proteins

Viewing posts tagged proteins

Relish protein level affects secondary traumatic brain injuries

Brain trauma is caused by both primary and secondary injuries. Primary injuries result from the physical damage to the brain, and secondary injuries from the bodies’ responses to those injuries. A recent publication in Genetics by Swanson et al. describes using mass spectrometry to investigate secondary injuries in the Relish (Rel) protein level in fly heads after a primary brain injury. They found changes in Rel levels were necessary for secondary traumatic brain injuries to occur.

Creating efficient and effective peptide fragmentation in tandem MS (MS/MS)

Photoactivation and photodissociation have long proven to be useful tools in tandem mass spectrometry, but implementation often involves cumbersome and potentially dangerous configurations. To remedy this problem, a fiber-optic cable was coupled to an infrared (IR) laser on a mass spectrometer. These advances allow for a more robust, straightforward, and safe instrumentation platform, permitting implementation of AI-ETD and IRMPD on commercial mass spectrometers and broadening the accessibility of these techniques.

This research is described in a recent Analytical Chemistry publication by Trent Peters-Clarke titled Optical Fiber-Enabled Photoactivation of Peptides and Proteins.

https://pubs.acs.org/doi/abs/10.1021/acs.analchem.0c02087

Multi-Omics of COVID-19 Collaboration with Albany Medical College

A collaboration with the lab of Dr. Ariel Jaitovich at Albany Medical College studied a Large-scale Multi-omic Analysis of COVID-19 Severity (in preprint). Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. A web-based tool (covid-omics.app) enables interactive exploration and illustrates its utility through a comparative analysis with published data and a machine learning prediction of COVID-19 severity.