Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms

Covalent docking-driven virtual screening of extensive small-molecule libraries against Bruton tyrosine kinase for the identification of highly selective and potent novel therapeutic candidates

Bruton tyrosine kinases (BTKs) play critical roles in various diseases, including chronic lymphatic leukemia (CLL), Waldenström Macroglobulinemia, Marginal Zone Lymphoma, Mantle Cell Lymphoma (MCL), and Graft Versus Host diseases. BTKs are a family of tyrosine kinases involved in B lymphocyte signal transduction, development, and maturation. Their overexpression can lead to cancer as they are essential for the activation of the B Cell Receptor (BCR) signaling pathway. Blocking the activation of BTKs presents a promising approach for treating CLL. This study was centered around the identification of small-molecule therapeutics that have an impact on human BTK. The covalently bound Ibrutinib molecule, recognized for its ability to inhibit BTK, was used as the query molecule. IUPAC text files containing molecular fragments of Ibrutinib were employed to virtually screen five different libraries comprising small-molecules, resulting in the screening of over 2.4 million synthesized compounds. Covalent docking simulations were applied to the selected small-molecules obtained through text mining from databases. Potent hit molecules capable of inhibiting BTKs through virtual screening algorithms were identified, paving the way for novel therapeutic strategies in the treatment of CLL.

Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis

Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.

Associative gene networks reveal novel candidates important for ADHD and dyslexia comorbidity

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is commonly associated with developmental dyslexia (DD), which are both prevalent and complicated pediatric neurodevelopmental disorders that have a significant influence on children's learning and development. Clinically, the comorbidity incidence of DD and ADHD is between 25 and 48%. Children with DD and ADHD may have more severe cognitive deficiencies, a poorer level of schooling, and a higher risk of social and emotional management disorders. Furthermore, patients with this comorbidity are frequently treated for a single condition in clinical settings, and the therapeutic outcome is poor. The development of effective treatment approaches against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and treatment. In this study, we developed bioinformatical methodology for the analysis of the comorbidity of these two diseases. As such, the search for candidate genes related to the comorbid conditions of ADHD and DD can help in elucidating the molecular mechanisms underlying the comorbid condition, and can also be useful for genotyping and identifying new drug targets.

RESULTS

Using the ANDSystem tool, the reconstruction and analysis of gene networks associated with ADHD and dyslexia was carried out. The gene network of ADHD included 599 genes/proteins and 148,978 interactions, while that of dyslexia included 167 genes/proteins and 27,083 interactions. When the ANDSystem and GeneCards data were combined, a total of 213 genes/proteins for ADHD and dyslexia were found. An approach for ranking genes implicated in the comorbid condition of the two diseases was proposed. The approach is based on ten criteria for ranking genes by their importance, including relevance scores of association between disease and genes, standard methods of gene prioritization, as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analyzed genes. Among the top 20 genes with the highest priority DRD2, DRD4, CNTNAP2 and GRIN2B are mentioned in the literature as directly linked with the comorbidity of ADHD and dyslexia. According to the proposed approach, the genes OPRM1, CHRNA4 and SNCA had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the most relevant genes are involved in biological processes related to signal transduction, positive regulation of transcription from RNA polymerase II promoters, chemical synaptic transmission, response to drugs, ion transmembrane transport, nervous system development, cell adhesion, and neuron migration.

CONCLUSIONS

The application of methods of reconstruction and analysis of gene networks is a powerful tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance for the comorbid condition of ADHD and dyslexia was employed to predict genes that play key roles in the development of the comorbid condition. The results can be utilized to plan experiments for the identification of novel candidate genes and search for novel pharmacological targets.

Early-adulthood spike in protein translation drives aging via juvenile hormone/germline signaling

Protein translation (PT) declines with age in invertebrates, rodents, and humans. It has been assumed that elevated PT at young ages is beneficial to health and PT ends up dropping as a passive byproduct of aging. In Drosophila, we show that a transient elevation in PT during early-adulthood exerts long-lasting negative impacts on aging trajectories and proteostasis in later-life. Blocking the early-life PT elevation robustly improves life-/health-span and prevents age-related protein aggregation, whereas transiently inducing an early-life PT surge in long-lived fly strains abolishes their longevity/proteostasis benefits. The early-life PT elevation triggers proteostatic dysfunction, silences stress responses, and drives age-related functional decline via juvenile hormone-lipid transfer protein axis and germline signaling. Our findings suggest that PT is adaptively suppressed after early-adulthood, alleviating later-life proteostatic burden, slowing down age-related functional decline, and improving lifespan. Our work provides a theoretical framework for understanding how lifetime PT dynamics shape future aging trajectories.

Effects of heterozygous deletion of autism-related gene Cullin-3 in mice

Autism Spectrum Disorder (ASD) is a developmental disorder in which children display repetitive behavior, restricted range of interests, and atypical social interaction and communication. CUL3, coding for a Cullin family scaffold protein mediating assembly of ubiquitin ligase complexes through BTB domain substrate-recruiting adaptors, has been identified as a high-risk gene for autism. Although complete knockout of Cul3 results in embryonic lethality, Cul3 heterozygous mice have reduced CUL3 protein, demonstrate comparable body weight, and display minimal behavioral differences including decreased spatial object recognition memory. In measures of reciprocal social interaction, Cul3 heterozygous mice behaved similarly to their wild-type littermates. In area CA1 of hippocampus, reduction of Cul3 significantly increased mEPSC frequency but not amplitude nor baseline evoked synaptic transmission or paired-pulse ratio. Sholl and spine analysis data suggest there is a small yet significant difference in CA1 pyramidal neuron dendritic branching and stubby spine density. Unbiased proteomic analysis of Cul3 heterozygous brain tissue revealed dysregulation of various cytoskeletal organization proteins, among others. Overall, our results suggest that Cul3 heterozygous deletion impairs spatial object recognition memory, alters cytoskeletal organization proteins, but does not cause major hippocampal neuronal morphology, functional, or behavioral abnormalities in adult global Cul3 heterozygous mice.

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%.

Effects of an exogenous ketone ester using multi-omics in skeletal muscle of aging C57BL/6J male mice

Exogenous ketone ester supplementation provides a means to increase circulating ketone concentrations without the dietary challenges imposed by ketogenic diets. Our group has shown that oral R,S-1,3, butanediol diacetoacetate (BD-AcAc2) consumption results in body weight loss or maintenance with moderate increases in circulating ketones. We have previously shown a diet consisting of 25% BD-AcAc2 can maintain lean body mass (LBM) and induce fat mass (FM) loss in young, healthy male mice, but the underlying mechanisms are still unknown. Therefore, the purpose of this study was to determine if a diet consisting of 25% BD-AcAc2 (ketone ester, KE) would alter body composition, transcriptional regulation, the proteome, and the lipidome of skeletal muscle in aged mice. We hypothesized that the KE group would remain weight stable with improvements in body composition compared to controls, resulting in a healthy aging phenotype. Male C57BL/6J mice (n = 16) were purchased from Jackson Laboratories at 72 weeks of age. After 1 week of acclimation, mice were weighed and randomly assigned to one of two groups (n = 8 per group): control (CON) or KE. A significant group by time interaction was observed for body weight (P < 0.001), with KE fed mice weighing significantly less than CON. FM increased over time in the control group but was unchanged in the KE group. Furthermore, LBM was not different between CON and KE mice despite KE mice weighing less than CON mice. Transcriptional analysis of skeletal muscle identified 6 genes that were significantly higher and 21 genes that were significantly lower in the KE group compared to CON. Lipidomic analysis of skeletal muscle identified no differences between groups for any lipid species, except for fatty acyl chains in triacylglycerol which was 46% lower in the KE group. Proteomics analysis identified 44 proteins that were different between groups, of which 11 were lower and 33 were higher in the KE group compared to CON. In conclusion, 72-week-old male mice consuming the exogenous KE, BD-AcAc2, had lower age-related gains in body weight and FM compared to CON mice. Furthermore, transcriptional and proteomics data suggest a signature in skeletal muscle of KE-treated mice consistent with markers of improved skeletal muscle regeneration, improved electron transport chain utilization, and increased insulin sensitivity.

Machine Learning Models Identify New Inhibitors for Human OATP1B1

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

Synthesis and Biological Evaluation of Thiazole-Based Derivatives with Potential against Breast Cancer and Antimicrobial Agents

Investigating novel, biologically-active coordination compounds that may be useful in the design of breast anticancer, antifungal, and antimicrobial agents is still the main challenge for chemists. In order to get closer to solving this problem, three new copper coordination compounds containing thiazole-based derivatives were synthesized. The structures of the synthesized compounds and their physicochemical characterization were evaluated based on elemental analysis, 1H and l3C nuclear magnetic resonance (NMR), flame atomic absorption spectroscopy (F-AAS), single-crystal X-ray diffraction, thermogravimetric analysis (TGA), and Fourier-transform infrared spectroscopy (FTIR). The pharmacokinetics were studied using SwissADME. The results obtained from the computational studies supported the results obtained from the MTT analysis, and the antimicrobial activity was expressed as the minimum inhibitory concentration (MIC).

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Novel Approach by Natural Language Processing for COVID-19 Knowledge Discovery

Background

Methods

Results

Conclusion

In silico design and in vitro assessment of anti-Helicobacter pylori compounds as potential small-molecule arginase inhibitors

Related to a variety of gastrointestinal disorders ranging from gastric ulcer to gastric adenocarcinoma, the infection caused by the gram-negative bacteria Helicobacter pylori (H. pylori) poses as a great threat to human health; hence, the search for new treatments is a global priority. The H. pylori arginase (HPA) protein has been widely studied as one of the main virulence factors of this bacterium, being involved in the prevention of nitric oxide-mediated bacterial cell death, which is a central component of innate immunity. Given the growing need for the development of new drugs capable of combating the infection by H. pylori, the present work describes the search for new HPA inhibitors, using virtual screening techniques based on molecular docking followed by the evaluation of the proposed modes of interaction at the HPA active site. In vitro studies of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), followed by cytotoxicity activity in gastric adenocarcinoma and non-cancer cells, were performed. The results highlighted compounds 6, 11, and 13 as potential inhibitors of HPA; within these compounds, the results indicated 13 presented an improved activity toward H. pylori killing, with MIC and MBC both at 64 µg/mL. Moreover, compound 13 also presented a selectivity index of 8.3, thus being more selective for gastric adenocarcinoma cells compared to the commercial drug cisplatin. Overall, the present work demonstrates the search strategy based on in silico and in vitro techniques is able to support the rational design of new anti-H. pylori drugs.

Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network

Protein-protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here, we use the MetaCore network and the Google matrix algorithms for prediction of protein-protein interactions dictating cardiac fibrosis, a primary cause of end-stage heart failure. The developed algorithms allow identification of interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

Atorvastatin and blood flow regulate expression of distinctive sets of genes in mouse carotid artery endothelium

Hypercholesterolemia is a well-known pro-atherogenic risk factor and statin is the most effective anti-atherogenic drug that lowers blood cholesterol levels. However, despite systemic hypercholesterolemia, atherosclerosis preferentially occurs in arterial regions exposed to disturbed blood flow (d-flow), while the stable flow (s-flow) regions are spared. Given their predominant effects on endothelial function and atherosclerosis, we tested whether (1) statin and flow regulate the same or independent sets of genes and (2) statin can rescue d-flow-regulated genes in mouse artery endothelial cells in vivo. To test the hypotheses, C57BL/6 J mice (8-week-old male, n=5 per group) were pre-treated with atorvastatin (10mg/kg/day, Orally) or vehicle for 5 days. Thereafter, partial carotid ligation (PCL) surgery to induce d-flow in the left carotid artery (LCA) was performed, and statin or vehicle treatment was continued. The contralateral right carotid artery (RCA) remained exposed to s-flow to be used as the control. Two days or 2 weeks post-PCL surgery, endothelial-enriched RNAs from the LCAs and RCAs were collected and subjected to microarray gene expression analysis. Statin treatment in the s-flow condition (RCA+statin versus RCA+vehicle) altered the expression of 667 genes at 2-day and 187 genes at 2-week timepoint, respectively (P<0.05, fold change (FC)≥±1.5). Interestingly, statin treatment in the d-flow condition (LCA+statin versus LCA+vehicle) affected a limited number of genes: 113 and 75 differentially expressed genes at 2-day and 2-week timepoint, respectively (P<0.05, FC≥±1.5). In contrast, d-flow in the vehicle groups (LCA+vehicle versus RCA+vehicle) differentially regulated 4061 genes at 2-day and 3169 genes at 2-week timepoint, respectively (P<0.05, FC≥±1.5). Moreover, statin treatment did not reduce the number of flow-sensitive genes (LCA+statin versus RCA+statin) compared to the vehicle groups: 1825 genes at 2-day and 3788 genes at 2-week, respectively, were differentially regulated (P<0.05, FC≥±1.5). These results revealed that both statin and d-flow regulate expression of hundreds or thousands of arterial endothelial genes, respectively, in vivo. Further, statin and d-flow regulate independent sets of endothelial genes. Importantly, statin treatment did not reverse d-flow-regulated genes except for a small number of genes. These results suggest that both statin and flow play important independent roles in atherosclerosis development and highlight the need to consider their therapeutic implications for both.

Drug repurposing for COVID-19 based on an integrative meta-analysis of SARS-CoV-2 induced gene signature in human airway epithelium

Drug repurposing has the potential to bring existing de-risked drugs for effective intervention in an ongoing pandemic-COVID-19 that has infected over 131 million, with 2.8 million people succumbing to the illness globally (as of April 04, 2021). We have used a novel `gene signature'-based drug repositioning strategy by applying widely accepted gene ranking algorithms to prioritize the FDA approved or under trial drugs. We mined publically available RNA sequencing (RNA-Seq) data using CLC Genomics Workbench 20 (QIAGEN) and identified 283 differentially expressed genes (FDR<0.05, log2FC>1) after a meta-analysis of three independent studies which were based on severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection in primary human airway epithelial cells. Ingenuity Pathway Analysis (IPA) revealed that SARS-CoV-2 activated key canonical pathways and gene networks that intricately regulate general anti-viral as well as specific inflammatory pathways. Drug database, extracted from the Metacore and IPA, identified 15 drug targets (with information on COVID-19 pathogenesis) with 46 existing drugs as potential-novel candidates for repurposing for COVID-19 treatment. We found 35 novel drugs that inhibit targets (ALPL, CXCL8, and IL6) already in clinical trials for COVID-19. Also, we found 6 existing drugs against 4 potential anti-COVID-19 targets (CCL20, CSF3, CXCL1, CXCL10) that might have novel anti-COVID-19 indications. Finally, these drug targets were computationally prioritized based on gene ranking algorithms, which revealed CXCL10 as the common and strongest candidate with 2 existing drugs. Furthermore, the list of 283 SARS-CoV-2-associated proteins could be valuable not only as anti-COVID-19 targets but also useful for COVID-19 biomarker development.

Biomarkers for Adverse Lung Injury Following Pediatric Cardiopulmonary Bypass

Supplemental Digital Content is available in the text.

Cardiopulmonary bypass triggers systemic inflammation, resulting in lung injury, and frequently leads to prolonged mechanical ventilation. Biomarkers of systemic inflammation are required to predict the risk of such complications. We hypothesize that specific serum proteins can be used as biomarkers to predict the severity of lung injury following cardiac surgery.

ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature

BACKGROUND

The rapid growth of scientific literature has rendered the task of finding relevant information one of the critical problems in almost any research. Search engines, like Google Scholar, Web of Knowledge, PubMed, Scopus, and others, are highly effective in document search; however, they do not allow knowledge extraction. In contrast to the search engines, text-mining systems provide extraction of knowledge with representations in the form of semantic networks. Of particular interest are tools performing a full cycle of knowledge management and engineering, including automated retrieval, integration, and representation of knowledge in the form of semantic networks, their visualization, and analysis. STRING, Pathway Studio, MetaCore, and others are well-known examples of such products. Previously, we developed the Associative Network Discovery System (ANDSystem), which also implements such a cycle. However, the drawback of these systems is dependence on the employed ontologies describing the subject area, which limits their functionality in searching information based on user-specified queries.

RESULTS

The ANDDigest system is a new web-based module of the ANDSystem tool, permitting searching within PubMed by using dictionaries from the ANDSystem tool and sets of user-defined keywords. ANDDigest allows performing the search based on complex queries simultaneously, taking into account many types of objects from the ANDSystem's ontology. The system has a user-friendly interface, providing sorting, visualization, and filtering of the found information, including mapping of mentioned objects in text, linking to external databases, sorting of data by publication date, citations number, journal H-indices, etc. The system provides data on trends for identified entities based on dynamics of interest according to the frequency of their mentions in PubMed by years.

CONCLUSIONS

The main feature of ANDDigest is its functionality, serving as a specialized search for information about multiple associative relationships of objects from the ANDSystem's ontology vocabularies, taking into account user-specified keywords. The tool can be applied to the interpretation of experimental genetics data, the search for associations between molecular genetics objects, and the preparation of scientific and analytical reviews. It is presently available at https://anddigest.sysbio.ru/ .

Potent novel inhibitors against hepatitis C virus NS3 (HCV NS3 GT-3a) protease domain

HCV NS3, a non-structural hepatitis C viral protein is used as one of the potential targets for inhibition by direct-acting antivirals. It is known that the success rate for HCV genotype-1 treatment remained very high, however, treatment of genotype-3a (GT-3a), is still quite challenging. In the current study, the HCV GT-3a full-length NS3 gene was amplified and sequenced. The complete nucleotide sequence was translated into the amino acid sequence and homology models of HCV-NS3 GT-3a were generated by HCV-NS3 genotype-1b as a template. The objective of the study was to screen novel therapeutic hits from large databases. For this aim, various small molecule databases including, BindingDB (∼45.000 compounds), NCI (∼265.000 compounds), and Specs-SC (∼212.000 compounds) were used. Firstly, all of the compounds were screened using binary-QSAR models from the MetaCore/MetaDrug server, and compounds were filtered based on therapeutic activity predictions by the anti-viral QSAR model. Filtered molecules were used in 26 different toxicity QSAR models and active non-toxic compounds were identified. These selected molecules were then used in docking and molecular dynamics (MD) simulations studies at the binding cavities of the NS3 protease domain of the GT-3a. Results were compared with known inhibitors and novel molecules are proposed against HCV-NS3 GT-3a. These molecules have high ligand efficiencies as compared to the reference molecules suggesting a better alternate to the existing suite of inhibitors. Thus, this study will be a step ahead in the development of new potential compounds as antiviral drugs for the GT-3a target.

Vascular permeability disruption explored in the proteomes of mouse lungs and human microvascular cells following acute bromine exposure

Bromine (Br₂) is an organohalide found in nature and is integral to many manufacturing processes. Br₂ is toxic to living organisms, and high concentrations can prove fatal. To meet industrial demand, large amounts of purified Br₂ are produced, transported, and stored worldwide, providing a multitude of interfaces for potential human exposure through either accidents or terrorism. To identify the key mechanisms associated with acute Br₂ exposure, we have surveyed the lung proteomes of C57BL/6 male mice and human lung-derived microvascular endothelial cells (HMECs) at 24 h following exposure to Br₂ in concentrations likely to be encountered in the vicinity of industrial accidents. Global discovery proteomics applications combined with systems biology analysis identified robust and highly significant changes in proteins associated with three biological processes: 1) exosome secretion, 2) inflammation, and 3) vascular permeability. We focused on the latter, conducting physiological studies on isolated perfused lungs harvested from mice 24 h after Br₂ exposure. These experiments revealed significant increases in the filtration coefficient (K_f) indicating increased permeability of the pulmonary vasculature. Similarly, confluent monolayers of Br₂ and Br-lipid-treated HMECs exhibited differential levels of zona occludens-1 that were found to be dissociated from cell wall localization, an increase in phosphorylation and internalization of E-cadherin, as well as increased actin stress fiber formation, all of which are consistent with increased permeability. Taken as a whole, our discovery proteomics and systems analysis workflow, combined with physiological measurements of permeability, revealed both profound and novel biological changes that contribute to our current understanding of Br₂ toxicity.

In-Silico Modeling in Drug Metabolism and Interaction: Current Strategies of Lead Discovery

BACKGROUND

Drug metabolism is a complex mechanism of human body systems to detoxify foreign particles, chemicals, and drugs through bio alterations. It involves many biochemical reactions carried out by invivo enzyme systems present in the liver, kidney, intestine, lungs, and plasma. After drug administration, it crosses several biological membranes to reach into the target site for binding and produces the therapeutic response. After that, it may undergo detoxification and excretion to get rid of the biological systems. Most of the drugs and its metabolites are excreted through kidney via urination. Some drugs and their metabolites enter into intestinal mucosa and excrete through feces. Few of the drugs enter into hepatic circulation where they go into the intestinal tract. The drug leaves the liver via the bile duct and is excreted through feces. Therefore, the study of total methodology of drug biotransformation and interactions with various targets is costly.

METHODS

To minimize time and cost, in-silico algorithms have been utilized for lead-like drug discovery. Insilico modeling is the process where a computer model with a suitable algorithm is developed to perform a controlled experiment. It involves the combination of both in-vivo and in-vitro experimentation with virtual trials, eliminating the non-significant variables from a large number of variable parameters. Whereas, the major challenge for the experimenter is the selection and validation of the preferred model, as well as precise simulation in real physiological status.

RESULTS

The present review discussed the application of in-silico models to predict absorption, distribution, metabolism, and excretion (ADME) properties of drug molecules and also access the net rate of metabolism of a compound.

CONCLUSION

It helps with the identification of enzyme isoforms; which are likely to metabolize a compound, as well as the concentration dependence of metabolism and the identification of expected metabolites. In terms of drug-drug interactions (DDIs), models have been described for the inhibition of metabolism of one compound by another, and for the compound-dependent induction of drug-metabolizing enzymes.

Machine learning prediction of oncology drug targets based on protein and network properties

Background

The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed.

Results

We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an Area Under the Curve (AUC) of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates.

Conclusions

We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

Lipidomics from sample preparation to data analysis: a primer

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid–liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

Carprofen-induced depletion of proton motive force reverses TetK-mediated doxycycline resistance in methicillin-resistant Staphylococcus pseudintermedius

We previously showed that doxycycline (DOX) and carprofen (CPF), a veterinary non-steroidal anti-inflammatory drug, have synergistic antimicrobial activity against methicillin-resistant Staphylococcus pseudintermedius (MRSP) carrying the tetracycline resistance determinant TetK. To elucidate the molecular mechanism of this synergy, we investigated the effects of the two drugs, individually and in combination, using a comprehensive approach including RNA sequencing, two-dimensional differential in-gel electrophoresis, macromolecule biosynthesis assays and fluorescence spectroscopy. Exposure of TetK-positive MRSP to CPF alone resulted in upregulation of pathways that generate ATP and NADH, and promote the proton gradient. We showed that CPF is a proton carrier that dissipates the electrochemical potential of the membrane. In the presence of both CPF and DOX, the energy compensation strategy was attenuated by downregulation of all the processes involved, such as citric acid cycle, oxidative phosphorylation and ATP-providing arginine deiminase pathway. Furthermore, protein biosynthesis inhibition increased from 20% under DOX exposure alone to 75% upon simultaneous exposure to CPF. We conclude that synergistic interaction of the drugs restores DOX susceptibility in MRSP by compromising proton-motive-force-dependent TetK-mediated efflux of the antibiotic. MRSP is unable to counterbalance CPF-mediated PMF depletion by cellular metabolic adaptations, resulting in intracellular accumulation of DOX and inhibition of protein biosynthesis.

Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.

Affinity Purification of NF1 Protein-Protein Interactors Identifies Keratins and Neurofibromin Itself as Binding Partners

Neurofibromatosis Type 1 (NF1) is caused by pathogenic variants in the NF1 gene encoding neurofibromin. Definition of NF1 protein–protein interactions (PPIs) has been difficult and lacks replication, making it challenging to define binding partners that modulate its function. We created a novel tandem affinity purification (TAP) tag cloned in frame to the 3’ end of the full-length murine Nf1 cDNA (mNf1). We show that this cDNA is functional and expresses neurofibromin, His-Tag, and can correct p-ERK/ERK ratios in NF1 null HEK293 cells. We used this affinity tag to purify binding partners with Strep-Tactin^®XT beads and subsequently, identified them via mass spectrometry (MS). We found the tagged mNf1 can affinity purify human neurofibromin and vice versa, indicating that neurofibromin oligomerizes. We identify 21 additional proteins with high confidence of interaction with neurofibromin. After Metacore network analysis of these 21 proteins, eight appear within the same network, primarily keratins regulated by estrogen receptors. Previously, we have shown that neurofibromin levels negatively regulate keratin expression. Here, we show through pharmacological inhibition that this is independent of Ras signaling, as the inhibitors, selumetinib and rapamycin, do not alter keratin expression. Further characterization of neurofibromin oligomerization and binding partners could aid in discovering new neurofibromin functions outside of Ras regulation, leading to novel drug targets.

Multi-Omics Profiling for NF1 Target Discovery in Neurofibromin (NF1) Deficient Cells

Loss of NF1 is an oncogenic driver. In efforts to define pathways responsible for the development of neurofibromas and other cancers, transcriptomic and proteomic changes are evaluated in a non-malignant NF1 null cell line. NF1 null HEK293 cells were created using CRISPR/Cas9 technology and they are compared to parental cells that express neurofibromin. A total of 1222 genes and 132 proteins are found to be differentially expressed. The analysis is integrated to identify eight transcripts/proteins that are differentially regulated in both analyses. Metacore Pathway analysis identifies Neurogenesis NGF/TrkA MAPK-mediated signaling alterations. Next, the data set is compared with other published studies that involve analysis of cells or tumors deficient for NF1 and it is found that 141 genes recur in the sample and others; only thirteen of these genes recur in two or more studies. Genes/proteins of interest are validated via q-RT-PCR or Western blot. It is shown that KRT8 and 14-3-3σ protein levels respond to exogenously introduced mNf1 cDNA. Hence, transcripts/proteins that respond to neurofibromin levels are identified and they can potentially be used as biomarkers.

Human and rodent temporal lobe epilepsy is characterized by changes in O-GlcNAc homeostasis that can be reversed to dampen epileptiform activity

Temporal Lobe Epilepsy (TLE) is frequently associated with changes in protein composition and post-translational modifications (PTM) that exacerbate the disorder. O-linked-β-N-acetyl glucosamine (O-GlcNAc) is a PTM occurring at serine/threonine residues that is derived from and closely associated with metabolic substrates. The enzymes O-GlcNActransferase (OGT) and O-GlcNAcase (OGA) mediate the addition and removal, respectively, of the O-GlcNAc modification. The goal of this study was to characterize OGT/OGA and protein O-GlcNAcylation in the epileptic hippocampus and to determine and whether direct manipulation of these proteins and PTM's alter epileptiform activity. We observed reduced global and protein specific O-GlcNAcylation and OGT expression in the kainate rat model of TLE and in human TLE hippocampal tissue. Inhibiting OGA with Thiamet-G elevated protein O-GlcNAcylation, and decreased both seizure duration and epileptic spike events, suggesting that OGA may be a therapeutic target for seizure control. These findings suggest that loss of O-GlcNAc homeostasis in the kainate model and in human TLE can be reversed via targeting of O-GlcNAc related pathways.

In silico approach in reveal traditional medicine plants pharmacological material basis

In recent years, studies of traditional medicinal plants have gradually increased worldwide because the natural sources and variety of such plants allow them to complement modern pharmacological approaches. As computer technology has developed, in silico approaches such as virtual screening and network analysis have been widely utilized in efforts to elucidate the pharmacological basis of the functions of traditional medicinal plants. In the process of new drug discovery, the application of virtual screening and network pharmacology can enrich active compounds among the candidates and adequately indicate the mechanism of action of medicinal plants, reducing the cost and increasing the efficiency of the whole procedure. In this review, we first provide a detailed research routine for examining traditional medicinal plants by in silico techniques and elaborate on their theoretical principles. We also survey common databases, software programs and website tools that can be used for virtual screening and pharmacological network construction. Furthermore, we conclude with a simple example that illustrates the whole methodology, and we present perspectives on the development and application of this in silico methodology to reveal the pharmacological basis of the effects of traditional medicinal plants.

From gene networks to drugs: systems pharmacology approaches for AUD

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

BACKGROUND

Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets.

RESULTS

Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system.

CONCLUSIONS

The application of methods of reconstruction and analysis of gene networks is a productive tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance to the comorbid condition of asthma and hypertension was employed that resulted in prediction of 10 genes, playing the key role in the development of the comorbid condition. The results can be utilised to plan experiments for identification of novel candidate genes along with searching for novel pharmacological targets.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

CD4+ T cells from HIV-1 patients with impaired Th1 effector responses to Mycobacterium tuberculosis exhibit diminished histone and nucleoprotein signatures

HIV+ patients have an increased risk for tuberculosis disease despite clinical management with ARTs. We established a culture model of Mtb-infection in PBMCs from HIV+ PPD+ donors on suppressive ART (median 6.4years) with negligible viral loads (median<50copies/mL) and stable CD4+ T cell counts (517cells/mm^3). We observed that HIV+ patient lymphocytes harbored a recruitment defect to Mtb-infected monocytes. To investigate these immune defects on a per cell basis, purified CD4+ T cells from HIV patients were assessed by label-free quantification protein mass spectrometry. CD4+ T cells from HIV patients displayed diminished nucleoprotein levels - notably of histone variant H2a.Z and ribonucleoprotein A1. Only within healthy donors, transcriptional regulatory histone variant H2a.Z expression was correlated to the extent of IFN-γ induction upon Mtb-infection. Our findings may explain why HIV patients exhibit prolonged immune cell dysfunction despite suppressive ART, and implicate a per cell defect of CD4+ T cells.

Targeting mantle cell lymphoma metabolism and survival through simultaneous blockade of mTOR and nuclear transporter exportin-1

Mantle cell lymphoma (MCL) is an aggressive B-cell lymphoma with poor prognosis, characterized by aberrant expression of growth-regulating and oncogenic effectors and requiring novel anticancer strategies. The nuclear transporter exportin-1 (XPO1) is highly expressed in MCL and is associated with its pathogenesis. mTOR signaling, a central regulator of cell metabolism, is frequently activated in MCL and is also an important therapeutic target in this cancer. This study investigated the antitumor effects and molecular/metabolic changes induced by the combination of the small-molecule selective inhibitor XPO1 inhibitor KPT-185 and the dual mTORC1/2 kinase inhibitor AZD-2014 on MCL cells. AZD-2014 enhanced the KPT-185-induced inhibition of cell growth and repression of cell viability. The combination of KPT-185 and AZD-2014 downregulated c-Myc and heat shock factor 1 (HSF1) with its target heat shock protein 70 (HSP70). As a consequence, the combination caused repression of ribosomal biogenesis demonstrated by iTRAQ proteomic analyses. Metabolite assay by CETOF-MS showed that AZD-2014 enhanced the KPT-185-induced repression of MCL cellular energy metabolism through the TCA (Krebs) cycle, and further repressed KPT-185-caused upregulation of glycolysis.Thus the simultaneous inhibition of XPO1 and mTOR signaling is a novel and promising strategy targeting prosurvival metabolism in MCL.

Argininosuccinate lyase interacts with cyclin A2 in cytoplasm and modulates growth of liver tumor cells

Arginine is a critical amino acid in specific cancer types including hepatocellular carcinoma (HCC) and melanoma. Novel molecular mechanisms and therapeutic targets in arginine metabolism-mediated cancer formation await further identification. Our laboratory has previously demonstrated that arginine metabolic enzyme argininosuccinate lyase (ASL) promoted HCC formation in part via maintenance of cyclin A2 protein expression and arginine production for channeling to nitric oxide synthase. In this study, we investigated the mechanism by which ASL regulates cyclin A2 expression. We found that ASL interacted with cyclin A2 in HCC cells and the localization of their interaction was in the cytoplasm. Mutation of essential residues for enzymatic activity of ASL did not affect the binding of ASL to cyclin A2. Moreover, the mutant ASL retained the ability to restore the decreased tumorigenicity caused by ASL shRNA. Furthermore, overexpression of ASL conferred resistance to arginine deprivation therapy. Finally, the important pathways and potential therapeutic targets in ASL-regulated HCC were identified by bioinformatics analyses with Metacore database and Connectivity Map database. Our analyses suggested that bisoprolol, celecoxib, and ipratropium bromide, are potential therapeutics for ASL-regulated HCC formation. Thus, ASL interacts with cyclin A2 in cytoplasm, and may promote HCC formation through this non-enzymatic function. Overexpression of ASL may be a contributing factor in drug resistance for arginine deprivation therapy.

Prediction of pharmacokinetic and toxicological parameters of a 4-phenylcoumarin isolated from geopropolis: In silico and in vitro approaches

In silico and in vitro methodologies have been used as important tools in the drug discovery process, including from natural sources. The aim of this study was to predict pharmacokinetic and toxicity (ADME/Tox) properties of a coumarin isolated from geopropolis using in silico and in vitro approaches. Cinnamoyloxy-mammeisin (CNM) isolated from Brazilian M. scutellaris geopropolis was evaluated for its pharmacokinetic parameters by in silico models (ACD/Percepta™ and MetaDrug™ software). Genotoxicity was assessed by in vitro DNA damage signaling PCR array. CNM did not pass all parameters of Lipinski's rule of five, with a predicted low oral bioavailability and high plasma protein binding, but with good predicted blood brain barrier penetration. CNM was predicted to show low affinity to cytochrome P450 family members. Furthermore, the predicted Ames test indicated potential mutagenicity of CNM. Also, the probability of toxicity for organs and tissues was classified as moderate and high for liver and kidney, and moderate and low for skin and eye irritation, respectively. The PCR array analysis showed that CNM significantly upregulated about 7% of all DNA damage-related genes. By exploring the biological function of these genes, it was found that the predicted CNM genotoxicity is likely to be mediated by apoptosis. The predicted ADME/Tox profile suggests that external use of CNM may be preferable to systemic exposure, while its genotoxicity was characterized by the upregulation of apoptosis-related genes after treatment. The combined use of in silico and in vitro approaches to evaluate these parameters generated useful hypotheses to guide further preclinical studies.

Serum protein profiling in diffuse large B-cell lymphoma

PURPOSE

The aim of this pilot study was to conduct a nontargeted exploratory proteomics profiling analysis on sera obtained from patients diagnosed with diffuse large B-cell lymphoma (DLBCL) with the goal of identifying disease-specific biomarkers.

EXPERIMENTAL DESIGN

Sera from 87 participants (57 chemotherapy-naïve diffuse DLBCL patients, 30 controls frequency-matched by age group and World Health Organization (WHO) BMI categories) that were part of a large San Francisco Bay Area case-control study of non-Hodgkin lymphoma were analyzed by liquid chromatography combined with tandem mass spectrometry.

RESULTS

Thirty-five proteins (p-adjusted <0.05) were identified as differentially abundant between the DLBCL patients at various disease stages as compared to the controls. Of these, five proteins were randomly selected for further confirmation by ELISA: adiponectin (AdipoQ), cluster of differentiation 14 (CD14), heparin sulfate proteoglycan core protein (HSPG2), extracellular matrix 1 (ECM1), and alpha-1-antichymotrypsin (ACT). These proteins were statistically significantly elevated by 68.8, 37.0, 61.6, 68.0, and 32.0%, respectively, in DLBCL patient sera as compared to controls.

CONCLUSION AND CLINICAL RELEVANCE

These preliminary data when combined with other cancer-related data regarding these proteins warrant continued research in clinical and large prospective studies to clarify the role for these biomarkers in DLBCL pathogenesis and/or prognosis.

A diet-induced animal model of non-alcoholic fatty liver disease and hepatocellular cancer

BACKGROUND & AIMS

The lack of a preclinical model of progressive non-alcoholic steatohepatitis (NASH) that recapitulates human disease is a barrier to therapeutic development.

METHODS

A stable isogenic cross between C57BL/6J (B6) and 129S1/SvImJ (S129) mice were fed a high fat diet with ad libitum consumption of glucose and fructose in physiologically relevant concentrations and compared to mice fed a chow diet and also to both parent strains.

RESULTS

Following initiation of the obesogenic diet, B6/129 mice developed obesity, insulin resistance, hypertriglyceridemia and increased LDL-cholesterol. They sequentially also developed steatosis (4-8weeks), steatohepatitis (16-24weeks), progressive fibrosis (16weeks onwards) and spontaneous hepatocellular cancer (HCC). There was a strong concordance between the pattern of pathway activation at a transcriptomic level between humans and mice with similar histological phenotypes (FDR 0.02 for early and 0.08 for late time points). Lipogenic, inflammatory and apoptotic signaling pathways activated in human NASH were also activated in these mice. The HCC gene signature resembled the S1 and S2 human subclasses of HCC (FDR 0.01 for both). Only the B6/129 mouse but not the parent strains recapitulated all of these aspects of human NAFLD.

CONCLUSIONS

We here describe a diet-induced animal model of non-alcoholic fatty liver disease (DIAMOND) that recapitulates the key physiological, metabolic, histologic, transcriptomic and cell-signaling changes seen in humans with progressive NASH.

LAY SUMMARY

We have developed a diet-induced mouse model of non-alcoholic steatohepatitis (NASH) and hepatic cancers in a cross between two mouse strains (129S1/SvImJ and C57Bl/6J). This model mimics all the physiological, metabolic, histological, transcriptomic gene signature and clinical endpoints of human NASH and can facilitate preclinical development of therapeutic targets for NASH.

Ribosomal Biogenesis and Translational Flux Inhibition by the Selective Inhibitor of Nuclear Export (SINE) XPO1 Antagonist KPT-185

Mantle cell lymphoma (MCL) is an aggressive B-cell lymphoma characterized by the aberrant expression of several growth-regulating, oncogenic effectors. Exportin 1 (XPO1) mediates the nucleocytoplasmic transport of numerous molecules including oncogenic growth-regulating factors, RNAs, and ribosomal subunits. In MCL cells, the small molecule KPT-185 blocks XPO1 function and exerts anti-proliferative effects. In this study, we investigated the molecular mechanisms of this putative anti-tumor effect on MCL cells using cell growth/viability assays, immunoblotting, gene expression analysis, and absolute quantification proteomics. KPT-185 exhibited a p53-independent anti-lymphoma effect on MCL cells, by suppression of oncogenic mediators (e.g., XPO1, cyclin D1, c-Myc, PIM1, and Bcl-2 family members), repression of ribosomal biogenesis, and downregulation of translation/chaperone proteins (e.g., PIM2, EEF1A1, EEF2, and HSP70) that are part of the translational/transcriptional network regulated by heat shock factor 1. These results elucidate a novel mechanism in which ribosomal biogenesis appears to be a key component through which XPO1 contributes to tumor cell survival. Thus, we propose that the blockade of XPO1 could be a promising, novel strategy for the treatment of MCL and other malignancies overexpressing XPO1.

Identification of a Selective G1-Phase Benzimidazolone Inhibitor by a Senescence-Targeted Virtual Screen Using Artificial Neural Networks

Cellular senescence is a barrier to tumorigenesis in normal cells, and tumor cells undergo senescence responses to genotoxic stimuli, which is a potential target phenotype for cancer therapy. However, in this setting, mixed-mode responses are common with apoptosis the dominant effect. Hence, more selective senescence inducers are required. Here we report a machine learning-based in silico screen to identify potential senescence agonists. We built profiles of differentially affected biological process networks from expression data obtained under induced telomere dysfunction conditions in colorectal cancer cells and matched these to a panel of 17 protein targets with confirmatory screening data in PubChem. We trained a neural network using 3517 compounds identified as active or inactive against these targets. The resulting classification model was used to screen a virtual library of ~2M lead-like compounds. One hundred and forty-seven virtual hits were acquired for validation in growth inhibition and senescence-associated β-galactosidase assays. Among the found hits, a benzimidazolone compound, CB-20903630, had low micromolar IC50 for growth inhibition of HCT116 cells and selectively induced senescence-associated β-galactosidase activity in the entire treated cell population without cytotoxicity or apoptosis induction. Growth suppression was mediated by G1 blockade involving increased p21 expression and suppressed cyclin B1, CDK1, and CDC25C. In addition, the compound inhibited growth of multicellular spheroids and caused severe retardation of population kinetics in long-term treatments. Preliminary structure-activity and structure clustering analyses are reported, and expression analysis of CB-20903630 against other cell cycle suppressor compounds suggested a PI3K/AKT-inhibitor-like profile in normal cells, with different pathways affected in cancer cells.

Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis

Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.

In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics

Background

Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies.

Methods

In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targets was applied to predict the related biological activities, signal pathways and processes networks of the compound by using MetaCore platform. After that, some most relevant regulating networks were considered, which included the nodes and relevant pathways of dioscin.

Results

71 potential targets of dioscin from humans, 7 from rats and 8 from mice were screened, and the prediction results showed that the most likely targets of dioscin were cyclin A2, calmodulin, hemoglobin subunit beta, DNA topoisomerase I, DNA polymerase lambda, nitric oxide synthase and UDP-N-acetylhexosamine pyrophosphorylase, etc. Many diseases including experimental autoimmune encephalomyelitis of human, temporal lobe epilepsy of rat and ankylosing spondylitis of mouse, may be inhibited by dioscin through regulating immune response alternative complement pathway, G-protein signaling RhoB regulation pathway and immune response antiviral actions of interferons, etc. The most relevant networks (5 from human, 3 from rat and 5 from mouse) indicated that dioscin may be a TOP1 inhibitor, which can treat cancer though the cell cycle– transition and termination of DNA replication pathway. Dioscin can down regulate EGFR and EGF to inhibit cancer, and also has anti-inflammation activity by regulating JNK signaling pathway.

Conclusions

The predictions of the possible targets, biological activities, signal pathways and relevant regulating networks of dioscin provide valuable information to guide further investigation of dioscin on pharmacodynamics and molecular mechanisms, which also suggests a practical and effective method for studies on the mechanism of other chemicals.

Bigger data, collaborative tools and the future of predictive drug discovery

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

Liver transcriptome analysis of Atlantic cod (Gadus morhua) exposed to PCB 153 indicates effects on cell cycle regulation and lipid metabolism

BACKGROUND

Polychlorinated biphenyls (PCBs) are persistent organic pollutants (POPs) with harmful effects in animals and humans. Although PCB 153 is one of the most abundant among PCBs detected in animal tissues, its mechanism of toxicity is not well understood. Only few studies have been conducted to explore genes and pathways affected by PCB 153 by using high throughput transcriptomics approaches. To obtain better insights into toxicity mechanisms, we treated juvenile Atlantic cod (Gadus morhua) with PCB 153 (0.5, 2 and 8 mg/kg body weight) for 2 weeks and performed gene expression analysis in the liver using oligonucleotide arrays.

RESULTS

Whole-genome gene expression analysis detected about 160 differentially regulated genes. Functional enrichment, interactome, network and gene set enrichment analysis of the differentially regulated genes suggested that pathways associated with cell cycle, lipid metabolism, immune response, apoptosis and stress response were among the top significantly enriched. Particularly, genes coding for proteins in DNA replication/cell cycle pathways and enzymes of lipid biosynthesis were up-regulated suggesting increased cell proliferation and lipogenesis, respectively.

CONCLUSIONS

PCB 153 appears to activate cell proliferation and lipogenic genes in cod liver. Transcriptional up-regulation of marker genes for lipid biosynthesis resembles lipogenic effects previously reported for persistent organic pollutants (POPs) and other environmental chemicals. Our results provide new insights into mechanisms of PCB 153 induced toxicity.

The effects of a MAP2K5 microRNA target site SNP on risk for anxiety and depressive disorders

Functional variants that contribute to genomewide association study (GWAS) signals are difficult to identify. MicroRNAs could contribute to some of these gene-trait relationships. We compiled a set of GWAS trait gene SNPs that were predicted to affect microRNA regulation of mRNA. Trait associations were tested in a sample of 6725 European-American (EA) and African-American (AA) subjects that were interviewed using the polydiagnostic SSADDA to diagnose major psychiatric disorders. A predicted miR-330-3p target site SNP (rs41305272) in mitogen-activated protein kinase kinase 5 (MAP2K5) mRNA was in LD (d' = 1.0, r(2) = 0.02) with a reported GWAS-identified variant for restless legs syndrome (RLS), a disorder frequently comorbid with anxiety and depression, possibly because of a shared pathophysiology. We examined the SNP's association with mood and anxiety-related disorders. Rs41305272 was associated with agoraphobia (Ag) in EAs (odds ratio [OR] = 1.95, P = 0.007; 195 cases) and AAs (OR = 3.2, P = 0.03; 148 cases) and major depressive disorder (MDD) in AAs (OR = 2.64, P = 0.01; 427 cases), but not EAs (465 cases). Rs41305272*T carrier frequency was correlated with the number of anxiety and depressive disorders diagnosed per subject. RLS was not evaluated in our subjects. Predicted miR-330-3p target genes were enriched in pathways relevant to psychiatric disorders. These findings suggest that microRNA target site information may be useful in the analysis of GWAS signals for complex traits. MiR-330-3p and MAP2K5 are potentially important contributors to mood and anxiety-related traits. With support from additional studies, these findings could add to the large number of risk genes identified through association to medical disorders that have primary psychiatric effects.