By Jason Alvarez (UCSF News)

A team of scientists at UC San Francisco and the National Institutes of Health have achieved another CRISPR first, one which may fundamentally alter the way scientists study brain diseases.

In a paper published Aug. 15 in the journal Neuron, the researchers describe a technique that uses a special version of CRISPR developed at UCSF to systematically alter the activity of genes in human neurons generated from stem cells, the first successful merger of stem cell-derived cell types and CRISPR screening technologies.

Though mutations and other genetic variants are known to be associated with an increased risk for many neurological diseases, technological bottlenecks have thwarted the efforts of scientists working to understand exactly how these genes cause disease. 

“Prior to this study, there were significant limitations that... Read more ...

Self-Propagating Amyloid and Tau Prions found in Post-Mortem Brain Samples, With Highest Levels in Patients Who Died Young

By Nicholas Weiler (UCSF News)

Two proteins central to the pathology of Alzheimer’s disease act as prions — misshapen proteins that spread through tissue like an infection by forcing normal proteins to adopt the same misfolded shape — according to new UC San Francisco research.

Using novel laboratory tests, the researchers were able to detect and measure specific, self-propagating prion forms of the proteins amyloid beta (Aß) and tau in postmortem brain tissue of 75 Alzheimer’s patients. In a striking finding, higher levels of these prions in human brain samples were strongly associated with early-onset forms of the disease and younger age at death.

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Martin Kampmann, PhD
Michael Keiser, PhD

Yesterday, the Chan Zuckerberg Initiative (CZI) launched the CZI Neurodegeneration Challenge Network. This new network brings together scientists from diverse research fields to understand the underlying causes of neurodegenerative diseases. Following an international open call for applications, two IND faculty, Martin Kampmann and Michael Keiser, were among the 17 recipients of a Ben Barres Early Career Acceleration Award, which provides $2.5 million in research funding for each recipient.... Read more ...

In their Technical Comment on “Predicting reaction performance in C–N cross-coupling using machine learning (Reports, 13 April 2018),” Kangway Chuang, Postdoctoral Fellow in the Keiser Lab, and Mike Keiser, Assistant Professor In-Residence, report that Ahneman et al.'s “experimental design [was] insufficient to distinguish models trained on chemical features from those trained solely on random-valued features in retrospective and prospective test scenarios, thus failing classical controls in machine learning.”

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