Genetic Switch Starts Aging Clock
Aging could happen at once, rather than over time -- and it may be possible to block the signal to begin the aging process.
We often think of aging as a slow decline of our bodies and internal organs. But a new study indicates that aging could happen much more quickly, perhaps the result of a switch being suddenly turned off that protects our cells from environmental stresses. Even stranger is that this switch turns off exactly at the time of peak sexual maturity.
The experiments were done in a worm species called C. elegans that is often used as a model for humans. And the research may have implications for understanding human diseases, according to Richard Morimoto, professor of biology at Northwestern University and an author on the paper published today in the journal Molecular Cell. Johnathan Labbadia, a postdoctoral fellow in Morimoto's lab, is the first author of the paper.
"The surprise is that aging is not just a continuum," Morimoto said, "but is very much represented by a precise genetic switch that is thrown at an exact moment. You could imagine all the subsequent events that are a continuum are the consequence of the lack of stress resilience. Muscles don't move as well and the nervous system doesn't respond."
Morimoto said knowing more about how the quality control system works in cells could help researchers one day figure out how to provide humans with a better cellular quality of life, and therefore delay degenerative diseases related to aging, such as neurodegenerative diseases.
Morimoto and Labbadia found the genetic switch occurs between two major tissues that determine the future of the species: the germline and the soma (the body tissues of the animal, such as muscle cells and neurons).
Once the germline has completed its job and produced eggs and sperm -- necessary for the next generation of animals -- it sends a signal to cell tissues to turn off protective mechanisms, starting the decline of the adult animal.
"What we discovered is exactly at this moment of reproductive maturity, all of these essential cell responses decline simultaneously," he said.
When the researchers blocked the signal between the germline to the other cells, the worm became bigger and stronger.
"We ended up creating a superworm that had been protected against all kinds of environmental insults that would kill the animal," Morimoto added.
One expert said the paper provides new insights into aging of cells.
"The study by Dr. Morimoto's lab in this paper provides a potential molecular mechanism linking the reproduction and aging," said Jose Velazquez, director of the division of aging biology at the National Institute on Aging in an e-mail to Discovery News. "The paper describes events happening in early adulthood in the worm. It seems to suggest that there is an important switch in cell stress response pathways at the time of sexual maturity. It's very interesting."
Morimoto said the next step is to look at human skin cells and see if he can find a similar cellular aging switch.
It's easy to mistake this photo for some kind of surreal landscape painting, but this image in fact shows off the imagination of Google's advanced image detection software. Similar to an artist with a blank canvas, Google's software constructed this image out of nothing, or essentially nothing, anyway. This photo began as random noise before software engineers coaxed this pattern out of their machines. How is it possible for software to demonstrate what appears to be an artistic sensibility? It all begins with what is basically an artificial brain.
Artificial neural networks are systems consisting of between 10 and 30 stacked layers of synthetic neurons. In order to train the network, "each image is fed into the input layer, which then talks to the next layer, until eventually the 'output' layer is reached,"
the engineers wrote in a blog post detailing their findings
. The layers work together to identify an image. The first layer detects the most basic information, such as the outline of the image. The next layers hone in on details about the shapes. The final output layer provides the "answer," or identification of the subject of an image. Shown is Google's image software before and after processing an image of two ibis grazing to detect their outlines.
Searching for shapes in clouds isn't just a human pastime anymore. Google engineers trained the software to identify patterns by feeding millions of images to the artificial neural network. Give the software constraints, and it will scout out patterns to recognize objects even in photos where the search targets are not present. In this photo, for example, Google's software, like a daydreamer staring at the clouds, finds all kinds of different animals in the sky. This pattern emerged because the neural network was trained primarily on images of animals.
How the machine is trained will determine its bias in terms of recognizing certain objects within an otherwise unfamiliar image. In this photo, a horizon becomes a pagoda; a tree is morphed into building; and a leaf is identified as a bird after image processing. The objects may have similar outlines to their counterparts, but all of the entries in the "before" images aren't a part of the software's image vocabulary, so the system improvises.
When the software acknowledges an object, it modifies a photo to exaggerate the presence of that known pattern. Even if the software is able to correctly recognize the animals it has been trained to spot, image detection may be a little overzealous in identifying familiar shapes, particularly after the engineers send the photo back, telling the software to find more of the same, and thereby creating a feedback loop. In this photo of a knight, the software appears to recognize the horse, but also renders the faces of other animals on the knight's helmet, globe and saddle, among other places.
Taken a step further, using the same image over several cycles in which the output is fed through over and over again, the artificial neural network will restructure an image into the shapes and patterns it has been trained to recognize. Again borrowing from an image library heavy on animals, this landscape scene is transformed into a psychedelic dream scene where clouds are apparently made of dogs.
At its most extreme, the neural network can transform an image that started as random noise into a recognizable but still somewhat abstract kaleidoscopic expression of objects with which the software is most familiar. Here, the software has detected a seemingly limitless number of arches in what was a random collection of pixels with no coherence whatsoever.
This landscape was created with a series of buildings. Google is developing this technology in order to boost its image recognition software. Future photo services might recognize an object, a location or a face in a photo. The engineers also suggest that the software could one day be a tool for artists that unlocks a new form of creative expression and may even shed light on the creative process more broadly.