SciBites:  Week of May 15th

SciBites: Week of May 15th

Scientists ID human protein essential for human cytomegalovirus replication

Human cytomegalovirus (HCMV), a highly prevalent herpesvirus, infects over 30 percent of people worldwide. While most remain symptom-free, HCMV can be dangerous or deadly for people with weakened immune systems or for babies infected before birth. Some HCMV treatments exist, but their benefits are limited, and scientists are investigating new ways to treat and prevent infection.

In a new study, scientists demonstrate that a human protein known as valosin-containing protein (VCP) is essential for replication of human cytomegalovirus (HCMV). The findings identify VCP as a potential new treatment target. Given the critical importance of VCP for HCMV replication, the scientists tested the effects of a chemical known to inhibit the activity of VCP. They found that the inhibitor, known as NMS-873, reduced HCMV replication and in infected cells. NMS-873 appeared to be ten times more potent than ganciclovir, the most commonly used antiviral treatment for HCMV.

Further research is needed to determine whether NMS-873 — originally developed as a potential anti-cancer drug — is safe and effective in humans.

New algorithm tracks neurons in bendy brain of freely crawling worm

Wiggly Caenorhabditis elegans can be difficult to study when on the move, especially for scientists trying to track neurons in the moving brain. This is because when the worm crawls, its brain bends as it moves. To tackle this problem, scientists at Princeton University have developed a new algorithm to track neurons in the brain of C. elegans while it crawls. The algorithm, presented in a new paper, could save hundreds of hours of manual labor in studies of animal behavior.

The new approach, dubbed Neuron Registration Vector Encoding, draws on computer vision and machine learning techniques. It uses 3-D fluorescent recordings of the C. elegans brain to assign a unique identity to each neuron it can detect. Based in part on the relative locations of the neurons, the algorithm keeps track of each neuron over time. Tracking is also enhanced by accounting for ways in which certain worm movements are known to deform the brain.

Image Credit:  Jeffrey P. Nguyen et al.  Figure 3: Automatically tracking neurons in a moving and deforming brain.

Author

Jen is the Editorial Media Manager at PLOS. Before her time at PLOS, she's worked in broadcast news, radio and online media.

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