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

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.

Cysteine containing dipeptides show a metal specificity that matches the composition of seawater.

Model prebiotic dipeptide sequences were identified by bioinformatics and DFT and molecular dynamics calculations. The peptides were then synthesized and evaluated for metal affinity and specificity. Cysteine containing dipeptides were not associated with metal affinities that followed the Irving-Williams series but did follow the concentration trends found in seawater.

Functional variants of the HMGA1 gene and type 2 diabetes mellitus.

CONTEXT: High-mobility group A1 (HMGA1) protein is a key regulator of insulin receptor (INSR) gene expression. We previously identified a functional HMGA1 gene variant in 2 insulin-resistant patients with decreased INSR expression and type 2 diabetes mellitus (DM). OBJECTIVE: To examine the association of HMGA1 gene variants with type 2 DM. DESIGN, SETTINGS, AND PARTICIPANTS: Case-control study that analyzed the HMGA1 gene in patients with type 2 DM and controls from 3 populations of white European ancestry. Italian patients with type 2 DM (n = 3278) and 2 groups of controls (n = 3328) were attending the University of Catanzaro outpatient clinics and other health care sites in Calabria, Italy, during 2003-2009; US patients with type 2 DM (n = 970) were recruited in Northern California clinics between 1994 and 2005 and controls (n = 958) were senior athletes without DM collected in 2004 and 2009; and French patients with type 2 DM (n = 354) and healthy controls (n = 50) were enrolled at the University of Reims in 1992. Genomic DNA was either directly sequenced or analyzed for specific HMGA1 mutations. Messenger RNA and protein expression for HMGA1 and INSR were measured in both peripheral lymphomonocytes and cultured Epstein-Barr virus-transformed lymphoblasts from patients with type 2 DM and controls. MAIN OUTCOME MEASURES: The frequency of HMGA1 gene variants among cases and controls. Odds ratios (ORs) for type 2 DM were estimated by logistic regression analysis. RESULTS: The most frequent functional HMGA1 variant, IVS5-13insC, was present in 7% to 8% of patients with type 2 DM in all 3 populations. The prevalence of IVS5-13insC variant was higher among patients with type 2 DM than among controls in the Italian population (7.23% vs 0.43% in one control group; OR, 15.77 [95% confidence interval {CI}, 8.57-29.03]; P < .001 and 7.23% vs 3.32% in the other control group; OR, 2.03 [95% CI, 1.51-3.43]; P < .001). In the US population, the prevalence of IVS5-13insC variant was 7.7% among patients with type 2 DM vs 4.7% among controls (OR, 1.64 [95% CI, 1.05-2.57]; P = .03). In the French population, the prevalence of IVS5-13insC variant was 7.6% among patients with type 2 DM and 0% among controls (P = .046). In the Italian population, 3 other functional variants were observed. When all 4 variants were analyzed, HMGA1 defects were present in 9.8% of Italian patients with type 2 DM and 0.6% of controls. In addition to the IVS5 C-insertion, the c.310G>T (p.E104X) variant was found in 14 patients and no controls (Bonferroni-adjusted P = .01); the c.*82G>A variant (rs2780219) was found in 46 patients and 5 controls (Bonferroni-adjusted P < .001); the c.*369del variant was found in 24 patients and no controls (Bonferroni-adjusted P < .001). In circulating monocytes and Epstein-Barr virus-transformed lymphoblasts from patients with type 2 DM and the IVS5-13insC variant, the messenger RNA levels and protein content of both HMGA1 and the INSR were decreased by 40% to 50%, and these defects were corrected by transfection with HMGA1 complementary DNA. CONCLUSIONS: Compared with healthy controls, the presence of functional HMGA1 gene variants in individuals of white European ancestry was associated with type 2 DM.

Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center.

The NIH-funded LINCS Consortium is creating an extensive reference library of cell-based perturbation response signatures and sophisticated informatics tools incorporating a large number of perturbagens, model systems, and assays. To date, more than 350 datasets have been generated including transcriptomics, proteomics, epigenomics, cell phenotype and competitive binding profiling assays. The large volume and variety of data necessitate rigorous data standards and effective data management including modular data processing pipelines and end-user interfaces to facilitate accurate and reliable data exchange, curation, validation, standardization, aggregation, integration, and end user access. Deep metadata annotations and the use of qualified data standards enable integration with many external resources. Here we describe the end-to-end data processing and management at the DCIC to generate a high-quality and persistent product. Our data management and stewardship solutions enable a functioning Consortium and make LINCS a valuable scientific resource that aligns with big data initiatives such as the BD2K NIH Program and concords with emerging data science best practices including the findable, accessible, interoperable, and reusable (FAIR) principles.

Drug and disease signature integration identifies synergistic combinations in glioblastoma.

Glioblastoma (GBM) is the most common primary adult brain tumor. Despite extensive efforts, the median survival for GBM patients is approximately 14 months. GBM therapy could benefit greatly from patient-specific targeted therapies that maximize treatment efficacy. Here we report a platform termed SynergySeq to identify drug combinations for the treatment of GBM by integrating information from The Cancer Genome Atlas (TCGA) and the Library of Integrated Network-Based Cellular Signatures (LINCS). We identify differentially expressed genes in GBM samples and devise a consensus gene expression signature for each compound using LINCS L1000 transcriptional profiling data. The SynergySeq platform computes disease discordance and drug concordance to identify combinations of FDA-approved drugs that induce a synergistic response in GBM. Collectively, our studies demonstrate that combining disease-specific gene expression signatures with LINCS small molecule perturbagen-response signatures can identify preclinical combinations for GBM, which can potentially be tested in humans.

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.

Cell-Free Translation Is More Variable than Transcription.

Although RNA synthesis can be reliably controlled with different T7 transcriptional promoters during cell-free gene expression with the PURE system, protein synthesis remains largely unaffected. To better control protein levels, we investigated a series of ribosome binding sites (RBSs). Although RBS strength did strongly affect protein synthesis, the RBS sequence could explain less than half of the variability of the data. Protein expression was found to depend on other factors besides the strength of the RBS, including the GC content of the coding sequence. The complexity of protein synthesis in comparison to RNA synthesis was observed by the higher degree of variability associated with protein expression. This variability was also observed in an E. coli cell extract-based system. However, the coefficient of variation was larger with E. coli RNA polymerase than with T7 RNA polymerase, consistent with the increased complexity of E. coli RNA polymerase.

Two-Way Chemical Communication between Artificial and Natural Cells.

Artificial cells capable of both sensing and sending chemical messages to bacteria have yet to be built. Here we show that artificial cells that are able to sense and synthesize quorum signaling molecules can chemically communicate with V. fischeri, V. harveyi, E. coli, and P. aeruginosa. Activity was assessed by fluorescence, luminescence, RT-qPCR, and RNA-seq. Two potential applications for this technology were demonstrated. First, the extent to which artificial cells could imitate natural cells was quantified by a type of cellular Turing test. Artificial cells capable of sensing and in response synthesizing and releasing N-3-(oxohexanoyl)homoserine lactone showed a high degree of likeness to natural V. fischeri under specific test conditions. Second, artificial cells that sensed V. fischeri and in response degraded a quorum signaling molecule of P. aeruginosa (N-(3-oxododecanoyl)homoserine lactone) were constructed, laying the foundation for future technologies that control complex networks of natural cells.

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Duplications of an iron-sulphur tripeptide leads to the formation of a protoferredoxin.

Based on UV-Vis, NMR, and EPR spectroscopies and DFT and molecular dynamics calculations, a model prebiotic [2Fe-2S] tripeptide was shown to accept and donate electrons. Duplications of the tripeptide sequence led to a protoferredoxin with increased stability. Duplications of primitive peptides may have contributed to the formation of contemporary ferredoxins.

In Vitro Selection for Small-Molecule-Triggered Strand Displacement and Riboswitch Activity.

An in vitro selection method for ligand-responsive RNA sensors was developed that exploited strand displacement reactions. The RNA library was based on the thiamine pyrophosphate (TPP) riboswitch, and RNA sequences capable of hybridizing to a target duplex DNA in a TPP regulated manner were identified. After three rounds of selection, RNA molecules that mediated a strand exchange reaction upon TPP binding were enriched. The enriched sequences also showed riboswitch activity. Our results demonstrated that small-molecule-responsive nucleic acid sensors can be selected to control the activity of target nucleic acid circuitry.

Gene position more strongly influences cell-free protein expression from operons than T7 transcriptional promoter strength.

The cell-free transcription-translation of multiple proteins typically exploits genes placed behind strong transcriptional promoters that reside on separate pieces of DNA so that protein levels can be easily controlled by changing DNA template concentration. However, such systems are not amenable to the construction of artificial cells with a synthetic genome. Herein, we evaluated the activity of a series of T7 transcriptional promoters by monitoring the fluorescence arising from a genetically encoded Spinach aptamer. Subsequently the influences of transcriptional promoter strength on fluorescent protein synthesis from one, two, and three gene operons were assessed. It was found that transcriptional promoter strength was more effective at controlling RNA synthesis than protein synthesis in vitro with the PURE system. Conversely, the gene position within the operon strongly influenced protein synthesis but not RNA synthesis.

Integrating artificial with natural cells to translate chemical messages that direct E. coli behaviour.

Previous efforts to control cellular behaviour have largely relied upon various forms of genetic engineering. Once the genetic content of a living cell is modified, the behaviour of that cell typically changes as well. However, other methods of cellular control are possible. All cells sense and respond to their environment. Therefore, artificial, non-living cellular mimics could be engineered to activate or repress already existing natural sensory pathways of living cells through chemical communication. Here we describe the construction of such a system. The artificial cells expand the senses of Escherichia coli by translating a chemical message that E. coli cannot sense on its own to a molecule that activates a natural cellular response. This methodology could open new opportunities in engineering cellular behaviour without exploiting genetically modified organisms.

Fluorescent proteins and in vitro genetic organization for cell-free synthetic biology.

To facilitate the construction of cell-free genetic devices, we evaluated the ability of 17 different fluorescent proteins to give easily detectable fluorescence signals in real-time from in vitro transcription-translation reactions with a minimal system consisting of T7 RNA polymerase and E. coli translation machinery, i.e., the PUREsystem. The data were used to construct a ratiometric fluorescence assay to quantify the effect of genetic organization on in vitro expression levels. Synthetic operons with varied spacing and sequence composition between two genes that coded for fluorescent proteins were then assembled. The resulting data indicated which restriction sites and where the restriction sites should be placed in order to build genetic devices in a manner that does not interfere with protein expression. Other simple design rules were identified, such as the spacing and sequence composition influences of regions upstream and downstream of ribosome binding sites and the ability of non-AUG start codons to function in vitro.

Cellular imitations.

Synthetic biologists typically construct new pathways within existing cells. While useful, this approach in many ways ignores the undefined but necessary components of life. A growing number of laboratories have begun to try to remove some of the mysteries of cellular life by building life-like systems from non-living component parts. Some of these attempts rely on purely chemical and physical forces alone without the aid of biological molecules, while others try to build artificial cells from the parts of life, such as nucleic acids, proteins, and lipids. Both bottom-up strategies suffer from the complication of trying to build something that remains undefined. The result has been the development of research programs that try to build systems that mimic in some way recognized living systems. Since it is difficult to quantify the mimicry of life, success often times is evaluated with a degree of subjectivity. Herein we highlight recent advances in mimicking the organization and behavior of cellular life from the bottom-up.