Nature Biotechnology, Published online: 28 July 2026; doi:10.1038/s41587-026-03264-4
The new laws break away from 20-year-old restrictive GMO directives to give an official nod to plants made with new genomic techniques.
Large Genomic Atlas Maps How Pregnancy Shapes Maternal Biology
Pregnancy is often described clinically through its complications: gestational diabetes, hypertensive disorders, fetal growth restriction, preterm birth. But beneath those diagnoses is a moving biological system. Maternal metabolism, immunity, hematology and endocrine function are continuously adapting to fetal development, and standard reference ranges only capture part of that complexity.
A new Nature Genetics study puts genomics into that timeline. Using noninvasive prenatal testing data from up to 121,579 unrelated Chinese pregnancies, researchers mapped genetic associations across 111 prenatal and postnatal phenotypes, from blood counts and glucose markers to liver enzymes, thyroid function, serum screening scores, non-invasive prenatal testing (NIPT) risk scores, gestational disorders and birth outcomes. The result is what the authors call “a dynamic genetic atlas of human gestation.”
Turning routine NIPT into a genomic resource
The scale of the study is notable because it uses data already generated in routine care. NIPT produces low-depth sequencing data from maternal plasma, primarily to screen for fetal aneuploidy. Here, the team used those data to infer maternal genotypes and link them to longitudinal clinical measurements from two hospitals in Shenzhen.
Across the 111 gestational phenotypes, the researchers identified 4,688 independent genome-wide significant signals, including 1,703 associations not previously reported for the same or related traits. Many new signals appeared in areas that have been relatively underexplored, including maternal serum screening and NIPT-based trisomy risk scores.
Prenatal screening outputs are usually interpreted as biochemical or fetal-risk measures. This study suggests that maternal genetic background also contributes to variation in some of those scores, which could eventually matter for calibration of risk models.
Pregnancy is not a static genetic context
The more interesting message is not just that gestational traits are heritable. It is that some genetic effects appear to be specific to pregnancy.
By comparing gestational results with nonpregnant female datasets from BioBank Japan and the Taiwan Biobank, the researchers found that 7.8% of high-confidence signals across 30 phenotypes were gestation-specific. These loci were enriched in pathways linked to fetal development, pregnancy maintenance, growth regulation and hormonal adaptation, with genes such as IGF1, ESR1, and PPARG emerging as network hubs.
The study also captured time-varying genetic effects. For 24 complete blood count traits measured repeatedly across pregnancy and postpartum, 18.7% of associated variants showed genotype-by-gestational-timing interactions. In other words, the effect of a variant could strengthen, weaken or shift depending on whether a patient was in the first trimester, late gestation, delivery or postpartum period.
For precision medicine, that is the key point. Pregnancy is not simply a confounder in female health studies; it is a distinct biological state with its own genetic architecture.
Pregnancy as an early health signal
The authors also connected gestational phenotypes with 80 later-life diseases and medication-use traits in BioBank Japan. Genetic correlation and Mendelian randomization analyses linked gestational glycemic traits with diabetes risk, and gestational adiposity, lipids and blood pressure with cardiovascular outcomes.
These results support the idea of pregnancy as an early-life cardiometabolic stress test. However, the interpretation needs care. When the researchers adjusted for related phenotypes from the Taiwan Biobank, many associations were attenuated, suggesting that pregnancy may reveal pre-existing genetic susceptibility rather than directly causing later disease.
The study is therefore less about predicting individual outcomes today than building the architecture for doing so tomorrow. Its limitations are important: the cohort was Chinese, several rarer complications such as preeclampsia were underpowered, and fetal-versus-maternal genetic contributions still need to be separated.
Even so, the direction is clear. Routine prenatal data may become more than a snapshot of pregnancy status. Combined with genomics, it could help define who is adapting normally, who is deviating early, and who may need closer follow-up long after delivery.
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A spatial view of tumor neoantigen recognition
Nature Biotechnology, Published online: 22 July 2026; doi:10.1038/s41587-026-03203-3
The cell-by-cell interactions between neoantigen-expressing tumor cells and tumor-specific T cells remained largely unexplored. A single-cell spatial transcriptomics framework enables mapping of neoantigen-expressing tumor cells and tumor-infiltrating T cells, as well as T cell receptor sequencing. This approach reveals distinct immune niches in the tumor microenvironment and their properties that determine tumor-specific responses.
ctDNA Blood Test Better Targets Radiopharmaceutical Prostate Cancer Therapy
A new ctDNA blood test from a Japanese team identifies those metastatic, castration-resistant prostate cancer (mCRPC) patients most likely to benefit from 223Ra radiopharmaceutical therapy. The test also monitors their progress throughout treatment.
The study appeared in the July issue of The Journal of Nuclear Medicine. The lead author is Masaki Shiota, MD, PhD, associate professor in the Department of Urology in the Graduate School of Medical Sciences at Kyushu University in Fukuoka, Japan.
Prostate cancer is very biologically heterogeneous. Patients who appear clinically similar may have different responses. Better tests are needed to match patients to therapies and determine the optimal treatment sequence or combination of therapies.
223Ra dichloride targets bone metastases and can improve overall survival and quality of life in CRPC patients. It is an alpha-particle–emitting radionuclide that behaves chemically like calcium. It is therefore preferentially incorporated into areas of increased bone turnover, including the osteoblastic lesions commonly produced by prostate cancer bone metastases.
But the treatment is expensive, clinical outcomes vary among patients, and there is not yet a reliable biomarker to predict or monitor treatment response.
This study used a research-based 88-gene panel with deep sequencing of plasma circulating tumor DNA (ctDNA) and matched leukocyte DNA. “The leukocyte analysis was important for distinguishing true tumor-derived alterations from germline variants and age-related clonal hematopoiesis,” Shiota told Inside Precision Medicine.
Prostate cancer is one of the most commonly diagnosed malignancies in men. Metastatic castration-sensitive prostate cancer arises either de novo as a cancer, or as a recurrence after radical local therapy for localized disease. It accounts for approximately 10% of newly diagnosed prostate cancer cases in Japan.
This team tracked associations between ctDNA profiles and clinical outcomes, including biomarker response, radiographic progression-free survival, and overall survival.
Patients with a higher amount of tumor DNA in the blood or certain gene changes, such as TP53, PTEN, and cell cycle pathway alterations, detected through ctDNA testing before treatment, had worse outcomes. Further, changes in tumor DNA reflected treatment response and disease trajectory.
“While 223Ra is an important treatment for prostate cancer that has spread to the bones, not all patients benefit equally,” said Shiota. “Our findings suggest that a blood-based genomic test may help identify patients who are more likely or less likely to benefit from the therapy. This could help doctors choose treatment more carefully and monitor patients more closely, with the goal of providing more personalized care.”
One of the biggest hurdles to the test’s advancement is reimbursement.
This depends on the country, the clinical indication, and the specific assay used. In Japan, Shiota explained, certain approved comprehensive genomic profiling tests using blood, such as FoundationOne Liquid CDx, can be covered by the national health insurance system under defined conditions. These tests are primarily used to identify potentially actionable alterations in patients with advanced solid tumors, particularly when obtaining adequate tumor tissue is difficult.
“The research assay used in our study is not currently reimbursed for selecting patients for radium-223 or for monitoring their response during treatment,” said Shiota. “Before insurance coverage could be considered for this particular use, our findings would need to be externally validated, and prospective studies would need to demonstrate that ctDNA-guided treatment decisions improve patient outcomes.”
ctDNA has several important applications in precision oncology. For example, it can: identify actionable genomic alterations and thereby help select targeted therapies, be used to monitor treatment response and detect emerging resistance mechanisms, capture tumor heterogeneity more comprehensively than a biopsy from a single metastatic site, detect minimal residual disease after treatment, and identify molecular relapse before it becomes apparent on conventional imaging.
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AI Protein Engineering Model Designed for Biomanufacturing
A cell engineering model designed specifically for the biomanufacturing environment could go a long way toward optimizing host cell performance, reducing development timelines, and streamlining biomanufacturing platform design.
“There’s a real challenge in determining how to engineer a host cell’s genome to maximize its performance in a biomanufacturing application.” That’s the difficulty laid out by Shawn Manchester, PhD, CEO, Triplebar Bio, and a view that’s shared by Yun Song, PhD, professor, University of California, Berkeley, and the industry innovation organization BioMADE. The three, with support from the National Science Foundation, are developing such an AI-informed cell engineering model to solve that challenge.
What’s most notable is that this AI-informed predictive and optimization model focuses on the biomanufacturing environment. The large-scale, application-specific training database is being designed to handle bench- to commercial-scale biomanufacturing.
Biomanufacturing-relevant environment
As Manchester says, “There aren’t a lot of organisms that have naturally evolved to make proteins they’ve never seen before, in an environment they’re not well-evolved for. Our ability to generate data in a setting that is relevant to biomanufacturing and to do so at the scale of hundreds of thousands of cells and data points is innovative…and it’s not something that many others are looking at. Most people are working on AI for protein engineering of therapeutics and other molecules as opposed to cell engineering for use in biomanufacturing.”
This project focuses on data capture from Pichia pastoris as the host cell producing five different proteins that are relevant for bioprocessing, food production, and defense. The learnings will be incorporated into Triplebar’s other programs, too, including optimization for its Chinese hamster ovary (CHO) cells, Manchester says.
“Improving the scalability of these proteins directly benefits [not just bioindustrial or biopharmaceutical applications, but a broad range of] biomanufacturing,” Brandon Simmons-Rawls, program manager, BioMADE, emphasizes.
This AI project combines two core technologies deployed at Triplebar:
- Droplet microfluidics, in which a cell is encapsulated in a water and oil emulsion alongside a fluorescent-based sensor to quantify protein production
- Multimodal transformer-based AI model
The genomes and transcriptomes of cells that make either more or less protein than baseline are sequenced and fed into the AI’s very large, labeled training sets to identify correlations between genotypes and phenotypes. These correlations are important patterns about how the genome works, Manchester explains, and can be used to generate new genomic designs for optimization of the host cell to the biomanufacturing process.
“We first pre-train models on all relevant sequencing data from thousands of genomes, which gives us an underlying structure to the genome. Then we fine-tune those models with the genotype-phenotype data we generate in the biomanufacturing-relevant environment. This allows us to understand which of those patterns in the genome relate to biomanufacturing-specific performance,” Manchester says.
Once this AI-enabled model rolls out, Manchester says he envisions it being used by bioindustrial and biopharma companies that “need to improve the productivity or efficiency of the organism they use in manufacturing. Results should be faster and more efficient than those derived using guess-and-check methods based on our current understanding of how these organisms work.
“The goal is for people to log onto this AI tool, identify what they’re making and what they’ve done, and ask the AI what else they can do to improve performance,” he continues. “The model will serve them specific genetic designs that they can deploy in their organism.”
Manchester predicts that more than 10% of the designs will produce meaningful improvements—significantly more than traditional, early-stage design-build-test cycles. This streamlines the cell engineering cycle by focusing on modifications likely to yield performance improvements that are most meaningful for biomanufacturing.
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A conceptual multi-agent architecture for mental health triage in post-conflict Arabic-speaking populations: a theoretical proposition and staged validation argument
Syria’s protracted conflict has produced a mental health crisis of extraordinary scale, with post-traumatic stress, depression, and anxiety estimated at several times global baselines, set against fewer than 0.37 psychiatrists per 100,000 people. Existing AI mental health tools have been developed and evaluated primarily for English-speaking, non-humanitarian populations, and their transfer to this setting is constrained by three simultaneous structural deficiencies—extreme clinical scarcity, Arabic natural-language-processing underperformance for dialect, and cultural misalignment with Syrian idioms of distress—which we term the Triple Gap. This article is a conceptual contribution in the Hypothesis and Theory genre, and its central claim is theoretical rather than technical: that AI-assisted mental-health triage at a safety floor adequate for crisis relevant care in this setting is conditional on the joint satisfaction of three constraints—linguistic adequacy for the local dialect, cultural validity for local idioms of distress and help-seeking, and bounded clinical responsibility through human oversight. These constraints interact, so that a system satisfying fewer than all three is expected to fail in clinically consequential rather than random ways. As one possible design response to this proposition—neither the only one nor a validated one—we describe a conceptual multi-agent architecture aligned with the WHO mhGAP task-shifting model: a four-stage pipeline (screening, risk stratification, routing, follow-up) constrained by a cross-cutting cultural-adaptation layer, augmented by candidate verification mechanisms with explicit abstention, and governed by human oversight in which clinical responsibility rests with a licensed clinician. The proposal is a hybrid clinical decision support hypothesis, not an autonomous system. Because no Syrian Arabic clinical corpus yet exists, the conversational components of the design cannot presently be evaluated; we therefore set out a staged sequencing argument for future work in which the construction of a Syrian Arabic Mental Health Evaluation Corpus (SAMHEC) is the first and rate-limiting condition. We present no prototype, no corpus, and no clinical, cultural, or safety evaluation, and we make no claim of clinical validity, safety, or readiness for deployment. The contribution is the integration of multi-agent triage with task-shifting and cultural adaptation into a single conditional argument whose adequacy can be established only through the staged empirical work we describe.
UCSD, IGI Partnership Expands CRISPR Innovation Across Health, Agriculture, and the Environment
California may be known as the Golden State, but it may well be referred to as the Genome Editing state after this week’s announcement of an inter-state partnership. The University of California (UC), San Diego, and the Innovative Genomics Institute (IGI), founded by Nobel laureate and CRISPR gene editing co-discoverer Jennifer Doudna, announced a new partnership to jointly develop cutting-edge genomic tools for addressing large-scale challenges in health and the environment.
Doudna founded the IGI in 2015 with the aim of translating the success of gene editing in the lab into practical solutions for large societal problems. Initially formed as a partnership between UC Berkeley and UC San Francisco with a focus on using CRISPR in human health, the IGI expanded to include sustainable agriculture and climate change applications of genome editing, adding UC Davis as a partner. UC San Diego is joining as the IGI’s fourth UC partner campus, combining research strengths in environmental and marine science, engineering and computer science, and biomedical and life sciences to develop the next generation of genome-editing tools and applications.
“I’m thrilled that the IGI is now partnering with UC San Diego,” said Doudna. “The mission of the IGI is to develop solutions that can not only scale to meet the biggest challenges in health and climate, but to make solutions that are accessible to those who need them most. UC San Diego helps us expand that real-world impact.”
[Margot Riggi]
The relationship between UC San Diego and IGI research labs has been building over the past decade. For several years, the IGI has been collaborating with UC San Diego researchers Joe Pogliano, PhD, professor in the Department of Molecular Biology, and Kit Pogliano, PhD, dean and professor in the School of Biological Sciences, on applied microbiology projects. They have worked with UC Berkeley-based labs on developing new therapies focusing on “jumbo phage” for antibiotic-resistant bacterial infections as well as a novel defense strategy against viral infections.
In 2023, UC San Diego researchers were awarded $10 million by the Howard Hughes Medical Institute to explore the biomedical promise of jumbo phage as therapeutic agents. Also in 2023, the IGI received a $70 million gift through the Audacious Project to develop microbiome-editing tools and apply them to problems caused by microbes and microbiome imbalances, including livestock methane emissions and inflammatory diseases like asthma. Other areas of the effort focus on developing more efficient tools for genome editing across a wide breadth of microorganisms to expand the impact of this technology.
In the environmental arena, UC San Diego researchers have worked closely with UC Berkeley labs since 2018 to develop tools for combatting crop pests, managing disease-carrying mosquito populations, and developing safe protocols for environmental applications of genomic technologies.
“UC San Diego brings exceptional strengths across biological sciences, medicine, engineering, computation, environmental research, and ocean science that align powerfully with the IGI’s mission,” said Pogliano.
In addition to the current collaborations, IGI’s executive director Brad Ringeisen foresees multiple areas of synergy: “UC San Diego’s strengths in engineering, medical devices, environmental research, and the microbiome complement IGI’s current research areas, and allow us to expand our societal impact in new ways,” he said.
New joint projects of particular interest include interdisciplinary programs designed to bolster the climate resilience of our oceans and soils through innovative scalable solutions, and combining UC Berkeley’s biotechnology discovery engine with UC San Diego’s biomedical research and health system to improve the drug discovery pipeline for rapid translation across areas like antimicrobial resistance and neurodegenerative disease.
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Genomas Brasil program: building precision public health within Brazil’s Unified Health System
Nature Medicine, Published online: 21 July 2026; doi:10.1038/s41591-026-04523-2
Brazil is embedding large-scale genomics into its universal public healthcare system, combining sequencing, infrastructure and clinical implementation at scale. The experience may provide a model for other countries in the global south that seek to deliver genomic equity and precision public health.
Early Genomic Indicators of Praziquantel Resistance in Schistosoma mansoni
The warning signs are subtle—genetic variants scattered across a vast parasite genome—but together they sketch a picture that disease‑control and elimination programs can’t afford to ignore. A new study published in Science Advances, “Extensive parasite transmission and variation in a functional receptor associated with drug resistance in endemic Schistosoma mansoni,” reveals genomic changes in the parasite that cause schistosomiasis, which may reduce its sensitivity to praziquantel, the only drug currently available to treat the disease.
The international team, led by researchers at the Wellcome Sanger Institute, the Royal Veterinary College, and the Medical College of Wisconsin, analyzed whole‑genome sequence data from 570 Schistosoma mansoni parasites collected across Africa and the Caribbean. As the paper noted, “Mass drug administration (MDA) with praziquantel is the cornerstone of schistosomiasis control and elimination efforts.” Yet after two decades of large‑scale treatment campaigns, the parasite’s genome is beginning to show signs of drug resistance.
The study uncovered extensive long‑distance transmission of S. mansoni and a striking degree of genetic diversity across endemic regions. But the most consequential finding lies in Sm.TRPMpzQ, a transient receptor potential (TRP) melastatin ion channel recently identified as praziquantel’s molecular target. Researchers found four naturally occurring variants in this receptor with reduced praziquantel sensitivity.
In some cases, parasites persisted even after treatment. As the paper reports, “Analyses of parasite infrapopulations collected from people pre‑ and post‑praziquantel treatment further identified instances of treatment failure, supporting the potential for praziquantel resistance.”
For global health programs that rely entirely on praziquantel, these findings represent an early but important signal.
Stephen Doyle, PhD, co‑senior author and group leader and UKRI Future Leaders Fellow at the Wellcome Sanger Institute, emphasized the shift this genomic insight enables: “Whole‑genome sequencing gives us an unprecedented window into how schistosome populations are structured and evolving across Africa, and by characterizing variation in the drug’s molecular target at scale, we can move from reactive surveillance to proactive monitoring.”
Professor Joanne Webster, DPhil, of the Royal Veterinary College and director of the Global Centre for Neglected Tropical Disease Research, underscored the stakes: “While praziquantel remains largely highly effective, our findings provide a sobering warning about the reliance on a single drug for schistosomiasis control and highlight the need for comprehensive surveillance to monitor the potential emergence of drug resistance.”
Schistosomiasis affects more than 250 million people worldwide, with 90% of infections occurring in sub‑Saharan Africa. With elimination targets set for 2030, the emergence of resistance could jeopardize decades of progress.
The authors argue that genomic surveillance should become a routine part of schistosomiasis control. Their dataset, the largest genomic analysis of S. mansoni from human infections to date, provides a baseline for tracking resistance‑linked variants as MDA programs continue.
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CD163+ perivascular macrophages in schizophrenia: a research framework for testing macrophage-related mechanisms
Elevated densities of CD163+ perivascular macrophages have been reported in schizophrenia post-mortem brain tissue, particularly in regions involved in neurodevelopment, dopaminergic signaling, and blood–brain barrier (BBB) regulation. However, the biological significance and developmental lineage of these findings remain unclear. While CD163 is linked to regulatory and scavenging functions, macrophage activation states form a continuum and cannot be inferred from any single marker. This Perspective outlines a structured, testable research framework to determine whether this accumulation reflects altered responsiveness to persistent intracellular, inflammatory, systemic, or treatment-related signals. To test this, we propose sequential methodological aims, including defining CD163+ cell localization and phenotypes in predefined brain regions, and assessing viral and non-viral molecular signals using spatial transcriptomic and cell-specific methods. This framework also involves comparing macrophage activation states across schizophrenia and other psychiatric and non-psychiatric control groups, using single-cell and single-nucleus sequencing. By not presuming a specific infectious aetiology, this approach will provide a general methodology to investigate macrophage-related mechanisms across diverse potential triggers. Within this model, HSV-1 is evaluated strictly as an illustrative proof-of-concept candidate for testing intracellular pathogen responses, rather than an exclusive cause, as epidemiological associations have been inconsistent and localization of viral materials within these cells has not been demonstrated. Similarly, Bacille Calmette–Guérin (BCG)-associated trained immunity is introduced strictly as a preliminary, ex vivo/in vitro approach to probe macrophage reprogramming and plasticity. Ultimately, this framework provides a systematic approach for investigating macrophage-related mechanisms and their potential drivers in schizophrenia without presupposing a specific underlying aetiology.