NIH-funded clinical trial shows that the new approach could also decrease time in the hospital.
Retron-Powered Approach Enables Genome Editing Across Diverse Bacterial Species
For decades, the ability to precisely rewrite bacterial genomes has been largely confined to a single workhorse organism: Escherichia coli. That limitation has slowed efforts to study pathogens, engineer sustainable biomanufacturing strains, and probe how microbes influence human health. While genome editing tools have transformed eukaryotic biology, most high‑efficiency bacterial editors simply haven’t worked outside E. coli.
A new study from the Gladstone Institutes aims to change that. In a large, nine‑lab collaboration, researchers have translated a retron‑based DNA editing system from E. coli into 14 additional bacterial species spanning three major phyla. The work, published in Nature Biotechnology and titled “Genome editing of phylogenetically distinct bacteria using cross-species retron-mediated recombineering,” demonstrates that retrons, bacterial immune elements that continuously produce short DNA strands, can be engineered into portable genome editing modules the authors call recombitrons. “Recombitrons—a genome editing tool created by pairing modified, donor-producing bacterial retrons with single-stranded binding and annealing proteins—have increased the efficiency of recombineering to install flexible, precise edits in the prokaryotic chromosome,” the authors wrote.
Retrons normally function as part of a viral defense system, generating DNA fragments that help bacteria detect and respond to infection. Seth Shipman, PhD, a Gladstone Investigator and senior author of the study, has spent years repurposing this machinery. “We’ve been easily editing E. coli genomes using retrons for years now, which has substantially increased the pace of our fundamental biology and our molecular technology development,” he said. “But we kept hearing from the broader field, asking when there would be a version of this technology that could be put to work in other bacterial species that matter for the environment, industrial processes, or human health.”
Shipman’s lab previously showed that retrons can act as cellular DNA-making factories, generating the donor strands needed for genome editing. In bacteria, the resulting editing tool built by pairing modified retrons with single‑stranded DNA–binding and annealing proteins is known as a recombitron. Until now, however, functional recombitrons existed only in E. coli.
To test whether the architecture could travel, the team designed a panel of 10 retron-based editing systems and partnered with other labs specializing in diverse bacterial species. “We designed all the molecular parts at Gladstone, then sent them to the collaborators, where they ran the experiment in their labs,” said first author Alejandro González‑Delgado, PhD. Samples were then returned to Gladstone for centralized analysis.
The results show broad functionality. The recombitrons worked in all 15 species tested, including clinically relevant pathogens such as Klebsiella pneumoniae and Pseudomonas aeruginosa, as well as fast‑growing biotechnology strains like Vibrio natriegens and Pseudomonas putida. Editing efficiencies varied widely—from fractions of a percent to more than 90%—but the team demonstrated that modifying retron structure or other system components could boost performance in lower‑efficiency hosts.
“Each retron worked differently in different bacteria,” González‑Delgado noted. “This reinforces why it’s important to have lots of different retrons, so scientists can choose the ones best suited to their favorite bacterial species.”
The study provides a roadmap for expanding genome editing into species that have historically been difficult to engineer. Researchers studying microbial pathogenesis, gut ecology, or industrial bioproduction can now match retron systems to their organism of interest.
“My lab builds molecular technology, and we want these technologies to be used as broadly as possible to uncover new biology and intervene in disease,” Shipman said. “We hope it will continue to spread from here.”
The post Retron-Powered Approach Enables Genome Editing Across Diverse Bacterial Species appeared first on GEN – Genetic Engineering and Biotechnology News.
Elon Musk and Sam Altman are going to court over OpenAI’s future
After a yearslong legal feud, Elon Musk and OpenAI CEO Sam Altman are heading to trial this week in Northern California in a case that could have sweeping consequences. Ahead of OpenAI’s highly anticipated IPO, the court could rule on whether the company is allowed to exist as a for-profit enterprise and might even oust its current executive leadership, including Altman.
Musk is suing OpenAI, alleging that Altman and OpenAI president Greg Brockman deceived him into bankrolling the company in its early days by promising to maintain it as a nonprofit dedicated to developing AI that benefits humanity, only to later restructure the company to operate a for-profit subsidiary. Musk cofounded OpenAI with Altman and others in 2015, but he left in 2018 after a bitter power struggle.
Musk is seeking as much as $134 billion in damages from OpenAI and Microsoft, one of OpenAI’s biggest financial backers. He is also asking the court to remove Altman and Brockman from their roles and to restore OpenAI as a nonprofit. Musk has asked the court to award any damages to OpenAI’s nonprofit rather than to him personally.
Nine jurors will deliver an advisory verdict, a non-binding recommendation, to guide the judge in deciding Musk’s claims against Altman. Musk, Altman, and Brockman will take the stand. Former OpenAI chief scientist Ilya Sutskever, former OpenAI CTO Mira Murati, and Microsoft CEO Satya Nadella are also expected to testify. Cringey texts, raw diary entries, and endless scheming behind the founding and growth of OpenAI are expected to come to light.
In an industry enveloped in secrecy, the trial will be a rare opportunity for the public to look behind the curtain and find out what’s going on in the companies creating the most transformative technology ever built.
What are they fighting about?
When OpenAI was originally founded as a nonprofit, backed by a $38 million donation from Musk, the company vowed to create open-source technology for the public’s benefit, unconstrained by a need to generate financial returns. But over the years, the company began to claim that intensifying competition could make it dangerous to share how it develops its AI models and that a nonprofit structure could not raise enough money to keep building AI. (MIT Technology Review was first to report on OpenAI’s internal conflicts around its mission.)
The court has already found that in 2017 Altman and Brockman wanted to establish a for-profit arm, while Musk proposed merging OpenAI with his electric-car company, Tesla. When Musk threatened to stop funding, Altman and Brockman told him that they were committed to keeping the company a nonprofit. Musk alleges that they pursued plans to pivot to a for-profit without informing him. According to OpenAI, Musk agreed that the company needed a for-profit entity and even wanted to be its CEO.
But even if Musk proves he was duped by Altman and Brockman, he may not have standing in the first place to sue them for restructuring the company to operate a for-profit subsidiary. Some legal scholars are puzzled over why the judge allowed him to bring this claim. “The idea that Elon Musk can sue because he was a donor or used to be on the board is pretty puzzling,” says Jill Horwitz, a law professor who studies nonprofit law at Northwestern University. “Typically, it’s up to the attorneys general to bring such a claim to enforce the charitable purposes. And that’s already happened.”
In October 2025, state attorneys general of California, where OpenAI is headquartered, and Delaware, where OpenAI is incorporated, struck a deal with OpenAI to approve its new corporate structure on a series of conditions. For example, a safety and security committee at the nonprofit would review safety-related decisions made by the for-profit subsidiary. Critics of the restructuring, including Musk, AI safety advocates, and civil society groups, have tried to stop it.
California’s attorney general has declined to join Musk’s lawsuit, saying that the office did not see how his action serves the public interest.
Still, whether the deal holds OpenAI to its nonprofit mission is an open question. “Elon Musk should have to show … what the deficiencies are in what’s been agreed to by OpenAI with the attorneys general,” says Rose Chan Loui, the director of the UCLA School of Law’s philanthropy and nonprofit program. Even with the terms in place, holding OpenAI to them depends on “how much they can enforce it and how much transparency they get into OpenAI’s work.”
More importantly, legal experts say the case is being considered under the wrong body of law. Musk argues that Altman and Brockman breached OpenAI’s charitable trust by creating a closed-source, for-profit subsidiary. As a result, the court has been analyzing the claim under the law of trusts. “But OpenAI is not a trust. OpenAI is a corporation. And so really they should be looking at … the law of charitable nonprofit organizations,” says Chan Loui.
What’s on the line?
Despite all the legal muddiness, the outcome of the trial could upend the AI race. Any one of the remedies that Musk seeks could cripple OpenAI as it races to go public by the end of the year. OpenAI, which is valued at over $850 billion, has described the litigation with Musk as a potential risk to its business. Musk’s rival company xAI, which makes the chatbot Grok, is expected to go public as a part of his rocket company SpaceX as early as June. If Musk prevails, xAI, which in combination with SpaceX is valued at $1.25 trillion, could get a big advantage in the AI race.
And the trial has helped expose the bitter schism between Musk and the company he once helped to found. An OpenAI spokesperson referred MIT Technology Review to a post on X: “This lawsuit has always been a baseless and jealous bid to derail a competitor.” Although Musk’s lawyers did not immediately respond to a request for comment, he has posted on X that “Scam Altman lies as easily as he breathes.”
MIT Technology Review will have ongoing coverage of Musk v. Altman until its conclusion. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.
<![CDATA[Round on the latest news in psychiatry from the last week. ]]>
Prepregnancy Lifestyle Risk Factors in Women Seeking Digital Fertility Services: Cross-Sectional Descriptive Study
<strong>Background:</strong> Approximately 1 in 3 pregnancies in the United States are complicated by one or more adverse pregnancy outcomes. This high prevalence contributes to the elevated rates of maternal and infant mortality in the United States. Modifiable prepregnancy or preconception lifestyle factors have been associated with adverse pregnancy outcomes in observational studies, which underscores the importance of preconception care. <strong>Objective:</strong> This cross-sectional descriptive study aimed to (1) estimate the prevalence of preconception lifestyle risk factors among women seeking services from a digital fertility platform, (2) characterize the study population and present relevant reference data, and (3) examine the distribution of prepregnancy lifestyle scores across demographic and clinical subgroups. <strong>Methods:</strong> The digital health company, Doveras Fertility, has built a prepregnancy digital health platform for individuals and couples seeking to optimize their fertility potential. Targeting users prior to initiation of pregnancy, the platform facilitates the assessment of baseline lifestyle risk factors. This paper reports on 396 adult women who sought the platform’s services in a 1-month period between May and June 2024 by completing a digital fertility questionnaire. Self-reported data were analyzed for 6 healthy prepregnancy lifestyle factors known to be associated with maternal health outcomes in prior observational studies, and each participant was given a composite score between 0 to 6 to represent the number of these healthy behaviors reported. The 6 healthy prepregnancy lifestyle factors include a BMI of 18.5 to 24.9 kg/m<sup>2</sup>, not currently smoking, ≥150 min/week of moderate to vigorous physical activity, healthy eating, no daily alcohol intake, and use of a prenatal multivitamin. <strong>Results:</strong> The study population was racially and ethnically diverse, with a mean age of 32.9 (SD 6.3) years. Most (235/396, 59%) participants received a composite score of 3 factors or fewer, and less than 5% (19/396) scored 6 out of 6. For context, this cohort had higher proportions of participants with unhealthy BMI and dietary patterns than those in the reference data. Regarding fertility, 46% (182/396) met the clinical definition of infertility (≥1 year trying to conceive), with the prevalence of infertility ranging from 16% (3/19) among those with the highest lifestyle scores to 59% (17/29) among those with the lowest. <strong>Conclusions:</strong> Most women seeking services from this digital fertility platform exhibited multiple lifestyle factors that have been previously associated with adverse pregnancy outcomes in observational studies. These results suggest that nearly all survey participants have potential risk factors for adverse maternal outcomes and therefore the potential to adopt at least one improvement in their lifestyle behavior. A digital platform may offer an accessible mechanism for identifying and characterizing preconception risk factors; however, future longitudinal studies are needed to evaluate whether platform-based interventions can effectively support behavior change and improve maternal health outcomes.
Multimodal Depression Detection Through Conversational Interactions with an Emotion-Aware Social Robot: Pilot Study
Background: Depression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, are reliable but impractical for frequent or large-scale screening. Self-report tools like the Patient Health Questionnaire-8 (PHQ-8) require disclosure and clinician oversight, limiting accessibility. Recent artificial intelligence–based approaches leverage multimodal behavioral cues (linguistic, acoustic, and visual) for automated depression detection but remain constrained by limited adaptability, scarce annotated data, weak emotional expression in real-world settings, and the high computational cost of deployment of socially assistive robots (SARs). Objective: This study introduces Depression Social Assistant Robot (DEPRESAR)-Fusion, a lightweight multimodal depression detection framework designed for natural interactions with emotion-aware SARs. The objective of this study was to enhance detection accuracy in everyday conversations while addressing the challenges of data scarcity, weak emotional cues, and computational efficiency. Methods: DEPRESAR-Fusion integrates acoustic, linguistic, and visual features with an emotion-aware response module powered by large language models to adapt conversational strategies dynamically. To stimulate richer emotional expression, participants were exposed to emotionally evocative videos before SAR interactions. To overcome data scarcity, we augmented training with (1) public depression-related social media corpora and (2) synthetic samples generated via large language models. The proposed multimodal fusion architecture was evaluated on benchmark clinical datasets for both binary depression classification and PHQ-8 regression tasks. Performance was compared against prior multimodal baselines using root mean square error, mean absolute error, and standard classification metrics. Results: Participants who viewed emotional stimuli before interacting with SARs exhibited significantly higher emotional expressiveness, leading to improved model performance. Regression tasks showed lower root mean square error and mean absolute error, while classification tasks achieved significantly higher accuracy than the nonstimulus condition. DEPRESAR-Fusion outperformed prior multimodal baselines across multiple benchmark datasets, achieving state-of-the-art performance in both binary classification and PHQ-8 regression. The system maintained a lightweight architecture suitable for real-time deployment on SARs. Conclusions: DEPRESAR-Fusion demonstrates that integrating emotion induction, data augmentation, and lightweight multimodal fusion can enable accurate and scalable depression detection in naturalistic SAR interactions. By bridging the gap between structured clinical assessments and everyday conversations, this approach highlights the potential of SAR-based systems as nonintrusive, artificial intelligence–driven tools for proactive mental health support.
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New U.S. recommendation on hepatitis B vaccine will have dire consequences, studies project
A new U.S. policy that recommends offering hepatitis B vaccine at birth only to babies perceived to be at risk of neonatal infection will lead to increased numbers of infected infants and more cases of chronic hepatitis B infection in children that will generate millions of extra dollars in health care costs, two studies published Monday project.
“Avoiding an increase in neonatal infections under the targeted recommendation would require historically unattained levels of maternal [hepatitis B] screening or birth-dose coverage among infants of unscreened mothers,” said one of the studies, from researchers at Boston University, the University of Florida, and Johns Hopkins University.
Supreme Court grapples with multibillion-dollar wave of lawsuits over Roundup cancer claims
WASHINGTON — The Supreme Court seemed divided Monday over whether to block thousands of lawsuits alleging the maker of the weedkiller Roundup failed to warn people it could cause cancer.
The case came before the justices after a tidal wave of litigation that included some multibillion-dollar verdicts against the global agrochemical manufacturer Bayer, which owns Roundup maker Monsanto.
Pencil Beam Laser Could Help Researchers Design Brain-Targeted Therapies
Scientists at MIT say they made a finding in optical physics that could enable a new bioimaging method that’s faster and higher-resolution than existing technology. They discovered that, under the right conditions, laser light clutter can spontaneously self-organize into a highly focused “pencil beam.”
Using this self-organized pencil beam, the team captured 3D images of the human blood-brain barrier 25 times faster than the gold-standard method, while maintaining comparable resolution, according to the scientists.
By showing individual cells absorbing drugs in real-time, this technology could help scientists test whether new drugs for neurodegenerative disease like Alzheimer’s or ALS reach their targets in the brain, with greater speed and resolution, they add.
“The common belief in the field is that if you crank up the power in this type of laser, the light will inevitably become chaotic. But we proved that this is not the case. We followed the evidence, embraced the uncertainty, and found a way to let the light organize itself into a novel solution for bioimaging,” says Sixian You, PhD, assistant professor in the MIT department of electrical engineering and computer science (EECS), a member of the research laboratory for electronics.
You is senior author of a paper “Self-localized ultrafast pencil beam for volumetric multiphoton imaging” on this imaging technique in Nature Medicine.
A better beam
When the researchers performed characterization experiments of this pencil beam, it was more stable and high-resolution than many similar beams. Other beams often suffer from “sidelobes,” blurry halos of light that can distort images.
Their beam was more pristine and tightly focused, according to You. Building on those experiments, the researchers demonstrated the use of this pencil-beam in biomedical imaging of the human blood-brain barrier.
Scientists and clinicians often want to see how drugs flow inside the vasculature of the blood-brain barrier and whether they reach their targets within the brain. But with standard optical settings, the best one can do is capture one 2D section of the vasculature at a time, and then repeat the process multiple times to generate a fuller image, You explains.
Using this new technique, the researchers created an ultrafast, high-precision pencil beam that enabled them to dynamically track how cells absorb proteins in real-time.
“The pharmaceutical industry is especially interested in using human-based models to screen for drugs that effectively cross the barrier, as animal models often fail to predict what happens in humans. That this new method doesn’t require the cells to have a fluorescent tag is a game-changer,” notes Roger Kamm, PhD, the Cecil and Ida Green Distinguished Professor of Biological Science and Mechanical Engineering.
“For the first time, we can now visualize the time-dependent entry of drugs into the brain and even identify the rate at which specific cell types internalize the drug.”
“Importantly, however, this approach is not limited to the blood-brain barrier but enables time-resolved tracking of diverse compounds and molecular targets across engineered tissue models, providing a powerful tool for biological engineering,” points out postdoctoral fellow Sarah Spitz, PhD.
The team reports that it captured cellular-level 3D images that were higher quality than with other methods, and generated these images about 25 times faster.
“Usually, you have a tradeoff between image resolution and depth of focus—you can only probe so far at a time. But with our method, we can overcome this tradeoff by creating a pencil-beam with both high resolution and a large depth of focus,” You says.
In the future, the researchers want to better understand the fundamental physics of the pencil-beam and the mechanisms behind its self-organization. They also plan to apply the technique to other scenarios, such as imaging neurons in the brain, and work toward commercializing the technology.
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Obesity Leaves Lasting DNA Methylation Memory in Immune Cells
A new study suggests that obesity leaves a durable molecular imprint on the immune system, one that persists long after weight loss and may continue to influence disease risk. Researchers at the University of Birmingham report that key immune cells retain an “epigenetic memory” of obesity, potentially sustaining inflammation and metabolic dysfunction even after patients return to a healthy weight.
The findings, published in EMBO Reports, provide a mechanistic explanation for a long-standing clinical observation: that individuals who lose weight often remain at elevated risk for conditions such as type 2 diabetes, cardiovascular disease, and certain cancers.
Immune cells retain a “memory” of obesity
The study focuses on CD4+ helper T cells, central regulators of immune coordination. By analyzing patient samples across multiple cohorts, including individuals undergoing pharmacological weight loss, rare genetic obesity syndromes, and lifestyle interventions, the researchers identified persistent epigenetic modifications in these cells.
Specifically, obesity was associated with changes in DNA methylation, a process in which chemical tags are added to DNA and alter gene expression without changing the underlying sequence. These modifications effectively encode a molecular memory of prior metabolic state.
As explained by the authors, these epigenetic marks can persist for years after weight loss. “The findings suggest that short-term weight loss may not immediately reduce the risk of some disease conditions associated with obesity,” said Claudio Mauro, PhD, senior author of the study. Instead, the immune system appears to retain a record of past metabolic stress that continues to influence cellular behavior.
Persistence beyond weight loss
The durability of this imprint is striking. The study estimates that obesity-associated DNA methylation patterns in T cells may persist for five to ten years after successful weight reduction. This suggests that immune remodeling lags far behind metabolic normalization.
Supporting this, the team observed similar patterns across diverse experimental systems, including human clinical samples and mouse models of diet-induced obesity. Together, these data point to a conserved biological mechanism rather than a transient or context-specific effect.
This persistent immune memory may help explain why relapse and long-term complications are common in obesity. As noted by Belinda Nedjai, PhD, of Queen Mary University of London, “the immune system retains a molecular record of past metabolic exposures, which may have implications for long-term disease risk and recovery.”
Disruption of cellular housekeeping and aging
At the functional level, the epigenetic changes identified in T cells appear to disrupt two critical biological processes: autophagy and immune senescence.
Autophagy, the process by which cells degrade and recycle damaged components, is essential for maintaining cellular health. The study suggests that obesity-associated DNA methylation impairs this pathway, reducing the cell’s ability to clear waste and maintain homeostasis.
In parallel, the researchers observed effects on immune aging, or senescence. Dysregulated T cells exhibited features of premature aging, potentially contributing to chronic inflammation and reduced immune resilience.
Together, these alterations could create a persistent pro-disease environment, even after weight loss. This reframes obesity not simply as a reversible metabolic state, but as a condition capable of inducing long-term immune reprogramming.
Implications for treatment strategies
The findings have direct implications for how obesity is managed clinically. If immune dysfunction persists for years after weight loss, then short-term interventions may be insufficient to fully restore health.
Instead, sustained weight maintenance—and potentially additional therapies targeting immune reprogramming—may be required. Mauro noted that “ongoing weight management following loss will see the ‘obesity memory’ slowly fade,” though this process may take years.
The study also points to potential therapeutic strategies. Drugs such as SGLT2 inhibitors, already used in diabetes treatment, may help accelerate the reversal of these epigenetic changes by reducing inflammation and promoting clearance of dysfunctional cells.
Rethinking obesity as a chronic immuno-metabolic disease
Beyond its immediate clinical implications, the study contributes to a broader conceptual shift in how obesity is understood. Rather than being defined solely by excess adiposity, obesity emerges as a condition that induces lasting systemic changes, particularly within the immune system.
As Andy Hogan, PhD, of Maynooth University emphasized, “obesity is a chronic progressive and relapsing disease,” and these findings help explain the biological basis of that persistence.
By identifying an epigenetic “memory” within immune cells, the work highlights a previously underappreciated dimension of metabolic disease: its capacity to reprogram immune function over the long term.
Looking ahead
The discovery of obesity-induced immune memory raises new questions about reversibility and intervention. Can these epigenetic marks be actively erased? And if so, how can therapies be designed to accelerate immune recovery?
Future research will likely focus on targeting these pathways directly, with the aim of restoring normal immune function and reducing long-term disease risk.
For now, the findings underscore a key message: losing weight is only part of the story. Fully reversing the biological impact of obesity may require sustained intervention—not just at the metabolic level, but at the level of the immune system itself.
The post Obesity Leaves Lasting DNA Methylation Memory in Immune Cells appeared first on Inside Precision Medicine.