New research has uncovered the spatial organization of genetic material in the nucleus of an embryo within hours of fertilization.
Category: genetics – Page 9
The Rise of Cyborgs: Merging Man with Machine | Terrifying Future of Human Augmentation
Posted in augmented reality, bioengineering, biotech/medical, cyborgs, education, ethics, finance, genetics, governance, life extension, media & arts, nanotechnology, neuroscience, robotics/AI, transhumanism, virtual reality, wearables | Leave a Comment on The Rise of Cyborgs: Merging Man with Machine | Terrifying Future of Human Augmentation
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.
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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).
An optogenetic method for controlling the movement of starfish oocytes has been developed, which could have applications for drug delivery.
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.
A new comparison and analysis of the genomes of species in the genus Malus, which includes the domesticated apple and its wild relatives, revealed the evolutionary relationships among the species and how their genomes have evolved over the past nearly 60 million years.
The research team identified structural variations among the genomes and developed methods for identifying genes associated with desirable traits, like tastiness and resistance to disease and cold, that could help guide future apple breeding programs.
A paper describing the research, conducted by an international team that includes Penn State biologists, was published in the journal Nature Genetics.
A research team has unveiled a crucial mechanism that helps regulate DNA damage repair, with important implications for improving cancer treatment outcomes.
The result was published in Cell Death & Differentiation. The team was led by Professor Zhao Guoping at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences.
The efficacy of radiotherapy is largely limited by the DNA damage repair capacity of tumor cells. When ionizing radiation induces DNA double-strand breaks—the primary lethal damage—tumor cells often exhibit abnormal overexpression of DNA repair proteins, establishing a robust damage response system that drives clinical radioresistance. To address this challenge, the team deciphered the regulatory network of epigenetic modifications in DNA damage repair.
Will a child who’s evaluated for autism later develop an intellectual disability? Can this be accurately predicted? Early-childhood experts in Quebec say they’ve have come up with a better way to find out.
In a study of 5,633 children drawn from three North American cohorts, clinician-researchers affiliated with Université de Montréal developed a new predictive model that combines a wide range of genetic variants with data on each stage of a young child’s development.
Their goal? To obtain reliable information as early as possible to predict the children’s developmental trajectory and thus offer more proactive support to those who may need it—namely, parents trying to better understand and anticipate their child’s needs.