheless, their phenotype is relatively mild, thanks to activation of mechanisms alleviating the loss of CpSRP54, involving upregulation of chaperones. We conclude that plants, green algae and diatoms have evolved differences in the pathways for co-translational and post-translational insertion of proteins into the thylakoid membranes

Microemulsions as Potential Carriers of Nisin: Effect of Composition on Structure and Efficacy

Water-in-oil (W/O) microemulsions based on either refined olive oil (ROO) or sunflower oil (SO), distilled monoglycerides (DMG), and ethanol were used as nisin carriers in order to ensure its effectiveness as a biopreservative. This work presents experimental evidence on the effects of ethanol concentration, hydration, the nature of oil, and the addition of nisin on the nanostructure of the proposed inverse microemulsions as revealed by electrical conductivity measurements, dynamic light scattering (DLS), small angle X-ray scattering (SAXS), and electron paramagnetic resonance (EPR) spectroscopy. Modeling of representative SAXS profiles was applied to gain further insight into the effects of ethanol and solubilized water content on the inverse swollen micelles’ size and morphology. With increasing ethanol content, the overall size of the inverse micelles decreased, whereas hydration resulted in an increase in the micellar size due to the penetration of water into the hydrophilic core of the inverse swollen micelles (hydration-induced swelling behavior). The dynamic properties of the surfactant monolayer were also affected by the nature of the used vegetable oil, the ethanol content, and the presence of the bioactive molecule, as evidenced by EPR spin probing experiments. According to simulation on the experimental spectra, two populations of spin probes at different polarities were revealed. The antimicrobial effect of the encapsulated nisin was evaluated using the well diffusion assay (WDA) technique against <i>Lactococccus lactis.</i> It was found that this encapsulated bacteriocin induced an inhibition of the microorganism growth. The effect was more pronounced at higher ethanol concentrations, but no significant difference was observed between the two used vegetable oils (ROO and SO)

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies

DataSheet_2_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.pdf

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.</p

DataSheet_1_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.xlsx

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.</p