bioinnovationbeacon.com

Author: admin

  • Fast, rewritable computing with DNA origami registers

    Fast, rewritable computing with DNA origami registers

    DNA stores the instructions for life and, along with enzymes and other molecules, computes everything from hair color to risk of developing diseases. Harnessing that prowess and immense storage capacity could lead to DNA-based computers that are faster and smaller than today’s silicon-based versions. As a step toward that goal, researchers report in ACS Central Science a fast, sequential DNA computing method that is also rewritable — just like current computers.

    “DNA computing as a liquid computing paradigm has unique application scenarios and offers the potential for massive data storage and processing of digital files stored in DNA,” says Fei Wang, a co-author of the study.

    In living organisms, DNA expression occurs sequentially: Genes are transcribed into RNA, which is translated into proteins. This process happens to many genes simultaneously and repeatedly. If researchers can duplicate this complex, elegant dance in DNA-based computers, these devices could be more powerful than current silicon-based machines. Researchers have demonstrated sequential DNA computing for very focused, specialized tasks. But until recently, not much progress had been made in developing more general and programmable DNA devices that could be used and reused for various applications.

    In previous research, Chunhai Fan, Wang and colleagues developed a programmable DNA integrated circuit with many logic gates that act as instructions for the circuit’s operations. Here’s how it worked:

    • Data, 0 or 1, was represented by a short piece of single-stranded DNA, called an oligonucleotide, that contained a series of bases: adenine, thymine, guanine and cytosine. (In nature, the sequence of bases codes for a gene.)
    • For example, two inputs of 1 (DNA strands 1 and 2) would interact with an OR logic gate DNA molecule.
    • Then in a fluid-filled tube, the input oligonucleotide interacted with a logic gate DNA molecule and generated an output oligonucleotide.
    • The output oligonucleotide bound to a different single-stranded DNA that was folded into an origami-like structure, called a register in computer lingo.
    • The oligonucleotide was “read” by reviewing its base sequence, released and used in a vial containing the next gate, and so on.

    This process took hours, and someone had to manually transfer the oligonucleotide from one gate to another vial for the next computing operation. So the team, along with Hui Lv and Sisi Jia, wanted to speed things up.

    To make the reaction processes more efficient and compact, the team first placed the DNA origami register onto a solid glass 2D surface. The output oligonucleotide floating in liquid from a specific logic gate then attached to the glass-mounted register. After the output oligonucleotide was read and the logic gate instructions determined, it detached, which reset the register so it could be rewritten, thereby avoiding the need to move or replace registers. The researchers also designed an amplifier that boosted the output signal so all the pieces — the gates, oligonucleotides and registers — could find one another more easily. In a proof-of-concept experiment, all the DNA computing reactions took place in a single tube within 90 minutes.

    “This research paves the way for developing large-scale DNA computing circuits with high speed and lays the foundation for visual debugging and automated execution of DNA molecular algorithms,” says Wang.

  • Better environmental performance boosts profits and cuts costs

    Better environmental performance boosts profits and cuts costs

    Sustainable practices in business are more than just an ethical responsibility; they make sound financial sense. Researchers from Kyushu University, in a study published on December 10, 2024, in Corporate Social Responsibility and Environmental Management, reveal that companies with better environmental performance and transparent disclosures can lower costs and boost profits.

    Investors are increasingly recognizing companies’ contributions toward carbon neutrality, driving the growth of environmental, social, and governance (ESG) investing. To support this trend, the Sustainability Accounting Standards Board (SASB) has provided an industry-specific framework to help companies effectively communicate their sustainability risks and opportunities to investors. Many companies now disclose their environmental information using this framework, and in many countries around the world, such transparency is becoming mandatory.

    Despite the advancements, the impact of corporate environmental strategies and performance on costs and profits remains unclear. To address this, Professor Hidemichi Fujii from Kyushu University’s Faculty of Economics and his team analyzed financial and environmental data from 8,547 companies across 34 countries spanning 2015 to 2022.

    The team developed two quantitative indicators to assess corporate environmental information: materiality-based scores and overall environmental scores.

    “Financial materiality is a relatively new concept. Environmental priorities vary across industries, as different companies face different key environmental challenges. Financial materiality helps investors assess whether disclosed information is relevant, and supports informed decision-making,” explained Siyu Shen, a graduate student at Kyushu University’s Graduate School of Economics and the paper’s first author.

    According to the SASB framework, environmental issues can be categorized into six areas, including greenhouse gas emissions, and water & wastewater management. For instance, water management is highly relevant to industries like mining but less critical for sectors such as finance. Materiality-based scores quantify only the relevant issues to see how efficiently a company addresses environmental challenges, while overall environmental scores evaluate all disclosed information to assess a company’s general environmental efforts.

    The researchers applied these two scores to assess companies’ environmental disclosures and performance. They found that companies with stronger environmental engagement can achieve better financial outcomes, including enhanced short- and long-term profits, and reduced costs. Notably, firms with superior environmental performance — rather than those focusing merely on disclosure — demonstrate better financial results and attract greater interest from investors.

    “Investors value what companies do for the environment more than what they say,” says Fujii. “By taking concrete action on environmental issues, companies signal sustainability and reliability to consumers and investors, lowering perceived risks, and strengthening their appeal as stable and ethical investments.”

    While overall environmental scores have a clear positive link to financial performance, materiality-based scores show only a limited correlation. This contradicted the team’s hypothesis, leading them to explore differences in how environmental efficiency is valued across countries.

    A closer look at the global data reveals that environmental efficiency is more strongly tied to financial performance in developed countries, such as America and Japan. In contrast, it remains less significant in developing countries like Chile and Indonesia.

    “This difference likely reflects variations in environmental regulations and public awareness across countries,” Shen explains. “In more economically developed countries, where companies have long been engaged in sustainability efforts, improving environmental efficiency can enhance profitability and market valuation. Meanwhile, in developing regions, as the overall regulatory frameworks are still developing, the priority is placed on environmental performance and transparency rather than efficiency.”

    The team is further investigating how macroeconomic factors, such as regulatory and social environments,influence corporate sustainability practices and financial outcomes across countries. Through a series of studies, they aim to provide scientific evidence of the impact of corporate environmental information disclosure and conservation efforts on economic performance. “We expect our international comparative studies to offer useful information for promoting effective policy planning to promote proactive responses to environmental issues,” Fujii adds.

  • Close encounters between distant DNA regions cause bursts of gene activity

    Close encounters between distant DNA regions cause bursts of gene activity

    Researchers at Kyushu University have revealed how spatial distance between specific regions of DNA is linked to bursts of gene activity. Using advanced cell imaging techniques and computer modeling, the researchers showed that the folding and movement of DNA, as well as the accumulation of certain proteins, changes depending on whether a gene is active or inactive. The study, published on December 6 in Science Advances, sheds insight into the complicated world of gene expression and could lead to new therapeutic techniques for diseases caused by improper regulation of gene expression.

    Gene expression is a fundamental process that occurs within cells, with two main phases: transcription, where DNA is copied into RNA, and translation, where the RNA is used to make proteins. For each cell to carry out its specific functions in the body, or to respond to changing conditions, the right amount of a protein must be produced at the right time, meaning genes must be carefully switched on and off.

    Previously, gene transcription was thought to occur in a continuous, smooth process. But with better technology to observe individual cells, scientists now know that transcription occurs in short, unpredictable bursts.

    “A gene will randomly switch on for a few minutes and large amounts of RNA will be produced. Then, the gene will suddenly switch off again,” says Professor Hiroshi Ochiai, from Kyushu University’s Medical Institute of Bioregulation and the study’s senior author. “It happens in nearly all genes, and in all living things, from plants, to animals, to bacteria.”

    This erratic and dynamic nature of transcription, known as transcriptional bursting, is a key mechanism for controlling gene activity in individual cells. It’s one reason why cells within the same tissue or culture environment show variability in their gene expression levels, which is crucial for processes like early embryonic development and cancer evolution. However, the exact mechanisms behind bursting remains unknown.

    In this study, the researchers decided to look into the role of DNA sequences known as enhancers and promoters, and how their spatial distance impacts transcriptional bursting. The promoter is usually located right next to the gene, and is where the protein that carries out transcription attaches to the DNA. Enhancers, on the other hand, are often many hundreds of thousands of bases away from the gene, but as DNA strands move and fold, enhancers can still end up close to genes in 3D space, amplifying gene activity.

    “We believe that enhancers play a crucial role in why transcription occurs in bursts of activity, but so far, the research is unclear,” says Ochiai.

    To test this idea, Ochiai and his team used an advanced imaging technique called seq-DNA/RNA-IF-FISH, which labels DNA, RNA and specific proteins with fluorescent probes. This triple-layered technique allowed the researchers to simultaneously capture the location of DNA, RNA and specific proteins in 3D space within individual mouse embryonic stem cells. With that information, the team could determine whether certain genes were on or off, see how the promoters and enhancers were interacting during bursts of activity, and where the proteins were accumulating, at an unprecedented level of detail.

    As an example, the researchers focused on a gene called Nanog, a 770,000-base length of DNA on chromosome 6, which has a promoter and three enhancer regions and is known to undergo transcriptional bursting in cultured mouse embryonic stem cells.

    The researchers found that in imaged cells where Nanog RNA was present (meaning the gene was active), the most distant enhancer was located in close spatial proximity to the Nanog gene. In contrast, when Nanog was inactive, the imaging showed that the same enhancer region was physically further away.

    Additionally, the scientists also found that proteins involved in regulating transcription also accumulated in the area around the enhancers and promoters when Nanog was active.

    To better understand the mechanism, Ochiai and his team used computer modeling to simulate how the different parts of DNA interact and move inside the cell, both when the Nanog gene is active and inactive.

    They developed their model by using data from their imaging experiments to make a “map” of how frequently different regions of DNA interacted with each other and how the DNA was folded in space. Using this map, the model then simulated how the DNA chain might randomly move.

    The model predicted that when in the active state, each enhancer region interacted for more than twice as long with the promoters, compared to when the gene was inactive.

    The model showed that these longer periods of interaction occurred due to “friction” around the DNA. Due to the accumulation of proteins and RNA when Nanog was active, the fluid became more viscous, and caused the modelled DNA strand to move slowly. Therefore, the gene was able to stay active for longer bursts of time. In contrast, the simulated DNA moved quicker when Nanog was inactive, meaning that the promoter and enhancers didn’t have time to interact.

    “The modeling suggests that bursting is stabilized due to these reinforcing loops,” concluded Ochiai. “Of course, this is just a simulation. The next step is to prove this mechanism also occurs in cells.”

  • Scientists ‘turn up the heat’ on understanding coffee wilt disease which threatens our favorite daily brew

    Scientists ‘turn up the heat’ on understanding coffee wilt disease which threatens our favorite daily brew

    Scientists, including those from Imperial College London, University of Oxford and CABI, have ‘turned up the heat’ on how repeated outbreaks of coffee wilt disease threatened arabica and robusta varieties of our favourite daily coffee brew.

    The scientists, who present their findings in the journal PLoS Biology, say the fungal pathogenFusarium xylarioides continues to pose a significant threat to coffee production and incomes across sub-Saharan Africa.

    Their work supports earlier findings, based on DNA markers and crossing experiments which suggested that F. xylarioides is a species complex containing distinct, host-specific populations.

    The scientists confirm the presence of genetically differentiated lineages with different host-specificity for arabica and robusta coffee and show that the fungal pathogen F. xylarioides repeatedly took up “horizontally transferred” segments of DNA from a related fungal Fusarium taxa which contributed to successive outbreaks of the disease.

    Primary source of income for more than 12 million households

    Coffee is a primary source of income for more than 12 million households in Africa and contributes a significant proportion of tax revenue in a number of these countries, with Ethiopia, for example, recording an export value of $762.8m annually. Yet repeated outbreaks of coffee wilt disease have continued to decimate coffee production in east and central Africa since the 1920s, affecting yields and farmer incomes.

    As part of this study, the scientists compared the genomes of 13 historical strains of F. xylarioides spanning six decades from CABI’s Culture Collection, to identify the evolutionary process behind these repeated outbreaks of coffee wilt disease.

    Dr Matthew Ryan, Senior Research Lead, Biological Resources at CABI and a co-author of the paper, co-supervised lead author of the research, Dr Lily Peck, of Imperial College London, together with Professor Tim Barracloughfrom the University of Oxford.

    Crucial to understand genetic structure and evolutionary potential

    Dr Peck said, “To improve the disease management of fungal pathogens such as Fusarium xylarioides, it is crucial to understand their genetic structure and evolutionary potential.

    “We found that F. xylarioides comprised at least four distinct lineages: one host-specific to arabica, one to robusta and two historic lineages isolated from various coffee species.

    “The presence/absence of large genomic regions across these lineages showed that horizontal transfers of effector genes, namely genes that are important in establishing successful infection, contributed to establishing host specificity.”

    Dr Peck also said that multiple transfers into F. xylarioides populations matched different parts of the Fusarium oxysporum mobile pathogenicity chromosome and were enriched in effector genes and transposons.

    Transposons, also known as “jumping genes,” are DNA sequences that can move around in genomes. They are found in many organisms, including bacteria, yeasts, humans, mice, fruit flies and frogs.

    Shown by transcriptomics to be highly expressed during infection

    Effector genes and other carbohydrate-active enzymes important in the breakdown of plant cell walls were shown by transcriptomics to be highly expressed during infection of arabica coffeeby the pathogenic arabica strains.

    Professor Barraclough said, “Widespread sharing of specific transposons between F. xylarioides and F. oxysporum, and the correspondence of a putative horizontally transferred region to a Starship (recently identified large mobile elements which are involved in fungal horizontal gene transfers), reinforce the inference of horizontal transfers and suggest that mobile elements were involved.

    “Our results support the hypothesis that horizontal gene transfers contributed to the repeated emergence of coffee wilt disease.”

    Potentially lucrative export of various coffee varieties

    Dr Ryan said the research findings potentially show what is happening more widely in relation to fungi and gives scientists the potential to further build on Dr Peck’s study in the future.

    “The fact that horizontal gene transfers between two different species of Fusarium contributed to the repeated emergence of coffee wilt disease is a key mechanism to understand what is happening more widely in fungi — to what extent, we just do not know,” he said.

    The scientists suggest that horizontal transfer could have occurred in a ‘shared niche,’ as both F. xylarioides and F. oxysporum are soil-borne pathogens and have been isolated from the roots and wood of coffee wilt-diseased trees in Ethiopia and central Africa, and from the roots of a banana tree in central Africa.

    They add that knowledge of horizontal transfer mechanisms and putative donor taxa might help to design future intercropping strategies that minimize the risk of transfer of effector genes between closely related Fusarium taxa.

  • Analyzing multiple mammograms improves breast cancer risk prediction

    Analyzing multiple mammograms improves breast cancer risk prediction

    A new study from Washington University School of Medicine in St. Louis describes an innovative method of analyzing mammograms that significantly improves the accuracy of predicting the risk of breast cancer development over the following five years. Using up to three years of previous mammograms, the new method identified individuals at high risk of developing breast cancer 2.3 times more accurately than the standard method, which is based on questionnaires assessing clinical risk factors alone, such as age, race and family history of breast cancer.

    The study is published Dec. 5 in JCO Clinical Cancer Informatics.

    “We are seeking ways to improve early detection, since that increases the chances of successful treatment,” said senior author Graham A. Colditz, MD, DrPH, associate director of Siteman Cancer Center, based at Barnes-Jewish Hospital and WashU Medicine, and the Niess-Gain Professor of Surgery. “This improved prediction of risk also may help research surrounding prevention, so that we can find better ways for women who fall into the high-risk category to lower their five-year risk of developing breast cancer.”

    This risk-prediction method builds on past research led by Colditz and lead author Shu (Joy) Jiang, PhD, a statistician, data scientist and associate professor of surgery in the Division of Public Health Sciences at WashU Medicine. The researchers showed that prior mammograms hold a wealth of information on early signs of breast cancer development that can’t be perceived even by a well-trained human eye. This information includes subtle changes over time in breast density, which is a measure of the relative amounts of fibrous versus fatty tissue in the breasts.

    For the new study, the team built an algorithm based on artificial intelligence that can discern subtle differences in mammograms and help identify those women at highest risk of developing a new breast tumor over a specific timeframe. In addition to breast density, their machine-learning tool considers changes in other patterns in the images, including in texture, calcification and asymmetry within the breasts.

    “Our new method is able to detect subtle changes over time in repeated mammogram images that are not visible to the eye,” said Jiang, yet these changes hold rich information that can help identify high-risk individuals.

    At the moment, risk-reduction options are limited and can include drugs such as tamoxifen that lower risk but may have unwanted side effects. Most of the time, women at high risk are offered more frequent screening or the option of adding another imaging method, such as an MRI, to try to identify cancer as early as possible.

    “Today, we don’t have a way to know who is likely to develop breast cancer in the future based on their mammogram images,” said co-author Debbie L. Bennett, MD, an associate professor of radiology and chief of breast imaging for the Mallinckrodt Institute of Radiology at WashU Medicine. “What’s so exciting about this research is that it indicates that it is possible to glean this information from current and prior mammograms using this algorithm. The prediction is never going to be perfect, but this study suggests the new algorithm is much better than our current methods.”

    AI improves prediction of breast cancer development

    The researchers trained their machine-learning algorithm on the mammograms of more than 10,000 women who received breast cancer screenings through Siteman Cancer Center from 2008 — 2012. These individuals were followed through 2020, and in that time 478 were diagnosed with breast cancer.

    The researchers then applied their method to predict breast cancer risk in a separate set of patients — more than 18,000 women who received mammograms through Emory University in the Atlanta area from 2013 — 2020. Subsequently, 332 women were diagnosed with breast cancer during the follow-up period, which ended in 2020.

    According to the new prediction model, women in the high-risk group were 21 times more likely to be diagnosed with breast cancer over the following five years than were those in the lowest-risk group. In the high-risk group, 53 out of every 1,000 women screened developed breast cancer over the next five years. In contrast, in the low-risk group, 2.6 women per 1,000 screened developed breast cancer over the following five years. Under the old questionnaire-based methods, only 23 women per 1,000 screened were correctly classified in the high-risk group, providing evidence that the old method, in this case, missed 30 breast cancer cases that the new method found.

    The mammograms were conducted at academic medical centers and community clinics, demonstrating that the accuracy of the method holds up in diverse settings. Importantly, the algorithm was built with robust representation of Black women, who are usually underrepresented in development of breast cancer risk models. The accuracy for predicting risk held up across racial groups. Of the women screened through Siteman, most were white, and 27% were Black. Of those screened through Emory, 42% were Black.

    In ongoing work, the researchers are testing the algorithm in women of diverse racial and ethnic backgrounds, including those of Asian, southeast Asian and Native American descent, to help ensure that the method is equally accurate for everyone.

    The researchers are working with WashU’s Office of Technology Management toward patents and licensing on the new method with the goal of making it broadly available anywhere screening mammograms are provided. Colditz and Jiang also are working toward founding a start-up company around this technology.

    Jiang S, Bennett DL, Rosner BA, Tamimi RM, Colditz GA. Development and validation of a dynamic 5-year breast cancer risk model using repeated mammograms. JCO Clinical Cancer Informatics. Dec. 5, 2024.

    This work was supported by Washington University School of Medicine in St. Louis.

    Jiang and Colditz have patents pending related to this work, predicting disease risk using radiomic images.

  • Historic outbreaks of coffee wilt disease linked to gene transfer from another fungus

    Historic outbreaks of coffee wilt disease linked to gene transfer from another fungus

    The fungus that causes coffee wilt disease repeatedly took up segments of DNA from a related fungal pathogen, which contributed to successive outbreaks of the disease. Lily Peck of Imperial College London, UK, reports these new findings in a study published December 5 in the open-access journal PLOS Biology.

    During the past century, outbreaks of coffee wilt disease, which are caused by the fungal pathogen Fusarium xylarioides, have severely impacted coffee production across sub-Saharan Africa. Despite being discovered in 1927, little is known about the pathogen’s genetics and evolution, and this understanding is crucial for managing future outbreaks.

    In the new study, researchers compared the genomes of 13 historic strains spanning six decades and multiple disease outbreaks to investigate how the pathogen has evolved and adapted to its host plants. They found that F. xylarioides is made up of at least four distinct lineages: one specialized to live on arabica coffee plants, one adapted to robusta coffee plants and two historic lineages that infected multiple coffee species. The researchers also found evidence that these strains had repeatedly received segments of DNA from another fungal pathogen, F. oxysporum, which enhanced F. xylarioides‘s ability to infect coffee plants. These additions of DNA, called horizontal gene transfer, likely contributed to the repeated emergence of coffee wilt disease on the African continent.

    Previous studies have shown that similar horizontal gene transfer events contributed to new outbreaks of other pathogens, including potato blight, certain wheat fungal pathogens and Aspergillus fumigatus, a fungus that most commonly infects immunocompromised individuals. The researchers concluded that the use of genomics techniques on historical strains of fungi, sampled during the last century and held in culture collections, would provide a powerful way to further investigate the role of horizontal gene transfer in fungal outbreaks.

    The authors add, “An historical approach shows the importance of horizontal transfers and large mobile elements in the emergence of new types of disease. If we can understand how new types of diseases evolve, we can give growers the knowledge they need to reduce the risk of new diseases emerging in the first place.”

  • CRISPR-Cas technology: Balancing efficiency and safety

    CRISPR-Cas technology: Balancing efficiency and safety

    Genome editing with various CRISPR-Cas molecule complexes has progressed rapidly in recent years. Hundreds of labs around the world are now working to put these tools to clinical use and are continuously advancing them.

    CRISPR-Cas tools allow researchers to modify individual building blocks of genetic material in a precise and targeted manner. Gene therapies based on such gene editing are already being used to treat inherited diseases, fight cancer and create drought- and heat-tolerant crops.

    Starting the repair

    The CRISPR-Cas9 molecular complex, also known as genetic scissors, is the most widely used tool by scientists around the world. It cuts the double-stranded DNA at the exact site where the genetic material needs to be modified. This contrasts with newer gene editing methods, which do not cut the double strand.

    The cut activates two natural repair mechanisms that the cell uses to repair such damage: a fast but imprecise one that reconnects only the ends of the cut DNA, and a slow and precise one that is slow and thorough but not activated in every case. The latter requires a copyable template for repair to accurately rejoin the DNA at the cut site.

    The slow variant is called homology-directed repair. Researchers want to use this method of repair because it allows the precise integration of individual DNA segments into a desired gene region. The approach is very flexible and can be used to repair different disease genes. “In principle, it could be used to cure any disease,” says Jacob Corn, Professor of Genome Biology at ETH Zürich.

    Boosting efficiency with one molecule

    To get the cell to use homology-directed repair, the researchers recently began using a molecule called AZD7648, which blocks fast repair and forces the cell to use homology-directed repair. This approach is expected to accelerate the development of more efficient gene therapies. Initial studies with these new therapies have been good. Too good to be true, as it turned out.

    A research group led by Jacob Corn has just discovered that the use of AZD7648 has serious side effects. The study has just been published in the journal Nature Biotechnology.

    Massive genetic changes

    Although AZD7648 promotes precise repair and thus precise gene editing using the CRISPR-Cas9 system as hoped, in a significant proportion of cells this has led to massive genetic changes in a part of the genome that was expected to be modified without scarring. The ETH researchers found that these changes resulted in the simple deletion of thousands and thousands of DNA building blocks, known as bases. Even whole chromosome arms broke off. This makes the genome unstable, with unpredictable consequences for the cells edited by the technique.

    “When we analyzed the genome at the sites where it had been edited, it looked correct and precise. But when we analyzed the genome more broadly, we saw massive genetic changes. These are not seen when you only analyze the short, edited section and its immediate neighborhood,” says Grégoire Cullot, a postdoctoral fellow in Corn’s group and first author of the study.

    Extent of damage is large

    The extent of the negative effects surprised the researchers. In fact, they suspect that they do not yet have a complete picture of the full extent of the damage because they did not look at the entire genome when analyzing the modified cells, only partial regions.

    New tests, approaches and regulations are therefore needed to clarify the extent and potential of the damage.

    The molecule AZD7648 is not unknown. It is currently in clinical trials as a potential cancer treatment.

    But how did the ETH researchers become aware of the problem? In other studies, the researchers showed how highly effective and precise CRISPR-Cas9 gene editing is when AZD7648 is added. “This made us suspicious, so we took a closer look,” says Jacob Corn.

    The ETH researchers then analysed the sequence of DNA building blocks not only around the edited site but also in the wider environment. They discovered these unwanted and catastrophic side effects caused by using AZD7648.

    Their study is the first to describe these side effects. Other research groups have also investigated them and support the ETH researchers’ findings. They also aim to publish their results. “We are the first to say that not everything is wonderful,” says Corn. “For us, this is a major setback because, like other scientists, we had hoped to use the new technique to accelerate the development of gene therapies.”

    The beginning of something new

    Nevertheless, Corn says this is not the end but the beginning of further advances in gene editing using CRISPR-Cas techniques. “The development of any new technology is a rocky road. One stumble does not mean we give up on the technology.”

    It may be possible to avert the danger by using not just one molecule to promote HDR in the future but a cocktail of different substances. “There are many possible candidates. We now need to find out which components such a cocktail would have to consist of in order not to damage the genome.”

    Gene therapies based on the CRISPR-Cas system have already been successfully used in clinical practice. In recent years, for example, a hundred patients suffering from the hereditary disease sickle cell anemia have been treated with CRISPR-Cas-based therapeutics — without AZD7648. “All patients are considered cured and have no side effects,” says Corn. “So, I am optimistic that gene therapies like this will become mainstream. The question is which approach is the right one and what we need to do to make the technique safe for as many patients as possible.”

  • Cellular traffic congestion in chronic diseases suggests new therapeutic targets

    Cellular traffic congestion in chronic diseases suggests new therapeutic targets

    Chronic diseases like type 2 diabetes and inflammatory disorders have a huge impact on humanity. They are a leading cause of disease burden and deaths around the globe, are physically and economically taxing, and the number of people with such diseases is growing.

    Treating chronic disease has proven difficult because there is not one simple cause, like a single gene mutation, that a treatment could target. At least, that’s how it has appeared to scientists. However, research from Whitehead Institute Member Richard Young and colleagues, published in the journal Cell on November 27, reveals that many chronic diseases have a common denominator that could be driving their dysfunction: reduced protein mobility. What this means is that around half of all proteins active in cells slow their movement when cells are in a chronic disease state, reducing the proteins’ functions. The researchers’ findings suggest that protein mobility may be a linchpin for decreased cellular function in chronic disease, making it a promising therapeutic target.

    In this paper, Young and colleagues in his lab, including postdoc Alessandra Dall’Agnese, graduate students Shannon Moreno and Ming Zheng, and research scientist Tong Ihn Lee, describe their discovery of this common mobility defect, which they call proteolethargy; explain what causes the defect and how it leads to dysfunction in cells; and propose a new therapeutic hypothesis for treating chronic diseases.

    “I’m excited about what this work could mean for patients,” says Dall’Agnese. “My hope is that this will lead to a new class of drugs that restore protein mobility, which could help people with many different diseases that all have this mechanism as a common denominator.”

    “This work was a collaborative, interdisciplinary effort that brought together biologists, physicists, chemists, computer scientists and physician-scientists,” Lee says. “Combining that expertise is a strength of the Young lab. Studying the problem from different viewpoints really helped us think about how this mechanism might work and how it could change our understanding of the pathology of chronic disease.”

    Commuter delays cause work stoppages in the cell

    How do proteins moving more slowly through a cell lead to widespread and significant cellular dysfunction? Dall’Agnese explains that every cell is like a tiny city, with proteins as the workers who keep everything running. Proteins have to commute in dense traffic in the cell, traveling from where they are created to where they work. The faster their commute, the more work they get done. Now, imagine a city that starts experiencing traffic jams along all the roads. Stores don’t open on time, groceries are stuck in transit, meetings are postponed. Essentially all operations in the city are slowed.

    The slow down of operations in cells experiencing reduced protein mobility follows a similar progression. Normally, most proteins zip around the cell bumping into other molecules until they locate the molecule they work with or act on. The slower a protein moves, the fewer other molecules it will reach, and so the less likely it will be able to do its job. Young and colleagues found that such protein slow-downs lead to measurable reductions in the functional output of the proteins. When many proteins fail to get their jobs done in time, cells begin to experience a variety of problems — as they are known to do in chronic diseases.

    Discovering the protein mobility problem

    Young and colleagues first suspected that cells affected in chronic disease might have a protein mobility problem after observing changes in the behavior of the insulin receptor, a signaling protein that reacts to the presence of insulin and causes cells to take in sugar from blood. In people with diabetes, cells become less responsive to insulin — a state called insulin resistance — causing too much sugar to remain in the blood. In researchpublished on insulin receptors in Nature Communications in 2022, Young and colleagues reported that insulin receptor mobility might be relevant to diabetes.

    Knowing that many cellular functions are altered in diabetes, the researchers considered the possibility that altered protein mobility might somehow affect many proteins in cells. To test this hypothesis, they studied proteins involved in a broad range of cellular functions, including MED1, a protein involved in gene expression; HP1α, a protein involved in gene silencing; FIB1, a protein involved in production of ribosomes; and SRSF2, a protein involved in splicing of messenger RNA. They used single-molecule tracking and other methods to measure how each of those proteins moves in healthy cells and in cells in disease states. All but one of the proteins showed reduced mobility (about 20-35%) in the disease cells.

    “I’m excited that we were able to transfer physics-based insight and methodology, which are commonly used to understand the single-molecule processes like gene transcription in normal cells, to a disease context and show that they can be used to uncover unexpected mechanisms of disease,” Zheng says. “This work shows how the random walk of proteins in cells is linked to disease pathology.”

    Moreno concurs: “In school, we’re taught to consider changes in protein structure or DNA sequences when looking for causes of disease, but we’ve demonstrated that those are not the only contributing factors. If you only consider a static picture of a protein or a cell, you miss out on discovering these changes that only appear when molecules are in motion.”

    Can’t commute across the cell, I’m all tied up right now

    Next, the researchers needed to determine what was causing the proteins to slow down. They suspected that the defect had to do with an increase in cells of the level of reactive oxygen species (ROS), molecules that are highly prone to interfering with other molecules and their chemical reactions. Many types of chronic-disease-associated triggers, such as higher sugar or fat levels, certain toxins, and inflammatory signals, lead to an increase in ROS, also known as an increase in oxidative stress. The researchers measured the mobility of the proteins again, in cells that had high levels of ROS and were not otherwise in a disease state, and saw comparable mobility defects, suggesting that oxidative stress was to blame for the protein mobility defect.

    The final part of the puzzle was why some, but not all, proteins slow down in the presence of ROS. SRSF2 was the only one of the proteins that was unaffected in the experiments, and it had one clear difference from the others: its surface did not contain any cysteines, an amino acid building block of many proteins. Cysteines are especially susceptible to interference from ROS because it will cause them to bond to other cysteines. When this bonding occurs between two protein molecules, it slows them down because the two proteins cannot move through the cell as quickly as either protein alone.

    About half of the proteins in our cells contain surface cysteines, so this single protein mobility defect can impact many different cellular pathways. This makes sense when one considers the diversity of dysfunctions that appear in cells of people with chronic diseases: dysfunctions in cell signaling, metabolic processes, gene expression and gene silencing, and more. All of these processes rely on the efficient functioning of proteins — including the diverse proteins studied by the researchers. Young and colleagues performed several experiments to confirm that decreased protein mobility does in fact decrease a protein’s function. For example, they found that when an insulin receptor experiences decreased mobility, it acts less efficiently on IRS1, a molecule to which it usually adds a phosphate group.

    From understanding a mechanism to treating a disease

    Discovering that decreased protein mobility in the presence of oxidative stress could be driving many of the symptoms of chronic disease provides opportunities to develop therapies to rescue protein mobility. In the course of their experiments, the researchers treated cells with an antioxidant drug — something that reduces ROS — called N-acetyl cysteine and saw that this partially restored protein mobility.

    The researchers are pursuing a variety of follow ups to this work, including the search for drugs that safely and efficiently reduce ROS and restore protein mobility. They developed an assay that can be used to screen drugs to see if they restore protein mobility by comparing each drug’s effect on a simple biomarker with surface cysteines to one without. They are also looking into other diseases that may involve protein mobility, and are exploring the role of reduced protein mobility in aging.

    “The complex biology of chronic diseases has made it challenging to come up with effective therapeutic hypotheses,” says Young, who is also a professor of biology at the Massachusetts Institute of Technology. “The discovery that diverse disease-associated stimuli all induce a common feature, proteolethargy, and that this feature could contribute to much of the dysregulation that we see in chronic disease, is something that I hope will be a real game changer for developing drugs that work across the spectrum of chronic diseases.”

  • New technology points to unexpected uses for snoRNA

    New technology points to unexpected uses for snoRNA

    Dynamic, reversible modifications of DNA and RNA regulate how genes are expressed and transcribed, which can influence cellular processes, disease development, and overall organismal health. Small nucleolar RNAs (snoRNAs) are a common but overlooked group of guide RNA molecules that steer chemical modifications to cellular ribosomal RNA (rRNA) targets, like an usher showing someone to their seat in a theater.

    Researchers from the University of Chicago recently developed a new approach for identifying new cellular RNA targets of snoRNAs. They uncovered thousands of previously unknown targets for snoRNAs in human cells and mouse brain tissues, including many that serve functions other than guiding rRNA modifications. Some of the newly discovered interactions with messenger RNA (mRNA) facilitate protein secretion, an important cellular process that could be harnessed for potential therapeutics and biotechnology applications.

    “Once you see so many targets for these snoRNAs, you realize there’s a lot more to be understood,” said Chuan He, PhD, John T. Wilson Distinguished Service Professor of Chemistry and Professor of Biochemistry and Molecular Biology at the University of Chicago and co-senior author of the paper. “We already see that they play a role in protein secretion, which has major implications for physiology, and it suggests a path forward to study hundreds of other snoRNAs.”

    The paper, “SnoRNA-facilitated protein secretion revealed by transcriptome-wide snoRNA target identification,” was published in November 2024 in the journal Cell.

    A molecular glue for protein secretion

    There are more than 1,000 known genes for encoding snoRNAs in the human genome, but scientists have only pinpointed the RNA targets for about 300 of them. These targets mostly involve guiding modifications for ribosomal RNA and small nuclear RNA involved in mRNA splicing. In the decades since snoRNAs were first discovered, researchers largely left the remaining 700 alone, assuming they performed similar functions. However, unlike other guide RNA molecules such as microRNAs that are all the same length, snoRNAs vary greatly in their length from 50-250 residues, suggesting that they can do many different things.

    Over the past 12 years, He’s lab has developed several biochemical and sequencing techniques for studying transcription, DNA modifications, and RNA modifications. In the new study, He worked with co-senior author Tao Pan, PhD, Professor of Biochemistry and Molecular Biology, to test a new tool called “snoKARR-seq” that links snoRNAs with their target binding RNAs. Bei Liu, PhD, a Chicago Fellow postdoctoral scholar who is co-mentored by He and Pan, led the project.

    “Chuan’s lab developed this killer technology to look at exactly what RNA each snoRNA is interacting with at the transcriptome level,” Pan said. “Now there’s a lot of open space for understanding comprehensively what these 1,000 human genes [that encode snoRNAs] are doing.”

    Most of the newly discovered snoRNA targets do not overlap with the known RNA modification sites, suggesting that snoRNAs may have a much broader function in cells. One unexpected discovery was that a snoRNA called SNORA73 interacts with mRNAs that encode secreted proteins and cell membrane proteins. Protein secretion is a fundamental biological process by which proteins are transported from a cell into the extracellular space, which is crucial for various functions, including communication between cells, immune responses, and digestion. The researchers saw that SNORA73 acts as a “molecular glue” between the mRNA and the protein synthesis machinery that helps facilitate this process.

    Further analysis of how SNORA73 binds with mRNA suggested that synthetic snoRNA sequences can be engineered to affect protein secretion. The researchers tested this hypothesis by tweaking a green fluorescent protein (GFP) reporter to interact with SNORA73. GFPs are often introduced in cells to make them glow under certain conditions so scientists can see the effects of experiments. When the researchers expressed SNORA73 genes with the engineered GFP that can be secreted from cells, it increased protein secretion by 30 to 50% over controls.

    These experiments showed that they could make use of the snoRNA machinery to manipulate the secretion of a given protein, which could be useful for developing therapeutics. For example, if a human disease involves a deficiency of secreted proteins, then bioengineers could hijack the system to deliver artificial snoRNAs to increase secretion of that protein.

    ‘The field is wide open’

    While the technology for synthesizing and delivering snoRNAs to the right locations isn’t quite ready yet, both He and Pan feel confident those challenges can be solved since it builds upon previous advances in technology using other forms of RNA. They also believe that since snoRNAs are specific to cell types, they could have much more diverse functions — and therapeutic possibilities — elsewhere.

    “Think about neuronal cells, stem cells, or cancer cells. There are just so many cell types one can study. So, I think the field is wide open,” He said. “Tao and I have been working together for more than 15 years, and it’s a great showcase of collaboration between the Biological Sciences Division and Physical Sciences Division at UChicago. This paper is another example that this kind of collaboration leads to opening a new field of biology.”

    Additional authors on the study include Tong Wu, Bernadette A. Miao, Fei Ji, Shun Liu, Pingluan Wang, Yutao Zhao, Yuhao Zhong, Arunkumar Sundaram, Tie-Bo Zeng, Marta Majcherska-Agrawal, and Robert J. Keenan from UChicago.

  • Persistent problems with AI-assisted genomic studies

    Persistent problems with AI-assisted genomic studies

    University of Wisconsin-Madison researchers are warning that artificial intelligence tools gaining popularity in the fields of genetics and medicine can lead to flawed conclusions about the connection between genes and physical characteristics, including risk factors for diseases like diabetes.

    The faulty predictions are linked to researchers’ use of AI to assist genome-wide association studies. Such studies scan through hundreds of thousands of genetic variations across many people to hunt for links between genes and physical traits. Of particular interest are possible connections between genetic variations and certain diseases.

    Genetics’ link to disease not always straightforward

    Genetics play a role in the development of many health conditions. While changes in some individual genes are directly connected to an increased risk for diseases like cystic fibrosis, the relationship between genetics and physical traits is often more complicated.

    Genome-wide association studies have helped to untangle some of these complexities, often using large databases of individuals’ genetic profiles and health characteristics, such as the National Institutes of Health’s All of Us project and the UK Biobank. However, these databases are often missing data about health conditions that researchers are trying to study.

    “Some characteristics are either very expensive or labor-intensive to measure, so you simply don’t have enough samples to make meaningful statistical conclusions about their association with genetics,” says Qiongshi Lu, an associate professor in the UW-Madison Department of Biostatistics and Medical Informatics and an expert on genome-wide association studies.

    The risks of bridging data gaps with AI

    Researchers are increasingly attempting to work around this problem by bridging data gaps with ever more sophisticated AI tools.

    “It has become very popular in recent years to leverage advances in machine learning, so we now have these advanced machine-learning AI models that researchers use to predict complex traits and disease risks with even limited data,” Lu says.

    Now, Lu and his colleagues have demonstrated the peril of relying on these models without also guarding against biases they may introduce. The team describe the problem in a paper recently published in the journal Nature Genetics. In it, Lu and his colleagues show that a common type of machine learning algorithm employed in genome-wide association studies can mistakenly link several genetic variations with an individual’s risk for developing Type 2 diabetes.

    “The problem is if you trust the machine learning-predicted diabetes risk as the actual risk, you would think all those genetic variations are correlated with actual diabetes even though they aren’t,” says Lu.

    These “false positives” are not limited to these specific variations and diabetes risk, Lu adds, but are a pervasive bias in AI-assisted studies.

    New statistical method can reduce false positives

    In addition to identifying the problem with overreliance on AI tools, Lu and his colleagues propose a statistical method that researchers can use to guarantee the reliability of their AI-assisted genome-wide association studies. The method helps removing bias that machine learning algorithms can introduce when they’re making inferences based on incomplete information.

    “This new strategy is statistically optimal,” Lu says, noting that the team used it to better pinpoint genetic associations with individuals’ bone mineral density.

    AI not the only problem with some genome-wide association studies

    While the group’s proposed statistical method could help improve the accuracy of AI-assisted studies, Lu and his colleagues also recently identified problems with similar studies that fill data gaps with proxy information rather than algorithms.

    In another recently published paper appearing in Nature Genetics, the researchers ring the alarm about studies that over-rely on proxy information in an attempt to establish connections between genetics and certain diseases.

    For instance, large health databases like the UK Biobank have a ton of genetic information about large populations, but they don’t have very much data regarding the incidence of diseases that tend to crop up later in life, like most neurodegenerative diseases.

    For Alzheimer’s disease specifically, some researchers have attempted to bridge that gap with proxy data gathered through family health history surveys, where individuals can report a parent’s Alzheimer’s diagnosis.

    The UW-Madison team found that such proxy-information studies can produce “highly misleading genetic correlation” between Alzheimer’s risk and higher cognitive abilities.

    “These days, genomic scientists routinely work with biobank datasets that have hundreds of thousands of individuals, however, as statistical power goes up, biases and the probability of errors are also amplified in these massive datasets,” says Lu. “Our group’s recent studies provide humbling examples and highlight the importance of statistical rigor in biobank-scale research studies.”