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Molecular architecture of lineage allocation and tissue organization in early mouse embryo (vol 572, 528, 2019) 期刊论文
NATURE, 2020, 577 (7791) : E6-E6
作者:  Peng, Guangdun;  Suo, Shengbao;  Cui, Guizhong;  Yu, Fang;  Wang, Ran;  Chen, Jun;  Chen, Shirui;  Liu, Zhiwen;  Chen, Guoyu;  Qian, Yun;  Tam, Patrick P. L.;  Han, Jing-Dong J.;  Jing, Naihe
收藏  |  浏览/下载:14/0  |  提交时间:2020/07/03
Nanoplasma-enabled picosecond switches for ultrafast electronics (vol 579, pg 534, 2020) 期刊论文
NATURE, 2020, 580 (7803) : E8-E8
作者:  Li, Jing;  Xu, Chuanliang;  Lee, Hyung Joo;  Ren, Shancheng;  Zi, Xiaoyuan;  Zhang, Zhiming;  Wang, Haifeng;  Yu, Yongwei;  Yang, Chenghua;  Gao, Xiaofeng;  Hou, Jianguo;  Wang, Linhui;  Yang, Bo;  Yang, Qing;  Ye, Huamao;  Zhou, Tie;  Lu, Xin;  Wang, Yan;  Qu, Min;  Yang, Qingsong;  Zhang, Wenhui;  Shah, Nakul M.;  Pehrsson, Erica C.;  Wang, Shuo;  Wang, Zengjun;  Jiang, Jun;  Zhu, Yan;  Chen, Rui;  Chen, Huan;  Zhu, Feng;  Lian, Bijun;  Li, Xiaoyun;  Zhang, Yun;  Wang, Chao;  Wang, Yue;  Xiao, Guangan;  Jiang, Junfeng;  Yang, Yue;  Liang, Chaozhao;  Hou, Jianquan;  Han, Conghui;  Chen, Ming;  Jiang, Ning;  Zhang, Dahong;  Wu, Song;  Yang, Jinjian;  Wang, Tao;  Chen, Yongliang;  Cai, Jiantong;  Yang, Wenzeng;  Xu, Jun;  Wang, Shaogang;  Gao, Xu;  Wang, Ting;  Sun, Yinghao
收藏  |  浏览/下载:18/0  |  提交时间:2020/07/03
Recycling and metabolic flexibility dictate life in the lower oceanic crust 期刊论文
NATURE, 2020, 579 (7798) : 250-+
作者:  Zhou, Peng;  Yang, Xing-Lou;  Wang, Xian-Guang;  Hu, Ben;  Zhang, Lei;  Zhang, Wei;  Si, Hao-Rui;  Zhu, Yan;  Li, Bei;  Huang, Chao-Lin;  Chen, Hui-Dong;  Chen, Jing;  Luo, Yun;  Guo, Hua;  Jiang, Ren-Di;  Liu, Mei-Qin;  Chen, Ying;  Shen, Xu-Rui;  Wang, Xi;  Zheng, Xiao-Shuang;  Zhao, Kai;  Chen, Quan-Jiao;  Deng, Fei;  Liu, Lin-Lin;  Yan, Bing;  Zhan, Fa-Xian;  Wang, Yan-Yi;  Xiao, Geng-Fu;  Shi, Zheng-Li
收藏  |  浏览/下载:37/0  |  提交时间:2020/05/13

The lithified lower oceanic crust is one of Earth'  s last biological frontiers as it is difficult to access. It is challenging for microbiota that live in marine subsurface sediments or igneous basement to obtain sufficient carbon resources and energy to support growth(1-3) or to meet basal power requirements(4) during periods of resource scarcity. Here we show how limited and unpredictable sources of carbon and energy dictate survival strategies used by low-biomass microbial communities that live 10-750 m below the seafloor at Atlantis Bank, Indian Ocean, where Earth'  s lower crust is exposed at the seafloor. Assays of enzyme activities, lipid biomarkers, marker genes and microscopy indicate heterogeneously distributed and viable biomass with ultralow cell densities (fewer than 2,000 cells per cm(3)). Expression of genes involved in unexpected heterotrophic processes includes those with a role in the degradation of polyaromatic hydrocarbons, use of polyhydroxyalkanoates as carbon-storage molecules and recycling of amino acids to produce compounds that can participate in redox reactions and energy production. Our study provides insights into how microorganisms in the plutonic crust are able to survive within fractures or porous substrates by coupling sources of energy to organic and inorganic carbon resources that are probably delivered through the circulation of subseafloor fluids or seawater.


  
Nagaoka ferromagnetism observed in a quantum dot plaquette 期刊论文
NATURE, 2020, 579 (7800) : 528-533
作者:  Yu, Yong;  Ma, Fei;  Luo, Xi-Yu;  Jing, Bo;  Sun, Peng-Fei;  Fang, Ren-Zhou;  Yang, Chao-Wei;  Liu, Hui;  Zheng, Ming-Yang;  Xie, Xiu-Ping;  Zhang, Wei-Jun;  You, Li-Xing;  Wang, Zhen;  Chen, Teng-Yun;  Zhang, Qiang;  Bao, Xiao-Hui;  Pan, Jian-Wei
收藏  |  浏览/下载:31/0  |  提交时间:2020/07/03

A quantum dot device designed to host four electrons is used to demonstrate Nagaoka ferromagnetism-a model of itinerant magnetism that has so far been limited to theoretical investigation.


Engineered, highly controllable quantum systems are promising simulators of emergent physics beyond the simulation capabilities of classical computers(1). An important problem in many-body physics is itinerant magnetism, which originates purely from long-range interactions of free electrons and whose existence in real systems has been debated for decades(2,3). Here we use a quantum simulator consisting of a four-electron-site square plaquette of quantum dots(4) to demonstrate Nagaoka ferromagnetism(5). This form of itinerant magnetism has been rigorously studied theoretically(6-9) but has remained unattainable in experiments. We load the plaquette with three electrons and demonstrate the predicted emergence of spontaneous ferromagnetic correlations through pairwise measurements of spin. We find that the ferromagnetic ground state is remarkably robust to engineered disorder in the on-site potentials and we can induce a transition to the low-spin state by changing the plaquette topology to an open chain. This demonstration of Nagaoka ferromagnetism highlights that quantum simulators can be used to study physical phenomena that have not yet been observed in any experimental system. The work also constitutes an important step towards large-scale quantum dot simulators of correlated electron systems.


  
Mechanism of adrenergic Ca(V)1.2 stimulation revealed by proximity proteomics 期刊论文
NATURE, 2020, 577 (7792) : 695-+
作者:  Peng, Guangdun;  Suo, Shengbao;  Cui, Guizhong;  Yu, Fang;  Wang, Ran;  Chen, Jun;  Chen, Shirui;  Liu, Zhiwen;  Chen, Guoyu;  Qian, Yun;  Tam, Patrick P. L.;  Han, Jing-Dong J.;  Jing, Naihe
收藏  |  浏览/下载:24/0  |  提交时间:2020/07/03

An in vivo approach to identify proteins whose enrichment near cardiac Ca(V)1.2 channels changes upon beta-adrenergic stimulation finds the G protein Rad, which is phosphorylated by protein kinase A, thereby relieving channel inhibition by Rad and causing an increased Ca2+ current.


Increased cardiac contractility during the fight-or-flight response is caused by beta-adrenergic augmentation of Ca(V)1.2 voltage-gated calcium channels(1-4). However, this augmentation persists in transgenic murine hearts expressing mutant Ca(V)1.2 alpha(1C) and beta subunits that can no longer be phosphorylated by protein kinase A-an essential downstream mediator of beta-adrenergic signalling-suggesting that non-channel factors are also required. Here we identify the mechanism by which beta-adrenergic agonists stimulate voltage-gated calcium channels. We express alpha(1C) or beta(2B) subunits conjugated to ascorbate peroxidase(5) in mouse hearts, and use multiplexed quantitative proteomics(6,7) to track hundreds of proteins in the proximity of Ca(V)1.2. We observe that the calcium-channel inhibitor Rad(8,9), a monomeric G protein, is enriched in the Ca(V)1.2 microenvironment but is depleted during beta-adrenergic stimulation. Phosphorylation by protein kinase A of specific serine residues on Rad decreases its affinity for beta subunits and relieves constitutive inhibition of Ca(V)1.2, observed as an increase in channel open probability. Expression of Rad or its homologue Rem in HEK293T cells also imparts stimulation of Ca(V)1.3 and Ca(V)2.2 by protein kinase A, revealing an evolutionarily conserved mechanism that confers adrenergic modulation upon voltage-gated calcium channels.


  
Improved protein structure prediction using potentials from deep learning 期刊论文
NATURE, 2020, 577 (7792) : 706-+
作者:  Ma, Runze;  Cao, Duanyun;  Zhu, Chongqin;  Tian, Ye;  Peng, Jinbo;  Guo, Jing;  Chen, Ji;  Li, Xin-Zheng;  Francisco, Joseph S.;  Zeng, Xiao Cheng;  Xu, Li-Mei;  Wang, En-Ge;  Jiang, Ying
收藏  |  浏览/下载:142/0  |  提交时间:2020/07/03

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence(1). This problem is of fundamental importance as the structure of a protein largely determines its function(2)  however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures(3). Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force(4) that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction(5) (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores(6) of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined(7).