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Category: genetics – Page 168

A new study in Nature hunted down another piece to the aging puzzle. In five species across the evolutionary scale—worms, flies, mice, rats, and humans—the team honed in on a critical molecular process that powers every single cell inside the body and degrades with age.

The process, called transcription, is the first step in turning our genetic material into proteins. Here, DNA letters are reworked into a “messenger” called RNA, which then shuttles the information to other parts of the cell to make proteins.

Scientists have long suspected that transcription may go awry with aging, but the new study offers proof that it doesn’t—with a twist. In all five of the species tested, as the organism grew older the process surprisingly sped up. But like trying to type faster when blindfolded, error rates also shot up.

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Continue reading “Vitamin B6: Did It Increase NAD? (Test Results)” | >

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Continue reading “Serine + Vitamin B6: Did It Reduce Homocysteine? (Test #1)” | >

Human lifespan is intricately connected to the aging process of individual cells, and this means that scientists have spent decades trying to unravel the mysteries of cellular aging and exploring methods to slow down the ticking of the aging clock.

Longevity. Technology: In 2020, a group of researchers from the University of California San Diego identified two distinct mechanisms of cellular aging and genetically manipulated them to extend cell lifespan [1]. Now, their research has progressed to employ synthetic biology and gene circuits to delay the deterioration associated with cellular aging [2]. The team’s innovative approach could revolutionize scientific methods of aging prevention and contribute to reprogramming aging pathways in various human cell types.

Publishing in Science, the researchers describe how cells in yeast, plants, animals and humans all contain gene regulatory circuits responsible for several physiological functions, including aging. These gene circuits, akin to electric circuits controlling household devices, can operate in different ways, and the UC San Diego team discovered that cells don’t necessarily age the same way – it all depends on their genetic material and environment. The researchers found that cells can age either through DNA stability decline or mitochondrial decline.

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I suspected both this and alzheimers are bacterial infections.

A common genus of microbe found in wet, boggy environments could play a key role in the development of Parkinson’s disease, by excreting compounds that trigger proteins inside brain cells to form toxic clumps.

The findings, made by a small team of researchers at the University of Helsinki and the University of Eastern Finland, build on the results of an earlier investigation showing that the severity of the neurodegenerative disorder in volunteers increased with concentrations of Desulfovibrio bacterial strains in their feces.

By now demonstrating a potential path from the presence of the bacteria in genetically edited worms to physical changes in the brain that coincide with Parkinson’s disease, the researchers hope to one day improve early diagnosis of the disease in humans, or even slow its progress.

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Proteins are made from chains of amino acids that fold into three-dimensional shapes, which in turn dictate protein function. Those shapes evolved over billions of years and are varied and complex, but also limited in number. With a better understanding of how existing proteins fold, researchers have begun to design folding patterns not produced in nature.

But a major challenge, says Kim, has been to imagine folds that are both possible and functional. “It’s been very hard to predict which folds will be real and work in a protein structure,” says Kim, who is also a professor in the departments of molecular genetics and computer science at U of T. “By combining biophysics-based representations of protein structure with diffusion methods from the image generation space, we can begin to address this problem.”

The new system, which the researchers call ProteinSGM, draws from a large set of image-like representations of existing proteins that encode their structure accurately. The researchers feed these images into a generative diffusion model, which gradually adds noise until each image becomes all noise. The model tracks how the images become noisier and then runs the process in reverse, learning how to transform random pixels into clear images that correspond to fully novel proteins.

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