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Nature

Resumen/Descripción – provisto por la editorial en inglés
Nature is a weekly international journal publishing the finest peer-reviewed research in all fields of science and technology on the basis of its originality, importance, interdisciplinary interest, timeliness, accessibility, elegance and surprising conclusions. Nature also provides rapid, authoritative, insightful and arresting news and interpretation of topical and coming trends affecting science, scientists and the wider public.
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Información

Tipo de recurso:

revistas

ISSN impreso

0028-0836

ISSN electrónico

1476-4687

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Tabla de contenidos

Phosphorus-mediated sp2–sp3 couplings for C–H fluoroalkylation of azines

Xuan ZhangORCID; Kyle G. Nottingham; Chirag Patel; Juan V. Alegre-Requena; Jeffrey N. Levy; Robert S. PatonORCID; Andrew McNallyORCID

Palabras clave: Multidisciplinary.

Pp. 217-222

Mesozoic cupules and the origin of the angiosperm second integument

Gongle ShiORCID; Fabiany HerreraORCID; Patrick S. HerendeenORCID; Elizabeth G. ClarkORCID; Peter R. CraneORCID

Palabras clave: Multidisciplinary.

Pp. 223-226

Evolutionary and biomedical insights from a marmoset diploid genome assembly

Chentao Yang; Yang ZhouORCID; Stephanie Marcus; Giulio FormentiORCID; Lucie A. Bergeron; Zhenzhen Song; Xupeng Bi; Juraj BergmanORCID; Marjolaine Marie C. Rousselle; Chengran ZhouORCID; Long Zhou; Yuan Deng; Miaoquan Fang; Duo XieORCID; Yuanzhen Zhu; Shangjin Tan; Jacquelyn MountcastleORCID; Bettina Haase; Jennifer Balacco; Jonathan Wood; William Chow; Arang RhieORCID; Martin PippelORCID; Margaret M. FabiszakORCID; Sergey KorenORCID; Olivier FedrigoORCID; Winrich A. FreiwaldORCID; Kerstin HoweORCID; Huanming Yang; Adam M. PhillippyORCID; Mikkel Heide SchierupORCID; Erich D. JarvisORCID; Guojie ZhangORCID

<jats:title>Abstract</jats:title><jats:p>The accurate and complete assembly of both haplotype sequences of a diploid organism is essential to understanding the role of variation in genome functions, phenotypes and diseases<jats:sup>1</jats:sup>. Here, using a trio-binning approach, we present a high-quality, diploid reference genome, with both haplotypes assembled independently at the chromosome level, for the common marmoset (<jats:italic>Callithrix jacchus</jats:italic>), an primate model system that is widely used in biomedical research<jats:sup>2,3</jats:sup>. The full spectrum of heterozygosity between the two haplotypes involves 1.36% of the genome—much higher than the 0.13% indicated by the standard estimation based on single-nucleotide heterozygosity alone. The de novo mutation rate is 0.43 × 10<jats:sup>−8</jats:sup> per site per generation, and the paternal inherited genome acquired twice as many mutations as the maternal. Our diploid assembly enabled us to discover a recent expansion of the sex-differentiation region and unique evolutionary changes in the marmoset Y chromosome. In addition, we identified many genes with signatures of positive selection that might have contributed to the evolution of <jats:italic>Callithrix</jats:italic> biological features. Brain-related genes were highly conserved between marmosets and humans, although several genes experienced lineage-specific copy number variations or diversifying selection, with implications for the use of marmosets as a model system.</jats:p>

Palabras clave: Multidisciplinary.

Pp. 227-233

Reconstruction of ancient microbial genomes from the human gut

Marsha C. WibowoORCID; Zhen Yang; Maxime BorryORCID; Alexander Hübner; Kun D. Huang; Braden T. TierneyORCID; Samuel Zimmerman; Francisco Barajas-Olmos; Cecilia Contreras-Cubas; Humberto García-OrtizORCID; Angélica Martínez-Hernández; Jacob M. Luber; Philipp Kirstahler; Tre BlohmORCID; Francis E. Smiley; Richard Arnold; Sonia A. Ballal; Sünje Johanna PampORCID; Julia Russ; Frank Maixner; Omar Rota-StabelliORCID; Nicola SegataORCID; Karl Reinhard; Lorena OrozcoORCID; Christina Warinner; Meradeth Snow; Steven LeBlanc; Aleksandar D. KosticORCID

<jats:title>Abstract</jats:title><jats:p>Loss of gut microbial diversity<jats:sup>1–6</jats:sup> in industrial populations is associated with chronic diseases<jats:sup>7</jats:sup>, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000–2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont <jats:italic>Methanobrevibacter smithii</jats:italic>. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces.</jats:p>

Palabras clave: Multidisciplinary.

Pp. 234-239

SARS-CoV-2 uses a multipronged strategy to impede host protein synthesis

Yaara FinkelORCID; Avi Gluck; Aharon Nachshon; Roni Winkler; Tal Fisher; Batsheva Rozman; Orel Mizrahi; Yoav LubelskyORCID; Binyamin ZuckermanORCID; Boris Slobodin; Yfat Yahalom-Ronen; Hadas Tamir; Igor UlitskyORCID; Tomer IsraelyORCID; Nir ParanORCID; Michal SchwartzORCID; Noam Stern-GinossarORCID

Palabras clave: Multidisciplinary.

Pp. 240-245

Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV

Alexey Stukalov; Virginie GiraultORCID; Vincent GrassORCID; Ozge KarayelORCID; Valter BergantORCID; Christian UrbanORCID; Darya A. HaasORCID; Yiqi HuangORCID; Lila OubrahamORCID; Anqi Wang; M. Sabri Hamad; Antonio Piras; Fynn M. Hansen; Maria C. Tanzer; Igor Paron; Luca ZinzulaORCID; Thomas Engleitner; Maria Reinecke; Teresa M. LavaccaORCID; Rosina Ehmann; Roman Wölfel; Jörg JoresORCID; Bernhard KusterORCID; Ulrike ProtzerORCID; Roland RadORCID; John ZiebuhrORCID; Volker ThielORCID; Pietro ScaturroORCID; Matthias MannORCID; Andreas PichlmairORCID

Palabras clave: Multidisciplinary.

Pp. 246-252

Adjuvanting a subunit COVID-19 vaccine to induce protective immunity

Prabhu S. Arunachalam; Alexandra C. WallsORCID; Nadia Golden; Caroline Atyeo; Stephanie FischingerORCID; Chunfeng Li; Pyone Aye; Mary Jane Navarro; Lilin Lai; Venkata Viswanadh EdaraORCID; Katharina RöltgenORCID; Kenneth Rogers; Lisa Shirreff; Douglas E. Ferrell; Samuel Wrenn; Deleah PettieORCID; John C. KraftORCID; Marcos C. Miranda; Elizabeth KeplORCID; Claire Sydeman; Natalie Brunette; Michael MurphyORCID; Brooke Fiala; Lauren Carter; Alexander G. White; Meera Trisal; Ching-Lin Hsieh; Kasi Russell-LodrigueORCID; Christopher Monjure; Jason Dufour; Skye SpencerORCID; Lara Doyle-Meyers; Rudolph P. Bohm; Nicholas J. ManessORCID; Chad RoyORCID; Jessica A. Plante; Kenneth S. Plante; Alex ZhuORCID; Matthew J. Gorman; Sally ShinORCID; Xiaoying Shen; Jane Fontenot; Shakti Gupta; Derek T. O’Hagan; Robbert Van Der Most; Rino RappuoliORCID; Robert L. Coffman; David Novack; Jason S. McLellanORCID; Shankar Subramaniam; David Montefiori; Scott D. BoydORCID; JoAnne L. FlynnORCID; Galit AlterORCID; Francois Villinger; Harry Kleanthous; Jay Rappaport; Mehul S. Suthar; Neil P. KingORCID; David VeeslerORCID; Bali PulendranORCID

Palabras clave: Multidisciplinary.

Pp. 253-258

High-dimensional characterization of post-acute sequelae of COVID-19

Ziyad Al-AlyORCID; Yan XieORCID; Benjamin Bowe

Palabras clave: Multidisciplinary.

Pp. 259-264

Swarm Learning for decentralized and confidential clinical machine learning

Stefanie Warnat-HerresthalORCID; Hartmut SchultzeORCID; Krishnaprasad Lingadahalli Shastry; Sathyanarayanan ManamohanORCID; Saikat Mukherjee; Vishesh GargORCID; Ravi Sarveswara; Kristian Händler; Peter Pickkers; N. Ahmad AzizORCID; Sofia Ktena; Florian TranORCID; Michael Bitzer; Stephan Ossowski; Nicolas CasadeiORCID; Christian HerrORCID; Daniel Petersheim; Uta Behrends; Fabian KernORCID; Tobias FehlmannORCID; Philipp Schommers; Clara Lehmann; Max AugustinORCID; Jan Rybniker; Janine Altmüller; Neha Mishra; Joana P. Bernardes; Benjamin Krämer; Lorenzo BonaguroORCID; Jonas Schulte-Schrepping; Elena De DomenicoORCID; Christian SieverORCID; Michael Kraut; Milind Desai; Bruno Monnet; Maria Saridaki; Charles Martin Siegel; Anna Drews; Melanie Nuesch-Germano; Heidi TheisORCID; Jan Heyckendorf; Stefan Schreiber; Sarah Kim-Hellmuth; Paul Balfanz; Thomas Eggermann; Peter Boor; Ralf Hausmann; Hannah Kuhn; Susanne Isfort; Julia Carolin Stingl; Günther Schmalzing; Christiane K. Kuhl; Rainer Röhrig; Gernot Marx; Stefan Uhlig; Edgar Dahl; Dirk Müller-Wieland; Michael Dreher; Nikolaus Marx; Jacob Nattermann; Dirk Skowasch; Ingo KurthORCID; Andreas KellerORCID; Robert Bals; Peter Nürnberg; Olaf Rieß; Philip Rosenstiel; Mihai G. NeteaORCID; Fabian TheisORCID; Sach Mukherjee; Michael Backes; Anna C. AschenbrennerORCID; Thomas UlasORCID; Angel Angelov; Alexander Bartholomäus; Anke Becker; Daniela Bezdan; Conny Blumert; Ezio Bonifacio; Peer Bork; Bunk Boyke; Helmut Blum; Thomas Clavel; Maria Colome-Tatche; Markus Cornberg; Inti Alberto De La Rosa Velázquez; Andreas Diefenbach; Alexander Dilthey; Nicole Fischer; Konrad Förstner; Sören Franzenburg; Julia-Stefanie Frick; Gisela Gabernet; Julien Gagneur; Tina Ganzenmueller; Marie Gauder; Janina Geißert; Alexander Goesmann; Siri Göpel; Adam Grundhoff; Hajo Grundmann; Torsten Hain; Frank Hanses; Ute Hehr; André Heimbach; Marius Hoeper; Friedemann Horn; Daniel Hübschmann; Michael Hummel; Thomas Iftner; Angelika Iftner; Thomas Illig; Stefan Janssen; Jörn Kalinowski; René Kallies; Birte Kehr; Oliver T. Keppler; Christoph Klein; Michael Knop; Oliver Kohlbacher; Karl Köhrer; Jan Korbel; Peter G. Kremsner; Denise Kühnert; Markus Landthaler; Yang Li; Kerstin U. Ludwig; Oliwia Makarewicz; Manja Marz; Alice C. McHardy; Christian Mertes; Maximilian Münchhoff; Sven Nahnsen; Markus Nöthen; Francine Ntoumi; Jörg Overmann; Silke Peter; Klaus Pfeffer; Isabell Pink; Anna R. Poetsch; Ulrike Protzer; Alfred Pühler; Nikolaus Rajewsky; Markus Ralser; Kristin Reiche; Stephan Ripke; Ulisses Nunes da Rocha; Antoine-Emmanuel Saliba; Leif Erik Sander; Birgit Sawitzki; Simone Scheithauer; Philipp Schiffer; Jonathan Schmid-Burgk; Wulf Schneider; Eva-Christina Schulte; Alexander Sczyrba; Mariam L. Sharaf; Yogesh Singh; Michael Sonnabend; Oliver Stegle; Jens Stoye; Janne Vehreschild; Thirumalaisamy P. Velavan; Jörg Vogel; Sonja Volland; Max von Kleist; Andreas Walker; Jörn Walter; Dagmar Wieczorek; Sylke Winkler; John Ziebuhr; Monique M. B. BretelerORCID; Evangelos J. Giamarellos-BourboulisORCID; Matthijs KoxORCID; Matthias BeckerORCID; Sorin Cheran; Michael S. Woodacre; Eng Lim GohORCID; Joachim L. SchultzeORCID; ;

<jats:title>Abstract</jats:title><jats:p>Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine<jats:sup>1,2</jats:sup>. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes<jats:sup>3</jats:sup>. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation<jats:sup>4,5</jats:sup>. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.</jats:p>

Palabras clave: Multidisciplinary.

Pp. 265-270

PIK3CA and CCM mutations fuel cavernomas through a cancer-like mechanism

Aileen A. RenORCID; Daniel A. SnellingsORCID; Yourong S. Su; Courtney C. HongORCID; Marco CastroORCID; Alan T. TangORCID; Matthew R. DetterORCID; Nicholas Hobson; Romuald GirardORCID; Sharbel Romanos; Rhonda Lightle; Thomas Moore; Robert ShenkarORCID; Christian Benavides; M. Makenzie BeamanORCID; Helge Müller-FielitzORCID; Mei Chen; Patricia Mericko; Jisheng Yang; Derek C. SungORCID; Michael T. Lawton; J. Michael Ruppert; Markus SchwaningerORCID; Jakob KörbelinORCID; Michael PotenteORCID; Issam A. Awad; Douglas A. MarchukORCID; Mark L. KahnORCID

Palabras clave: Multidisciplinary.

Pp. 271-276