A dynamic model of the ABA Signaling pathway with its core components: translation rate of PP2C determines the kinetics of ABA-induced gene expression

The abscisic acid (ABA) signaling pathway is the key defense mechanism against drought stress in plants, yet the connectivity of cellular molecules related to gene expression in response to ABA is little understood. A dynamic model of the core components of the ABA signaling pathway was built using ordinary differential equations to understand the connectivity. Parameter values of protein-protein interactions and enzymatic reactions in the model were implemented from the data obtained by previously conducted experiments. On the other hand, parameter values of gene expression and translation were determined by comparing the kinetics of gene expression in the model to those of ABA-induced RD29A (response to desiccation 29A) in actual plants. Based on the analyses of the optimized model, we hypothesized that the translation rate of PP2C (protein phosphatase type 2C) is downregulated by ABA to increase the ABRE (ABA-responsive element) promoter activity. The hypotheses were preliminarily supported by newly conducted experiments using transgenic Arabidopsis plants that carry a luciferase expression cassette driven by the RD29A promoter (RD29A::LUC). The model suggests that identifying a mechanism that alters PP2C translation rate would be one of the next research frontiers in the ABA signaling pathway.

SARS-CoV-2 Nsp5 Protein Causes Acute Lung Inflammation, A Dynamical Mathematical Model

In the present work we propose a dynamical mathematical model of the lung cells inflammation process in response to SARS-CoV-2 infection. In this scenario the main protease Nsp5 enhances the inflammatory process, increasing the levels of NF kB, IL-6, Cox2, and PGE2 with respect to a reference state without the virus. In presence of the virus the translation rates of NF kB and IkB arise to a high constant value, and when the translation rate of IL-6 also increases above the threshold value of 7 pg mL−1 s−1 the model predicts a persistent over stimulated immune state with high levels of the cytokine IL-6. Our model shows how such over stimulated immune state becomes autonomous of the signals from other immune cells such as macrophages and lymphocytes, and does not shut down by itself. We also show that in the context of the dynamical model presented here, Dexamethasone or Nimesulide have little effect on such inflammation state of the infected lung cell, and the only form to suppress it is with the inhibition of the activity of the viral protein Nsp5. To that end, our model suggest that drugs like Saquinavir may be useful. In this form, our model suggests that Nsp5 is effectively a central node underlying the severe acute lung inflammation during SARS-CoV-2 infection. The persistent production of IL-6 by lung cells can be one of the causes of the cytokine storm observed in critical patients with COVID19. Nsp5 seems to be the switch to start inflammation, the consequent overproduction of the ACE2 receptor, and an important underlying cause of the most severe cases of COVID19.

Estrogen-related receptor alpha and Rplp1 ribosome protein-dependent translation coordinately regulate starvation response and decrease NASH progression

Although general translation declines during fasting, maintaining the translation of a subset or proteins is necessary for metabolic homeostasis and cell viability. Using unbiased proteome analysis of hepatic cells during starvation, we identified a novel pathway in which Esrra-mediated transcription of Rplp1-dependent translation of lysosomal proteins declined during early starvation and recovered after prolonged starvation to restore autophagy-lysosome function. Interestingly, hepatic Esrra-Rplp1-dependent translation rate of lysosomal proteins also was impaired in patients and mice with non-alcoholic steatohepatitis (NASH), and translational response to starvation was dysregulated in mice with NASH. Remarkably, activation of Esrra pharmacologically, genetically, or by alternate day fasting restored protein translation, increased expression of lysosomal proteins, induced autophagy, and reduced lipotoxicity, inflammation, and fibrosis in cell culture and in vivo models of NASH. Thus, hepatic Esrra is essential for ribosome-dependent translation of lysosomal proteins during starvation, and prevention of lipotoxicity and progression in NASH.

mTOR-dependent translation drives tumor infiltrating CD8+ effector and CD4+ Treg cells expansion

We performed a systematic analysis of the translation rate of tumor-infiltrating lymphocytes (TILs) and the microenvironment inputs affecting it, both in humans and in mice. Measurement of puromycin incorporation, a proxy of protein synthesis, revealed an increase of translating CD4+ and CD8+ cells in tumors, compared to normal tissues. High translation levels are associated with phospho-S6 labeling downstream of mTORC1 activation, whereas low levels correlate with hypoxic areas, in agreement with data showing that T cell receptor stimulation and hypoxia act as translation stimulators and inhibitors, respectively. Additional analyses revealed the specific phenotype of translating TILs. CD8+ translating cells have enriched expression of IFN-γ and CD-39, and reduced SLAMF6, pointing to a cytotoxic phenotype. CD4+ translating cells are mostly regulatory T cells (Tregs) with enriched levels of CTLA-4 and Ki67, suggesting an expanding immunosuppressive phenotype. In conclusion, the majority of translationally active TILs is represented by cytotoxic CD8+ and suppressive CD4+ Tregs, implying that other subsets may be largely composed by inactive bystanders.

DnaK response to expression of protein mutants is dependent on translation rate and stability

Chaperones play a central part in the quality control system in cells by clearing misfolded and aggregated proteins. The chaperone DnaK acts as a sensor for molecular stress by recognising short hydrophobic stretches of misfolded proteins. As the level of unfolded protein is a function of protein stability, we hypothesised that the level of DnaK response upon overexpression of recombinant proteins would be correlated to stability. Using a set of mutants of the lambda-repressor with varying thermal stabilities and a fluorescent reporter system, the effect of stability on DnaK response and protein abundance was investigated. Our results demonstrate that the initial DnaK response is largely dependent on protein synthesis rate but as the recombinantly expressed protein accumulates and homeostasis is approached the response correlates strongly with stability. Furthermore, we observe a large degree of cell-cell variation in protein abundance and DnaK response in more stable proteins.

Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome

Background: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple studies had been done to identify and study nonsynonymous mutations, which change amino acid residues of SARS-CoV-2 proteins. On the other hand, there is little study on the effects of SARS-CoV-2 synonymous mutations. Although these mutations do not alter amino acids, some studies suggest that they may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome. Methods: A total of 30,229 SARS-CoV-2 genomic sequences were retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database and aligned using MAFFT. Then, the mutations and their respective frequency were identified. A prediction of RNA secondary structures and their base pair probabilities was performed to study the effect of synonymous mutations on RNA structure and stability. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2. Results: A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a, followed by C313U and C9286U mutation in nsp1 and nsp4 of ORF1a, respectively. Conclusion: Among the synonymous mutations identified, C913U mutation in ORF1a and C26735U in membrane (M) protein may affect RNA secondary structure, reducing the stability of RNA folding and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis

Background Detecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we aimed to identify prostate cancer specific biomarkers which can separate between non-aggressive and aggressive prostate cancer based on DNA methylation patterns. Results We performed a comparison analysis of DNA methylation status between normal prostate tissues and prostate adenocarcinoma (PRAD) samples at different Gleason stages. The candidate biomarkers were selected by excluding the biomarkers existing in multiple cancers (pan-cancer) and requiring significant difference between PRAD and other urinary samples. By least absolute shrinkage and selection operator (LASSO) selection, 8 biomarkers (cg04633600, cg05219445, cg05796128, cg10834205, cg16736826, cg23523811, cg23881697, cg24755931) were identified and in-silico validated by model constructions. First, all 8 biomarkers could separate PRAD at different stages (Gleason 6 vs. Gleason 3 + 4: AUC = 0.63; Gleason 6 vs. Gleason 4 + 3 and 8–10: AUC = 0.87). Second, 5 biomarkers (cg04633600, cg05796128, cg23523811, cg23881697, cg24755931) effectively detected PRAD from normal prostate tissues (AUC ranged from 0.88 to 0.92). Last, 6 biomarkers (cg04633600, cg05219445, cg05796128, cg23523811, cg23881697, cg24755931) completely distinguished PRAD with other urinary samples (AUC = 1). Conclusions Our study identified and in-silico validated a panel of prostate cancer specific DNA methylation biomarkers with diagnosis value.

DnaK response to expression of protein mutants is dependent on translation rate and stability

Chaperones play a central part in the quality control system in cells by clearing misfolded and aggregated proteins. The chaperone DnaK acts as a sensor for molecular stress by recognising short hydrophobic stretches of misfolded proteins. As the level of unfolded protein is a function of protein stability, we hypothesised that the level of DnaK response upon overexpression of recombinant proteins would be correlated to stability. Using a set of mutants of the λ-repressor with varying thermal stabilities and a fluorescent reporter system, the effect of stability on DnaK response and protein abundance was investigated. Our results demonstrate that the initial DnaK response is largely dependent on protein synthesis rate but as the recombinantly expressed protein accumulates and homeostasis is approached the response correlates strongly with stability. Furthermore, we observe a large degree of cell-cell variation in protein abundance and DnaK response in more stable proteins.

METTL3-dependent MALAT1 delocalization drives c-Myc induction in thymic epithelial tumors

Background Thymic epithelial tumors (TETs) are rare neoplasms, originating from epithelial thymic cells. The oncogenic potential of these rare neoplasms is still largely undefined, and a deeper molecular characterization could result in a relevant advance in their management, greatly improving diagnosis, prognosis and treatment choice. Deregulation of N6-methyladenosine (m6A) RNA modification, catalyzed by the METTL3/METTL14 methyltransferase complex, is emerging as a relevant event in cell differentiation and carcinogenesis. Various studies have reported that altered expression of METTL3 is associated with an aggressive malignant phenotype and favors migration and invasiveness, but its role in Thymic Tumors remains unknown. Results In this study, we characterized that METTL3 contributes to Thymic Epithelial Tumor phenotype. We evidenced that METTL3 is overexpressed in tumor tissue compared to normal counterpart. Silencing of METTL3 expression in thymic carcinoma cells results in reduced cell proliferation and overall translation rate. Of note, METTL3 is responsible for the induction of c-MYC expression in TET cells. Specifically, high expression of c-MYC protein is enabled by lncRNA MALAT1, which is methylated and delocalized by METTL3. Interestingly, blocking of c-MYC by using JQ1 inhibitor cooperates with METTL3 depletion in the inhibition of proliferation and induction of cell death. Conclusion This study highlighted METTL3 as a tumor promoter in Thymic tumors and c-MYC as a promising target to be exploited for the treatment of TET.

Intracellular Life Cycle Kinetics of SARS-CoV-2 Predicted Using Mathematical Modelling

SARS-CoV-2 infection represents a global threat to human health. Various approaches were employed to reveal the pathogenetic mechanisms of COVID-19. Mathematical and computational modelling is a powerful tool to describe and analyze the infection dynamics in relation to a plethora of processes contributing to the observed disease phenotypes. In our study here, we formulate and calibrate a deterministic model of the SARS-CoV-2 life cycle. It provides a kinetic description of the major replication stages of SARS-CoV-2. Sensitivity analysis of the net viral progeny with respect to model parameters enables the identification of the life cycle stages that have the strongest impact on viral replication. These three most influential parameters are (i) degradation rate of positive sense vRNAs in cytoplasm (negative effect), (ii) threshold number of non-structural proteins enhancing vRNA transcription (negative effect), and (iii) translation rate of non-structural proteins (positive effect). The results of our analysis could be used for guiding the search for antiviral drug targets to combat SARS-CoV-2 infection.