The Chan Zuckerberg Initiative’s Collaborative Pairs Pilot Project Award is part of its Neurodegeneration Challenge Network
More than 30 inherited disorders are caused by the unstable expansion of repetitive DNA sequences, including Huntington’s disease, ALS, Fragile X syndrome, and Friedreich’s ataxia. Jennifer E. Phillips-Cremins, associate professor in Penn Engineering’s Department of Bioengineering and in the Perelman School of Medicine’s Department of Genetics, has shown another link between these disorders: the location of these expanding genes relative to the complicated folding patterns the genome exhibits to fit inside the nucleus of a cell.
Now, Phillips-Cremins is among 60 researchers taking part in a $4.5 Million Chan Zuckerberg Initiative project that aims to apply novel, interdisciplinary approaches toward investigating neurodegenerative disorders. The CZI Collaborative Pairs Pilot Project will fund 30 teams that combine clinical and basic science expertise and have at least one early- or mid-career researcher.
Imagine if a computer could learn from molecules found in nature and use an algorithm to generate new ones. Then imagine those molecules could get printed and tested in a lab against some of the nastiest, most dangerous bacteria out there — bacteria quickly becoming resistant to our current antibiotic options.
Or consider a bandage that can sense an infection with fewer than 100 bacterial cells present in an open wound. What if that bandage could then send a signal to your phone letting you know an infection had started and asking you to press a button to trigger the release of the treatment therapy it contained?
These ideas aren’t science fiction. They’re projects happening right now, in various stages, in the lab of Penn synthetic biologist César de la Fuente, who joined the University as a Presidential Professor in May 2019. His ultimate goal is to develop the first computer-made antibiotics. But beyond that, his lab — which includes three postdoctoral fellows, a visiting professor, and a handful of graduate students and undergrads — has many other endeavors that sit squarely at the intersection of computer science and microbiology.
Antibiotic resistance is becoming a dangerous problem, both in the United States and worldwide. According to the Centers for Disease Control and Prevention, each year in the U.S., at least 2.8 million people get infections that antibiotics can’t help, and more than 35,000 die from those infections. Around the world, common ailments like pneumonia and food-borne illness are getting harder to treat.
New antibiotics are needed, and according to de la Fuente, it’s time to look beyond the traditional approach.
“We’ve relied on nature as a source of antibiotics for many, many years. My whole hypothesis is that nature has perhaps run out of inspiration,” says de la Fuente, who has appointments in the Perelman School of Medicine and the School of Engineering and Applied Science. “We haven’t been able to discover any new scaffolds for many years. Can we digitize that information, nature’s chemistry, to be able to create and discover new molecules?”
To do that, his team turned to amino acids, the building blocks of protein molecules. The 20 that occur naturally bond in countless sequences and lengths, then fold to form different proteins. The sequencing possibilities are expansive, more than the number of stars in the universe. “We could never synthesize all of them and just see what happens,” says postdoc Marcelo Melo. “We have to combine the chemical knowledge — decades of chemistry on these tell us how they behave — with the computational side, because a computer can find patterns unlike any human could.”
Using machine learning, the researchers provide the computer with natural molecules that successfully work against bacteria. The computer learns from those examples, then generates new, artificial molecules. “We try this back and forth and hopefully we find patterns, new patterns that we can explore, instead of blindly searching,” Melo says.
The computer can then test each artificial sequence virtually, setting aside the most successful components and tossing the rest, in a form of computational natural selection. Those pieces with the highest potential get used to create new sequences, theoretically producing better and better ones each time.
De la Fuente’s team has seen some promising results already: “A lot of the molecules we’ve synthesized have worked,” he says. “The best ones worked in animal models. They were able to reduce infections in mice — which was pretty cool, given that the computer generated the whole thing.” Still, de la Fuente says the work is years away from producing anything close to a shelf-ready antibiotic.
When the immune system detects a foreign pathogen, a cascade of chemical signals call white blood cells to the scene. Neutrophils are the most common and abundant type of these cells and while they start accumulating at the site of an infection within minutes, they are essentially at the mercy of the circulatory system’s one-way flow of traffic to get them where they need to go.
Now, research from the University of Pennsylvania’s School of Engineering and Applied Science shows how these cells can be coaxed to fight the direction of blood flow, crawling upstream along the walls of veins and arteries.
The in vitro study suggested that this technique could get neutrophils to the sites of infections faster when they are restricted to the direction of blood flow.
Daniel A. Hammer, Alfred G. and Meta A. Ennis Professor in the Department of Bioengineering, and Alexander Buffone, Jr., a research associate in his lab, led the research. Nicholas R. Anderson, a graduate student in the Hammer lab, also contributed to the study.
Tissue gets stiffer when it’s compressed. That property can become even more pronounced with injury or disease, which is why doctors palpate tissue as part of a diagnosis, such as when they check for lumps in a cancer screening. That stiffening response is a long-standing biomedical paradox, however: tissue consists of cells within a complex network of fibers, and common sense dictates that when you push the ends of a string together, it loosens tension, rather than increasing it.
Now, in a study published in Nature, University of Pennsylvania’s School of Engineering and Applied Science researchers have solved this mystery by better understanding the mechanical interplay between that fiber network and the cells it contains.
The researchers found that when tissue is compressed, the cells inside expand laterally, pulling on attached fibers and putting more overall tension on the network. Targeting the proteins that connect cells to the surrounding fiber network might therefore be the optimal way of reducing overall tissue stiffness, a goal in medical treatments for everything from cancer to obesity.
The study was led by Paul Janmey, Professor in the Perelman School of Medicine’s Department of Physiology and in Penn Engineering’s Department of Bioengineering, and Vivek Shenoy, Eduardo D. Glandt President’s Distinguished Professor in Penn Engineering’s Department of Materials Science and Engineering, Mechanical Engineering and Applied Mechanics, and Bioengineering, along with Anne van Oosten and Xingyu Chen, graduate students in Janmey’s and Shenoy’s labs. Van Oosten is now a postdoctoral fellow at Leiden University in The Netherlands.
Shenoy is Director of Penn’s Center for Engineering Mechanobiology, which studies how physical forces influence the behavior of biological systems; Janmey is the co-director of one of the Center’s working groups, organized around the question, “How do cells adapt to and change their mechanical environment?”
Together, they have been interested in solving the paradox surrounding tissue stiffness.