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Article
Thrombocytopenia is common in severe sepsis and is associated with an increased risk of mortality. A new study shows that platelet pyroptosis initiated during infection promotes a feedforward loop of neutrophil-mediated inflammation that worsens outcomes during sepsis.
 
Article
An analysis of floods or droughts that hit the same place twice shows that using risk management alone does not reduce the effect of extreme events. Addressing the social drivers of hazard impact, equitably, is essential. Repeated disasters reveal exposure inequity.
 
Location of flood and drought paired events coloured according to changes in impact and their indicators of change
a, Location of flood and drought paired events (n = 45). Numbers are paired-event IDs. b, Indicators of change, sorted by impact change. Impact is considered to be controlled by hazard, exposure and vulnerability, which are exacerbated by risk management shortcomings. Maps of the paired events coloured according to drivers and management shortcomings are shown in Extended Data Fig. 1.
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Correlation matrix and histograms of indicators of change
a, c, Correlation matrix of indicators of change for flood (a) and drought (c) paired events. Colours of squares indicate Spearman’s rank correlation coefficients and their size, the P value. b, d,Histograms of indicators of change of flood (b) and drought (d) stratified by decrease (n = 15 and n = 5 paired events for flood and drought, respectively) and increase (n = 5 and n = 8 paired events, respectively) in impact. The asterisk denotes the success stories of Box 1; double asterisks denote pairs for which the second event was much more hazardous than the first (that is, 'unprecedented'). Mgmt shortc, management shortcomings.
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Relationship between change in hazard and change in impacts
Categories are: lower hazard and lower impact, ten cases; higher hazard and higher impact, 11 cases; lower hazard and higher impact, one case; higher hazard and lower impact, two cases. Circles and triangles indicate drought and flood paired events, respectively; their colours indicate change in vulnerability. Green circle highlights success stories (n = 2) of reduced impact (−1) despite a small increase in hazard (+1). Purple ellipse indicates paired events (n = 7) with large increase in hazard (+2)—that is, events that were subjectively unprecedented and probably not previously experienced by local residents.
Source data
Article
Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing³. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change³.
 
Microstructure of AM AlCoCrFeNi2.1 EHEA
a, Printed heatsink fan, octet lattice (strut size of about 300 μm) and gear (from left to right). b, Three-dimensional reconstructed optical micrographs of as-printed AlCoCrFeNi2.1 EHEA. The interlayer boundary, melt pool boundaries and laser scan tracks are illustrated by the blue line, orange lines and red arrows, respectively. The build direction (BD) is vertical. c, A cross-sectional EBSD IPF map of as-printed AlCoCrFeNi2.1 EHEA, showing a magnified local region where neighbouring nanolamellar eutectic colonies exhibit different crystallographic orientations. To better display the finer bcc nanolamellae, the inset shows a two-colour EBSD phase map with fcc lamellae in blue and bcc lamellae in red. It is noted that the bcc nanolamellae are under-indexed owing to their small thicknesses close to the resolution limit of EBSD (see Supplementary Fig. 3 for the morphology of dual-phase nanolamellar eutectic colonies). d, Secondary electron micrograph of the nanolamellar structure. e, Bright-field TEM image of the bcc and the fcc nanolamellae (indicated by a red dot and a green dot, respectively), with the insets showing PED patterns tilted to the zone axes (B) [1¯11]bcc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${[\bar{1}11]}_{{\rm{bcc}}}$$\end{document} and [011]fcc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${[011]}_{{\rm{fcc}}}$$\end{document}, respectively. f, Lamellar thickness distribution of the bcc (left) and the fcc (right) lamellae in as-printed AlCoCrFeNi2.1 EHEA. g, HAADF-STEM image showing the modulated nanostructures within bcc lamellae. h, APT maps of elemental distribution in a 100 × 78 × 5 nm³ section with an fcc/bcc interface in the centre. Chemical fluctuations within bcc lamellae are manifested by the nanoscale Ni–Al-rich and Co–Cr–Fe-rich regions. The compositions of dual phases are extracted from one-dimensional concentration profile analysis and are listed in Extended Data Table 1.
Tensile properties of AM AlCoCrFeNi2.1 EHEAs
a, Tensile stress–strain curves of as-printed and annealed AlCoCrFeNi2.1 EHEAs. The yield strength (σ0.2) and ultimate tensile strength (σu) are marked on the curves. The inset shows the schematic of a dogbone-shaped specimen under tensile loading. b, Tensile yield strength versus uniform elongation of AM AlCoCrFeNi2.1 EHEAs compared with those of high-performance AM metal alloys with high strength (σ0.2 > 800 MPa) in the literature, including bulk metallic glass composites (BMGCs), steels, Ni-based superalloys, titanium (Ti)-based alloys and HEAs. The solid and hollow symbols represent the properties of as-printed and post-annealed samples, respectively (see the detailed data, symbol description and associated references in Supplementary Table 5).
Lattice strains and stress partitioning in fcc and bcc phases during uniaxial tension
a, Evolution of lattice strain against macroscopic true stress for representative fcc (including {111}, {200}, {220} and {311}) and bcc (including {110}, {211} and {321}) crystallographic plane families along the loading direction. Experimental and simulation results are represented by symbols and solid lines, respectively. The macroscopic yield strength is marked with the red dashed line. b, DP-CPFE simulation results of the macroscopic stress–strain response with the corresponding stress partitioning in the bcc and the fcc phases. c, Neutron-diffraction spectra at different tensile strains (ε) along the loading direction during deformation. d, Dislocation density against strain in the bcc and the fcc phases, derived from the diffraction spectra in c and the modified Williamson–Hall method (Supplementary Section 3). Error bars represent the standard deviation.
Meso- and atomic-scale deformation structures
a–c, Virtual bright-field PED micrographs revealing the evolution of dislocation substructures in bcc (indicated by the red dot) and fcc (indicated by the green dot) nanolamellae at tensile strains of about 0% (a), 5% (b) and 15% (c). The advantage of PED over conventional dislocation imaging is the elimination of most dynamical effects, leading to a crisper dislocation contrast. d–f, High-magnification bright-field TEM micrographs of the deformation substructures at tensile strains of about 0% (d), 5% (e) and 15% (f). Deformation-induced stacking faults, highlighted by yellow arrows, were observed in fcc nanolamellae at 5% strain. The phase interfaces are indicated by the yellow dashed lines. g–i, HRTEM micrographs showing the atomic-level bcc and fcc phase interface at tensile strains of about 0% (g), 5% (h) and 15% (i), along with the FFT patterns (insets). j–l, IFFT micrographs for the yellow boxed regions in g–i, respectively. It is noted that the IFFT patterns reveal only the edge components of dislocations (highlighted by the yellow dashed circles) by showing extra half lattice planes, but the screw components are not readily visible.
Article
Additive manufacturing produces net-shaped components layer by layer for engineering applications1–7. The additive manufacture of metal alloys by laser powder bed fusion (L-PBF) involves large temperature gradients and rapid cooling2,6, which enables microstructural refinement at the nanoscale to achieve high strength. However, high-strength nanostructured alloys produced by laser additive manufacturing often have limited ductility3. Here we use L-PBF to print dual-phase nanolamellar high-entropy alloys (HEAs) of AlCoCrFeNi2.1 that exhibit a combination of a high yield strength of about 1.3 gigapascals and a large uniform elongation of about 14 per cent, which surpasses those of other state-of-the-art additively manufactured metal alloys. The high yield strength stems from the strong strengthening effects of the dual-phase structures that consist of alternating face-centred cubic and body-centred cubic nanolamellae; the body-centred cubic nanolamellae exhibit higher strengths and higher hardening rates than the face-centred cubic nanolamellae. The large tensile ductility arises owing to the high work-hardening capability of the as-printed hierarchical microstructures in the form of dual-phase nanolamellae embedded in microscale eutectic colonies, which have nearly random orientations to promote isotropic mechanical properties. The mechanistic insights into the deformation behaviour of additively manufactured HEAs have broad implications for the development of hierarchical, dual- and multi-phase, nanostructured alloys with exceptional mechanical properties. An additive manufacturing strategy is used to produce dual-phase nanolamellar high-entropy alloys that show a combination of enhanced high yield strength and high tensile ductility.
 
Piezoelectricity and microstructural and morphology characterization of the MnO/BTO grains and membranes
a, Optical image of the sintered MnO/BTO and BTO membranes. ESEM images of the surface (b; scale bar, 1 μm) and cross section (c; scale bar, 1 μm), AC-STEM&TEM (d; scale bar, 200 nm) and EDS elemental mapping images (f; scale bars, 200 nm), HRTEM image and the corresponding selected 0; scale bar, 5 nm⁻¹) (e; scale bar, 5 nm) of the sintered MnO/BTO grains. The output voltage of the membranes in response to different weights in O/W emulsion (g,h) and air (i,j).
Piezoelectric effect and fouling reduction induced by hydraulic pressure
Flux (a) under the corresponding time-dependent pressure changes condition (b) and rejection of oil (oil with Oil Red O) (see inset in a). The piezoelectric voltage and current outputs of the membranes in response to hydraulic pressure during filtration (c,d). Fouling first then self-cleaning operation, and flux decay under constant pressure of 2 bar, and recovered (e) by periodic hydraulic pressure cycles (f). Note that the oil content was 2,500 ppm (a,e).
Universal PiezoMem antifouling
Representative foulants (a), mixed foulants (b), real landfill leachate (c) and the ESEM images of the PiezoMem and non-PiezoMem fouled by the representative foulants under periodically changing hydraulic pressure 70-(2-7) s filtration (d; scale bars, 5 μm). The f in the last row of panel d is the ratio of the foulant-coated surface area in the PiezoMem and the non-PiezoMem.
Mechanism of membrane self-cleaning
a, Electron paramagnetic resonance spectra with DMPO (·OH) or TEMP (¹O2) as trapping agents induced through pulse hydraulic pressure on PiezoMem. b, Elemental mapping images for the membrane surface after periodic cycling filtration with SiO2 and Al2O3. Scale bars, 5 μm. c, Flux of the (non-)PiezoMem varied with negative and neutral charge SiO2 particles. Electric field strength of the generated piezoelectricity on PiezoMem (d) and corresponding dielectrophoresis (DEP) force simulated by COMSOL (e).
Article
Pressure-driven membranes is a widely used separation technology in a range of industries, such as water purification, bioprocessing, food processing and chemical production1,2. Despite their numerous advantages, such as modular design and minimal footprint, inevitable membrane fouling is the key challenge in most practical applications3. Fouling limits membrane performance by reducing permeate flux or increasing pressure requirements, which results in higher energetic operation and maintenance costs4–7. Here we report a hydraulic-pressure-responsive membrane (PiezoMem) to transform pressure pulses into electroactive responses for in situ self-cleaning. A transient hydraulic pressure fluctuation across the membrane results in generation of current pulses and rapid voltage oscillations (peak, +5.0/−3.2 V) capable of foulant degradation and repulsion without the need for supplementary chemical cleaning agents, secondary waste disposal or further external stimuli3,8–13. PiezoMem showed broad-spectrum antifouling action towards a range of membrane foulants, including organic molecules, oil droplets, proteins, bacteria and inorganic colloids, through reactive oxygen species (ROS) production and dielectrophoretic repulsion. The PiezoMem membrane responsive to hydraulic pressure is introduced, showing the ability to convert pressure pulses into electroactive responses for in situ self-cleaning and enabling broad-spectrum antifouling action towards a range of membrane foulants.
 
Delocalization of a TZM by the NHSE
a, A 1D NH-SSH chain with non-reciprocal intercell hopping wx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{x}$$\end{document} and wx+δx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{x}+{\delta }_{x}$$\end{document}. The blue box encloses the unit cell. b, The complex energy of the NH-SSH chain under a periodic boundary condition (PBC) and an open boundary condition (OBC). The chain has 60 sites with vx=−2.33\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{x}=-\,2.33$$\end{document}, wx=−0.38\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{x}=-\,0.38$$\end{document} and δx=−0.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}=-\,0.5$$\end{document}. c, The spatial distribution ψ̅\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar{\psi }$$\end{document} of all the wavefunctions in an open chain shows the NHSE. d, An interface formed by a topologically trivial NH-SSH chain and a topological H-SSH chain. e, The energy spectra of the interface system shown in d and their dependence on δx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}$$\end{document}. Here vd=−1.03\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{d}=-\,1.03$$\end{document}. The red dots mark the TZM, which is pinned at zero energy for all values of δx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}$$\end{document}. f, The real-space wavefunctions of the TZM for different δx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}$$\end{document}. It is seen that the increase of δx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\delta }_{x}\right|$$\end{document} delocalizes the TZM until δx=δxc=−1.95\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{{xc}}=-\,1.95$$\end{document}, at which the TZM becomes fully extended in the NH-SSH chain. The TZM relocalizes at the left end of the system for even larger δx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\delta }_{x}\right|$$\end{document}. g, When δx=δxc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{{xc}}$$\end{document}, tuning vd\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{d}$$\end{document} can produce a constant-amplitude mode in the NH-SSH chain.
Experimental observation of the delocalization effect
a, The schematic drawing of an isolated rotational oscillator, which serves as on-site orbital in our experimental lattices. b, A photograph of the 1D NH-SSH and H-SSH interface system. The white numbers label the sites. The TZM is excited at site 11, which is marked by the white arrow. The teal and orange arrows are the measurement positions of the data shown in c. The left inset is a top-down view of the lattice. The coupling springs are coloured for clarity. The blue box encloses one unit cell. c, Response spectra measured at site 8 and site 9 with excitation at site 11. The TZM response is seen in the orange curve (site 9). d, Measured oscillation amplitudes of the TZM for different values of δx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}$$\end{document}. The TZM is nearly extended at δx=−1.88\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}=-\,1.88$$\end{document}. The excitation is at site 11 at 12 Hz. e, The measured oscillation amplitude of the near-constant-amplitude TZM (vd=vx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{d}={v}_{x}$$\end{document}).
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Morphing of the TEM in a 2D stacked topological lattice
a, The schematic drawing of the 2D stacked topological lattice. The blue box marks a unit cell. b, A photograph of the experimental lattice realizing a. The coupling springs are coloured for clarity. c, The energy spectra of a 9×7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$9\times 7$$\end{document}-site lattice with different non-reciprocal hopping in the x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x$$\end{document} direction. Here vx=−0.41\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{x}=-\,0.41$$\end{document}, wx=−2.6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{x}=-\,2.6$$\end{document} and vy=−0.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{y}=-\,0.1$$\end{document}. The TEMs are marked by the red box. d, The real part of the energy spectra of the open lattice with three different non-reciprocal hopping distributions. Note that the energies are complex here. e–i, The theoretical response fields with the lattice excited at the left edge are plotted for δx=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}=0$$\end{document} (e) and δx=δxc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{{xc}}$$\end{document} (f). g–i, The theoretical response fields with non-uniform distributions of δx1y\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x1}\left(y\right)$$\end{document} (g),δx2y\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x2}\left(y\right)$$\end{document} (h) and δx1y\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x1}\left(y\right)$$\end{document} (i). The non-reciprocal hopping distributions are indicated by the overlaying curves. The lattice is excited at the left edge. j–n, The long-exposure photos showing the oscillation profiles in the lattice with harmonic excitation at 12.5 Hz at the left edge. The localized Hermitian TEM (j) becomes an extended mode over the entire lattice in k. It also deforms to a pyramidal (l), triangular (m) and V (n) shape by different δxy\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}\left(y\right)$$\end{document}. The grids in j–n mark the unit cells.
Morphing of a TCM by HO-NHSE
a, The model of a non-Hermitian topological quadrupole insulator. b, A photograph of the experimental lattice realizing a. c, The measured response spectra of the TCM at the lower-left corner of the Hermitian topological quadrupole insulator. The grey shaded region corresponds to the response of the bulk modes. d, The energy spectra of an open lattice consisting of 9×7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$9\times 7$$\end{document} sites with δx=δy=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{y}=0$$\end{document} (orange), δx=δxc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{{xc}}$$\end{document}, δy=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{y}=0$$\end{document} (teal) and δx=δxc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{{xc}}$$\end{document}, δy=δyc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{y}={\delta }_{{yc}}$$\end{document} (blue). e–g, The wavefunctions of the TCM when δx=δy=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{y}=0$$\end{document} (e), δx=δxc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{{xc}}$$\end{document}, δy=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{y}=0$$\end{document} (f) and δx=δxc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}={\delta }_{{xc}}$$\end{document}, δy=δyc\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{y}={\delta }_{{yc}}$$\end{document} (g). h–j, The measured vibration fields corresponding to the TCM (h), the extension of the TCM along the lower edge (i) and into the 2D surface (j) under the harmonic excitation at 12.4 Hz at the corner. In i and j, δx=−2.18\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{x}=-\,2.18$$\end{document} and δy=−1.7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{y}=-\,1.7$$\end{document}. The grids in h–j mark the unit cells.
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Article
Topological modes (TMs) are usually localized at defects or boundaries of a much larger topological lattice1,2. Recent studies of non-Hermitian band theories unveiled the non-Hermitian skin effect (NHSE), by which the bulk states collapse to the boundary as skin modes3–6. Here we explore the NHSE to reshape the wavefunctions of TMs by delocalizing them from the boundary. At a critical non-Hermitian parameter, the in-gap TMs even become completely extended in the entire bulk lattice, forming an ‘extended mode outside of a continuum’. These extended modes are still protected by bulk-band topology, making them robust against local disorders. The morphing of TM wavefunction is experimentally realized in active mechanical lattices in both one-dimensional and two-dimensional topological lattices, as well as in a higher-order topological lattice. Furthermore, by the judicious engineering of the non-Hermiticity distribution, the TMs can deform into a diversity of shapes. Our findings not only broaden and deepen the current understanding of the TMs and the NHSE but also open new grounds for topological applications. It is experimentally demonstrated that the non-Hermitian skin effect can convert localized topological modes into extended modes of unconventional shapes while preserving the topological characteristics, which presents opportunities for topological manipulations of waves and light.
 
Location and geology of the Oxaya early Miocene ignimbrite formation, Central Andes
a, Location of study area. b, Simplified geological map of study area showing distribution of the Oxaya Formation and main structural features modified by van Zalinge et al.²⁷, after Garcia et al.²⁸. c, Simplified stratigraphy²⁷ of the Oxaya Formation and underlying stratigraphic units based on locality M and drill hole data (b).
Geochronological data for the four ignimbrites of the Oxaya Formation
Four kinds of data are shown: U–Pb ages (CA-ID-TIMS) of individual euhedral zircons revised from van Zalinge et al.²⁷ using the Bayesian method²⁹, ⁴⁰Ar–³⁹Ar ages of individual sanidine fragments and U–Pb ages (LA-ICP-MS) of individual inherited zircons. The fourth, our preferred eruption age, comes about from integration of the CA-ID-TIMS U–Pb ages of zircons and the ⁴⁰Ar–³⁹Ar ages of sanidines. The data are ordered by age from left (youngest) to right (oldest). 2-sigma uncertainty (analytical precision) intervals are shown for individual crystal (sanidine and zircon) ages, whereas Bayesian eruption ages are 95% confidence intervals (full external precision).
The evolution of ⁴⁰Ar–³⁹Ar ages in sanidine as a function of time and temperature owing to diffusion and ⁴⁰Ar in growth
Models show the effects of storage temperatures (a), magma residence times (b) and post-emplacement welding and cooling (c) of the ignimbrites on sanidine ⁴⁰Ar–³⁹Ar ages in the cores of sanidine crystals. We show model ages for the cores of crystals, in which the core is defined as the volume at the centre of the original crystal with a diameter 50% of the original crystal. We show ⁴⁰Ar–³⁹Ar ages of crystal cores for two reasons. First, we know that the ⁴⁰Ar–³⁹Ar measurements were made on sanidine crystal fragments and that the grains were fragmented during mineral separation. Second, diffusive loss of ⁴⁰Ar will cause rim-to-core age gradients; therefore it is the crystal cores that will provide the oldest ages in our observed ⁴⁰Ar–³⁹Ar age distributions. For each set of models, we assume that all sanidines crystallized at 26.5 Ma and that eruption occurred at 21.8 Ma. a, Modelled sanidine crystal core ⁴⁰Ar–³⁹Ar age as a function of original grain size and storage temperature. b, Modelled sanidine crystal core ⁴⁰Ar–³⁹Ar age as a function of original grain size and magma residence time, assuming that no diffusive ⁴⁰Ar loss occurred before magma entrainment. Models are shown for two magma temperatures, 700 and 770 °C, based on geothermometry³⁰. c, Modelled sanidine crystal core ⁴⁰Ar–³⁹Ar age at 600 °C, the maximum temperature during post-emplacement welding and cooling of the ignimbrites, assuming no previous diffusive ⁴⁰Ar loss during pre-eruption storage or magma residence.
Simplified conceptual model of a transcrustal magmatic system
Development of a shallow magma chamber followed by a caldera-forming ignimbrite eruption is shown. a, Silicic melt is segregated from a middle-crustal to lower-crustal hot zone, leading to an incipient Rayleigh–Taylor instability and transfer to an upper-crustal magma chamber by a dike. Depiction is after the incubation period in which a large batholith system has been emplaced in the upper crust and hosts the development of the large magma chamber. b, Rapid emplacement of the shallow magma chamber is accompanied by deformation of co-genetic earlier plutonic rocks before eruptions. c, The roof rocks are disrupted and incorporated into the erupting magma chamber.
Article
Generation of silicic magmas leads to emplacement of granite plutons, huge explosive volcanic eruptions and physical and chemical zoning of continental and arc crust1–7. Whereas timescales for silicic magma generation in the deep and middle crust are prolonged8, magma transfer into the upper crust followed by eruption is episodic and can be rapid9–12. Ages of inherited zircons and sanidines from four Miocene ignimbrites in the Central Andes indicate a gap of 4.6 Myr between initiation of pluton emplacement and onset of super-eruptions, with a 1-Myr cyclicity. We show that inherited zircons and sanidine crystals were stored at temperatures <470 °C in these plutons before incorporation in ignimbrite magmas. Our observations can be explained by silicic melt segregation in a middle-crustal hot zone with episodic melt ascent from an unstable layer at the top of the zone with a timescale governed by the rheology of the upper crust. After thermal incubation of growing plutons, large upper-crustal magma chambers can form in a few thousand years or less by dike transport from the hot-zone melt layer. Instability and disruption of earlier plutonic rock occurred in a few decades or less just before or during super-eruptions. Analysis of inherited zircons and sanidines from Miocene ignimbrites in the Central Andes shows that plutons were emplaced for up to 4 million years prior to onset of volcanism and that disruption of plutonic rock occurs a few decades or less just before or during super-eruptions.
 
Fabrication of nLEDs and their optical properties
a–c, Schematics and corresponding scanning electron microscopy images of nLEDs fabricated by means of conventional top-down processing methods. Dry etching (a), wet etching (b) and deposition of a SiO2 surface passivation layer by means of a sol–gel method (c). Scale bars, 1 μm. Inset is a schematic of the sol–gel reaction on the GaN LED nanorod. Scale bars, 500 nm (left) and 200 nm (right). d, PL image (top; blue and yellow emissions are co-displayed) and fluorescence excitation–emission spectra (bottom) of nanorods with plasma-enhanced ALD SiO2 passivation (left) and sol–gel SiO2 passivation (right). Scale bars, 3 μm. e, Panchromatic CL images of the submicron LED rod array on wafer with plasma-enhanced ALD SiO2 (left) and sol–gel SiO2 (right) (λ = 300–700 nm). Scale bars, 3 μm. f, g, PL spectra (f) and PL decay traces (g) of the nanorods averaged over the area indicated in the inset images.
Variation in EL and current-density–voltage curves of nLEDs according to the surface passivation method: plasma-enhanced ALD and sol–gel SiO2 deposition
a, EL and PL composite image of a single nanorod within a pixel based on passivation type: plasma-enhanced ALD SiO2 (left) and sol–gel SiO2 (right). Scale bars, 1 μm. b, EL intensity profile across the MQWs in the horizontal direction in Fig. 2a, measured with a confocal microscope placed directly above the nanorods. c, EQE curves for the nLEDs. Each curve is obtained from 60 pixels, with each pixel comprising six and nine nanorods for plasma-enhanced ALD SiO2 and sol–gel SiO2 passivation, respectively. d, e, The current-density–voltage (J–V) characteristics of the nLEDs in linear scale (d) and log scale (e). f, Ideality factors obtained from the J–V curves at 2.5 V.
Surface analysis of the nLEDs after each fabrication step
a, HAADF-STEM images of the sidewall in MQWs according to the fabrication step. The white arrows represent the amorphization of the dry-etched surface. The yellow arrows indicate the plasma damage concentrated on the sidewall of the InGaN quantum wells. Scale bars, 2 nm. b, XPS core-level spectra of nLEDs with a 2-nm-thick SiO2 coating: Ga 3d (left) and N 1s (right). c, Ga 3d state ratios obtained from the XPS spectra. d, ESR spectra of the nanorods after each fabrication step. e, N–N split interstitials of the nanorods based on the ESR spectra.
Defects in the sidewalls of InGaN quantum wells fabricated using different passivation methods
a, High-resolution STEM images of the InGaN quantum wells. Each coloured layer indicates the region at which the corresponding electron energy loss spectrum was obtained, that is, bulk (green, sol–gel; and yellow, plasma-enhanced ALD) and surface (blue, sol–gel; and red, plasma-enhanced ALD). Scale bars, 2 nm. b, N-K-edge spectra of the regions indicated in a. Spectral features of the N-K-edge ELNES, which were theoretically verified using DFT calculations, as shown in the bottom panel.
Article
Indium gallium nitride (InGaN)-based micro-LEDs (μLEDs) are suitable for meeting ever-increasing demands for high-performance displays owing to their high efficiency, brightness and stability1–5. However, μLEDs have a large problem in that the external quantum efficiency (EQE) decreases with the size reduction6–9. Here we demonstrate a blue InGaN/GaN multiple quantum well (MQW) nanorod-LED (nLED) with high EQE. To overcome the size-dependent EQE reduction problem8,9, we studied the interaction between the GaN surface and the sidewall passivation layer through various analyses. Minimizing the point defects created during the passivation process is crucial to manufacturing high-performance nLEDs. Notably, the sol–gel method is advantageous for the passivation because SiO2 nanoparticles are adsorbed on the GaN surface, thereby minimizing its atomic interactions. The fabricated nLEDs showed an EQE of 20.2 ± 0.6%, the highest EQE value ever reported for the LED in the nanoscale. This work opens the way for manufacturing self-emissive nLED displays that can become an enabling technology for next-generation displays. Using a sol–gel passivation method, the fabrication of blue InGaN nanorod-LEDs with the highest external quantum efficiency value ever reported for LEDs in the nanoscale is demonstrated.
 
Article
A growing interest in cannabis has led to new career opportunities. A growing interest in cannabis has led to new career opportunities.
 
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Microsoft Excel and Google Sheets are powerful and widely used. But there’s a right way and a wrong way to use them, data scientists say. Microsoft Excel and Google Sheets are powerful and widely used. But there’s a right way and a wrong way to use them, data scientists say.
 
Article
Transplanting human cells into animal brains brings insights into development and disease along with new ethical questions. Transplanting human cells into animal brains brings insights into development and disease along with new ethical questions.
 
Article
Some studies suggest that the risk of cardiovascular problems, such as a heart attack or stroke, remains high even many months after a SARS-CoV-2 infection clears up. Researchers are starting to pin down the frequency of these issues and what is causing the damage. Some studies suggest that the risk of cardiovascular problems, such as a heart attack or stroke, remains high even many months after a SARS-CoV-2 infection clears up. Researchers are starting to pin down the frequency of these issues and what is causing the damage.
 
Article
Retraction Watch has witnessed a retraction boom since its founding 12 years ago. But the scientific community must do much more. Retraction Watch has witnessed a retraction boom since its founding 12 years ago. But the scientific community must do much more.
 
Article
A metallochaperone protein that ensures that zinc ions are delivered to a crucial cellular enzyme has now been discovered. The finding underscores the subtleties of controlling cellular zinc allocation when the metal is scarce. Zng1 is an evolutionary conserved chaperone protein for zinc ions.
 
Article
Low levels of social interaction across class lines have generated widespread concern 1–4 and are associated with worse outcomes, such as lower rates of upward income mobility 4–7 . Here we analyse the determinants of cross-class interaction using data from Facebook, building on the analysis in our companion paper ⁷ . We show that about half of the social disconnection across socioeconomic lines—measured as the difference in the share of high-socioeconomic status (SES) friends between people with low and high SES—is explained by differences in exposure to people with high SES in groups such as schools and religious organizations. The other half is explained by friending bias—the tendency for people with low SES to befriend people with high SES at lower rates even conditional on exposure. Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness). Using fluctuations in the share of students with high SES across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Thus, socioeconomic integration can increase economic connectedness in communities in which friending bias is low. By contrast, when friending bias is high, increasing cross-SES interactions among existing members may be necessary to increase economic connectedness. To support such efforts, we release privacy-protected statistics on economic connectedness, exposure and friending bias for each ZIP (postal) code, high school and college in the United States at https://www.socialcapital.org .
 
Article
Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health1–8. However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers9, we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date10,11. Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality12–14. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org. Analyses of data on 21 billion friendships from Facebook in the United States reveal associations between social capital and economic mobility.
 
Cryptococcus promotes arginase-1 expression in macrophages via a soluble, capsule-independent mechanism
a, Volcano plot of RNA-seq data showing uniquely differentially expressed genes (filtered on genes with more than twofold change over S. cerevisiae challenge) between uninfected and 6 h wild-type C. neoformans-infected BMDMs based on DESeq2 analysis (multiplicity of infection (MOI) = 10). Three biological replicates were used for each condition. b, Normalized RNA-seq data for BMDMs stimulated for 6 h with either wild-type (WT) C. neoformans (Cn), cap60ΔC. neoformans, S. cerevisiae (all at MOI = 10), LPS (100 ng ml⁻¹), or zymosan (10 μg ml⁻¹). Three biological replicates were used for each condition. c, Intracellular fluorescence-activated cell sorting (FACS) detection of arginase-1 after 24 h of infection with either C. neoformans or S. cerevisiae at the indicated MOI. Five biologically independent samples. d, FACS detection of intracellular arginase-1 in Il4ra+/⁻ and Il4ra−/− BMDMs stimulated for 24 h with IL-4 (40 ng ml⁻¹) or WT C. neoformans (MOI = 10). Four biologically independent samples. e, FACS detection of intracellular arginase-1 under identical conditions as in d, but with the stimuli either added directly to the BMDMs or to the top of a 0.2-μm Transwell insert (image created with BioRender.com). Four biologically independent samples. f, FACS detection of arginase-1 in BMDMs stimulated for 24 h with IL-4 (40 ng ml⁻¹), live wild-type C. neoformans, heat-killed (55 °C for 15 min) wild-type C. neoformans or S. cerevisiae (all MOI = 10). Four biologically independent samples. Data are mean ± s.d. ***P < 0.001, ****P < 0.0001 by one-way ANOVA with Bonferroni test.
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Identification of CPL1 as a fungal effector by forward genetics
a, Genetic screening strategy to identify fungal effectors that drive arginase-1 expression. b, Outline of CPL1 protein domain architecture. c, Complementation assay using FACS detection of arginase-1 in BMDMs stimulated for 24 h with either wild-type, cpl1Δ or cpl1Δ + CPL1 C. neoformans strains at the indicated MOI. Four biologically independent samples. d, FACS detection of arginase-1 in BMDMs stimulated for 24 h with wild-type, cpl1Δ or pGAL7-CPL1 C. neoformans strains at the indicated MOI. Four biologically independent samples. e, India ink staining for capsular polysaccharides in the indicated strains after overnight culture in 10% Sabouraud medium. Scale bar, 5 μm. Data are representative of two independent experiments. f, FACS detection of arginase-1 in BMDMs stimulated for 24 h with the indicated capsule mutant strains at MOI = 10. Three biologically independent samples. g, Spotting assay for wild-type versus cpl1Δ C. neoformans growth on YPAD plates incubated at the indicated temperatures. h, Quantitative PCR with reverse transcription (RT–qPCR) of CPL1 mRNA in cultures grown to A600 of 1.0 in the indicated conditions. AU, arbitrary units relative to ACT1. Three biologically independent samples. i, Representative FACS detection of intracellular iNOS in BMDMs pre-infected with the indicated strains at MOI = 10 for 2 h followed by 24 h stimulation with LPS (100 ng ml⁻¹) and IFNγ (50 ng ml⁻¹). j, Total nitric oxide in supernatants from BMDMs treated as in i. Seven biologically independent samples. Data are mean ± s.d. **P < 0.01, ***P < 0.001, ****P < 0.0001 by one-way ANOVA with Bonferroni test.
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CPL1 potentiates IL-4 signalling via TLR4
a, Silver-stained SDS–PAGE of recombinant CPL1. n = 5 independent experiments. b, FACS detection of arginase-1 in BMDMs stimulated for 24 h with mock or purified rCPL1 alone (left) or with IL-4 (right). Three biologically independent samples. c, RNA-seq heat map of the top 50 BMDM IL-4-induced genes comparing the fold changes of the indicated stimulations relative to PBS. d, No of C. neoformans after 48 h of incubation at 37 °C and 5% CO2 in supernatants from BMDMs stimulated for 24 h as in c. CFU, colony-forming units. Six biologically independent samples. e, FACS detection of IL-4Rα on BMDMs stimulated for 24 h with rCPL1 or mock. f, Western blot for indicated proteins in BMDMs stimulated as in c for 8 h. Data are representative of three independent experiments. g, FACS detection of arginase-1 in STAT3flox/flox BMDMs transduced with empty vector or iCre retrovirus and stimulated as in c. Five biologically independent samples. h, FACS detection of arginase-1 in wild-type, Tlr2−/−, Tlr4−/− or Tlr2−/−Tlr4−/− BMDMs stimulated for 24 h with rCPL1. Three biologically independent samples. i, As h but with IL-4 stimulation. Three biologically independent samples. j, FACS detection of TLR4 on BMDMs stimulated for 1 h with LPS (100 ng ml⁻¹) or rCPL1. Three biological experiments. k, Luminescence in HEK293T cells transfected with the indicated plasmids plus NF-kB luciferase and stimulated for 6 h with either rCPL1 or LPS (100 ng ml⁻¹). Three biologically independent samples. l, FACS detection of arginase-1 in BMDMs stimulated for 24 h with mock, rCPL1 or rCPL1(Y160A) alone (left) or in combination with IL-4 (right). Three biologically independent samples. Concentrations are 10 ng ml⁻¹ for IL-4 and 111 nM for rCPL1 unless otherwise noted. Data are mean ± s.d. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by one-way ANOVA with Bonferroni test.
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CPL1 promotes arginase-1 expression in pulmonary interstitial macrophages and is required for virulence
a, FACS sub-gating of CD45⁺YARG⁺ lung cells from YARG mice infected intranasally with 5 × 10⁴ CFU wild-type C. neoformans for 10 days. b, Representative histogram of YARG expression in lung interstitial macrophages from mice injected intranasally with either saline or 5 × 10⁴ CFU wild-type C. neoformans for 10 days. c, FACS detection of YARG expression in interstitial macrophages from mice injected intranasally with either saline (n = 5 mice), or wild-type (n = 11 mice), cpl1Δ (n = 14 mice) or qsp1Δ (n = 5 mice) Kn99a (5 × 10⁴ CFU) for 10 days. One-way ANOVA with Bonferroni test. d, Kaplan–Meier survival curve analysis of mice infected with wild-type (n = 10 mice) or cpl1Δ (n = 10 mice) C. neoformans. ****P < 0.0001 by Mantel–Cox test. e, Representative histogram (left) and quantification (right) showing YARG expression in WT, Il4ra−/− or Stat6−/− (all n = 4 mice) infected for 10 days as in a. f, Lung titre of wild-type and cpl1ΔC. neoformans in the indicated mouse genotypes (n = 6 mice for each genotype) 10 days after infection. One-way ANOVA with Bonferroni test. g, FACS detection of arginase-1 in lung interstitial macrophages (IMs) from WT or Tlr4−/− mice infected as in a. Unpaired two-sided t-test. h, Representative FACS detection of C. neoformans–mCherry expression in alveolar macrophages (left) or interstitial macrophages (right) after 10 days of infection. i, Representative FACS histograms of C. neoformans–mCherry expression in interstitial macrophages from YARG mice gated on YARG⁻ interstitial macrophages (left) or YARG⁺ interstitial macrophages (right). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
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Article
Invasive fungal pathogens are major causes of human mortality and morbidity1,2. Although numerous secreted effector proteins that reprogram innate immunity to promote virulence have been identified in pathogenic bacteria, so far, there are no examples of analogous secreted effector proteins produced by human fungal pathogens. Cryptococcus neoformans, the most common cause of fungal meningitis and a major pathogen in AIDS, induces a pathogenic type 2 response characterized by pulmonary eosinophilia and alternatively activated macrophages3–8. Here, we identify CPL1 as an effector protein secreted by C. neoformans that drives alternative activation (also known as M2 polarization) of macrophages to enable pulmonary infection in mice. We observed that CPL1-enhanced macrophage polarization requires Toll-like receptor 4, which is best known as a receptor for bacterial endotoxin but is also a poorly understood mediator of allergen-induced type 2 responses9–12. We show that this effect is caused by CPL1 itself and not by contaminating lipopolysaccharide. CPL1 is essential for virulence, drives polarization of interstitial macrophages in vivo, and requires type 2 cytokine signalling for its effect on infectivity. Notably, C. neoformans associates selectively with polarized interstitial macrophages during infection, suggesting a mechanism by which C. neoformans generates its own intracellular replication niche within the host. This work identifies a circuit whereby a secreted effector protein produced by a human fungal pathogen reprograms innate immunity, revealing an unexpected role for Toll-like receptor 4 in promoting the pathogenesis of infectious disease. Cryptococcus neoformans secretes CPL1 protein, which induces alternative activation of macrophages via Toll-like receptor 4 in mice and is essential for fungal virulence.
 
PI3K–AKT signalling stimulates de novo CoA synthesis
a, The CoA de novo synthesis pathway. b, Insulin (100 nM) and PI3K inhibitor (GDC-0941 or GDC-0032; 2 μM) treatments with concurrent ¹³C3¹⁵N1-VB5 labelling (3 h), preceded by serum/growth factor deprivation (18 h) and inhibitor pretreatment (15 min) in MCF10A cells. c, PIK3CA+/+ and PIK3CAp.H1047R/+-knockin MCF10A cells with PI3K inhibitor (GDC-0941; 2 μM) treatment. Labelling and conditions were otherwise as described in b. d, Acid-extracted CoA and short-chain acyl-CoAs with the cells and conditions as described in b, except with 4 h treatments and labelling. e, Radioactive ¹⁴C-VB5 labelling (3 h) with the cells and conditions otherwise as described in b, followed by a chase (1 h) in medium without VB5. Disintegrations per minute (DPM) normalized to protein. f, AKT inhibitor (GDC-0068, 2 μΜ) and mTORC1 inhibitor (rapamycin, 100 nM) treatments with concurrent ¹³C3¹⁵N1-VB5 labelling (3 h) of MCF10A cells expressing doxycycline (Dox)-inducible HA-tagged wild-type (WT) or constitutively active (E17K) AKT. Treatments and labelling were preceded by doxycycline incubation (48 h), serum and growth factor deprivation (18 h) and inhibitor pretreatment (15 min). g, AKT inhibitor (GDC-0068, 2 μΜ) and ACLY inhibitor (NDI-091143, 15 μM) treatments with the cells, labelling and conditions otherwise as described in f. For b–d, f and g, metabolites were measured using LC–MS/MS and normalized to protein; labelled metabolites (mass + 4 [M + 4]); fractional abundance is [M + 4]/total. For the percentage change graphs, the left-most treatment group mean was set to 0%. For b–g, n = 3 biological replicates (circles). Data are mean ± s.e.m. Statistical analysis was performed using one-way analysis of variance (ANOVA) with Tukey test; asterisks (*) indicate significant differences compared with the treatment groups marked with daggers (†) or between treatments indicated with brackets (P < 0.05). Immunoblotting analysis probed for total and phosphorylated (p) proteins.
PANK2 and PANK4 are direct AKT substrates
a, CoA synthesis pathway enzymes. The full names and accession numbers are provided in Supplementary Table 1. b, Enzymes of the CoA synthesis pathway that are candidate AKT substrates. Amino acid residues that fall within low- to high-quality AKT substrate motifs according to the kinase–substrate prediction program Scansite, and that are reported to be phosphorylated in the phosphoproteomic database Phosphosite are listed. Numbering is based on the human sequence. c, Endogenous PANK1, PANK2 and PANK4 immunoprecipitation (IP) from MCF10A cells with insulin (100 nM) and PI3K inhibitor (GDC-0941, 2 μM) treatments (30 min), preceded by serum and growth factor deprivation (18 h) and inhibitor pretreatment (15 min). d, Endogenous PANK4 immunoprecipitation from orthotopic mammary allograft tumours in C57BL/6J treated with vehicle or PI3K inhibitor (BYL-719, 45 mg kg⁻¹) daily for 10 days. e, Endogenous PANK4 immunoprecipitation from skeletal muscle (gastrocnemius) of C57BL/6J mice treated with PI3K inhibitor (BYL-719, 50 mg kg⁻¹) for 1 h. f, Endogenous PANK2 and PANK4 immunoprecipitation from MCF10A cells treated with insulin (100 nM), AKT inhibitor (MK-2206, 2 μM), and mTORC1 inhibitor (rapamycin, 20 nM) with conditions otherwise as in c. g,h, In vitro AKT kinase assays. Untagged PANK2 (g) or PANK4 (h) (WT or alanine point mutants) were immunopurified from respective reconstituted knockout cells treated with PI3K and AKT inhibitors. PANK immunopurifications were incubated with purified GST–AKT (30 min). i, Diagram of the human PANK2 and PANK4 domains and AKT-targeted phosphorylation sites. The asterisks indicate the location of evolutionary mutations inactivating PANK4 kinase domain. For c–h, immunoblotting analysis probed for total and phosphorylated proteins including AKT phospho-substrate motifs; representative of two independent experiments. IgG, control IgG immunoprecipitation.
PANK4 suppresses CoA synthesis and phosphorylation of PANK4 Thr406 reduces this suppression
a, PANK kinase inhibitor (0–10 μM; inhib.) treatments with concurrent ¹³C3¹⁵N1-VB5 labelling (3 h) of MCF10A cells expressing doxycycline-inducible HA-tagged wild-type or constitutively active (E17K) AKT. Treatment and labelling were preceded by doxycycline incubation (48 h), serum and growth factor deprivation (18 h) and inhibitor pretreatment (15 min). b, Individual siRNA-mediated knockdowns of PANK1, PANK2 and PANK4 (siP1, siP2 and siP4, respectively) or non-targeting siRNA (siC) in MCF10A AKTp.E17K/+ cells with ¹³C3¹⁵N1-VB5 labelling (24 h) preceded by serum and growth factor deprivation (18 h). c, Wild-type PANK4 and PANK4-KO AKTp.E17K/+ MCF10A cells with ¹³C3¹⁵N1-VB5 labelling (3 h) preceded by serum and growth factor deprivation (18 h). Divided blots are from same SDS–PAGE gel and image. d, PANK4-KO cells stably expressing vector (Vec) or untagged human PANK4 (WT or T406A) with conditions and labelling otherwise as described in c. Divided blots are from same SDS–PAGE gel and image. e, ACLY inhibitor (NDI-091143, 20 μM) treatment with concurrent ¹³C3¹⁵N1-VB5 labelling (3 h) of PANK4-KO cells stably expressing vector or untagged human PANK4 (T406A or T406E). Treatments and labelling were preceded by serum and growth factor deprivation (18 h) and inhibitor pretreatment (2 h). f, Two-dimensional (2D) proliferation of cell lines from d and e with growth factors. Statistical comparison was performed on day 4 data. For a–e, metabolites were measured using LC–MS/MS and normalized to protein; labelled metabolites (M + 4). For the graphs of percentage change, the mean value of the left-most treatment group was set to 0%. Immunoblotting analysis probed for total or phosphorylated proteins. For a–f, n = 3 biological replicates (circles). Data are mean ± s.e.m. Statistical analysis was performed using two-tailed Student’s t-tests (c), one-way ANOVA with Tukey test (a and d–f) and two-way ANOVA with Sidak test (b); asterisks (*) indicate significant differences compared with the treatment groups marked with daggers (†) or between treatments indicated with brackets (P < 0.05).
PANK4 functions as a metabolite phosphatase
a, Amino acid alignment of PANK4 with conserved catalytic aspartates (blue, bold) and other conserved residues (grey) of previously characterized DUF89 domains (non-PANK4 orthologues). At, Arabidopsis thaliana; Hs, Homo sapiens; Ph, Pyrococcus horikoshii; Sc, Saccharomyces cerevisiae. b, PANK4 phosphatase assay. Flag-tag immunopurifications from cells expressing vector or Flag–PANK4 (WT, D623A or D659A). Substrates: para-nitrophenyl phosphate (PNPP) and 4′-phosphopantetheine (p-PaSH). The wild-type mean was set to 1. c, PANK4 phosphatase assay. Flag–PANK4 as in b. Substrates: 4′-phosphopantothenate (p-Pa) and 4′-phosphopantetheine. The 4′-phosphopantetheine mean was set to 1. d, PANK4-KO AKTp.E17K/+ MCF10A cells stably expressing vector or untagged PANK4 (WT, D623A or D659A). ¹³C3¹⁵N1-VB5 labelling (3 h) was performed with serum replacement and growth factors. Metabolites were measured using LC–MS/MS and normalized to protein. Labelled metabolites (mass + 4). Vector mean set to 0%. e, Unlabelled polar metabolomics using cells and conditions in d.Two independent experiments, ‘a’ and ‘b, were analysed (Methods). f, Unlabelled lipidomics using the cells and conditions as described in d. The analysis incorporates three independent experiments (Methods). g, Seahorse oxygen-consumption assay using the cells in d without serum or growth factors. OCR, oxygen consumption rate. h, 2D proliferation with the cells and conditions as described in d. Representative of three independent experiments. i, Three-dimensional (3D) soft agar colony formation using the cells in d with serum and growth factors. j, Orthotopic mammary xenograft tumours using SUM159 PANK4-KO cells with stable expression of vector or untagged PANK4 (WT or D623A) in nude mice. Individual tumour growth curves (left). Kaplan–Meier survival curves using tumour volume (750 mm³) or ulceration (X) end points (right). k, Model of PI3K-dependent CoA synthesis regulation. For b, d and j, immunoblotting analysis probed for total and phosphorylated proteins. For b–d and g–i, n = 3 (b–d, g and h), n = 4 (j) or n = 6 (i) biological replicates (circles). Data are mean ± s.e.m. For b–d and g–j, statistical analysis was performed using one-way ANOVA with Tukey test; asterisks (*) indicate significant differences compared with the treatment groups marked with daggers (†) or between treatments indicated with brackets (P < 0.05).
Source data
Article
In response to hormones and growth factors, the class I phosphoinositide-3-kinase (PI3K) signalling network functions as a major regulator of metabolism and growth, governing cellular nutrient uptake, energy generation, reducing cofactor production and macromolecule biosynthesis1. Many of the driver mutations in cancer with the highest recurrence, including in receptor tyrosine kinases, Ras, PTEN and PI3K, pathologically activate PI3K signalling2,3. However, our understanding of the core metabolic program controlled by PI3K is almost certainly incomplete. Here, using mass-spectrometry-based metabolomics and isotope tracing, we show that PI3K signalling stimulates the de novo synthesis of one of the most pivotal metabolic cofactors: coenzyme A (CoA). CoA is the major carrier of activated acyl groups in cells4,5 and is synthesized from cysteine, ATP and the essential nutrient vitamin B5 (also known as pantothenate)6,7. We identify pantothenate kinase 2 (PANK2) and PANK4 as substrates of the PI3K effector kinase AKT8. Although PANK2 is known to catalyse the rate-determining first step of CoA synthesis, we find that the minimally characterized but highly conserved PANK49 is a rate-limiting suppressor of CoA synthesis through its metabolite phosphatase activity. Phosphorylation of PANK4 by AKT relieves this suppression. Ultimately, the PI3K–PANK4 axis regulates the abundance of acetyl-CoA and other acyl-CoAs, CoA-dependent processes such as lipid metabolism and proliferation. We propose that these regulatory mechanisms coordinate cellular CoA supplies with the demands of hormone/growth-factor-driven or oncogene-driven metabolism and growth. The PI3K–PANK4 axis regulates coenzyme A synthesis, the abundance of acetyl-CoA, and CoA-dependent processes such as lipid metabolism, and these regulatory mechanisms coordinate cellular CoA supplies with the demands of hormone and growth-factor-driven or oncogene-driven metabolism and growth.
 
Cas1–Cas2 integrates retron RT-DNA
a, Schematic representation of retroelement-based transcriptional recording into CRISPR arrays. dsDNA, double-stranded DNA. b, Schematic representation of the biological components of the retron-based recorder. c, Urea PAGE visualization of RT-DNA from retron Eco1 ncRNA variants. From left to right (excluding ladder): wild-type Eco1 (WT), Eco1 v32 and Eco1 v35. For gel source data, see Supplementary Fig. 1. d, Schematic of the experimental promoters used to test retron-recorder parts and a cartoon of the hypothetical duplex RT-DNA prespacer structure. Eryth, erythromycin. e, Quantification of arrays expanded with retron-derived spacers using Eco1 variants v32 (orange) and v35 (green). Open circles represent three biological replicates. f, Quantification of arrays expanded with retron-derived spacers with a wild-type (12-bp) and extended (27-bp) a1/a2 region. Open circles represent five biological replicates. g, Time series of array expansions from retron-derived spacers. Open circles represent six biological replicates and filled circles are the means. Data from three replicates were collected from time 1.5 to 9 h. Data from a separate three replicates were collected from time 9 to 22.5 h. Data from all six replicates were collected at 24 h. h, Time series of array expansions from non-retron-derived spacers. Open circles represent biological replicates and filled circles are the means, collected as described in g. i, Proportion of total new spacers that are retron derived. Open circles represent biological replicates and the dashed line is the mean, collected as described in g. All statistics are given in Supplementary Table 1.
Diversification of retron-based barcodes
a, Hypothetical structure of a duplexed RT-DNA prespacer with a 6-base barcode and retron-derived spacer. b, Quantification of array expansions from barcoded variants of retron Eco1 v35, showing both retron-derived (green/pink) and non-retron-derived (black) spacers for each variant. Open circles represent three biological replicates. dRT, dead-RT. c, Left, heatmap of the in silico ability to distinguish among all barcoded Eco1 v35 variants. Right, heatmap of the in silico ability to distinguish among a reduced set of barcoded Eco1 v35 variants. d, Heatmap of the standard deviation between three separate trials of a barcode discrimination test. Left, full set; right, reduced set. All statistics are given in Supplementary Table 1.
Mechanism of RT-DNA spacer acquisition
a, Hypothetical structure of a duplexed Eco1 v32 RT-DNA prespacer and retron-derived spacer, with mismatched regions highlighted. b, Quantification of mismatch region sequences in spacers from cells expressing Eco1 v32 versus cells electroporated with oligonucleotide mimic. Bars represent the mean (± s.d.) of four and five biological replicates for the retron- and oligonucleotide-derived conditions, respectively. c, Urea PAGE visualization of Eco1 RT-DNA. DBR1 treatment resolves the 2′–5′ linkage. For gel source data, see Supplementary Fig. 1. d, Quantification of mismatch region sequences in spacers from cells electroporated with purified debranched Eco1 v32 RT-DNA. Bars represent the mean (± s.d.) of four biological replicates. e, Quantification of array expansions from different prespacer substrates. Open circles represent, from left to right, three, two and five biological replicates. f, Schematic of Eco4 RT-DNA, in both orientations, with mismatch sequences highlighted. g, Quantification of mismatch region sequences in cells expressing Eco4 versus cells electroporated with oligonucleotide mimic. Bars represent the mean (± s.d.) of three biological replicates. h, Urea PAGE visualization of Eco4 RT-DNA. DBR1 does not cause a size shift in Eco4 RT-DNA. For gel source data, see Supplementary Fig. 1. i, Quantification of array expansions from retron Eco4. Open circles represent three biological replicates. All statistics are given in Supplementary Table 1.
Temporal recordings of gene expression
a, Schematic of signal plasmid pSBK.134 used to express ncRNAs A and B and the recording plasmid used to express the Eco1 RT, Cas1 and Cas2. b, Accumulation of retron-derived spacers from pSBK.134 after 24 h of induction from the respective promoters (four biological replicates). c, Retron-derived spacers when ncRNAs were induced in the order A and then B from pSBK.134. Filled circles represent the mean (± s.e.m.) of four biological replicates. aTc, anhydrotetracycline; Cho, choline chloride. d, Retron-derived spacers when ncRNAs were induced in the order B and then A from pSBK.134. Filled circles represent the mean (± s.e.m.) of four biological replicates. e, Non-retron-derived spacers in cells harbouring pSBK.134, in both induction conditions. Filled circles represent the mean (± s.e.m.) of four biological replicates. f, Schematic of signal plasmid pSBK.136 used to express ncRNAs A and B and the recording plasmid. g, Accumulation of retron-derived spacers from pSBK.136 after 24 h of induction from the respective promoters (four biological replicates). The outlier sample determined by Grubbs’ test is denoted as a grey ‘X’. h, Retron-derived spacers when ncRNAs were induced in the order A and then B from pSBK.136. Filled circles represent the mean (± s.e.m.) of three biological replicates. Sal, sodium salicylate. i, Retron-derived spacers when ncRNAs were induced in the order B and then A from pSBK.136. Filled circles represent the mean (± s.e.m.) of four biological replicates. j, Non-retron-derived spacers in cells harbouring pSBK.136, in both induction conditions. Filled circles represent the mean (± s.e.m.) of four biological replicates. k, Graphical representation of the rules used to determine the order of expression from arrays. l, Ordering analysis of recording experiments with signal plasmid pSBK.134. Open circles correspond to six biological replicates. m, Ordering analysis of recording experiments with signal plasmid pSBK.136. Open circles correspond to five biological replicates. All statistics are given in Supplementary Table 1.
Modelling the limits of retron recording
a, Simulation of 100 replicates each of A-then-B and B-then-A recordings using acquisition rate data from pSBK.134 recordings. Each point represents the calculated ordering score from a single replicate of 1 million arrays. b, Simulation of 100 replicates each of A-then-B and B-then-A recordings using acquisition rate data from pSBK.136 recordings. Each point represents the calculated ordering score from a single replicate of 1 million arrays. c, Simulation of varying the number of arrays analysed per sample using acquisition rate data from pSBK.134 recordings. Each box with whiskers represents 100 simulated replicates, with whiskers extending from the minimum to the maximum. d, Simulation of varying the length of each epoch in a retron recording using acquisition rate data from pSBK.134 (blue). Simulation findings are overlaid with real retron recordings of the same length (purple). Each box with whiskers represents 100 simulated replicates of 1 million reads each, with whiskers spanning from the minimum to the maximum. Each overlaid point is a single biological replicate. Recording experiments with 6-, 12- and 48-h epochs were carried out in triplicate. Recording experiments with an epoch length of 24 h are the same as in Fig. 4l. e, Simulation of varying the strength of signal B when signal A remains constant. 1× acquisition rates were obtained from pSBK.134 recordings. Each box with whiskers represents 50 simulated replicates of 1 million arrays each. Whiskers span from the minimum to the maximum. f, Simulation of varying the strength of signal B when signal A is decreased or increased by a factor of 8. 1× acquisition rates were obtained from pSBK.134 recordings. Each box with whiskers represents 50 simulated replicates of 1 million arrays each. Whiskers span from the minimum to the maximum.
Article
Biological processes depend on the differential expression of genes over time, but methods to make physical recordings of these processes are limited. Here we report a molecular system for making time-ordered recordings of transcriptional events into living genomes. We do this through engineered RNA barcodes, based on prokaryotic retrons1, that are reverse transcribed into DNA and integrated into the genome using the CRISPR–Cas system2. The unidirectional integration of barcodes by CRISPR integrases enables reconstruction of transcriptional event timing based on a physical record through simple, logical rules rather than relying on pretrained classifiers or post hoc inferential methods. For disambiguation in the field, we will refer to this system as a Retro-Cascorder. Retro-Cascorder, a system for time-ordered recording of transcriptional output, uses retrons as a tag to mediate DNA barcode acquisition in a CRISPR array.
 
Structural and functional analysis of A. muciniphila PE
a, Flow diagram for fractionation of A. muciniphila PE. Amounts in active fractions are shown in red. FA composition of PE fraction also shown. b, TNFα production by mBMDCs treated with A. muciniphila lipid extract fractions as measured by ELISA. The fraction indicated in red was used for structural characterization. Pam3CSK4 was used as a control agonist. Data are presented as mean values ± s.d. of technical replicates (n = 4). c, The structure of a15:0-i15:0 PE. d, The relative abundance of FAs in A. muciniphila PE. e, Dose response of TNFα production by mBMDCs treated with natural (Nat.) and synthetic (Syn.) a15:0-i15:0 PE lipids as measured by ELISA. Data are presented as mean values ± s.d. of technical replicates (n = 4). f, a15:0-i15:0 PE and complete PE (AmPE) trigger release of TNFα and IL-6 but not IL-10 or IL-12p70 from mBMDCs, as measured by flow cytometry. LPS was used as a control. Data are presented as mean values ± s.d. of technical replicates (n = 3). g, TNFα release is lost in TLR2 knockout mBMDCs but not in TLR4 knockout mBMDCs as measured by ELISA. Pam3CSK4 was used as a TLR2 control agonist, and LPS was used as a TLR4 control agonist. Data are presented as mean values ± s.d. of technical replicates (n = 4). All experiments were repeated independently at least twice with similar results. DMSO, dimethyl sulfoxide.
Biosynthesis and laboratory synthesis of A. muciniphila PE
a, Key genes involved in the putative biosynthetic pathway for A. muciniphila BAA-835 PE. b, Leucine or isoleucine feeding increases TNFα induction by A. muciniphila in a TLR2-dependent fashion as measured by ELISA. Pam3CSK4 and LPS were used as controls. Data are presented as mean values ± s.d. of technical replicates (n = 4). Unpaired t-test with two-tailed P value; ****P < 0.0001. c, Outline of synthetic scheme for a15:0-i15:0 PE and analogues. d, Overlay of mass spectrometric data from the natural and synthetic a15:0-i15:0. e, TNFα induction by natural and synthetic a15:0-i15:0 PE. a15:0-i15:0 PE induces production in mBMDCs, whereas n14:0-n14:0, n15:0-n15:0, n16:0-n16:0, a15:0-a15:0 and i15:0-i15:0 PE have no detectable TNFα induction, as measured by ELISA. i15:0-a15:0, the positional isomer, shows partial induction. Pam3CSK4 and LPS were used as controls. Data are presented as mean values ± s.d. of technical replicates (n = 4). All experiments were repeated independently at least twice with similar results. Ile, isoleucine; Val, valine; Leu, leucine.
TLR2–TLR1 binding model and T cell activation by a15:0-i15:0 PE
a, View of the TLR2–TLR1 complex from the Protein Data Bank (PDB ID 2z7x) with the modelled a15:0-i15:0 PE ligand in the ‘bridging’ conformation, showing the branches with C13 coloured green and C12 purple. b, An overview of the modelled TLR2–TLR1-a15:0-i15:0 PE complex in the surface representation. The dashed circle indicates the buried lipid head group. c, TLR1 and TLR2 are required for natural and synthetic A. muciniphila lipids to induce TNFα production in human monocyte-derived dendritic cells (MDDCs). The production of TNFα was measured by ELISA 18 h after adding natural or synthetic A. muciniphila lipids, Pam3CSK4, FSL-1 or LPS to cell culture media of human MDDCs following nucleofection. d, IL-23A and IL-12B induction by natural and synthetic a15:0-i15:0 PE lipids. e–g, Effects of treatment of human MDDCs with a15:0-i15:0 PE in combination with Pam3CSK4 or LPS. With long (18 h) delay times, low doses of a15:0-i15:0 PE suppress immune responses to Pam3CSK4 and moderate immune responses to LPS (e). Both effects disappear with shorter delay times (3 h in f or none in g). LPS and Pam3CSK4 were used at final concentrations of 100 ng ml⁻¹. Data in c (n = 3), d (n = 6) and e–g (n = 4) are representative of two independent experiments, showing mean values ±  s.d. P values in a were calculated by two-way analysis of variance. *P < 0.05; **P < 0.001; ****P < 0.0001; NS, not significant.
Article
Multiple studies have established associations between human gut bacteria and host physiology, but determining the molecular mechanisms underlying these associations has been challenging1–3. Akkermansia muciniphila has been robustly associated with positive systemic effects on host metabolism, favourable outcomes to checkpoint blockade in cancer immunotherapy and homeostatic immunity4–7. Here we report the identification of a lipid from A. muciniphila’s cell membrane that recapitulates the immunomodulatory activity of A. muciniphila in cell-based assays8. The isolated immunogen, a diacyl phosphatidylethanolamine with two branched chains (a15:0-i15:0 PE), was characterized through both spectroscopic analysis and chemical synthesis. The immunogenic activity of a15:0-i15:0 PE has a highly restricted structure–activity relationship, and its immune signalling requires an unexpected toll-like receptor TLR2–TLR1 heterodimer9,10. Certain features of the phospholipid’s activity are worth noting: it is significantly less potent than known natural and synthetic TLR2 agonists; it preferentially induces some inflammatory cytokines but not others; and, at low doses (1% of EC50) it resets activation thresholds and responses for immune signalling. Identifying both the molecule and an equipotent synthetic analogue, its non-canonical TLR2–TLR1 signalling pathway, its immunomodulatory selectivity and its low-dose immunoregulatory effects provide a molecular mechanism for a model of A. muciniphila’s ability to set immunological tone and its varied roles in health and disease. Overall, this study describes the molecular mechanism of a druggable pathway that recapitulates in cellular assays the immunomodulatory effects associated with Akkermansia muciniphila, a prominent member of the gut microbiota.
 
Principles of the microwave-induced transport in dual circuits
a, JJ transport. b, CQPS transport. In a and bI–V characteristics without microwaves (blue curve) and with microwaves (red curve) are shown schematically. Insets show energy diagrams for the microwave-assisted transport between reservoirs separated by tunnel barriers (an insulator and a nanowire) biased by Q0Vd.c. in JJ and Φ0Id.c. in CQPS. c,d, Effective electrical circuits for the transport measurements for JJ (c) and for CQPS (d). Tunnelling of Cooper pairs in the JJ is replaced by tunnelling of vortices through a CQPS nanowire. A capacitance C and a resistor R parallel to the JJ are replaced by an inductance L and an admittance Y in series to the CQPS junction.
Device and transport
a, The device layout. The superconducting 100 nm wide wire with a constriction of approximately 20 × 50 nm² geometrical size (zoomed out) is embedded into the circuit with four compact series meandering inductances made of the NbN films with kinetic inductances L′≈1.7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${L}^{{\prime} }\approx 1.7$$\end{document} μH, L″ ≈ 0.5 μH. Inductances are connected to series Pd resistances (R′=11.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}^{{\prime} }=11.5$$\end{document} kΩ) and Pd contact pads. The circuit is connected to current, I⁺/I⁻, and voltage, V⁺/V⁻, leads. The microwaves are delivered through an on-chip coplanar line, coupled to the circuit via capacitances Cκ. An inset shows a CQPS junction: a small nanowire constriction. b, I–V characteristics in a wide voltage range demonstrate high re-trapping (Ir) and excess (Iexc) currents. c, An I–V characteristic of the central part. A clear blockade is found with the re-trapping voltage Vc*=2.3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${V}_{{\rm{c}}}^{* }=2.3$$\end{document} μV.
Inverse Shapiro steps in four-probe I–V measurements
Horizontal lines show the expected position of plateaus at nQ0f. a, fI = 14.924 GHz. b, fI = 14.924 GHz with an a.c. current 2.6 times higher than in a. c, fII = 19.845 GHz. d, fIII = 25.963 GHz.
Oscillations of dV/dI peaks
a, dV/dI characteristics in a two-dimensional plot experimentally measured at 14.924 GHz. b, Simulations, accounting for the heating effect from Pd resistors. c, Cross-sections at positions n of the quantized steps. Solid lines are simulations. Each plot is offset by n × 1.5 kΩ in the y axis. d, dV/dI for n = ±1 peak position difference calculated as Ĩ1=(I1max−I−1max)/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\widetilde{I}}_{1}=({I}_{1}^{\max }-{I}_{-1}^{\max })/2$$\end{document} from various samples with different Vc*\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${V}_{c}^{* }$$\end{document} (specified in the plot legend). The typical accuracy for each point is 2 × 10⁻³ defined by fitting the peak position. An inset shows q/Q0 − 1 with q=Ĩ1/f\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$q={\widetilde{I}}_{1}/f$$\end{document}. The red dotted line is I = Q0f.
Article
The a.c. Josephson effect predicted in 19621 and observed experimentally in 19632 as quantized ‘voltage steps’ (the Shapiro steps) from photon-assisted tunnelling of Cooper pairs is among the most fundamental phenomena of quantum mechanics and is vital for metrological quantum voltage standards. The physically dual effect, the a.c. coherent quantum phase slip (CQPS), photon-assisted tunnelling of magnetic fluxes through a superconducting nanowire, is envisaged to reveal itself as quantized ‘current steps’3,4. The basic physical significance of the a.c. CQPS is also complemented by practical importance in future current standards, a missing element for closing the quantum metrology triangle5,6. In 2012, the CQPS was demonstrated as superposition of magnetic flux quanta in superconducting nanowires 7. However, the direct flat current steps in superconductors, the only unavailable basic effect of superconductivity to date, was unattainable due to lack of appropriate materials and challenges in circuit engineering. Here we report the direct observation of the dual Shapiro steps in a superconducting nanowire. The sharp steps are clear up to 26 GHz frequency with current values 8.3 nA and limited by the present set-up bandwidth. The current steps were theoretically predicted in small Josephson junctions 30 years ago5. However, unavoidable broadening in Josephson junctions prevents their direct experimental observation8,9. We solve this problem by placing a thin NbN nanowire in an inductive environment. Direct observation of the physical dual a.c. Josephson effect, a series of quantized current steps in a superconducting nanowire, is reported and may offer a way to establish new metrological standards for currents.
 
Drosophila Sestrin binds GATOR2 and regulates mTORC1 in vivo in response to dietary leucine
a, Data from an equilibrium binding assay showing that purified Flag–Sestrin bound leucine, Kd ≈ 100 μM. The values are the mean ± s.d. of three technical replicates from a representative experiment. b, The L431E alteration blocks leucine binding by Drosophila Sestrin. HA-tagged wild-type Sestrin and Sestrin(L431E) were prepared from HEK293T cells expressing the appropriate cDNAs. The binding assays were performed as in a. The values are the mean ± s.d. of three technical replicates from a representative experiment. The P values were determined using an unpaired t-test with Welch correction, and the Holm-Šídák multiple comparison method. c, Leucine starvation enhances the Sestrin–GATOR2 interaction. Flag–immunoprecipitates (IPs) were prepared from S2R+ cells stably expressing Flag-tagged und (negative control) or WDR59 (a GATOR2 component) and starved or not of leucine. Immunoprecipitates and lysates were analysed by immunoblotting for the indicated proteins. Addition to the immunoprecipitates of 1 mM leucine, but not other amino acids, disrupted the Sestrin–GATOR2 interaction. d, Dietary leucine regulates in vivo the interaction of Sestrin with GATOR2 depending on the leucine-binding site of Sestrin. Immunoprecipitates were prepared from lysates of fat bodies from wild-type (OreR) or SesnL431E larvae expressing the MYC-tagged control protein GFP or the MYC-tagged GATOR2 component WDR24 in the fat body (lpp-gal4). Animals were fed the indicated diets for 4.5 h before sample collection. Amino acid replete: chemically defined diet containing all amino acids; leucine free or valine free: chemically defined diet lacking leucine or valine, respectively. e, Sestrin binding to leucine regulates mTORC1 signalling in vivo. Shown are immunoblots of Sestrin, S6K and phospho-S6K in fat bodies prepared as in d from larvae with the indicated genotypes. Nprl2 and Mio encode core components of the GATOR1 and GATOR2 complexes, respectively. The dietary composition and feeding period were as in d. The data are representative of three (a,b) or two (c–e) independent experiments with similar results.
Source Data
Drosophila require Sestrin to adapt to a low-leucine diet
a,b, Loss of Sestrin reduces survival during development after leucine starvation. The bar charts show survival (%) of larvae raised for 10 days on a chemically defined diet containing 10% of the leucine in the control diet. The P values were determined using a two-proportion z-test (two-sided). The bars show the percentage of surviving larvae in each genotype and the error bars represent the 95% Wald confidence interval. c, Sestrin is required for larval growth on a low-leucine diet. Shown are age-synchronized animals of the indicated genotypes raised for 9 days on either an amino-acid-replete diet or a reduced (10%)-leucine diet. Scale bar, 1 mm. d–i, Loss of Sestrin reduces survival of adult flies after leucine starvation. Sesn⁻/− animals show reduced lifespan when fed a diet lacking leucine (0% leucine). Shown are survival curves of age-synchronized adult male (♂) and female (♀) animals of the indicated genotypes fed the indicated diets. In c, wild type (w¹¹¹⁸) n = 157; Sesn⁻/−n = 217; in d, wild type (w¹¹¹⁸) n = 221; Sesn⁻/−n = 225; in e, wild type (w¹¹¹⁸) n = 206; Sesn⁻/n = 203; in f, wild type (w¹¹¹⁸) n = 205; Sesn⁻/−n = 226; in g, wild type (w¹¹¹⁸) n = 222; Sesn⁻/⁻n = 230; in h, wild type (w¹¹¹⁸) n = 221; Sesn⁻/⁻n = 228. See statistics in Supplementary Data 1 and Methods. The data in a–i are representative of three independent experiments with similar results.
Source Data
Flies prefer to eat leucine-containing food in a fashion that depends on the capacity of Sestrin to bind leucine
a, A schematic of the two-choice food preference assay (see Methods for details). AA, amino acids. b, Wild-type female animals develop a preference for leucine over the course of several hours. The data show the fold difference in relative food intake for the leucine-coated compared to water-coated apples. n ≥ 11 per time point. c, Rapamycin prevents flies from developing a preference for the leucine-coated apple. n ≥ 5 per condition. d–f, SesnL431E and Sesn⁻/− animals fail to develop a preference for the leucine-containing apple. In d,e, n ≥ 4 per condition; in f, n ≥ 6 per condition. g, Immunoblotting for Sestrin following knockdown of Sesn in adult flies. Akt serves as a loading control. h, Ubiquitous knockdown of Sesn reduces the preference of adult female flies for leucine. The data show the fold difference in food intake for the leucine-coated apple relative to the water-coated apple. n ≥ 5 per condition. i, The approach used to achieve temporal control of Sesn knockdown in j,k. j, Sesn immunoblot showing Gal80ts-mediated depletion of Sestrin in adult, but not developing, animals. Extracts were prepared from flies raised at the indicated temperatures. S6K serves as a loading control. Note that heat shock induces Sestrin protein levels in control flies. k, Knockdown of Sesn during adulthood is sufficient to decrease the preference of female flies for leucine-containing apples. n ≥ 13 per condition. a,i, Created with BioRender.com. In b–f,h,k the values are mean ± s.d. of biological replicates from a representative experiment. Each experiment was repeated three (d–k) or two (b,c) times with similar results. Statistical analyses were carried out using one-way analysis of variance (ANOVA) followed by Dunnett’s multiple comparisons test (b), two-way ANOVA followed by Šídák's multiple comparisons test (c–e), one-way ANOVA followed by Šídák's multiple comparisons test (f) and two-tailed unpaired t-test (h,k).
Source Data
Sestrin-regulated mTORC1 signalling in glial cells controls the preference of flies for leucine-containing food
a, A genetic screen identifies a role for Sesn in glial cells in mediating the leucine preference. Sesn RNA-mediated interference was performed in various tissues with the indicated Gal4 lines. Knockdown of Sesn in glial cells (Repo-Gal4) and ubiquitously (da-Gal4), but not in other tissues, reduces the preference for the leucine-coated versus water-coated apple. For each Gal4 line, the data are normalized to the leucine preference of control flies. See non-normalized data in Extended Data Fig. 7a. n ≥ 5 per condition. b,c, Confocal projection of brains of adult female flies of the indicated genotypes expressing 4MBOX–GFP, a reporter for the MITF transcription factor (TF) that is negatively regulated by mTORC1. Animals were fed the indicated diets for 1 day and brains were stained for GFP and Repo. The images in b,c were taken with 10× and 40× objectives, respectively. Scale bars, 50 μm (b) and 10 μm (c). d, In wild-type flies, but not SesnL431E or Sesn⁻/− flies, leucine starvation increases the number of GFP-positive peri-oesophageal glial cells. Each point represents the ratio of the number of GFP- to Repo-positive cells in the oesophageal area of one fly brain. n ≥ 3 per condition. e, Proposed role of the Sestrin–mTORC1 pathway in regulating the preference of flies for leucine-containing food. In a,d, the values are mean ± s.d. of biological replicates from a representative experiment. The data are representative of three independent experiments with similar results. Statistical analysis was performed using two-tailed unpaired t-test (a), and two-way ANOVA followed by Šídák's multiple comparisons test (d).
Source Data
Article
Mechanistic target of rapamycin complex 1 (mTORC1) regulates cell growth and metabolism in response to multiple nutrients, including the essential amino acid leucine1. Recent work in cultured mammalian cells established the Sestrins as leucine-binding proteins that inhibit mTORC1 signalling during leucine deprivation2,3, but their role in the organismal response to dietary leucine remains elusive. Here we find that Sestrin-null flies (Sesn−/−) fail to inhibit mTORC1 or activate autophagy after acute leucine starvation and have impaired development and a shortened lifespan on a low-leucine diet. Knock-in flies expressing a leucine-binding-deficient Sestrin mutant (SesnL431E) have reduced, leucine-insensitive mTORC1 activity. Notably, we find that flies can discriminate between food with or without leucine, and preferentially feed and lay progeny on leucine-containing food. This preference depends on Sestrin and its capacity to bind leucine. Leucine regulates mTORC1 activity in glial cells, and knockdown of Sesn in these cells reduces the ability of flies to detect leucine-free food. Thus, nutrient sensing by mTORC1 is necessary for flies not only to adapt to, but also to detect, a diet deficient in an essential nutrient. Fruitflies require Sestrin to regulate mTORC1 signalling in response to dietary leucine, survive a diet low in leucine, and control leucine-sensitive physiological characteristics, which establishes Sestrin as a physiologically relevant leucine sensor.
 
High-resolution ctDNA and metastatic tissue whole genomes
a, Study design. NEPC, neuroendocrine prostate cancer. b, Per-patient breakdown of plasma cfDNA and metastatic tissue samples. Rows indicate the ordinal timing of sequential collections. c, Plasma cancer fraction inferred by WGS correlates with previous estimates from deep targeted sequencing (73-gene panel). d, Per-sample overview of genomic information (main study cohort). The ‘non-integer copy number’ category only includes non-integer values of less than 4; signature weights are derived from bulk sequencing (signatures with weights less than 0.15 are grouped as ‘Other’). HRRd, homologous recombination repair defective; MMRd, mismatch repair defective; SNV, single-nucleotide variant. e, Comparison of evidence for subclonal copy number alterations detected in cfDNA (n = 58) versus tumour tissue (n = 15); samples that constituted admixtures of at least two major clonal populations with different WGD statuses were excluded. Representative examples of same-patient whole-genome tissue and cfDNA copy number profiles. LBD = ligand binding domain.
Cancer clonal composition of ctDNA
a, Overview of subclonal reconstruction methodology from bulk DNA. b, Per-patient illustration of inferred tumour populations. The top plots show cancer fraction and sample metadata. The third row illustrates the fractional representation of each subclone in a given patient sample (column). Phylogenetic tree branch length is proportional to the number of mutations assigned to a given population (truncal branch is scaled 0.25×). WGD events (annotated with stars) are positioned by their timing relative to mutations. Triplications (indicated by *) are not positioned by timing. c, Bar plot showing the proportion of the genome that is copy-altered (relative to expected ploidy accounting for WGD(s)), dichotomized by gains versus deletions in ctDNA and tissue. d, Bar plot showing the mean number of whole-chromosome copy number alterations (relative to expected ploidy) in diploid versus WGD samples; dots represent individual samples (dots are jittered along the x and y dimensions). e, Fraction of genomic regions that are copy-altered versus mean genome copy number. Samples that constituted admixtures of two or more major clonal populations with different WGD states were excluded. f,g, Box plots showing per-patient C>T ageing-associated (COSMIC 1) (f) and DNA-repair-defect-associated (COSMIC 3 or 6+15) (g) signature (n = 4) weights inferred from mutation subsets categorized by evolutionary timing. Connecting lines indicate same-patient clonal populations. MRCA, most recent common ancestor. h, Per-sample segmented copy number profiles of the AR gene and enhancer locus. LBD mutations and structural variants, and gene and enhancer copy number status, are annotated.
Population and genomic discordance among synchronous metastatic tissue biopsy and cfDNA pairs
a, Leveraging multi-sample subclonal reconstruction to estimate the proportion of ctDNA derived from somatic populations present in individual metastatic tissue biopsies. Three representative examples illustrating low, medium and high ctDNA contributions. b, Scatter plot illustrating high correlation between mCRPC driver gene copy number across matched cfDNA–tissue pairs; marker size reflects the proportion of ctDNA originating from the biopsied population. The Pearson correlation for each gene is shown. c, Mutation concordance (presence/absence) between cfDNA–tissue pairs. VAF, variant allele frequency. d, Per-patient somatic copy number differences between time-matched ctDNA and tissue. e, Average absolute ctDNA–tissue copy number differences across the genome (left) and the AR locus (right) on chromosome X. f, Scatter plot illustrating high correlation between AR gene and enhancer copy number between matched cfDNA–tissue pairs.
Genome evolution in ctDNA during systemic treatment with AR signalling inhibitors
a, Genomic landscape across all 76 cfDNA and tissue samples (regardless of time point). Somatic copy number (top row), patients with genomic rearrangement breakpoints (1-Mb window; second row), patients with mutations (100-kb window; third row) and patients with protein-altering mutations (divided by the coding sequence (CDS) length in kb; bottom row). In the copy number panel (top row), red and blue colour indicate copy numbers that are larger and smaller, respectively, than the average base ploidy of all samples (accounting for WGDs). In the bottom row, only genes that are mutated in three or more patients are shown. b, Aggregate of acquired genomic changes between consecutive cfDNA samples after treatment with AR signalling inhibitors in 21 patients. Average copy number change after treatment (top row), patients with breakpoints acquired after treatment (1-Mb window; second row), patients with mutations acquired after treatment (100-kb window; third row) and patients with protein-altering mutations acquired after treatment (bottom row). In the bottom row, only genes with acquired mutations in two or more patients are shown. c, Average copy number (CN) change at the AR locus showing copy number increases that affect the enhancer and gene body. d, AR gene body and enhancer copy number in serial cfDNA samples from 21 patients. The estimated number of LBD mutant AR copies in each sample is indicated with coloured bar segments. Wild-type AR copies are shown in black. e, Clonal population dynamics (inferred from subclonal reconstruction) across treatment timelines. The x axis represents monotonically interpolated measurements of prostate-specific antigen (PSA) at four-week intervals, indicating changes in tumour burden. Subclone fractions are interpolated between cfDNA time points using exponential functions (see Supplementary Information, 'Visualization of tumour burden and subclone fractions over time'). Right, segmented AR locus copy number structures across consecutive cfDNA time points (gene body and enhancer are highlighted with colour).
CtDNA nucleosome architecture of mCRPC
a, Evidence of nucleosome occupancy footprints in plasma cfDNA. Right, method for quantifying gene expression from TSS nucleosome depletion. b, The magnitude of cfDNA nucleosome depletion mirrors mRNA expression from patient-matched metastatic tissue whole-transcriptome sequencing at both the gene TSS (left) and the first exon–intron junction (right). Nucleosome depletion is shown using orthogonal methods (WPS and relative read-depth depletion). c, Per-sample cfDNA TSS nucleosome depletion stratified by patient-matched tissue mRNA expression. d, Spearman correlation of TSS and first exon–intron junction nucleosome depletion (in cfDNA) versus tissue gene expression percentile in 13 patients with synchronous cfDNA–tissue samples. Each line is an individual sample. e, Spearman correlation of TSS nucleosome depletion (in cfDNA) versus tissue gene expression percentile in 61 cfDNA samples including those without matched tissue (leveraging publicly available mCRPC tissue transcriptome data). Results for all genes after excluding housekeeping and haematopoietic lineage genes are shown in different colours. f, Differential first exon–intron junction read-depth depletion by first exon length (long exons represent top 25% of all RefSeq MANE first exons; short exons represent bottom 25%) across n = 61 cfDNA samples. g, Gene RNA expression correlates with gene body coverage depletion in cfDNA (n = 61) but not tissue (n = 16). h, Per-sample (row) cfDNA read-depth depletion at 3,224 ARBSs ordered by magnitude of nucleosome depletion. The AR copy number and presence of mutations are annotated on the left. Negative controls are shown at the bottom; ctDNA-positive mCRPC samples are shown at the top. i, Clonal population shifts in patient AE-180 across sequential progressions on enzalutamide and abiraterone. Population phylogeny shown on the right. j, Temporal decrease in AR signalling detected from average read-depth depletion around ARBS. The inset plot shows the temporal dynamics of lactate dehydrogenase (LDH; associated with liver metastases) and alkaline phosphatase (ALP) concentrations per upper-limit of normal (ULN), indicating changes in disease composition.
Article
Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring1. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete2–12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery. Deep whole-genome sequencing of serial blood samples and matched metastatic tissue reveals that circulating tumour DNA profiling enables detailed study of treatment-driven subclone dynamics, epigenomics and genome-wide somatic evolution in metastatic human cancers.
 
Fish-like motion activates a conserved social behaviour network
a, Schematic of stimulus presentation for activity mapping. b, Attraction towards stimuli shown in a. n = 17 (no stimulus) or n = 9 (continuous; bout-like) single animals; and n = 8 animals tested in 4 pairs (conspecific). Data are mean (black dots) ± 1 s.d. Exact P values were calculated using two-tailed t-tests compared with the no-stimulus group: P = 0.16 (continuous); P = 5.2 × 10⁻⁸ (bout-like); P = 8.1 × 10⁻¹¹ (conspecific). Bonferroni-corrected α values: NS, P > 0.05/3 (NS); ***P < 0.001/3. c, Representative slices of maximum-intensity-normalized c-fos signal merged across all 28 registered animals. The views are horizontal (top row), sagittal (bottom left) and coronal (bottom right). The solid grey lines indicate the corresponding planes across the slices. The dashed line indicates the midline. Coloured patches indicate activity clusters (Extended Data Fig. 1). A, anterior; D, dorsal; L, lateral. d, The average normalized c-fos signal at the three representative horizontal planes indicated in c. n = 6 (no stimulus), n = 8 (continuous), n = 6 (bout-like) and n = 8 (conspecific) animals. e, The effect size (Cohen’s d) of normalized bulk c-fos induction compared with the no-stimulus condition. Negative values indicate a lower signal compared with the no-stimulus condition. The dendrogram represents hierarchical clustering. Statistical analysis was performed using two-tailed t-tests in each activity cluster versus the no-stimulus group. *P < 0.05/3, **P < 0.01/3, ***P < 0.001/3 (α values were Bonferroni-corrected per activity cluster). Animal numbers are the same as in d. Additional statistical information is provided as Source Data. Scale bars, 200 µm. DT, dorsal thalamus; En, entopeduncular nucleus; Hc1, caudal hypothalamus 1; Hc2, caudal hypothalamus 2; Hc3, caudal hypothalamus 3; Hi1, intermediate hypothalamus 1; Hi2, intermediate hypothalamus 2; Hi3, intermediate hypothalamus 3; Hrl, rostral hypothalamus, lateral; mHr, rostral hypothalamus, medial; MOd, medulla oblongata, dorsal; MOi, medulla oblongata, intermediate; nMLF, nucleus of the medial longitudinal fasciculus; OB, olfactory bulb; P, pallium; Pl, pallium, lateral; PM, magnocellular preoptic nucleus; Pn, pineal; PPa, anterior parvocellular preoptic nucleus; PPp, posterior parvocellular preoptic nucleus; Pr, pretectum; PT, posterior tuberculum; Ri, inferior raphe; SPd, subpallium, dorsal; SPv, subpallium, ventral; TeOa, tectum, anterior; TeOd, tectum, dorsal; TeOv, tectum, ventral; Tg, lateral tegmentum; TS, torus semicircularis; VT, ventral thalamus.
Source data
Dorsal thalamus neurons are activated by fish-like motion
a, Schematic of the experimental set-up. b, Example imaging planes in the TeO and DT with all segmented neuronal ROIs, representative for n = 11 animals (left). Right, representative normalized ∆F/F traces of one tectal and one thalamic neuron. c, Horizontal view of all responsive neurons (n = 28,306 total, 2,573 ± 1,175 per fish) from 11 fish (18–22 d.p.f.) aligned to a juvenile reference brain (left). Colour indicates mean baseline ∆F/F (no stimulus), and responses to continuous and bout-like motion. The yellow line indicates the DT. Right, mean responses of all DT neurons per fish (n = 258 ± 198, 2,837 total) from n = 6 animals with a number of recorded DT-BPNs of >30. Data are mean ± 1 s.d. d, Mean ∆F/F responses of example neurons from b to all stimulus frequencies. e, The distribution of all responsive neurons from n = 11 fish in the reference brain. The colour map shows the BPI. Opacity scales with absolute BPI (0–0.5). f, The distribution of BPNs (312 ± 143 neurons per fish, 3,437 total). Colour reflects a Gaussian KDE; contours delineate densities of 0.1, 0.15 and 0.3 BPNs per 1,000 μm³. n = 11 fish. g, DT-BPN tuning to stimulus frequency. The mean peak across neurons was 1.2 Hz  ± 1.6 Hz. n = 563 neurons. The black lines represent the mean values of individual animals. Data are from a subset of animals in e with a number of recorded DT-BPNs of >30. n = 6 animals. h, DT-BPN tuning to average speed at 1.5 Hz or 60 Hz and acceleration. The cartoons show stimulus displacement over time. Data are mean ± 1 s.d. of all of the neurons shown above. n = 291 neurons. The black lines indicate individual animals. n = 4 fish, 73 ± 10 neurons per fish. i, DT-BPN and PreT responses to local dot motion and whole-field motion and their anatomical distribution. Circles (left) show the mean of individual animals. n = 4 fish, 77 ± 15 (DT-BPNs), 114 ± 48 (PreT) neurons per fish. Data are mean ± 1 s.d. j, The distribution of BPNs in 7 d.p.f. larvae (n = 4 fish, 230 ± 87 neurons per fish) as in f. Scale bars, 100 μm (b and i) and 200 μm (c, e, f and j).
Source data
Connectivity of the larval thalamic bout-preference region
a, Frontal view of an EM reconstruction of neurons in the bout-preference region (BPN KDE, red) of the DT. Axons are shown in blue. b, Top view of the neurons shown in a. c, Magnified view of the thalamic arborization field, outlined in b. Synapses with identified presynaptic partners are shown as blue spheres. One representative synapse of a tectal PVPN axon (white arrow) onto a putative BPN’s dendrite (pBPN, black arrow) is indicated below (randomly chosen). d, Frontal view of tectal PVPNs (green) and their postsynaptic pBPN partners (red). e, Example of a single PVPN (green), which makes ipsilateral synaptic contacts to at least four identified pBPNs (red). f, Side view of the left (top) and the right (bottom) tectal SFGS layers, showing the PVPNs (green) and their presynaptic retinal ganglion cell axons (different colours). PVPN axons are not shown for clarity. g, Side view of the pBPNs (red, axons in blue) and their axonal target regions (Supplementary Video 2). AF4 (yellow) is shown as a reference. h, Circuit diagram. Identified cell types are indicated next to arrows with cell numbers in parentheses. For c, scale bar, 0.5 µm.
The TeO–DT circuit is necessary for social attraction
a, Schematic of the shoaling test after chemogenetic ablation. ctr., control. b, Two-photon image of 21 d.p.f. SAGFF(lf)81c:Gal4, UAS:NTR-mCherry and elavl3:H2B-GCaMP6s animals 24 h after ablation versus control treatment. Representative of three similar fish. c,d, Reduced neighbour density (c) and attraction (d) in 81c:NTR-ablated animals. Short-range repulsion is intact. n = 13 (ablated) and n = 15 (control) animals. The neighbour maps in c show the probability of finding the stimulus in space with the animal at the centre of the map, heading up. Each map is 60 mm × 60 mm. AU, arbitrary units. e, Schematic of volumetric two-photon imaging in the DT after 81c:NTR ablation. f, Mean and example ΔF traces of all DT-BPNs for ablated and control animals show that 81c:NTR ablation strongly reduces responses to bout-like motion. The vertical lines mark the start of stimulus presentation. The shaded areas denote 1 s.d. around the mean. n = 25 (ablated) and n = 849 (control). g,h, Fewer DT-BPNs in 81c:NTR (-ablated) animals. g, The anatomical location of all DT-BPNs across animals coloured by mean ΔF/F to bout-like motion. n = 25 (ablated) and n = 849 (control). The yellow area shows the DT. h, Quantification of DT-BPNs per animal. n = 7 (ablated) and 9 (control) animals. Cohen’s d effect size is shown. The P value was calculated using the two-sided Mann–Whitney U-test. i, Attraction is strongly reduced in s1026tEt:NTR-ablated animals. Short-range repulsion is intact. n = 7 (ablated), n = 21 (control) animals. j, Bilateral two-photon laser ablation of neurons in the DT-BPN region in juvenile zebrafish. k, Reduced attraction after DT laser ablation. n = 7 (ablated) and n = 9 (embedding control) animals. Short-range repulsion is intact. Data in d, h, i and k represent individual animals and mean ± 1 s.d. Cohen’s d effect size is shown. The P values were calculated using two-tailed Student’s t-tests with no correction. For b, e, g and j, scale bars, 100 µm.
Source data.
Article
Social affiliation emerges from individual-level behavioural rules that are driven by conspecific signals1–5. Long-distance attraction and short-distance repulsion, for example, are rules that jointly set a preferred interanimal distance in swarms6–8. However, little is known about their perceptual mechanisms and executive neural circuits3. Here we trace the neuronal response to self-like biological motion9,10, a visual trigger for affiliation in developing zebrafish2,11. Unbiased activity mapping and targeted volumetric two-photon calcium imaging revealed 21 activity hotspots distributed throughout the brain as well as clustered biological-motion-tuned neurons in a multimodal, socially activated nucleus of the dorsal thalamus. Individual dorsal thalamus neurons encode local acceleration of visual stimuli mimicking typical fish kinetics but are insensitive to global or continuous motion. Electron microscopic reconstruction of dorsal thalamus neurons revealed synaptic input from the optic tectum and projections into hypothalamic areas with conserved social function12–14. Ablation of the optic tectum or dorsal thalamus selectively disrupted social attraction without affecting short-distance repulsion. This tectothalamic pathway thus serves visual recognition of conspecifics, and dissociates neuronal control of attraction from repulsion during social affiliation, revealing a circuit underpinning collective behaviour. A tectothalamic pathway for social affiliation in developing zebrafish dissociates neuronal control of attraction from repulsion during affiliation, revealing a circuit underpinning of collective behaviour
 
The hippocampus CA1 encodes conjunctive representations of context
a, Head-fixed virtual reality setup with training paradigm (top). Training comprises 20 s multisensory experience with context-specific reinforcement. Bottom left, lick rates in reward, neutral and aversive context across reinforced and probe trials. n = 12 mice, 24 sessions; **P < 0.01, ***P < 0.005, ****P < 0.0001, two-way ANOVA with Sidak’s post hoc comparison. Bottom right, moving average lick rates across probe trials (thin lines) from one mouse aligned to context entry. Thick line shows session average. b, Top, retrieval paradigm. Left, lick rate on full (AVOT) or partial features (O, T, AT, AO, OT and AOT) of the respective contexts for three mice. Data are mean ± s.e.m.; *P < 0.05, **P < 0.01, ***P < 0.005, two-way ANOVA with multiple comparisons. Right, moving average lick responses from representative mouse. Reinf., reinforcement. c, Histology (scale bar, 500 μm) and z-projection images of two-photon recording (mean over time; scale bars, 160 μm) of GCaMP expressing dorsal CA1 neurons with GRIN implants capturing same field of view across training and retrieval. d,e, Activity of a reward (d) and aversive (e) context-selective neuron in reward (red) and aversive (black) probe trials (mean response in opaque line) with heat map showing individual trial responses. f, Representative neurons exhibiting conjunctive representation of reward (right) or aversive (left) contexts showing significant responses to all features of only one context. g, Fraction of context-selective (in training), feature-selective or conjunctive neurons (in retrieval). n = 3 mice, 5 sessions for training, 9 sessions in retrieval; **P = 0.003; paired t-test during retrieval. h, Significant divergence on retrieval trials of reward (R, red) versus aversive (F, black) feature (AVOT, AT, OT, AOT) population trajectories from a representative mouse. i, Performance of a linear SVM to classify context, trained on three features and tested on a held-out feature of the same context, demonstrating shared underlying dynamics for all features of a given context. n = 3 mice, 9 sessions; data are mean ± s.e.m. j, Schematic illustrating the question of where features are represented and how they access CA1 conjunctive representations. Details of statistical analyses are shown in Supplementary Table 1.
Source data.
AC, but not LEC, is necessary for feature-based memory recall
a, RetroAAV-tdT injection in dorsal hippocampus labels afferents from AC, LEC, claustrum, medial septum, anterior thalamus, hypothalamus, contralateral hippocampus and amygdala (denoted with asterisks) Scale bar, 3,000 µm; further detail in Extended Data Fig. 5a. b,c, AC or LEC inhibition with simultaneous CA1 imaging (b) shows robust inhibition of some neurons (top) but not others (bottom) (c). The shaded area represents light delivery. d, There is no significant difference between fraction of CA1 neurons inhibited during LEC inhibition (n = 4 mice, 7 sessions) versus AC inhibition (n = 3 mice, 6 sessions) (top), but CA1 context neurons are preferentially inhibited during AC inhibition (bottom) versus LEC (Student’s t-test, **P = 0.001) or compared to chance (dashed line). *P = 0.015, Wilcoxon signed-rank test. Boxes show mean and quartiles and whiskers extend to minimum and maximum. e,f, Left, rasters of binarized activity from 50 neurons in CA1 with inhibition of LEC (e) or AC (f) in reward and aversive feature trials grouped by context-responsive versus non-context-responsive neurons from one mouse. Right, activity of neurons. A significant left shift (*P < 0.05, Wilcoxon signed-rank test) is observed on inhibited trials across both groups during LEC inhibition but only in context-specific neurons during AC inhibition (**P < 0.01). g, Inhibition of context versus non-context neuron activity across all trial types (AVOT, AOT and OT) combined for aversive and reward trials. n = 3 mice, 6 sessions for LEC (top); n = 4 mice, 7 sessions for AC (bottom); each session is an individual data point. F(1,36) = 38.92, P < 0.0001 for AC; F(1,30) = 2.801, P = 0.11 for LEC, two-way ANOVA with Sidak’s post hoc comparison. h,i, Bilateral optogenetic inhibition of LEC (h) or AC (i) during memory retrieval showing behavioural performance during inhibited (light-on) versus control (light-off) trials for each trial type, in opsin (GtACR) versus control (mCherry) cohorts. Data points represent individual mice. F(1,17) = 43.79, P < 0.0001 for AC-GtACR; F(1,16) = 0.82, P = 0.37 for LEC GtACR, two-way ANOVA with Sidak’s post hoc comparison. Details of statistical analyses are shown in Supplementary Table 1.
Source data.
Population codes for feature representation in AC
a, Schematic and z-projection images of two-photon recording in AC. Scale bars, 160 µm. b, Reward (top) and aversive (bottom) context-selective neurons, sorted by onset time, displayed for reward (left) and aversive (right) probe trials from one mouse. c, Fraction of context-selective (in training), feature-selective or conjunctive (in retrieval) neurons in AC. n = 7 mice, 9 sessions in training, 11 sessions in retrieval; paired t-test in retrieval, **P = 0.002. d, Comparison of conjunctive and feature responses in AC and CA1. n = 3 mice, 9 sessions for CA1; n = 7 mice, 11 sessions for AC; ****P < 0.0001, ***P = 0.0005, two-way ANOVA with Sidak’s post hoc comparison. e, Representative recordings from four neurons, each in a different colour, exhibiting feature selectivity in AC and conjunctive representation in CA1 during retrieval. f,g, dF/F activity of feature-responsive neurons to all other feature presentations in AC (left) and CA1 (right). n = 3 mice each; data are mean ± s.e.m (f) or heat map (g), with the adjacent column indicating whether the neuron was statistically classified as feature-selective (white) or not (black). h, dF/F response of a feature-selective ensemble relative to other features of the same context (dark) versus the opposite context (light) in AC and CA1. n = 7 mice, 11 sessions for AC; n = 3 mice, 9 sessions for CA1; ****q < 0.0001, *q = 0.027, multiple paired t-test. Further details in Extended Data Fig. 9c. i, Ratio of context-to-feature separation across a maximally separating hyperplane in state space on retrieval trials. n = 7 mice, 11 sessions for AC; n = 3 mice, 9 sessions for CA1; data are mean ± s.e.m. j, Top, timeline of TRAP2 paradigm to inhibit feature-coding neurons. Bottom, percentage of AC neurons that expressed GtACR in the TRAP habituation and TRAP feature-coding cohort. k, Left, TRAP mice display deficits in feature-based recall on R6, which is rescued during light-off on R7 (F(1,25) = 64.39, P < 0.0001). Right, there is no behavioural deficit in the TRAP habituation cohort (F(1,20) = 0.14, P = 0.71; two-way ANOVA with Sidak’s post hoc comparison). Details of statistical analyses are shown in Supplementary Table 1.
Source data.
Dynamic interactions between feature representations in AC and contextual representations in CA1 representation facilitate memory recall
a,b, Side (left) and top view (right, adapted from Allen Institute Repository: 3D Brain Explorer beta (https://connectivity.brain-map.org/3d-viewer?v=1)) of GRIN lens implantation to access CA1 and AC simultaneously (a), with z-projection images (mean over time) from one mouse (b). Scale bar, 160 μm. c, Left, proportion of context-selective neurons responding to context onset during training as a function of latency for a representative mouse, as determined by cumulative distribution function. P < 0.0001, Kolmogorov–Smirnov test. Middle, as left, but for retrieval, with feature ensembles in AC and context ensembles in CA1 (P = 0.001). Right, difference of mean onset time of AC and CA1 during training and retrieval determined by fitting onset curve to an exponential function. n = 3 mice. Data are mean ± s.d. d, Interaction between AC feature and CA1 context ensembles (left), with dF/F correlation between feature ensembles in AC (all feature types grouped) with context ensembles in CA1 across neutral (blue) and aversive (black) feature trials (right). Data are mean ± s.e.m; n = 3 mice, 5 retrieval sessions, 3 features each; Kruskal–Wallis one-way ANOVA with Dunn’s post hoc test. e, Highly connected AC–CA1 neurons (long-range partners, left) and their correlations with feature neurons in AC (all feature types grouped, P = 0.055) and context neurons in CA1. n = 3 mice, 5 sessions; **P = 0.009, Student’s paired t-test (boxes show mean and quartile). f, Left, average activity of AC shock-responsive neurons on trial 1 and trial 5 for a representative mouse. Shaded area indicates s.e.m., black dotted line shows context onset and red dotted line shows first shock delivery. Middle, mean dF/F during first 10 s in context, normalized to ITI, across all mice. n = 3 mice; data are mean ± s.e.m. Right, proportion of active shock-responsive neurons as a function of time in context in trials 1 and 5. n = 3 mice. g, As f, but for CA1. h, Schematic of a working model in which LEC inputs to CA1 are a dedicated storage circuit and ACC inputs to CA1 are a dedicated retrieval circuit. Details of statistical analyses are shown in Supplementary Table 1.
Source data.
Article
Memory formation involves binding of contextual features into a unitary representation1–4, whereas memory recall can occur using partial combinations of these contextual features. The neural basis underlying the relationship between a contextual memory and its constituent features is not well understood; in particular, where features are represented in the brain and how they drive recall. Here, to gain insight into this question, we developed a behavioural task in which mice use features to recall an associated contextual memory. We performed longitudinal imaging in hippocampus as mice performed this task and identified robust representations of global context but not of individual features. To identify putative brain regions that provide feature inputs to hippocampus, we inhibited cortical afferents while imaging hippocampus during behaviour. We found that whereas inhibition of entorhinal cortex led to broad silencing of hippocampus, inhibition of prefrontal anterior cingulate led to a highly specific silencing of context neurons and deficits in feature-based recall. We next developed a preparation for simultaneous imaging of anterior cingulate and hippocampus during behaviour, which revealed robust population-level representation of features in anterior cingulate, that lag hippocampus context representations during training but dynamically reorganize to lead and target recruitment of context ensembles in hippocampus during recall. Together, we provide the first mechanistic insights into where contextual features are represented in the brain, how they emerge, and how they access long-range episodic representations to drive memory recall. Longitudinal imaging and functional perturbations during behaviour identified a brain region that represents constituent features of a contextual memory and enables feature-mediated memory recall.
 
Sequential genome editing with DNA Typewriter
a, Schematic of two successive editing events at the type guide, which shifts in position with each editing event. The DNA Tape consists of a tandem array of CRISPR–Cas9 target sites (grey boxes), all but the first of which are truncated at their 5′ ends and therefore inactive. The 5-bp insertion includes a 2-bp pegRNA-specific barcode as well as a 3-bp key that activates the next monomer. Because genome editing is sequential in this scheme, the temporal order of recorded events can simply be read out by their physical order along the array. b, Schematic of prime editing with DNA Typewriter. Prime editing recognizes a CRISPR–Cas9 target and modifies it with the edit specified by the pegRNA². With DNA Typewriter, an insertional editing event generates a new prime editing target at the subsequent monomer. c, Schematic of ordered recording via DNA Typewriter. Individual pegRNAs are potentially event driven³⁶ or constitutively expressed, together with the PE2 enzyme. d–f, Specificity of genome editing on versions of TAPE-1 with two (d), three (e) or five (f) monomers. Cells bearing stably integrated TAPE-1 target arrays were transfected with a pool of plasmids expressing pegRNAs and PE2. Each class of outcomes is inclusive of all possible NNGGA insertions; collectively, the classes shown include 2ⁿ – 1 possible outcomes, where n is the number of monomers. We observe that editing of any given target site is highly dependent on the preceding sites in the array having already been edited. g, Edit scores of 16 barcodes used in the experiment with 5×TAPE-1. Edit scores for each insertion are calculated as the log2-scaled ratio between the insertion frequencies and the abundances of pegRNAs in the plasmid pool, averaged over n = 3 transfection replicates.
Transfection programmes for 16 sequential epochs
a, Schematic of five transfection programmes over 8 or 16 epochs. For programmes 1 and 2, pegRNAs with single barcodes were introduced in each epoch for 16 epochs.The specific orders aimed to maximize (programme 1) or minimize (programme 2) the edit distances between temporally adjacent transfections. For programme 3, pegRNAs with two different barcodes were introduced at a 1:1 ratio for 16 epochs, with one barcode always shared between adjacent epochs (and between epochs 1 and 16). For programmes 4 and 5, pegRNAs with two different barcodes were introduced either at a constant ratio (1:3) or at varying ratios in each epoch (1:1, 1:2, 1:4 or 1:8) for eight epochs, respectively. b, Barcode frequencies across five insertion sites in 5×TAPE-1 in programmes 1 and 2 following epoch 16. Barcodes introduced in early epochs are more frequently observed at the first site. c–g, Bigram transition matrices for programmes 1 (c), 2 (d), 3 (e), 4 (f) and 5 (g). Barcodes are ordered from early (left/top) to late (right/bottom). h, Calculated versus intended relative frequencies between programmes 4 and 5. Programme ratios were calculated by combining sequencing reads from n = 3 independent transfection experiments.
Recording and decoding short digital text messages with DNA Typewriter
a, Base64 binary-to-text was modified to assign 64 NNNGGA barcodes for TAPE-1 to 64 text characters. b, Illustration of the encoding strategy for “WHAT HATH GOD WROUGHT?”, which has 22 characters including whitespaces. The message is grouped into sets of four characters, converted to NNN barcodes according to the TAPE64 encoding table, and plasmids corresponding to each set are mixed at a ratio of 7:5:3:1 for transfection. To encode 22 characters, we sequentially transfected 5 sets of 4 characters and 1 set of 2 characters 3 days apart into PE2(+) 5×TAPE-1(+) HEK293T cells. c–e, Decoding of three messages based on sequencing of the following 5×TAPE-1 arrays: “WHAT HATH GOD WROUGHT?” (c), “MR. WATSON, COME HERE!” (d) and “BOUND FOREVER, DNA” (e). For each message, the full set of NNNGGA insertions was first identified and cotransfected sets of characters were then identified from the bigram transition matrix (left). Within each set of characters inferred to have been cotransfected, ordering was based on corrected unigram counts (middle), resulting in the final decoded message (right). Misordered characters within each recovered message are coloured purple, missing characters are coloured red with strikethrough, and unintended characters are coloured light blue. Both two-dimensional histogram and corrected read counts were calculated by combining sequencing reads over n = 3 independent transfection experiments. Read counts were corrected using the edit score for each insertion barcode.
Reconstruction of a monophyletic cell lineage tree using DNA Typewriter and scRNA-seq
a, Schematic of the lentiviral vector used in the DNA Typewriter-based lineage tracing experiment³⁸. The integration cassette includes a 5×TAPE-1 sequence associated with an 8-bp random barcode (TargetBC) and a pegRNA expression cassette. The pegRNA targets TAPE-1 and inserts 6 bp, in which the first 3 bp is the random barcode (InsertBC) and the last 3 bp is the key sequence of GGA for TAPE-1. Each TargetBC-5×TAPE-1 array is embedded in the 3′ UTR of the eGFP gene with an RNA capture sequence at its 3′ end and transcribed from the eEF1α promoter. b, Schematic of the monophyletic lineage tracing experiment. A HEK293T line with Dox-inducible PE2 expression was transfected with the lentiviral construct shown in a at a high MOI. A monoclonal line was then established and expanded in the presence of Dox. During expansion, pegRNAs expressed by TargetBC-defined integrants compete to mediate insertions at the type guides of TAPE-1 arrays within the same cell. c, Cumulative editing of each site within TAPE-1. Each coloured line shows the cumulative editing rate for 1 of 13 TargetBCs. Grey bars denote the cumulative editing of TAPE-1 sites across all 13 independent TargetBCs within the n = 1 single-cell experiment. d, Histogram of the number of edits across 59 editable sites in each cell. The red dashed line denotes the average. e, Histogram of the number of differences across the 59 editable sites for all possible pairs of the 3,257 sampled cells. The red dashed line denotes the average. f, Distribution of the number of pairwise differences between each cell and its ‘nearest neighbour’ among the 3,257 sampled cells.
Reconstruction of a monophyletic cell lineage tree using DNA Typewriter
a, A monophyletic lineage tree of the 3,257 cells with all 13 TargetBC Tape arrays recovered. The UPGMA clustering method was used to construct the tree from a distance matrix that takes into account the order of edits within the TAPE-1 arrays, by discounting matches for which earlier sites along the same DNA Tape were not also identically edited. b, A lineage tree constructed by order-aware UPGMA for a subset of 32 cells drawn from the larger tree, specifically the two 16-cell clades marked with light blue in the circular tree. Numbers next to branching points denote bootstrap values out of 100 resamplings. The 59 sites of the 13 TargetBC-associated Tape arrays are represented to the right, with InsertBCs coloured by edit identity. Cells are identified by the 16-bp CellBCs (10x Chromium v3 chemistry) listed on the far right. A higher-resolution version of the entire tree of 3,257 cells in the same format is provided in Supplementary Fig. 1.
Article
DNA is naturally well suited to serve as a digital medium for in vivo molecular recording. However, contemporary DNA-based memory devices are constrained in terms of the number of distinct ‘symbols’ that can be concurrently recorded and/or by a failure to capture the order in which events occur¹. Here we describe DNA Typewriter, a general system for in vivo molecular recording that overcomes these and other limitations. For DNA Typewriter, the blank recording medium (‘DNA Tape’) consists of a tandem array of partial CRISPR–Cas9 target sites, with all but the first site truncated at their 5′ ends and therefore inactive. Short insertional edits serve as symbols that record the identity of the prime editing guide RNA² mediating the edit while also shifting the position of the ‘type guide’ by one unit along the DNA Tape, that is, sequential genome editing. In this proof of concept of DNA Typewriter, we demonstrate recording and decoding of thousands of symbols, complex event histories and short text messages; evaluate the performance of dozens of orthogonal tapes; and construct ‘long tape’ potentially capable of recording as many as 20 serial events. Finally, we leverage DNA Typewriter in conjunction with single-cell RNA-seq to reconstruct a monophyletic lineage of 3,257 cells and find that the Poisson-like accumulation of sequential edits to multicopy DNA tape can be maintained across at least 20 generations and 25 days of in vitro clonal expansion.
 
Multiple ecdysteroids are produced in MAGs and transferred to the female LRT during mating
MAGs and female LRTs (encompassing the atrium, spermatheca and parovarium) were dissected from 4-day-old (4 d) virgin males and from virgin and mated females (at 0.5, 3 and 12 h.p.m.). Ecdysteroids in these tissues were analysed by HPLC–MS/MS (mean ± s.e.m.; unpaired t-test, two-sided, false discovery rate (FDR)-corrected; NS, not significant; *P < 0.05, **P < 0.01. 3D20E: 3 h versus 0.5 h, P = 0.035; 12 h versus 3 h, P = 0.0015; 12 h versus 0.5 h, P = 0.030. 3D20E22P: 3 h versus 0.5 h, P = 0.25; 12 h versus 3 h, P = 0.0032; 12 h versus 0.5 h, P = 0.015). Data were pooled from three biological replicates. Peak area was calculated for each ecdysteroid of interest and normalized by mosquito numbers. Ecdysteroids are indicated by colour as follows: E, green; 20E, orange; 20E22P, purple; 3D20E, blue; 3D20E22P, pink. Insets increase the scale on the y-axis to show lower ecdysteroid levels.
Source Data
Identification of two EcK genes and one EPP gene in mosquito reproductive tissues
a, The custom-built bioinformatic pipeline used to search for genes encoding EcKs, EOs and EPPs in the reproductive tissues of each sex. Numbers next to arrows indicate the number of male and female candidates at each step. This analysis identified one EPP gene (EPP) and one EcK gene (EcK1) expressed in males, and one EcK gene (EcK2) expressed in both sexes but did not yield a candidate EO gene. b, Heat map comparing expression of candidate genes in tissues of virgin (V) and mated (M) A. gambiae and Anopheles albimanus. Spca, spermatheca; MAGs, male accessory glands; body, the rest of the body including the thorax, wings, legs, fatbody and guts in both sexes and ovaries in females. EcK2 was highly expressed in both the MAGs and atria of A. gambiae, whereas EPP was found only in the MAGs. c, Proteomics analysis of the male ejaculome transferred to the female atrium at 3, 12 and 24 h.p.m., showing the 67 most abundant proteins. Females were reared with food containing ¹⁵N to label (and mask) all proteins. Unlabelled males were mated with labelled females, and the female LRTs were dissected at 3, 12 and 24 h.p.m. for proteomic analysis (see Supplementary Table 1 for a complete list of ejaculome proteins). Inset, EPP, Eck1 and EcK2 detected in the MAGs of virgin males by proteomic analysis of these tissues. d, EPP detected by western blot in the MAGs and mated female LRTs but not in virgin females or in the rest of the body of either males or females. The membrane was probed simultaneously with anti-actin (loading control) and anti-EPP antibodies. All males were virgins. For gel source data, see Supplementary Fig. 1. The western blot was performed twice with similar results.
Source Data
Sexually transferred EPP regulates female remating rates and fertility
a, Decreased phosphatase activity in MAGs caused by EPP silencing using double-stranded EPP RNA (dsEPP) or double-stranded GFP RNA (dsGFP) control. A pool of 20 MAGs was used in each replicate (P = 0.0046, paired t-test, two-sided), indicated by separate dots. b, Females mated to EPP-silenced males have a significantly lower proportion of dephosphorylated 3D20E at 3 h.p.m. (P = 0.0043, unpaired t-test, two-sided), whereas 20E levels are unaffected (P = 0.063, unpaired t-test, two-sided). The data are presented as mean ± s.e.m., derived from three pools of 13, 16 and 19 females each. c, Females mated to EPP-silenced males have a significantly higher rate of remating (P = 0.0002, Fisher’s exact test, two-sided). The females were first force-mated to ensure their mated status; 2 days later, they were exposed to additional males carrying transgenic sperm to assess remating rates by quantitative PCR detection of the transgene. d, Blood-fed females mated to EPP-silenced males have a significant decrease in fertility (P < 0.0001; Mann–Whitney test, two-sided) and a slight decrease in egg numbers (P = 0.088, Mann–Whitney test, two-sided), whereas the oviposition rate is not affected (P = 0.94, Fisher’s exact test, two-sided). In all panels, n indicates the number of biologically independent mosquito samples. NS, not significant. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.001.
Source Data
3D20E is more potent than 20E at inducing mating refractoriness and oviposition
(a,b) 3D20E chemically synthesized from 20E (a) with very high levels of conversion/efficiency (data presented as mean ± s.e.m., derived from three independent synthesis reactions) (b). c, Mass spectra (lower half) perfectly matched those of ecdysteroids found in the mated female LRT (upper half). d, Injection of 0.63 µg or 0.21 µg 3D20E induced significantly higher refractoriness to mating than 20E (0.63 µg, P = 0.02; 0.21 µg, P < 0.0001; Fisher’s exact tests, two-sided) and 10% ethanol controls (0.63 µg, P < 0.0001; 0.21 µg, P < 0.0001; Fisher’s exact tests, two-sided), whereas 20E was significantly higher than controls only at the higher dose (0.63 µg, P = 0.0002; 0.21 µg, P = 0.54; Fisher’s exact tests, two-sided). e, 3D20E injections induced significantly higher oviposition rates than 10% ethanol controls in virgin females (0.21 µg, P < 0.0001; 0.13 µg, P = 0.0003; Fisher’s exact tests, two-sided), whereas 20E was significant compared with controls only at the higher dose (0.21 µg, P = 0.022; 0.13 µg, P = 0.0823; Fisher’s exact tests, two-sided). 3D20E induced significantly higher oviposition rates than 20E at higher doses (0.21 µg, P = 0.0019; 0.13 µg, P = 0.075; Fisher’s exact tests, two-sided). In all panels, n indicates the number of biologically independent mosquito samples. NS, not significant. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.001. Data were pooled from three replicates.
Source Data
Article
Insects, unlike vertebrates, are widely believed to lack male-biased sex steroid hormones¹. In the malaria mosquito Anopheles gambiae, the ecdysteroid 20-hydroxyecdysone (20E) appears to have evolved to both control egg development when synthesized by females² and to induce mating refractoriness when sexually transferred by males³. Because egg development and mating are essential reproductive traits, understanding how Anopheles females integrate these hormonal signals can spur the design of new malaria control programs. Here we reveal that these reproductive functions are regulated by distinct sex steroids through a sophisticated network of ecdysteroid-activating/inactivating enzymes. We identify a male-specific oxidized ecdysteroid, 3-dehydro-20E (3D20E), which safeguards paternity by turning off female sexual receptivity following its sexual transfer and activation by dephosphorylation. Notably, 3D20E transfer also induces expression of a reproductive gene that preserves egg development during Plasmodium infection, ensuring fitness of infected females. Female-derived 20E does not trigger sexual refractoriness but instead licenses oviposition in mated individuals once a 20E-inhibiting kinase is repressed. Identifying this male-specific insect steroid hormone and its roles in regulating female sexual receptivity, fertility and interactions with Plasmodium parasites suggests the possibility for reducing the reproductive success of malaria-transmitting mosquitoes.
 
Women are less likely to be named authors on any given document in all fields and at all career stages
Graphs plot the probability that a potential author on a scientific document (article or patent) is a woman against the probability that an actual author is a woman. A potential author is defined as an employee in a laboratory between 2013 and 2016 from which an article or patent was published between 2014 and 2016. There are 17,929,271 potential article authorships and 3,203,831 potential patent inventorships in our sample. The markers in each panel are sized by the total number of actual authorships in the category. The diagonal represents parity in the gender composition of potential and actual authorships. Individual data on potential and actual authorships are shown in Supplementary Fig. 5. Left, disparity across job titles. Right, disparity across research fields. Observations are weighted by the inverse of the number of teams per employee times the inverse of the number of potential articles or patents per employee.
Women are still less likely to be named even when controls are included
Graphs show the probability that an individual in a team is an author on a given article (left) or patent (right) published by that team. Left, the likelihood of attribution on an article is estimated from 17,929,271 potential authorship observations. Right, the likelihood of attribution on a patent is estimated from 3,203,831 inventorship observations. The data associated with each bar are generated by predicting the dependent variable from ordinary least squares regressions of the likelihood of being named on gender and the indicated controls (reported in Extended Data Table 4). For the purpose of plotting probabilities and gender differences holding all else fixed (Δ), we hold all of the controls at their respective means. Because men have higher values than women on average on the controlled factors that increase the probability of attribution, the predicted probabilities for men decline and those for women increase as more controls are included. Controls, from left to right: (1) none; (2) whether a potential author is the PI of the team, the number of days worked on the team and publication date (calendar year × month); (3) job title of the potential author/inventor; (4) research field of the team; (5) individual indicator variables for each team (these team indicators subsume the fields indicator). The observations are weighted by the inverse of the number of teams per employee times the inverse of the number of potential articles or patents per employee. Individual data on the probability of women or men being named on articles or patents are visualized in Supplementary Fig. 6. Error bars are centred on the mean and extend to the 95% confidence interval based on 1.96 × s.e. Standard errors are clustered by team and employee.
Women are much less likely to be named on high-impact articles
The probability that an individual in a team is an author on an article (left) or inventor on a patent (right) in relation to the number of citations that the document receives. Estimates were obtained from an ordinary least squares regression of the probability of being named with an indicator for gender against the log of total forward citations plus one (Extended Data Table 7). Left, the regression is estimated based on 17,929,271 potential article authorships. Right, the regression is estimated based on 3,203,831 potential patent inventorships. The observations are weighted by the inverse of the number of teams per employee times the inverse of the number of potential articles or patents per employee. Estimates include controls for publication date (calendar year × month), PI status, number of days worked on the team, job title and research team fixed effects. Each data point represents the estimated difference in the probability of a woman being named an author (left) or inventor (right) at each citation level. Error bars extend from the point estimate of the estimated marginal effect by ±1.96 × the standard error and show the 95% confidence interval of the marginal effect. Standard errors are clustered by team and employee.
Women are more likely to report that their contributions were underestimated or that there was discrimination
A survey was sent to 28,000 scientists who had published in an academic journal listed in the Web of Science and who listed themselves with a public profile on the ORCID database. The bar chart shows the percentage of 871 men and women who provided answers to the survey question (Q2b): ‘What is the most likely reason that you were not listed as an author on that paper?’. Respondents were able to select more than one option, thus the total number of responses is higher than the number of respondents. The probability is computed as the arithmetic mean of the binary responses. Individual data on the reason an individual is not named are visualized in Supplementary Fig. 7. Error bars are centred on that mean and extend to the 95% confidence interval based on 1.96 × s.e.m. The difference in the probability of selecting ‘Contribution did not justify authorship’ between men and women is 0.1294 (P = 0.0000; two-sided t-test; test value = 4.1060). The difference in the probability of selecting ‘Others underestimated my contributions’ between men and women is −0.0984 (P = 0.0036; two-sided t-test; test value = −2.9218). The difference in the probability of selecting ‘Discrimination/stereotyping/bias’ between men and women is −0.0780 (P = 0.0003; two-sided t-test; test value = −3.6623). Additional t-tests of the differences in the probability of indicating a reason across men and women can be found in the text.
Women report making more contributions than men on authored papers
We sent a survey to 28,000 scientists who had published in academic journals listed in the Web of Science and who had a public profile in the ORCID database. Of these, 2,297 responded and completed the question (Q1a): ‘How did you contribute to the paper? Check all that apply.’ The graph shows the percentage of these respondents who selected each category. Probability was computed as the arithmetic mean of binary indicators representing whether the respondent selected each category. Each respondent was asked about a paper associated with them on Web of Science. Respondents were able to select more than one option, thus the total number of responses is therefore higher than the number of respondents. Individual data on the contribution by gender are visualized in Supplementary Fig. 8. Error bars are centred on the mean and extend to the 95% confidence interval based on 1.96 × s.e.m.
Article
There is a well-documented gap in the observed number of scientific works produced by women and men in science, with clear consequences for the retention and promotion of women in science1. The gap might be a result of productivity differences2-5, or it might be due to women’s contributions not being acknowledged6,7. This paper finds that at least part of this gap is due to the latter: women in research teams are significantly less likely to be credited with authorship than are men. The findings are consistent across three very different sources of data. Analysis of the first source - large scale administrative data on research teams, team scientific output, and attribution of credit - show that women are significantly less likely to be named on any given article or patent produced by their team relative to their peers. The gender gap in attribution is found across almost all scientific fields and career stages. The second source – an extensive survey of authors – similarly shows that women’s scientific contributions are systematically less likely to be recognized. The third source – qualitative responses – suggests that the reason is that their work is often not known, not appreciated, or ignored. At least some of the observed gender gap in scientific output may not be due to differences in scientific contribution, but to differences in attribution.
 
Article
The heart, the first organ to develop, undergoes complex morphogenesis that when defective results in congenital heart disease (CHD). With current therapies, more than 90% of CHD patients survive into adulthood but often suffer premature death from heart failure (HF) and non-cardiac causes 1. To gain insight into poorly understood disease progression, we performed single nuclear RNA sequencing (snRNA-seq) and analyzed 157,273 nuclei from donors and CHD patients, including hypoplastic left heart syndrome (HLHS) and Tetralogy of Fallot (TOF), two common forms of cyanotic CHD lesions, as well as, dilated (DCM) and hypertrophic (HCM) cardiomyopathies. We observed CHD specific cell states in cardiomyocytes (CMs) which had evidence of insulin resistance and increased FOXO and CRIM1 expression. Cardiac fibroblasts (CFs) in HLHS had enrichment for a low HIPPO and high YAP cell state characteristic of activated CFs. Imaging Mass Cytometry (IMC) uncovered the spatially resolved perivascular microenvironment consistent with an immunodeficient state in CHD. Peripheral immune cell profiling suggested deficient monocytic immunity in CHD in agreement with CHD predilection to infection and cancer 2. Our comprehensive CHD phenotyping provides a roadmap for future personalized medicine in CHD.
 
Cellular composition in the LV from healthy donors and those with cardiomyopathy
a, Uniform manifold approximation and projection (UMAP) representation of 592,689 nuclei isolated from LVs of 42 donors. b, Dendrogram demonstrating the similarity of cluster centroids. c, Stacked bar plot depicting the cell-type composition of each sample (n = 80; 1 or 2 technical replicates per patient) with colour coding reflecting cell types in b. d, PCA of pseudo-bulk snRNA-seq of LV samples from 42 donors by disease status and sex. The per cent variance captured by each principal component is shown in parentheses on each respective axis. HCMrEF, HCM with reduced LVEF; HCMpEF, HCM with preserved LVEF.
Source data
Transcriptional differences between NF and cardiomyopathy LVs
a, Log fold change and two-sided P-value for expression changes between DCM (n = 11) and NF (n = 16) (left), HCM (n = 15) and NF (centre), and HCM and DCM (right) hearts for each gene tested using limma–voom differential expression analysis (Methods). Genes are coloured by cell type with larger, opaque dots representing genes with FDR < 0.01 based on the Benjamini–Hochberg procedure. b, The number of significantly differentially expressed genes (FDR < 0.01) by cell type for each disease comparison in a. c, log2 counts per million (CPM) across patients for genes with a significant disease–sex interaction in either DCM (n = 11) versus NF (n = 16) or HCM (n = 15) versus NF (n = 16) hearts using limma–voom differential expression analysis (FDR < 0.1 based on the Benjamini–Hochberg procedure). In box plots, the center line respresents the median, box limits show upper and lower quartiles and whiskers span 1.5× the interquartile range. d, Reactome pathway enrichment for differential expression between DCM versus NF (D) and HCM versus NF (H) by cell type. The size of each square represents a two-sided P-value from gene set enrichment analysis (GSEA) and shading represents the normalized enrichment score (NES). Only pathways with a Benjamini–Hochberg FDR < 0.05 in both the GSEA and hypergeometric test for over-representation in at least one cell type are shown (Methods). Pathways with FDR < 0.05 in the GSEA test are denoted with a black outline. Pint, two-sided P-value for interaction between cardiomyopathy and sex.
Source data
DCM- and HCM-specific activated fibroblast populations
a, The number of activated fibroblasts per disease state, coloured by donor. b, Expression profiles of activated fibroblast marker genes (n = 42 donors, 592,689 nuclei). Expression is presented as mean log-normalized expression. c, UMAP representation of all fibroblasts from two patients (P1304: n = 8,798 fibroblasts; P1425: n = 7,164 fibroblasts) with the largest activated fibroblast populations. Connectivity induced by the minimum spanning tree from Slingshot is overlaid. d, UMAP representation with overlaid Slingshot-inferred trajectories coloured by pseudo-time. The highlighted trajectory represents the most connected transition from quiescent to activated fibroblast. e, UMAP representation with inferred RNA velocity overlaid as a stream plot. f, Predicted expression of genes showing interesting patterns based on a negative binomial generalized additive model (NB-GAM) for each gene across pseudo-time in patient P1304 (left) and patient P1425 (right). Normalized expression is smoothed expression from NB-GAM scaled to the maximum value for each gene. g, Estimated fraction of activated fibroblasts from deconvolution analysis of bulk RNA-sequencing data for overlapping data from snRNA-seq (left) and non-overlapping data (right) from the MAGNet study. The number of individuals for each disease state is shown under the respective x-axis label. h, Deconvolution of bulk RNA-sequencing data from two external datasets displayed as in g. PPCM, peripartum cardiomyopathy; EC, Endothelial cell.
Source data
Cellular assay of myofibroblast transition in cardiac fibroblasts
a, A subset of genes from the activated fibroblast trajectory analysis (n = 27) were knocked out in cardiac fibroblasts on 384-well plates using 1–4 sgRNAs per gene, stimulated with TGFβ1, and assessed for changes in cellular phenotypes using high-content imaging. CM, cardiomyocyte. Parts of this figure were created with BioRender.com. b, Representative image of cardiac fibroblasts before (left) and after (right) TGFβ1 stimulation with NTC sgRNA. c, Bar plots representing the mean fraction of myofibroblasts after TGFβ1 stimulation across all wells for a given sgRNA (n = 1–4 wells, with exact number shown under each bar), coloured by gene. Data are mean ± s.e.m. Individual well values for each sgRNA are shown as dots. d, Representative images before and after TGFβ1 stimulation for three control genes and two target genes showing strong effects. Details on statistics and reproducibility are provided in Methods.
Source data
Article
Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function1,2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts3,4, but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFβ1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.
 
Strategies for site-selective glycosylation and catalyst optimization
a, Site-selectivity by protecting-group control versus by non-covalent catalyst control. b, Catalyst optimization for site-selective galactosylation (1) with β-3a and site-selective mannosylation (2) with β-3a and β-3b. Selectivities were determined by ¹H NMR analysis of crude unacylated product mixtures. R, generic substituent; LG, leaving group; PG, protecting group; TBDPS, tert-butyldiphenylsilyl; MS, molecular sieves.
Scope studies
a, Nucleophile scope of site-selective galactosylations. b, Nucleophile scope of site-selective mannosylations. High stereoselectivities (>20:1 β:α) were observed in every case. Selectivities were determined by ¹H NMR analysis of crude unacylated product mixtures. Yields of site-selective galactosylations reflect isolated yields of acylated (1,2)-product after the two-step galactosylation/acylation sequence. Yields of site-selective mannosylations reflect isolated yields of a mixture of unacylated (1,2)- and (1,3)-products. aReaction performed at 40 °C. bReaction performed at 23 °C. cReaction performed at 4 °C. d48 h reaction time. Additional substrates are provided in Supplementary Fig. 10. DMAP, 4-dimethylaminopyridine; BOM, benzyloxymethyl.
Linear-free-energy relationship study and catalyst optimization for galactosylation of α-3a
a, Correlation of experimental site-selectivity (ΔΔG‡ = −RTln(r.r.)) with calculated interaction energies between substituted catalyst arenes and galactose. b, Catalyst optimization for galactosylation of α-3a. Selectivities were determined by ¹H NMR analysis of crude unacylated product mixtures. aMTBE instead of isopropyl ether. Yields reflect the isolated yields of acylated (1,2)-product after the two-step galactosylation/acylation sequence. Counterpoise corrections were performed to correct for basis set superposition error and obtain corrected electronic energies. R, ideal gas constant; T, temperature; r.r., regioisomeric ratio; MTBE, methyl tert-butyl ether.
Kinetic and computational studies
a, Michaelis–Menten kinetic analyses of reactions catalysed by (1,2)-selective cat-6 and unselective cat-2. Error bars represent the difference between two replicates. b, Computed (1,2)-transition-state structure for galactosylation of β-galactose and differences in C–H/π contacts between (1,2)- and (1,3)-transition states. Selectivities were determined by ¹H NMR analysis of crude unacylated product mixtures. ArF, 3,5-bis(trifluoromethyl)phenyl.
Article
The identification of general and efficient methods for the construction of oligosaccharides stands as one of the great challenges for the field of synthetic chemistry1,2. Selective glycosylation of unprotected sugars and other polyhydroxylated nucleophiles is a particularly significant goal, requiring not only control over the stereochemistry of the forming bond but also differentiation between similarly reactive nucleophilic sites in stereochemically complex contexts3,4. Chemists have generally relied on multi-step protecting-group strategies to achieve site control in glycosylations, but practical inefficiencies arise directly from the application of such approaches5–7. We describe here a new strategy for small-molecule-catalyst-controlled, highly stereo- and site-selective glycosylations of unprotected or minimally protected mono- and disaccharides using precisely designed bis-thiourea small-molecule catalysts. Stereo- and site-selective galactosylations and mannosylations of a wide assortment of polyfunctional nucleophiles is thereby achieved. Kinetic and computational studies provide evidence that site selectivity arises from stabilizing C–H/π interactions between the catalyst and the nucleophile, analogous to those documented in sugar-binding proteins. This work demonstrates that highly selective glycosylation reactions can be achieved through control of stabilizing noncovalent interactions, a potentially general strategy for selective functionalization of carbohydrates.
 
Article
Glucose uptake is essential for cancer glycolysis and is involved in non-shivering thermogenesis of adipose tissues1–6. Most cancers use glycolysis to harness energy for their infinite growth, invasion and metastasis2,7,8. Activation of thermogenic metabolism in brown adipose tissue (BAT) by cold and drugs instigates blood glucose uptake in adipocytes4,5,9. However, the functional effects of the global metabolic changes associated with BAT activation on tumour growth are unclear. Here we show that exposure of tumour-bearing mice to cold conditions markedly inhibits the growth of various types of solid tumours, including clinically untreatable cancers such as pancreatic cancers. Mechanistically, cold-induced BAT activation substantially decreases blood glucose and impedes the glycolysis-based metabolism in cancer cells. The removal of BAT and feeding on a high-glucose diet under cold exposure restore tumour growth, and genetic deletion of Ucp1—the key mediator for BAT-thermogenesis—ablates the cold-triggered anticancer effect. In a pilot human study, mild cold exposure activates a substantial amount of BAT in both healthy humans and a patient with cancer with mitigated glucose uptake in the tumour tissue. These findings provide a previously undescribed concept and paradigm for cancer therapy that uses a simple and effective approach. We anticipate that cold exposure and activation of BAT through any other approach, such as drugs and devices either alone or in combination with other anticancer therapeutics, will provide a general approach for the effective treatment of various cancers.
 
Acetylation-regulated chromatin compaction prevents microtubule perforation in mitosis
a, The contribution of condensin and histone deacetylases to mitotic chromosome compaction and congression to the spindle centre. HeLa cells with homozygously mAID-tagged SMC4 were treated with 5-PhIAA to deplete condensin (ΔCondensin) or with TSA to suppress mitotic histone deacetylation as indicated. Live-cell images with microtubules stained by SiR–tubulin; DNA was stained with Hoechst 33342. Projection of 5 z-sections. b, Quantification of chromosome congression by the fraction of chromatin localizing to the central spindle region. n = 51 (control), n = 65 (ΔCondensin), n = 34 (ΔCondensin + TSA), n = 61 (TSA) cells. The bars indicate the mean. Significance was tested using two-tailed Mann–Whitney U-tests (P < 10⁻¹⁵ (ΔCondensin + TSA); P < 10⁻¹⁵ (TSA); precision limit of floating-point arithmetic). c, Quantification of chromatin density in cells treated as described in a. n = 31 (control), n = 89 (ΔCondensin), n = 99 (ΔCondensin + TSA) and n = 74 (TSA) cells. The bars indicate the mean. Significance was tested using two-tailed Mann–Whitney U-tests (P < 10⁻¹⁵ (ΔCondensin + TSA); P < 10⁻¹⁵ (TSA); precision limit of floating-point arithmetic). AU, arbitrary units. d, Electron tomography analysis of wild-type prometaphase HeLa cells in the absence or presence of TSA. Magenta, chromatin surfaces; green, microtubules in cytoplasm; cyan, microtubules in chromatin. The red circles show the perforation sites. e,f, Quantification of microtubule density in chromatin (e) and cytoplasmic (f) regions as shown in d. n = 10 tomograms from 7 cells for each condition. The bars indicate the mean. Significance was tested using two-tailed Mann–Whitney U-tests (P = 1.083 × 10⁻⁵ (e); P = 0.247 (f)). Biological replicates: n = 2 (a–f). Scale bars, 5 µm (a), 2 µm (d, 250 nm section); 200 nm (tomogram slices and 3D model).
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Acetylation regulates chromatin solubility in mitotic cytoplasm
a, Chromosome fragmentation in live mitotic HeLa cells by AluI injection (t = 0 min). Chromatin was visualized with H2B–mCherry. Projection of 3 z-sections. Time is shown as min:s. b, Quantification of chromatin density for cells as in a. n = 11 cells, 3 regions of interest (ROIs) each. The bars indicate the mean. Significance was tested using a two-tailed Mann–Whitney U-test (P = 0.332). c, Chromatin mobility in undigested metaphase chromosomes and after AluI injection, measured by fluorescence recovery after photobleaching in live metaphase cells expressing H2B–mCherry. The circles indicate the photobleaching region at t = 0 s. Time is shown as s. d, Quantification of fluorescence in n = 8 (undigested) or n = 10 (AluI-digested) cells as described in c. Data are mean ± s.d. e, AluI injection as described in a for a TSA-treated mitotic cell. Time is shown as min:s. f, Quantification of chromatin density, normalized to the mean of untreated pre-injection cells shown in b. n = 11 cells, 3 ROIs each. The bars indicate the mean. Significance was tested using a two-tailed Mann–Whitney U-test (P < 10⁻¹⁵; precision limit of floating-point arithmetic). g–i, Ki-67 localization in mitotic cells. g, HeLa cells expressing eGFP–Ki-67 and H2B–mCherry were treated with taxol for mitotic arrest (control); cells were treated with TSA or microinjected with AluI as indicated. Ki-67 localization was analysed in chromosomes oriented perpendicularly to the optical plane (insets). h, Line profiles across the chromatin–cytoplasm boundary as indicated by the yellow lines in g were aligned to the first peak in eGFP–Ki-67 fluorescence and normalized to the mean of Ki-67 fluorescence at the first peak of control. n = 19 (control), n = 24 (TSA) and n = 22 (AluI) cells. Data are mean ± s.d. i, Quantification of Ki-67 surface confinement by the ratio of Ki-67 fluorescence on the surface (S) over inside (I). n = 19 (control), n = 24 (TSA) and n = 22 (AluI) cells. The bars indicate the mean. Significance was tested using two-tailed Mann–Whitney U-tests (P = 9.305 × 10⁻¹⁰ (TSA); P = 0.476 (AluI)). Biological replicates: n = 3 (a,b,g–i); n = 2 (c–f). Scale bars, 5 µm (a, e and g, main images), 1 µm (a, e and g, insets) and 3 µm (c).
Source Data
Chromatin condensates limit access of tubulin and other negatively charged macromolecules
a, The localization of tubulin (tub.) relative to mitotic chromosomes. Rhodamine-labelled tubulin was injected into live mitotic cells that were untreated, treated with nocodazole alone (control) or in combination with TSA. b, Quantification of the tubulin concentration for the data shown in a. n = 27 cells. The bars indicate the mean. Significance was tested using a two-tailed Mann–Whitney U-test (P < 1 × 10⁻¹⁵; precision limit of floating-point arithmetic). c, Live-cell images of a HeLa cell expressing DsRed or DsRed fused at its N terminus to electrically charged polypeptides. DNA was stained with Hoechst 33342. The numbers in parentheses indicate the predicted elementary charge of the tetramers formed by DsRed fusion constructs. d, Quantification of DsRed concentration for the data shown in c. n = 26 (DsRed), n = 26 (DsRed(−7e)), n = 26 (DsRed(+9e)) cells. The bars indicate the mean. Significance was tested using two-tailed Mann–Whitney U-tests (P = 0.4 × 10⁻¹⁴ (DsRed(−7e)); P = 0.4 × 10⁻¹⁴ (DsRed(+9e)). e, The localization of tubulin relative to reconstituted nucleosome (nuc.) array droplets. Nucleosome array droplets were formed by incubation in phase separation buffer and fluorescently labelled tubulin was then added in the presence of nocodazole, or in the absence of nocodazole with subsequent temperature increase to 20 °C to induce microtubule polymerization. f, Quantification of the tubulin concentration or microtubule density in nucleosome array condensates relative to buffer for the data shown in e. n = 94 droplets, n = 13 fields of polymerized microtubules. The bars indicate the mean. Biological replicates: n = 2 (a–d); n = 3 (e,f). Technical replicates: n = 3 (a,b); n = 2 (c,d); n = 3 (e,f). For a, c and e, scale bars, 5 µm (main images) and 1 µm (insets).
Source Data
Microtubules push liquified chromatin away from the spindle pole
a, Time-lapse microscopy analysis of liquified chromatin during monopolar spindle assembly. AluI was injected into live mitotic HeLa cells expressing H2B–mCherry and meGFP–CENP-A, stained with SiR–tubulin, in the presence of nocodazole (noco) and STLC. Nocodazole was then removed at t = 0 min during time-lapse imaging to induce monopolar spindle assembly. Projection of 5 z-sections. Time is shown as min:s. b, Quantification of bulk chromatin (H2B–mCherry) and centromeric chromatin (meGFP–CENP-A) localizing at the cell periphery relative to the region around the spindle monopole at t = 36 min. n = 15 cells. The bars indicate the mean. Significance was tested by a two-tailed Mann–Whitney U-test (P = 1.289 × 10⁻⁸). c, Model of chromatin compaction and condensin-mediated DNA looping in mitotic chromosome and spindle assembly. The illustration shows a top-down view of a chromosome cross-section. Biological replicates: n = 3 (a,b). Scale bars, 5 µm.
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Article
Dividing eukaryotic cells package extremely long chromosomal DNA molecules into discrete bodies to enable microtubule-mediated transport of one genome copy to each of the newly forming daughter cells1–3. Assembly of mitotic chromosomes involves DNA looping by condensin4–8 and chromatin compaction by global histone deacetylation9–13. Although condensin confers mechanical resistance to spindle pulling forces14–16, it is not known how histone deacetylation affects material properties and, as a consequence, segregation mechanics of mitotic chromosomes. Here we show how global histone deacetylation at the onset of mitosis induces a chromatin-intrinsic phase transition that endows chromosomes with the physical characteristics necessary for their precise movement during cell division. Deacetylation-mediated compaction of chromatin forms a structure dense in negative charge and allows mitotic chromosomes to resist perforation by microtubules as they are pushed to the metaphase plate. By contrast, hyperacetylated mitotic chromosomes lack a defined surface boundary, are frequently perforated by microtubules and are prone to missegregation. Our study highlights the different contributions of DNA loop formation and chromatin phase separation to genome segregation in dividing cells.
 
A leukocyte receptor network by systematic protein interaction mapping
a, SAVEXIS enables efficient and high-throughput screening for protein binding interactions between recombinant extracellular domains. b, Schematic showing the diverse structural architectures of leukocyte surface proteins within the pan-leukocyte library of 630 proteins. The number of proteins from each class is noted above, and the recombinant expression strategy is illustrated below. c, Summarized matrix of protein–protein pairs for immune receptors with interactions either identified by screening or previously reported in the literature. The average signal intensity for a given bait–prey measurement orientation across the primary and secondary screens is indicated by the shaded intensity, and the colour indicates which interactions are novel. d, Screening successfully finds most previously reported interactions with minimal false positives. Receiver operating characteristic (ROC) curve for average measurements of protein–protein pairs against reference sets of expected positive and randomized negative interactions. AUC, area under the curve. e, Organized interaction network of immune receptor interactions. The colour indicates which interactions are novel, and the line thickness is proportional to the magnitude of evidence from the screening measurements.
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Validating and assembling a quantitative immune interactome
a, Novel receptor interactions are detectable on the surfaces of live human cells. For six examples that encompass different architectural classes, flow cytometry traces are shown for the binding of fluorescent-conjugated protein to HEK293 cells overexpressing its identified counter-receptor (blue) or control cells (red). b, SPR substantiates and quantifies the binding of novel leukocyte receptor–ligand pairs. For the same six example protein pairs, sensorgram data (black) are shown with Langmuir model fitting curves overlaid (red) for all interactions for which a robust fit could be calculated. Ig-SF, immunoglobulin superfamily; LRR-SF, leucine rich repeat superfamily. c, The quantitative interactome of immune cell-surface proteins. Proteins are shown as circular charts indicating the proportion of expression in each leukocyte population. Binding affinity between proteins is indicated by the size and intensity of red edges (expressed in terms of the binding dissociation constant (KD), where smaller values reflect stronger binding). Abbreviations for cell type names are defined in Supplementary Table 5. d, Immune cell subsets use related but varying distributions of binding affinities when communicating with other cell types. For each pairing of two cells, a histogram of inferred interactions is shown alongside a colour shade that indicates the average affinity. e, Inflammatory activation broadly reconfigures receptors towards those with less-transient binding kinetics. After differential expression testing of surface proteins between activated and stimulated leukocytes (n = 4 samples per condition), the binding affinities of interactions involving downregulated (downreg) receptors are compared to the binding affinities of upregulated (upreg) receptors. Data are shown as Tukey box plots with Holm-corrected P values from a two-sided Welch’s t-test. f, Intercellular connectivity can be mathematically predicted by integrating protein expression, binding affinity and cell parameters using physics-based equations. A detailed description of the model can be found in the Supplementary Equations. g, Model predictions for baseline rates of immune cell association agree with published data measuring in vitro immune cell association. Each data point has two colours that correspond to the two physically interacting cell types. Shading depicts the 95% compatibility interval for the least-squares linear regression fit.
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An interactive atlas of immune cell connections across the human body
a, Systematic integration of single-cell datasets to map cellular connectivity across tissues with substantial immune populations. Cell types are positioned around each circle, with each position along it marking a cell-surface protein. Linkages formed by physically interacting surface proteins between cell types are marked by curved lines, coloured by interaction identity. Full abbreviations are listed in Supplementary Table 5. b, Functionalities available through our interactive atlas of physical immune interactions (https://www.sanger.ac.uk/tool/immune-interaction/immune-interaction). c, Myeloid cells act as interaction hubs in immune tissues. Eigenvector centrality metrics of myeloid cells compared to all other populations after converting the total interaction count for all cell–cell pairs into a weighted undirected graph. Data are shown as Tukey box plots with Benjamini–Hochberg P values calculated from a two-sided Welch’s t-test. d, Spatial transcriptomics of a human lymph node confirms that our identified interaction partners are physically colocalized in situ. An example data point of a tissue section analysed for the JAG1 + VASN interaction is shown. The percentage of measured spots in which the expression of one protein of an interacting pair is spatially connected to the other protein of the pair is compared for previously reported interactions (green), novel interactions (blue) and a negative control of the same proteins with interaction links randomly permuted (yellow) (n = 100). Data are shown as Tukey box plots with P values calculated from a Tukey's honest significance test. e, Single-molecule RNA hybridization on human lymph nodes defines regions in which newly identified interaction partners are expressed in spatially bordering cells. A single lymphoid follicle enriched in CD45⁺ leukocytes is magnified (left), showing the zonation of JAG1- and VASN-expressing cells into the corona and the germinal centre, respectively (middle). An inset (right) highlights a region of bordering cells expressing each marker. Scale bars, 200 μm (left); 100 μm (middle); 50 μm (right).
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Multiplex leukocyte assays identify functional pathways for receptor proteins
a, High-content microscopy set-up for perturbing human peripheral blood mononuclear cells (PBMCs) with recombinant proteins and measuring changes in cellular activation and connectivity. Scale bar, 30 µm. b, Proteins with identified receptor interactions elicit responses on lymphocyte action. Polarization of lymphocyte populations in resting and weakly activating background conditions (y axis) after addition of soluble protein extracellular domains (x axis). Stimulation (red) or inhibition (blue) relative to control is shown of a cell polarization marker of lymphocyte activation. Phenotypes that have P values below the adjusted significance threshold are outlined in bold. n = 10 samples. c, Interacting cellular communities can be extracted from high-content imaging data. Representative microscopy fields (left) and computed physical cell contacts (right, white lines) are depicted for leukocytes perturbed with recombinant SEMA4D and SIRPA as examples. Scale bar, 30 µm. d, Rewiring of cellular interactions by perturbing receptor pathways. Measured changes in cell–cell interactions (x axis) induced by recombinant proteins (panels) across measurement time points and background conditions (y axis). The same colour scale as in c is used to identify cell pairs along the x axis. n = 10 samples. e, Observed interaction changes conform to mathematical model predictions. Average magnitudes of cell–cell interaction changes (y axis) after the addition of recombinant proteins are compared for cell pairs predicted by the model to be likely to change after perturbing that surface protein (‘true’) and those predicted not to change (‘false’). Each panel considers a different recombinant protein added in the experiment and the corresponding model predictions for that same protein. The two colours for each data point depict the identity of the cell pair according to the colour scale in c. n = 10 samples for the experimental data.
Source data
Article
The human immune system is composed of a distributed network of cells circulating throughout the body, which must dynamically form physical associations and communicate using interactions between their cell-surface proteomes¹. Despite their therapeutic potential², our map of these surface interactions remains incomplete3,4. Here, using a high-throughput surface receptor screening method, we systematically mapped the direct protein interactions across a recombinant library that encompasses most of the surface proteins that are detectable on human leukocytes. We independently validated and determined the biophysical parameters of each novel interaction, resulting in a high-confidence and quantitative view of the receptor wiring that connects human immune cells. By integrating our interactome with expression data, we identified trends in the dynamics of immune interactions and constructed a reductionist mathematical model that predicts cellular connectivity from basic principles. We also developed an interactive multi-tissue single-cell atlas that infers immune interactions throughout the body, revealing potential functional contexts for new interactions and hubs in multicellular networks. Finally, we combined targeted protein stimulation of human leukocytes with multiplex high-content microscopy to link our receptor interactions to functional roles, in terms of both modulating immune responses and maintaining normal patterns of intercellular associations. Together, our work provides a systematic perspective on the intercellular wiring of the human immune system that extends from systems-level principles of immune cell connectivity down to mechanistic characterization of individual receptors, which could offer opportunities for therapeutic intervention.
 
Top-cited authors
Richard K Wilson
  • Washington University in St. Louis
Elaine Mardis
  • Nationwide Children's Hospital
Gad Getz
  • Broad Institute of MIT and Harvard
Lucinda Fulton
  • Washington University in St. Louis
Robert Fulton
  • Washington University in St. Louis