Jennifer Phillips-Cremins Wins CZI Grant to Study 3D Genome’s Role in Neurodegenerative Disease

The Chan Zuckerberg Initiative’s Collaborative Pairs Pilot Project Award is part of its Neurodegeneration Challenge Network

Jennifer Phillips-Cremins, Ph.D.

Read the full story on the Penn Engineering blog.

Computer-generated Antibiotics, Biosensor Band-Aids, and the Quest to Beat Antibiotic Resistance

By Michele W. Berger

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 synthetic biologist , 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.

Computer-generated antibiotics

Antibiotic resistance is becoming a dangerous problem, both in the United States and worldwide. According to the , 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.

De la Fuente poses near Penn’s “Biopond”
De la Fuente earned his bachelor’s degree in biotechnology, then a doctorate in microbiology and immunology and a postdoc in synthetic biology and computational biology. Combining these fields led him to the innovative work his lab does today.

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 and the . “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.

Continue reading on .

Penn Engineers Coax White Blood Cells to Crawl Upstream, Enabling Faster Route to Infections

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.

Alexander Buffone and Daniel Hammer

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.

They published their findings in Biophysical Journal.

Read the full post on the Penn Engineering Blog.

Penn Engineers Solve the Paradox of Why Tissue Gets Stiffer When Compressed

The researchers’ experiments involved making synthetic tissues with artificial “cells.” The fibrin network that surrounds these beads pull on them when compressed; by changing the number of beads in their experimental tissues, the researchers could suss out how cell-fiber interplay contributes to the tissue’s overall properties.

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.

Headshots of Paul Janmey and Vivek Shenoy

Paul Janmey and Vivek Shenoy

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.

Read the full story on the Penn Engineering Medium Blog.