Writing in PLOS Computational Biology, a research team led by Lu Chai, Jie Gao and colleagues introduces DeCTCF, an integrative framework that decodes CTCF binding sequences by combining experimental data with predictions from a powerful artificial intelligence model. The work analyzed 236,552 CTCF binding sequences drawn from chromatin immunoprecipitation sequencing, or ChIP-seq, experiments across 118 human cell lines. Rather than treating all of these binding sites as a uniform population, the researchers asked whether they could be sorted into meaningful functional groups, and whether those groups could explain the protein’s remarkably diverse roles across tissues and cell types.

The key innovation lies in how DeCTCF characterizes each binding site. Instead of relying solely on the raw DNA sequence underneath a CTCF peak, the framework taps into Sei, a pretrained deep learning model trained on vast collections of regulatory elements. Sei can take a stretch of DNA and predict a rich panel of epigenomic features for it, effectively forecasting what kinds of regulatory activities that sequence is likely to support in different cellular contexts. By feeding predicted epigenomic profiles into the analysis, DeCTCF gains a layer of functional information that sequence alone cannot provide, allowing binding sites with similar sequence motifs to be distinguished by their predicted regulatory surroundings.

Using this approach, the team clustered the hundreds of thousands of CTCF binding sites into 20 distinct groups. Each cluster represents a population of binding sites that share not only sequence characteristics but also predicted epigenomic behavior. The clusters were not arbitrary statistical artifacts. When the researchers examined their properties, they found that the groups could be annotated into coherent functional modules, revealing an underlying modular organization in how CTCF operates across the genome. One module stood out as particularly prominent: a major cluster associated with three-dimensional chromatin architecture, consistent with CTCF’s well-established role as an anchor point for chromatin loops.

Beyond the architectural module, the analysis uncovered three lineage-associated modules, clusters of CTCF binding sites whose predicted features tie them to particular cellular lineages rather than to generic genome folding. These lineage modules are especially intriguing because they suggest that subsets of CTCF binding sites are deployed in a cell-type-specific manner, potentially recruiting different molecular partners depending on the developmental or tissue context. In other words, the same architectural protein may carry out different regulatory conversations in different cell types, and the binding sites it occupies carry telltale signatures of those conversations.

The study provides a concrete example of this principle in stem cells. Several clusters enriched in the three stem cell lines included in the dataset also showed enrichment of ZIC-family proteins, a group of transcription factors known to be active in early development. These same clusters were associated with gene sets related to pluripotency and neurodevelopment, the biological programs that define what stem cells are and what they can become. This convergence of evidence, linking a CTCF cluster, a candidate co-factor family and a coherent gene expression program, illustrates how the framework can generate testable hypotheses about which co-factors cooperate with CTCF in specific cellular contexts.

The researchers also examined the shape of CTCF ChIP-seq signals at the cluster level and found striking associations with chromatin loop annotations. Binding sites displaying a single-peak signal profile were reproducibly associated with higher loop interaction scores, suggesting that these sites act as strong anchors for chromatin loops. In contrast, sites with double- and triple-peak profiles showed distinct loop-pairing preferences, hinting that the local arrangement of CTCF occupancy, not merely its presence or absence, influences how distant regions of the genome are brought into contact. This observation adds a geometric dimension to the functional classification, connecting the epigenomic identity of a binding site to its role in physical genome architecture.

Technically, the study demonstrates the growing power of transfer learning in genomics. Training a model from scratch to interpret hundreds of thousands of regulatory sequences across more than a hundred cell types would require enormous labeled datasets that few laboratories possess. By leveraging Sei’s pretrained representations, DeCTCF shows that knowledge embedded in large-scale epigenomic prediction models can be repurposed for downstream biological questions, in this case the functional dissection of a single architectural protein’s binding landscape. The strategy turns a general-purpose sequence model into a specialized analytical instrument, a pattern that is likely to become increasingly common as pretrained genomic foundation models mature.

The broader significance of the work lies in reframing how scientists think about CTCF. Rather than a monolithic factor performing a single genome-folding task, CTCF emerges as a modular organizer whose binding sites fall into functionally distinguishable classes, each with its own predicted epigenomic context, candidate co-factors and architectural behavior. This modular view helps explain how one protein can participate simultaneously in chromosome compaction, enhancer-promoter communication and lineage-specific gene regulation without those roles collapsing into one another. It also provides a practical resource: the 20-cluster classification offers other researchers a framework for interpreting CTCF binding data in their own cell types of interest.

There remain important caveats and open questions. The lineage associations are computational predictions and statistical enrichments, not demonstrations of mechanism, and the candidate co-factor relationships, such as the link between ZIC-family proteins and stem cell clusters, will need experimental validation in the laboratory. The reliance on predicted epigenomic features also means that the classification inherits any biases of the underlying Sei model. Nevertheless, by systematically mapping CTCF’s modular organization across 118 human cell lines and connecting binding-site classes to chromatin loops, co-factors and developmental gene programs, DeCTCF delivers a comprehensive and testable picture of how the genome’s master organizer achieves its remarkable contextual versatility. The study points toward a future in which the regulatory grammar of the genome can be read, cluster by cluster, directly from its sequence.

Subject of Research: Computational classification of CTCF binding sites using predicted epigenomic features to reveal modular genome organization

Article Title: DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features

Article References: Chai, L., Gao, J., Guo, T., Ba, T., Li, Z., Liu, J., Wang, Y., & Zhang, L. (2026). DeCTCF: Decoding CTCF binding sequences by leveraging predicted epigenomic features. PLOS Computational Biology, 22(10), e1014848. https://doi.org/10.1371/journal.pcbi.1014848

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014848

Keywords: CTCF, DeCTCF, epigenomics, chromatin architecture, Sei model, deep learning, ChIP-seq, genome organization, transcription factors, stem cells, ZIC proteins, computational biology

For decades, evolutionary biologists have tended to treat the genome primarily as a linear string of letters, with innovation arising through mutations that alter or duplicate individual genes. The new research challenges that picture. When the Vienna team reconstructed the 3D organisation of the genome in octopus, squid and cuttlefish, they discovered that an ancient and massive burst of genome reorganisation, occurring hundreds of millions of years ago, dramatically reshuffled the chromosomes of the cephalopod ancestor. That reshuffling brought previously distant stretches of DNA into close physical proximity, creating new opportunities for regulatory interactions that had never existed before.

The genome is not simply a sequence. Within the nucleus, it is folded into a complex three-dimensional structure in which loops and domains bring distant regions into contact, allowing regulatory elements such as enhancers to influence genes located far away along the linear chromosome. Understanding how that folded structure itself evolves, the researchers argue, is becoming increasingly important for understanding how new forms of biological complexity arise. Lead author Dr Thea Rogers emphasised that the genome’s three-dimensional arrangement is a genuine object of study in its own right, one whose evolution can shape the evolution of gene regulation and, ultimately, of traits.

The central concept the researchers propose is what they call regulatory entanglement. When a burst of genome reorganisation throws previously separate regions of DNA into contact, those regions can begin to interact and influence each other’s activity. Over long stretches of evolutionary time, these new interactions can become embedded, forming increasingly interconnected regulatory networks. The result is a genome in which novel patterns of gene expression can be generated while essential functions remain intact. In effect, regulatory entanglement offers a mechanism for balancing innovation against stability, one of the central tensions in genome evolution.

This balancing act may be particularly important for animals like cephalopods, whose nervous systems depend on the precisely timed and tissue-specific expression of enormous numbers of genes during development. Regulatory entanglement provides a way for genomes to experiment with new connections, generating fresh patterns of gene regulation that can be refined by natural selection, without dismantling the regulatory machinery required for basic survival. The researchers suggest that this process may have played a key role in the emergence of the coleoid cephalopods’ unusually complex nervous systems, facilitating the fine-tuned gene activity needed to build and operate such elaborate neural tissue.

One of the most striking findings of the study is that not all levels of genome structure respond to reorganisation in the same way. The team found that large structural units known as chromatin domains, which span substantial sections of chromosomes, remained largely stable across hundreds of millions of years of evolution. Whatever upheavals the cephalopod genome endured, these broad compartments persisted, apparently preserving the coarse-grained organisation required for normal cellular function. This stability implies that there are deep constraints on how far a genome’s architecture can be rearranged before fundamental processes break down.

In sharp contrast, finer-scale structures known as chromatin loops proved far more dynamic. These loops, which physically bring distant regions of DNA into contact so that they can regulate one another, varied widely across species, across different tissues, and across developmental stages. Crucially, the researchers found that these flexible loops were often located near genes involved in key cephalopod traits, including genes linked to the nervous system. That pattern suggests that the finer-scale wiring of the 3D genome is precisely where large-scale changes in DNA organisation have their greatest impact, and where evolutionary innovation is most likely to be concentrated.

Taken together, these results challenge a long-standing assumption in evolutionary biology: that genome architecture is simply a passive consequence of evolution, shaped by selection acting on genes but not itself shaping the course of evolutionary change. The Vienna team’s findings indicate the opposite. The three-dimensional organisation of DNA can actively influence how evolution unfolds, by determining which regulatory combinations become available for selection to act upon. In cephalopods, an ancient reorganisation of the genome may have opened an enormous space of new regulatory possibilities, providing raw material for the evolution of traits as demanding as a distributed, highly capable nervous system.

The implications extend well beyond cephalopods. If regulatory entanglement is a general feature of genome evolution, then the same principles could help explain the emergence of complexity in other lineages where large-scale genome rearrangements have occurred, from the genome duplications that reshaped early vertebrates to the chromosomal reshuffling seen in flowering plants. The study also highlights the growing importance of three-dimensional genomics, a field that combines chromosome-conformation data with measures of gene activity to reveal how the physical folding of DNA governs gene regulation. As such datasets accumulate across the tree of life, researchers will increasingly be able to ask not just which genes an organism possesses, but how the arrangement of those genes in nuclear space constrains and enables evolutionary change.

For the coleoid cephalopods, the study offers a compelling new chapter in the story of how Earth’s most alien minds came to be. Hundreds of millions of years ago, their ancestor’s genome was broken apart and reassembled in a new configuration, entangling regulatory elements that had never before communicated. From that tangled regulatory landscape, capable of producing novel patterns of gene activity while preserving the essentials of life, there eventually emerged animals that can learn by observation, change the texture of their skin, and edit their own RNA in response to the environment. The 3D structure of DNA, long treated as mere packaging, now appears to be one of the reasons such creatures exist at all.

Subject of Research: The role of three-dimensional genome architecture and regulatory entanglement in the evolution of complex cephalopod nervous systems

Article Title: 3D structure of DNA may explain how cephalopods evolved complex brains

Article References: 3D structure of DNA may explain how cephalopods evolved complex brains. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: cephalopods, octopus, genome architecture, 3D genome, regulatory entanglement, chromatin loops, chromatin domains, gene regulation, evolutionary innovation, nervous system evolution, Nature Communications, epigenomics

The research, led by Xiaoli Xie, Delin Tan, Jingyun Mo and colleagues under the supervision of Huiqing Jiang, Tingting Li and Jia Wang, set out to address a conspicuous gap in cancer genomics. While alterations in three-dimensional chromosome organization—topologically associating domains, chromatin loops and A/B compartments—are well documented in many tumors, the behavior of the X chromosome itself during carcinogenesis has attracted remarkably little attention. Yet the X chromosome carries more than 800 genes, many involved in cell proliferation, metabolism and immune regulation, and liver disease is notorious for its pronounced sex bias: men face a substantially higher risk of hepatocellular carcinoma than women, a pattern the team suspected might be written into the architecture of the sex chromosomes.

To follow genomic architecture across disease progression, the researchers built what they describe as a 4D nucleome landscape, combining chromosome conformation capture technologies such as Hi-C with ChIP-seq, RNA-seq, whole-genome sequencing and DNA methylation profiling, sampled at multiple time points as chemically induced liver disease unfolded in mice. Using F1 hybrid mice, in which the two parental X chromosomes can be distinguished by allele-specific sequence variants, the team could separate the folding of the single active X chromosome in males from the paired active and inactive X chromosomes in females. This allelic resolution proved critical, because it exposed a striking asymmetry: the conformational changes and gene activation occurring on the male active X chromosome during hepatocarcinogenesis were far more pronounced than those on the female active X chromosome, mirroring the male bias seen in human liver disease.

The most surprising finding emerged when the team turned to human hepatic tissue. In normal liver samples from both men and women, the active X chromosome is not organized as a single continuous domain but is split into two large mega-domains separated by a defined boundary located near the 90-megabase region of the chromosome. This bipartite configuration is specific to the liver—it was not observed in other tissues such as colon and lung—and, notably, it appears largely independent of the polyploid state of hepatocytes, which commonly carry doubled or quadrupled genome copies without ill effect. The bipartite active X chromosome, in other words, is a liver-specific architectural feature, maintained even in cells with unusual ploidy.

That stability, however, collapses early in disease. Analyzing fibrotic, cirrhotic and tumoral liver tissue from patients of both sexes, the researchers found that the mega-domain boundary weakens and the two halves of the active X chromosome merge into a single, unified domain. The merging is not merely a passive byproduct of genomic chaos in tumors. When the two halves fuse, the strengthened long-range contacts between them occur predominantly between A-type, active compartments, and these new A–A interactions are associated with the activation of X-linked genes. Genes on the X chromosome were among the most disproportionately upregulated in hepatocellular carcinoma samples, a pattern confirmed in data from The Cancer Genome Atlas, where the ratio of upregulated to total genes on the X chromosome ranked at or near the top in male tumors.

To establish causality rather than correlation, the team employed CRISPR–Cas3, a genome-editing system capable of deleting megabase-scale stretches of DNA that standard Cas9 cannot handle. By excising the boundary element that separates the two mega-domains of the active X chromosome, the researchers recreated, in isolation, the architectural event that accompanies liver cancer. The result was unambiguous: disruption of the bipartite conformation alone was sufficient to trigger a premalignant transition. In liver organoids, boundary deletion transformed normally cystic structures into thickened, compact spheroids reminiscent of early tumor growth, accompanied by the activation of X-linked genes. The boundary, it seems, functions as a tumor-suppressive architectural element whose loss unlocks oncogenic expression programs.

Having demonstrated that structural dissolution drives malignant change, the researchers then asked which of the activated X-linked genes actually matter for liver cancer. Using complementary CRISPR-based screening approaches—CRISPRa to boost the expression of genes that were weakly expressed, and CRISPRi to silence genes that were highly expressed—the team winnowed the long list of upregulated X-linked genes down to a set of functional drivers in hepatocellular carcinoma, including metabolic and stress-response genes such as G6PD and members of the ARMCX family. The identification of these effective genes provides a bridge between an abstract chromosomal conformation and concrete molecular targets that could, in principle, be therapeutically interrogated.

The study’s technical scope is considerable. The raw data, encompassing Hi-C, ChIP-seq, RNA-seq, whole-genome sequencing and bisulfite sequencing from both mouse and human samples, have been deposited in public archives, and all analysis code has been released through GitHub and Zenodo. Genome-wide, the researchers also documented the broader erosion of liver genome organization during carcinogenesis: topologically associating domains weakened, CTCF and H3K27ac signals at boundaries diminished, chromatin loops faded, and DNA methylation increased. Against this backdrop of global architectural decay, the specific, boundary-dependent dissolution of the bipartite active X chromosome stands out as an unusually discrete and mechanistically tractable event—one of the earliest and most consistent architectural signatures of hepatocarcinogenesis in both mice and humans.

The findings also carry implications for the long-standing puzzle of sex bias in liver cancer. Because males possess only a single X chromosome, every hepatocyte depends entirely on the architectural integrity of that one active X for proper dosage regulation of X-linked genes. The more pronounced conformational disruption and gene activation observed on the male X during disease progression offers a structural explanation for why male livers appear more vulnerable to malignant transformation, complementing earlier hypotheses about tumor-suppressor genes that escape X inactivation. Intriguingly, the work resonates with prior studies showing that the inactive X chromosome also folds into a bipartite structure in mice—one organized around the macroH2A-rich DXZ4-like boundary—suggesting that bipartite architecture may be a general feature of X chromosome biology whose functions differ profoundly between the active and inactive states.

For the field of cancer genomics, the message is that the fourth dimension of the genome—its behavior across time and disease states—can reveal vulnerabilities invisible to static snapshots. A liver-specific, megabase-scale boundary on the active X chromosome emerges from this study as both an early casualty of hepatocarcinogenesis and a functional guardian against it. If confirmed and extended, monitoring the integrity of this bipartite structure, or the X-linked genes it restrains, could eventually inform risk stratification in patients with chronic liver disease, while the boundary itself and the genes it controls offer fresh mechanistic entry points into one of the world’s deadliest cancers.

Subject of Research: Dissolution of the bipartite active X chromosome structure during hepatocellular carcinoma development

Article Title: 4D nucleome landscape reveals dissolution of bipartite active X chromosome in liver cancer development

Article References: Xie, X., Tan, D., Mo, J., Xu, Y., Ni, Q., Liu, T., Wang, H., Chen, B., Liao, Y., Lin, Y., Chen, W., Luo, S., Fang, Q., Zhao, M., Yu, Y., Zheng, C., Cui, R., Wang, J., Chang, H., … Wang, J. (2026). 4D nucleome landscape reveals dissolution of bipartite active X chromosome in liver cancer development. Nature Cell Biology, 28(10), 2106-2118. https://doi.org/10.1038/s41556-026-02063-y

Image Credits: AI Generated

DOI: 10.1038/s41556-026-02063-y

Keywords: X chromosome, 4D nucleome, hepatocellular carcinoma, genome architecture, Hi-C, bipartite mega-domain, liver fibrosis, CRISPR-Cas3, X-linked gene activation, sex bias in cancer, chromatin loops, premalignant transformation

At the heart of the work is a two-part engineering strategy that combines prime editing with recombinase technology. Prime editing is used to place recombinase recognition sites—such as loxP—directly into chosen genomic locations without the double-strand breaks that often accompany older editing paradigms. By targeting repetitive elements, the protocol converts naturally abundant genomic scaffolds into high-density landing pads for future rearrangements.

The target focus is notable: LINE-1 elements and other repeat sequences offer multiple insertion opportunities across the genome. By multiplexing prime edits, the method can install recombinase sites at hundreds to thousands of repeat loci within a single cell population. This dense and programmable substrate is designed to be far more scalable than approaches that rely on sparse modification events or stochastic integration.

Once these sites are present, recombinase-mediated rearrangements can be induced to generate controlled genomic structural changes. Crucially, the authors position these rearrangements as a way to systematically test how large-scale genome architecture influences cellular function. Instead of inferring structure–function links indirectly, the workflow aims to connect induced rearrangements to measurable phenotypes such as survival under selective conditions.

For researchers conducting genome-wide functional analyses, the protocol also functions as an essentiality mapping platform. By tracking which edited cells persist when selection pressures are applied, the method enables direct mapping between genome organization and fitness. This reframes structural perturbation as an experimentally quantifiable variable in functional screens.

The authors emphasize that this approach differs from strategies dependent on double-strand breaks or random transposon insertion. Those methods may be powerful but are often limited by lower reproducibility, reduced programmability, or confounding stress responses. Here, the reliance on prime editing for site installation supports a denser and more predictable set of recombination substrates.

In terms of implementation, the full workflow reportedly requires roughly 12–18 weeks and intermediate-to-advanced expertise spanning genome editing, mammalian cell culture, and sequencing-based analysis. The protocol is therefore positioned not as a simple tweak to existing editing workflows, but as a structured pipeline intended for high-throughput, mechanism-driven genome structure studies.

Overall, the study offers an ambitious but actionable route to programmable genome rearrangement at unprecedented scale. By enabling thousands of recombinase sites in repetitive elements, it opens a new experimental window for viral science news: mapping genome architecture to cellular fitness—and potentially guiding rational synthetic genome design.

Subject of Research: Large-scale genome structure interrogation using recombinase-mediated rearrangements of multiplexed prime edits in repetitive elements.

Article Title: Large-scale genome structure interrogation via recombinase-mediated rearrangements of multiplexed prime edits in repetitive elements.

Article References: Riedmayr, L.M., Koeppel, J., Church, G.M. et al. Large-scale genome structure interrogation via recombinase-mediated rearrangements of multiplexed prime edits in repetitive elements. Nat Protoc (2026). https://doi.org/10.1038/s41596-026-01409-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41596-026-01409-y

Keywords: Prime editing; recombinase; loxP; genome architecture; LINE-1; genome rearrangements; genome-wide functional screens; synthetic genome design.