Acute myeloid leukaemia, or AML, disrupts the production of healthy blood cells and progresses rapidly, making early and effective treatment critical. Around one-third of patients carry a mutation in a gene called NPM1, one of the most frequently altered genes in this disease. The NPM1 gene provides instructions for a protein that normally resides in the nucleus, the compartment housing the cell’s genetic material. In patients with the mutation, the protein accumulates in the cytoplasm, the surrounding fluid of the cell, and this mislocalisation plays a central role in driving the cancer. Understanding precisely why the protein ends up in the wrong place has therefore been a major goal of leukaemia research.

Until now, most explanations have focused on transport: how the mutated protein shuttles between the nucleus and the cytoplasm. The new study, led by Dr Daniel Booth and Dr Claire Seedhouse at the University of Nottingham’s School of Medicine, shows that this picture is incomplete. Something important happens during cell division itself, when the cell dismantles its nucleus and its chromosomes are exposed in the cytoplasm. The research reveals a previously unknown mechanism that helps explain how the mutation disrupts the normal organisation of proteins inside leukaemia cells.

At the heart of the discovery is the mitotic chromosome periphery, a protective layer that surrounds chromosomes during cell division and has received relatively little scientific attention. Using advanced microscopy, genetic engineering and protein-mapping techniques, the researchers found that healthy NPM1 attaches to this layer during mitosis. This attachment acts like a bookmark: when division is complete and the nucleus re-forms, the protein uses the chromosome surface as a reference point to find its way back to its correct location. In leukaemia cells, this process fails. Dr Booth described the chromosome surface as a temporary docking station that helps proteins return to the right place after a cell divides, and noted that in leukaemia cells this docking does not work properly.

The mutated NPM1 protein behaves in two distinct abnormal ways. It fails to attach properly to the chromosome periphery, instead forming abnormal clusters or dispersing throughout the cell. Worse still, the mutant protein actively interferes with the healthy NPM1 protein produced by the normal copy of the gene, further disrupting its ability to return to the right place after division. Together, these findings reveal two previously unrecognised ways in which cell division contributes to the abnormal positioning of NPM1 in leukaemia, adding a new dimension to a problem that researchers had largely framed as one of transport between cellular compartments.

Perhaps the most exciting aspect of the study is that the researchers showed the process could be partially restored in laboratory experiments. By increasing levels of Ki-67, a protein that helps organise the chromosome periphery, the team created a thicker protective layer around the chromosomes. This improved the attachment of mutant NPM1 to the chromosomes in laboratory-grown leukaemia cells, helping to restore its correct positioning and the normal organisation of the cell. The result suggests that manipulating the chromosome periphery could, in principle, correct one of the fundamental defects that sustains the disease.

The involvement of Ki-67 adds a surprising twist. The protein is widely used in clinics as a marker of cell proliferation and is generally associated with more aggressive cancers, so one might expect higher Ki-67 levels to signal a worse prognosis. However, when the team analysed publicly available patient data, a more complex picture emerged: among patients with this particular form of AML, higher Ki-67 levels showed an unexpected trend towards better survival. The researchers stress that this observation requires further investigation, but it hints that the protein’s role in organising the chromosome periphery may be more protective than its reputation suggests.

Critically, the team confirmed that the newly identified mechanism occurs in samples taken directly from AML patients, not only in laboratory-grown cells. This strengthens the clinical relevance of the findings and suggests that the defective docking of NPM1 at the chromosome periphery is a genuine feature of the disease in patients rather than an artefact of cell culture. Dr Seedhouse said that what is particularly exciting is that the team has shown the process can be improved experimentally, adding that although the work is at an early stage, it reveals a completely new way of thinking about how this form of leukaemia develops and, potentially, how it might be treated.

The discovery challenges the longstanding view that abnormal movement of NPM1 between the nucleus and the cytoplasm is the principal explanation for its mislocalisation. Instead, it identifies interactions between proteins and chromosomes during cell division as another important contributor to the disease. If the findings can be translated into therapies, they would represent a fundamentally new strategy: rather than blocking the mutant protein’s transport, future treatments might aim to strengthen the chromosome periphery so that proteins can find their way home after each cell division. Such an approach could potentially complement existing therapies for AML, a disease in which new treatment options remain urgently needed.

The implications may also extend well beyond leukaemia. The abnormal positioning of proteins inside cells is involved in a range of diseases, and understanding how cells organise their proteins during division could open new avenues of research into other conditions. By uncovering an unexpected link between chromosome biology and blood cancer, the Nottingham team has revealed a new direction for understanding, and potentially treating, diseases driven by misplaced proteins, and has highlighted the mitotic chromosome periphery, long a neglected corner of cell biology, as a promising target for future medicine.

Subject of Research: How the NPM1 mutation disrupts protein positioning at the mitotic chromosome periphery in acute myeloid leukaemia

Article Title: Scientists uncover overlooked mechanism driving aggressive leukemia

Article References: Scientists uncover overlooked mechanism driving aggressive leukemia. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: acute myeloid leukaemia, NPM1, cell division, chromosome periphery, Ki-67, protein mislocalisation, University of Nottingham, blood cancer, mitosis, leukaemia research, Nature, mutation

The question underlying the work is one of the oldest in science: how could life have emerged from non-living matter? For a minimal cell to exist, Prof. Göpfrich explains, it must at least retain its genetic material while also being capable of change. Creating such a minimal living cell from scratch using non-living components is one of the central goals of synthetic biology, the field in which Göpfrich and her research group at the ZMBH work. Their chosen building blocks are RNA droplets: liquid-like, membrane-less nanostructures composed of synthetically produced ribonucleic acid that spontaneously self-assemble. Unlike conventional artificial cells enclosed in lipid membranes, these droplets rely on the intrinsic tendency of RNA molecules to condense into dense, viscous phases, much like oil droplets forming in water. That simplicity is precisely what makes them an attractive model for studying the earliest steps toward living systems.

A crucial property of these droplets is that they can encapsulate the genetic template from which they are transcribed. As William Verstraeten, a doctoral candidate in Prof. Göpfrich’s research group, notes, that establishes a basic prerequisite for a system that can evolve. The genetic blueprint and the molecular machinery reading it are held together in the same tiny compartment, so any mutation introduced into the template is immediately expressed as new RNA inside the very droplet that carries it. This coupling between genotype and phenotype at the nanoscale is something that even the simplest living cells achieve, and reproducing it in a purely synthetic, membrane-less system is a significant technical achievement in its own right.

What happened next surprised the researchers. When they deliberately modified the DNA template to introduce mutations, they expected the droplets to dissolve or lose their structural integrity. Instead, the opposite occurred: the droplets formed large cavities, known as vacuoles, which remained stable for days or even weeks. According to Göpfrich, a mutation rate of just around three percent was sufficient to produce these complex changes in the primitive synthetic cells. In other words, only a small fraction of the genetic material needed to be altered before the entire architecture of the droplet reorganized itself, sprouting internal compartments that no one had designed. The result suggests that elaborate structure can be encoded in, and triggered by, remarkably few molecular changes.

Identifying exactly which mutations were responsible required specialized expertise. The team led by Prof. Dr. Michael Platten at the German Cancer Research Center applied a specialized sequencing technology to determine which genetic changes produced the structural transformations inside the droplets. This analysis made it possible to create a model mutant, a defined genetic sequence whose introduction reliably reproduces the vacuole-forming effect. The ability to reproduce the phenotype at will transforms the observation from a curiosity into a tool: the researchers can now program droplet architecture by writing specific sequences into the template, effectively using mutation as a design language for synthetic cells.

The physical mechanism behind the cavities was worked out in collaboration with Prof. Dr. Christoph Weber from the Institute of Physics at the University of Augsburg. As he explains, the vacuoles form when more and more new RNA enters the already viscous interior of the droplets during transcription. The rapid accumulation of mass can cause local instabilities, from which the cavities form. In essence, the mutated RNA sequences produce transcripts that pile up inside the crowded droplet faster than the condensed phase can accommodate them, and the resulting mechanical stress tears open voids within the droplet body. The experiments confirmed that this process is subject to strict size and composition thresholds: vacuole formation requires RNA droplets to have a radius of more than 20 micrometers and to contain more than approximately two percent genetically modified material. Below those limits, the droplets remain homogeneous; above them, internal compartments reliably appear.

The consequences of those internal cavities extend far beyond appearance. Further investigations showed that the vacuoles redistribute mass inside the droplets, producing tiny movements on the micrometer scale. Mutated RNA droplets traveled significantly farther than their non-mutated counterparts and collided with one another more frequently. Those collisions had a consequential outcome: the droplets fused and grew. In a population of synthetic cells, therefore, the mutants are effectively more mobile and more interactive, qualities that in an evolutionary scenario could translate into a genuine advantage. The vacuoles also serve a storage function. As Göpfrich points out, they take up small molecules and can store this cargo inside them, giving the otherwise featureless droplets something analogous to internal organelles, compartments in which specific chemistry could in principle be concentrated and protected.

For Göpfrich, the deeper lesson is architectural. Mutagenesis, she emphasizes, provides a new architecture and new functions that would be very difficult to design and build from scratch. Traditional synthetic biology assembles systems part by part, an approach that becomes exponentially harder as the desired functions grow more complex. The Heidelberg results suggest an alternative strategy: encode a simple starting system, let mutations accumulate, and select the variants that acquire useful behaviors. Evolution, in this view, is not merely a subject of study but a manufacturing method, one capable of discovering structural solutions that human intuition and engineering would struggle to specify in advance.

That philosophy now anchors an ambitious applied agenda. As part of Heidelberg University’s SynthImmune Cluster of Excellence, the researchers aim to harness this process of mutation and selection to generate molecular functions from minimal synthetic components. Their stated goal is to use the tool to develop synthetic immune effectors capable of evolving specific immune functions on their own. If successful, the approach could yield therapeutic molecules that adapt within the body, refining their activity through cycles of mutation and selection rather than requiring every variant to be designed at the bench. The same principle, the researchers hope, could be extended to other medical applications where evolving function outperforms fixed design.

The work also carries conceptual weight for origins-of-life research. The experiments were conducted as part of the German Research Foundation-funded Collaborative Research Centre / Transregio 392, Molecular Evolution in Prebiotic Environments, which brings together the research groups of Kerstin Göpfrich in Heidelberg and Christoph Weber in Augsburg to study how evolutionary processes could have operated before cells existed. A system in which a few percent of mutations convert a static droplet into a mobile, compartmentalized, cargo-storing structure is a concrete demonstration that the threshold for emergent complexity can be low. The research was additionally supported by the European Research Council, the International Human Frontier Science Program Organization, the Alfried Krupp von Bohlen und Halbach Foundation and the Carl-Zeiss-Stiftung. The findings, published in Nature Chemical Engineering on 7 October 2026 under the title Genetic encoding and mutagenesis of RNA droplet phenotypes, suggest that the path from chemistry to function may be shorter, and more forgiving, than anyone had assumed.

Subject of Research: Mutation-driven emergence of complex phenotypes in synthetic RNA droplet cells

Article Title: Synthetic cells: function through mutation

Article References: Synthetic cells: function through mutation. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: synthetic cells, RNA droplets, mutation, vacuoles, synthetic biology, origin of life, molecular evolution, Heidelberg University, German Cancer Research Center, Nature Chemical Engineering, SynthImmune, cargo uptake

The research was led by Razi Fachar Eldeen of the Faculty of Mathematics and Prof. Naama Brenner of the Wolfson Faculty of Chemical Engineering, both members of the Technion’s Network Biology Research Laboratory. Their work addresses a question that has long hovered at the edge of evolutionary theory. Fitness landscapes are frequently degenerate, meaning that many different combinations of traits, or many different sequences of mutational steps, can yield identical levels of reproductive success. In such situations, classical theory offers no obvious reason for evolution to prefer one route over another. If all directions lead to the same fitness value, one might expect the population to perform an undirected random walk, diffusing evenly across the plateau of equivalent outcomes.

That intuition, the Technion team found, is wrong. By analyzing evolution on degenerate fitness landscapes using computational simulation and mathematical modeling, they discovered that the geometry of the landscape, specifically its curvature, exerts a systematic influence even when fitness itself is identical everywhere along the available paths. Rather than drifting aimlessly, populations are drawn toward flatter regions of the landscape. In these flat zones, an organism’s traits are less sensitive to mutation and other perturbations: small changes in genotype or environment produce smaller changes in phenotype and fitness. The result is a directional bias that emerges purely from the shape of the terrain, not from any explicit reward for being robust.

The distinction between height and curvature is the conceptual heart of the finding. Fitness measures how well an organism survives and reproduces in its current environment, and selection directly rewards improvements in fitness. Curvature, by contrast, describes how fitness changes as traits are perturbed. A population sitting on a sharp peak may enjoy high fitness, but a single mutation can send it tumbling down a steep slope. A population on a broad, flat plateau may have the same measured fitness, yet its descendants remain viable across a much wider range of genetic and environmental variation. The Technion study shows that over evolutionary time, this second-order property is not invisible to the dynamics. Even without selection explicitly favoring tolerance to change, the population’s trajectory bends toward the flatter ground.

Prof. Brenner framed the question that motivated the work in terms of the classical climbing metaphor. Evolution, she noted, is commonly described as a process of climbing fitness peaks, in which organisms become better adapted to their environment and thus improve their chances of survival. However, organisms sometimes face multiple evolutionary trajectories that all yield the same level of fitness. This raises the question of how evolution selects among these paths of equivalent fitness. The answer the team arrived at is that the selection among equal-fitness paths is governed by robustness: when fitness is equal, evolution tends to favor directions that confer greater robustness and tolerance to change, properties that themselves provide a survival advantage over longer timescales.

This conclusion carries a significant implication for how scientists think about the origins of biological robustness. Robustness, the capacity of a living system to maintain its function despite genetic mutations, environmental fluctuations, and internal noise, is ubiquitous in nature. Gene regulatory networks buffer against perturbations, developmental programs produce reliable body plans despite variable conditions, and metabolic pathways continue functioning when individual components fail. Traditionally, such resilience is attributed to direct selection: lineages that survived disruptions left more descendants, so robustness was favored trait by trait. The Technion findings suggest an additional and more spontaneous route. Robustness and resilience to disruption can emerge as a byproduct of evolutionary dynamics themselves, without any explicit selection pressure favoring these properties, simply because populations on degenerate landscapes drift toward the flat, tolerant regions.

The methodological approach relied on computational simulation and modeling, allowing the researchers to explore the behavior of populations on landscapes where many trajectories lead to similar fitness levels. In the figures accompanying the study, the lighter-colored regions represent these areas of equivalent fitness. One panel depicts the classical expectation: evolution proceeding as an undirected random walk across the plateau. The other depicts what the study actually found: differences in landscape curvature driving organisms deterministically toward flatter regions, where they gain greater robustness and tolerance to change. The contrast between the two panels captures the shift in perspective that the work proposes. The flat side of the landscape, far from being an evolutionary dead zone where nothing interesting happens, turns out to be an attractor, a destination that the dynamics actively seek out.

Why should curvature produce such a systematic drift? The intuition can be sketched in terms of how populations explore the space of possible traits. On a steeply curved region of the landscape, most mutational steps away from the current state lead to large losses of fitness, and such steps are efficiently eliminated by selection. On a flat region, many mutational steps are nearly neutral, so the population can accumulate and retain far more genetic and phenotypic variation. Over time, the population effectively spends more of its history in the flat, mutation-tolerant zones, and its lineage becomes anchored there. The flat regions act as sinks in the space of evolutionary trajectories: paths leading into them are retained, while paths leading out of them are repeatedly pruned by the accumulated weight of small deleterious changes. The drift is therefore deterministic in the statistical sense, even though the individual mutations that carry it are random.

The implications extend beyond evolutionary biology into unexpected territory. The researchers point out that deep learning exhibits a strikingly parallel phenomenon. When artificial neural networks are trained, they likewise tend to converge on flat minima of their loss landscapes, and it is well established in the machine learning literature that solutions found in flat basins tend to generalize better to new data than solutions perched on sharp minima. In both domains, a system navigating a high-dimensional landscape in which many points offer equivalent performance ends up preferentially occupying the flat regions, and in both domains this preference is associated with greater stability and tolerance to perturbation. The Technion study thus builds a conceptual bridge between evolutionary dynamics and learning dynamics, suggesting that the tendency to seek flatness may reflect deep structural properties of adaptation processes in high-dimensional spaces, whether the adapting system is a population of organisms or a network of artificial neurons.

The study may ultimately help explain patterns of biological variation observed in nature, deepen understanding of the origins of robustness in living systems, and inform theoretical work across disciplines that grapple with adaptation in complex landscapes. It reframes the flat portions of fitness landscapes, long treated as the boring interludes between dramatic climbs, as regions with their own intrinsic dynamics and their own evolutionary consequences. The research was supported in part by the U.S.-Israel Binational Science Foundation, and it appeared in the Proceedings of the National Academy of Sciences under the title describing evolution on degenerate fitness landscapes as non-random, with curvature driving directional drift. What emerges from the work is a revised picture of evolution’s compass: even on ground where every direction looks equally good, the shape of the terrain quietly decides where life will go, steering it toward the places where it can best withstand whatever comes next.

Subject of Research: Directional drift of evolving populations toward flat, robust regions of degenerate fitness landscapes

Article Title: The flat side of evolution: Even when multiple evolutionary paths are equally advantageous, the choice among them is not random

Article References: The flat side of evolution: Even when multiple evolutionary paths are equally advantageous, the choice among them is not random. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: evolution, fitness landscapes, robustness, degeneracy, curvature, natural selection, Technion, computational modeling, deep learning, PNAS, mutation, adaptation

The tool, called EWF 2.0, was developed by Jaromir Sant of the University of Turin and the Collegio Carlo Alberto, together with Paul A. Jenkins, Jere Koskela and Dario Spanò of the University of Warwick and Newcastle University. It extends an earlier line of work known as the EWF algorithms, which allow researchers to draw exact samples from the Wright–Fisher diffusion — that is, to generate simulated allele frequency trajectories whose statistical properties match the theoretical model perfectly, without the approximation errors that plague standard numerical schemes. The crucial innovation in version 2.0 is that the population size is no longer required to be constant. Instead, the demographic history can change over time, which mathematically turns the drift coefficient of the diffusion into a time-inhomogeneous one: a process whose random dynamics depend explicitly on when you look at it.

To appreciate why this matters, it helps to understand what exact simulation means in this context. The Wright–Fisher diffusion is a stochastic differential equation, and the most common way to simulate such equations is to discretise time into small steps and approximate the motion between them. These Euler-type schemes are easy to implement but introduce bias: the simulated trajectories are not true draws from the model, and the bias can be difficult to quantify, particularly for quantities that depend on the fine structure of the path rather than just its endpoints. Exact algorithms sidestep this problem entirely. Using clever probabilistic constructions — often based on rejection sampling ideas in which a candidate path is proposed and then accepted or rejected according to criteria that guarantee the output has exactly the right distribution — they produce trajectories that are statistically indistinguishable from the real thing, whatever the intended application.

The difficulty with time-varying demography is that these rejection-based constructions were designed for diffusions with fixed coefficients. When the population size changes, the drift term of the allele frequency dynamics acquires a time-dependent factor, and the bounds and transformations that make the earlier algorithms valid no longer hold. Previous versions of EWF could handle time-varying mutation and selection in certain settings, but the demographic component had to remain static. Since virtually every dataset from natural populations — humans, livestock, pathogens, crop species — carries the imprint of a fluctuating census history, this limitation was a serious obstacle for anyone hoping to use exact simulation in realistic inference pipelines.

EWF 2.0 removes that obstacle. According to the authors, the algorithm accommodates a specified demographic history alongside mutation parameters, a selection function and a set of sampling times, and from those inputs it generates exact draws from the law of the corresponding Wright–Fisher diffusion. Perhaps more strikingly, it can also simulate diffusion bridges: trajectories that are conditioned to start at one allele frequency and end at another. Bridge simulation is a notoriously hard problem, because the conditioning fundamentally alters the dynamics of the process, and the authors note that existing methods cannot handle time-varying mutation and selection rates in this setting. Bridges are precisely what many statistical applications need, since inferring past evolutionary forces often amounts to asking which hidden paths most plausibly connect observed genetic data points collected at different times.

The applications of such a tool extend across modern population genetics. Ancient DNA studies, for example, now routinely provide snapshots of allele frequencies at multiple time points separated by hundreds or thousands of generations. Reconstructing the trajectories between those snapshots — and quantifying the uncertainty around them — requires simulating bridges under a model that includes the demographic history inferred from archaeological and genomic evidence. Similarly, forward-time simulations used to benchmark inference methods, to test the power of genome-wide association designs, or to explore how selection responds to environmental change all benefit from trajectories that are exact rather than approximate. Any bias in the simulator propagates into the conclusions drawn from it, so the guarantee of exactness is not a mathematical luxury but a practical safeguard.

The authors validated their implementation using distributional tests, including Kolmogorov–Smirnov tests and quantile-quantile plots, which compare the empirical distribution of simulated quantities against theoretical expectations. Agreement in these tests provides strong evidence that the algorithm samples from the intended law, since even subtle biases in a simulation scheme tend to show up as systematic deviations in such comparisons. Equally important from a practical standpoint is the performance result: despite its greater generality, EWF 2.0 retains the same runtime as previous versions of the algorithm. Adding the flexibility of time-varying demography therefore comes at no computational penalty, which the authors highlight as ensuring efficiency and scalability for large-scale simulation studies.

The software is written up as an open-access methods paper and the code is freely available on GitHub, lowering the barrier for other groups to adopt it. In a field where simulation underpins everything from method validation to likelihood-free inference, a tool that combines exactness, demographic realism and competitive speed is likely to find rapid uptake. The work was supported by funding from the Italian Ministry of University and Research through a PRIN 2022 grant financed by the European Union’s Next Generation EU programme, reflecting the increasingly international and computationally oriented character of statistical genetics research.

Looking at the broader picture, the advance illustrates a familiar pattern in computational science: the gap between the models researchers want to use and the models their tools can actually handle. The Wright–Fisher diffusion with time-varying demography has been part of the theoretical literature for decades, and approximate simulators have long been able to produce plausible-looking trajectories under it. What was missing was the guarantee — the assurance that a simulated history is a genuine draw from the specified model rather than an artefact of numerical approximation. By extending exact simulation into the time-inhomogeneous regime, EWF 2.0 brings a foundational class of population genetic models fully within reach of rigorous simulation-based inference, and it does so without asking practitioners to sacrifice speed. For studies probing how genes rose and fell across the shifting demographic tides of the past, that combination may prove hard to resist.

Subject of Research: Exact simulation of allele frequency trajectories under the Wright–Fisher diffusion with time-varying demography

Article Title: EWF 2.0: exact sampling of allele trajectories using the Wright–Fisher diffusion with time-varying demography

Article References: Sant, J., Jenkins, P. A., Koskela, J., & Spanò, D. (2026). EWF 2.0: exact sampling of allele trajectories using the Wright–Fisher diffusion with time-varying demography. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06576-z

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06576-z

Keywords: Wright–Fisher diffusion, exact simulation, population genetics, allele frequency, demography, genetic drift, selection, mutation, diffusion bridge, computational biology, stochastic modelling, BMC Bioinformatics