Yongqiang Liu, Hongru Wang, Zhimin Jiang, Wei Wang, Ruineng Xu, Qihui Wang, Zhihua Zhang, Aifu Li, Yan Liang, Shujun Ou, Xiujie Liu, Shouyun Cao, Hongning Tong, Yonghong Wang, Feng Zhou, Hong Liao, Bin Hu, Chengcai Chu <<<

The intensive application of inorganic nitrogen underlies marked increases in crop production, but imposes detrimental effects on ecosystems: it is therefore crucial for future sustainable agriculture to improve the nitrogen-use efficiency of crop plants. Here we report the genetic basis of nitrogen-use efficiency associated with adaptation to local soils in rice (Oryza sativa L.). Using a panel of diverse rice germplasm collected from different ecogeographical regions, we performed a genome-wide association study on the tillering response to nitrogen-the trait that is most closely correlated with nitrogen-use efficiency in rice-and identified OsTCP19 as a modulator of this tillering response through its transcriptional response to nitrogen and its targeting to the tiller-promoting gene DWARF AND LOW-TILLERING (DLT). A 29-bp insertion and/or deletion in the OsTCP19 promoter confers a differential transcriptional response and variation in the tillering response to nitrogen among rice varieties. The allele of OsTCP19 associated with a high tillering response to nitrogen is prevalent in wild rice populations, but has largely been lost in modern cultivars: this loss correlates with increased local soil nitrogen content, which suggests that it might have contributed to geographical adaptation in rice. Introgression of the allele associated with a high tillering response into modern rice cultivars boosts grain yield and nitrogen-use efficiency under low or moderate levels of nitrogen, which demonstrates substantial potential for rice breeding and the amelioration of negative environment effects by reducing the application of nitrogen to crops. <<<

Daniel J Emerson, Peiyao A Zhao, Ashley L Cook, R Jordan Barnett, Kyle N Klein, Dalila Saulebekova, Chunmin Ge, Linda Zhou, Zoltan Simandi, Miriam K Minsk, Katelyn R Titus, Weitao Wang, Wanfeng Gong, Di Zhang, Liyan Yang, Sergey V Venev, Johan H Gibcus, Hongbo Yang, Takayo Sasaki, Masato T Kanemaki, Feng Yue, Job Dekker, Chun-Long Chen, David M Gilbert, Jennifer E Phillips-Cremins <<<

DNA replication occurs through an intricately regulated series of molecular events and is fundamental for genome stability. At present, it is unknown how the locations of replication origins are determined in the human genome. Here we dissect the role of topologically associating domains (TADs), subTADs and loops in the positioning of replication initiation zones (IZs). We stratify TADs and subTADs by the presence of corner-dots indicative of loops and the orientation of CTCF motifs. We find that high-efficiency, early replicating IZs localize to boundaries between adjacent corner-dot TADs anchored by high-density arrays of divergently and convergently oriented CTCF motifs. By contrast, low-efficiency IZs localize to weaker dotless boundaries. Following ablation of cohesin-mediated loop extrusion during G1, high-efficiency IZs become diffuse and delocalized at boundaries with complex CTCF motif orientations. Moreover, G1 knockdown of the cohesin unloading factor WAPL results in gained long-range loops and narrowed localization of IZs at the same boundaries. Finally, targeted deletion or insertion of specific boundaries causes local replication timing shifts consistent with IZ loss or gain, respectively. Our data support a model in which cohesin-mediated loop extrusion and stalling at a subset of genetically encoded TAD and subTAD boundaries is an essential determinant of the locations of replication origins in human S phase. <<<

Chromosome replication is performed by a complex and intricate ensemble of proteins termed the replisome, where the DNA polymerases Polδ and Polε, DNA polymerase α-primase (Polα) and accessory proteins including AND-1, CLASPIN and TIMELESS-TIPIN (respectively known as Ctf4, Mrc1 and Tof1-Csm3 in Saccharomyces cerevisiae) are organized around the CDC45-MCM-GINS (CMG) replicative helicase. Because a functional human replisome has not been reconstituted from purified proteins, how these factors contribute to human DNA replication and whether additional proteins are required for optimal DNA synthesis are poorly understood. Here we report the biochemical reconstitution of human replisomes that perform fast and efficient DNA replication using 11 purified human replication factors made from 43 polypeptides. Polε, but not Polδ, is crucial for optimal leading-strand synthesis. Unexpectedly, Polε-mediated leading-strand replication is highly dependent on the sliding-clamp processivity factor PCNA and the alternative clamp loader complex CTF18-RFC. We show how CLASPIN and TIMELESS-TIPIN contribute to replisome progression and demonstrate that, in contrast to the budding yeast replisome, AND-1 directly augments leading-strand replication. Moreover, although AND-1 binds to Polα, the interaction is dispensable for lagging-strand replication, indicating that Polα is functionally recruited via an AND-1-independent mechanism for priming in the human replisome. Collectively, our work reveals how the human replisome achieves fast and efficient leading-strand and lagging-strand DNA replication, and provides a powerful system for future studies of the human replisome and its interactions with other DNA metabolic processes. <<<

Jing Xiong, Seong Su Kang, Zhihao Wang, Xia Liu, Tan-Chun Kuo, Funda Korkmaz, Ashley Padilla, Sari Miyashita, Pokman Chan, Zhaohui Zhang, Pavel Katsel, Jocoll Burgess, Anisa Gumerova, Kseniia Ievleva, Damini Sant, Shan-Ping Yu, Valeriia Muradova, Tal Frolinger, Daria Lizneva, Jameel Iqbal, Ki A Goosens, Sakshi Gera, Clifford J Rosen, Vahram Haroutunian, Vitaly Ryu, Tony Yuen, Mone Zaidi, Keqiang Ye <<<

Alzheimer's disease has a higher incidence in older women, with a spike in cognitive decline that tracks with visceral adiposity, dysregulated energy homeostasis and bone loss during the menopausal transition. Inhibiting the action of follicle-stimulating hormone (FSH) reduces body fat, enhances thermogenesis, increases bone mass and lowers serum cholesterol in mice. Here we show that FSH acts directly on hippocampal and cortical neurons to accelerate amyloid-β and Tau deposition and impair cognition in mice displaying features of Alzheimer's disease. Blocking FSH action in these mice abrogates the Alzheimer's disease-like phenotype by inhibiting the neuronal C/EBPβ-δ-secretase pathway. These data not only suggest a causal role for rising serum FSH levels in the exaggerated Alzheimer's disease pathophysiology during menopause, but also reveal an opportunity for treating Alzheimer's disease, obesity, osteoporosis and dyslipidaemia with a single FSH-blocking agent. <<<

Stefanie Warnat-Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N Ahmad Aziz, Sofia Ktena, Florian Tran, Michael Bitzer, Stephan Ossowski, Nicolas Casadei, Christian Herr, Daniel Petersheim, Uta Behrends, Fabian Kern, Tobias Fehlmann, Philipp Schommers, Clara Lehmann, Max Augustin, Jan Rybniker, Janine Altmüller, Neha Mishra, Joana P Bernardes, Benjamin Krämer, Lorenzo Bonaguro, Jonas Schulte-Schrepping, Elena De Domenico, Christian Siever, Michael Kraut, Milind Desai, Bruno Monnet, Maria Saridaki, Charles Martin Siegel, Anna Drews, Melanie Nuesch-Germano, Heidi Theis, Jan Heyckendorf, Stefan Schreiber, Sarah Kim-Hellmuth, COVID-19 Aachen Study (COVAS), Jacob Nattermann, Dirk Skowasch, Ingo Kurth, Andreas Keller, Robert Bals, Peter Nürnberg, Olaf Rieß, Philip Rosenstiel, Mihai G Netea, Fabian Theis, Sach Mukherjee, Michael Backes, Anna C Aschenbrenner, Thomas Ulas, Deutsche COVID-19 Omics Initiative (DeCOI), Monique M B Breteler, Evangelos J Giamarellos-Bourboulis, Matthijs Kox, Matthias Becker, Sorin Cheran, Michael S Woodacre, Eng Lim Goh, Joachim L Schultze <<<

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. 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. <<<

Junyue Cao, Malte Spielmann, Xiaojie Qiu, Xingfan Huang, Daniel M Ibrahim, Andrew J Hill, Fan Zhang, Stefan Mundlos, Lena Christiansen, Frank J Steemers, Cole Trapnell, Jay Shendure <<<

Mammalian organogenesis is a remarkable process. Within a short timeframe, the cells of the three germ layers transform into an embryo that includes most of the major internal and external organs. Here we investigate the transcriptional dynamics of mouse organogenesis at single-cell resolution. Using single-cell combinatorial indexing, we profiled the transcriptomes of around 2 million cells derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting 'mouse organogenesis cell atlas' (MOCA) provides a global view of developmental processes during this critical window. We use Monocle 3 to identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. We explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle. <<<

Ugur Sahin, Petra Oehm, Evelyna Derhovanessian, Robert A Jabulowsky, Mathias Vormehr, Maike Gold, Daniel Maurus, Doreen Schwarck-Kokarakis, Andreas N Kuhn, Tana Omokoko, Lena M Kranz, Mustafa Diken, Sebastian Kreiter, Heinrich Haas, Sebastian Attig, Richard Rae, Katarina Cuk, Alexandra Kemmer-Brück, Andrea Breitkreuz, Claudia Tolliver, Janina Caspar, Juliane Quinkhardt, Lisa Hebich, Malte Stein, Alexander Hohberger, Isabel Vogler, Inga Liebig, Stephanie Renken, Julian Sikorski, Melanie Leierer, Verena Müller, Heidrun Mitzel-Rink, Matthias Miederer, Christoph Huber, Stephan Grabbe, Jochen Utikal, Andreas Pinter, Roland Kaufmann, Jessica C Hassel, Carmen Loquai, Özlem Türeci <<<

Treating patients who have cancer with vaccines that stimulate a targeted immune response is conceptually appealing, but cancer vaccine trials have not been successful in late-stage patients with treatment-refractory tumours. We are testing melanoma FixVac (BNT111)-an intravenously administered liposomal RNA (RNA-LPX) vaccine, which targets four non-mutated, tumour-associated antigens that are prevalent in melanoma-in an ongoing, first-in-human, dose-escalation phase I trial in patients with advanced melanoma (Lipo-MERIT trial, ClinicalTrials.gov identifier NCT02410733). We report here data from an exploratory interim analysis that show that melanoma FixVac, alone or in combination with blockade of the checkpoint inhibitor PD1, mediates durable objective responses in checkpoint-inhibitor (CPI)-experienced patients with unresectable melanoma. Clinical responses are accompanied by the induction of strong CD4 and CD8 T cell immunity against the vaccine antigens. The antigen-specific cytotoxic T-cell responses in some responders reach magnitudes typically reported for adoptive T-cell therapy, and are durable. Our findings indicate that RNA-LPX vaccination is a potent immunotherapy in patients with CPI-experienced melanoma, and suggest the general utility of non-mutant shared tumour antigens as targets for cancer vaccination. <<<

Lena M Kranz, Mustafa Diken, Heinrich Haas, Sebastian Kreiter, Carmen Loquai, Kerstin C Reuter, Martin Meng, Daniel Fritz, Fulvia Vascotto, Hossam Hefesha, Christian Grunwitz, Mathias Vormehr, Yves Hüsemann, Abderraouf Selmi, Andreas N Kuhn, Janina Buck, Evelyna Derhovanessian, Richard Rae, Sebastian Attig, Jan Diekmann, Robert A Jabulowsky, Sandra Heesch, Jessica Hassel, Peter Langguth, Stephan Grabbe, Christoph Huber, Özlem Türeci, Ugur Sahin <<<

Lymphoid organs, in which antigen presenting cells (APCs) are in close proximity to T cells, are the ideal microenvironment for efficient priming and amplification of T-cell responses. However, the systemic delivery of vaccine antigens into dendritic cells (DCs) is hampered by various technical challenges. Here we show that DCs can be targeted precisely and effectively in vivo using intravenously administered RNA-lipoplexes (RNA-LPX) based on well-known lipid carriers by optimally adjusting net charge, without the need for functionalization of particles with molecular ligands. The LPX protects RNA from extracellular ribonucleases and mediates its efficient uptake and expression of the encoded antigen by DC populations and macrophages in various lymphoid compartments. RNA-LPX triggers interferon-α (IFNα) release by plasmacytoid DCs and macrophages. Consequently, DC maturation in situ and inflammatory immune mechanisms reminiscent of those in the early systemic phase of viral infection are activated. We show that RNA-LPX encoding viral or mutant neo-antigens or endogenous self-antigens induce strong effector and memory T-cell responses, and mediate potent IFNα-dependent rejection of progressive tumours. A phase I dose-escalation trial testing RNA-LPX that encode shared tumour antigens is ongoing. In the first three melanoma patients treated at a low-dose level, IFNα and strong antigen-specific T-cell responses were induced, supporting the identified mode of action and potency. As any polypeptide-based antigen can be encoded as RNA, RNA-LPX represent a universally applicable vaccine class for systemic DC targeting and synchronized induction of both highly potent adaptive as well as type-I-IFN-mediated innate immune mechanisms for cancer immunotherapy. <<<

Longxing Cao, Brian Coventry, Inna Goreshnik, Buwei Huang, William Sheffler, Joon Sung Park, Kevin M Jude, Iva Marković, Rameshwar U Kadam, Koen H G Verschueren, Kenneth Verstraete, Scott Thomas Russell Walsh, Nathaniel Bennett, Ashish Phal, Aerin Yang, Lisa Kozodoy, Michelle DeWitt, Lora Picton, Lauren Miller, Eva-Maria Strauch, Nicholas D DeBouver, Allison Pires, Asim K Bera, Samer Halabiya, Bradley Hammerson, Wei Yang, Steffen Bernard, Lance Stewart, Ian A Wilson, Hannele Ruohola-Baker, Joseph Schlessinger, Sangwon Lee, Savvas N Savvides, K Christopher Garcia, David Baker <<<

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder-target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications. <<<

Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A A Kohl, Anna Potapenko, Andrew J Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, Demis Hassabis <<<

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. <<<

Bruce A Curtis, Goro Tanifuji, Fabien Burki, Ansgar Gruber, Manuel Irimia, Shinichiro Maruyama, Maria C Arias, Steven G Ball, Gillian H Gile, Yoshihisa Hirakawa, Julia F Hopkins, Alan Kuo, Stefan A Rensing, Jeremy Schmutz, Aikaterini Symeonidi, Marek Elias, Robert J M Eveleigh, Emily K Herman, Mary J Klute, Takuro Nakayama, Miroslav Oborník, Adrian Reyes-Prieto, E Virginia Armbrust, Stephen J Aves, Robert G Beiko, Pedro Coutinho, Joel B Dacks, Dion G Durnford, Naomi M Fast, Beverley R Green, Cameron J Grisdale, Franziska Hempel, Bernard Henrissat, Marc P Höppner, Ken-Ichiro Ishida, Eunsoo Kim, Luděk Kořený, Peter G Kroth, Yuan Liu, Shehre-Banoo Malik, Uwe G Maier, Darcy McRose, Thomas Mock, Jonathan A D Neilson, Naoko T Onodera, Anthony M Poole, Ellen J Pritham, Thomas A Richards, Gabrielle Rocap, Scott W Roy, Chihiro Sarai, Sarah Schaack, Shu Shirato, Claudio H Slamovits, David F Spencer, Shigekatsu Suzuki, Alexandra Z Worden, Stefan Zauner, Kerrie Barry, Callum Bell, Arvind K Bharti, John A Crow, Jane Grimwood, Robin Kramer, Erika Lindquist, Susan Lucas, Asaf Salamov, Geoffrey I McFadden, Christopher E Lane, Patrick J Keeling, Michael W Gray, Igor V Grigoriev, John M Archibald <<<

Cryptophyte and chlorarachniophyte algae are transitional forms in the widespread secondary endosymbiotic acquisition of photosynthesis by engulfment of eukaryotic algae. Unlike most secondary plastid-bearing algae, miniaturized versions of the endosymbiont nuclei (nucleomorphs) persist in cryptophytes and chlorarachniophytes. To determine why, and to address other fundamental questions about eukaryote-eukaryote endosymbiosis, we sequenced the nuclear genomes of the cryptophyte Guillardia theta and the chlorarachniophyte Bigelowiella natans. Both genomes have >21,000 protein genes and are intron rich, and B. natans exhibits unprecedented alternative splicing for a single-celled organism. Phylogenomic analyses and subcellular targeting predictions reveal extensive genetic and biochemical mosaicism, with both host- and endosymbiont-derived genes servicing the mitochondrion, the host cell cytosol, the plastid and the remnant endosymbiont cytosol of both algae. Mitochondrion-to-nucleus gene transfer still occurs in both organisms but plastid-to-nucleus and nucleomorph-to-nucleus transfers do not, which explains why a small residue of essential genes remains locked in each nucleomorph. <<<

Dexterous magnetic manipulation of ferromagnetic objects is well established, with three to six degrees of freedom possible depending on object geometry. There are objects for which non-contact dexterous manipulation is desirable that do not contain an appreciable amount of ferromagnetic material but do contain electrically conductive material. Time-varying magnetic fields generate eddy currents in conductive materials, with resulting forces and torques due to the interaction of the eddy currents with the magnetic field. This phenomenon has previously been used to induce drag to reduce the motion of objects as they pass through a static field, or to apply force on an object in a single direction using a dynamic field, but has not been used to perform the type of dexterous manipulation of conductive objects that has been demonstrated with ferromagnetic objects. Here we show that manipulation, with six degrees of freedom, of conductive objects is possible by using multiple rotating magnetic dipole fields. Using dimensional analysis, combined with multiphysics numerical simulations and experimental verification, we characterize the forces and torques generated on a conductive sphere in a rotating magnetic dipole field. With the resulting model, we perform dexterous manipulation in simulations and physical experiments. <<<

Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability. Deep-learning accelerators aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting physical substrates beyond conventional electronics. Approaches so far have been unable to apply the backpropagation algorithm to train unconventional novel hardware in situ. The advantages of backpropagation have made it the de facto training method for large-scale neural networks, so this deficiency constitutes a major impediment. Here we introduce a hybrid in situ-in silico algorithm, called physics-aware training, that applies backpropagation to train controllable physical systems. Just as deep learning realizes computations with deep neural networks made from layers of mathematical functions, our approach allows us to train deep physical neural networks made from layers of controllable physical systems, even when the physical layers lack any mathematical isomorphism to conventional artificial neural network layers. To demonstrate the universality of our approach, we train diverse physical neural networks based on optics, mechanics and electronics to experimentally perform audio and image classification tasks. Physics-aware training combines the scalability of backpropagation with the automatic mitigation of imperfections and noise achievable with in situ algorithms. Physical neural networks have the potential to perform machine learning faster and more energy-efficiently than conventional electronic processors and, more broadly, can endow physical systems with automatically designed physical functionalities, for example, for robotics, materials and smart sensors. <<<

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. <<<