SpectralNET – an application for spectral graph analysis and visualization

BACKGROUND: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. RESULTS: Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). CONCLUSION: SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request

Distinct Biological Network Properties between the Targets of Natural Products and Disease Genes

We show that natural products target proteins with a high number of protein−protein functional interactions (high biological network connectivity) and that these protein targets have higher network connectivity than disease genes. This feature may facilitate disruption of essential biological pathways, resulting in competitor death. This result also suggests that additional sources of small molecules will be required to discover drugs targeting the root causes of human disease in the future.Chemistry and Chemical Biolog

Chemical Space Overlap with Critical Protein–Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries

There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein–protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as “hot spots”. Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the “screenable chemical universe based on intuitive data organization”, SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR·uPA, TEAD4·Yap1, and CaVα·CaVβ. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR·uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4·Yap1. Virtual screening of conformationally restricted compounds targeting uPAR·uPA and TEAD4·Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions

Levels of Matrix Metalloproteinase-9 within Cerebrospinal Fluid in a Rabbit Model of Coccidioidal Meningitis and Vasculitis

Matrix metalloproteinase (MMP)-9 is produced by the central nervous system and inflammatory cells in a variety of inflammatory conditions in both animals and humans. MMP-9 promotes inflammation, breakdown of the blood-brain barrier, and vasculitis. Because vasculitis is seen frequently in patients with coccidioidal meningitis (CM), this study evaluated the presence of MMP-9 within the cerebrospinal fluid (CSF) of rabbits infected intracisternally with Coccidioides immitis arthroconidia. Infected rabbits demonstrated systemic and neurological sequelae to infection, including CSF pleocytosis. Levels of MMP-9 within CSF were assayed by use of zymography and compared with MMP-2 levels, which served as an internal control. Elevated levels of MMP-9 were detectable by day 3, continued to increase through day 10, and declined by day 15 after infection. MMP-9 may contribute to inflammation and vasculitis in this animal model. Future work can focus on evaluation of MMP inhibitors, to gain a better perspective of the role of this MMP in C

Multiplex Cytological Profiling Assay to Measure Diverse Cellular States

Computational methods for image-based profiling are under active development, but their success hinges on assays that can capture a wide range of phenotypes. We have developed a multiplex cytological profiling assay that “paints the cell” with as many fluorescent markers as possible without compromising our ability to extract rich, quantitative profiles in high throughput. The assay detects seven major cellular components. In a pilot screen of bioactive compounds, the assay detected a range of cellular phenotypes and it clustered compounds with similar annotated protein targets or chemical structure based on cytological profiles. The results demonstrate that the assay captures subtle patterns in the combination of morphological labels, thereby detecting the effects of chemical compounds even though their targets are not stained directly. This image-based assay provides an unbiased approach to characterize compound- and disease-associated cell states to support future probe discovery

Predicting compound activity from phenotypic profiles and chemical structures

Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources—chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)—to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6–10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process

CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network

Abstract The Cancer Target Discovery and Development (CTD2) Network aims to use functional genomics to accelerate the translation of high-throughput and high-content genomic and small-molecule data towards use in precision oncology. As part of this goal, and to share its conclusions with the research community, the Network developed the ‘CTD2 Dashboard’ [https://ctd2-dashboard.nci.nih.gov/], which compiles CTD2 Network-generated conclusions, termed ‘observations’, associated with experimental entities, collected by its member groups (‘Centers’). Any researcher interested in learning about a given gene, protein, or compound (a ‘subject’) studied by the Network can come to the CTD2 Dashboard to quickly and easily find, review, and understand Network-generated experimental results. In particular, the Dashboard allows visitors to connect experiments about the same target, biomarker, etc., carried out by multiple Centers in the Network. The Dashboard’s unique knowledge representation allows information to be compiled around a subject, so as to become greater than the sum of the individual contributions. The CTD2 Network has broadly defined levels of validation for evidence (‘Tiers’) pertaining to a particular finding, and the CTD2 Dashboard uses these Tiers to indicate the extent to which results have been validated. Researchers can use the Network’s insights and tools to develop a new hypothesis or confirm existing hypotheses, in turn advancing the findings towards clinical applications. Database URL: https://ctd2-dashboard.nci.nih.gov

Survey of Pathogens in Juvenile Salmon Oncorhynchus Spp. Migrating through Pacific Northwest Estuaries

Although the adverse impact of pathogens on salmon populations in the Pacific Northwest is often discussed and recognized, little is currently known regarding the incidence and corresponding significance of delayed disease-induced mortalities. In the study reported herein, we surveyed the presence and prevalence of selected micro- and macroparasites in out-migrant juvenile coho salmon Oncorhynchus kisutch and Chinook salmon O. tshawytscha from 12 coastal estuaries in the Pacific Northwest over a 6-year period (1996-2001). The major finding of this study was the widespread occurrence of pathogens in wild salmon from Pacific Northwest estuaries. The six most prevalent pathogens infecting both juvenile Chinook and coho salmon were Renibacterium salmoninarum, Nanophyetus salmincola, an erythrocytic cytoplasmic virus (erythrocytic inclusion body syndrome or erythrocytic necrosis virus), and three gram-negative bacteria (Listonella anguillarum, Yersinia ruckeri, and Aeromonas salmonicida). The most prevalent pathogen in both Chinook and coho salmon was N. salmincola, followed by the pathogens R. salmoninarum and the erythrocytic cytoplasmic virus. Statistically significant differences in the prevalence of R.. salmoninarum and N. salmincola were observed between Chinook and coho salmon. Based on the prevalence of pathogens observed in this study, disease appears to be a potentially significant factor governing the population numbers of salmon in the Pacific Northwest. Development of a detailed understanding of the principal components influencing the ecology of infectious disease will aid in the development of management and control strategies to mitigate disease in and hence further the recovery of salmon stocks listed under the Endangered Species Act

What Every Business Student Needs to Know About Information Systems

Whether Information Systems should or should not be part of the core business school curriculum is a recurring discussion in many universities. In this article, a task force of 40 prominent information systems scholars address the issue. They conclude that information systems is absolutely an essential body of knowledge for business school students to acquire as well as a key element of the business school\u27s long-run strategic positioning within the university. Originally prepared in response to draft accreditation guidelines prepared by AACSB International, the article includes a compilation of the concepts that the authors believe to be the core information systems knowledge that all business school students should be familiar with

Hydrogen Sulfide and Neurogenic Inflammation in Polymicrobial Sepsis: Involvement of Substance P and ERK-NF-κB Signaling

Hydrogen sulfide (H2S) has been shown to induce transient receptor potential vanilloid 1 (TRPV1)-mediated neurogenic inflammation in polymicrobial sepsis. However, endogenous neural factors that modulate this event and the molecular mechanism by which this occurs remain unclear. Therefore, this study tested the hypothesis that whether substance P (SP) is one important neural element that implicates in H2S-induced neurogenic inflammation in sepsis in a TRPV1-dependent manner, and if so, whether H2S regulates this response through activation of the extracellular signal-regulated kinase-nuclear factor-κB (ERK-NF-κB) pathway. Male Swiss mice were subjected to cecal ligation and puncture (CLP)-induced sepsis and treated with TRPV1 antagonist capsazepine 30 minutes before CLP. DL-propargylglycine (PAG), an inhibitor of H2S formation, was administrated 1 hour before or 1 hour after sepsis, whereas sodium hydrosulfide (NaHS), an H2S donor, was given at the same time as CLP. Capsazepine significantly attenuated H2S-induced SP production, inflammatory cytokines, chemokines, and adhesion molecules levels, and protected against lung and liver dysfunction in sepsis. In the absence of H2S, capsazepine caused no significant changes to the PAG-mediated attenuation of lung and plasma SP levels, sepsis-associated systemic inflammatory response and multiple organ dysfunction. In addition, capsazepine greatly inhibited phosphorylation of ERK1/2 and inhibitory κBα, concurrent with suppression of NF-κB activation even in the presence of NaHS. Furthermore, capsazepine had no effect on PAG-mediated abrogation of these levels in sepsis. Taken together, the present findings show that H2S regulates TRPV1-mediated neurogenic inflammation in polymicrobial sepsis through enhancement of SP production and activation of the ERK-NF-κB pathway