Nature最新文献

The survival of malignant cells within tumours is often seen as depending on ruthless competition for nutrients and other resources^1,2. Although competition is certainly critical for tumour evolution and cancer progression, cooperative interactions within tumours are also important, albeit poorly understood^3,4. Cooperative populations at all levels of biological organization risk extinction if their population size falls below a critical tipping point^5,6. Here we examined whether cooperation among tumour cells may be a potential therapeutic target. We identified a cooperative mechanism that enables tumour cells to proliferate under the amino acid-deprived conditions found in the tumour microenvironment. Disruption of this mechanism drove cultured tumour populations to the critical extinction point and resulted in a marked reduction in tumour growth in vivo. Mechanistically, we show that tumour cells collectively digest extracellular oligopeptides through the secretion of aminopeptidases. The resulting free amino acids benefit both aminopeptidase-secreting cells and neighbouring cells. We identified CNDP2 as the key enzyme that hydrolyses these peptides extracellularly, and loss of this aminopeptidase prevents tumour growth in vitro and in vivo. These data show that cooperative scavenging of nutrients is key to survival in the tumour microenvironment and reveal a targetable cancer vulnerability.

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.

arising from: H. K. Wayment-Steele et al. Nature https://doi.org/10.1038/s41586-023-06832-9 (2024).

Although typically associated with a single folded conformation, some globular proteins remodel their secondary and/or tertiary structures in response to cellular stimuli¹; AlphaFold2 (AF2)² readily generates one dominant protein structure for these fold-switching proteins, but it often fails to predict their alternative experimentally observed structures^3,4. By introducing a method called AF-cluster, Wayment-Steele et al.⁵ attempted to steer AF2 to predict alternative structures using three protein families as test cases. However, AF-cluster underperforms relative to random sequence sampling. This demonstrates conclusively that AF-cluster lacks predictive and explanatory power.

Most current strategies for carbon management require CO₂ removal (CDR) from the atmosphere on the multi-hundred gigatonne (Gt) scale by 2100 (refs. ^1,2,3,4,5). Mg-rich silicate minerals can remove >10⁵ Gt CO₂ and sequester it as stable and innocuous carbonate minerals or dissolved bicarbonate ions^3,6,7. However, the reaction rates of these minerals under ambient conditions are far too slow for practical use. Here we show that CaCO₃ and CaSO₄ react quantitatively with diverse Mg-rich silicates (for example, olivine, serpentine and augite) under thermochemical conditions to form Ca₂SiO₄ and MgO. On exposure to ambient air under wet conditions, Ca₂SiO₄ is converted to CaCO₃ and silicic acid, and MgO is partially converted into a Mg carbonate within weeks, whereas the input Mg silicate shows no reactivity over 6 months. Alternatively, Ca₂SiO₄ and MgO can be completely carbonated to CaCO₃ and Mg(HCO₃)₂ under 1 atm CO₂ at ambient temperature within hours. Using CaCO₃ as the Ca source, this chemistry enables a CDR process in which the output Ca₂SiO₄/MgO material is used to remove CO₂ from air or soil and the CO₂ process emissions are sequestered. Analysis of the energy requirements indicates that this process could require less than 1 MWh per tonne CO₂ removed, approximately half the energy of CO₂ capture with leading direct air capture technologies. The chemistry described here could unlock Mg-rich silicates as a vast resource for safe and permanent CDR.

The fusion of non-Abelian anyons is a fundamental operation in measurement-only topological quantum computation¹. In one-dimensional topological superconductors (1DTSs)^2,3,4, fusion amounts to a determination of the shared fermion parity of Majorana zero modes (MZMs). Here we introduce a device architecture⁵ that is compatible with future tests of fusion rules. We implement a single-shot interferometric measurement of fermion parity^{6,7,8,9,10,11} in indium arsenide–aluminium heterostructures with a gate-defined superconducting nanowire^12,13,14. The interferometer is formed by tunnel-coupling the proximitized nanowire to quantum dots. The nanowire causes a state-dependent shift of the quantum capacitance of these quantum dots of up to 1 fF. Our quantum-capacitance measurements show flux h/2e-periodic bimodality with a signal-to-noise ratio (SNR) of 1 in 3.6 μs at optimal flux values. From the time traces of the quantum-capacitance measurements, we extract a dwell time in the two associated states that is longer than 1 ms at in-plane magnetic fields of approximately 2 T. We discuss the interpretation of our measurements in terms of both topologically trivial and non-trivial origins. The large capacitance shift and long poisoning time enable a parity measurement with an assignment error probability of 1%.

Meiotic recombination starts with SPO11 generation of DNA double-strand breaks (DSBs)¹. SPO11 is critical for meiosis in most species, but it generates dangerous DSBs with mutagenic² and gametocidal³ potential. Cells must therefore utilize the beneficial functions of SPO11 while minimizing its risks⁴—how they do so remains poorly understood. Here we report reconstitution of DNA cleavage in vitro with purified recombinant mouse SPO11 bound to TOP6BL. SPO11–TOP6BL complexes are monomeric (1:1) in solution and bind tightly to DNA, but dimeric (2:2) assemblies cleave DNA to form covalent 5′ attachments that require SPO11 active-site residues, divalent metal ions and SPO11 dimerization. SPO11 can also reseal DNA that it has nicked. Structure modelling with AlphaFold 3 suggests that DNA is bent prior to cleavage⁵. In vitro cleavage displays a sequence bias that partially explains DSB site preferences in vivo. Cleavage is inefficient on complex DNA substrates, partly because SPO11 is readily trapped in DSB-incompetent (presumably monomeric) binding states that exchange slowly. However, cleavage is improved with substrates that favour dimer assembly or by artificially dimerizing SPO11. Our results inform a model in which intrinsically weak dimerization restrains SPO11 activity in vivo, making it exquisitely dependent on accessory proteins that focus and control DSB formation.