A map of DNA methylation changes in human organs could help researchers to discover more targets for anti-ageing therapies.
Category: genetics
Memory consolidation requires reactivation of only three neurons during sleep, research reveals
Researchers at Tsukuba University in Japan report that memories acquired while awake are stored in a more permanent form (called memory consolidation) during the REM stage of sleep, and that this process requires the reactivation of only a few specialized neurons involved in memory formation. They found that three of these neurons are crucial for memory consolidation during REM sleep.
The researchers focused on adult-born neurons (ABNs) in the hippocampal region of the temporal lobe, which are rare neurons known to be essential for maintaining proper memory function as the loss of these cells is observed in Alzheimer’s disease. However, it has remained unclear why the loss of this small neuronal population has such devastating effects on memory.
In the Nature Communications study, specially genetically modified mice, in which the activity of ABNs could be monitored, were exposed to a fear experience, and the researchers examined if the activities of these ABNs during initial memory formation were reproduced during REM sleep, when dreaming is believed to occur.
New AI model predicts which genetic mutations truly drive disease
Scientists at Mount Sinai have created an artificial intelligence system that can predict how likely rare genetic mutations are to actually cause disease. By combining machine learning with millions of electronic health records and routine lab tests like cholesterol or kidney function, the system produces “ML penetrance” scores that place genetic risk on a spectrum rather than a simple yes/no. Some variants once thought dangerous showed little real-world impact, while others previously labeled uncertain revealed strong disease links.
Less is more: Gene loss drives adaptive evolution of a pandemic bacterium
A study published in Nature Ecology & Evolution reveals a surprising evolutionary insight: sometimes, losing genes rather than gaining them can help bacterial pathogens survive and thrive.
The study was conducted by a group of scientists and coordinated by Jaime Martínez Urtaza, from the Department of Genetics and Microbiology of the Universitat Autònoma de Barcelona (UAB); Yang Chao and Falush Daniel, from the Shanghai Institute of Immunity and Infection, Chinese Academy of Science; and Wang Hui, from the Shanghai Jiao Tong University.
When we think of evolution, we often imagine organisms changing or gaining new genes to adapt, such as growing wings, developing resistance, or evolving new behaviors. Across the tree of life, both spontaneous mutations and gene acquisition are classic tools of adaptation. However, in this study, researchers went down a lesser known and scarcely explored evolutionary path, the one of gene loss.
AI and lab tests to predict genetic disease risk
When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance.
The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the journal Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk.
Traditional genetic studies often rely on a simple yes/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don’t fit neatly into binary categories. The researchers trained AI models to quantify disease on a spectrum, offering more nuanced insight into how disease risk plays out in real life.
Using more than 1 million electronic health records, the researchers built AI models for 10 common diseases. They then applied these models to people known to have rare genetic variants, generating a score between 0 and 1 that reflects the likelihood of developing the disease.
A higher score, closer to 1, suggests a variant may be more likely to contribute to disease, while a lower score indicates minimal or no risk. The team calculated “ML penetrance” scores for more than 1,600 genetic variants.
Some of the results were surprising, say the investigators. Variants previously labeled as “uncertain” showed clear disease signals, while others thought to cause disease had little effect in real-world data.
Direct plasma membrane-to-ER lipid transfer outpaces vesicular trafficking, study reveals
Max Planck Institute of Molecular Cell Biology and Genetics led a study showing that directional, non-vesicular lipid transport drives fast, species-selective lipid sorting, outpacing slower, less specific vesicular trafficking, and yielding a quantitative map of retrograde lipid transport in cells.
Thousands of lipid species occupy distinct organelle membranes, with task differences that determine cellular function. Gaps in live-cell imaging capabilities have limited clarity on how individual lipids move between organelles to maintain those tasks.
Biosynthesis of lipids begins in the endoplasmic reticulum (ER), followed by distribution toward the plasma membrane and subsequent recycling back into the ER or catabolism in lysosomes, peroxisomes, and mitochondria.
CRISPR’s efficiency triples in lab tests with DNA-wrapped nanoparticles
With the power to rewrite the genetic code underlying countless diseases, CRISPR holds immense promise to revolutionize medicine. But until scientists can deliver its gene-editing machinery safely and efficiently into relevant cells and tissues, that promise will remain out of reach.
Now, Northwestern University chemists have unveiled a new type of nanostructure that dramatically improves CRISPR delivery and potentially extends its scope of utility.
Called lipid nanoparticle spherical nucleic acids (LNP-SNAs), these tiny structures carry the full set of CRISPR editing tools—Cas9 enzymes, guide RNA and a DNA repair template—wrapped in a dense, protective shell of DNA. Not only does this DNA coating shield its cargo, but it also dictates which organs and tissues the LNP-SNAs travel to and makes it easier for them to enter cells.
Capturing language change through the genes
Throughout human history, there have been many instances where two populations came into contact—especially in the past few thousand years because of large-scale migrations as a consequence of conquests, colonialization, and, more recently, globalization. During these encounters, not only did populations exchange genetic material, but also cultural elements.
When populations interact, they may borrow technologies, beliefs, practices, and also, crucially, aspects of language. With this, sounds, words or grammatical patterns can be exchanged from one language to the other. For example, English borrowed “sausage” from French after the Norman conquests, while French later borrowed “sandwich” from English.
However, studying these linguistic exchanges can be challenging due to the limited historical records of human contacts, especially on a global scale. As a result, our understanding of how languages evolved over time through such interactions remains incomplete.
AI tool targets RNA structures to unravel secrets of the dark genome
We mapped the human genome decades ago, but most of it is still a black box. Now, UNSW scientists have developed a tool to peer inside and what they find could reshape how we think about disease.
Your genome is the genetic map of you, and we understand almost none of it.
Our handle on the bits of the genome that tell the body how to do things (“make eyes blue,” “build heart tissue,” “give this person sickle cell anemia”) is OK, but there are vast areas of the genome that don’t appear to do anything.