Lifeboat Foundation

Researchers at the Texas A&M College of Veterinary Medicine and Biomedical Sciences (VMBS) are helping uncover new information about the Y chromosome in horses, which will help owners identify optimal lineages for breeding and help conservationists preserve breed diversity.

“Because of its complex structure, the Y chromosome is much harder to sequence, making our knowledge of it far from complete,” said Dr. Gus Cothran, a professor emeritus in the VMBS’ Department of Veterinary Integrative Biosciences (VIBS). “In fact, scientists used to believe that the Y chromosome lacked genetic variety, which we believed meant that it didn’t contribute much to species diversity.”

However, Cothran’s new research collaboration, led by the University of Veterinary Medicine Vienna, has uncovered that the Y chromosome does have meaningful variation and is important for species diversity.

We might like to think of ourselves as autonomous entities but, in reality, we’re more like walking ecosystems, teeming with bacteria, viruses, and other microbes. It turns out that differences in these microbes might be as crucial to evolution and natural variation as genetic mutations are.

This novel perspective was discussed in a recent publication by Seth Bordenstein, director of Penn State’s One Health Microbiome Center, who is a professor of biology and entomology and holds the Dorothy Foehr Huck and J. Lloyd Huck Endowed Chair in Microbiome Sciences.

He, along with 21 colleagues from around the globe, collectively known as the Holobiont Biology Network, propose that understanding the relationships between microbes and their hosts will lead to a more profound understanding of biological variation.

Summary: Scientists have reprogrammed mouse cells into pluripotent stem cells using a gene from choanoflagellates, single-celled organisms related to animals. This breakthrough demonstrates that key genes driving stem cell formation existed in unicellular ancestors nearly a billion years ago.

The resulting stem cells were used to create a chimeric mouse, showcasing how ancient genetic tools can integrate with modern mammalian biology. This discovery redefines the evolutionary origins of stem cells and may inform regenerative medicine advancements.

Can weight loss leave a lasting imprint on our fat cells?

Losing weight is often touted as a cornerstone of better health, particularly for people dealing with obesity and its associated health risks.

Anyone who has ever tried to get rid of a few extra kilos knows the frustration: the weight drops initially, only to be back within a matter of weeks—the yo-yo effect has struck. Researchers at ETH Zurich have now been able to show that this is all down to epigenetics.

Continue reading “Fat cells have epigenetics-based memory: Researchers discover mechanism behind weight loss yo-yo effect” »

Called Evo, the AI was inspired by the large language models, or LLMs, underlying popular chatbots such as OpenAI’s ChatGPT and Anthropic’s Claude. These models have taken the world by storm for their prowess at generating human-like responses. From simple tasks, such as defining an obtuse word, to summarizing scientific papers or spewing verses fit for a rap battle, LLMs have entered our everyday lives.

If LLMs can master written languages—could they do the same for the language of life?

This month, a team from Stanford University and the Arc Institute put the theory to the test. Rather than training Evo on content scraped from the internet, they trained the AI on nearly three million genomes—amounting to billions of lines of genetic code—from various microbes and bacteria-infecting viruses.

Researchers at the National Institutes of Health (NIH) and their collaborators have discovered a new way in which RAS genes, which are commonly mutated in cancer, may drive tumor growth beyond their well-known role in signaling at the cell surface.

Mutant RAS, they found, helps to kick off a series of events involving the transport of specific nuclear proteins that lead to uncontrolled tumor growth, according to a study published November 11, 2024, in Nature Cancer.

RAS genes are the second most frequently mutated genes in cancer, and mutant RAS proteins are key drivers of some of the deadliest cancers, including nearly all pancreatic cancers, half of colorectal cancers, and one-third of lung cancers.

Microorganisms—bacteria, viruses and other tiny life forms—may drive biological variation in visible life as much, if not more, than genetic mutations, creating new lineages and even new species of animals and plants, according to Seth Bordenstein, director of Penn State’s One Health Microbiome Center, professor of biology and entomology, and the Dorothy Foehr Huck and J. Lloyd Huck Endowed Chair in Microbiome Sciences.

Bordenstein and 21 other scientists from around the world published a paper in Science, summarizing research that they said drives a deeper understanding of biological variation by uniting life’s seen and unseen realms.

Continue reading “Q&A: Holobiont biology, a new concept for exploring how microbiome shapes evolution of visible life” »

Evo is a large language model that is not trained on words but on the genomes of millions of microbes. It can accurately predict the effects of mutations.

To determine the type and severity of a cancer, pathologists typically analyze thin slices of a tumor biopsy under a microscope. But to figure out what genomic changes are driving the tumor’s growth—information that can guide how it is treated—scientists must perform genetic sequencing of the RNA isolated from the tumor, a process that can take weeks and costs thousands of dollars.

Now, Stanford Medicine researchers have developed an artificial intelligence-powered computational program that can predict the activity of thousands of genes within tumor cells based only on standard microscopy images of the biopsy.

The tool, described online in Nature Communications Nov. 14, was created using data from more than 7,000 diverse tumor samples. The team showed that it could use routinely collected biopsy images to predict genetic variations in breast cancers and to predict patient outcomes.