EZH2 K63-polyubiquitination affecting migration in extranodal natural killer/T-cell lymphoma.

BACKGROUND: Overexpressed EZH2 is oncogenically involved in the pathogenesis of different cancerous contexts including extranodal natural killer/T cell lymphoma (ENKTL). However, the underlying mechanisms of EZH2 upregulation have not been fully clarified and it is still difficult to target EZH2 in ENKTL. RESULTS: Current study identifies an E3 ligase TRIP12 that triggers K63-linked polyubiquitination of EZH2 in ENKTL and unexpectedly, stabilizes EZH2. As determined by gene expression profiling (GEP), TRIP12 and EZH2 levels correlate with each other in ENKTL patient samples. Aided by quantitative mass spectrometry (MS) and follow-up analysis, we identify K634 as the ubiquitination site of EZH2. Further study confirms that TRIP12-mediated EZH2 K634 ubiquitination enhances the interaction between EZH2 and SUZ12 or CDK1 and increases the level of EZH2 T487 phosphorylation. This study further demonstrates the TRIP12-EZH2 signaling might be regulated by cytoplasmic HSP60. Importantly, the TRIP12-EZH2 axis mediates ENKTL cell migration via accelerating epithelial-mesenchymal transition (EMT). Moreover, our study finds out dexamethasone treatment manipulates TRIP12-EZH2 signaling and may represent a novel therapeutic strategy against ENKTL metastasis. CONCLUSIONS: Altogether, TRIP12 induces K63-linked site-specific polyubiquitination of EZH2 for stabilization, which promotes ENKTL cell migration and could be targeted by dexamethasone treatment.

Immune pathway upregulation and lower genomic instability distinguish EBV-positive nodal T/NK-cell lymphoma from ENKTL and PTCL-NOS.

Primary Epstein-Barr virus (EBV)-positive nodal T/NK-cell lymphoma (PTCL-EBV) is a poorly understood disease which shows features resembling extranodal NK/T-cell lymphoma (ENKTL) and is currently not recognized as a distinct entity but categorized as a variant of primary T-cell lymphoma not otherwise specified (PTCL-NOS). Herein, we analyzed copynumber aberrations (n=77) with a focus on global measures of genomic instability and homologous recombination deficiency and performed gene expression (n=84) and EBV miRNA expression (n=24) profiling as well as targeted mutational analysis (n=16) to further characterize PTCL-EBV in relation to ENKTL and PTCL-NOS. Multivariate analysis revealed that patients with PTCL-EBV had a significantly worse outcome compared to patients with PTCL-NOS (P=0.002) but not to those with ENKTL. Remarkably, PTCL-EBV exhibited significantly lower genomic instability and homologous recombination deficiency scores compared to ENKTL and PTCL-NOS. Gene set enrichment analysis revealed that many immune-related pathways, interferon α/γ response, and IL6_JAK_STAT3 signaling were significantly upregulated in PTCLEBV and correlated with lower genomic instability scores. We also identified that NFκB-associated genes, BIRC3, NFKB1 (P50) and CD27, and their proteins are upregulated in PTCL-EBV. Most PTCL-EBV demonstrated a type 2 EBV latency pattern and, strikingly, exhibited downregulated expression of most EBV miRNA compared to ENKTL and their target genes were also enriched in immune-related pathways. PTCL-EBV also showed frequent mutations of TET2, PIK3CD and STAT3, and are characterized by microsatellite stability. Overall, poor outcome, low genomic instability, upregulation of immune pathways and downregulation of EBV miRNA are distinctive features of PTCL-EBV. Our data support the concept that PTCL-EBV could be considered as a distinct entity, provide novel insights into the pathogenesis of the disease and offer potential new therapeutic targets for this tumor.

T-Cell Lymphoma Clonality by Copy Number Variation Analysis of T-Cell Receptor Genes.

T-cell lymphomas arise from a single neoplastic clone and exhibit identical patterns of deletions in T-cell receptor (TCR) genes. Whole genome sequencing (WGS) data represent a treasure trove of information for the development of novel clinical applications. However, the use of WGS to identify clonal T-cell proliferations has not been systematically studied. In this study, based on WGS data, we identified monoclonal rearrangements (MRs) of T-cell receptors (TCR) genes using a novel segmentation algorithm and copy number computation. We evaluated the feasibility of this technique as a marker of T-cell clonality using T-cell lymphomas (TCL, n = 44) and extranodal NK/T-cell lymphomas (ENKTLs, n = 20), and identified 98% of TCLs with one or more TCR gene MRs, against 91% detected using PCR. TCR MRs were absent in all ENKTLs and NK cell lines. Sensitivity-wise, this platform is sufficiently competent, with MRs detected in the majority of samples with tumor content under 25% and it can also distinguish monoallelic from biallelic MRs. Understanding the copy number landscape of TCR using WGS data may engender new diagnostic applications in hematolymphoid pathology, which can be readily adapted to the analysis of B-cell receptor loci for B-cell clonality determination.

Combining kinase inhibitors for optimally co-targeting cancer and drug escape by exploitation of drug target promiscuities.

Cancers resist targeted therapeutics by drug-escape signaling. Multitarget drugs co-targeting cancer and drug-escape mediators (DEMs) are clinically advantageous. DEM coverage may be expanded by drug combinations. This work evaluated to what extent the kinase DEMs (KDEMs) can be optimally co-targeted by drug combinations based on target promiscuities of individual drugs. We focused on 41 approved and 28 clinical trial small molecule kinase inhibitor drugs with available experimental kinome and clinical pharmacokinetic data. From the kinome inhibitory profiles of these drugs, drug combinations were assembled for optimally co-targeting an established cancer target (EGFR, HER2, ABL1, or MEK1) and 9-16 target-associated KDEMs at comparable potency levels as that against the cancer target. Each set of two-, three-, and four-drug combinations co-target 36-71%, 44-89%, 50-88%, and 27-55% KDEMs of EGFR, HER2, ABL1, and MEK1, respectively, compared with the 36, 33, 38, and 18% KDEMs maximally co-targeted by an existing drug or drug combination approved or clinically tested for the respective cancer. Some co-targeted KDEMs are not covered by any existing drug or drug combination. Our work suggested that novel drug combinations may be constructed for optimally co-targeting cancer and drug escape by the exploitation of drug target promiscuities.

Drug sales confirm clinical advantage of multi-target inhibition of drug escapes by anticancer kinase inhibitors.

The clinical advantage of co-targeting cancer drug escape has been indicated by the percentage of these co-targeting drugs among all multi-target drugs in clinics and clinical trials. This clinical advantage needs to be further interrogated from such perspectives as the clinical impact of multi-target inhibition of drug-escape mediators. This impact may be reflected by drug sales data, that is, multi-target inhibition of higher number of drug-escape mediators favors the expanded coverage of drug-resistant patients leading to higher sales. We investigated whether this expectation is followed by the 25 FDA-approved anticancer kinase inhibitors, which were divided into 11 groups of comparable therapeutic mechanisms and approval years. We found 19 (76%) drugs to follow and 3 (12%) drugs not to follow this expectation. The remaining two (8%) and one (4%) drugs cannot be assessed due to insufficient data and incomparability. Therefore, drug sales strongly indicate the clinical advantage of multi-target inhibition of cancer drug escapes.

CMAUP: a database of collective molecular activities of useful plants.

The beneficial effects of functionally useful plants (e.g. medicinal and food plants) arise from the multi-target activities of multiple ingredients of these plants. The knowledge of the collective molecular activities of these plants facilitates mechanistic studies and expanded applications. A number of databases provide information about the effects and targets of various plants and ingredients. More comprehensive information is needed for broader classes of plants and for the landscapes of individual plant's multiple targets, collective activities and regulated biological pathways, processes and diseases. We therefore developed a new database, Collective Molecular Activities of Useful Plants (CMAUP), to provide the collective landscapes of multiple targets (ChEMBL target classes) and activity levels (in 2D target-ingredient heatmap), and regulated gene ontologies (GO categories), biological pathways (KEGG categories) and diseases (ICD blocks) for 5645 plants (2567 medicinal, 170 food, 1567 edible, 3 agricultural and 119 garden plants) collected from or traditionally used in 153 countries and regions. These landscapes were derived from 47 645 plant ingredients active against 646 targets in 234 KEGG pathways associated with 2473 gene ontologies and 656 diseases. CMAUP (http://bidd2.nus.edu.sg/CMAUP/) is freely accessible and searchable by keywords, plant usage classes, species families, targets, KEGG pathways, gene ontologies, diseases (ICD code) and geographical locations.

NPASS: natural product activity and species source database for natural product research, discovery and tool development.

There has been renewed interests in the exploration of natural products (NPs) for drug discovery, and continuous investigations of the therapeutic claims and mechanisms of traditional and herbal medicines. In-silico methods have been employed for facilitating these studies. These studies and the optimization of in-silico algorithms for NP applications can be facilitated by the quantitative activity and species source data of the NPs. A number of databases collectively provide the structural and other information of ∼470 000 NPs, including qualitative activity information for many NPs, but only ∼4000 NPs are with the experimental activity values. There is a need for the activity and species source data of more NPs. We therefore developed a new database, NPASS (Natural Product Activity and Species Source) to complement other databases by providing the experimental activity values and species sources of 35 032 NPs from 25 041 species targeting 5863 targets (2946 proteins, 1352 microbial species and 1227 cell-lines). NPASS contains 446 552 quantitative activity records (e.g. IC50, Ki, EC50, GI50 or MIC mainly in units of nM) of 222 092 NP-target pairs and 288 002 NP-species pairs. NPASS, http://bidd2.nus.edu.sg/NPASS/, is freely accessible with its contents searchable by keywords, physicochemical property range, structural similarity, species and target search facilities.

Pharmacological relationships and ligand discovery of G protein-coupled receptors revealed by simultaneous ligand and receptor clustering.

Conventional ligand and receptor similarity methods have been extensively used for exposing pharmacological relationships and drug lead discovery. They may in some cases neglect minor relationships useful for target hopping particularly against the remote family members. To complement the conventional methods for capturing these minor relationships, we developed a new method that uses a SLARC (Simultaneous Ligand And Receptor Clustering) 2D map to simultaneously characterize the ligand structural and receptor binding-site sequence relationships of a receptor family. The SLARC maps of the rhodopsin-like GPCR family comprehensively revealed scaffold hopping, target hopping, and multi-target relationships for the ligands of both homologous and remote family members. Their usefulness in new ligand discovery was validated by guiding the prospective discovery of novel indole piperazinylpyrimidine dual-targeting adenosine A2A receptor antagonist and dopamine D2 agonist compounds. The SLARC approach is useful for revealing pharmacological relationships and discovering new ligands at target family levels.

Database and Bioinformatics Studies of Probiotics.

Probiotics have been widely explored for health benefits, animal cares, and agricultural applications. Recent advances in microbiome, microbiota, and microbial dark matter research have fueled greater interests in and paved ways for the study of the mechanisms of probiotics and the discovery of new probiotics from uncharacterized microbial sources. A probiotics database named PROBIO was developed to facilitate these efforts and the need for the information on the known probiotics, which provides the comprehensive information about the probiotic functions of 448 marketed, 167 clinical trial/field trial, and 382 research probiotics for use or being studied for use in humans, animals, and plants. The potential applications of the probiotics data are illustrated by several literature-reported investigations, which have used the relevant information for probing the function and mechanism of the probiotics and for discovering new probiotics. PROBIO can be accessed free of charge at http://bidd2.nus.edu.sg/probio/homepage.htm .

Predicting the Enzymatic Hydrolysis Half-lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination.

The enzymatic hydrolysis of chemicals, which is important for in vitro drug metabolism assays, is an important indicator of drug stability profiles during drug discovery and development. Herein, we employed a stepwise feature elimination (SFE) method with nonlinear support vector machine regression (SVR) models to predict the in vitro half-lives in human plasma/blood of various esters. The SVR model was developed using public databases and literature-reported data on the half-lives of esters in human plasma/blood. In particular, the SFE method was developed to prevent over fitting and under fitting in the nonlinear model, and it provided a novel and efficient method of realizing feature combinations and selections to enhance the prediction accuracy. Our final developed model with 24 features effectively predicted an external validation set using the time-split method and presented reasonably good R2 values (0.6) and also predicted two completely independent validation datasets with R2 values of 0.62 and 0.54; thus, this model performed much better than other prediction models.

HEROD: a human ethnic and regional specific omics database.

MOTIVATION: Genetic and gene expression variations within and between populations and across geographical regions have substantial effects on the biological phenotypes, diseases, and therapeutic response. The development of precision medicines can be facilitated by the OMICS studies of the patients of specific ethnicity and geographic region. However, there is an inadequate facility for broadly and conveniently accessing the ethnic and regional specific OMICS data. RESULTS: Here, we introduced a new free database, HEROD, a human ethnic and regional specific OMICS database. Its first version contains the gene expression data of 53 070 patients of 169 diseases in seven ethnic populations from 193 cities/regions in 49 nations curated from the Gene Expression Omnibus (GEO), the ArrayExpress Archive of Functional Genomics Data (ArrayExpress), the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Geographic region information of curated patients was mainly manually extracted from referenced publications of each original study. These data can be accessed and downloaded via keyword search, World map search, and menu-bar search of disease name, the international classification of disease code, geographical region, location of sample collection, ethnic population, gender, age, sample source organ, patient type (patient or healthy), sample type (disease or normal tissue) and assay type on the web interface. AVAILABILITY AND IMPLEMENTATION: The HEROD database is freely accessible at http://bidd2.nus.edu.sg/herod/index.php. The database and web interface are implemented in MySQL, PHP and HTML with all major browsers supported. CONTACT: phacyz@nus.edu.sg.

Discovery of novel dual VEGFR2 and Src inhibitors using a multistep virtual screening approach.

AIM: Simultaneous inhibition of VEGFR2 and Src may enhance the efficacy of VEGFR2-targeted cancer therapeutics. Hence, development of dual inhibitors on VEGFR2 and Src can be a useful strategy for such treatments. MATERIALS & METHODS: A multistep virtual screening protocol, comprising ligand-based support vector machines method, drug-likeness rules filter and structure-based molecular docking, was developed and employed to identify dual inhibitors of VEGFR2 and Src from a large commercial chemical library. Kinase inhibitory assays and cell viability assays were then used for experimental validation. RESULTS: A set of compounds belonging to six different molecular scaffolds was identified and sent for biological evaluation. Compound 3c belonging to the 2-amino-3-cyanopyridine scaffold exhibited good antiproliferative effect and dual-target activities against VEGFR2 and Src. CONCLUSION: This study demonstrated the ability of the multistep virtual screening approach to identify novel multitarget agents.

A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks.

The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates.

Applying the designed multiple ligands approach to inhibit dihydrofolate reductase and thioredoxin reductase for anti-proliferative activity.

The development of multi-targeting drugs is currently being explored as an attractive alternative to combination therapy, especially for the treatment of complex diseases such as cancer. Dihydrofolate reductase (DHFR) and thioredoxin reductase (TrxR) are enzymes belonging to two unrelated cellular pathways that are known to contribute towards cancer cell growth and survival. In order to evaluate whether simultaneous inhibition of DHFR and TrxR by dihydrotriazines (DHFR-targeting) and chalcones (TrxR-targeting) may be beneficial, breast MCF-7 and colorectal HCT116 carcinoma cells were treated with combinations of selected dihydrotriazines and chalcones at a 1:1 M ratio. Two combinations demonstrated synergy at low-to-moderate concentrations. Based on this result, four merged dihydrotriazine-chalcone compounds were designed and synthesized. Two compounds, 11a [DHFR IC50 = 56.4 µM, TrxR IC50 (60 min) = 12.6 µM] and 11b [DHFR IC50 = 2.4 µM, TrxR IC50 (60 min) = 10.1 µM], demonstrated in vitro inhibition of DHFR and TrxR. The compounds showed growth inhibitory activity against MCF-7 and HCT116 cells, but lacked cytotoxicity. Molecular docking experiments showed 11b to possess rational binding modes to both the enzymes. In conclusion, this study has not only identified the dihydrotriazine and chalcone scaffolds as good starting points for the development of dual inhibitors of DHFR and TrxR, but also demonstrated the synthetic feasibility of producing a chemical entity that could result in simultaneous inhibition of DHFR and TrxR. Future efforts to improve the antiproliferative profiles of such compounds will look at alternative ways of integrating the two pharmacophoric scaffolds.

Clustered distribution of natural product leads of drugs in the chemical space as influenced by the privileged target-sites.

Some natural product leads of drugs (NPLDs) have been found to congregate in the chemical space. The extent, detailed patterns, and mechanisms of this congregation phenomenon have not been fully investigated and their usefulness for NPLD discovery needs to be more extensively tested. In this work, we generated and evaluated the distribution patterns of 442 NPLDs of 749 pre-2013 approved and 263 clinical trial small molecule drugs in the chemical space represented by the molecular scaffold and fingerprint trees of 137,836 non-redundant natural products. In the molecular scaffold trees, 62.7% approved and 37.4% clinical trial NPLDs congregate in 62 drug-productive scaffolds/scaffold-branches. In the molecular fingerprint tree, 82.5% approved and 63.0% clinical trial NPLDs are clustered in 60 drug-productive clusters (DCs) partly due to their preferential binding to 45 privileged target-site classes. The distribution patterns of the NPLDs are distinguished from those of the bioactive natural products. 11.7% of the NPLDs in these DCs have remote-similarity relationship with the nearest NPLD in their own DC. The majority of the new NPLDs emerge from preexisting DCs. The usefulness of the derived knowledge for NPLD discovery was demonstrated by the recognition of the new NPLDs of 2013-2014 approved drugs.

Fluorescence array-based sensing of metal ions using conjugated polyelectrolytes.

Array-based sensing offers several advantages for detecting a series of analytes with common structures or properties. In this study, four anionic conjugated polyelectrolytes (CPEs) with a common poly(p-pheynylene ethynylene) (PPE) backbone and varying pendant ionic side chains were designed. The conjugation length, repeat unit pattern, and ionic side chain composition were the main factors affecting the fluorescence patterns of CPE polymers in response to the addition of different metal ions. Eight metal ions, including Pb(2+), Hg(2+), Fe(3+), Cr(3+), Cu(2+), Mn(2+), Ni(2+), and Co(2+), categorized as water contaminants by the Environmental Protection Agency, were selected as analytes in this study. Fluorescence intensity response patterns of the four-PPE sensor array toward each of the metal ions were recorded, analyzed, and transformed into canonical scores using linear discrimination analysis (LDA), which permitted clear differentiation between metal ions using both two-dimensional and three-dimensional graphs. In particular, the array could readily differentiate between eight toxic metal ions in separate aqueous solutions at 100 nM. Our four-PPE sensor array also provides a practical application to quantify Pb(2+) and Hg(2+) concentrations in blind samples within a specific concentration range.

CFam: a chemical families database based on iterative selection of functional seeds and seed-directed compound clustering.

Similarity-based clustering and classification of compounds enable the search of drug leads and the structural and chemogenomic studies for facilitating chemical, biomedical, agricultural, material and other industrial applications. A database that organizes compounds into similarity-based as well as scaffold-based and property-based families is useful for facilitating these tasks. CFam Chemical Family database http://bidd2.cse.nus.edu.sg/cfam was developed to hierarchically cluster drugs, bioactive molecules, human metabolites, natural products, patented agents and other molecules into functional families, superfamilies and classes of structurally similar compounds based on the literature-reported high, intermediate and remote similarity measures. The compounds were represented by molecular fingerprint and molecular similarity was measured by Tanimoto coefficient. The functional seeds of CFam families were from hierarchically clustered drugs, bioactive molecules, human metabolites, natural products, patented agents, respectively, which were used to characterize families and cluster compounds into families, superfamilies and classes. CFam currently contains 11,643 classes, 34,880 superfamilies and 87,136 families of 490,279 compounds (1691 approved drugs, 1228 clinical trial drugs, 12,386 investigative drugs, 262,881 highly active molecules, 15,055 human metabolites, 80,255 ZINC-processed natural products and 116,783 patented agents). Efforts will be made to further expand CFam database and add more functional categories and families based on other types of molecular representations.

Design, synthesis, and biological evaluation of curcumin analogues as multifunctional agents for the treatment of Alzheimer's disease.

A series of novel curcumin analogues were designed, synthesized, and evaluated as potential multifunctional agents for the treatment of AD. The in vitro studies showed that these compounds had better inhibitory properties against Aß aggregation than curcumin. Superior anti-oxidant properties (better than the reference compound Trolox) of these compounds were observed by the oxygen radical absorbance capacity (ORAC) method and a cell-based assay using DCFH-DA as a probe. In addition they were able to chelate metals such as iron and copper and decrease metal-induced Aß aggregation. The structure-activity relationships were discussed. The results suggested that our curcumin analogues could be selected as multifunctional agents for further investigation of AD treatment.