César de la Fuente, Presidential Assistant Professor in Bioengineering, Microbiology, Psychiatry, and Chemical and Biomolecular Engineering, co-led a team of researchers who created autonomous particles covered with patches of protein “motors,” with the goal that these bots can eventually carry livesaving drugs through bodily fluids.
César de la Fuente, Presidential Assistant Professor in Psychiatry, Bioengineering, Microbiology, and in Chemical and Biomolecular Engineering has been honored with a 2022 Young Investigator Award by the Royal Spanish Society of Chemistry (RSEQ) for his pioneering research efforts to combine the power of machines and biology to help prevent, detect, and treat infectious diseases.
The rise of drug-resistant bacteria infections is one of the world’s most severe global health issues, estimated to cause 10 million deaths annually by the year 2050. Some of the most virulent and antibiotic-resistant bacterial pathogens are the leading cause of life-threatening, hospital-acquired infections, particularly dangerous for immunocompromised and critically ill patients. Traditional and continual synthesis of antibiotics will simply not be able to keep up with bacteria evolution.
To avoid the continual process of synthesizing new antibiotics to target bacteria as they evolve, Penn Engineers have looked at a new, natural resource for antibiotic molecules.
A recent study on the search for encrypted peptides with antimicrobial properties in the human proteome has located naturally occurring antibiotics within our own bodies. By using an algorithm to pinpoint specific sequences in our protein code, a team of Penn researchers along with collaborators, led by César de la Fuente, Presidential Assistant Professor in Psychiatry, Bioengineering, Microbiology, and Chemical and Biomolecular Engineering, and Marcelo Torres, a post doc in de la Fuente’s lab, were able to locate novel peptides, or amino acid chains, that when cleaved, indicated their potential to fend off harmful bacteria.
Now, in a new study published in ACS Nano, the team along with Angela Cesaro, the lead author and post doc in de la Fuente’s lab, have identified three distinct antimicrobial peptides derived from a protein in human plasma and demonstrate their abilities in mouse models. Angela Cesaro performed a great part of the activities during her PhD under the supervision of corresponding author, Professor Angela Arciello, from the University of Naples Federico II. The collaborative study also includes Utrecht University in the Netherlands.
“We identified the cardiovascular system as a hot spot for potential antimicrobials using an algorithmic approach,” says de la Fuente. “Then we looked closer at a specific protein in the plasma.”
Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering
Bassett runs the Complex Systems lab which tackles problems at the intersection of science, engineering, and medicine using systems-level approaches, exploring fields such as curiosity, dynamic networks in neuroscience, and psychiatric disease. They are a pioneer in the emerging field of network science which combines mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits.
Jason A. Burdick, Robert D. Bent Professor in Bioengineering
Burdick runs the Polymeric Biomaterials Laboratory which develops polymer networks for fundamental and applied studies with biomedical applications with a specific emphasis on tissue regeneration and drug delivery. The specific targets of his research include: scaffolding for cartilage regeneration, controlling stem cell differentiation through material signals, electrospinning and 3D printing for scaffold fabrication, and injectable hydrogels for therapies after a heart attack.
César de la Fuente, Presidential Assistant Professor in Bioengineering and Chemical & Biomedical Engineering in Penn Engineering and in Microbiology and Psychiatry in the Perelman School of Medicine
De la Fuente runs the Machine Biology Group which combines the power of machines and biology to prevent, detect, and treat infectious diseases. He pioneered the development of the first antibiotic designed by a computer with efficacy in animals, designed algorithms for antibiotic discovery, and invented rapid low-cost diagnostics for COVID-19 and other infections.
Carl H. June, Richard W. Vague Professor in Immunotherapy in the Perelman School of Medicine and member of the Bioengineering Graduate Group
June is the Director for the Center for Cellular Immunotherapies and the Parker Institute for Cancer Therapy and runs the June Lab which develops new forms of T cell based therapies. June’s pioneering research in gene therapy led to the FDA approval for CAR T therapy for treating acute lymphoblastic leukemia (ALL), one of the most common childhood cancers.
Vivek Shenoy, Eduardo D. Glandt President’s Distinguished Professor in Bioengineering, Mechanical Engineering and Applied Mechanics (MEAM), and in Materials Science and Engineering (MSE)
Shenoy runs the Theoretical Mechanobiology and Materials Lab which develops theoretical concepts and numerical principles for understanding engineering and biological systems. His analytical methods and multiscale modeling techniques gain insight into a myriad of problems in materials science and biomechanics.
The highly anticipated annual list identifies researchers who demonstrated significant influence in their chosen field or fields through the publication of multiple highly cited papers during the last decade. Their names are drawn from the publications that rank in the top 1% by citations for field and publication year in the Web of Science™ citation index.
Bassett and Burdick were both on the Highly Cited Researchers list in 2019 and 2020.
The methodology that determines the “who’s who” of influential researchers draws on the data and analysis performed by bibliometric experts and data scientists at the Institute for Scientific Information™ at Clarivate. It also uses the tallies to identify the countries and research institutions where these scientific elite are based.
David Pendlebury, Senior Citation Analyst at the Institute for Scientific Information at Clarivate, said: “In the race for knowledge, it is human capital that is fundamental and this list identifies and celebrates exceptional individual researchers who are having a great impact on the research community as measured by the rate at which their work is being cited by others.”
The full 2021 Highly Cited Researchers list and executive summary can be found online here.
The impending danger of bacterial resistance to antibiotics is well-documented within the scientific community. Bacteria are the most efficient evolvers, and their ability to develop tolerance to drugs, in addition to antibiotic overuse and misuse, means that researchers have had to get particularly resourceful to ensure the future of modern medicine.
WIRED’s Max G. Levy recently spoke with de la Fuente and postdoctoral researcher and study collaborator Marcelo Torres about the urgency of the team’s work, and why developing these solutions is critical to the survival of civilization as we know it. The team’s algorithm, based on pattern recognition software used to analyze images, makes an otherwise insurmountable feat tangible.
De la Fuente’s lab specializes in using AI to discover and design new drugs. Rather than making some all-new peptide molecules that fit the bill, they hypothesized that an algorithm could use machine learning to winnow down the huge repository of natural peptide sequences in the human proteome into a select few candidates.
“We know those patterns—the multiple patterns—that we’re looking for,” says de la Fuente. “So that allows us to use the algorithm as a search function.”
While biologists and chemists race to develop new antibiotics to combat constantly mutating bacteria, predicted to lead to 10 million deaths by 2050, engineers are approaching the problem through a different lens: finding naturally occurring antibiotics in the human genome.
The billions of base pairs in the genome are essentially one long string of code that contains the instructions for making all of the molecules the body needs. The most basic of these molecules are amino acids, the building blocks for peptides, which in turn combine to form proteins. However, there is still much to learn about how — and where — a particular set of instructions are encoded.
Now, bringing a computer science approach to a life science problem, an interdisciplinary team of Penn researchers have used a carefully designed algorithm to discover a new suite of antimicrobial peptides, hiding deep within this code.
The study, published in Nature Biomedical Engineering, was led by César de la Fuente, Presidential Assistant Professor in Bioengineering, Microbiology, Psychiatry, and Chemical and Biomolecular Engineering, spanning both Penn Engineering and Penn Medicine, and his postdocs Marcelo Torres and Marcelo Melo. Collaborators Orlando Crescenzi and Eugenio Notomista of the University of Naples Federico II also contributed to this work.
“The human body is a treasure trove of information, a biological dataset. By using the right tools, we can mine for answers to some of the most challenging questions,” says de la Fuente. “We use the word ‘encrypted’ to describe the antimicrobial peptides we found because they are hidden within larger proteins that seem to have no connection to the immune system, the area where we expect to find this function.”
Testing is key to understanding and controlling the spread of COVID-19, which has already taken more than four million lives around the world. However, current tests are limited by the tradeoff between accuracy and the time it takes to analyze a sample.
Another challenge of current COVID-19 tests is cost. Most tests are expensive to produce and require trained personnel to administer and analyze them. Testing in low-and middle-income communities has therefore been largely inaccessible, leaving individuals at greater risk of viral spread.
To address cost, time and accuracy, a new electrochemical test developed by Penn researchers uses electrodes made from graphite — the same material found in pencil lead. Developed by César de la Fuente, Presidential Assistant Professor in Bioengineering, Microbiology and Psychiatry with a secondary appointment in Chemical and Biomolecular Engineering, these electrodes reduce the cost to $1.50 per test and require only 6.5 minutes to deliver 100-pecent-accurate results from saliva samples and up to 88 percent accuracy in nasal samples.
While his previous research highlights the invention of RAPID (Real-time Accurate Portable Impedimetric Detection prototype 1.0), a COVID-19 testing kit which uses screen-printed electrodes, this new research published in PNAS presents LEAD (Low-cost Electrochemical Advanced Diagnostic), using the same concept as RAPID but with less expensive materials. De la Fuente’s current test reduces costs from $4.67 per test (RAPID) to $1.50 per test (LEAD) just by changing the building material of the electrodes.
“Both RAPID and LEAD work on the same principle of electrochemistry,” says de la Fuente. “However, LEAD is easier to assemble, it can be used by anyone and the materials are cheaper and more accessible than those of RAPID. This is important because we are using an abundant material, graphite, the same graphite used in pencils, to build the electrode to make testing more accessible to lower-income communities.”
This figure, adapted from the paper, shows the functionalization steps of LEAD which prepares the electrodes to bind to the sample. The height of the peaks indicates whether the sample is negative or positive. Because the SARS-CoV-2 spike protein in a positive sample binds to the electrode, it inhibits the emitted signal and produces a smaller peak.
César de la Fuente, PhD, Presidential Assistant Professor in Bioengineering, Chemical and Biomolecular Engineering, Psychiatry, and Microbiology, was featured in the Philadelphia Business Journal’s Class of 2021 “40 Under 40” list. Currently focused on antibiotic discovery, creating tools for microbiome engineering, and low-cost diagnostics, de le Fuente pioneered the world’s first computer-designed antibiotic with efficacy in animal models.
De la Fuente was previously included in the AIChE’s “35 Under 35” list in 2020 and most recently published his work demonstrating a rapid COVID-19 diagnostic test which delivers highly accurate results within four minutes.
Read “40 Under 40: Philadelphia Business Journal’s complete Class of 2021” here.
Read other BE blog posts featuring Dr. de la Fuente here.
César de la Fuente, Presidential Assistant Professor in Bioengineering, Chemical and Biomolecular Engineering, Microbiology, and Psychiatry, was the inaugural recipient of the Nemirovsky Engineering and Medicine Opportunity (NEMO) Prize from Penn Health-Tech in 2020 for his low-cost, rapid COVID test. Now with promising results recently published in the journal Matter (showing 90 percent accuracy in as little as four minutes), Penn Health-Tech caught up with de la Fuente to discuss his experience over the past year:
“How did [your project] evolve in the past year?
‘We started with one prototype and now have three entirely different prototypes for the test. Two use electrochemistry, and we are now working on a new technology that uses calorimetry. With calorimetry, when the cotton swabs are exposed to the virus, they change color. This means users are able to see if they’re affected by a virus through a simple color change, making it more of a visual detection method.'”
Even as COVID-19 vaccinations are being rolled out, testing for active infections remains a critical tool in fighting the pandemic. Existing rapid tests that can directly detect the virus rely on reverse transcription polymerase chain reaction (RT-PCR), a common genetic assay that nevertheless requires trained technicians and lab space to conduct.
Alternative testing methods that can be scaled up and deployed in places where those are in short supply are therefore in high demand.
Penn researchers have now demonstrated such a method, which senses the virus by measuring the change in an electrical signal when a piece of the SARS-CoV-2 virus binds to a biosensor in their device, which they call RAPID 1.0.
The work, published in the journal Matter, was led by César de la Fuente, a Presidential Assistant Professor who has appointments in Engineering’s departments of Chemical and Biomolecular Engineering, and Bioengineering, as well as in Psychiatry and Microbiology in the Perelman School of Medicine.
“Prior to the pandemic, our lab was working on diagnostics for bacterial infections. But then, COVID-19 hit. We felt a responsibility to use our expertise to help—and the diagnostic space was ripe for improvements,” de la Fuente said. “We feel strongly about the health inequities witnessed during the pandemic, with testing access and the vaccine rollout, for example. We believe inexpensive diagnostic tests like RAPID could help bridge some of those gaps.”
The RAPID technology uses electrochemical impedance spectroscopy (EIS), which transforms the binding event between the SARS-CoV-2 viral spike protein and its receptor in the human body, the protein ACE2 (which provides the entry point for the coronavirus to hook into and infect human cells), into an electrical signal that clinicians and technicians can detect. That signal allows the test to discriminate between infected and healthy human samples. The signal can be read through a desktop instrument or a smartphone.