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Category: AI in Biotechnology

  • Chatbot Iris offers students individual support

    Chatbot Iris offers students individual support

    How can a chatbot support students in lectures and with assigned exercises? Researchers at the Technical University of Munich (TUM) have developed the chatbot Iris, which offers computer science students personalized assistance with programming assignments. A study has now confirmed the chatbot’s success: Iris improves the understanding of programming concepts and represents a valuable complement to human tutors.

    A packed auditorium with over 1000 students. This is not a rare sight in introductory informatics lectures. To meet the needs of each individual student under these conditions, Stephan Krusche, professor of Software Engineering, and his team have been building the Artemis learning platform since 2016. It resembles well-known learning platforms, but offers more possibilities. For example, students not only receive their assignments through the platform, but also get immediate feedback when they make mistakes or show an incomplete understanding of concepts.

    Chatbot Iris: excellent tutor

    In October of last year, Stephan Krusche’s team at the TUM Centre for Educational Technologies added the chatbot to the platform. Iris is based on the GPT large language model of OpenAI. It is intended to help students with assigned problems, with the support geared to their individual needs. To adapt the system to the learning context, the chatbot operates as a so-called excellent tutor. That means that, rather than immediately suggesting the right solution or specific steps, Iris offers subtle hints or asks leading questions. This approach encourages students to think for themselves. To boost learning efficiency, the chatbot only answers questions directly relevant to the learning content.

    “To test the success of Iris, we surveyed over 200 students from three introductory informatics lectures,” says Patrick Bassner, a doctoral candidate at the Chair of Software Engineering. “The goal was to arrive at conclusions on how students rate the effectiveness of the support from Iris, whether students are more comfortable asking a chatbot questions than a human tutor and whether students subjectively trust Iris.”

    Iris promotes learning and students’ independence

    The responses show that the students value Iris as a valuable support, but also that a large share of those surveyed still rely on human tutors. The chatbot understands questions well and students feel comfortable and safe when using it. Iris gives them the feeling that the chats will remain private and that they are not being judged when asking questions. Especially in large lectures, this saves students from having to overcome their inhibitions to ask questions in front of the class. When studying for exams, the use of Iris is becoming commonplace. However, students feel confident enough to solve problems on exams without the support of the chatbot.

    “The integration of Iris into Artemis has shown that AI-controlled tutors can play a valuable role in university teaching,” says Stephan Krusche. “They offer personalized support that benefits learning and boosts students’ ability to help themselves.”

    The researchers plan to further optimize the chatbot Iris and want to see it being used in other subject areas.

  • Prying open the AI black box

    Prying open the AI black box

    Artificial intelligence continues to squirm its way into many aspects of our lives. But what about biology, the study of life itself? AI can sift through hundreds of thousands of genome data points to identify potential new therapeutic targets. While these genomic insights may appear helpful, scientists aren’t sure how today’s AI models come to their conclusions in the first place. Now, a new system named SQUID arrives on the scene armed to pry open AI’s black box of murky internal logic.

    SQUID, short for Surrogate Quantitative Interpretability for Deepnets, is a computational tool created by Cold Spring Harbor Laboratory (CSHL) scientists. It’s designed to help interpret how AI models analyze the genome. Compared with other analysis tools, SQUID is more consistent, reduces background noise, and can lead to more accurate predictions about the effects of genetic mutations.

    How does it work so much better? The key, CSHL Assistant Professor Peter Koo says, lies in SQUID’s specialized training.

    “The tools that people use to try to understand these models have been largely coming from other fields like computer vision or natural language processing. While they can be useful, they’re not optimal for genomics. What we did with SQUID was leverage decades of quantitative genetics knowledge to help us understand what these deep neural networks are learning,” explains Koo.

    SQUID works by first generating a library of over 100,000 variant DNA sequences. It then analyzes the library of mutations and their effects using a program called MAVE-NN (Multiplex Assays of Variant Effects Neural Network). This tool allows scientists to perform thousands of virtual experiments simultaneously. In effect, they can “fish out” the algorithms behind a given AI’s most accurate predictions. Their computational “catch” could set the stage for experiments that are more grounded in reality.

    “In silico [virtual] experiments are no replacement for actual laboratory experiments. Nevertheless, they can be very informative. They can help scientists form hypotheses for how a particular region of the genome works or how a mutation might have a clinically relevant effect,” explains CSHL Associate Professor Justin Kinney, a co-author of the study.

    There are tons of AI models in the sea. More enter the waters each day. Koo, Kinney, and colleagues hope that SQUID will help scientists grab hold of those that best meet their specialized needs.

    Though mapped, the human genome remains an incredibly challenging terrain. SQUID could help biologists navigate the field more effectively, bringing them closer to their findings’ true medical implications.

  • Scientists preserve DNA in an amber-like polymer

    Scientists preserve DNA in an amber-like polymer

    In the movie “Jurassic Park,” scientists extracted DNA that had been preserved in amber for millions of years, and used it to create a population of long-extinct dinosaurs.

    Inspired partly by that film, MIT researchers have developed a glassy, amber-like polymer that can be used for long-term storage of DNA, whether entire human genomes or digital files such as photos.

    Most current methods for storing DNA require freezing temperatures, so they consume a great deal of energy and are not feasible in many parts of the world. In contrast, the new amber-like polymer can store DNA at room temperature while protecting the molecules from damage caused by heat or water.

    The researchers showed that they could use this polymer to store DNA sequences encoding the theme music from Jurassic Park, as well as an entire human genome. They also demonstrated that the DNA can be easily removed from the polymer without damaging it.

    “Freezing DNA is the number one way to preserve it, but it’s very expensive, and it’s not scalable,” says James Banal, a former MIT postdoc. “I think our new preservation method is going to be a technology that may drive the future of storing digital information on DNA.”

    Banal and Jeremiah Johnson, the A. Thomas Geurtin Professor of Chemistry at MIT, are the senior authors of the study, published in the Journal of the American Chemical Society. Former MIT postdoc Elizabeth Prince and MIT postdoc Ho Fung Cheng are the lead authors of the paper.

    Capturing DNA

    DNA, a very stable molecule, is well-suited for storing massive amounts of information, including digital data. Digital storage systems encode text, photos, and other kind of information as a series of 0s and 1s. This same information can be encoded in DNA using the four nucleotides that make up the genetic code: A, T, G, and C. For example, G and C could be used to represent 0 while A and T represent 1.

    DNA offers a way to store this digital information at very high density: In theory, a coffee mug full of DNA could store all of the world’s data. DNA is also very stable and relatively easy to synthesize and sequence.

    In 2021, Banal and his postdoc advisor, Mark Bathe, an MIT professor of biological engineering, developed a way to store DNA in particles of silica, which could be labeled with tags that revealed the particles’ contents. That work led to a spinout called Cache DNA.

    One downside to that storage system is that it takes several days to embed DNA into the silica particles. Furthermore, removing the DNA from the particles requires hydrofluoric acid, which can be hazardous to workers handling the DNA.

    To come up with alternative storage materials, Banal began working with Johnson and members of his lab. Their idea was to use a type of polymer known as a degradable thermoset, which consists of polymers that form a solid when heated. The material also includes cleavable links that can be easily broken, allowing the polymer to be degraded in a controlled way.

    “With these deconstructable thermosets, depending on what cleavable bonds we put into them, we can choose how we want to degrade them,” Johnson says.

    For this project, the researchers decided to make their thermoset polymer from styrene and a cross-linker, which together form an amber-like thermoset called cross-linked polystyrene. This thermoset is also very hydrophobic, so it can prevent moisture from getting in and damaging the DNA. To make the thermoset degradable, the styrene monomers and cross-linkers are copolymerized with monomers called thionolactones. These links can be broken by treating them with a molecule called cysteamine.

    Because styrene is so hydrophobic, the researchers had to come up with a way to entice DNA — a hydrophilic, negatively charged molecule — into the styrene.

    To do that, they identified a combination of three monomers that they could turn into polymers that dissolve DNA by helping it interact with styrene. Each of the monomers has different features that cooperate to get the DNA out of water and into the styrene. There, the DNA forms spherical complexes, with charged DNA in the center and hydrophobic groups forming an outer layer that interacts with styrene. When heated, this solution becomes a solid glass-like block, embedded with DNA complexes.

    The researchers dubbed their method T-REX (Thermoset-REinforced Xeropreservation). The process of embedding DNA into the polymer network takes a few hours, but that could become shorter with further optimization, the researchers say.

    To release the DNA, the researchers first add cysteamine, which cleaves the bonds holding the polystyrene thermoset together, breaking it into smaller pieces. Then, a detergent called SDS can be added to remove the DNA from polystyrene without damaging it.

    Storing information

    Using these polymers, the researchers showed that they could encapsulate DNA of varying length, from tens of nucleotides up to an entire human genome (more than 50,000 base pairs). They were able to store DNA encoding the Emancipation Proclamation and the MIT logo, in addition to the theme music from “Jurassic Park.”

    After storing the DNA and then removing it, the researchers sequenced it and found that no errors had been introduced, which is a critical feature of any digital data storage system.

    The researchers also showed that the thermoset polymer can protect DNA from temperatures up to 75 degrees Celsius (167 degrees Fahrenheit). They are now working on ways to streamline the process of making the polymers and forming them into capsules for long-term storage.

    Cache DNA, a company started by Banal and Bathe, with Johnson as a member of the scientific advisory board, is now working on further developing DNA storage technology. The earliest application they envision is storing genomes for personalized medicine, and they also anticipate that these stored genomes could undergo further analysis as better technology is developed in the future.

    “The idea is, why don’t we preserve the master record of life forever?” Banal says. “Ten years or 20 years from now, when technology has advanced way more than we could ever imagine today, we could learn more and more things. We’re still in the very infancy of understanding the genome and how it relates to disease.”

    The research was funded by the National Science Foundation.

  • ‘Self-taught’ AI tool helps to diagnose and predict severity of common lung cancer

    ‘Self-taught’ AI tool helps to diagnose and predict severity of common lung cancer

    A computer program based on data from nearly a half-million tissue images and powered by artificial intelligence can accurately diagnose cases of adenocarcinoma, the most common form of lung cancer, a new study shows.

    Researchers at NYU Langone Health’s Perlmutter Cancer Center and the University of Glasgow developed and tested the program. They say that because it incorporates structural features of tumors from 452 adenocarcinoma patients, who are among the more than 11,000 patients in the United States National Cancer Institute’s Cancer Genome Atlas, the program offers an unbiased, detailed, and reliable second opinion for patients and oncologists about the presence of the cancer and the likelihood and timing of its return (prognosis).

    The research team also points out that the program is independent and “self-taught,” meaning that it determined on its own which structural features were statistically most significant to gauging the severity of disease and had the greatest impact on tumor recurrence.

    Publishing in the journal Nature Communications online June 11, the study program, also called an algorithm, or specifically, histomorphological phenotype learning (HPL), was found to accurately distinguish between similar lung cancers, adenocarcinoma and squamous cell cancers, 99% of the time. The HPL program was also found to be 72% accurate at predicting the likelihood and timing of cancer’s return after therapy, bettering the 64% accuracy in the predictions made by pathologists who directly examined the same patients’ tumor images, researchers say.

    “Our new histomorphological phenotype learning program has the potential to offer cancer specialists and their patients a quick and unbiased diagnostic tool for lung adenocarcinoma that, once further testing is complete, can also be used to help validate and even guide their treatment decisions,” said study lead investigator Nicolas Coudray, PhD, a bioinformatics programmer at NYU Grossman School of Medicine and Perlmutter Cancer Center.

    “Patients, physicians, and researchers know they can rely on this predictive modeling because it is self-taught, provides explainable decisions, and is based only on the knowledge drawn specifically from each patient’s tissue, including such features as its proportion of dying cells, tumor-fighting immune cells, and how densely packed the tumor cells are, among other features,” said Coudray.

    “Lung tissue samples can now be analyzed in minutes by our computer program to provide fairly accurate predictions of whether their cancer will return, predictions that are better than current standards of care for making a prognosis in lung adenocarcinoma,” said study co-senior investigator Aristotelis Tsirigos, PhD. Tsirigos is a professor in the Departments of Pathology and Medicine at NYU Grossman School of Medicine and Perlmutter Cancer Center, where he also serves as co-director of precision medicine and director of its Applied Bioinformatics Laboratories.

    Tsirigos says that thanks to such tools and other advances in the lung cancer biology, pathologists will be examining tissue scans on their computer screens, and less and less on microscopes, and then using their AI program to analyze the image and produce its own image of the scan. The new image, or “landscape,” they add, will offer a detailed breakdown of the tissue’s content. It might note, for example, that there is 5% necrosis and 10% tumor infiltration and what that means in terms of survival. That reading may statistically equate to an 80% chance of remaining cancer-free for two years or more, based on information from all the patient data in the program.

    To develop the HPL program, the researchers first analyzed lung adenocarcinoma tissue slides from the Cancer Genome Atlas. Adenocarcinoma was chosen for the test model because the disease is known for characteristic features. As an example, they note that its tumor cells tend to group in so-called acinar, or saclike patterns and spread predictably along the surface lining of lung cells.

    From their analysis of the slides, whose visual images were digitally scanned and broken into 432,231 small quadrants or tiles, researchers found 46 key characteristics, what they term histomorphological phenotype clusters, from both normal and diseased tissue, a subset of which were statistically linked to either cancer’s early return or to long-term survival. The findings were then confirmed by further and separate testing on tissue images from 276 men and women who were treated for adenocarcinoma at NYU Langone from 2006 to 2021.

    Researchers say their goal is to use the HPL algorithm to assign to each patient a score between 0 and 1 that reflects their statistical chance of survival and tumor recurrence for up to five years. Because the program is self-learning, they stress HPL will become increasingly more accurate as more data is added over time. To build public trust, researchers have posted their programming code online and have plans to make the new HPL tool freely available upon completion of further testing.

    Characteristics linked to tumors recurring included high tile percentages of dead cancer cells and tumor-fighting immune cells called lymphocytes, and the dense clustering of tumor cells in the outer linings of the lungs. Features tied to increased likelihood for survival were high percentages of unchanged or preserved lung sac tissue, and lack of or mild presence of inflammatory cells.

    Tsirigos says the team next plans to look at developing HPL-like programs for other cancers, such as breast, ovarian, and colorectal, that are similarly based on distinctive and key morphological features and additional molecular data. The team also has plans to expand and improve the accuracy of the current adenocarcinoma HPL program by including other data from hospital electronic health records about other illnesses and diseases, or even income and home ZIP code.

    Funding support for the new study was provided by National Institutes of Health grant P30CA016087, United Kingdom Research Council grants Ep/R018634/1 and BB/V016067/1, and European Union Horizon 2020 grant no. 101016851.

    Besides Tsirigos and Coudray, other NYU Langone researchers involved in this study are Anna Yeaton, Bojing Liu, Hortense Le, Luis Chiriboga, Afreen Karimkhan, Navneet Natula, Christopher Park, Harvey Pass, and Andre Moreira. Study co-lead investigator Adalberto Claudio Quiros, study co-investigators Xinyu Yang and John Le Quesne, and study co-senior investigator Ke Yuan are all at the University of Glasgow, UK. Study co-investigator David Moore is at the University College London, UK.

  • Artificial intelligence blood test provides a reliable way to identify lung cancer

    Artificial intelligence blood test provides a reliable way to identify lung cancer

    Using artificial intelligence technology to identify patterns of DNA fragments associated with lung cancer, researchers from the Johns Hopkins Kimmel Cancer Center and other institutions have developed and validated a liquid biopsy that may help identify lung cancer earlier.

    In a prospective study published June 3 in Cancer Discovery, the team demonstrated that artificial intelligence technology could identify people more likely to have lung cancer based on DNA fragment patterns in the blood. The study enrolled about 1,000 participants with and without cancer who met the criteria for traditional lung cancer screening with low-dose computed tomography (CT). Individuals were recruited to participate at 47 centers in 23 U.S. states. By helping to identify patients most at risk and who would benefit from follow-up CT screening, this new blood test could potentially boost lung cancer screening and reduce death rates, according to computer modeling by the team.

    “We have a simple blood test that could be done in a doctor’s office that would tell patients whether they have potential signs of lung cancer and should get a follow-up CT scan,” says the study’s corresponding author, Victor E. Velculescu, M.D., Ph.D., professor of oncology and co-director of the Cancer Genetics and Epigenetics program at the Johns Hopkins Kimmel Cancer Center.Lung cancer is the deadliest cancer in the United States, according to the National Cancer Institute, and worldwide, according to the World Health Organization. Yearly screening with CT scans in high-risk patients can help detect lung cancers early, when they are most treatable, and help avert lung cancer deaths. Screening is recommended by the U.S. Preventive Services Task Force for 15 million people nationally who are between ages 50 and 80 and have a smoking history, yet only about 6%-10% of eligible individuals are screened each year. People may be reticent to follow through on screening, Velculescu explains, due to the time it takes to arrange and go to an appointment, and the low doses of radiation they are exposed to from the scan.

    To help overcome some of these hurdles, Velculescu and his colleagues developed a test over the past five years that uses artificial intelligence to detect patterns of DNA fragments found in patients with lung cancer. It takes advantage of differences in how DNA is packaged in normal and cancer cells. DNA is neatly and consistently folded up in healthy cells, almost like a rolled-up ball of yarn, but DNA in cancer cells is more disorganized. When both types of cells die, fragments of DNA end up in the blood. The DNA fragments in patients with cancer tend to be more chaotic and irregular than the DNA fragments found in individuals who do not have cancer.

    The team trained artificial intelligence software to identify the specific patterns of DNA fragments seen in the blood of 576 people with or without lung cancer. Then, they verified that the method worked in a second group of 382 people with and without cancer. Based on their analyses, the test has a negative predictive value of 99.8%, meaning that only 2 in 1,000 individuals tested may be missed and have lung cancer.

    The group’s computer simulations showed that if the test boosted the rate of lung cancer screening to 50% within five years, it could quadruple the number of lung cancers detected and increase the proportion of cancers detected early — when they are most treatable — by about 10%. That could prevent about 14,000 cancer deaths over five years.

    “The test is inexpensive and could be done at a very large scale,” Velculescu says. “We believe it will make lung cancer screening more accessible and help many more people get screened. This will lead to more cancers being detected and treated early.”

    The test is currently available through DELFI Diagnostics for use as a laboratory-based test under the Clinical Laboratory Improvement Amendments. However, the team plans to seek approval from the U.S. Food and Drug Administration for lung cancer screening. Velculescu colleagues also plan to study whether a similar approach could be used to detect other types of cancer.

    Robert B. Scharpf of Johns Hopkins co-authored the study. Additional co-authors were from the Cleveland Clinic, DELFI Diagnostics, Medicus Economics LLC, Miami Cancer Institute, the Pan American Center for Oncology, Washington University, Centura Health, Vanderbilt Health, Stratevi, Massachusetts General Hospital, the Medical University of South Carolina, the Department of Veterans Affairs, the Perelman School of Medicine at the University of Pennsylvania, New York University Langone Health, Allegheny Health Network and Memorial Sloan Kettering Cancer Center.

    The work was supported in part by DELFI Diagnostics, the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, Stand Up To Cancer-LUNGevity-American Lung Association Lung Cancer Interception Dream Team Translational Research Grant, Stand Up To Cancer-DCS International Translational Cancer Research Dream Team Grant, the Gray Foundation, The Honorable Tina Brozman Foundation, the Commonwealth Foundation, the Cole Foundation and the National Institutes of Health.

    Velculescu and Scharpf are inventors on patent applications submitted by The Johns Hopkins University related to cell-free DNA for cancer detection that have been licensed to DELFI Diagnostics, LabCorp, Qiagen, Sysmex, Agios, Genzyme, Esoterix, Ventana and ManaT Bio. Velculescu divested his equity in Personal Genome Diagnostics (PGDx) to LabCorp in February 2022. Velculescu is a founder of DELFI Diagnostics, serves on the board of directors, and owns DELFI Diagnostics stock. Scharpf is a founder and consultant of DELFI Diagnostics and owns DELFI Diagnostics stock. Velculescu, Scharpf and Johns Hopkins receive royalties and fees from the company. The Johns Hopkins University also owns equity in DELFI Diagnostics. Velculescu is an adviser to Viron Therapeutics and Epitope. These relationships are managed by Johns Hopkins in accordance with its conflict-of-interest policies.

  • Novel software that combines gene activity and tissue location to decode disease mechanisms

    Novel software that combines gene activity and tissue location to decode disease mechanisms

    In disease research, it’s important to know gene expression and where in a tissue the expression is happening, but marrying the two sets of information can be challenging.

    “Single-cell technologies, especially in the emerging field of spatial transcriptomics, help scientists see where in a tissue the genes are turned on or off. It combines information about gene activity with the exact locations within the disease tissues,” explains Fan Zhang, PhD, assistant professor of medicine with a secondary appointment in the Department of Biomedical Informatics at the University of Colorado School of Medicine.

    “This is really valuable because it lets physicians and researchers see not just which genes are active, but also where they are active, which can give key insights into how different cells behave and interact in diseased conditions,” she continues.

    Effectively combining location and genetic information has been a tough obstacle for researchers — until now.

    Zhang and her lab developed a new computational machine learning method — called Spatial Transcriptomic multi-viEW, or “STew” for short — that enables the joint analysis of spatial variation and gene expression changes in a scalable way that can handle large amounts of cells.

    This new technology may help researchers learn more about the spatial biology behind many different diseases and lead them to better treatment therapies.

    A path toward an accurate target for effective treatment

    The new technology is accurate in finding significant patterns that show where specific cell activities happen, which is important for understanding how cells work and how clinical tissues are structured in diseases. Zhang’s lab has already successfully applied STew on human tissues, including human brains, skin with inflammation, and breast cancer tumors.

    For Zhang, who studies inflammatory diseases using computational AI tools and translational approaches, finding a good target for treatment is often a challenge, but STew could help change that.

    “With inflamed joints, for example, the genes causing inflammation could be closer to the blood vessel through interacting with mesenchymal structures, or they could be farther away, but knowing that exact location and cell-cell communication patterns helps us better understand the underlying mechanisms,” she says.

    By merging spatial biology and molecular diversity, STew gives researchers a new dimension in classifying patient heterogeneity.

    “If you only use gene expression to classify patients, you don’t have the full picture,” Zhang says. “Once you add in spatial information, you have a more comprehensive understanding.”

    “We expect STew to be effective in uncovering critical molecular and cellular signals in various clinical conditions, like different types of tumors and autoimmune disorders, opening new avenues for dysregulated immune pathways for therapeutic intervention for theses disease,” she continues.

    A novel software-driven route to empowering collaboration

    There’s another perk that comes with the development of STew: collaboration. Scientific discoveries often benefit from experts from different fields working together.

    Because STew has a wide application, Zhang says the software will bring researchers together in new and exciting ways that will ultimately benefit the field of medicine and offer promise to patients in need of treatments.

    “We want to encourage researchers across specialties, skillsets, and even departments, to collaborate in ways that they previously might not have been able to do,” Zhang says. “We can accomplish more together, so it’s important to boost data-driven and AI tool-motivated collaboration in a way that is meaningful.”

  • New AI algorithm may improve autoimmune disease prediction and therapies

    New AI algorithm may improve autoimmune disease prediction and therapies

    A new advanced artificial intelligence (AI) algorithm may lead to better — and earlier — predictions and novel therapies for autoimmune diseases, which involve the immune system mistakenly attacking their body’s own healthy cells and tissues. The algorithm digs into the genetic code underlying the conditions to more accurately model how genes associated with specific autoimmune diseases are expressed and regulated and to identify additional genes of risk.

    The work, developed by a team led by Penn State College of Medicine researchers, outperforms existing methodologies and identified 26% more novel gene and trait associations, the researchers said. They published their work today (May 20) in Nature Communications.

    “We all carry some DNA mutations, and we need to figure out how any one of these mutations may influence gene expression linked to disease so we can predict disease risk early. This is especially important for autoimmune disease,” said Dajiang Liu, distinguished professor, vice chair for research, and director of artificial intelligence and biomedical informatics at the Penn State College of Medicine and co-senior author of the study. “If an AI algorithm can more accurately predict disease risk, it means we can carry out interventions earlier.”

    Genetics often underpin disease development. Variations in DNA can influence gene expression, or the process by which the information in DNA is converted into functional products like a protein. How much or how little a gene is expressed can influence disease risk.

    Genome-wide association studies (GWAS), a popular approach in human genetics research, can home in on regions of the genome associated with a particular disease or trait but can’t pinpoint the specific genes that affect disease risks. It’s like sharing your location with a friend with the precise location setting turned off on your smartphone — the city might be obvious, but the address is obscured. Existing methods are also limited in the granularity of its analysis. Gene expression can be specific to certain types of cells. If the analysis doesn’t distinguish between distinct cell types, the results may overlook real causal relationships between genetic variants and gene expression.

    The research team’s method, dubbed EXPRESSO for EXpression PREdiction with Summary Statistics Only, applies a more advanced artificial intelligence algorithm and analyzes data from single-cell expression quantitative trait loci, a type of data that links genetic variants to the genes they regulate. It also integrates 3D genomic data and epigenetics — which measures how genes may be modified by environment to influence disease — into its modeling. The team applied EXPRESSO to GWAS datasets for 14 autoimmune diseases, including lupus, Crohn’s disease, ulcerative colitis and rheumatoid arthritis.

    “With this new method, we were able to identify many more risk genes for autoimmune disease that actually have cell-type specific effects, meaning that they only have effects in a particular cell type and not others,” said Bibo Jiang, assistant professor at the Penn State College of Medicine and senior author of the study.

    The team then used this information to identify potential therapeutics for autoimmune disease. Currently, there aren’t good long-term treatment options, they said.

    “Most treatments are designed to mitigate symptoms, not cure the disease. It’s a dilemma knowing that autoimmune disease needs long-term treatment, but the existing treatments often have such bad side effects that they can’t be used for long. Yet, genomics and AI offer a promising route to develop novel therapeutics,” said Laura Carrel, professor of biochemistry and molecular biology at the Penn State College of Medicine and co-senior author of the study.

    The team’s work pointed to drug compounds that could reverse gene expression in cell types associated with an autoimmune disease, such as vitamin K for ulcerative colitis and metformin, which is typically prescribed for type 2 diabetes, for type 1 diabetes. These drugs, already approved by the Food and Drug Administration as safe and effective for treating other diseases, could potentially be repurposed.

    The research team is working with collaborators to validate their findings in a laboratory setting and, ultimately, in clinical trials.

    Lida Wang, a doctoral student in the biostatistics program, and Chachrit Khunsriraksakul, who earned a doctorate in bioinformatics and geonomics in 2022 and his medical degree in May from Penn State, co-led the study. Other Penn State College of Medicine authors on the paper include: Havell Markus, who is pursuing a doctorate and a medical degree; Dieyi Chen, doctoral candidate; Fan Zhang, graduate student; and Fang Chen, postdoctoral scholar. Xiaowei Zhan, associate professor at UT Southwestern Medical Center, also contributed to the paper.

    Funding from the National Institutes of Health (grant numbers R01HG011035, R01AI174108 and R01ES036042) and the Artificial Intelligence and Biomedical Informatics pilot grant from the Penn State College of Medicine supported this work.

  • Diamond glitter: A play of colors with artificial DNA crystals

    Diamond glitter: A play of colors with artificial DNA crystals

    Using DNA origami, LMU researchers have built a diamond lattice with a periodicity of hundreds of nanometers — a new approach for manufacturing semiconductors for visible light.

    The shimmering of butterfly wings in bright colors does not emerge from pigments. Rather, it is photonic crystals that are responsible for the play of colors. Their periodic nanostructure allows light at certain wavelengths to pass through while reflecting other wavelengths. This causes the wing scales, which are in fact transparent, to appear so magnificently colored. For research teams, the manufacture of artificial photonic crystals for visible light wavelengths has been a major challenge and motivation ever since they were predicted by theorists more than 35 years ago. “Photonic crystals have a versatile range of applications. They have been employed to develop more efficient solar cells, innovative optical waveguides, and materials for quantum communication. However, they have been very laborious to manufacture,” explains Dr. Gregor Posnjak. The physicist is a postdoc in the research group of LMU Professor Tim Liedl, whose work is funded by the “e-conversion” Cluster of Excellence and the European Research Council. Using DNA nanotechnology, the team has developed a new approach for the manufacture of photonic crystals. Their results have now been published in the journal Science.

    Diamond structure out of strands of DNA

    In contrast to lithographic techniques, the LMU team uses a method called DNA origami to design and synthesize building blocks, which then self-assemble into a specific lattice structure. “It’s long been known that the diamond lattice theoretically has an optimal geometry for photonic crystals. In diamonds, each carbon atom is bonded to four other carbon atoms. Our challenge consisted in enlarging the structure of a diamond crystal by a factor of 500, so that the spaces between the building blocks correspond with the wavelength of light,” explains Tim Liedl. “We increased the periodicity of the lattice to 170 nanometers by replacing the individual atoms with larger building blocks — in our case, through DNA origami,” says Posnjak.

    The perfect molecule folding technique

    What sounds like magic is actually a specialty of the Liedl group, one of the world’s leading research teams in DNA origami and self-assembly. For this purpose, the scientists use a long, ring-shaped DNA strand (consisting of around 8,000 bases) and a set of 200 short DNA staples. “The latter control the folding of the longer DNA strand into virtually any shape at all — akin to origami masters, who fold pieces of paper into intricate objects. As such, the clamps are a means of determining how the DNA origami objects combine to form the desired diamond lattice,” says the LMU postdoctoral researcher. The DNA origami building blocks form crystals of approximately ten micrometers in size, which are deposited on a substrate and then passed on to a cooperating research group from the Walter Schottky Institute at the Technical University of Munich (TUM): The team led by Professor Ian Sharp (also funded by the “e-conversion” Cluster of Excellence) is able to deposit individual atomic layers of titanium dioxide on all surfaces of the DNA origami crystals. “The DNA origami diamond lattice serves as scaffolding for titanium dioxide, which, on account of its high index of refraction, determines the photonic properties of the lattice. After coating, our photonic crystal does not allow UV light with a wavelength of about 300 nanometers to pass through, but rather reflects it,” explains Posnjak. The wavelength of the reflected light can be controlled via the thickness of the titanium dioxide layer.

    DNA origami could boost photonics

    For photonic crystals that work in the infrared range, classic lithographic techniques are suitable but laborious and expensive. In the wavelength range of visible and UV light, lithographic methods have not been successful to date. “Consequently, the comparatively easy manufacturing process using the self-assembly of DNA origami in an aqueous solution offers a powerful alternative for producing structures in the desired size cost-effectively and in larger quantities,” says Prof. Tim Liedl. He is convinced that the unique structure with its large pores, which are chemically addressable, will stimulate further research — for example, in the domain of energy harvesting and storage. In the same issue of Science, a collaboration led by prof. Petr Šulc of Arizona State University and TUM presents a theoretical framework for designing diverse crystalline lattices from patchy colloids, and experimentally demonstrates the method by utilizing DNA origami building blocks to form a pyrochlore lattice, which potentially also could be used for photonic applications.