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Biosynthesis of medicinal tropane alkaloids in yeast
TA acyl acceptor and donor biosynthesis
HDH discovery and scopolamine biosynthesis
Engineering vacuolar littorine biosynthesis
Discussion
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Fig. 1 Engineered biosynthetic pathway for de novo production of scopolamine in yeast and optimization of PLA-glucoside biosynthesis.
Fig. 2 Identification and characterization of hyoscyamine dehydrogenase in A.
Fig. 3 Engineering littorine synthase for activity in yeast.
Fig. 4 Optimization of substrate transport limitations and medicinal TA production.
Extended Data Fig. 1 Design of genomic integrations for pathway construction in yeast.
Extended Data Fig. 2 Substrate specificity and structure-guided active site engineering of UGT84A27 in engineered yeast.
Extended Data Fig. 3 Coexpression analysis, active site mutagenesis, and orthologue identification for AbHDH.
Extended Data Fig. 4 Screening H6H orthologues from TA-producing Solanaceae in yeast.
Extended Data Fig. 5 Phylogenetic analysis of HDH.
Extended Data Fig. 6 Analysis of AbLS localization, N-glycosylation, and proteolytic processing patterns in yeast and tobacco.
Extended Data Fig. 7 Analysis of putative endoproteolytic propeptide removal in AbLS.
Extended Data Fig. 8 Fluorescence microscopy of tobacco alkaloid transporters expressed in CSY1296 for alleviation of vacuolar TA transport limitations.
Extended Data Fig. 9 Effect of extra gene copies on accumulation of TA pathway intermediates and products in scopolamine-producing strain CSY1296.
Extended Data Fig. 10 Time courses of de novo TA and precursor production in pseudo-fed-batch cultures of CSY1297 and CSY1298.
s41586-020-2725-7.pdf
Bridging of DNA breaks activates PARP2–HPF1 to modify chromatin
PARP2–HPF1 bridges two nucleosomes
Bridging of DNA break activates PARP2
PARP2–HPF1 catalytic cycle
Auto-PARylation dissociates the complex
Discussion
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Fig. 1 PARP2–HPF1 bridges two mononucleosomes.
Fig. 2 Bridging of DNA break activates PARP2.
Fig. 3 PARP2 catalytic domain rearranges to open NAD+ and substrate-binding sites.
Fig. 4 PARylated PARP2–HPF1 dissociates from the chromatin.
Extended Data Fig. 1 Assembly and cryo-EM of PARP2–HPF1 bound to mononucleosomes.
Extended Data Fig. 2 Classification of the PARP2–HPF1–nucleosome complex.
Extended Data Fig. 3 Focused classification, refinement and model building: focus on the PARP2–HPF1 complex.
Extended Data Fig. 4 PARP2 interaction with nucleosomes.
Extended Data Fig. 5 Interaction of HPF1 with the nucleosome stabilizes the PARP2–HPF1–nucleosome complex.
Extended Data Fig. 6 Bridging of two nucleosomes is required for PARP2 activation.
Extended Data Fig. 7 Bridging of two nucleosomes induces conformational changes in PARP2.
Extended Data Fig. 8 Model for PARP2–HPF1 in open state 1.
Extended Data Fig. 9 PARP2–HPF1 in open state 2.
Extended Data Fig. 10 Mutations that cause resistance to PARP inhibitors.
Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics.
s41586-020-2732-8.pdf
Plasticity of ether lipids promotes ferroptosis susceptibility and evasion
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Fig. 1 Genome-wide CRISPR screens identify peroxisome components as contributors to ferroptosis susceptibility.
Fig. 2 The polyunsaturated ether lipid biosynthesis pathway mediates the pro-ferroptotic roles of peroxisomes.
Fig. 3 Cancer cells initially dependent on GPX4 downregulate PUFA-ePLs to evade ferroptosis.
Fig. 4 Neurons and cardiomyocytes acquire increased PUFA-ePLs and gain sensitivity to ferroptosis during differentiation.
Extended Data Fig. 1 CRISPR screens identify peroxisome components as contributors to ferroptosis sensitivity.
Extended Data Fig. 2 Peroxisomes contribute to ferroptosis sensitivity in renal and ovarian carcinoma cells.
Extended Data Fig. 3 Peroxisomes contribute to ferroptosis sensitivity via the ether lipid biosynthesis pathway.
Extended Data Fig. 4 AGPS/FAR1 depletion blocks ether phospholipid synthesis and lipid peroxidation.
Extended Data Fig. 5 The ether lipid biosynthesis pathway, but not other peroxisomal pathways, contributes to ferroptosis susceptibility.
Extended Data Fig. 6 Peroxisomes and the ether lipid biosynthesis pathway contribute to ferroptosis in liver, endometrial and kidney cancers.
Extended Data Fig. 7 AGPAT3 contributes to PUFA-ePL synthesis downstream of peroxisomes.
Extended Data Fig. 8 Polyunsaturated ether lipid nanoparticles increase cellular sensitivity to ferroptosis.
Extended Data Fig. 9 Polyunsaturated plasmalogens promote lipid peroxidation in GPX4-inhibited cells.
Extended Data Fig. 10 PUFA-ePL downregulation is associated with acquired ferroptosis resistance in vivo.
Extended Data Fig. 11 ER-resident enzyme plasmanylethanolamine desaturase/TMEM189 is dispensible for ferroptosis sensitivity in selected cancer cells.
Extended Data Fig. 12 Neurons and cardiomyocytes acquire increased ether-phospholipid levels and elevated sensitivity to ferroptosis.
s41586-020-2444-0.pdf
A substrate-specific mTORC1 pathway underlies Birt–Hogg–Dubé syndrome
TFEB phosphorylation does not require RHEB
Rag GTPases mediate mTORC1–TFEB interaction
TFEB phosphorylation requires active RagC/D
TFEB drives the kidney phenotype of BHD mice
Discussion
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Fig. 1 TFEB phosphorylation is insensitive to the RHEB–TSC axis.
Fig. 2 Unconventional recruitment of an mTORC1 substrate by Rag GTPases.
Fig. 3 Activation of RagC has a differential effect on mTORC1 substrates.
Fig. 4 TFEB depletion rescues renal pathology and lethality in FLCN-knockout mice.
Extended Data Fig. 1 TFEB phosphorylation is insensitive to serum starvation.
Extended Data Fig. 2 TFEB phosphorylation is insensitive to the RHEB–TSC axis.
Extended Data Fig. 3 Rag GTPases are required for TFEB phosphorylation.
Extended Data Fig. 4 Rag GTPases are required for TFEB phosphorylation regardless of mTORC1 activation status.
Extended Data Fig. 5 The mTORC1 substrate-recruitment mechanism of TFEB is determined by its N-terminal region.
Extended Data Fig. 6 Addition of a TOS motif to a Rag-binding-deficient TFEB mutant rescues its phosphorylation and subcellular localization.
Extended Data Fig. 7 Activation of RagA is essential for mTOR lysosomal recruitment and TFEB cytosolic localization.
Extended Data Fig. 8 TFEB phosphorylation and cytosolic retention requires active RagC/D.
Extended Data Fig. 9 Genomic and mRNA analysis of transgenic mouse lines.
Extended Data Fig. 10 TFEB is constitutively nuclear and active in FLCN-knockout kidneys, and its depletion rescues mTORC1 hyperactivation.
s41586-020-2425-3.pdf
The liver–brain–gut neural arc maintains the Treg cell niche in the gut
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Fig. 1 Potential interaction between APCs and neurons in the gut.
Fig. 2 The hepatic vagal sensory afferent pathway is essential for NTS activation during colitis.
Fig. 3 The liver–brain–gut axis regulates colonic Treg cell homeostasis through muscarinic signalling in APCs.
Fig. 4 Perturbation of hepatic vagal afferents exacerbates mouse colitis in a muscarinic signalling-dependent manner.
Extended Data Fig. 1 Muscarinic signalling in colonic APC activates induction of Treg.
Extended Data Fig. 2 Colitis activates liver-brain axis.
Extended Data Fig. 3 Anatomy of the hepatic vagus nerve in mice.
Extended Data Fig. 4 Effects of vagotomy on maintenance and stability of colonic pTreg.
Extended Data Fig. 5 Afferent vagal, but not spinal cord, activation from the liver is involved in colonic Treg homeostasis.
Extended Data Fig. 6 Hemi-subdiaphragmatic vagotomy revealed functional asymmetries of the vagus nerve.
Extended Data Fig. 7 Effects of VGx and HVx on intrinsic enteric neuron.
Extended Data Fig. 8 Effects of mAChR and α7nAChR on maintenance of colonic Treg.
Extended Data Fig. 9 The effects of gut-microbiota on colonic Treg maintenance of the liver-brain-gut axis.
Extended Data Fig. 10 Effects of HVx on colitis.
s41586-020-2575-3.pdf
Chloroquine does not inhibit infection of human lung cells with SARS-CoV-2
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Fig. 1 Chloroquine does not block infection of human lung cells with SARS-CoV-2.
Table 1 Half-maximal inhibitory concentrations of the tested drugs.
s41586-020-2558-4.pdf
Hydroxychloroquine use against SARS-CoV-2 infection in non-human primates
In vitro efficacy of HCQ against SARS-CoV-2 infection
Infection of macaques with SARS-CoV-2
Treatment with HCQ
Relation between HCQ concentration and virus kinetics
Pathogenesis and host response to HCQ treatment
Conclusions
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Fig. 1 Study design and viral loads in the respiratory tract of SARS-CoV-2-infected cynomolgus macaques treated with HCQ and AZTH.
Fig. 2 Time course of lung lesions by CT analysis of SARS-CoV-2-infected cynomolgus macaques treated with HCQ.
Fig. 3 Pharmacokinetic and viral kinetic parameters in cynomolgus macaques.
Fig. 4 Cytokines and chemokines in the plasma of SARS-CoV-2-infected cynomolgus macaques treated with HCQ.
Extended Data Fig. 1 In vitro evaluation of the antiviral activity of HCQ against SARS-CoV-2.
Extended Data Fig. 2 Viral loads of SARS-CoV-2-infected cynomolgus macaques treated with HCQ.
Extended Data Fig. 3 Representative transversal slices of lung CT scans from SARS-CoV-2-infected cynomolgus macaques treated with HCQ.
Extended Data Fig. 4 Plasma and blood HCQ concentrations of six uninfected NHPs.
Extended Data Fig. 5 Complete blood count of SARS-CoV-2-infected cynomolgus macaques treated with HCQ.
Extended Data Fig. 6 Cytokines and chemokines in the plasma of SARS-CoV-2-exposed cynomolgus macaques treated with HCQ.
Extended Data Fig. 7 Plasma ALT levels of cynomolgus macaques treated with HCQ.
Extended Data Fig. 8 Biochemistry analysis of cynomolgus macaques treated with HCQ.
s41586-020-2726-6.pdf
Red blood cell tension protects against severe malaria in the Dantu blood group
Dantu limits invasion of red blood cells
Surface protein composition is also affected
Membrane tension and invasion efficiency
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Fig. 1 Reduced invasion of Dantu-variant RBCs by several P.
Fig. 2 RBC membrane protein characteristics vary across Dantu genotypes but do not correlate directly with invasion efficiency.
Fig. 3 Biomechanical properties of the RBC membrane differ across Dantu genotypes and correlate with invasion.
Extended Data Fig. 1 Erythrocytic cycle of malaria parasites.
Extended Data Fig. 2 Invasion process across Dantu genotype groups studied by time-lapse video microscopy.
Extended Data Fig. 3 Distribution of reticulocytes and RNA concentrations across Dantu genotypes.
Extended Data Fig. 4 Plasma membrane profiling by tandem mass tag (TMT)-based MS3 mass spectrometry.
Extended Data Fig. 5 Representative membrane fluctuation spectra for non-Dantu, Dantu-heterozygous and Dantu-homozygous RBCs.
Extended Data Fig. 6 Relationship between biophysical properties in non-Dantu and Dantu-homozygote RBCs.
Extended Data Fig. 7 Reduction of membrane tension in non-Dantu and Dantu-homozygous RBCs on treatment with phloretin.
Extended Data Fig. 8 Comparing parasite invasion and biomechanical properties of frozen and fresh RBCs.
Extended Data Fig. 9 Decoupling tension and bending modulus with flickering analysis.
Extended Data Fig. 10 Membrane flickering spectroscopy amplitude analysis.
Table 1 Clinical and demographic characteristics of study participants.
s41586-020-2724-8.pdf
Homeostatic mini-intestines through scaffold-guided organoid morphogenesis
Establishment of tissue homeostasis
Stereotypical cell-fate patterning
Emergence of rare cell types
Regenerative potential of mini-gut tubes
Modelling long-term parasite infection
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Fig. 1 Establishment of long-term homeostatic culture of tubular mini-guts.
Fig. 2 Cell-fate patterning and cellular diversity of tubular mini-guts.
Fig. 3 Perspectives for modelling intestine biology and disease.
Extended Data Fig. 1 Bioengineering intestinal stem cell epithelia with a tubular, in-vivo-like architecture.
Extended Data Fig. 2 Establishment of shape-controlled organoid culture from a variety of epithelial stem and progenitor cells.
Extended Data Fig. 3 Establishment of long-term culture and in vitro tissue homeostasis.
Extended Data Fig. 4 Mini-gut tubes undergo rapid cell turnover and comprise key functional intestinal cell types.
Extended Data Fig. 5 Canonical markers from the various intestinal cell types are accurately reproduced in vitro.
Extended Data Fig. 6 Cell types identified in vitro closely resemble their in vivo counterparts.
Extended Data Fig. 7 Identification of rare cell types in the mini-guts.
Extended Data Fig. 8 Capacity of mini-gut tubes to regenerate after radiation-induced damage.
Extended Data Fig. 9 Modelling C.
Extended Data Fig. 10 Perspectives for mimicking organ-level complexity in mini-gut tubes through spatially controlled co-cultures.
s41586-020-2702-1.pdf
The calcium-permeable channel OSCA1.3 regulates plant stomatal immunity
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Fig. 1 OSCA1.
Fig. 2 OSCA1.
Fig. 3 OSCA1.
Fig. 4 OSCA1.
Extended Data Fig. 1 Predicted topology of OSCA1.
Extended Data Fig. 2 OSCA1.
Extended Data Fig. 3 PBL1 also phosphorylates OSCA1.
Extended Data Fig. 4 OSCA1.
Extended Data Fig. 5 OSCA1.
Extended Data Fig. 6 T-DNA insertion lines used in this study and transcript levels.
Extended Data Fig. 7 Expression pattern of OSCA genes from Clade 1.
Extended Data Fig. 8 Flg22-induced calcium influx measured in leaf discs is comparable between wild-type and osca1.
Extended Data Fig. 9 Flg22-induced calcium fluxes in osca1.
Extended Data Fig. 10 AtPep1-induced decrease in stomatal conductance is impaired in osca1.
s41586-020-2720-z.pdf
Evolution of the endothelin pathway drove neural crest cell diversification
Ednra controls head skeleton development
Lamprey Ednr paralogues cooperate
Edn signalling acts through soxE and dlx
Conserved role for Ednra in the heart
Ednrb function in PNS has diverged
Lamprey Ednrs have dedicated ligands
Evolutionary history of edn and ednr genes
Conclusions
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Fig. 1 Lamprey and X.
Fig. 2 Skeletogenic NCC development is disrupted in lamprey Δednr, lamprey Δdlx and X.
Fig. 3 Lamprey ednr genes have a minor role in the PNS and display specialized ligand interactions.
Fig. 4 The co-option, duplication and specialization of Edn signalling pathways drove the expansion and diversification of NCC subpopulations.
Extended Data Fig. 1 Petromyzon marinus wild-type and mutant larval alcian blue stained head skeletons and lecticanA expression.
Extended Data Fig. 2 Petromyzon marinus Δednra phenotype and genotype summary.
Extended Data Fig. 3 Petromyzon marinus dlxA, -D, -B, hand, ID, lecticanA (lecA), myc, msxA, phox2, soxE1, soxE2, and twistA expression in Δednr lampreys at st.
Extended Data Fig. 4 Xenopus laevis Δednra and Δedn1 head skeleton defects and genotyping.
Extended Data Fig. 5 Petromyzon marinus Δednrb and Δednra+b phenotypes and genotyping.
Extended Data Fig. 6 Petromyzon marinus Δdlx genotyping post-ISH and alcian blue staining.
Extended Data Fig. 7 Xenopus laevis Δedn3 pigmentation phenotype and genotype summary.
Extended Data Fig. 8 Xenopus laevis Δedn3 peripheral nervous system in larvae and subadult frogs.
Extended Data Fig. 9 Petromyzon marinus ΔednA and ΔednE phenotype and genotype summary.
Extended Data Fig. 10 ednr and edn synteny and phylogeny.
Extended Data Fig. 11 Phenotypes of larvae injected with 22 different negative control sgRNAs.
s41586-020-2721-y.pdf
Metabolic trait diversity shapes marine biogeography
Temperature-dependent O2 tolerance
Physiological trait diversity
Linking physiology to biogeography
Implications
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Fig. 1 Relationships among species traits that govern the temperature-dependent vulnerability to hypoxia of marine animals.
Fig. 2 Spatial distributions of the Metabolic Index and species with distinct temperature sensitivities.
Fig. 3 Temperature and state-space habitat for three marine species from different phyla, ocean basins and latitude ranges.
Fig. 4 Diversity of the ecological trait governing energetic habitat barriers.
Fig. 5 Thermal tolerance of species measured in laboratory studies (CTmax) and predicted from the Metabolic Index (ATmax).
Extended Data Fig. 1 Species metabolic rates and hypoxia tolerances from laboratory studies.
Extended Data Fig. 2 Correlations and diversity in traits that govern geographical range boundaries.
Extended Data Fig. 3 Temperature sensitivity of processes that govern the O2 supply.
Extended Data Fig. 4 Spatial distributions of the Metabolic Index, temperature and compared to occurrences of species that occupy diverse latitude and depth ranges.
Extended Data Fig. 5 Maps of the Metabolic Index, temperature and compared to species distributions.
Extended Data Fig. 6 Spatial distributions of the P.
Extended Data Fig. 7 Predictive skill of the Metabolic Index in delineating the species geographical range, compared with temperature or alone.
Extended Data Fig. 8 Critical value of the Metabolic Index at the limit of species geographical range (Φcrit).
Extended Data Fig. 9 Relationship between Φcrit and the ratio of maximum-to-resting metabolic rates (MMR/RMR), among all species with empirical estimates of both parameters.
Extended Data Fig. 10 Relationship across species between thermal tolerance of species measured in laboratory studies and predicted from the Metabolic Index.
Extended Data Table 1 Summary statistical tests of the relationships between metabolic and hypoxia traits and between distributions of Φcrit and SMS.
s41586-020-2705-y.pdf
Bending the curve of terrestrial biodiversity needs an integrated strategy
Reversing biodiversity trends by 2050
Contribution of different interventions
Discussion and conclusions
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Fig. 1 Estimated recent and future global biodiversity trends resulting from land-use change, with and without coordinated efforts to reverse trends.
Fig. 2 Contributions of various efforts to reverse land-use change-induced biodiversity trends.
Extended Data Fig. 1 Datasets used to provide spatially explicit input for modelling increased conservation efforts into the land-use models.
Extended Data Fig. 2 Spatial patterns in projected changes in the value of biodiversity indicators for BASE and IAP scenarios (and the difference between the IAP and BASE scenarios) for the 17 IPBES subregions by 2050 and 2100 (compared to 2010 value).
Extended Data Fig. 3 Projected future global trends in drivers of habitat loss and degradation.
Extended Data Fig. 4 Projected global trends in land-use change across all scenarios.
Extended Data Fig. 5 Spatial patterns of projected habitat loss and restoration by 2100.
Extended Data Fig. 6 Estimated recent and future global biodiversity trends that resulted from land-use change for all seven scenarios.
Extended Data Fig. 7 Spatial patterns of the date of peak loss in the twenty-first century and the share of avoided future peak loss.
Extended Data Fig. 8 Global relative changes in the price index of non-energy crops, total greenhouse gas emissions from agriculture, forestry and other land uses, total irrigation water withdrawal and nitrogen fertilizer use between 2010 and 2050.
Table 1 The seven scenarios describing the efforts to reverse declining biodiversity trends.
Table 2 Key features of the nine estimated BDIs.
Extended Data Table 1 Prolongation of historical biodiversity trends in the BASE scenario.
Extended Data Table 2 Key statistics for the date of peak loss, share of avoided loss and relative recovery speed.
s41586-020-2686-x.pdf
Mapping carbon accumulation potential from global natural forest regrowth
Potential drivers of accumulation rates
Mapping carbon accumulation rates
Climate mitigation potential of regrowth
Evaluation of our results
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Fig. 1 Variation in carbon accumulation among biomes and previous land use/disturbance.
Fig. 2 Mapping carbon accumulation potential.
Fig. 3 Predicted rates compared to IPCC defaults.
Extended Data Fig. 1 Variation in carbon accumulation among biomes.
Extended Data Fig. 2 Accumulation of coarse woody debris and litter carbon through time.
Extended Data Fig. 3 Variation in carbon stocks among biomes.
Extended Data Fig. 4 Effect of disturbance intensity on carbon accumulation.
Extended Data Fig. 5 Map of extent of extrapolation per pixel across all covariate layers.
Extended Data Fig. 6 Fine-scale variation in rates.
Extended Data Fig. 7 Coverage of field data.
Extended Data Table 1 General approaches for restoring forest or tree cover.
Extended Data Table 2 Effect of disturbance intensity on carbon accumulation.
s41586-020-2727-5.pdf
The hysteresis of the Antarctic Ice Sheet
Long-term stability simulations
Ice-sheet hysteresis
Ocean-induced versus atmosphere-induced changes
Discussion and conclusion
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Fig. 1 Antarctic ice velocities and surrounding ocean temperatures.
Fig. 2 Hysteresis of the Antarctic Ice Sheet.
Fig. 3 Ice-sheet volume differences between retreat and regrowth.
Fig. 4 Long-term ice loss for different warming levels.
Fig. 5 Ocean-driven versus atmosphere-driven ice loss.
Extended Data Fig. 1 Comparison of modelled and observed ice geometry.
Extended Data Fig. 2 Comparison of modelled and observed ice velocities.
Extended Data Fig. 3 Regrown Antarctica.
Extended Data Fig. 4 Hysteresis sensitivity to model parameter variations.
Extended Data Fig. 5 Long-term ice loss for different warming levels.
Extended Data Fig. 6 Ocean-driven versus atmosphere-driven ice loss (regrowth branch).
s41586-020-2733-7.pdf
Light-driven post-translational installation of reactive protein side chains
Results
Photocatalytic carbon-centred radical protein modification
Optimization of BACED and pySOOF reagents
Diverse side chains inserted into proteins
On-protein heterolytic reactivity
On-protein homolytic reactivity
Probing of post-translational enzymes
Alkylator proteins trap buried protein–protein interfaces through mimicry
Discussion
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Fig. 1 Site-selective, light-driven post-translational protein editing.
Fig. 2 On-protein homolytic and heterolytic reactivity via installation of a radical precursor and electrophile side chains.
Fig. 3 Insertion of native, difluoro-labelled and electrophile-containing side chains into proteins provides insight into enzymes that post-translationally modify them.
Extended Data Fig. 1 Overview of radical side-chain installation and relevant previous literature.
Extended Data Fig. 2 Complementary strategies for mild protein-compatible photoredox reactions.
Extended Data Fig. 3 Investigation and optimization of BACED chemistry.
Extended Data Fig. 4 Mechanistic investigation of the role of catechol in BACED reactions.
Extended Data Fig. 5 Initial experiments without iron using various hydride sources, and optimization study with sodium borohydride for pySOOF.
Extended Data Fig. 6 Optimization study of Fe(ii)-mediated protein modification reaction with pySOOF.
Extended Data Fig. 7 Investigations on pySOOF reagent reactivity and on-protein mechanism.
Extended Data Fig. 8 Substrate scopes for BACED and pySOOF.
Extended Data Fig. 9 Upscaling of the protein modification with pySOOF and 19F NMR analysis.
Extended Data Fig. 10 Application of difluorinated amino acid-labelled proteins in 19F NMR studies.
Extended Data Fig. 11 Effective molarity driven protein–protein crosslinking with electrophile-containing side chains.
s41586-020-2718-6.pdf
Colloidal diamond
Particle synthesis
Particle design and crystallization
Calculation of photonic bandgap
Next steps
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Fig. 1 Schematic and space-filling models of a colloidal diamond lattice.
Fig. 2 Synthesis of compressed tetrahedral patchy clusters.
Fig. 3 Crystallization of cubic diamond colloidal crystals.
Fig. 4 Relative bandgap versus compression ratio.
Extended Data Fig. 1 Controlling compression and size ratios.
Extended Data Fig. 2 Fluorescent microscope image of DNA-coated compressed tetrahedral clusters.
Extended Data Fig. 3 Self-assembly of DNA-coated compressed clusters.
Extended Data Fig. 4 Self-assembly of DNA-coated compressed clusters.
Extended Data Fig. 5 Inverse cubic diamond lattice of clusters.
s41586-020-2735-5.pdf
Third-order nanocircuit elements for neuromorphic engineering
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Fig. 1 Element construction and static measurements.
Fig. 2 Experimental measurements and modelling of action potentials.
Fig. 3 Experimental demonstration of universal Boolean logic via nonmonotonic spiking behaviour.
Fig. 4 Experimental demonstration of neuromorphic analogue computing.
s41586-020-2729-3.pdf
Host–microbiota maladaptation in colorectal cancer
The intestinal host–microbiota interface
The aetiology of CRC
Dysbiosis in CRC
Genotoxicity induced by CRC-associated bacteria
The effect of microorganism-driven metabolism
Influx of immune-stimulating microorganisms
Inflammation-driven bacterial niche
‘Oncomicrobes’ alter immune composition
Technologies to investigate microbiome causality
Future outlook
Conclusion
Acknowledgements
Fig. 1 A schematic of the host–microbiota interactions in health and in colorectal cancer.
Fig. 2 Known inflammatory mechanisms by which the microbiota contributes to CRC.
Fig. 3 Approaches to advance the translation of microbiome-based therapeutics in CRC.
s41586-020-2674-1.pdf
Zebrafish prrx1a mutants have normal hearts
Reporting summary
Acknowledgements
Fig. 1 Cardiac laterality in prrx1a-mutant embryos.
Fig. 2 Induction of off-target laterality phenotypes by injection of prrx1a-MO1.
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Reply to: Zebrafish prrx1a mutants have normal hearts
Reporting summary
Acknowledgements
Fig. 1 prrx1a-crispant embryos show mesocardia and a smaller atrium without early defects in the LRO.
Fig. 2 EMT transcription factors in heart laterality in zebrafish.
s41586-020-2678-x.pdf
Author Correction: FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes
Table 1 The originally published, incorrect primer sequences and the corrected primer sequences.
s41586-020-2656-3.pdf
Publisher Correction: The National Lung Matrix Trial of personalized therapy in lung cancer