A new study found that a gene recently recognized as a biomarker for Alzheimer’s disease is actually a cause of it, due to its previously unknown secondary function. Researchers at the University of California San Diego used artificial intelligence to help both unravel this mystery of Alzheimer’s disease and discover a potential treatment that obstructs the gene’s moonlighting role.
The research team published their results on April 23 in the journal Cell.
About one in nine people aged 65 and older has Alzheimer’s disease, the most common cause of dementia. While some particular genes, when mutated, can lead to Alzheimer’s, that connection only accounts for a small percentage of all Alzheimer’s patients. The vast majority of patients do not have a mutation in a known disease-causing gene; instead, they have “spontaneous” Alzheimer’s, and the causes for that are unclear.
Advances in high-throughput phenotyping (HTP) platforms together with genotyping technologies have revolutionized breeding of varieties with desired traits relying on genomic prediction. Yet, we lack an understanding of the expression of multiple traits at different time points across the entire growth period of the plant.
A research team, including IPK Leibniz Institute and the Max Planck Institute of Molecular Plant Physiology, has developed a computational approach to solve this problem. The results were published in the journal Nature Plants.
The phenome of a plant comprises the entirety of traits expressed at any given time, and is the integrated outcome of the effects of genetic factors, environmental conditions and their complex interactions. Understanding how the crop phenome changes over time can help predict individual traits at specific time points in crop development. However, this problem is challenging not only because of the intricate dependence between individual traits, but also due to differences in how the phenomes of specific genotypes change over the plant life cycle.
A groundbreaking gene therapy has restored sight in four young children born with severe blindness due to a rare genetic deficiency. Scientists at UCL and Moorfields Eye Hospital successfully injected healthy copies of the defective gene into the retina, leading to life-changing improvements. Gr
Human cyborgs are individuals who integrate advanced technology into their bodies, enhancing their physical or cognitive abilities. This fusion of man and machine blurs the line between science fiction and reality, raising questions about the future of humanity, ethics, and the limits of human potential. From bionic limbs to brain-computer interfaces, cyborg technology is rapidly evolving, pushing us closer to a world where humans and machines become one.
Alternative RNA splicing is like a movie editor cutting and rearranging scenes from the same footage to create different versions of a film. By selecting which scenes to keep and which to leave out, the editor can produce a drama, a comedy, or even a thriller—all from the same raw material. Similarly, cells splice RNA in different ways to produce a variety of proteins from a single gene, fine-tuning their function based on need. However, when cancer rewrites the script, this process goes awry, fueling tumor growth and survival.
In a recent study reported in the Feb. 15 issue of Nature Communications, scientists from The Jackson Laboratory (JAX) and UConn Health not only show how cancer hijacks this tightly regulated splicing and rearranging of RNA but also introduce a potential therapeutic strategy that could slow or even shrink aggressive and hard-to-treat tumors. This discovery could transform how we treat aggressive cancers, such as triple-negative breast cancer and certain brain tumors, where current treatment options are limited.
At the heart of this work, led by Olga Anczuków, an associate professor at JAX and co-program leader at the NCI-designated JAX Cancer Center, are tiny genetic elements called poison exons, nature’s own “off switch” for protein production. When these exons are included in an RNA message, they trigger its destruction before a protein can be made—preventing harmful cellular activity. In healthy cells, poison exons regulate the levels of key proteins, keeping the genetic machinery in check. But in cancer, this safety mechanism often fails.
Scientists found a genetic link between autism and DM1, where repeat DNA sequences disrupt brain gene splicing. This sheds light on ASD’s development and opens new paths for targeted treatments. Researchers from The Hospital for Sick Children (SickKids) and the University of Nevada, Las Vegas (UN
Genome editing has advanced at a rapid pace with promising results for treating genetic conditions—but there is always room for improvement. A new paper by investigators from Mass General Brigham showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy.
In their study, the authors developed a machine learning algorithm—known as PAMmla—that can predict the properties of approximately 64 million genome-editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets. The results are published in Nature.
“Our study is a first step in dramatically expanding our repertoire of effective and safe CRISPR-Cas9 enzymes. In our manuscript, we demonstrate the utility of these PAMmla-predicted enzymes to precisely edit disease-causing sequences in primary human cells and in mice,” said corresponding author Ben Kleinstiver, Ph.D., Kayden-Lambert MGH Research Scholar associate investigator at Massachusetts General Hospital (MGH).
Genome editing has advanced at a rapid pace with promising results for treating genetic conditions-but there is always room for improvement. A new paper by investigators from Mass General Brigham published in Nature showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy. In their study, authors developed a machine learning algorithm-known as PAMmla-that can predict the properties of about 64 million genome editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets. Their results are published in Nature.
“Our study is a first step in dramatically expanding our repertoire of effective and safe CRISPR-Cas9 enzymes. In our manuscript we demonstrate the utility of these PAMmla-predicted enzymes to precisely edit disease-causing sequences in primary human cells and in mice,” said corresponding author Ben Kleinstiver, PhD, Kayden-Lambert MGH Research Scholar associate investigator at Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham healthcare system. “Building on these findings, we are excited to have these tools utilized by the community and also apply this framework to other properties and enzymes in the genome editing repertoire.”
CRISPR-Cas9 enzymes can be used to edit genes at locations throughout the genomes, but there are limitations to this technology. Traditional CRISPR-Cas9 enzymes can have off-target effects, cleaving or otherwise modifying DNA at unintended sites in the genome. The newly published study aims to improve this by using machine learning to better predict and tailor enzymes to hit their targets with greater specificity. The approach also offers a scalable solution-other attempts at engineering enzymes have had a lower throughput and typically yielded orders of magnitude fewer enzymes.
