Dušica Vidovic

Antidiabetic actions of a non-agonist PPARγ ligand blocking Cdk5-mediated phosphorylation

PPARγ is the functioning receptor for the thiazolidinedione (TZD) class of antidiabetes drugs including rosiglitazone and pioglitazone. These drugs are full classical agonists for this nuclear receptor, but recent data have shown that many PPARγ-based drugs have a separate biochemical activity, blocking the obesity-linked phosphorylation of PPARγ by Cdk5 (ref. 2). Here we describe novel synthetic compounds that have a unique mode of binding to PPARγ, completely lack classical transcriptional agonism and block the Cdk5-mediated phosphorylation in cultured adipocytes and in insulin-resistant mice. Moreover, one such compound, SR1664, has potent antidiabetic activity while not causing the fluid retention and weight gain that are serious side effects of many of the PPARγ drugs. Unlike TZDs, SR1664 also does not interfere with bone formation in culture. These data illustrate that new classes of antidiabetes drugs can be developed by specifically targeting the Cdk5-mediated phosphorylation of PPARγ.

Suppression of TH17 differentiation and autoimmunity by a synthetic ROR ligand

T-helper cells that produce interleukin-17 (TH17 cells) are a recently identified CD4+ T-cell subset with characterized pathological roles in autoimmune diseases. The nuclear receptors retinoic-acid-receptor-related orphan receptors α and γt (RORα and RORγt, respectively) have indispensible roles in the development of this cell type. Here we present SR1001, a high-affinity synthetic ligand—the first in a new class of compound—that is specific to both RORα and RORγt and which inhibits TH17 cell differentiation and function. SR1001 binds specifically to the ligand-binding domains of RORα and RORγt, inducing a conformational change within the ligand-binding domain that encompasses the repositioning of helix 12 and leads to diminished affinity for co-activators and increased affinity for co-repressors, resulting in suppression of the receptors’ transcriptional activity. SR1001 inhibited the development of murine TH17 cells, as demonstrated by inhibition of interleukin-17A gene expression and protein production. Furthermore, SR1001 inhibited the expression of cytokines when added to differentiated murine or human TH17 cells. Finally, SR1001 effectively suppressed the clinical severity of autoimmune disease in mice. Our data demonstrate the feasibility of targeting the orphan receptors RORα and RORγt to inhibit specifically TH17 cell differentiation and function, and indicate that this novel class of compound has potential utility in the treatment of autoimmune diseases.

Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High- Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS)

The National Institutes of Health Library of Integrated Network-based Cellular Signatures (LINCS) program is generating extensive multidimensional data sets, including biochemical, genome-wide transcriptional, and phenotypic cellular response signatures to a variety of small-molecule and genetic perturbations with the goal of creating a sustainable, widely applicable, and readily accessible systems biology knowledge resource. Integration and analysis of diverse LINCS data sets depend on the availability of sufficient metadata to describe the assays and screening results and on their syntactic, structural, and semantic consistency. Here we report metadata specifications for the most important molecular and cellular components and recommend them for adoption beyond the LINCS project. We focus on the minimum required information to model LINCS assays and results based on a number of use cases, and we recommend controlled terminologies and ontologies to annotate assays with syntactic consistency and semantic integrity. We also report specifications for a simple annotation format (SAF) to describe assays and screening results based on our metadata specifications with explicit controlled vocabularies. SAF specifically serves to programmatically access and exchange LINCS data as a prerequisite for a distributed information management infrastructure. We applied the metadata specifications to annotate large numbers of LINCS cell lines, proteins, and small molecules. The resources generated and presented here are freely available.

Discovery of a novel submicromolar inhibitor of the lymphoid specific tyrosine phosphatase.

We report here a class of thiazolidine-2,4-diones and 2-thioxothiazolidin-4-ones as potent inhibitors of the lymphoid specific tyrosine phosphatase (Lyp) identified from high throughput screens. Chemical modification by incorporating the known phosphotyrosine (pTyr) mimics led to the discovery of a salicylate-based inhibitor with submicromolar potency.

Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action

The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.

Inhibition of lymphoid tyrosine phosphatase by benzofuran salicylic acids

The lymphoid tyrosine phosphatase (Lyp, PTPN22) is a critical negative regulator of T cell antigen receptor (TCR) signaling. A single-nucleotide polymorphism (SNP) in the ptpn22 gene correlates with the incidence of various autoimmune diseases, including type 1 diabetes, rheumatoid arthritis, and systemic lupus erythematosus. Since the disease-associated allele is a more potent inhibitor of TCR signaling, specific Lyp inhibitors may become valuable in treating autoimmunity. Using a structure-based approach, we synthesized a library of 34 compounds that inhibited Lyp with IC(50) values between 0.27 and 6.2 μM. A reporter assay was employed to screen for compounds that enhanced TCR signaling in cells, and several inhibitors displayed a dose-dependent, activating effect. Subsequent probing for Lyps direct physiological targets by immunoblot analysis confirmed the ability of the compounds to inhibit Lyp in T cells. Selectivity profiling against closely related tyrosine phosphatases and in silico docking studies with the crystal structure of Lyp yielded valuable information for the design of Lyp-specific compounds.

A Combined Ligand- and Structure-Based Virtual Screening Protocol Identifies Submicromolar PPARγ Partial Agonists

Peroxisome proliferator‐activated receptor γ (PPARγ) is involved in expression of genes that control glucose and lipid metabolism. PPARγ is the molecular target of the thiazolidinedione (TZD) class of antidiabetic drugs. However, despite their clinical use these drugs are associated with numerous adverse effects, which are related to their full activation of PPARγ transcriptional responses. PPARγ partial agonists are the focus of development efforts towards second‐generation PPARγ modulators with favorable pharmacology, potent insulin sensitization without the severe full agonists’ adverse effects. In order to identify novel PPARγ partial agonist lead compounds, we developed a virtual screening protocol based on three‐dimensional ligand‐shape similarity and docking. Prioritization gave 235 compounds for experimental screening from the National Institutes of Health (NIH) Molecular Libraries Small Molecule Repository (MLSMR)—a chemical library containing 340 000 compounds. Seven novel potent partial agonists were confirmed in cell‐based transactivation and competitive binding assays. Our results illustrate a well‐designed virtual screening campaign successfully identifying novel lead compounds as potential entry points for the development of antidiabetic drugs.

Discovery of Potent Non-urea Inhibitors of Soluble Epoxide Hydrolase

Soluble epoxide hydrolase (sEH) is a novel target for the treatment of hypertension and vascular inflammation. A new class of potent non-urea sEH inhibitors was identified via high throughput screening (HTS) and chemical modification. IC(50)s of the most potent compounds range from micromolar to low nanomolar.

Pharos: Collating protein information to shed light on the druggable genome

The ‘druggable genome’ encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage ‘serendipitous browsing’, whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: Integrated access to diverse large-scale cellular perturbation response data

Abstract The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content.