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  • New mechanism for maintaining genome stability discovered

    New mechanism for maintaining genome stability discovered

    Genetically speaking, it’s a bacterium’s worst-case scenario: during transcription, newly minted RNA sticks to its DNA template, forming a 3-stranded structure known as an R-loop. While these structures have some important roles to play in a cell, R-loops in the wrong place at the wrong time can be disastrous, leading to DNA breaks, mutations, and cell death.

    Now, new research in Nature Structural & Molecular Biology describes how the enzyme RapA prevents R-loop formation in E. coli, with far-reaching implications for how all cells maintain genomic stability. The findings demonstrate that, under certain circumstances, the RNA polymerase (RNAP) enzyme responsible for copying DNA into RNA can generate rampant R-loops — if not for the intervention of a protein called RapA.

    “R-loops are generally bad news, so cells have many redundant mechanisms to prevent them from forming,” says Seth Darst, head of the Laboratory of Molecular Biophysics. “We discovered that RapA, a protein that we’ve been curious about for years, is one of those key mechanisms.”

    The jaws of life

    All living things rely on RNAP to transcribe DNA into RNA. In bacteria, researchers have long known that transcription begins when RNAP clamps onto a DNA strand and initiates the process after receiving the green light from sigma proteins. But the details of how transcription ends are still fuzzy. New studies have shown, for instance, that RNAP often remains clamped to the DNA even after the newly finished RNA transcript is released — why and how was poorly understood.

    Back in the 1990s, the Darst lab had discovered RapA, a ATPase that clearly interacted with RNAP — but with no obvious function. “At the time, we really couldn’t figure out what RapA was doing,” he says. But after a few decades, when a separate research group found that E. coli exposed to stressful, high-salt conditions could not grow without RapA, Darst’s interest in the mysterious protein reignited.

    His team set about using cryo-EM to examine how RNAP remains clamped to DNA after transcription termination, and how RapA interacts with it. They chose negatively supercoiled DNA — an underwound and twisted form of the double helix — because it better mimics the natural state of bacterial DNA than the linear DNA often used in structural studies. “Ours is one of the first studies to use negatively supercoiled DNA in a cryo-EM experiment,” says first author Joshua Brewer, who designed the experiment. “This method helped us better visualize the topological state of the DNA, how the proteins rearrange themselves, and how they interact with the DNA.”

    They were surprised to find that RNAP seldom sits idly when it remains clamped to DNA after transcription is complete. Instead, it can initiate transcription again, this time without the normal safeguard of sigma proteins. In the absence of sigma, transcription initiation forms harmful R-loops unless RapA can intervene first, prying open the RNAP clamp. “RNAP is like a big claw that closes around DNA,” Darst says. “RapA binds RNAP and pulls the clamp open so that it falls off the DNA before it can create R-loops.”

    Beyond bacteria

    A clearer picture of RapA’s role began to emerge. When the team subjected bacteria engineered without RapA to stressful, high-salt conditions, the microbes experienced genetic instability — evidence that RNAP is more likely to remain clamped to DNA and more likely to form R-loops under certain circumstances.

    They also learned that, while E. coli also possess Rho, a well-studied enzyme capable of pulling R-loops apart, Rho cannot fully compensate when RapA is absent. “When you knock out RapA, Rho has to work overtime,” Brewer says. “It appears that RapA and Rho work as complementary — not redundant — safeguards for genome stability when E. coli are subject to high-salt stress.”

    The implications could be vast. Darst, Brewer, and colleagues suspect that RapA — or something like it (an RNAP release factor, so to speak) — exists not only in E. coli but in all bacteria and, perhaps, in all cells. Uncovering similar mechanisms in other organisms could inspire new strategies to target diseases linked to transcription-related genome instability.

    “We predict that other enzymes likely serve similar functions across the tree of life,” says Darst. “The more we learn about these mechanisms, the more we deepen our understanding of how cells safeguard their genomes.”

  • New method forecasts computation, energy costs for sustainable AI models

    New method forecasts computation, energy costs for sustainable AI models

    The process of updating deep learning/AI models when they face new tasks or must accommodate changes in data can have significant costs in terms of computational resources and energy consumption. Researchers have developed a novel method that predicts those costs, allowing users to make informed decisions about when to update AI models to improve AI sustainability.

    “There have been studies that focused on making deep learning model training more efficient,” says Jung-Eun Kim, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University. “However, over a model’s life cycle, it will likely need to be updated many times. One reason is that, as our work here shows, retraining an existing model is much more cost effective than training a new model from scratch.

    “If we want to address sustainability issues related to deep learning AI, we must look at computational and energy costs across a model’s entire life cycle — including the costs associated with updates. If you cannot predict what the costs will be ahead of time, it is impossible to engage in the type of planning that makes sustainability efforts possible. That makes our work here particularly valuable.”

    Training a deep learning model is a computationally intensive process, and users want to go as long as possible without having to update the AI. However, two types of shifts can happen that make these updates inevitable. First, the task that the AI is performing may need to be modified. For example, if a model was initially tasked with only classifying digits and traffic symbols, you may need to modify the task to identify vehicles and humans as well. This is called a task shift.

    Second, the data users provide to the model may change. For example, you may need to make use of a new kind of data, or perhaps the data you are working with is being coded in a different way. Either way, the AI needs to be updated to accommodate the change. This is called a distribution shift.

    “Regardless of what is driving the need for an update, it is extremely useful for AI practitioners to have a realistic estimate of the computational demand that will be required for the update,” Kim says. “This can help them make informed decisions about when to conduct the update, as well as how much computational demand they will need to budget for the update.”

    To forecast what the computational and energy costs will be, the researchers developed a new technique they call the REpresentation Shift QUantifying Estimator (RESQUE).

    Essentially, RESQUE allows users to compare the dataset that a deep learning model was initially trained on to the new dataset that will be used to update the model. This comparison is done in a way that estimates the computational and energy costs associated with conducting the update.

    Those costs are presented as a single index value, which can then be compared with five metrics: epochs, parameter change, gradient norm, carbon and energy. Epochs, parameter change and gradient norm are all ways of measuring the amount of computational effort necessary to retrain the model.

    “However, to provide insight regarding what this means in a broader sustainability context, we also tell users how much energy, in kilowatt hours, will be needed to retrain the model,” Kim says. “And we predict how much carbon, in kilograms, will be released into the atmosphere in order to provide that energy.”

    The researchers conducted extensive experiments involving multiple data sets, many different distribution shifts, and many different task shifts to validate RESQUE’s performance.

    “We found that the RESQUE predictions aligned very closely with the real-world costs of conducting deep learning model updates,” Kim says. “Also, as I noted earlier, all of our experimental findings tell us that training a new model from scratch demands far more computational power and energy than retraining an existing model.”

    In the short term, RESQUE is a useful methodology for anyone who needs to update a deep learning model.

    “RESQUE can be used to help users budget computational resources for updates, allow them to predict how long the update will take, and so on,” Kim says.

    “In the bigger picture, this work offers a deeper understanding of the costs associated with deep learning models across their entire life cycle, which can help us make informed decisions related to the sustainability of the models and how they are used. Because if we want AI to be viable and useful, these models must be not only dynamic but sustainable.”

  • New AI predicts inner workings of cells

    New AI predicts inner workings of cells

    In the same way that ChatGPT understands human language, a new AI model developed by Columbia computational biologists captures the language of cells to accurately predict their activities.

    Using a new artificial intelligence method, researchers at Columbia University Vagelos College of Physicians and Surgeons can accurately predict the activity of genes within any human cell, essentially revealing the cell’s inner mechanisms. The system, described in the current issue of Nature, could transform the way scientists work to understand everything from cancer to genetic diseases.

    “Predictive generalizable computational models allow to uncover biological processes in a fast and accurate way. These methods can effectively conduct large-scale computational experiments, boosting and guiding traditional experimental approaches,” says Raul Rabadan, professor of systems biology and senior author of the new paper.

    Traditional research methods in biology are good at revealing how cells perform their jobs or react to disturbances. But they cannot make predictions about how cells work or how cells will react to change, like a cancer-causing mutation.

    “Having the ability to accurately predict a cell’s activities would transform our understanding of fundamental biological processes,” Rabadan says. “It would turn biology from a science that describes seemingly random processes into one that can predict the underlying systems that govern cell behavior.”

    In recent years, the accumulation of massive amounts of data from cells and more powerful AI models are starting to transform biology into a more predictive science. The 2024 Nobel Prize in Chemistry was awarded to researchers for their groundbreaking work in using AI to predict protein structures. But the use of AI methods to predict the activities of genes and proteins inside cells has proven more difficult.

    New AI method predicts gene expression in any cell

    In the new study, Rabadan and his colleagues tried to use AI to predict which genes are active within specific cells. Such information about gene expression can tell researchers the identity of the cell and how the cell performs its functions.

    “Previous models have been trained on data in particular cell types, usually cancer cell lines or something else that has little resemblance to normal cells,” Rabadan says. Xi Fu, a graduate student in Rabadan’s lab, decided to take a different approach, training a machine learning model on gene expression data from millions of cells obtained from normal human tissues. The inputs consisted of genome sequences and data showing which parts of the genome are accessible and expressed.

    The overall approach resembles the way ChatGPT and other popular “foundation” models work. These systems use a set of training data to identify underlying rules, the grammar of language, and then apply those inferred rules to new situations. “Here it’s exactly the same thing: we learn the grammar in many different cellular states, and then we go into a particular condition — it can be a diseased or it can be a normal cell type — and we can try to see how well we predict patterns from this information,” says Rabadan.

    Fu and Rabadan soon enlisted a team of collaborators, including co-first authors Alejandro Buendia, now a Stanford PhD student formerly in the Rabadan lab, and Shentong Mo of Carnegie Mellon, to train and test the new model.

    After training on data from more than 1.3 million human cells, the system became accurate enough to predict gene expression in cell types it had never seen, yielding results that agreed closely with experimental data.

    New AI methods reveal drivers of a pediatric cancer

    Next, the investigators showed the power of their AI system when they asked it to uncover still hidden biology of diseased cells, in this case, an inherited form of pediatric leukemia.

    “These kids inherit a gene that is mutated, and it was unclear exactly what it is these mutations are doing,” says Rabadan, who also co-directs the cancer genomics and epigenomics research program at Columbia’s Herbert Irving Comprehensive Cancer Center.

    With AI, the researchers predicted that the mutations disrupt the interaction between two different transcription factors that determine the fate of leukemic cells. Laboratory experiments confirmed AI’s prediction. Understanding the effect of these mutations uncovers specific mechanisms that drive this disease.

    AI could reveal “dark matter” in genome

    The new computational methods should also allow researchers to start exploring the role of genome’s “dark matter” — a term borrowed from cosmology that refers to the vast majority of the genome, which does not encode known genes — in cancer and other diseases.

    “The vast majority of mutations found in cancer patients are in so-called dark regions of the genome. These mutations do not affect the function of a protein and have remained mostly unexplored. says Rabadan. “The idea is that using these models, we can look at mutations and illuminate that part of the genome.”

    Already, Rabadan is working with researchers at Columbia and other universities, exploring different cancers, from brain to blood cancers, learning the grammar of regulation in normal cells, and how cells change in the process of cancer development.

    The work also opens new avenues for understanding many diseases beyond cancer and potentially identifying targets for new treatments. By presenting novel mutations to the computer model, researchers can now gain deep insights and predictions about exactly how those mutations affect a cell.

    Coming on the heels of other recent advances in artificial intelligence for biology, Rabadan sees the work as part of a major trend: “It’s really a new era in biology that is extremely exciting; transforming biology into a predictive science.”

  • How good are AI doctors at medical conversations?

    How good are AI doctors at medical conversations?

    Artificial intelligence tools such as ChatGPT have been touted for their promise to alleviate clinician workload by triaging patients, taking medical histories and even providing preliminary diagnoses.

    These tools, known as large-language models, are already being used by patients to make sense of their symptoms and medical tests results.

    But while these AI models perform impressively on standardized medical tests, how well do they fare in situations that more closely mimic the real world?

    Not that great, according to the findings of a new study led by researchers at Harvard Medical School and Stanford University.

    For their analysis, published Jan. 2 in Nature Medicine, the researchers designed an evaluation framework — or a test — called CRAFT-MD (Conversational Reasoning Assessment Framework for Testing in Medicine) and deployed it on four large-language models to see how well they performed in settings closely mimicking actual interactions with patients.

    All four large-language models did well on medical exam-style questions, but their performance worsened when engaged in conversations more closely mimicking real-world interactions.

    This gap, the researchers said, underscores a two-fold need: First, to create more realistic evaluations that better gauge the fitness of clinical AI models for use in the real world and, second, to improve the ability of these tools to make diagnosis based on more realistic interactions before they are deployed in the clinic.

    Evaluation tools like CRAFT-MD, the research team said, can not only assess AI models more accurately for real-world fitness but could also help optimize their performance in clinic.

    “Our work reveals a striking paradox — while these AI models excel at medical board exams, they struggle with the basic back-and-forth of a doctor’s visit,” said study senior author Pranav Rajpurkar, assistant professor of biomedical informatics at Harvard Medical School. “The dynamic nature of medical conversations — the need to ask the right questions at the right time, to piece together scattered information, and to reason through symptoms — poses unique challenges that go far beyond answering multiple choice questions. When we switch from standardized tests to these natural conversations, even the most sophisticated AI models show significant drops in diagnostic accuracy.”

    A better test to check AI’s real-world performance

    Right now, developers test the performance of AI models by asking them to answer multiple choice medical questions, typically derived from the national exam for graduating medical students or from tests given to medical residents as part of their certification.

    “This approach assumes that all relevant information is presented clearly and concisely, often with medical terminology or buzzwords that simplify the diagnostic process, but in the real world this process is far messier,” said study co-first author Shreya Johri, a doctoral student in the Rajpurkar Lab at Harvard Medical School. “We need a testing framework that reflects reality better and is, therefore, better at predicting how well a model would perform.”

    CRAFT-MD was designed to be one such more realistic gauge.

    To simulate real-world interactions, CRAFT-MD evaluates how well large-language models can collect information about symptoms, medications, and family history and then make a diagnosis. An AI agent is used to pose as a patient, answering questions in a conversational, natural style. Another AI agent grades the accuracy of final diagnosis rendered by the large-language model. Human experts then evaluate the outcomes of each encounter for ability to gather relevant patient information, diagnostic accuracy when presented with scattered information, and for adherence to prompts.

    The researchers used CRAFT-MD to test four AI models — both proprietary or commercial and open-source ones — for performance in 2,000 clinical vignettes featuring conditions common in primary care and across 12 medical specialties.

    All AI models showed limitations, particularly in their ability to conduct clinical conversations and reason based on information given by patients. That, in turn, compromised their ability to take medical histories and render appropriate diagnosis. For example, the models often struggled to ask the right questions to gather pertinent patient history, missed critical information during history taking, and had difficulty synthesizing scattered information. The accuracy of these models declined when they were presented with open-ended information rather than multiple choice answers. These models also performed worse when engaged in back-and-forth exchanges — as most real-world conversations are — rather than when engaged in summarized conversations.

    Recommendations for optimizing AI’s real-world performance

    Based on these findings, the team offers a set of recommendations both for AI developers who design AI models and for regulators charged with evaluating and approving these tools.

    These include:

    • Use of conversational, open-ended questions that more accurately mirror unstructured doctor-patient interactions in the design, training, and testing of AI tools
    • Assessing models for their ability to ask the right questions and to extract the most essential information
    • Designing models capable of following multiple conversations and integrating information from them
    • Designing AI models capable of integrating textual (notes from conversations) with and non-textual data (images, EKGs)
    • Designing more sophisticated AI agents that can interpret non-verbal cues such as facial expressions, tone, and body language

    Additionally, the evaluation should include both AI agents and human experts, the researchers recommend, because relying solely on human experts is labor-intensive and expensive. For example, CRAFT-MD outpaced human evaluators, processing 10,000 conversations in 48 to 72 hours, plus 15-16 hours of expert evaluation. In contrast, human-based approaches would require extensive recruitment and an estimated 500 hours for patient simulations (nearly 3 minutes per conversation) and about 650 hours for expert evaluations (nearly 4 minutes per conversation). Using AI evaluators as first line has the added advantage of eliminating the risk of exposing real patients to unverified AI tools.

    The researchers said they expect that CRAFT-MD itself will also be updated and optimized periodically to integrate improved patient-AI models.

    “As a physician scientist, I am interested in AI models that can augment clinical practice effectively and ethically,” said study co-senior author Roxana Daneshjou, assistant professor of Biomedical Data Science and Dermatology at Stanford University. “CRAFT-MD creates a framework that more closely mirrors real-world interactions and thus it helps move the field forward when it comes to testing AI model performance in health care.”

    Authorship, funding, disclosures

    Additional authors included Jaehwan Jeong and Hong-Yu Zhou, Harvard Medical School; Benjamin A. Tran, Georgetown University; Daniel I. Schlessinger, Northwestern University; Shannon Wongvibulsin, University of California-Los Angeles; Leandra A. Barnes, Zhuo Ran Cai and David Kim, Sandford University; and Eliezer M. Van Allen, Dana-Farber Cancer Institute.

    The work was supported by the HMS Dean’s Innovation Award and a Microsoft Accelerate Foundation Models Research grant awarded to Pranav Rajpurkar. SJ received further support through the IIE Quad Fellowship.

    Daneshjou reported receiving personal fees from DWA, personal fees from Pfizer, personal fees from L’Oreal, personal fees from VisualDx, stock options from MDAlgorithms and Revea outside the submitted work, and a patent for TrueImage pending. Schlessinger is the co-founder of FixMySkin Healing Balms, a shareholder in Appiell Inc. and K-Health, a consultant with Appiell Inc and LuminDx, and an investigator for Abbvie and Sanofi. Van Allen serves as an advisor to Enara Bio, Manifold Bio, Monte Rosa, Novartis Institute for Biomedical Research, Serinus Bio. E.M.V.A provides research support to Novartis, BMS, Sanofi, NextPoint. Van Allen holds equity in Tango Therapeutics, Genome Medical, Genomic Life, Enara Bio, Manifold Bio, Microsoft, Monte Rosa, Riva Therapeutics, Serinus Bio, Syapse. Van Allen has filed for institutional patents on chromatin mutations and immunotherapy response, and methods for clinical interpretation; intermittent legal consulting on patents for Foaley & Hoag, and serves on the editorial board of Science Advances.

  • Fly vs. wasp: Stealing a defense move helps thwart a predator

    Fly vs. wasp: Stealing a defense move helps thwart a predator

    In the continual arms race between parasites and their hosts, innovation was thought to be the key to a successful attack or defense that one-ups the competition.

    But sometimes, as in the corporate world, outright theft can be a quicker way to achieve dominance.

    University of California, Berkeley biologists have shown that several species of fruit fly have stolen a successful defense from bacteria to survive predation by parasitic wasps, which in some flies can turn half of all fly larvae into surrogate wombs for baby wasps — a gruesome fate that inspired the creature in the 1979 movie “Alien.”

    Bacteria and other microbes are famous for stealing genes from other microbes or viruses; this so-called horizontal gene transfer is the source of troublesome antibiotic resistance among disease-causing microbes. But it’s thought to be less common in multicellular organisms, such as insects and humans. Understanding how common it is in animals and how these genes are co-opted and shared can help scientists understand the evolution of animal immune defenses and could point the way to human therapies to fight parasitic or infectious diseases or cancer, itself a kind of parasite.

    “It’s a model for understanding how immune systems evolve, including our immune system, which also contains horizontally transferred genes,” said Noah Whiteman, UC Berkeley professor of molecular and cell biology and of integrative biology and director of the campus’s Essig Museum of Entomology.

    Last year, the researchers and their colleagues in Hungary used CRISPR genome editing to knock out the gene responsible for the defense in one widespread fly species, Drosophila ananassae, and found that nearly all the genetically modified flies died from predation by parasitic wasps.

    In a new study published Dec. 20 in the journal Current Biology, the biologists demonstrated that this defense — a gene that encodes a toxin — can be edited into the genome of the common laboratory fruit fly, Drosophila melanogaster, to make them resistant to parasitoid wasps as well. The gene essentially becomes part of the fly’s immune system, one weapon in its armamentarium to fend off parasites.

    The results demonstrate how crucial the stolen defense is to fly survival and highlights a strategy that may be more common in animals that scientists suspect.

    “This shows that horizontal gene transfer is an underappreciated way that rapid evolution happens in animals,” said UC Berkeley doctoral student Rebecca Tarnopol, first author of the Current Biology paper. “People appreciate horizontal gene transfer as one of the major drivers of rapid adaptation in microbes, but these events were thought to be super uncommon in animals. But at least in insects, it seems like they’re fairly frequent.”

    According to Whiteman, senior author of the paper, “The study shows that in order to keep up with the barrage of parasites that are continually evolving new ways to overcome host defenses, a good strategy for animals is to borrow genes from even more rapidly evolving viruses and bacteria, and that’s just what these flies have done.”

    Gene flow from virus to bacteria to fly

    Whiteman studies how insects evolve to resist the toxins that plants produce to prevent being eaten. In 2023, he published a book, “Most Delicious Poison,” about the plant toxins that humans have come to enjoy, such as caffeine and nicotine.

    One plant-herbivore interaction he focuses on is that between the common fruit fly Scaptomyza flava and sour-tasting mustard plants, like the cresses that grow in streams throughout the world.

    “The larvae, the immature stages of the fly, live in the leaves of the plant. They’re leaf miners, they leave little trails in the leaves,” Whiteman said. “They’re true parasites of the plant and the plant’s trying to kill them with its specialized chemicals. We study that arms race.”

    What he’s learned, however, likely applies to many other insects, among the most successful herbivores on Earth.

    “These are obscure little flies, but if you think about the fact that half of all living insect species are herbivores, it’s a very popular life history. Understanding the evolution of that is really important for understanding evolution in general in terms of how successful herbivores are,” he said.

    Several years ago, after sequencing the fly’s genome in search of genes that allow it to resist mustard toxins, he discovered an unusual gene that he learned was widespread in bacteria. A search through earlier published genome sequences turned up the same gene in a related fly, Drosophila ananassae, as well as in a bacteria that lives inside an aphid. Researchers studying the aphid uncovered a complicated story: The gene actually comes from a bacterial virus, or bacteriophage, that infects the bacteria that live inside the aphid. The bacteriophage gene, expressed by the bacteria, makes the aphid resistant to a parasitic wasp that plagues it.

    These wasps lay their eggs inside the larvae, or maggots, and remain there until the larvae turn into immobile pupae, at which point the wasp eggs mature into wasp larvae that consume the fly pupa, eventually emerging as adults.

    When Tarnopol first used gene editing to express the toxin gene in all cells of D. melanogaster, all the flies died. But when Tarnopol expressed the gene only in certain immune cells, the fly became as resistant to parasites as its cousin, D. ananassae.

    Whiteman, Tarnopol, and their colleagues subsequently discovered that the gene found in the genome of D. ananassae — a fusion between two toxin genes, cytolethal distending toxin B (cdtB) and apoptosis inducing protein of 56kDa (aip56), that the researchers called fusionB — codes for an enzyme that cuts up DNA.

    To discover how this nuclease is able to kill a wasp egg, the UC Berkeley researchers reached out to István Andó at the Institute of Genetics of the HUN-REN Biological Research Centre in Szeged, Hungary, which had previously shown that these same flies have a cellular defense against wasp eggs that essentially walls off the eggs from the fly’s body and kills them. Andó and his lab colleagues created antibodies to the toxin that allowed them to track it through the fly’s body and found that the nuclease essentially floods the fly’s body to surround and kill the egg.

    “We’ve been finding this huge untapped world of humoral immune factors that might be at play in the immune system of invertebrates,” Tarnopol said. “Our paper is one of the first ones to show, at least in Drosophila, that this type of immune response might be a common mechanism by which natural enemies like wasps and nematodes are dealt with. They are way more lethal in nature than some of the microbial infections that most people work with.”

    Whiteman and his colleagues are still exploring the complexities of these interactions between fly and wasp, and the cellular and genetic changes that allowed the flies to synthesize a toxin without killing itself.

    “If the gene is expressed in the wrong tissue, the fly is going to die. That gene is never going to sweep through populations through natural selection,” Whiteman said. “But if it lands in a place in the genome that’s near some enhancer or some regulatory component that expresses it a little bit in fat body tissue, then you can see how it can get this leg up really quickly, you get this amazing advantage.”

    Horizontal gene transfer in any organism would pose similar problems, he said, but in the arms race between predator and prey, it may be worth it.

    “When you’re a poor little fruit fly, how do you deal with these pathogens and parasites that are rapidly evolving to take advantage of you?” he said. “One way is to borrow genes from bacteria and viruses because they’re rapidly evolving. It’s an ingenious strategy — instead of waiting around for your own genes to help you, take them from other organisms that are more rapidly evolving than themselves. And that seems to have happened many times independently in insects, given that so many different ones have taken up this gene. It gives us a picture of a new kind of dynamism that is occurring even in animals that have just innate immune systems and don’t have adaptive immunity.”

    Whiteman’s work was funded by the National Institute of General Medical Sciences of the National Institutes of Health (R35GM119816). Other co-authors of the paper are Josephine Tamsil, Ji Heon Ha, Kirsten Verster and Susan Bernstein of UC Berkeley, Gyöngyi Cinege, Edit Ábrahám, Lilla B. Magyar and Zoltán Lipinszki of Hungary and Bernard Kim of Stanford University.

  • Researchers take ‘significant leap forward’ with quantum simulation of molecular electron transfer

    Researchers take ‘significant leap forward’ with quantum simulation of molecular electron transfer

    Researchers at Rice University have made a meaningful advance in the simulation of molecular electron transfer — a fundamental process underpinning countless physical, chemical and biological processes. The study, published in Science Advances, details the use of a trapped-ion quantum simulator to model electron transfer dynamics with unprecedented tunability, unlocking new opportunities for scientific exploration in fields ranging from molecular electronics to photosynthesis.

    Electron transfer, critical to processes such as cellular respiration and energy harvesting in plants, has long posed challenges to scientists due to the complex quantum interactions involved. Current computational techniques often fall short of capturing the full scope of these processes. The multidisciplinary team at Rice, including physicists, chemists and biologists, addressed these challenges by creating a programmable quantum system capable of independently controlling the key factors in electron transfer: donor-acceptor energy gaps, electronic and vibronic couplings and environmental dissipation.

    Using an ion crystal trapped in a vacuum system and manipulated by laser light, the researchers demonstrated the ability to simulate real-time spin dynamics and measure transfer rates across a range of conditions. The findings not only validate key theories of quantum mechanics but also pave the way for novel insights into light-harvesting systems and molecular devices.

    “This is the first time that this kind of model was simulated on a physical device while including the role of the environment and even tailoring it in a controlled way,” said lead researcher Guido Pagano, assistant professor of physics and astronomy. “It represents a significant leap forward in our ability to use quantum simulators to investigate models and regimes that are relevant for chemistry and biology. The hope is that by harnessing the power of quantum simulation, we will eventually be able to explore scenarios that are currently inaccessible to classical computational methods.”

    The team achieved a significant milestone by successfully replicating a standard model of molecular electron transfer using a programmable quantum platform. Through the precise engineering of tunable dissipation, the researchers explored both adiabatic and nonadiabatic regimes of electron transfer, demonstrating how these quantum effects operate under varying conditions. Additionally, their simulations identified optimal conditions for electron transfer, which parallel the energy transport mechanisms observed in natural photosynthetic systems.

    “Our work is driven by the question: Can quantum hardware be used to directly simulate chemical dynamics?” Pagano said. “Specifically, can we incorporate environmental effects into these simulations as they play a crucial role in processes essential to life such as photosynthesis and electron transfer in biomolecules? Addressing this question is significant as the ability to directly simulate electron transfer in biomolecules could provide valuable insights for designing new light-harvesting materials.”

    The implications for practical applications are far-reaching. Understanding electron transfer processes at this level could lead to breakthroughs in renewable energy technologies, molecular electronics and even the development of new materials for quantum computing.

    “This experiment is a promising first step to gain a deeper understanding of how quantum effects influence energy transport, particularly in biological systems like photosynthetic complexes,” said Jose N. Onuchic, study co-author, the Harry C. and Olga K. Wiess Chair of Physics and professor of physics and astronomy, chemistry and biosciences. “The insights we gain in this type of experiment could inspire the design of more efficient light-harvesting materials.”

    Peter G. Wolynes, study co-author, the D.R. Bullard-Welch Foundation Professor of Science and professor of chemistry, biosciences and physics and astronomy, emphasized the broader significance of the findings: “This research bridges the gap between theoretical predictions and experimental verification, offering an exquisitely tunable framework for exploring quantum processes in complex systems.”

    The team plans to extend its simulations to include more complex molecular systems such as those involved in photosynthesis and DNA charge transport. The researchers also hope to investigate the role of quantum coherence and delocalization in energy transfer, leveraging the unique capabilities of their quantum platform.

    “This is just the beginning,” said Han Pu, co-lead author of the study and professor of physics and astronomy. “We are excited to explore how this technology can help unravel the quantum mysteries of life and beyond.”

    The study’s other co-authors include graduate students Visal So, Midhuna Duraisamy Suganthi, Abhishek Menon, Mingjian Zhu and research scientist Roman Zhuravel.

    This research was made possible thanks to the Welch Foundation Award C-2154, the Office of Naval Research Young Investigator Program (No. N00014-22-1-2282), a National Science Foundation CAREER Award (No. PHY-2144910), the Army Research Office (W911NF22C0012), the Office of Naval Research (No. N00014-23-1-2665), the NSF (PHY-2207283, PHY-2019745 and PHY-2210291) and the D. R. Bullard-Welch Chair at Rice (No. C0016). The authors acknowledge that this material is based upon work supported by the U.S Department of Energy, Office of Science, Office of Nuclear Physics under the Early Career Award No. DE-SC0023806. The isotopes used in this research were supplied by the U.S. Department of Energy Isotope Program managed by the Office of Isotope R&D and Production.

  • A user manual for yeast’s genetic switches

    A user manual for yeast’s genetic switches

    When introducing genes into yeast to make it produce drugs and other useful substances, it is also necessary to reliably switch the production on or off. A Kobe University team found three gene regulation design principles that provide a flexible guideline for the effective control of microbiological production.

    It’s said that DNA is the blueprint of life, telling our cells what to produce. But DNA also contains the switches telling those cells when to produce something and how much of it. Therefore, when introducing new genes into cells to produce useful chemicals such as drugs or raw materials for chemical production, it is also necessary to include a genetic switch, a piece of DNA called a “promoter,” that tells the cells to start production as needed. Kobe University bioengineer TOMINAGA Masahiro says: “The problem is that these promoters cannot be used in a plug-and-play manner unless researchers deeply understand how they interact with other genetic elements. Indeed, there are not so many cases in which researchers use artificial promoters to precisely control the cellular production and achieve their research purpose.” Sometimes the production is too low, sometimes it is “leaky,” meaning that it cannot be turned off at will. This is especially true for bioengineering yeast, which is more complex in its genetic regulation compared to bacteria. But this increased complexity also enables its use to produce many useful chemicals.

    As experts in modifying yeast cells, Tominaga and colleagues from the team led by ISHII Jun took a systematic approach to working out how to design effective promoters. “We came up with the idea that by carefully describing our process of improving a prototype promoter, we could prepare a ‘user manual’ for how to achieve high-performance and precise control so that these genetic systems could be more widely used,” Tominaga explains.

    In a paper now published in the journal Nature Communications, they describe three design principles for yeast promoters. First, if researchers not only need large amounts of the product but also the ability to switch the production on or off at will, they should introduce multiple copies of the regulatory elements enabling this within the promoter. This reduces leakiness and increases the productivity. Second, the distance between promoter elements should be as small as possible to enhance the productivity even more. And third, the promoter should be insulated from surrounding DNA by including extra DNA before it to further reduce leakiness. Tominaga says: “We showed that a promoter’s performance can be improved more than 100-fold by simply modifying its surrounding sequence. This is the first study to clearly propose a solution to the problem why potent yeast promoters work in some environments and not in others.”

    The Kobe University bioengineers demonstrated the usefulness of their system by showcasing the production of two pharmaceutically useful proteins, so-called “biologics.” Not only could they produce these two biologics in separate yeast strains but also in the same strain and with the ability to independently control which biologic is produced at any time. The latter is important because it has potential applications in hospitals, as the team explains in the study: “In addition to the conventional fermentation of single biologics, the rapid and single-dose production of multiple biologics with a single yeast strain at the point of care is crucial for emergencies that require production speed and flexibility rather than purity and productivity.” They also achieved the notoriously difficult production of a coronavirus protein that can be used for the production of treatments, further showcasing both the usefulness and the flexibility of their design principles.

    Tominaga explains his wider outlook on the implications of this study: “Synthetic biology advocates creating new biological functions by rewriting genome sequences. The reality is however that we are often confused by unexpected changes resulting from our edits. We hope that our study is the first step towards the ability to design every single base in the genome with clear intentions.”

    This research was funded by the Japan Agency for Medical Research and Development (grants JP21ae0121002, JP21ae0121005, JP21ae0121006, JP21ae0121007, JP20ae0101055 and JP20ae0101060), the Japan Science and Technology Agency (grants JPMJCR21N2 and JPMJGX23B4) and the Japan Society for the Promotion of Science (grants JP23K26469, JP23H01776 and JP18K14374). It was conducted in collaboration with researchers from the Pharma Foods International Co. Ltd and National Institute of Health Sciences.

  • Activating the hidden pharmaceutical potential of bacteria

    Activating the hidden pharmaceutical potential of bacteria

    Microorganisms produce a wide variety of natural products that can be used as active ingredients to treat diseases such as infections or cancer. The blueprints for these molecules can be found in the microbes’ genes, but often remain inactive under laboratory conditions. A team of researchers at the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) has now developed a groundbreaking genetic method that leverages a natural bacterial mechanism for the transfer of genetic material and uses it for the production of new active ingredients. The team published its results in the journal Science.

    In contrast to humans, bacteria have the remarkable ability to exchange genetic material with each other. A well-known example with far-reaching consequences is the transfer of antibiotic resistance genes between bacterial pathogens. This gene transfer allows them to adapt quickly to different environmental conditions and is a major driver of the spread of antibiotic resistance. Researchers at the HIPS and the German Center for Infection Research (DZIF) have now harnessed this natural principle to amplify and isolate genetic blueprints for new bioactive natural products from bacteria, known as biosynthetic gene clusters. Their innovative approach, called “ACTIMOT,” makes it possible to either produce the natural products encoded in the gene clusters directly in the native bacterium or to transfer them into more suitable microbial production strains to produce the new molecules there. The HIPS is a site of the Helmholtz Centre for Infection Research (HZI) in collaboration with Saarland University.

    ACTIMOT — short for “Advanced Cas9-mediaTed In vivo MObilization and mulTiplication of BGCs” — leverages the CRISPR-Cas9 technology, which has become known as “gene scissors,” and accordingly allows precise interventions in the genetic material of bacteria. Since biosynthetic gene clusters are often less active under laboratory conditions, they are extracted from the genome using ACTIMOT and inserted into a mobile genetic unit that is then multiplied by the bacterium itself. All these steps are performed exploiting the molecular mechanism that also allows bacteria to transfer resistance genes amongst each other. In many instances, the amplification of the gene clusters on these so-called plasmids is already sufficient to enable the production of the encoded natural products. If this does not succeed, the formed plasmids can be easily transferred into an alternative production strain to produce the encoded natural products. The authors provide successful examples of both approaches in the present study.

    “Many biosynthetic gene clusters remain suppressed under laboratory conditions for various reasons, and current efforts to reveal the natural products they encode only address a limited number of them,” says Chengzhang Fu, junior research group leader at HIPS and last author of the study. “Our approach mimics the natural bacterial gene transfer process to directly liberate and amplify entire biosynthetic gene clusters within the native bacterial cell, granting access to previously hidden natural products. Using this technology, we can access the biosynthetic potential of bacteria much faster and easier, as compared to existing methods.”

    The team has already demonstrated that ACTIMOT can indeed lead to new discoveries: During the study, the researchers discovered 39 new natural products from four previously unknown natural product classes. These discoveries have given the team confidence that ACTIMOT can significantly accelerate the discovery of new drug candidates. “Microorganisms offer us incredible potential for the production of new chemical matter that we can use, among other things, to develop urgently needed active ingredients,” says Rolf Müller, head of department and scientific director of the HIPS and coordinator of the ‘New Antibiotics’ research area at the DZIF, who also took a leading role in the study. “So far, large parts of this microbial treasure remain hidden from us. ACTIMOT will help us to further exploit the biosynthetic potential of bacteria and thus significantly advance the development of new active agents.”

    In the current study, ACTIMOT has been used with bacteria of the genus Streptomyces. However, the authors are already planning to expand it to other bacterial species with a high potential for the production of unknown natural products. Beyond this, ACTIMOT holds potential for application in various other areas, including the large-scale production of high-value natural products, the exploration of unknown gene pathways, and the identification of starting points for natural product optimization.

  • Intermittent fasting inhibits hair regeneration in mice

    Intermittent fasting inhibits hair regeneration in mice

    Intermittent fasting has proven benefits for metabolic health, but a new study shows that it could slow hair growth — at least in mice. Researchers report December 13 in the Cell Press journal Cell that mice subjected to intermittent fasting regimes showed improved metabolic health but slower hair regeneration compared to mice with 24/7 access to food. A similar process might occur in humans, based on a small clinical trial that the team also conducted, but it’s likely to be less severe since humans have a much slower metabolic rate and different hair growth patterns compared to mice.

    “We don’t want to scare people away from practicing intermittent fasting because it is associated with a lot of beneficial effects — it’s just important to be aware that it might have some unintended effects,” says senior author and stem cell biologist Bing Zhang of Westlake University in Zhejiang, China.

    In addition to its metabolic benefits, previous studies have shown that fasting can improve the stress resistance of stem cells associated with blood, intestinal, and muscle tissue, but little is known about how it impacts peripheral tissues such as skin and hair. Zhang’s team hypothesized that fasting might also be beneficial for skin tissue regeneration, the process by which old and damaged cells are replaced.

    To test this, they examined hair regrowth in mice that were shaved and then subjected to different intermittent fasting regimes. Some mice were fed on a time-restricted feeding (TRF) schedule that involved 8 hours of food access and 16 hours of fasting each day, while other mice were subjected to alternate-day feeding (ADF).

    They were surprised to find that fasting inhibited hair regeneration. While control mice that had unlimited access to food had regrown most of their hair after 30 days, mice on both intermittent fasting regimes showed only partial hair regrowth after 96 days.

    The team showed that this inhibited hair growth occurs because hair follicle stem cells (HFSCs) are unable to cope with the oxidative stress associated with switching from using glucose to fat. HFSCs go through phases of activity and dormancy, and hair regrowth depends on these cells becoming active. While the control mice’s HFSCs began to become activated around day 20 post-shaving and remained active until their hair had regrown, the intermittent fasting mice’s activated HFSCs underwent apoptosis (programmed cell death) during extended fasting periods.

    Using genetic engineering methods, the team showed that this fasting-induced apoptosis was driven by an increased concentration of free fatty acids near the hair follicles, which caused a build-up of harmful radical oxygen species within the HFSCs. Free fatty acids also caused human HFSCs to undergo apoptosis in vitro.

    “During fasting, adipose tissue starts to release free fatty acids, and these fatty acids enter the HFSCs that were recently activated, but these stem cells don’t have the right machinery to use them,” says Zhang.

    In comparison, epidermal stem cells, which are responsible for maintaining the epidermal skin barrier, were unaffected by intermittent fasting. The major difference between these stem cell types is that epidermal stem cells have a higher antioxidant capacity. When the team tested whether antioxidants could mitigate the effects of fasting on hair growth, they showed that both topical application of vitamin E and genetic upregulation of antioxidant capacity helped HFSCs survive fasting.

    The team also conducted a small clinical trial with 49 healthy young adults to examine whether fasting similarly affects hair regrowth in humans. They showed that a time-restricted diet involving 18 hours of fasting per day reduced the average speed of hair growth by 18% compared to controls, but larger studies would be needed to verify this effect given the study’s small sample size and short duration (10 days).

    “The human population is very heterogeneous, so the effects might be different for different people,” says Zhang. “Mice also have a very high metabolic rate compared with humans, so fasting and metabolic switching have a more severe effect on mouse HFSCs. We see a milder effect in humans — there are still apoptotic stem cells, but many HFSCs survive. So, there is still hair regrowth; it’s just a little bit slower than usual.”

    In future work, the researchers plan to collaborate with local hospitals to investigate how fasting impacts other types of stem cells in the skin and other body systems.

    “We plan to examine how this process affects the regeneration activities in other tissues,” says Zhang. “We also want to figure out how fasting impacts skin wound healing and identify metabolites that could help the survival of HFSCs and promote hair growth during fasting.”

    This research was supported by the National Natural Science Foundation of China, the Key R&D Program of Zhejiang, the Westlake Laboratory of Life Sciences and Biomedicine, the Research Center for Industries of the Future (RCIF), and the Center of Synthetic Biology and Integrated Bioengineering at Westlake University.

  • Noninvasive imaging method can penetrate deeper into living tissue

    Noninvasive imaging method can penetrate deeper into living tissue

    Metabolic imaging is a noninvasive method that enables clinicians and scientists to study living cells using laser light, which can help them assess disease progression and treatment responses.

    But light scatters when it shines into biological tissue, limiting how deep it can penetrate and hampering the resolution of captured images.

    Now, MIT researchers have developed a new technique that more than doubles the usual depth limit of metabolic imaging. Their method also boosts imaging speeds, yielding richer and more detailed images.

    This new technique does not require tissue to be preprocessed, such as by cutting it or staining it with dyes. Instead, a specialized laser illuminates deep into the tissue, causing certain intrinsic molecules within the cells and tissues to emit light. This eliminates the need to alter the tissue, providing a more natural and accurate representation of its structure and function.

    The researchers achieved this by adaptively customizing the laser light for deep tissues. Using a recently developed fiber shaper — a device they control by bending it — they can tune the color and pulses of light to minimize scattering and maximize the signal as the light travels deeper into the tissue. This allows them to see much further into living tissue and capture clearer images.

    Greater penetration depth, faster speeds, and higher resolution make this method particularly well-suited for demanding imaging applications like cancer research, tissue engineering, drug discovery, and the study of immune responses.

    “This work shows a significant improvement in terms of depth penetration for label-free metabolic imaging. It opens new avenues for studying and exploring metabolic dynamics deep in living biosystems,” says Sixian You, assistant professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the Research Laboratory for Electronics, and senior author of a paper on this imaging technique.

    She is joined on the paper by lead author Kunzan Liu, an EECS graduate student; Tong Qiu, an MIT postdoc; Honghao Cao, an EECS graduate student; Fan Wang, professor of brain and cognitive sciences; Roger Kamm, the Cecil and Ida Green Distinguished Professor of Biological and Mechanical Engineering; Linda Griffith, the School of Engineering Professor of Teaching Innovation in the Department of Biological Engineering; and other MIT colleagues. The research will appear in Science Advances.

    Laser-focused

    This new method falls in the category of label-free imaging, which means tissue is not stained beforehand. Staining creates contrast that helps a clinical biologist see cell nuclei and proteins better. But staining typically requires the biologist to section and slice the sample, a process that often kills the tissue and makes it impossible to study dynamic processes in living cells.

    In label-free imaging techniques, researchers use lasers to illuminate specific molecules within cells, causing them to emit light of different colors that reveal various molecular contents and cellular structures. However, generating the ideal laser light with certain wavelengths and high-quality pulses for deep-tissue imaging has been challenging.

    The researchers developed a new approach to overcome this limitation. They use a multimode fiber, a type of optical fiber which can carry a significant amount of power, could couple it with a compact device called a “fiber shaper.” This shaper allows them to precisely modulate the light propagation by adaptively changing the shape of the fiber. Bending the fiber changes the color and intensity of the laser.

    Building on prior work, the researchers adapted the first version of the fiber shaper for deeper multimodal metabolic imaging.

    “We want to channel all this energy into the colors we need with the pulse properties we require. This gives us higher generation efficiency and a clearer image, even deep within tissues,” says Cao.

    Once they had built the controllable mechanism, they developed an imaging platform to leverage the powerful laser source to generate longer wavelengths of light, which are crucial for deeper penetration into biological tissues.

    “We believe this technology has the potential to significantly advance biological research. By making it affordable and accessible to biology labs, we hope to empower scientists with a powerful tool for discovery,” Liu says.

    Dynamic applications

    When the researchers tested their imaging device, the light was able to penetrate more than 700 micrometers into a biological sample, whereas the best prior techniques could only reach about 200 micrometers.

    “With this new type of deep imaging, we want to look at biological samples and see something we have never seen before,” Liu adds.

    The deep imaging technique enabled them to see cells at multiple levels within a living system, which could help researchers study metabolic changes that happen at different depths. In addition, the faster imaging speed allows them to gather more detailed information on how a cell’s metabolism affects the speed and direction of its movements.

    This new imaging method could offer a boost to the study of organoids, which are engineered cells that can grow to mimic the structure and function of organs. Researchers in the Kamm and Griffith labs pioneer the development of brain and endometrial organoids that can grow like organs for disease and treatment assessment.

    However, it has been challenging to precisely observe internal developments without cutting or staining the tissue, which kills the sample.

    This new imaging technique allows researchers to noninvasively monitor the metabolic states inside a living organoid while it continues to grow.

    With these and other biomedical applications in mind, the researchers plan to aim for even higher-resolution images. At the same time, they are working to create low-noise laser sources, which could enable deeper imaging with less light dosage.

    They are also developing algorithms that react to the images to reconstruct the full 3D structures of biological samples in high resolution.

    In the long run, they hope to apply this technique in the real world to help biologists monitor drug response in real-time to aid in the development of new medicines.

    “By enabling multimodal metabolic imaging that reaches deeper into tissues, we’re providing scientists with an unprecedented ability to observe nontransparent biological systems in their natural state. We’re excited to collaborate with clinicians, biologists, and bioengineers to push the boundaries of this technology and turn these insights into real-world medical breakthroughs,” You says.

    This research is funded, in part, by MIT startup funds, a U.S. National Science Foundation CAREER Award, an MIT Irwin Jacobs and Joan Klein Presidential Fellowship, and an MIT Kailath Fellowship.