Transferring large synthetic DNA molecules across species has historically been impeded by the fragility of the DNA constructs and the host immune and cellular machinery’s constraints. Conventional approaches often fail to maintain the structural and epigenetic integrity of these synthetic genomes during delivery. The NICE method innovatively addresses this by isolating intact yeast nuclei containing synthetic DNA while preserving their native chromatin architecture. Harnessing the unique epigenomic landscape of Saccharomyces cerevisiae—a model organism notably devoid of cytosine methylation and repressive histone modifications—researchers encapsulated synthetic DNA within these nuclei, creating a natural protective vehicle for transfer.

The core of this pioneering protocol lies in isolating nuclei from genetically engineered yeast cells in a manner that maintains megabase-size synthetic chromosomes intact and transcriptionally naive. This isolation preserves the chromatin’s native state, avoiding the epigenetic reprogramming that can occur outside its natural nuclear context. Subsequent to isolation, these nuclei are introduced into mouse early embryos, which are in a developmental phase characterized by a naive epigenetic state, conducive to the establishment of de novo chromatin modifications and gene regulatory programs. This stage-specific receptivity opens avenues for real-time observation of epigenetic remodeling events following introduction of foreign synthetic DNA.

Crucially, NICE enables near-perfect delivery efficiency through microinjection techniques that transfer isolated yeast nuclei directly into embryos. This reliable methodology stands in stark contrast to traditional gene transfer mechanisms, often fraught with inefficiencies, fragmented DNA integration, or loss of epigenetic information. The protocol reports reaching 100% successful delivery rates of intact synthetic chromosomal DNA into the nuclear environment of mouse zygotes, paving the way for robust exploration of synthetic genomics within mammalian developmental systems.

Complementing the physical transfer process, the methodology incorporates rigorous quality control steps, including pulsed-field gel electrophoresis, to verify the integrity and size of the isolated DNA molecules. This ensures that synthetic chromosomes subjected to the protocol retain their structural fidelity throughout the isolation and transfer stages. Importantly, the isolated nuclei maintain a high concentration and purity level, allowing for long-term storage at ultra-low temperatures (-80 °C) for periods exceeding six months without compromising their viability or the synthetic DNA’s structural integrity.

This innovative fusion of yeast biology with mammalian embryo technology exploits the former’s distinct chromatin characteristics, effectively circumventing common barriers in cross-species genomic manipulation. Saccharomyces cerevisiae, lacking complex DNA methylation systems and certain repressive histone marks typical in higher eukaryotes, provides a simplified genomic environment where synthetic DNA chromosomes can be constructed and maintained with fewer epigenetic modifications. The encapsulation of such DNA within yeast nuclei thus facilitates its safe transport and gradual epigenetic adaptation once inside the mammalian embryo.

The implications of this work are far-reaching. By establishing a controlled system to study the onset and development of epigenetic modifications upon integration of synthetic DNA, scientists can dissect the influence of chromatin states on transcriptional activation in unprecedented detail. This knowledge is vital to understanding complex gene regulatory networks, potentially revolutionizing developmental biology, synthetic biology, and regenerative medicine.

Intriguingly, the protocol’s adaptability makes it compatible with various synthetic DNA constructs, opening avenues beyond yeast chromosomes to include human synthetic genomes or other large DNA assemblies. This versatility signals a new era where entire synthetic chromosomes—encoding complex traits, metabolic pathways, or therapeutic genes—can be efficiently translocated into mammalian systems for functional assays or clinical applications.

Notably, the successful integration and preservation of synthetic DNA in a naive embryonic environment provide a unique opportunity to follow chromatin remodeling and gene expression dynamics from the earliest stages of mammalian development. This allows researchers to map how foreign genetic material becomes epigenetically assimilated, which genes are activated or silenced, and how these patterns influence organismal phenotypes over time.

The high degree of control and specificity afforded by NICE elevates it as a prime tool for genome engineering initiatives that require megabase-scale modifications, such as synthetic chromosome construction, large gene cluster transplantation, or the introduction of novel functional elements. The method’s scalability and robustness ensure it can be integrated into existing workflows in genetic engineering laboratories with proficiency in mammalian embryo microinjection.

While the protocol demands familiarity with mammalian microinjection techniques, its temporal efficiency is noteworthy. The entire process—from yeast nuclei isolation and quality verification to delivery into mammalian embryos—is completed within approximately five days. This rapid turnaround accelerates experimental cycles and expedites the iterative process of synthetic genome design, delivery, and functional evaluation.

The ability to store isolated yeast nuclei at -80 °C for extended periods also enhances experimental flexibility and planning, allowing researchers to generate batches of nuclei in advance and use them as needed without repeated preparation. This storage capability is a critical practical advancement for labs aiming to conduct large-scale or longitudinal studies involving synthetic genomic elements.

This method sets a precedent for future work in synthetic genomics, exemplifying how cross-kingdom genetic material transfer can be finely controlled to preserve intricate epigenomic configurations and facilitate downstream biological inquiries. Researchers anticipate that NICE will invigorate efforts to create synthetic mammalian genomes, model complex diseases, or engineer organisms with enhanced functionalities.

In summary, NICE represents a tour de force in genome engineering by seamlessly integrating yeast synthetic chromosomes into mammalian embryonic systems with unmatched accuracy, purity, and viability. It transcends prior limitations of DNA delivery technologies, enabling comprehensive studies of de novo epigenetic regulation and gene expression patterns emanating from synthetic DNA. This transformative approach unlocks new vistas for understanding genetic regulation and advancing synthetic biology with profound implications for medicine and biotechnology.

Subject of Research: Delivery of synthetic megabase-scale DNA into mammalian embryos using isolated yeast nuclei.

Article Title: Yeast nuclei–mediated precise delivery of synthetic megabase-scale human DNA into mammalian embryos.

Article References:
Liu, Y., Zhou, J., Liu, Z. et al. Yeast nuclei–mediated precise delivery of synthetic megabase-scale human DNA into mammalian embryos. Nat Protoc (2026). https://doi.org/10.1038/s41596-026-01374-6

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41596-026-01374-6

At its core, this innovation employs a powerful genomic sequence model called Evo, which was trained on an unprecedented scale of prokaryotic genomic data. Unlike protein design that typically focuses on narrow regions of sequence space or requires laborious structural predictions, Evo taps into the latent functional information encoded not just in isolated gene sequences but within the broader genomic neighborhoods they inhabit. This contextual understanding enables the model to generate de novo gene variants that successfully encode desired functions at experimental success rates ranging between 17 to 50 percent after testing relatively few variants. Such rates surpass many existing protein engineering methodologies, highlighting the potency of conditioning on genomic context.

Remarkably, many designed proteins from this approach display no significant sequence similarity to any known proteins, including those with related functions. This unprecedented novelty blurs the line between de novo protein design and evolution-guided diversification, presenting an ‘existence proof’ that these language models can generalize far beyond the natural sequence repertoires catalogued in biological databases. It opens new avenues to design proteins with unprecedented functional diversity, drawing upon evolutionary principles encoded in genome architecture yet generating sequences never before seen in nature.

What sets semantic design apart from prior techniques is its fundamentally different paradigm for creating functional biological molecules. It does not require any task-specific fine-tuning that risks overfitting to known examples, nor does it rely on natural language prompts derived from existing knowledge bases. Instead, semantic design excavates the rich reservoir of functional diversity hidden within genomic sequences and their ecological and evolutionary contexts. This method can thus access proteins and functions that have not yet been characterized by science, catering to a realm of biological utility beyond current annotations or hypotheses.

A striking demonstration of this technique’s versatility is provided through the generation of novel antitoxins that imply a broader compatibility across diverse toxin–antitoxin systems than previously reported, as well as an anti-CRISPR protein linked to a protein family with a different presumed function. These findings exemplify how semantic design can reveal cross-functional relationships and hidden compatibilities that defy conventional wisdom in molecular biology. It also underscores the advantage of bypassing mechanistic or structural assumptions, as filtering based on predicted structure quality would have discarded many of these successfully designed proteins.

Semantic design emerges not as a replacement but as a complementary strategy alongside classical protein engineering and directed evolution. Its ability to explore vast synthetic sequence space beyond the constraints of well-characterized natural genes presents an exciting toolkit for rational design and innovation. Particularly for functions like anti-CRISPR activity, where multiple structural and mechanistic paths exist, genomic conditioning can selectively guide design towards functional outputs less accessible to traditional approaches.

Crucially, although Evo 1.5 was the model employed for these landmark achievements, the semantic design framework is agnostic to the particular model architecture or training dataset. Any sufficiently trained language model on prokaryotic or phage genomes can be integrated into this framework. As model capabilities improve and our understanding of gene synteny—the relative order and arrangement of genes in genomes—deepens, the power and scope of semantic design are expected to grow commensurately.

The traditional paradigm in biological sequence discovery relies heavily on the concept of “guilt by association,” where hypotheses about gene function are inferred from evolutionary conservation and similarity across species. This constraint limits exploration to the slowly accumulated diversity shaped over billions of years of life’s history. By contrast, semantic design enables a rapid and expansive sampling of synthetic sequences tailored to specific biological systems. To democratize access to this unprecedented resource, the team has released SynGenome, a publicly available database containing over 120 billion base pairs of AI-generated genomic sequences, providing a valuable platform for researchers worldwide to uncover novel synthetic biological parts.

Despite its transformative potential, semantic design faces inherent challenges. Autoregressive sequence generation methods sometimes produce repetitive or hallucinated sequences that appear plausible but lack true functionality. Moreover, genes generated through contextual conditioning may encode regulatory elements rather than the direct functional proteins initially targeted, necessitating rigorous in silico screening and empirical validation. The approach is currently most effective in prokaryotic systems, reflecting the genomic structures and functional architectures captured by training data, and extending semantic design to eukaryotic organisms will require novel strategies attuned to their complex genome organization.

Looking forward, the rapidly growing corpus of genomic data, coupled with advances in language model architectures and inference algorithms, promises to elevate semantic design to new heights. More sophisticated models that can generate entire multi-component biological systems, as demonstrated with toxin–antitoxin pairs, foreshadow the ability to engineer complex synthetic circuits, metabolic pathways, or even whole genomes. These capabilities could accelerate the creation of bespoke living systems tailored for medicine, industry, and environmental applications.

Beyond mere synthetic biology, semantic design opens a window into an expanded biological reality, uncovering sequences and functions veiled from natural observation. This synthetic genomic space is a frontier ripe for discovery, with the potential to reveal new molecular machines and evolutionary principles. By integrating rich semantic information encoded in genomes with computational creativity, scientists are reshaping our capacity to design life itself, heralding a new epoch of bioengineering that transcends the limits of natural evolution.

In essence, this work marks a profound shift in how we conceive and manipulate the building blocks of life. It boldly illustrates that language models trained on biological sequences are not only tools for data analysis but are potent generative engines capable of inventing functional, novel genes and proteins. As the field advances, semantic design could fundamentally alter the trajectory of biotechnology, synthetic biology, and our understanding of the molecular basis of life.

Subject of Research:
Design of functional de novo genes using genomic language models trained on prokaryotic DNA sequences.

Article Title:
Semantic design of functional de novo genes from a genomic language model.

Article References:
Merchant, A.T., King, S.H., Nguyen, E. et al. Semantic design of functional de novo genes from a genomic language model. Nature (2025). https://doi.org/10.1038/s41586-025-09749-7

Image Credits:
AI Generated

DOI:
https://doi.org/10.1038/s41586-025-09749-7