Structure activity relationship of pyridoxazinone substituted RHS analogs of oxabicyclooctane-linked 1,5-naphthyridinyl novel bacterial topoisomerase inhibitors as broad-spectrum antibacterial agents (Part-6).

Oxabicyclooctane linked 1,5-naphthyridinyl-pyridoxazinones are novel broad-spectrum bacterial topoisomerase inhibitors (NBTIs) targeting bacterial DNA gyrase and topoisomerase IV at a site different than quinolones. Due to lack of cross-resistance to known antibiotics they present excellent opportunity to combat drug-resistant bacteria. A structure activity relationship of the pyridoxazinone moiety is described in this Letter. Chemical synthesis and activities of NBTIs with substitutions at C-3, C-4 and C-7 of the pyridoxazinone moiety with halogens, alkyl groups and methoxy group has been described. In addition, substitutions of the linker NH proton and its transformation into amide analogs of AM-8085 and AM-8191 have been reported. Fluoro, chloro, and methyl groups at C-3 of the pyridoxazinone moiety retained the potency and spectrum. In addition, a C-3 fluoro analog showed 4-fold better oral efficacy (ED50 3.9 mg/kg) as compared to the parent AM-8085 in a murine bacteremia model of infection of Staphylococcus aureus. Even modest polarity (e.g., methoxy) is not tolerated at C-3 of the pyridoxazinone unit. The basicity and NH group of the linker is important for the activity when CH2 is at the linker position-8. However, amides (with linker position-8 ketone) with a position-7 NH or N-methyl group retained potency and spectrum suggesting that neither basicity nor hydrogen-donor properties of the linker amide NH is essential for the activity. This would suggest likely an altered binding mode of the linker position-7,8 amide containing compounds. The amides showed highly improved hERG (functional IC50 >30 µM) profile.

Structure activity relationship of C-2 ether substituted 1,5-naphthyridine analogs of oxabicyclooctane-linked novel bacterial topoisomerase inhibitors as broad-spectrum antibacterial agents (Part-5).

Oxabicyclooctane linked novel bacterial topoisomerase inhibitors (NBTIs) are new class of recently reported broad-spectrum antibacterial agents. They target bacterial DNA gyrase and topoisomerase IV and bind to a site different than quinolones. They show no cross-resistance to known antibiotics and provide opportunity to combat drug-resistant bacteria. A structure activity relationship of the C-2 substituted ether analogs of 1,5-naphthyridine oxabicyclooctane-linked NBTIs are described. Synthesis and antibacterial activities of a total of 63 analogs have been summarized representing alkyl, cyclo alkyl, fluoro alkyl, hydroxy alkyl, amino alkyl, and carboxyl alkyl ethers. All compounds were tested against three key strains each of Gram-positive and Gram-negative bacteria as well as for hERG binding activities. Many key compounds were also tested for the functional hERG activity. Six compounds were evaluated for efficacy in a murine bacteremia model of Staphylococcus aureus infection. Significant tolerance for the ether substitution (including polar groups such as amino and carboxyl) at C-2 was observed for S. aureus activity however the same was not true for Enterococcus faecium and Gram-negative strains. Reduced clogD generally showed reduced hERG activity and improved in vivo efficacy but was generally associated with decreased overall potency. One of the best compounds was hydroxy propyl ether (16), which mainly retained the potency, spectrum and in vivo efficacy of AM8085 associated with the decreased hERG activity and improved physical property.

Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets.

SUMMARY: Recently, several high profile studies collected cell viability data from panels of cancer cell lines treated with many drugs applied at different concentrations. Such drug sensitivity data for cancer cell lines provide suggestive treatments for different types and subtypes of cancer. Visualization of these datasets can reveal patterns that may not be obvious by examining the data without such efforts. Here we introduce Drug/Cell-line Browser (DCB), an online interactive HTML5 data visualization tool for interacting with three of the recently published datasets of cancer cell lines/drug-viability studies. DCB uses clustering and canvas visualization of the drugs and the cell lines, as well as a bar graph that summarizes drug effectiveness for the tissue of origin or the cancer subtypes for single or multiple drugs. DCB can help in understanding drug response patterns and prioritizing drug/cancer cell line interactions by tissue of origin or cancer subtype. AVAILABILITY AND IMPLEMENTATION: DCB is an open source Web-based tool that is freely available at: http://www.maayanlab.net/LINCS/DCB CONTACT: avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

BACKGROUND: System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. RESULTS: Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. CONCLUSIONS: Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

Renoprotective effect of combined inhibition of angiotensin-converting enzyme and histone deacetylase.

The Connectivity Map database contains microarray signatures of gene expression derived from approximately 6000 experiments that examined the effects of approximately 1300 single drugs on several human cancer cell lines. We used these data to prioritize pairs of drugs expected to reverse the changes in gene expression observed in the kidneys of a mouse model of HIV-associated nephropathy (Tg26 mice). We predicted that the combination of an angiotensin-converting enzyme (ACE) inhibitor and a histone deacetylase inhibitor would maximally reverse the disease-associated expression of genes in the kidneys of these mice. Testing the combination of these inhibitors in Tg26 mice revealed an additive renoprotective effect, as suggested by reduction of proteinuria, improvement of renal function, and attenuation of kidney injury. Furthermore, we observed the predicted treatment-associated changes in the expression of selected genes and pathway components. In summary, these data suggest that the combination of an ACE inhibitor and a histone deacetylase inhibitor could have therapeutic potential for various kidney diseases. In addition, this study provides proof-of-concept that drug-induced expression signatures have potential use in predicting the effects of combination drug therapy.

Sets2Networks: network inference from repeated observations of sets.

BACKGROUND: The skeleton of complex systems can be represented as networks where vertices represent entities, and edges represent the relations between these entities. Often it is impossible, or expensive, to determine the network structure by experimental validation of the binary interactions between every vertex pair. It is usually more practical to infer the network from surrogate observations. Network inference is the process by which an underlying network of relations between entities is determined from indirect evidence. While many algorithms have been developed to infer networks from quantitative data, less attention has been paid to methods which infer networks from repeated co-occurrence of entities in related sets. This type of data is ubiquitous in the field of systems biology and in other areas of complex systems research. Hence, such methods would be of great utility and value. RESULTS: Here we present a general method for network inference from repeated observations of sets of related entities. Given experimental observations of such sets, we infer the underlying network connecting these entities by generating an ensemble of networks consistent with the data. The frequency of occurrence of a given link throughout this ensemble is interpreted as the probability that the link is present in the underlying real network conditioned on the data. Exponential random graphs are used to generate and sample the ensemble of consistent networks, and we take an algorithmic approach to numerically execute the inference method. The effectiveness of the method is demonstrated on synthetic data before employing this inference approach to problems in systems biology and systems pharmacology, as well as to construct a co-authorship collaboration network. We predict direct protein-protein interactions from high-throughput mass-spectrometry proteomics, integrate data from Chip-seq and loss-of-function/gain-of-function followed by expression data to infer a network of associations between pluripotency regulators, extract a network that connects 53 cancer drugs to each other and to 34 severe adverse events by mining the FDA's Adverse Events Reporting Systems (AERS), and construct a co-authorship network that connects Mount Sinai School of Medicine investigators. The predicted networks and online software to create networks from entity-set libraries are provided online at http://www.maayanlab.net/S2N. CONCLUSIONS: The network inference method presented here can be applied to resolve different types of networks in current systems biology and systems pharmacology as well as in other fields of research.

Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers.

Common inflammatome gene signatures as well as disease-specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug targets and co-expressed gene modules linked to metabolic disorders and cancer. A large proportion of genes in this signature are tightly connected in tissue-specific Bayesian networks (BNs) built from multiple independent mouse and human cohorts. Both the inflammatome signature and the corresponding consensus BNs are highly enriched for immune response-related genes supported as causal for adiposity, adipokine, diabetes, aortic lesion, bone, muscle, and cholesterol traits, suggesting the causal nature of the inflammatome for a variety of diseases. Integration of this inflammatome signature with the BNs uncovered 151 key drivers that appeared to be more biologically important than the non-drivers in terms of their impact on disease phenotypes. The identification of this inflammatome signature, its network architecture, and key drivers not only highlights the shared etiology but also pinpoints potential targets for intervention of various common diseases.

Adenovirus IL-13-induced airway disease in mice: a corticosteroid-resistant model of severe asthma.

Interleukin 13 (IL-13) is considered to be a key driver of the development of airway allergic inflammation and remodeling leading to airway hyperresponsiveness (AHR). How precisely IL-13 leads to the development of airway inflammation, AHR, and mucus production is not fully understood. In order to identify key mediators downstream of IL-13, we administered adenovirus IL-13 to specifically induce IL-13-dependent inflammation in the lungs of mice. This approach was shown to induce cardinal features of lung disease, specifically airway inflammation, elevated cytokines, AHR, and mucus secretion. Notably, the model is resistant to corticosteroid treatment and is characterized by marked neutrophilia, two hallmarks of more severe forms of asthma. To identify IL-13-dependent mediators, we performed a limited-scale two-dimensional SDS-PAGE proteomic analysis and identified proteins significantly modulated in this model. Intriguingly, several identified proteins were unique to this model, whereas others correlated with those modulated in a mouse ovalbumin-induced pulmonary inflammation model. We corroborated this approach by illustrating that proteomic analysis can identify known pathways/mediators downstream of IL-13. Thus, we have characterized a murine adenovirus IL-13 lung model that recapitulates specific disease traits observed in human asthma, and have exploited this model to identify effectors downstream of IL-13. Collectively, these findings will enable a broader appreciation of IL-13 and its impact on disease pathways in the lung.