Connecting omics signatures and revealing biological mechanisms with iLINCS.

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity.

Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers for the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Its key aim is to generate proteomic and transcriptomic signatures that can predict cardiotoxic adverse effects of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shotgun proteomics experiments (308 cell line/drug combinations +64 control lysates) have been conducted. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures, generated in tandem, to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and enable possible mitigation. Both raw and processed proteomics data have passed several quality control steps and been made publicly available on the PRIDE database. This broad protein kinase inhibitor-stimulated human cardiomyocyte proteomic data and signature set is valuable for prediction of drug toxicities.

piNET: a versatile web platform for downstream analysis and visualization of proteomics data.

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

Epigenetic pathways and glioblastoma treatment: insights from signaling cascades.

There is an urgent need to identify novel therapies for glioblastoma (GBM) as most therapies are ineffective. A first step in this process is to identify and validate targets for therapeutic intervention. Epigenetic modulators have emerged as attractive drug targets in several cancers including GBM. These epigenetic regulators affect gene expression without changing the DNA sequence. Recent studies suggest that epigenetic regulators interact with drivers of GBM cell and stem-like cell proliferation. These drivers include components of the Notch, Hedgehog, and Wingless (WNT) pathways. We highlight recent studies connecting epigenetic and signaling pathways in GBM. We also review systems and big data approaches for identifying patient specific therapies in GBM. Collectively, these studies will identify drug combinations that may be effective in GBM and other cancers.

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.

Novel kinase inhibitors by reshuffling ligand functionalities across the human kinome.

Protein kinases remain among the most versatile and prospective therapeutic drug targets with currently 15 distinct compounds approved for use in humans and numerous clinical development programs. The vast majority of kinase inhibitors bind at the ATP site. Here we present an integrated workflow to amplify the rapidly increasing space of structurally resolved small molecule kinase ligands to generate novel inhibitors. Our approach considers both receptor-based similarity constraints in cocomplexes and ligand-based filtering/refinement methods to generate novel, drug-like matter. After building a comprehensive database of the structural kinome and identifying ATP-competitive ligands, we leverage local site similarities and site alignments to shuffle ligand fragments across the kinome. After extensive curation and standardization, our automated protocol starting from 936 cocrystal ATP-competitive binding sites generated about 150,000 new ligand structures among them over 26,000 lead-/drug-like compounds; the majority of those are novel based on structural similarity and scaffolds. In a retrospective analysis we demonstrate that our protocol produced known potent kinase inhibitors and we show how docking can be applied to prioritize the most likely efficacious compounds. Our workflow emulates a common strategy in medicinal chemistry to identify and swap corresponding moieties from known inhibitors to generate novel and potent leads. Here, we systematize and automate this approach leveraging available knowledge covering the entire human Kinome.

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. 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.

Potent inhibitors of Huntingtin protein aggregation in a cell-based assay.

A quinazoline that decreases polyglutamine aggregate burden in a cell-based assay was identified from a high-throughput screen of a chemical-compound library, provided by the NIH Molecular Libraries Small Molecule Repository (MLSMR). A structure and activity study yielded leads with submicromolar potency.