SUMMARY 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.

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 1,183 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 investigating hallmarks of TF activity profiles inferred from the transcriptomes of three different cancer types. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

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

Summary 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 A 2 (PLA 2 )-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 cPLA 2 α-Prostaglandin E 2 -EP4 signaling, which could be suppressed using the anti-psoriatic calcipotriol. cPLA 2 α 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.

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).