Dynamic Boolean modeling of molecular and cellular interactions in psoriasis predicts drug target candidates.

Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation, and a perturbed keratinocyte life cycle. Despite the availability of drugs for psoriasis management, the disease remains incurable. Treatment response variability calls for new tools and approaches to comprehend the mechanisms underlying disease development. We present a Boolean multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network analysis, the model predictions suggest that targeting neutrophil activation in conjunction with inhibition of either prostaglandin E2 (PGE2) or STAT3 shows promise comparable to interleukin-17 (IL-17) inhibition, one of the most effective treatment options for moderate and severe cases. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling in psoriasis and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification.

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities.

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

Characterisation of Colorectal Cancer Cell Lines through Proteomic Profiling of Their Extracellular Vesicles.

Colorectal cancer (CRC) is one of the most prevalent cancers, driven by several factors including deregulations in intracellular signalling pathways. Small extracellular vesicles (sEVs) are nanosized protein-packaged particles released from cells, which are present in liquid biopsies. Here, we characterised the proteome landscape of sEVs and their cells of origin in three CRC cell lines HCT116, HT29 and SW620 to explore molecular traits that could be exploited as cancer biomarker candidates and how intracellular signalling can be assessed by sEV analysis instead of directly obtaining the cell of origin itself. Our findings revealed that sEV cargo clearly reflects its cell of origin with proteins of the PI3K-AKT pathway highly represented in sEVs. Proteins known to be involved in CRC were detected in both cells and sEVs including KRAS, ARAF, mTOR, PDPK1 and MAPK1, while TGFB1 and TGFBR2, known to be key players in epithelial cancer carcinogenesis, were found to be enriched in sEVs. Furthermore, the phosphopeptide-enriched profiling of cell lysates demonstrated a distinct pattern between cell lines and highlighted potential phosphoproteomic targets to be investigated in sEVs. The total proteomic and phosphoproteomics profiles described in the current work can serve as a source to identify candidates for cancer biomarkers that can potentially be assessed from liquid biopsies.

Halogenated boroxine increases propensity to apoptosis in leukemia (UT-7) but not non-tumor cells in vitro.

A hallmark of the development of solid and hematological malignancies is the dysregulation of apoptosis, which leads to an imbalance between cell proliferation, cell survival and death. Halogenated boroxine [K2 (B3 O3 F4 OH)] (HB) is a derivative of cyclic anhydride of boronic acid, with reproducible anti-tumor and anti-proliferative effects in different cell models. Notably, these changes are observed to be more profound in tumor cells than in normal cells. Here, we investigated the underlying mechanisms through an extensive evaluation of (a) deregulated target genes and (b) their interactions and links with main apoptotic pathway genes upon treatment with an optimized concentration of HB. To provide deeper insights into the mechanism of action of HB, we performed identification, visualization, and pathway association of differentially expressed genes (DEGs) involved in regulation of apoptosis among tumor and non-tumor cells upon HB treatment. We report that HB at a concentration of 0.2 mg·mL-1 drives tumor cells to apoptosis, whereas non-tumor cells are not affected. Comparison of DEG profiles, gene interactions and pathway associations suggests that the HB effect and tumor-'selectivity' can be explained by Bax/Bak-independent mitochondrial depolarization by ROS generation and TRAIL-like activation, followed by permanent inhibition of NFκB signaling pathway specifically in tumor cells.

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

Introduction: 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. Methods: Extensive 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. Results: Results 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. Discussion: The 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.

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

The 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 and 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 KO 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 the 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.

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.

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 Lactococccus lactis. 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).