WashU Medicine researchers designed a cellular immunotherapy that turns astrocytes (green), a type of cell in the brain, into super cleaners that sweep away Alzheimer’s-related proteins. With this new feature, the cells successfully reduced the amount of harmful amyloid beta plaques (blue) in the brains of mice.

According to a new genomic study of Australia’s koala populations, rapid demographic rebound may be able to restore once-lost genetic variation and drive recombination in ways that re-establish long-term evolutionary potential in previously bottlenecked populations. Population bottlenecks can lead to evolutionary dead ends by eroding genetic diversity and intensifying inbreeding. Over time, these genetic repercussions can reduce fertility, survival, and resilience to environmental change, driving a so-called “extinction vortex” in which dwindling numbers and declining genetic health greatly amplify extinction risk. However, this genetic decline is not always irreversible. When populations rebound rapidly, their growth can sometimes restore genetic diversity. Theory suggests that ever-growing numbers – even in small founding groups – can promote genetic reshuffling and the introduction of new mutations, counteracting the loss of variation and easing the harmful effects of inbreeding. Thus, in theory, rapid demographic expansion may be a key mechanism for buffering against the genetic pitfalls typically associated with severe population declines. Here, Collin Ahrens and colleagues leveraged the precipitous decline – and the severe genetic bottleneck that resulted – and rapid rebound of koalas as a natural experiment to test whether population recovery was able to facilitate genetic recovery. Ahrens et al. used whole-genome data from 418 koalas drawn from 27 populations across Australia and found that, although koalas endured severe population decline and genetic bottleneck that left them with low genetic diversity, populations now show signs of genetic recovery. The findings suggest that this rebound may be driven in part by recombination, which shuffles existing genetic material into new combinations, helping to restore functional diversity as populations expand. Overall, the findings indicate that recovery of bottlenecked populations can occasionally occur through rapid population growth, which has important implications for conservation management strategies.

Genetically altered astrocytes that express chimeric antigen receptors offer a promising immunotherapy system capable of clearing accumulations of amyloid-β in the brains of mice – a hallmark pathological feature of Alzheimer’s disease, according to a new study. Alzheimer’s disease (AD), the leading cause of dementia in aging populations worldwide, is marked by a distinctive pathological cascade: amyloid-β (Aβ) plaques accumulate in the brain, triggering harmful changes in tau proteins, which may ultimately drive widespread neurodegeneration. Although the amyloid cascade hypothesis remains debated, recently approved anti-Aβ antibody therapies have shown moderate success in slowing AD progression. However, these treatments require large doses and are associated with potentially fatal side effects. To address these shortcomings, researchers are exploring the use of engineered chimeric antigen receptors (CARs) that could program brain cells to recognize and clear Aβ with far more efficiency and precision. Although CAR immunotherapies have been transformative in cancer treatment and shown promise in early in vitro laboratory studies, significant hurdles remain towards their use in treating AD, including safe delivery of CAR cells to the brain. As a result, their effectiveness in mitigating AD in vivo has yet to be demonstrated. Here, Yun Chen and colleagues explored whether engineered CAR-expressing astrocytes – CAR-As – could be leveraged to overcome these challenges. According to Chen et al., these modified astrocytes enhanced Aβ clearance in laboratory experiments and, when delivered noninvasively into the brains of mice, substantially reduced Aβ buildup in living animals. Moreover, the authors found that a single early treatment in a mouse model of AD prevented the development of Aβ pathology. The findings establish a proof-of-concept that CAR-A designs may offer a powerful and potentially durable strategy for clearing Aβ accumulation and mitigating disease progression in AD. “The study of Chen et al. helps establish a foundation for increasingly innovative CAR strategies in AD,” write Jake Boles and David Gate in a related Perspective. “As CAR technologies mature and the ability to selectively neutralize toxic proteins improves, these approaches hold substantial promise for AD and other neurodegenerative disorders.”

The Buck Institute for Research on Aging today announced the launch of Healthspan Horizons, a new initiative designed to address one of the most urgent challenges in modern medicine: how to measure, understand, and extend healthspan—the years of life spent in good health.

A recent research study found that a combination of the GLP-1 receptor agonist semaglutide and bimagrumab, an antibody that blocks activin signaling pathways, results in greater weight loss while also preserving lean mass, such as skeletal muscle and connective tissue. In the paper “Bimagrumab and semaglutide alone or in combination for the treatment of obesity: a phase 2 randomized clinical trial,” published Monday in the journal Nature Medicine, Dr. Steven Heymsfield of Pennington Biomedical Research Center describes the results of the BELIEVE study, which showed that the therapy combination addressed lean mass loss associated with GLP-1-based therapies.  

Researchers at the University of Ottawa Heart Institute, the University of Ottawa and McGill University have launched ARCHIMEDES (Advanced Research Collaboration for Health Integration, Medical Exploration, and Data Synthesis), a national platform designed to give Canadian health researchers secure access to diverse health data, enable responsible data sharing across institutions and health data types, and support the development of advanced analyses, including AI algorithms.