GSTDTAP

浏览/检索结果: 共12条,第1-10条 帮助

已选(0)清除 条数/页:   排序方式:
Structural basis of DNA targeting by a transposon-encoded CRISPR-Cas system 期刊论文
NATURE, 2020, 577 (7789) : 271-+
作者:  Halpin-Healy, Tyler S.;  Klompe, Sanne E.;  Sternberg, Samuel H.;  Fernandez, Israel S.
收藏  |  浏览/下载:17/0  |  提交时间:2020/07/03

Bacteria use adaptive immune systems encoded by CRISPR and Cas genes to maintain genomic integrity when challenged by pathogens and mobile genetic elements(1-3). Type I CRISPR-Cas systems typically target foreign DNA for degradation via joint action of the ribonucleoprotein complex Cascade and the helicase-nuclease Cas3(4,5), but nuclease-deficient type I systems lacking Cas3 have been repurposed for RNA-guided transposition by bacterial Tn7-like transposons(6,7). How CRISPR- and transposon-associated machineries collaborate during DNA targeting and insertion remains unknown. Here we describe structures of a TniQ-Cascade complex encoded by the Vibrio cholerae Tn6677 transposon using cryo-electron microscopy, revealing the mechanistic basis of this functional coupling. The cryo-electron microscopy maps enabled de novo modelling and refinement of the transposition protein TniQ, which binds to the Cascade complex as a dimer in a head-to-tail configuration, at the interface formed by Cas6 and Cas7 near the 3'  end of the CRISPR RNA (crRNA). The natural Cas8-Cas5 fusion protein binds the 5'  crRNA handle and contacts the TniQ dimer via a flexible insertion domain. A target DNA-bound structure reveals critical interactions necessary for protospacer-adjacent motif recognition and R-loop formation. This work lays the foundation for a structural understanding of how DNA targeting by TniQ-Cascade leads to downstream recruitment of additional transposase proteins, and will guide protein engineering efforts to leverage this system for programmable DNA insertions in genome-engineering applications.


  
Hidden neural states underlie canary song syntax 期刊论文
NATURE, 2020
作者:  Bao, Han;  Duan, Junlei;  Jin, Shenchao;  Lu, Xingda;  Li, Pengxiong;  Qu, Weizhi;  Wang, Mingfeng;  Novikova, Irina;  Mikhailov, Eugeniy E.;  Zhao, Kai-Feng;  Molmer, Klaus;  Shen, Heng;  Xiao, Yanhong
收藏  |  浏览/下载:33/0  |  提交时间:2020/07/03

Neurons in the canary premotor cortex homologue encode past song phrases and transitions, carrying information relevant to future choice of phrases as '  hidden states'  during song.


Coordinated skills such as speech or dance involve sequences of actions that follow syntactic rules in which transitions between elements depend on the identities and order of past actions. Canary songs consist of repeated syllables called phrases, and the ordering of these phrases follows long-range rules(1)in which the choice of what to sing depends on the song structure many seconds prior. The neural substrates that support these long-range correlations are unknown. Here, using miniature head-mounted microscopes and cell-type-specific genetic tools, we observed neural activity in the premotor nucleus HVC(2-4)as canaries explored various phrase sequences in their repertoire. We identified neurons that encode past transitions, extending over four phrases and spanning up to four seconds and forty syllables. These neurons preferentially encode past actions rather than future actions, can reflect more than one song history, and are active mostly during the rare phrases that involve history-dependent transitions in song. These findings demonstrate that the dynamics of HVC include '  hidden states'  that are not reflected in ongoing behaviour but rather carry information about prior actions. These states provide a possible substrate for the control of syntax transitions governed by long-range rules.


  
Origin of complexity in haemoglobin evolution 期刊论文
NATURE, 2020
作者:  Cheema, Suraj S.;  Kwon, Daewoong;  Shanker, Nirmaan;  dos Reis, Roberto;  Hsu, Shang-Lin;  Xiao, Jun;  Zhang, Haigang;  Wagner, Ryan;  Datar, Adhiraj;  McCarter, Margaret R.;  Serrao, Claudy R.;  Yadav, Ajay K.;  Karbasian, Golnaz;  Hsu, Cheng-Hsiang;  Tan, Ava J.;  Wang, Li-Chen;  Thakare, Vishal;  Zhang, Xiang;  Mehta, Apurva;  Karapetrova, Evguenia;  Chopdekar, Rajesh, V;  Shafer, Padraic;  Arenholz, Elke;  Hu, Chenming;  Proksch, Roger;  Ramesh, Ramamoorthy;  Ciston, Jim;  Salahuddin, Sayeef
收藏  |  浏览/下载:80/0  |  提交时间:2020/07/03

Most proteins associate into multimeric complexes with specific architectures(1,2), which often have functional properties such as cooperative ligand binding or allosteric regulation(3). No detailed knowledge is available about how any multimer and its functions arose during evolution. Here we use ancestral protein reconstruction and biophysical assays to elucidate the origins of vertebrate haemoglobin, a heterotetramer of paralogous alpha- and beta-subunits that mediates respiratory oxygen transport and exchange by cooperatively binding oxygen with moderate affinity. We show that modern haemoglobin evolved from an ancient monomer and characterize the historical '  missing link'  through which the modern tetramer evolved-a noncooperative homodimer with high oxygen affinity that existed before the gene duplication that generated distinct alpha- and beta-subunits. Reintroducing just two post-duplication historical substitutions into the ancestral protein is sufficient to cause strong tetramerization by creating favourable contacts with more ancient residues on the opposing subunit. These surface substitutions markedly reduce oxygen affinity and even confer cooperativity, because an ancient linkage between the oxygen binding site and the multimerization interface was already an intrinsic feature of the protein'  s structure. Our findings establish that evolution can produce new complex molecular structures and functions via simple genetic mechanisms that recruit existing biophysical features into higher-level architectures.


Experimental analysis of reconstructed ancestral globins reveals that haemoglobin'  s complex tetrameric structure and oxygen-binding functions evolved by simple genetic and biophysical mechanisms.


  
Olfactory receptor and circuit evolution promote host specialization 期刊论文
NATURE, 2020
作者:  Chen, Tse-An;  Chuu, Chih-Piao;  Tseng, Chien-Chih;  Wen, Chao-Kai;  Wong, H. -S. Philip;  Pan, Shuangyuan;  Li, Rongtan;  Chao, Tzu-Ang;  Chueh, Wei-Chen;  Zhang, Yanfeng;  Fu, Qiang;  Yakobson, Boris I.;  Chang, Wen-Hao;  Li, Lain-Jong
收藏  |  浏览/下载:28/0  |  提交时间:2020/07/03

The evolution of animal behaviour is poorly understood(1,2). Despite numerous correlations between interspecific divergence in behaviour and nervous system structure and function, demonstrations of the genetic basis of these behavioural differences remain rare(3-5). Here we develop a neurogenetic model, Drosophila sechellia, a species that displays marked differences in behaviour compared to its close cousin Drosophila melanogaster(6,7), which are linked to its extreme specialization on noni fruit (Morinda citrifolia)(8-16). Using calcium imaging, we identify olfactory pathways in D. sechellia that detect volatiles emitted by the noni host. Our mutational analysis indicates roles for different olfactory receptors in long- and short-range attraction to noni, and our cross-species allele-transfer experiments demonstrate that the tuning of one of these receptors is important for species-specific host-seeking. We identify the molecular determinants of this functional change, and characterize their evolutionary origin and behavioural importance. We perform circuit tracing in the D. sechellia brain, and find that receptor adaptations are accompanied by increased sensory pooling onto interneurons as well as species-specific central projection patterns. This work reveals an accumulation of molecular, physiological and anatomical traits that are linked to behavioural divergence between species, and defines a model for investigating speciation and the evolution of the nervous system.


A neurogenetic model, Drosophila sechellia-a relative of Drosophila melanogaster that has developed an extreme specialization for a single host plant-sheds light on the evolution of interspecific differences in behaviour.


  
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:156/0  |  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).


  
Shift in size of bumblebee queens over the last century 期刊论文
GLOBAL CHANGE BIOLOGY, 2019
作者:  Gerard, Maxence;  Martinet, Baptiste;  Maebe, Kevin;  Marshall, Leon;  Smagghe, Guy;  Vereecken, Nicolas J.;  Vray, Sarah;  Rasmont, Pierre;  Michez, Denis
收藏  |  浏览/下载:32/0  |  提交时间:2020/02/17
Bergmann'  s rule  body size  bumblebees  genetic structure  global change  habitat fragmentation  
Assessment of the degree of order in the organisational structure of electricity regulatory institution in China based on shannon entropy 期刊论文
ENERGY POLICY, 2019, 132: 429-439
作者:  Wang, Zheng-Xin;  He, Ling-Yang;  Li, Dan-Dan
收藏  |  浏览/下载:20/0  |  提交时间:2019/11/27
Electricity regulation  Organisational structure  Degree of order  Shannon entropy  Genetic algorithm  
Western and eastern post-glacial migration pathways shape the genetic structure of sycamore maple (Acer pseudoplatanus L.) in Germany 期刊论文
FOREST ECOLOGY AND MANAGEMENT, 2019, 432: 83-93
作者:  Neophytou, Charalambos;  Konnert, Monika;  Fussi, Barbara
收藏  |  浏览/下载:12/0  |  提交时间:2019/04/09
Sycamore maple  Genetic structure  Origin  Microsatellites  Post-glacial migration  Introgression  
Historical seed use and transfer affects geographic specificity in genetic diversity and structure of old planted Pinus thunbergii populations 期刊论文
FOREST ECOLOGY AND MANAGEMENT, 2018, 408: 211-219
作者:  Iwaizumi, Masakazu G.;  Miyata, Shousuke;  Hirao, Tomonori;  Tamura, Miho;  Watanabe, Atsushi
收藏  |  浏览/下载:6/0  |  提交时间:2019/04/09
Genetic diversity  Genetic structure  Historical seed transfer  Pinus thunbergii  Planted population  
Extensive sib-mating in a refugial population of beech (Fagus sylvatica) growing along a lowland river 期刊论文
FOREST ECOLOGY AND MANAGEMENT, 2018, 407: 66-74
作者:  Ouayjan, Adib;  Hampe, Arndt
收藏  |  浏览/下载:22/0  |  提交时间:2019/04/09
Forest genetic resources  Long-term population persistence  Mating system  Pollen cloud  Riparian forest  Spatial genetic structure