Publications in Nature Communications

Inhibitors of the protein kinase WEE1 have emerged as promising agents for cancer therapy. In this study, we uncover synergistic interactions between WEE1 small-molecule inhibitors and defects in mRNA translation, mediated by activation of the integrated stress response (ISR) through the kinase GCN2. Using a pooled CRISPRi screen, we identify GSPT1 and ALKBH8 as factors whose depletion confer hypersensitivity to the WEE1 inhibitor, AZD1775. We demonstrate that this synergy depends on ISR activation, which is induced by the off-target activity of WEE1 inhibitors. Furthermore, PROTAC-based WEE1 inhibitors and molecular glues show reduced or no ISR activation, suggesting potential strategies to minimise off-target toxicity. Our findings reveal that certain WEE1 inhibitors elicit dual toxicity via ISR activation and genotoxic stress, with ISR activation being independent of WEE1 itself or cell-cycle status. This dual mechanism highlights opportunities for combination therapies, such as pairing WEE1 inhibitors with agents targeting the mRNA translation machinery. This study also underscores the need for more precise WEE1 targeting strategies to mitigate off-target effects, with implications for optimising the therapeutic potential of WEE1 inhibitors.

The enzyme AMPD1 is expressed in skeletal muscle and is involved in ATP production. All available Neandertal genomes carry a lysine-to-isoleucine substitution at position 287 in AMPD1. This variant, which occurs at an allele frequency of 0–8% outside Africa, was introduced to modern humans by gene flow from Neandertals. Here, we show that the catalytic activity of the purified Neandertal AMPD1 is ~25% lower than the ancestral enzyme, and when introduced in mice, it reduces AMPD activity in muscle extracts by ~80%. Among present-day Europeans, another AMPD1 variant encoding a stop codon occurs at an allele frequency of 9–14%. Individuals heterozygous for this variant are less likely to be top-performing athletes in various sports, but otherwise reduced AMPD1 activity is well tolerated in present-day humans. While being conserved among vertebrates, AMPD1 seems to have become less functionally important among Neandertals and modern humans.

The ability to robustly predict guide RNA (gRNA) activity is a long-standing goal for CRISPR applications, as it would reduce the need to pre-screen gRNAs. Quantification of formation of short insertions and deletions (indels) after DNA cleavage by transcribed gRNAs has been typically used to measure and predict gRNA activity. We evaluate the effect of chemically synthesized Cas9 gRNAs on different cellular DNA cleavage outcomes and find that the activity of different gRNAs is largely similar and often underestimated when only indels are scored. We provide a simple linear model that reliably predicts synthetic gRNA activity across cell lines, robustly identifies inefficient gRNAs across different published datasets, and is easily accessible via online genome browser tracks. In addition, we develop a homology-directed repair efficiency prediction tool and show that unintended large-scale repair events are common for Cas9 but not for Cas12a, which may be relevant for safety in gene therapy applications.

Mutations in FUS and TARDBP cause amyotrophic lateral sclerosis (ALS), but the precise mechanisms of selective motor neuron degeneration remain unresolved. To address if pathomechanisms are shared across mutations and related to either gain- or loss-of-function, we performed single-cell RNA sequencing across isogenic induced pluripotent stem cell-derived neuron types, harbouring FUS P525L, FUS R495X, TARDBP M337V mutations or FUS knockout. Transcriptional changes were far more pronounced in motor neurons than interneurons. About 20% of uniquely dysregulated motor neuron transcripts were shared across FUS mutations, half from gain-of-function. Most indicated mitochondrial impairments, with attenuated pathways shared with mutant TARDBP M337V as well as C9orf72-ALS patient motor neurons. Mitochondrial motility was impaired in ALS motor axons, even with nuclear localized FUS mutants, demonstrating shared toxic gain-of-function mechanisms across FUS- and TARDBP-ALS, uncoupled from protein mislocalization. These early mitochondrial dysfunctions unique to motor neurons may affect survival and represent therapeutic targets in ALS.

Hole spin qubits are emerging as the workhorse of semiconducting quantum processors because of their large spin-orbit interaction, enabling fast, low-power, all-electric operations. However, this interaction also causes non-uniformities, resulting in site-dependent qubit energies and anisotropies. Although these anisotropies enable single-spin control, if not properly harnessed, they can hinder scalability. Here, we report on microwave-driven singlet-triplet qubits in planar germanium and use them to investigate spin anisotropies. For in-plane magnetic fields, the spins are largely anisotropic and electrically tunable, allowing access to all transitions and coherence times exceeding 3 μs are extracted. For out-of-plane fields they have an isotropic response. Even in this field direction, where the qubit lifetime is strongly affected by nuclear spins, we find 400 ns coherence times. Our work adds a valuable tool to investigate and harness the spin anisotropies, applicable to two-dimensional devices, facilitating the path towards scalable quantum processors.

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 g_c/2π = 566 ± 2 MHz. This broadly applicable method opens the path for novel qubits and high-fidelity, long-distance two-qubit gates.

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.

The shrinkage of glaciers and the vanishing of glacier-fed streams (GFSs) are emblematic of climate change. However, forecasts of how GFS microbiome structure and function will change under projected climate change scenarios are lacking. Combining 2,333 prokaryotic metagenome-assembled genomes with climatic, glaciological, and environmental data collected by the Vanishing Glaciers project from 164 GFSs draining Earth’s major mountain ranges, we here predict the future of the GFS microbiome until the end of the century under various climate change scenarios. Our model framework is rooted in a space-for-time substitution design and leverages statistical learning approaches. We predict that declining environmental selection promotes primary production in GFSs, stimulating both bacterial biomass and biodiversity. Concomitantly, predictions suggest that the phylogenetic structure of the GFS microbiome will change and entire bacterial clades are at risk. Furthermore, genomic projections reveal that microbiome functions will shift, with intensified solar energy acquisition pathways, heterotrophy and algal-bacterial interactions. Altogether, we project a ‘greener’ future of the world’s GFSs accompanied by a loss of clades that have adapted to environmental harshness, with consequences for ecosystem functioning.