Illustration of the 55LCC complex. (Image: Courtesy of Cameron Baines/Phospho Biomedical Animation)
When cells in the human body divide, they must first make accurate copies of their DNA. The DNA replication exercise is one of the most important processes in all living organisms and is fraught with risks of mutation, which can lead to cell death or cancer. Now, findings from biologists from the Perelman School of Medicine and from the University of Leeds have identified a multiprotein “machine” in cells that helps govern the pausing or stopping of DNA replication to ensure its smooth progress. Illustration of the 55LCC complex. (Image: Courtesy of Cameron Baines/Phospho Biomedical Animation)
The discovery, published in Cell, advances the understanding of DNA replication, helps explain a puzzling set of genetic diseases, and could inform the development of future treatments for neurologic and developmental disorders.
“We’ve found what appears to be a critical quality-control mechanism in cells,” says senior co-corresponding author Roger Greenberg, the J. Samuel Staub, M.D. Professor in the department of Cancer Biology, director of the Penn Center for Genome Integrity, and director of basic science at the Basser Center for BRCA at Penn Medicine. “Trillions of cells in our body divide every single day, and this requires accurate replication of our genomes. Our work describes a new mechanism that regulates protein stability in replicating DNA. We now know a bit more about an important step in this complex biological process.”
“The proposed studies lay the foundation to make a major scientific impact in the childhood leukemia field and ultimately improve outcomes for children,” says Vining.
Diagram of the optoPlateReader, a high-throughput, feedback-enabled optogenetics and spectroscopy device initially developed by Penn 2021 iGEM team.
For bioengineers today, light does more than illuminate microscopes. Stimulating cells with light waves, a field known as optogenetics, has opened new doors to understanding the molecular activity within cells, with potential applications in drug discovery and more.
Thanks to recent advances in optogenetic technology, much of which is cheap and open-source, more researchers than ever before can construct arrays capable of running multiple experiments at once, using different wavelengths of light. Computing languages like Python allow researchers to manipulate light sources and precisely control what happens in the many “wells” containing cells in a typical optogenetic experiment.
However, researchers have struggled to simultaneously gather data on all these experiments in real time. Collecting data manually comes with multiple disadvantages: transferring cells to a microscope may expose them to other, non-experimental sources of light. The time it takes to collect the data also makes it difficult to adjust metabolic conditions quickly and precisely in sample cells.
Now, a team of Penn Engineers has published a paper in Communications Biology, an open access journal in the Nature portfolio, outlining the first low-cost solution to this problem. The paper describes the development of optoPlateReader (or oPR), an open-source device that addresses the need for instrumentation to monitor optogenetic experiments in real time. The oPR could make possible features such as automated reading, writing and feedback in microwell plates for optogenetic experiments.
Left to right: Will Benman, Gloria Lee, Saachi Datta, Juliette Hooper, Grace Qian, David Gonzalez-Martinez, and Lukasz Bugaj (with Max).
The paper follows up on the award-winning work of six University of Pennsylvania alumni — Saachi Datta, M.D. Candidate at Stanford School of Medicine; Juliette Hooper, Programmer Analyst in Penn’s Perelman School of Medicine; Gabrielle Leavitt, M.D. Candidate at Temple University; Gloria Lee, graduate student at Oxford University; Grace Qian, Drug Excipient and Residual Analysis Research Co-op at GSK; and Lana Salloum, M.D. Candidate at Albert Einstein College of Medicine — who claimed multiple prizes at the 2021 International Genetically Engineered Machine Competition (iGEM) as Penn undergraduates.
The International Genetically Engineered Machine Competition (or iGEM) is the largest synthetic biology community and the premiere synthetic biology competition for both university and high school students from around the world. Hundreds of interdisciplinary teams of students compete annually, combining molecular biology techniques and engineering concepts to create novel biological systems and compete for prizes and awards through oral presentations and poster sessions.
The optoPlateReader was initially developed by Penn’s 2021 iGEM team, combining a light-stimulation device with a plate reader. At the iGEM competition, the invention took home Best Foundational Advance (best in track), Best Hardware (best from all undergraduate teams), and Best Presentation (best from all undergraduate teams), as well as a Gold Medal Distinction and inclusion in the Top 10 Overall and Top 10 Websites lists. (Read more about the 2021 iGEM team on the BE Blog.)
The original iGEM project focused on the design, construction, and testing of the hardware and software that make up the oPR, the focus of the new paper. After iGEM concluded, the team showed that the oPR could be used with real biological samples, such as cultures of bacteria. This work demonstrated that the oPR could be applied to real research questions, a necessary precursor to publication, and that the device could simultaneously monitor and manipulate living samples.
The main application for the oPR is in metabolic production (such as the creation of pharmaceuticals and bio-fuels). The oPR is able to issue commands to cells via light but can also take live readings about their current state. In the oPR, certain colors of light cause cells to carry out different tasks, and optical measurements give information on growth rates and protein production rates.
In this way, the new device is able to support production processes that can adapt in real time to what cells need, altering their behavior to maximize yield. For example, if an experiment produces a product that is toxic to cells, the oPR could instruct those cells to “turn on” only when the population of cells is dense and “turn off” when the concentration of that product becomes toxic and the cellular population needs to recover. This ability to pivot in real time could assist industries that rely on bioproduction.
The main challenges in developing this device were in incorporating the many light emitting diodes (LEDs) and sensors into a tiny space, as well as insulating the sensors from the nearby LEDs to ensure that the measured light came from the sample and not from the instrument itself. The team also had to create software that could coordinate the function of nearly 100 different sets of LEDs and sensors. Going forward, the team hopes to spread the word about the open-source oPR to other researchers studying metabolic production to enable more efficient research.
Lukasz Bugaj, Assistant Professor in Bioengineering and senior author of the paper, served as the team’s mentor along with Brian Chow, formerly an Associate Professor in Bioengineering and a founding member of the iGEM program at MIT, and Jose Avalos, Associate Professor of Chemical and Biological Engineering at Princeton University.
Key to the project’s development was the guidance of Bioengineering graduate students Will Benman, David Gonzalez Martinez, and Gabrielle Ho, as well as that of Saurabh Malani, a graduate student at Princeton University.
Visualization of a CAR T cell (in red) attacking a cancer cell (in blue) (Meletios Varras via Getty Images)
For patients with certain types of cancer, CAR T cell therapy has been nothing short of life changing. Developed in part by Carl June, Richard W. Vague Professor at Penn Medicine, and approved by the Food and Drug Administration (FDA) in 2017, CAR T cell therapy mobilizes patients’ own immune systems to fight lymphoma and leukemia, among other cancers.
However, the process for manufacturing CAR T cells themselves is time-consuming and costly, requiring multiple steps across days. The state of the art involves extracting patients’ T cells, then activating them with tiny magnetic beads, before giving the T cells genetic instructions to make chimeric antigen receptors (CARs), the specialized receptors that help T cells eliminate cancer cells.
Now, Penn Engineers have developed a novel method for manufacturing CAR T cells, one that takes just 24 hours and requires only one step, thanks to the use of lipid nanoparticles (LNPs), the potent delivery vehicles that played a critical role in the Moderna and Pfizer-BioNTech COVID-19 vaccines.
In a new paper in Advanced Materials, Michael J. Mitchell, Associate Professor in Bioengineering, describes the creation of “activating lipid nanoparticles” (aLNPs), which can activate T cells and deliver the genetic instructions for CARs in a single step, greatly simplifying the CAR T cell manufacturing process. “We wanted to combine these two extremely promising areas of research,” says Ann Metzloff, a doctoral student in Bioengineering and NSF Graduate Research Fellow in the Mitchell lab and the paper’s lead author. “How could we apply lipid nanoparticles to CAR T cell therapy?”
LeAnn Dourte, Practice Associate Professor in Bioengineering, has been one of the most active members of the Penn Engineering faculty in pioneering the Structured, Active, In-Class Learning (SAIL) model of education. In a recent issue of the Penn Almanac, Dourte boils down her practical advice for faculty looking to make their courses more interactive and dynamic into one simple philosophy: “Just change 10 minutes.”
“The effectiveness of these 10-minute activities hinges on their alignment with learning objectives. Students are always on the lookout for anything that they see as busy-work, so articulating the purpose of such activities is paramount to their success. These are some of the goals I think about when I design activities with my learning objectives in mind. While some of these approaches are specific to subjects with quantitative problem solving, many have applications across disciplines.”
Dourte’s article articulates her active learning approach, along with a list of specific learning objectives, including encouraging diverse perspectives, promoting error recognition and correction, and more.
Penn Engineers have developed a way to target lung diseases, including lung cancer, with lipid nanoparticles (LNPs). (wildpixel via Getty Images)
Penn Engineers have developed a new means of targeting the lungs with lipid nanoparticles (LNPs), the miniscule capsules used by the Moderna and Pfizer-BioNTech COVID-19 vaccines to deliver mRNA, opening the door to novel treatments for pulmonary diseases like cystic fibrosis.
In a paper in Nature Communications, Michael J. Mitchell, Associate Professor in the Department of Bioengineering, demonstrates a new method for efficiently determining which LNPs are likely to bind to the lungs, rather than the liver. “The way the liver is designed,” says Mitchell, “LNPs tend to filter into hepatic cells, and struggle to arrive anywhere else. Being able to target the lungs is potentially life-changing for someone with lung cancer or cystic fibrosis.”
Previous studies have shown that cationic lipids — lipids that are positively charged — are more likely to successfully deliver their contents to lung tissue. “However, the commercial cationic lipids are usually highly positively charged and toxic,” says Lulu Xue, a postdoctoral fellow in the Mitchell Lab and the paper’s first author. Since cell membranes are negatively charged, lipids with too strong a positive charge can literally rip apart target cells.
Typically, it would require hundreds of mice to individually test the members of a “library” of LNPs — chemical variants with different structures and properties — to find one with a low charge that has a higher likelihood of delivering a medicinal payload to the lungs.
Instead, Xue, Mitchell and their collaborators used what is known as “barcoded DNA” (b-DNA) to tag each LNP with a unique strand of genetic material, so that they could inject a pool of LNPs into just a handful of animal models. Then, once the LNPs had propagated to different organs, the b-DNA could be scanned, like an item at the supermarket, to determine which LNPs wound up in the lungs.
Like space shuttles using booster rockets to breach the atmosphere, lipid nanoparticles (LNPs) equipped with the new molecule more successfully deliver medicinal payloads. (Love Employee via Getty Images)
Inspired by the design of space shuttles, Penn Engineering researchers have invented a new way to synthesize a key component of lipid nanoparticles (LNPs), the revolutionary delivery vehicle for mRNA treatments including the Pfizer-BioNTech and Moderna COVID-19 vaccines, simplifying the manufacture of LNPs while boosting their efficacy at delivering mRNA to cells for medicinal purposes.
In a paper in Nature Communications, Michael J. Mitchell, Associate Professor in the Department of Bioengineering, describes a new way to synthesize ionizable lipidoids, key chemical components of LNPs that help protect and deliver medicinal payloads. For this paper, Mitchell and his co-authors tested delivery of an mRNA drug for treating obesity and gene-editing tools for treating genetic disease.
Previous experiments have shown that lipidoids with branched tails perform better at delivering mRNA to cells, but the methods for creating these molecules are time- and cost-intensive. “We offer a novel construction strategy for rapid and cost-efficient synthesis of these lipidoids,” says Xuexiang Han, a postdoctoral student in the Mitchell Lab and the paper’s co-first author.
Bispecific T cell engagers are emerging as a powerful class of immunotherapy to treat cancer but are sometimes hindered by unwanted outcomes, such as on-target, off-tumor toxicity; cytokine release syndrome; and neurotoxicity. Now, researchers Penn researchers have developed a novel “switchable” bispecific T cell engager that mitigates these negative effects by co-opting a drug already approved by the FDA. (Image: iStock / CIPhotos)
In the ever-evolving battle against cancer, immunotherapy presents a turning point. It began with harnessing the body’s immune system to fight cancer, a concept rooted more than a century ago but only gaining significant momentum in recent years. Pioneering this shift were therapies like CAR T cell therapy, which reprograms a patient’s T cells to attack cancer cells. Within this domain, bispecific T cell engagers, or bispecific antibodies, have emerged as effective treatments for many blood-borne cancers in the clinic and are being evaluated for solid tumor therapy.
These antibodies simultaneously latch onto both a cancer cell and a T cell, effectively bridging the gap between the two. This proximity triggers the T cells to unleash their lethal arsenal, thereby killing the cancer cells. However, bispecific T cell engagers, like many cancer therapies, face hurdles such as cell-specific targeting limitations, known as on-target off-tumor toxicity, which means the tumor is correctly targeted but so are other healthy cells in the body, leading to healthy tissue damage. Moreover, bispecific antibodies may also lead to immune system overactivation, a precursor for cytokine release syndrome (CRS), and neurotoxicity.
Now, researchers led by Michael Mitchell of the University of Pennsylvania have found a way to circumvent many of these deleterious effects by developing a bispecific T cell nanoengager that is equipped with an “off switch.” Their findings are published in Nature Biomedical Engineering.
“We’re excited to show that bispecific antibodies can be tweaked in a way that allows us to tap into their powerful cancer-killing potential without inducing toxicity to healthy tissues,” says Mitchell, associate professor of bioengineering at Penn’s School of Engineering and Applied Science. “This new controllable drug-delivery mechanism, which we call switchable bispecific T cell nanoengagers, or SiTEs, adds this switchable component to the antibody via administering an FDA-approved small-molecule drug, amantadine.”
TDP-43 may be one of the most dangerous proteins in the human body, implicated in neurodegenerative conditions like ALS and Alzheimer’s disease. But the protein remains mysterious: how TDP-43 interacts with the immune system, for instance, is still unclear.
Now, Ning Jenny Jiang, J. Peter and Geri Skirkanich Associate Professor of Innovation in Bioengineering, has been selected for the Collaborative Pairs Pilot Project Awards, sponsored by the Chan Zuckerberg Initiative (CZI), to investigate the relationship between TDP-43 and the immune system.
Launched in 2018, the Collaborative Pairs Pilot Project Awards support pairs of investigators to explore “innovative, interdisciplinary approaches to address critical challenges in the fields of neurodegenerative disease and fundamental neuroscience.” Professor Jiang will partner with Pietro Fratta, MRC Senior Clinical Fellow and MNDA Lady Edith Wolfson Fellow at the University College London Queen Square Institute of Neurology.
The TDP-43 protein is associated with neurodegenerative diseases affecting the central nervous system, including ALS and Alzehimer’s disease. While the loss of neurons and muscle degeneration cause the progressive symptoms, the diseases themselves may be a previously unidentified trigger for abnormal immune system activity.
One possible link is the intracellular mislocalization of TDP-43 (known as TDP-43 proteinopathy), when the protein winds up in the wrong location, which the Jiang and Fratta Labs will investigate. Successfully proving this link could result in potentially game-changing new therapies for these neurodegenerative diseases.
The Jiang Lab at Penn Engineering specializes in systems immunology, using high-throughput sequencing and single-cell and quantitative analysis to understand how the immune system develops and ages, as well as the molecular signatures of immune related diseases. Jiang joined Penn Bioengineering in 2021.
Since arriving on campus, Jiang has teamed with the recently formed Penn Anti-Cancer Engineering Center (PACE), which seeks to understand the forces that determine how cancer grows and spreads, and Engineers in the Center for Precision Engineering (CPE4H), which focuses on innovations in diagnostics and delivery in the development of customizable biomaterials and implantable devices for individualized care.
Jiang was elected a member of the American Institute for Medical and Biological Engineering (AIMBE) College of Fellows in 2021, and has previously won multiple prestigious awards including the NSF CAREER, a Cancer Research Institute Lloyd J. Old STAR Award, and a CZI Neurodegeneration Challenge Network Ben Barres Early Career Acceleration Award.
Jiang is a leader in high-throughput and high-dimensional analysis of T cells, a type of white blood cell crucial to the functioning of a healthy immune system. A recent study in Nature Immunology described the Jiang Lab’s TetTCR-SeqHD technology, the first approach to provide a multifaceted analysis of antigen-specific T cells in a high-throughput manner.
The CZI Collaborative Pairs Pilot Project Awards will provide $200,000 of funding over 18 months with a chance to advance to the second phase of $3.2 million in funding over a four-year period.
Read the full list of grantees on the CZI’s Neurodegeneration Challenge Network (NDCN) Projects website here.
Computing at the speed of light may reduce the energy cost of training AI. (Narongrit Doungmanee via Getty Images)
Penn Engineers have developed a new chip that uses light waves, rather than electricity, to perform the complex math essential to training AI. The chip has the potential to radically accelerate the processing speed of computers while also reducing their energy consumption.
The silicon-photonic (SiPh) chip’s design is the first to bring together Benjamin Franklin Medal Laureate and H. Nedwill Ramsey Professor Nader Engheta’s pioneering research in manipulating materials at the nanoscale to perform mathematical computations using light — the fastest possible means of communication — with the SiPh platform, which uses silicon, the cheap, abundant element used to mass-produce computer chips.
The interaction of light waves with matter represents one possible avenue for developing computers that supersede the limitations of today’s chips, which are essentially based on the same principles as chips from the earliest days of the computing revolution in the 1960s.
In a paper in Nature Photonics, Engheta’s group, together with that of Firooz Aflatouni, Associate Professor in Electrical and Systems Engineering, describes the development of the new chip. “We decided to join forces,” says Engheta, leveraging the fact that Aflatouni’s research group has pioneered nanoscale silicon devices.
Their goal was to develop a platform for performing what is known as vector-matrix multiplication, a core mathematical operation in the development and function of neural networks, the computer architecture that powers today’s AI tools.
Nader Engheta is the H. Nedwill Ramsey Professor in Electrical and Systems Engineering, Bioengineering, Materials Science and Engineering, and in Physics and Astronomy.
