a-web-tool-for-generating-high-quality-machine-readable-biological

A Web Tool for Generating High Quality Machine-readable Biological Pathways

PathWhiz is a web server built to facilitate the creation of colorful, interactive, visually pleasing pathway diagrams that are rich in biological ...

University of Alberta

Cell-free mitochondiral DNA (mtDNA) is an immunogenic molecule associated with many inflammatory conditions. We evaluated the relationship between cell-free mtDNA in cerebrospinal fluid (CSF) and neurocognitive performance and inflammation during HIV infection. In a cross-sectional analysis, we evaluated the association of mtDNA levels with clinical assessments, inflammatory markers, and neurocognitive performance in 28 HIV-infected individuals. In CSF, we measured mtDNA levels by droplet digital PCR, and soluble CD14 and CD163, neurofilament light, and neopterin by ELISA. In blood and CSF, we measured soluble IP-10, MCP-1, TNF-α, and IL-6 by ELISA, and intracellular expression of IL-2, IFN-γ, and TNF-α in CD4(+) and CD8(+) T cells by flow cytometry. We also evaluated the relationship between CSF pleocytosis and mtDNA longitudinally in another set of five individuals participating in an antiretroviral treatment (ART) interruption study. Cell-free CSF mtDNA levels strongly correlated with neurocognitive performance among individuals with neurocognitive impairment (NCI) (r = 0.77, p = 0.001). CSF mtDNA also correlated with levels of IP-10 in CSF (r = 0.70, p = 0.007) and MCP-1 in blood plasma (r = 0.66, p = 0.01) in individuals with NCI. There were no significant associations between inflammatory markers and mtDNA in subjects without NCI, and levels of mtDNA did not differ between subjects with and without NCI. MtDNA levels preceded pleocytosis and HIV RNA following ART interruption. Cell-free mtDNA in CSF was strongly associated with the severity of neurocognitive dysfunction and inflammation only in individuals with NCI. Our findings suggest that within a subset of subjects cell-free CSF mtDNA is associated with inflammation and degree of NCI.

Cell-free mitochondrial DNA in CSF is associated with early viral rebound, inflammation, and severity of neurocognitive deficits in HIV infection.

The application of metabolomics towards cancer research has led to a renewed appreciation of metabolism in cancer development and progression. It has also led to the discovery of metabolite cancer biomarkers and the identification of a number of novel cancer causing metabolites. The rapid growth of metabolomics in cancer research is also leading to challenges. In particular, with so many cancer-associate metabolites being identified, it is often difficult to keep track of which compounds are associated with which cancers. It is also challenging to track down information on the specific pathways that particular metabolites, drugs or drug metabolites may be affecting. Even more frustrating are the difficulties associated with identifying metabolites from NMR or MS spectra. Fortunately, a number of metabolomics databases are emerging that are designed to address these challenges. One such database is the Human Metabolome Database (HMDB). The HMDB is currently the world's largest and most comprehensive, organism-specific metabolomics database. It contains more than 40,000 metabolite entries, thousands of metabolite concentrations, >700 metabolic and disease-associated pathways, as well as information on dozens of cancer biomarkers. This review is intended to provide a brief summary of the HMDB and to offer some guidance on how it can be used in metabolomic studies of cancer.

Cancer Metabolomics and the Human Metabolome Database.

Droplet digital PCR (ddPCR) is a quantitative assay that requires DNA fragmentation to maximize reaction efficiency. Here, we measured the proportion of mitochondrial DNA (mtDNA) carrying the "common deletion," a rare event, to compare quantification sensitivities between alternative DNA fragmentation methods (sonication and QIAshredder spin columns) against enzymatic digestion (traditionally used). QIAshredder showed the highest sensitivity when compared to sonication, followed by digestion. Also, both sonication and QIAshredder fragmentation had shorter processing times than enzymatic digestion; therefore, QIAshredder fragmentation and sonication are alternative DNA processing methods that maximize ddPCR quantification for the detection of rare events.

Random shearing as an alternative to digestion for mitochondrial DNA processing in droplet digital PCR.

YMDB or the Yeast Metabolome Database (http://www.ymdb.ca/) is a comprehensive database containing extensive information on the genome and metabolome of Saccharomyces cerevisiae Initially released in 2012, the YMDB has gone through a significant expansion and a number of improvements over the past 4 years. This manuscript describes the most recent version of YMDB (YMDB 2.0). More specifically, it provides an updated description of the database that was previously described in the 2012 NAR Database Issue and it details many of the additions and improvements made to the YMDB over that time. Some of the most important changes include a 7-fold increase in the number of compounds in the database (from 2007 to 16 042), a 430-fold increase in the number of metabolic and signaling pathway diagrams (from 66 to 28 734), a 16-fold increase in the number of compounds linked to pathways (from 742 to 12 733), a 17-fold increase in the numbers of compounds with nuclear magnetic resonance or MS spectra (from 783 to 13 173) and an increase in both the number of data fields and the number of links to external databases. In addition to these database expansions, a number of improvements to YMDB's web interface and its data visualization tools have been made. These additions and improvements should greatly improve the ease, the speed and the quantity of data that can be extracted, searched or viewed within YMDB. Overall, we believe these improvements should not only improve the understanding of the metabolism of S. cerevisiae, but also allow more in-depth exploration of its extensive metabolic networks, signaling pathways and biochemistry.

Miguel Ramirez-Gaona

Department of Computer Science

University of Alberta

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Miguel Ramirez-Gaona

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Computer Science

University of Alberta

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Department of Computer Science