A multiomic network approach uncovers disease modifying mechanisms of inborn errors of metabolism.

For many inborn errors of metabolism (IEM) the understanding of disease mechanisms remains limited in part explaining their unmet medical needs. We hypothesize that the expressivity of IEM disease phenotypes is affected by the activity of specific modifier pathways, which is controlled by rare and common polygenic variation. To identify these modulating pathways, we used RNA sequencing to generate molecular signatures of IEM in disease relevant tissues.

Validation of the REverSe TRanscrIptase Chain Termination assay for measuring tenofovir diphosphate in dried blood spots from a clinical pharmacokinetic trial.

Background: Tenofovir diphosphate concentration in red blood cells is an objective measure of long-term oral pre-exposure prophylaxis (PrEP) or antiretroviral therapy (ART) adherence. However, current methods for measuring tenofovir diphosphate are equipment and capital intensive, limiting widespread adoption.

Objectives: Low cost, rapid diagnostics for measuring tenofovir diphosphate may drive clinical adoption of routine drug level measurement as a tool for adherence monitoring of tenofovir disoproxil fumarate-based PrEP or ART.

SimMS: a GPU-accelerated cosine similarity implementation for tandem mass spectrometry.

Motivation: Untargeted metabolomics involves a large-scale comparison of the fragmentation pattern of a mass spectrum against a database containing known spectra. Given the number of comparisons involved, this step can be time-consuming.

Results: In this work, we present a GPU-accelerated cosine similarity implementation for Tandem Mass Spectrometry (MS), with an approximately 1000-fold speedup compared to the MatchMS reference implementation, without any loss of accuracy.

Cryo-EM reveals a new allosteric binding site at the M mAChR.

The M muscarinic acetylcholine receptor (M mAChR) represents a promising therapeutic target for neurological disorders. However, the high conservation of its orthosteric binding site has posed significant challenges for drug development. While selective positive allosteric modulators (PAMs) offer a potential solution, a structural understanding of the M mAChR and its allosteric binding sites has remained limited.

High-Dimensional Variable Selection With Competing Events Using Cooperative Penalized Regression.

Variable selection is an important step in the analysis of high-dimensional data, yet there are limited options for survival outcomes in the presence of competing risks. Commonly employed penalized Cox regression considers each event type separately through cause-specific models, neglecting possibly shared information between them. We adapt the feature-weighted elastic net (fwelnet), an elastic net generalization, to survival outcomes and competing risks.