Large-Scale Screening of Per- and Polyfluoroalkyl Substance Binding Interactions and Their Mixtures with Nuclear Receptors.

Despite their significant impact, comprehensive screenings and detailed analyses of per- and polyfluoroalkyl substance (PFAS) binding strengths at the orthosteric and allosteric sites of NRs are currently lacking. This study addresses this gap by focusing on the binding interaction analysis of both common and uncommon PFAS with the nuclear receptors (NRs) vitamin D receptor (VDR), peroxisome proliferator-activated receptor gamma (PPARγ), pregnane X receptor (PXR), and estrogen receptor alpha (ERα). Advanced docking simulations were used to screen 9507 PFAS chemicals at the orthosteric and allosteric sites of PPARγ, PXR, VDR, and ERα. All receptors exhibited strong binding interactions at the orthosteric and allosteric site with a significant number of PFAS. We verified the accuracy of the docking protocol through multiple docking controls and validations. A mixture modeling analysis indicates that PFAS can bind in various combinations with themselves and endogenous ligands simultaneously, to disrupt the endocrine system and cause carcinogenic responses. These findings reveal that PFAS can interfere with nuclear receptor activity by displacing endogenous or native ligands by binding to the orthosteric and allosteric sites. The purpose of this study is to explore the mechanisms through which PFAS exert their endocrine-disrupting effects, potentially leading to more targeted therapeutic strategies. Importantly, this study is the first to explore the binding of PFAS at allosteric sites and to model PFAS mixtures at nuclear receptors. Given the high concentration and persistence of PFAS in humans, this study further emphasizes the urgent need for further research into the carcinogenic mechanisms of PFAS and the development of therapeutic strategies that target nuclear receptors.

Novel Methodology for the Design of Personalized Cancer Vaccine Targeting Neoantigens: Application to Pancreatic Ductal Adenocarcinoma.

Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient's tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations were ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program called SCGeneID, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, SCGeneID identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targeted 100 highly prevalent gene mutations in two patients. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures, which are freely available to download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes, which were also found in the reference study, where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide cancer vaccine targeting neoantigens using human patient data to identify gene mutations associated with the specific tumor of interest.

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations.

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens.

Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect's vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

AutoPepVax, a Novel Machine-Learning-Based Program for Vaccine Design: Application to a Pan-Cancer Vaccine Targeting EGFR Missense Mutations.

The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope-HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope-MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax's comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations.

Predicting Dose-Dependent Carcinogenicity of Chemical Mixtures Using a Novel Hybrid Neural Network Framework and Mathematical Approach.

This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as per- and polyfluorinated substances (PFASs), which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNNMixCancer that utilizes a hybrid neural network (HNN) integrated into a machine-learning framework. This framework incorporates a mathematical model to simulate chemical mixtures, enabling the creation of classification models for binary (carcinogenic or noncarcinogenic) and multiclass classification (categorical carcinogenicity) and regression (carcinogenic potency). Through extensive experimentation, we demonstrate that our HNN model outperforms other methodologies, including random forest, bootstrap aggregating, adaptive boosting, support vector regressor, gradient boosting, kernel ridge, decision tree with AdaBoost, and KNeighbors, achieving a superior accuracy of 92.7% in binary classification. To address the limited availability of experimental data and enrich the training data, we generate an assumption-based virtual library of chemical mixtures using a known carcinogenic and noncarcinogenic single chemical for all the classification models. Remarkably, in this case, all methods achieve accuracies exceeding 98% for binary classification. In external validation tests, our HNN method achieves the highest accuracy of 80.5%. Furthermore, in multiclass classification, the HNN demonstrates an overall accuracy of 96.3%, outperforming RF, Bagging, and AdaBoost, which achieved 91.4%, 91.7%, and 80.2%, respectively. In regression models, HNN, RF, SVR, GB, KR, DT with AdaBoost, and KN achieved average R2 values of 0.96, 0.90, 0.77, 0.94, 0.96, 0.96, and 0.97, respectively, showcasing their effectiveness in predicting the concentration at which a chemical mixture becomes carcinogenic. Our method exhibits exceptional predictive power in prioritizing carcinogenic chemical mixtures, even when relying on assumption-based mixtures. This capability is particularly valuable for toxicology studies that lack experimental data on the carcinogenicity and toxicity of chemical mixtures. To our knowledge, this study introduces the first method for predicting the carcinogenic potential of chemical mixtures. The HNNMixCancer framework offers a novel alternative for dose-dependent carcinogen prediction. Ongoing efforts involve implementing the HNN method to predict mixture toxicity and expanding the application of HNNMixCancer to include multiple mixtures such as PFAS mixtures and co-occurring chemicals.

A Peptide Vaccine Design Targeting KIT Mutations in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is a leading blood cancer subtype that can be caused by 27 gene mutations. Previous studies have explored potential vaccine and drug treatments against AML, but many were proven immunologically insignificant. Here, we targeted this issue and applied various clinical filters to improve immune response. KIT is an oncogenic gene that can cause AML when mutated and is predicted to be a promising vaccine target because of its immunogenic responses when activated. We designed a multi-epitope vaccine targeting mutations in the KIT oncogene using CD8+ and CD4+ epitopes. We selected the most viable vaccine epitopes based on thresholds for percentile rank, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, allergenicity, and stability. The efficacy of data was observed through world and regional population coverage of our vaccine design. Then, we obtained epitopes for optimized population coverage from PCOptim-CD, a modified version of our original Java-based program code PCOptim. Using 24 mutations on the KIT gene, 12 CD8+ epitopes and 21 CD4+ epitopes were obtained. The CD8+ dataset had a 98.55% world population coverage, while the CD4+ dataset had a 65.14% world population coverage. There were five CD4+ epitopes that overlapped with the top CD8+ epitopes. Strong binding to murine MHC molecules was found in four CD8+ and six CD4+ epitopes, demonstrating the feasibility of our results in preclinical murine vaccine trials. We then created three-dimensional (3D) models to visualize epitope-MHC complexes and TCR interactions. The final candidate is a non-toxic and non-allergenic multi-epitope vaccine against KIT mutations that cause AML. Further research would involve murine trials of the vaccine candidates on tumor cells causing AML.

ES-Screen: A Novel Electrostatics-Driven Method for Drug Discovery Virtual Screening.

Electrostatic interactions drive biomolecular interactions and associations. Computational modeling of electrostatics in biomolecular systems, such as protein-ligand, protein-protein, and protein-DNA, has provided atomistic insights into the binding process. In drug discovery, finding biologically plausible ligand-protein target interactions is challenging as current virtual screening and adjuvant techniques such as docking methods do not provide optimal treatment of electrostatic interactions. This study describes a novel electrostatics-driven virtual screening method called 'ES-Screen' that performs well across diverse protein target systems. ES-Screen provides a unique treatment of electrostatic interaction energies independent of total electrostatic free energy, typically employed by current software. Importantly, ES-Screen uses initial ligand pose input obtained from a receptor-based pharmacophore, thus independent of molecular docking. ES-Screen integrates individual polar and nonpolar replacement energies, which are the energy costs of replacing the cognate ligand for a target with a query ligand from the screening. This uniquely optimizes thermodynamic stability in electrostatic and nonpolar interactions relative to an experimentally determined stable binding state. ES-Screen also integrates chemometrics through shape and other physicochemical properties to prioritize query ligands with the greatest physicochemical similarities to the cognate ligand. The applicability of ES-Screen is demonstrated with in vitro experiments by identifying novel targets for many drugs. The present version includes a combination of many other descriptor components that, in a future version, will be purely based on electrostatics. Therefore, ES-Screen is a first-in-class unique electrostatics-driven virtual screening method with a unique implementation of replacement electrostatic interaction energies with broad applicability in drug discovery.

SARS-CoV-2 Omicron (BA.1 and BA.2) specific novel CD8+ and CD4+ T cell epitopes targeting spike protein.

The Omicron (BA.1/B.1.1.529) variant of SARS-CoV-2 harbors an alarming 37 mutations on its spike protein, reducing the efficacy of current COVID-19 vaccines. In this study, we identified CD8+ and CD4+ T cell epitopes from SARS-CoV-2 S protein mutants. To identify the highest quality CD8 and CD4 epitopes from the Omicron variant, we selected epitopes with a high binding affinity towards both MHC I and MHC II molecules. We applied other clinical checkpoint predictors, including immunogenicity, antigenicity, allergenicity, instability and toxicity. Subsequently, we found eight Omicron (BA.1/B.1.1.529) specific CD8+ and eleven CD4+ T cell epitopes with a world population coverage of 76.16% and 97.46%, respectively. Additionally, we identified common epitopes across Omicron BA.1 and BA.2 lineages that target mutations critical to SARS-CoV-2 virulence. Further, we identified common epitopes across B.1.1.529 and other circulating SARS-CoV-2 variants, such as B.1.617.2 (Delta). We predicted CD8 epitopes' binding affinity to murine MHC alleles to test the vaccine candidates in preclinical models. The CD8 epitopes were further validated using our previously developed software tool PCOptim. We then modeled the three-dimensional structures of our top CD8 epitopes to investigate the binding interaction between peptide-MHC and peptide-MHC-TCR complexes. Notably, our identified epitopes are targeting the mutations on the RNA-binding domain and the fusion sites of S protein. This could potentially eliminate viral infections and form long-term immune responses compared to relatively short-lived mRNA vaccines and maximize the efficacy of vaccine candidates against the current pandemic and potential future variants.

Predicting Dose-Range Chemical Toxicity using Novel Hybrid Deep Machine-Learning Method.

Humans are exposed to thousands of chemicals, including environmental chemicals. Unfortunately, little is known about their potential toxicity, as determining the toxicity remains challenging due to the substantial resources required to assess a chemical in vivo. Here, we present a novel hybrid neural network (HNN) deep learning method, called HNN-Tox, to predict chemical toxicity at different doses. To develop a hybrid HNN-Tox method, we combined two neural network frameworks, the Convolutional Neural Network (CNN) and the multilayer perceptron (MLP)-type feed-forward neural network (FFNN). Combining the CNN and FCNN in the field of environmental chemical toxicity prediction is a novel approach. We developed several binary and multiclass classification models to assess dose-range chemical toxicity that is trained based on thousands of chemicals with known toxicity. The performance of the HNN-Tox was compared with other machine-learning methods, including Random Forest (RF), Bootstrap Aggregation (Bagging), and Adaptive Boosting (AdaBoost). We also analyzed the model performance dependency on varying features, descriptors, dataset size, route of exposure, and toxic dose. The HNN-Tox model, trained on 59,373 chemicals annotated with known LD50 and routes of exposure, maintained its predictive ability with an accuracy of 84.9% and 84.1%, even after reducing the descriptor size from 318 to 51, and the area under the ROC curve (AUC) was 0.89 and 0.88, respectively. Further, we validated the HNN-Tox with several external toxic chemical datasets on a large scale. The HNN-Tox performed optimally or better than the other machine-learning methods for diverse chemicals. This study is the first to report a large-scale prediction of dose-range chemical toxicity with varying features. The HNN-Tox has broad applicability in predicting toxicity for diverse chemicals and could serve as an alternative methodology approach to animal-based toxicity assessment.

A New Hybrid Neural Network Deep Learning Method for Protein-Ligand Binding Affinity Prediction and De Novo Drug Design.

Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neural network) and the FFNN (fast forward neural network) hybrid neural network framework. The HNN-denovo uses protein pocket structure and protein-ligand interactions as input features. The HNN-affinity uses protein sequences and ligand features as input features. The HNN method combines the CNN and FCNN machine architecture for the protein structure or protein sequence and ligand descriptors. To train the model, the HNN methods used thousands of known protein-ligand binding affinity data retrieved from the PDBBind database. We also developed the Random Forest (RF), Gradient Boosting (GB), Decision Tree with AdaBoost (DT), and a consensus model. We compared the HNN results with models developed based on the RF, GB, and DT methods. We also independently compared the HNN method results with the literature reported deep learning protein-ligand binding affinity predictions made by the DLSCORE, KDEEP, and DeepAtom. The predictive performance of the HNN methods (max Pearson's R achieved was 0.86) was consistently better than or comparable to the DLSCORE, KDEEP, and DeepAtom deep learning learning methods for both balanced and unbalanced data sets. The HNN-affinity can be applied for the protein-ligand affinity prediction even in the absence of protein structure information, as it considers the protein sequence as standalone feature in addition to the ligand descriptors. The HNN-denovo method can be efficiently implemented to the structure-based de novo drug design campaign. The HNN-affinity method can be used in conjunction with the deep learning molecular docking protocols as a standalone. Further, it can be combined with the conventional molecular docking methods as a multistep approach to rapidly screen billions of diverse compounds. The HNN method are highly scalable in the cloud ML platform.

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.

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.

Proglumide Reverses Nonalcoholic Steatohepatitis by Interaction with the Farnesoid X Receptor and Altering the Microbiome.

Nonalcoholic steatohepatitis (NASH) is associated with obesity, metabolic syndrome, and dysbiosis of the gut microbiome. Cholecystokinin (CCK) is released by saturated fats and plays an important role in bile acid secretion. CCK receptors are expressed on cholangiocytes, and CCK-B receptor expression increases in the livers of mice with NASH. The farnesoid X receptor (FXR) is involved in bile acid transport and is a target for novel therapeutics for NASH. The aim of this study was to examine the role of proglumide, a CCK receptor inhibitor, in a murine model of NASH and its interaction at FXR. Mice were fed a choline deficient ethionine (CDE) diet to induce NASH. Some CDE-fed mice received proglumide-treated drinking water. Blood was collected and liver tissues were examined histologically. Proglumide's interaction at FXR was evaluated by computer modeling, a luciferase reporter assay, and tissue FXR expression. Stool microbiome was analyzed by RNA-Sequencing. CDE-fed mice developed NASH and the effect was prevented by proglumide. Computer modeling demonstrated specific binding of proglumide to FXR. Proglumide binding in the reporter assay was consistent with a partial agonist at the FXR with a mean binding affinity of 215 nM. FXR expression was significantly decreased in livers of CDE-fed mice compared to control livers, and proglumide restored FXR expression to normal levels. Proglumide therapy altered the microbiome signature by increasing beneficial and decreasing harmful bacteria. These data highlight the potential novel mechanisms by which proglumide therapy may improve NASH through interaction with the FXR and consequent alteration of the gut microbiome.

A novel chemo-phenotypic method identifies mixtures of salpn, vitamin D3, and pesticides involved in the development of colorectal and pancreatic cancer.

Environmental chemical (EC) exposures and our interactions with them has significantly increased in the recent decades. Toxicity associated biological characterization of these chemicals is challenging and inefficient, even with available high-throughput technologies. In this report, we describe a novel computational method for characterizing toxicity, associated biological perturbations and disease outcome, called the Chemo-Phenotypic Based Toxicity Measurement (CPTM). CPTM is used to quantify the EC "toxicity score" (Zts), which serves as a holistic metric of potential toxicity and disease outcome. CPTM quantitative toxicity is the measure of chemical features, biological phenotypic effects, and toxicokinetic properties of the ECs. For proof-of-concept, we subject ECs obtained from the Environmental Protection Agency's (EPA) database to the CPTM. We validated the CPTM toxicity predictions by correlating 'Zts' scores with known toxicity effects. We also confirmed the CPTM predictions with in-vitro, and in-vivo experiments. In in-vitro and zebrafish models, we showed that, mixtures of the motor oil and food additive 'Salpn' with endogenous nuclear receptor ligands such as Vitamin D3, dysregulated the nuclear receptors and key transcription pathways involved in Colorectal Cancer. Further, in a human patient derived cell organoid model, we found that a mixture of the widely used pesticides 'Tetramethrin' and 'Fenpropathrin' significantly impacts the population of patient derived pancreatic cancer cells and 3D organoid models to support rapid PDAC disease progression. The CPTM method is, to our knowledge, the first comprehensive toxico-physicochemical, and phenotypic bionetwork-based platform for efficient high-throughput screening of environmental chemical toxicity, mechanisms of action, and connection to disease outcomes.

Single-Cell and Bulk RNASeq Profiling of COVID-19 Patients Reveal Immune and Inflammatory Mechanisms of Infection-Induced Organ Damage.

The SARS-CoV-2 virus's ability to induce hypercytokinemia and cause multiple organ failure makes it imperative to find effective treatments. To understand the mechanism of viral infection and its effects on organ tissues, we analyzed multiple single-cell and bulk RNAseq data from COVID-19 patients' organ samples. Various levels of severity of infection were accounted for, with comparative analyses between mild, moderate, and severely infected patients. Our analysis uncovered an upregulation of the innate immune response via several inflammatory genes, IL-2, IL-6, IL-8, IL-17A, and NF-κB. Consequently, we found that the upregulation of these downstream effects can lead to organ injury. The downregulated pathways such as eukaryotic initiation factor 2 (eIF2) and eIF4-mediated host translation, were found to lead to an increased viral translation. We also found that the loss of inhibitory peptides can suppress an overactive innate immune response via NF-κB and interleukin-mediated pathways. Investigation of viral-host protein mapping showed that the interaction of viral proteins with host proteins correlated with the down- and upregulation of host pathways such as decreased eIF2-mediated host translation and increased hypertrophy and fibrosis. Inflammation was increased via the stimulation of pro-inflammatory cytokines and suppression of host translation pathways that led to reduced inflammatory inhibitors. Cardiac hypertrophy and organ fibrosis were the results of increased inflammation in organs of severe and critical patients. Finally, we identified potential therapeutic targets for the treatment of COVID-19 and its deleterious effects on organs. Further experimental investigation would conclusively determine the effects of COVID-19 infection on organs other than the lungs and the effectiveness of the proposed therapeutic targets.

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 ß-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.

Immune characterization and profiles of SARS-CoV-2 infected patients reveals potential host therapeutic targets and SARS-CoV-2 oncogenesis mechanism.

The spread of SARS-CoV-2 and the increasing mortality rates of COVID-19 create an urgent need for treatments, which are currently lacking. Although vaccines have been approved by the FDA for emergency use in the U.S., patients will continue to require pharmacologic intervention to reduce morbidity and mortality as vaccine availability remains limited. The rise of new variants makes the development of therapeutic strategies even more crucial to combat the current pandemic and future outbreaks. Evidence from several studies suggests the host immune response to SARS-CoV-2 infection plays a critical role in disease pathogenesis. Consequently, host immune factors are becoming more recognized as potential biomarkers and therapeutic targets for COVID-19. To develop therapeutic strategies to combat current and future coronavirus outbreaks, understanding how the coronavirus hijacks the host immune system during and after the infection is crucial. In this study, we investigated immunological patterns or characteristics of the host immune response to SARS-CoV-2 infection that may contribute to the disease severity of COVID-19 patients. We analyzed large bulk RNASeq and single cell RNAseq data from COVID-19 patient samples to immunoprofile differentially expressed gene sets and analyzed pathways to identify human host protein targets. We observed an immunological profile of severe COVID-19 patients characterized by upregulated cytokines, interferon-induced proteins, and pronounced T cell lymphopenia, supporting findings by previous studies. We identified a number of host immune targets including PERK, PKR, TNF, NF-kB, and other key genes that modulate the significant pathways and genes identified in COVID-19 patients. Finally, we identified genes modulated by COVID-19 infection that are implicated in oncogenesis, including E2F transcription factors and RB1, suggesting a mechanism by which SARS-CoV-2 infection may contribute to oncogenesis. Further clinical investigation of these targets may lead to bonafide therapeutic strategies to treat the current COVID-19 pandemic and protect against future outbreaks and viral escape variants.

Small compounds targeting tyrosine phosphorylation of Scaffold Protein Receptor for Activated C Kinase1A (RACK1A) regulate auxin mediated lateral root development in Arabidopsis.

Receptor for activated C kinase 1 (RACK1) is WD-40 type scaffold protein, conserved in all eukaryote organisms. Many reports implicated RACK1 in plant hormone signal transduction pathways including in auxin and diverse stress signaling pathways; however, the precise molecular mechanism of its role is not understood. Previously, a group of small compounds targeting the Arabidopsis RACK1A functional site-Tyr248 have been developed. Here, the three different small compounds are used to elucidate the role of RACK1A in auxin mediated lateral root development. Through monitoring the auxin response in the architecture of lateral roots and auxin reporter assays, a small molecule- SD29-12 was found to stabilize the auxin induced RACK1A Tyr248 phosphorylation, thereby stimulating auxin signaling and inducing lateral roots formation. In contrast, two other compounds, SD29 and SD29-14, inhibited auxin induced RACK1A Tyr248 phosphorylation resulting in the inhibition of auxin sensitivity and alternation in the lateral roots formation. Taken together, auxin induced RACK1A Tyr248 phosphorylation is found to be the critical regulatory mechanism for auxin-mediated lateral root development. This work leads to the molecular understanding of the role RACK1A plays in the auxin induced lateral root development signaling pathways. The auxin signal stimulating compound has the potential to be used as auxin-based root inducing bio-stimulant.