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Publications in Applied Sciences by NOMIS researchers

Culture-independent meta-pangenomics enabled by long-read metagenomics reveals associations with pediatric undernutrition

NOMIS Researcher(s)

Published in

September 9, 2025

The human gut microbiome is linked to child malnutrition, yet traditional microbiome approaches lack resolution. We hypothesized that complete metagenome-assembled genomes (cMAGs), recovered through long-read (LR) DNA sequencing, would enable pangenome and microbial genome-wide association study (GWAS) analyses to identify microbial genetic associations with child linear growth. LR methods produced 44–64× more cMAGs per gigabase pair (Gbp) than short-read methods, with PacBio (PB) yielding the most accurate and cost-effective assemblies. In a Malawian longitudinal pediatric cohort, we generated 986 cMAGs (839 circular) from 47 samples and applied this database to an expanded set of 210 samples. Machine learning identified species predictive of linear growth. Pangenome analyses revealed microbial genetic associations with linear growth, while genome instability correlated with declining length-for-age Z score (LAZ). This resource demonstrates the power of comparing cMAGs with health trajectories and establishes a new standard for microbiome association studies.

Research field(s)
Bioinformatics, Microbiology, Pediatrics

DOI
10.1016/j.cell.2025.08.020

Targeted DNA ADP-ribosylation triggers templated repair in bacteria and base mutagenesis in eukaryotes

NOMIS Researcher(s)

Published in

September 4, 2025

Base editors create precise genomic edits by directing nucleobase deamination or removal without inducing double-stranded DNA breaks. However, a vast chemical space of other DNA modifications remains to be explored for genome editing. Here we harness the bacterial antiphage toxin DarT2 to append ADP-ribosyl moieties to DNA, unlocking distinct editing outcomes in bacteria versus eukaryotes. Fusing an attenuated DarT2 to a Cas9 nickase, we program site-specific ADP-ribosylation of thymines within a target DNA sequence. In tested bacteria, targeting drives homologous recombination, offering flexible and scar-free genome editing without base replacement or counterselection. In tested yeast, plant and human cells, targeting drives substitution of the modified thymine to adenine or a mixture of adenine and cytosine with limited insertions or deletions, offering edits inaccessible to current base editors. Altogether, our approach, called append editing, leverages the addition of chemical moieties to DNA to expand current modalities for precision gene editing.

Research field(s)
Biotechnology, Biochemistry & Molecular Biology, Genetics & Heredity, Microbiology

DOI
10.1038/s41587-025-02802-w

The actin cortex acts as a mechanical memory of morphology in confined migrating cells

NOMIS Researcher(s)

Published in

August 25, 2025

Cell migration in narrow microenvironments occurs in numerous physiological processes. It involves successive cycles of confinement and release that drive important morphological changes. However, it remains unclear whether migrating cells can retain a memory of their past morphological states that could potentially facilitate their navigation through confined spaces. We demonstrate that local geometry governs a switch between two cell morphologies, thereby facilitating cell passage through long and narrow gaps. We combined cell migration assays on standardized microsystems with biophysical modelling and biochemical perturbations to show that migrating cells have a long-term memory of past confinement events. The morphological cell states correlate across transitions through actin cortex remodelling. These findings indicate that mechanical memory in migrating cells plays an active role in their migratory potential in confined environments.

Research field(s)
Biomedical Engineering, Biochemistry & Molecular Biology, Biophysics

DOI
10.1038/s41567-025-02980-z

A male-essential miRNA is key for avian sex chromosome dosage compensation

NOMIS Researcher(s)

Published in

July 16, 2025

Birds have a sex chromosome system in which females are heterogametic (ZW) and males are homogametic (ZZ)1. The differentiation of avian sex chromosomes from ancestral autosomes entails the loss of most genes from the W chromosome during evolution1,2. However, the extent to which mechanisms evolved that counterbalance this substantial reduction in female gene dosage remains unclear. Here we report functional in vivo and evolutionary analyses of a Z-linked microRNA (miR-2954) with strong male-biased expression, previously proposed to mediate avian sex chromosome dosage compensation3. We knocked out miR-2954 in chicken, which resulted in early embryonic lethality in homozygous knockout males, probably driven by specific upregulation of dosage-sensitive Z-linked target genes. Evolutionary gene expression analyses further revealed that these dosage-sensitive target genes underwent both transcriptional and translational upregulation on the single Z in female birds. Altogether, this work unveils a scenario in which evolutionary pressures following W gene loss drove transcriptional and translational upregulation of dosage-sensitive Z-linked genes in females but also their transcriptional upregulation in males. The resulting excess of transcripts in males, resulting from the combined activity of two upregulated dosage-sensitive Z gene copies, was in turn offset by the emergence of a highly targeted miR-2954-mediated transcript degradation mechanism during avian evolution. This study uncovered a unique sex chromosome dosage compensation system in birds, in which a microRNA has become essential for male survival.

Research field(s)
Bioinformatics, Biochemistry & Molecular Biology, Developmental Biology, Genetics & Heredity, Evolutionary Biology

DOI
10.1038/s41586-025-09256-9

A foundation model to predict and capture human cognition

NOMIS Researcher(s)

Published in

July 2, 2025

Establishing a unified theory of cognition has been an important goal in psychology1,2. A first step towards such a theory is to create a computational model that can predict human behaviour in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behaviour in any experiment expressible in natural language. We derived Centaur by fine-tuning a state-of-the-art language model on a large-scale dataset called Psych-101. Psych-101 has an unprecedented scale, covering trial-by-trial data from more than 60,000 participants performing in excess of 10,000,000 choices in 160 experiments. Centaur not only captures the behaviour of held-out participants better than existing cognitive models, but it also generalizes to previously unseen cover stories, structural task modifications and entirely new domains. Furthermore, the model’s internal representations become more aligned with human neural activity after fine-tuning. Taken together, our results demonstrate that it is possible to discover computational models that capture human behaviour across a wide range of domains. We believe that such models provide tremendous potential for guiding the development of cognitive theories, and we present a case study to demonstrate this.

Research field(s)
Information & Communication Technologies, Psychology & Cognitive Sciences, Psychology & Cognitive Sciences, Behavioral Science & Comparative Psychology

DOI
10.1038/s41586-025-09215-4

Capturing the complexity of human strategic decision-making with machine learning

NOMIS Researcher(s)

Published in

June 25, 2025

Strategic decision-making is a crucial component of human interaction. Here we conduct a large-scale study of strategic decision-making in the context of initial play in two-player matrix games, analysing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on this dataset predicts human choices with greater accuracy than leading theories of strategic behaviour, revealing systematic variation unexplained by existing models. By modifying this network, we develop an interpretable behavioural model that uncovers key insights: individuals’ abilities to respond optimally and reason about others’ actions are highly context dependent, influenced by the complexity of the game matrices. Our findings illustrate the potential of machine learning as a tool for generating new theoretical insights into complex human behaviours.

Research field(s)
Information & Communication Technologies, Psychology & Cognitive Sciences

DOI
10.1038/s41562-025-02230-5

Realism Drives Interpersonal Reciprocity but Yields to AI-Assisted Egocentrism in a Coordination Experiment

NOMIS Researcher(s)

Published in

April 25, 2025

Virtual reality technologies that enhance realism and artificial intelligence (AI) systems that assist human behavior are increasingly interwoven in social applications. However, how these technologies might jointly influence interpersonal coordination remains unclear. We conducted an experiment with 240 participants in 120 pairs who interacted through remote-controlled robot cars in a physical space or virtual cars in a digital space, with or without autosteering assistance, using the chicken game, an established model of interpersonal coordination. We find that both realism and AI assistance help improve user performance but through opposing mechanisms. Real-world contexts enhanced communication, fostering reciprocal actions and collective benefits. In contrast, autosteering assistance diminished the need for interpersonal coordination, shifting participants’ focus towards self-interest. Notably, when combined, the egocentric effects of autosteering assistance outweighed the prosocial effects of realism. The design of HCI systems that involve social coordination will, we believe, need to take such effects into account.

Research field(s)
Information & Communication Technologies, Behavioral Science & Comparative Psychology

DOI
10.1145/3706598.3713371

Binary climate data visuals amplify perceived impact of climate change

NOMIS Researcher(s)

Published in

April 17, 2025

For much of the global population, climate change appears as a slow, gradual shift in daily weather. This leads many to perceive its impacts as minor and results in apathy (the ‘boiling frog’ effect). How can we convey the urgency of the crisis when its impacts appear so subtle? Here, through a series of large-scale cognitive experiments (N = 799), we find that presenting people with binary climate data (for example, lake freeze history) significantly increases the perceived impact of climate change (Cohen’s d = 0.40, 95% confidence interval 0.26–0.54) compared with continuous data (for example, mean temperature). Computational modelling and follow-up experiments (N = 398) suggest that binary data enhance perceived impact by creating an ‘illusion’ of sudden shifts. Crucially, our approach does not involve selective data presentation but rather compares different datasets that reflect equivalent trends in climate change over time. These findings, robustly replicated across multiple experiments, provide a cognitive basis for the ‘boiling frog’ effect and offer a psychologically grounded approach for policymakers and educators to improve climate change communication while maintaining scientific accuracy.

Research field(s)
Information & Communication Technologies, Psychology & Cognitive Sciences, Behavioral Science & Comparative Psychology

DOI
10.1038/s41562-025-02183-9

Comprehensive interrogation of synthetic lethality in the DNA damage response

NOMIS Researcher(s)

Published in

April 9, 2025

The DNA damage response (DDR) is a multifaceted network of pathways that preserves genome stability1,2. Unravelling the complementary interplay between these pathways remains a challenge3,4. Here we used CRISPR interference (CRISPRi) screening to comprehensively map the genetic interactions required for survival during normal human cell homeostasis across all core DDR genes. We captured known interactions and discovered myriad new connections that are available online. We defined the molecular mechanism of two of the strongest interactions. First, we found that WDR48 works with USP1 to restrain PCNA degradation in FEN1/LIG1-deficient cells. Second, we found that SMARCAL1 and FANCM directly unwind TA-rich DNA cruciforms, preventing catastrophic chromosome breakage by the ERCC1–ERCC4 complex. Our data yield fundamental insights into genome maintenance, provide a springboard for mechanistic investigations into new connections between DDR factors and pinpoint synthetic vulnerabilities that could be exploited in cancer therapy.

Partnership(s)
,

Research field(s)
Bioinformatics, Biochemistry & Molecular Biology, Genetics & Heredity, Oncology & Carcinogenesis

DOI
10.1038/s41586-025-08815-4

Strong charge-photon coupling in planar germanium enabled by granular aluminium superinductors

NOMIS Researcher(s)

Published in

March 1, 2025

High kinetic inductance superconductors are gaining increasing interest for the realisation of qubits, amplifiers and detectors. Moreover, thanks to their high impedance, quantum buses made of such materials enable large zero-point fluctuations of the voltage, boosting the coupling rates to spin and charge qubits. However, fully exploiting the potential of disordered or granular superconductors is challenging, as their inductance and, therefore, impedance at high values are difficult to control. Here, we report a reproducible fabrication of granular aluminium resonators by developing a wireless ohmmeter, which allows in situ measurements during film deposition and, therefore, control of the kinetic inductance of granular aluminium films. Reproducible fabrication of circuits with impedances (inductances) exceeding 13 kΩ (1 nH per square) is now possible. By integrating a 7.9 kΩ resonator with a germanium double quantum dot, we demonstrate strong charge-photon coupling with a rate of gc/2π = 566 ± 2 MHz. This broadly applicable method opens the path for novel qubits and high-fidelity, long-distance two-qubit gates.

Research field(s)
Nanoscience & Nanotechnology, Quantum

DOI
10.1038/s41467-025-57252-4

AI-based approach to dissect the variability of mouse stem cell-derived embryo models

NOMIS Researcher(s)

Published in

February 19, 2025

Recent advances in stem cell-derived embryo models have transformed developmental biology, offering insights into embryogenesis without the constraints of natural embryos. However, variability in their development challenges research standardization. To address this, we use deep learning to enhance the reproducibility of selecting stem cell-derived embryo models. Through live imaging and AI-based models, we classify 900 mouse post-implantation stem cell-derived embryo-like structures (ETiX-embryos) into normal and abnormal categories. Our best-performing model achieves 88% accuracy at 90 h post-cell seeding and 65% accuracy at the initial cell-seeding stage, forecasting developmental trajectories. Our analysis reveals that normally developed ETiX-embryos have higher cell counts and distinct morphological features such as larger size and more compact shape. Perturbation experiments increasing initial cell numbers further supported this finding by improving normal development outcomes. This study demonstrates deep learning’s utility in improving embryo model selection and reveals critical features of ETiX-embryo self-organization, advancing consistency in this evolving field.

Research field(s)
Bioinformatics, Artificial Intelligence & Image Processing, Biophysics, Developmental Biology

DOI
10.1038/s41467-025-56908-5

Resolving the three-dimensional interactome of human accelerated regions during human and chimpanzee neurodevelopment

NOMIS Researcher(s)

Published in

January 30, 2025

Human accelerated regions (HARs) have been implicated in human brain evolution. However, insight into the genes and pathways they control is lacking, hindering the understanding of their function. Here, we identify 2,963 conserved gene targets for 1,590 HARs and their orthologs in human and chimpanzee neural stem cells (NSCs). Conserved gene targets are enriched for neurodevelopmental functions and are overrepresented among differentially expressed genes (DEGs) identified in human NSCs (hNSCs) and chimpanzee NSCs (cNSCs) as well as in human versus non-human primate brains. Species-specific gene targets do not converge on any function and are not enriched among DEGs. HAR targets also show cell-type-specific expression in the human fetal brain, including in outer radial glia, which are linked to cortical expansion. Our findings support that HARs influence brain evolution by altering the expression of ancestral gene targets shared between human and chimpanzee rather than by gaining new targets in human and facilitate hypothesis-directed studies of HAR biology.

Research field(s)
Bioinformatics, Developmental Biology, Evolutionary Biology

DOI
10.1016/j.cell.2025.01.007

Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning

NOMIS Researcher(s)

Published in

January 14, 2025

Efficient and accurate nanocarrier development for targeted drug delivery is hindered by a lack of methods to analyze its cell-level biodistribution across whole organisms. Here we present Single Cell Precision Nanocarrier Identification (SCP-Nano), an integrated experimental and deep learning pipeline to comprehensively quantify the targeting of nanocarriers throughout the whole mouse body at single-cell resolution. SCP-Nano reveals the tissue distribution patterns of lipid nanoparticles (LNPs) after different injection routes at doses as low as 0.0005 mg kg−1—far below the detection limits of conventional whole body imaging techniques. We demonstrate that intramuscularly injected LNPs carrying SARS-CoV-2 spike mRNA reach heart tissue, leading to proteome changes, suggesting immune activation and blood vessel damage. SCP-Nano generalizes to various types of nanocarriers, including liposomes, polyplexes, DNA origami and adeno-associated viruses (AAVs), revealing that an AAV2 variant transduces adipocytes throughout the body. SCP-Nano enables comprehensive three-dimensional mapping of nanocarrier distribution throughout mouse bodies with high sensitivity and should accelerate the development of precise and safe nanocarrier-based therapeutics.

Research field(s)
Nanoscience & Nanotechnology, Biomedical Engineering

DOI
10.1038/s41587-024-02528-1

Tropical forest clearance impacts biodiversity and function, whereas logging changes structure

NOMIS Researcher(s)

Published in

January 9, 2025

The impacts of degradation and deforestation on tropical forests are poorly understood, particularly at landscape scales. We present an extensive ecosystem analysis of the impacts of logging and conversion of tropical forest to oil palm from a large-scale study in Borneo, synthesizing responses from 82 variables categorized into four ecological levels spanning a broad suite of ecosystem properties: (i) structure and environment, (ii) species traits, (iii) biodiversity, and (iv) ecosystem functions. Responses were highly heterogeneous and often complex and nonlinear. Variables that were directly impacted by the physical process of timber extraction, such as soil structure, were sensitive to even moderate amounts of logging, whereas measures of biodiversity and ecosystem functioning were generally resilient to logging but more affected by conversion to oil palm plantation.

Research field(s)
Forestry, Conservation Biology, Ecology, Environmental Sciences

DOI
10.1126/science.adf9856

Spatial transcriptomic clocks reveal cell proximity effects in brain ageing

NOMIS Researcher(s)

Published in

December 18, 2024

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain ageing is complex and is accompanied by many cellular changes2. Furthermore, the influence that aged cells have on neighbouring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in ageing tissues have not yet been developed. Here we generate a spatially resolved single-cell transcriptomics brain atlas of 4.2 million cells from 20 distinct ages across the adult lifespan and across two rejuvenating interventions—exercise and partial reprogramming. We build spatial ageing clocks, machine learning models trained on this spatial transcriptomics atlas, to identify spatial and cell-type-specific transcriptomic fingerprints of ageing, rejuvenation and disease, including for rare cell types. Using spatial ageing clocks and deep learning, we find that T cells, which increasingly infiltrate the brain with age, have a marked pro-ageing proximity effect on neighbouring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating proximity effect on neighbouring cells. We also identify potential mediators of the pro-ageing effect of T cells and the pro-rejuvenating effect of neural stem cells on their neighbours. These results suggest that rare cell types can have a potent influence on their neighbours and could be targeted to counter tissue ageing. Spatial ageing clocks represent a useful tool for studying cell–cell interactions in spatial contexts and should allow scalable assessment of the efficacy of interventions for ageing and disease.

Research field(s)
Bioinformatics, Biochemistry & Molecular Biology, Immunology, Neurology & Neurosurgery

DOI
10.1038/s41586-024-08334-8

Arrayed CRISPR libraries for the genome-wide activation, deletion and silencing of human protein-coding genes

NOMIS Researcher(s)

Published in

December 4, 2024

Arrayed CRISPR libraries extend the scope of gene-perturbation screens to non-selectable cell phenotypes. However, library generation requires assembling thousands of vectors expressing single-guide RNAs (sgRNAs). Here, by leveraging massively parallel plasmid-cloning methodology, we show that arrayed libraries can be constructed for the genome-wide ablation (19,936 plasmids) of human protein-coding genes and for their activation and epigenetic silencing (22,442 plasmids), with each plasmid encoding an array of four non-overlapping sgRNAs designed to tolerate most human DNA polymorphisms. The quadruple-sgRNA libraries yielded high perturbation efficacies in deletion (75–99%) and silencing (76–92%) experiments and substantial fold changes in activation experiments. Moreover, an arrayed activation screen of 1,634 human transcription factors uncovered 11 novel regulators of the cellular prion protein PrPC, screening with a pooled version of the ablation library led to the identification of 5 novel modifiers of autophagy that otherwise went undetected, and ‘post-pooling’ individually produced lentiviruses eliminated template-switching artefacts and enhanced the performance of pooled screens for epigenetic silencing. Quadruple-sgRNA arrayed libraries are a powerful and versatile resource for targeted genome-wide perturbations.

Research field(s)
Bioinformatics, Biotechnology, Biochemistry & Molecular Biology, Genetics & Heredity

DOI
10.1038/s41551-024-01278-4

Learning human-like representations to enable learning human values

NOMIS Researcher(s)

Published in

November 8, 2024

How can we build AI systems that can learn any set of individual human values both quickly and safely, avoiding causing harm or violating societal standards for acceptable behavior during the learning process? We explore the effects of representational alignment between humans and AI agents on learning human values. Making AI systems learn human-like representations of the world has many known benefits, including improving generalization, robustness to domain shifts, and few-shot learning performance. We demonstrate that this kind of representational alignment can also support safely learning and exploring human values in the context of personalization. We begin with a theoretical prediction, show that it applies to learning human morality judgments, then show that our results generalize to ten different aspects of human values — including ethics, honesty, and fairness — training AI agents on each set of values in a multi-armed bandit setting, where rewards reflect human value judgments over the chosen action. Using a set of textual action descriptions, we collect value judgments from humans, as well as similarity judgments from both humans and multiple language models, and demonstrate that representational alignment enables both safe exploration and improved generalization when learning human values.

Research field(s)
Artificial Intelligence & Image Processing, Psychology & Cognitive Sciences

DOI
10.48550/arXiv.2312.14106

Bayes in the age of intelligent machines

NOMIS Researcher(s)

Published in

September 21, 2024

The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference. We argue that this is not the case and that these systems in fact offer new opportunities for Bayesian modeling. Specifically, we argue that artificial neural networks and Bayesian models of cognition lie at different levels of analysis and are complementary modeling approaches, together offering a way to understand human cognition that spans these levels. We also argue that the same perspective can be applied to intelligent machines, in which a Bayesian approach may be uniquely valuable in understanding the behavior of large, opaque artificial neural networks that are trained on proprietary data.

Research field(s)
Artificial Intelligence & Image Processing, Psychology & Cognitive Sciences

DOI
10.1177/09637214241262329

Learning from aggregated opinion

NOMIS Researcher(s)

Published in

July 24, 2024

The capacity to leverage information from others’ opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others’ testimony. Yet a distinct form of social information—aggregated opinion—increasingly guides our judgments and decisions. We investigated how people learn from such information by conducting three experiments with participants recruited online within the United States (N = 886) comparing the predictions of three computational models: a Bayesian solution to this problem that can be implemented by a simple strategy for combining proportions with prior beliefs, and two alternatives from epistemology and economics. Across all studies, we found the strongest concordance between participants’ judgments and the predictions of the Bayesian model, though some participants’ judgments were better captured by alternative strategies. These findings lay the groundwork for future research and show that people draw systematic inferences from aggregated opinion, often in line with a Bayesian solution.

Research field(s)
Artificial Intelligence & Image Processing, Psychology & Cognitive Sciences

DOI
10.1177/09567976241251741

Thresholds for adding degraded tropical forest to the conservation estate

NOMIS Researcher(s)

Published in

July 17, 2024

Logged and disturbed forests are often viewed as degraded and depauperate environments compared with primary forest. However, they are dynamic ecosystems1 that provide refugia for large amounts of biodiversity2,3, so we cannot afford to underestimate their conservation value4. Here we present empirically defined thresholds for categorizing the conservation value of logged forests, using one of the most comprehensive assessments of taxon responses to habitat degradation in any tropical forest environment. We analysed the impact of logging intensity on the individual occurrence patterns of 1,681 taxa belonging to 86 taxonomic orders and 126 functional groups in Sabah, Malaysia. Our results demonstrate the existence of two conservation-relevant thresholds. First, lightly logged forests (<29% biomass removal) retain high conservation value and a largely intact functional composition, and are therefore likely to recover their pre-logging values if allowed to undergo natural regeneration. Second, the most extreme impacts occur in heavily degraded forests with more than two-thirds (>68%) of their biomass removed, and these are likely to require more expensive measures to recover their biodiversity value. Overall, our data confirm that primary forests are irreplaceable5, but they also reinforce the message that logged forests retain considerable conservation value that should not be overlooked.

Research field(s)
Conservation Biology, Forestry, Ecology, Environmental Sciences

DOI
10.1038/s41586-024-07657-w

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