Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model.

Lipids are ubiquitous metabolites with diverse functions; abnormalities in lipid metabolism appear to be related to complications from multiple diseases, including type 2 diabetes. Through technological advances, the entire lipidome has been characterized and researchers now need computational approaches to better understand lipid network perturbations in different diseases. Using a mouse model of type 2 diabetes with microvascular complications, we examined lipid levels in plasma and in renal, neural, and retinal tissues to identify shared and distinct lipid abnormalities.

Transcriptional networks of murine diabetic peripheral neuropathy and nephropathy: common and distinct gene expression patterns.

Aims/hypothesis: Diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN) are two common microvascular complications of type 1 and type 2 diabetes mellitus that are associated with a high degree of morbidity. In this study, using a variety of systems biology approaches, our aim was to identify common and distinct mechanisms underlying the pathogenesis of these two complications.

Methods: Our previously published transcriptomic datasets of peripheral nerve and kidney tissue, derived from murine models of type 1 diabetes (streptozotocin-injected mice) and type 2 diabetes (BKS-db/db mice) and their respective controls, were collected and processed using a unified analysis pipeline so that comparisons could be made.

A graph algebra for scalable visual analytics.

Visual analytics (VA), which combines analytical techniques with advanced visualization features, is fast becoming a standard tool for extracting information from graph data. Researchers have developed many tools for this purpose, suggesting a need for formal methods to guide these tools' creation. Increased data demands on computing requires redesigning VA tools to consider performance and reliability in the context of analysis of exascale datasets.

THINK Back: KNowledge-based Interpretation of High Throughput data.

Results of high throughput experiments can be challenging to interpret. Current approaches have relied on bulk processing the set of expression levels, in conjunction with easily obtained external evidence, such as co-occurrence. While such techniques can be used to reason probabilistically, they are not designed to shed light on what any individual gene, or a network of genes acting together, may be doing.

The identification of gene expression profiles associated with progression of human diabetic neuropathy.

Diabetic neuropathy is a common complication of diabetes. While multiple pathways are implicated in the pathophysiology of diabetic neuropathy, there are no specific treatments and no means to predict diabetic neuropathy onset or progression. Here, we identify gene expression signatures related to diabetic neuropathy and develop computational classification models of diabetic neuropathy progression.

Simplifying access to a Clinical Data Repository using schema summarization.

The University of Michigan Clinical Data Repository (CDR) integrates over 25 data sources, and as a result has a schema that is too complex to be directly queried by clinical researchers. Schema summarization uses abstract elements and links to summarize a complex schema and allows users with limited knowledge of the underlying database structure to effectively issue queries to the CDR for clinical and translational research.