Identification and Characterization of a New Tubulin-Binding

We studied the mechanism of action of 3,5-dibromo-4-(3,4-dimethoxyphenyl)-1H-pyrrole-2-carboxylic acid ethyl ester (JG-03-14) and found that it is a potent microtubule depolymerizer. JG-03-14 caused a dose-dependent loss of cellular microtubules, formation of aberrant mitotic spindles, accumulation of cells in the G2/M phase of the cell cycle, and Bcl-2 phosphorylation. These events culminated in the initiation of apoptosis, as evidenced by the caspase 3-dependent cleavage of poly(ADP-ribose) polymerase (PARP). JG-03-14 has antiproliferative activity against a wide range of cancer cell lines, with an average IC50 value of 62 nM, and it is a poor substrate for transport by P-glycoprotein. JG-03-14 inhibited the polymerization of purified tubulin in vitro, consistent with a direct interaction between the compound and tubulin. JG-03-14 potently inhibited the binding of [3H]colchicine to tubulin, suggesting that it bound to tubulin at a site overlapping the colchicine site. JG-03-14 had antitumor effects in the PC3 xenograft model, in which it caused greater than 50% reduction in tumor burden after 14 days of treatment. Molecular modeling studies indicated that the dimethoxyphenyl group of JG-03-14 occupies a space similar to that of the trimethoxyphenyl group of colchicine. However, the 2,3,5-trisubstituted pyrrole group, which is connected to the dimethoxyphenyl moiety, interacted with both α and β tubulin in space not shared with colchicine, suggesting significant differences compared with colchicine in the mechanism of binding to tubulin. Our results suggest that this tetransubstituted pyrrole represents a new, biologically active chemotype for the colchicine site on tubulin

The Molecular Basis of Vitamin D Receptor and β-Catenin Crossregulation

The signaling/oncogenic activity of β-catenin can be repressed by activation of the vitamin D receptor (VDR). Conversely, high levels of β-catenin can potentiate the transcriptional activity of 1,25-dihydroxyvitamin D3 (1,25D). We show here that the effects of β-catenin on VDR activity are due to interaction between the activator function-2 (AF-2) domain of the VDR and C terminus of β-catenin. Acetylation of the β-catenin C terminus differentially regulates its ability to activate TCF or VDR-regulated promoters. Mutation of a specific residue in the AF-2 domain, which renders the VDR trancriptionally inactive in the context of classical coactivators, still allows interaction with β-catenin and ligand-dependent activation of VDRE-containing promoters. VDR antagonists, which block the VDRE-directed activity of the VDR and recruitment of classical coactivators, do allow VDR to interact with β-catenin, which suggests that these and perhaps other ligands would permit those functions of the VDR that involve β-catenin interaction.The authors wish to acknowledge the support of National Institutes of Health grants DK058196 and U54 CA100971 (S.W.B.), the AACR-Bristol Myers Squibb Translational Fellowship in Colon Cancer (S.S.), and the following LCCC Core Facilities: macromolecular analysis, microscopy, and tissue culture

The E3 ubiquitin ligase component, Cereblon, is an evolutionarily conserved regulator of Wnt signaling

Immunomodulatory drugs (IMiDs) are important for the treatment of multiple myeloma and myelodysplastic syndrome. Binding of IMiDs to Cereblon (CRBN), the substrate receptor of the CRL4CRBN E3 ubiquitin ligase, induces cancer cell death by targeting key neo-substrates for degradation. Despite this clinical significance, the physiological regulation of CRBN remains largely unknown. Herein we demonstrate that Wnt, the extracellular ligand of an essential signal transduction pathway, promotes the CRBN-dependent degradation of a subset of proteins. These substrates include Casein kinase 1α (CK1α), a negative regulator of Wnt signaling that functions as a key component of the β-Catenin destruction complex. Wnt stimulation induces the interaction of CRBN with CK1α and its resultant ubiquitination, and in contrast with previous reports does so in the absence of an IMiD. Mechanistically, the destruction complex is critical in maintaining CK1α stability in the absence of Wnt, and in recruiting CRBN to target CK1α for degradation in response to Wnt. CRBN is required for physiological Wnt signaling, as modulation of CRBN in zebrafish and Drosophila yields Wnt-driven phenotypes. These studies demonstrate an IMiD-independent, Wnt-driven mechanism of CRBN regulation and provide a means of controlling Wnt pathway activity by CRBN, with relevance for development and disease

Retinoic Acid Mediates Regulation of Network Formation by COUP-TFII and VE-Cadherin Expression by TGFβ Receptor Kinase in Breast Cancer Cells

Tumor development, growth, and metastasis depend on the provision of an adequate vascular supply. This can be due to regulated angiogenesis, recruitment of circulating endothelial progenitors, and/or vascular transdifferentiation. Our previous studies showed that retinoic acid (RA) treatment converts a subset of breast cancer cells into cells with significant endothelial genotypic and phenotypic elements including marked induction of VE-cadherin, which was responsible for some but not all morphological changes. The present study demonstrates that of the endothelial-related genes induced by RA treatment, only a few were affected by knockdown of VE-cadherin, ruling it out as a regulator of the RA-induced endothelial genotypic switch. In contrast, knockdown of the RA-induced gene COUP-TFII prevented the formation of networks in Matrigel but had no effect on VE-cadherin induction or cell fusion. Two pan-kinase inhibitors markedly blocked RA-induced VE-cadherin expression and cell fusion. However, RA treatment resulted in a marked and broad reduction in tyrosine kinase activity. Several genes in the TGFβ signaling pathway were induced by RA, and specific inhibition of the TGFβ type I receptor blocked both RA-induced VE-cadherin expression and cell fusion. Together these data indicate a role for the TGFβ pathway and COUP-TFII in mediating the endothelial transdifferentiating properties of RA

IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method

In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, ‘IntegralVac,’ by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing deep learning tools served as a positive control to ensure successful prediction was possible. As evidenced by increased accuracy and AUC, IntegralVac improved the prediction rate of top-ranked epitopes. We also examined the compatibility between other servers’ clinical checkpoint filters and IntegralVac. This was to ensure that the other servers had a means for predicting additional checkpoint filters that we wanted to implement in IntegralVac. The clinical checkpoint filters, including allergenicity, antigenicity, and toxicity, were used as additional predictors to improve IntegralVac’s prediction accuracy. We generated immunogenicity scores by cross-comparing sequence inputs with each other and determining the overlap between each individual peptide sequence. The IntegralVac increased the immunogenicity prediction accuracy to 90.1% AUC and the binding affinity accuracy to 95.4% compared to the control NetMHCPan server. The IntegralVac opens new avenues for future in silico methods, by building upon established models for continued prediction accuracy improvement

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes

Predicting Environmental Chemical Toxicity using a New Hybrid Deep Machine Learning Method

Humans are exposed to thousands of potentially toxic chemicals including environmental chemicals such as industrial wastes, food products, solvents, air pollutants, fertilizers, pesticides, insecticides, carcinogens, drugs, metals/metalloids, and other industrial chemicals. Approximately 300,000 such chemicals currently in use, unfortunately little is known about their potential toxicity. Determining human toxicity potential of chemicals remains a challenge due to a substantial resource required to assess a chemical in-vivo, and only a few thousand single chemicals in commercial use has been evaluated. In this study, to predict the environmental chemical toxicity, we developed a new hybrid neural network (HNN) deep learning model consisting of a Convolutional Neural Network (CNN) and multilayer perceptron (MLP) type feed forward neural network (FFNN). Our HNN deep learning model trained based on thousands of chemicals, presented the best performance for majority of the cases. Taken together, our hybrid HNN deep learning models has a wide applicability in the prediction of toxicity of any chemical category and its mixtures

Large Scale Profiling of SARS-CoV-2-Infected Patients Identified Potential Therapeutic Host Targets and Drug Candidates for COVID-19

Given the rapid spread of SARS-CoV-2 and rising death toll of COVID-19 in the current absence of effective treatments, it is imperative that therapeutics are developed and made available to patients as quickly as possible. Publicly available COVID-19 patient data can be used to identify host therapeutic targets, tailoring treatments to the disease signatures observed in patients. In this study, we identify potential host therapeutic targets based on gene expression alterations observed in COVID-19 patients. We analyzed RNAseq data from airway samples of COVID-19 patients and healthy controls to detect significantly differentially expressed genes and pathways that present potential therapeutic targets. Our analysis revealed expression changes in key genes involved in activation of immune pathways, as well as genes targeted by SARS-CoV2 to interfere with normal host cell functioning. Critical changes were observed in a number of genes, including EIF2AK2, which was shown to play important roles in activating the interferon response and interfering with host cell translational machinery in SARS-CoV-2 infection, presenting a prospective therapeutic target. We also identified drugs with potential to modulate multiple therapeutic targets within the most significant pathways. Our results both validate key genes, pathways, and drug candidates that have been reported by other studies and suggest others that have not been well-characterized and warrant further investigation by future studies. Further investigation of these therapeutic targets and their drug interactions may lead to effective therapeutic strategies to combat the current COVID-19 pandemic and protect against future outbreaks.<br /