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Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants (vol 11, 955, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 955页

作者： Donovan, Margaret K. R. D'Antonio-Chronowska, Agnieszka D'Antonio, Matteo Frazer, Kelly A. University of California San Diego Bioinformatics and Systems Biology Graduate Program La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Biomedical Informatics La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Pediatrics and Rady Children’s Hospital La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Pediatrics and Rady Children’s Hospital La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Institute for Genomic Medicine La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242)

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Gene expression Genetic association study RNA sequencing

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CryoEM structure of yeast cytoplasmic exosome complex

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细胞研究（英文版） 2016年第7期26卷 822-837页

作者： Jun-Jie Liu Chu-Ya Niu Yao Wu Dan Tan Yang Wang Ming-Da Ye Yang Liu Ministry of Education Key Laboratory of Protein Sciences Tsinghua-Peking Joint Center for Life Sciences Beijing Advanced Innovation Center for Structural Biology School of Life Sciences Tsinghua University Beijing 100084 China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Tsinghua University Beijing 100084 China Ministry of Education Key Laboratory of Protein Sciences Tsinghua-Peking Joint Center for Life Sciences Beijing Advanced Innovation Center for Structural Biology School of Life Sciences Tsinghua University Beijing 100084 China National Institute of Biological Sciences Beijing 102206 China Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science Tsinghua University Beijing 100084 China MOE Key Laboratory of Bioinformatics Tsinghua-Peking Joint Center for Life Sciences Center for Synthetic and Systems Biology School of Life Sciences Tsinghua University Beijing 100084 China

The eukaryotic multi-subunit RNA exosome complex plays crucial roles in 3′-to-5′ RNA processing and decay. Rrp6 and Ski7 are the major cofactors for the nuclear and cytoplasmic exosomes, respectively. In the cytoplasm, Ski7 helps the exosome to target mRNAs for degradation and turnover via a through-core pathway. However, the interaction between Ski7 and the exosome complex has remained unclear. The transaction of RNA substrates within the exosome is also elusive. In this work, we used single-particle cryo-electron microscopy to solve the structures of the Ski7-exosome complex in RNA-free and RNA-bound forms at resolutions of 4.2 Å and 5.8 Å, respectively. These structures reveal that the N-terminal domain of Ski7 adopts a structural arrangement and interacts with the exosome in a similar fashion to the C-terminal domain of nuclear Rrp6. Further structural analysis of exosomes with RNA substrates harboring 3′ overhangs of different length suggests a switch mechanism of RNA-induced exosome activation in the through-core pathway of RNA processing.

关键词： exosome RNA decay Ski7 Rrp6 cryo-EM

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Core packing of well-defined x-ray and NMR structures is the same

arXiv

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arXiv 2022年

作者： Grigas, Alex T. Liu, Zhuoyi Regan, Lynne O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh United Kingdom Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Numerous studies have investigated the differences and similarities between protein structures determined by solution NMR spectroscopy and those determined by x-ray crystallography. A fundamental question is whether any observed differences are due to differing methodologies, or to differences in the behavior of proteins in solution versus in the crystalline state. Here, we compare the properties of the hydrophobic cores of high-resolution protein crystal structures and those in NMR structures, determined using increasing numbers and types of restraints. Prior studies have reported that many NMR structures have denser cores compared to those of high-resolution x-ray crystal structures. Our current work investigates this result in more detail, and finds that these NMR structures tend to violate basic features of protein stereochemistry, such as small non-bonded atomic overlaps and few Ramachandran and side chain dihedral angle outliers. We find that NMR structures solved with more restraints, and which do not significantly violate stereochemistry, have hydrophobic cores that have a similar size and packing fraction as their counterparts determined by x-ray crystallography at high-resolution. These results lead us to conclude that, at least regarding the core packing properties, high-quality structures determined by NMR and x-ray crystallography are the same, and the differences reported earlier are most likely a consequence of methodology, rather than fundamental differences between the protein in the two different environments. Significance: Dense packing of hydrophobic residues is key to protein structure and stability. Previously, it has been noted that structures solved by NMR spectroscopy have denser cores than x-ray crystal structures. Here, we calculate the core size and packing fraction of NMR structures with experimental restraints in the PDB. Their cores are typically smaller, but denser. However, NMR structures with accurate stereochemistry possess core pa

关键词： X ray crystallography

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Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

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Biomedical Data Science 1000年第1期5卷 141-161页

作者： Bian Li Bowen Jin John A. Capra William S. Bush 1Department of Biological Sciences and Center for Structural Biology Vanderbilt University Nashville Tennessee USA 2Graduate Program in Systems Biology and Bioinformatics Department of Nutrition School of Medicine Case Western Reserve University Cleveland Ohio USA 3Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics University of California San Francisco California USA email: tony@*** 4Cleveland Institute for Computational Biology Department of Population and Quantitative Health Sciences Case Western Reserve University Cleveland Ohio USA email: wsb36@case.edu

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integratethese data sources will play increasingly important roles in disease gene discovery and variant interpretation.

关键词： protein 3D structure population genetics variant interpretation disease gene discovery data integration

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Traveling fronts in self-replicating persistent random walks with multiple internal states

arXiv

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arXiv 2020年

作者： Ishihara, Keisuke George, Ashish B. Cornelius, Ryan Korolev, Kirill S. Max Planck Institute for the Physics of Complex Systems Nöthnitzer Strasse 38 Dresden01187 Germany Max Planck Institute of Molecular Cell Biology and Genetics Pfotenhauer Strasse 108 Dresden01307 Germany Department of Physics Boston University BostonMA02215 United States Graduate Program in Bioinformatics Boston University BostonMA02215 United States

Self-activation coupled to a transport mechanism results in traveling waves that describe polymerization reactions, forest fires, tumor growth, and even the spread of epidemics. Diffusion is a simple and commonly used model of particle transport. Many physical and biological systems are, however, better described by persistent random walks that switch between multiple states of ballistic motion. So far, traveling fronts in persistent random walk models have only been analyzed in special, simplified cases. Here, we formulate the general model of reaction-transport processes in such systems and show how to compute the expansion velocity for arbitrary number of states. For the two-state model, we obtain a closed-form expression for the velocity and report how it is affected by different transport and replication parameters. We also show that nonzero death rates result in a discontinuous transition from quiescence to propagation. We compare our results to a recent observation of a discontinuous onset of propagation in mi-crotubule asters and comment on the universal nature of the underlying mechanism. Copyright © 2020, The Authors. All rights reserved.

关键词： Deforestation

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Construction of Synthetic Escherichia Coli Producing s-linalool

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Procedia Computer Science 2012年 11卷 88-95页

作者： Ratana Thanasomboon Dujduan Waraho Supapon Cheevadhanarak Asawin Meechai Systems Biology and Bioinformatics research laboratory King Mongkut's University of Technology Thonburi Bangkok 10140 Thailand Biological Engineering Program King Mongkut's University of Technology Thonburi Bangkok10140Thailand Division of Biotechnology School of Bioresources and Technology3 King Mongkut's University of Technology Thonburi Bangkok10140 Thailand Department ofChemical Engineering King Mongkut's University of Technology Thonburi Bangkok10140 Thailand

Floral scents are used in many industries and demand for them increases every year. However, the shortage of extracted fragrance supplies from plants leads to high market costs of floral scents. Production of these fragrances from microorganisms is an alternative. In this work we applied the synthetic biology approach to construct a synthetic Escherichia coli system to produce linalool, which is the main component in floral scents. The E. coli system was synthesized in a laboratory by constructing 2 new BioBrick parts, GPPS and LIS Bricks. They were employed to construct GPPS and LIS generator devices by combing them with existing BioBricks from the Registry of BioBrick Standards; i.e., BBa_R0040, BBa_B0030, BBa_B0015. The synthetic E. coli system was created by transforming the generators into a non-scent E. coli YYC912 chassis. The system was tested by culturing synthetic cells in LB medium containing kanamycin and tetramycin. It was found that the synthetic E. coli successfully synthesized linalool and exhibited the strongest linalool scent at 96 hours of cultivation. This synthetic cell will be further used as a base strain for future construction of strains that can make higher linalool products for use on the industry level.

关键词： synthetic biology BioBrick Escherichia Coli linalool

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Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

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Current Opinion in Structural biology 2023年 78卷 102517页

作者： Banerjee, Anupam Saha, Satyaki Tvedt, Nathan C. Yang, Lee-Wei Bahar, Ivet Computational and Systems Biology University of Pittsburgh School of Medicine Pittsburgh 15261 PA United States Computational and Applied Mathematics and Statistics The College of William and Mary Williamsburg 23185 VA United States Institute of Bioinformatics and Structural Biology and PhD Program in Biomedical Artificial Intelligence National Tsing Hua University Hsinchu 300044 Taiwan Physics Division National Center for Theoretical Sciences Taipei 106319 Taiwan

Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities. © 2022 The Authors

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Epigenetic landscapes reveal transcription factors that regulate CD8⁺ T cell differentiation (vol 18, pg 573, 2017)

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NATURE IMMUNOLOGY 2017年第6期18卷 573-582页

作者： Yu, Bingfei Zhang, Kai Milner, J. Justin Toma, Clara Chen, Runqiang Scott-Browne, James P. Pereira, Renata M. Crotty, Shane Chang, John T. Pipkin, Matthew E. Wang, Wei Goldrath, Ananda W. Division of Biological Sciences University of California San Diego La Jolla California USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla California USA Division of Vaccine Discovery La Jolla Institute for Allergy and Immunology La Jolla California USA Department of Immunology and Microbial Science The Scripps Research Institute Jupiter Florida USA Division of Signaling and Gene Expression La Jolla Institute for Allergy and Immunology La Jolla California USA Division of Infectious Diseases Department of Medicine University of California San Diego La Jolla California USA Department of Medicine University of California San Diego La Jolla California USA Department of Chemistry and Biochemistry University of California San Diego La Jolla California USA Department of Cellular and Molecular Medicine University of California San Diego La Jolla California USA.

Dynamic changes in the expression of transcription factors (TFs) can influence the specification of distinct CD8+ T cell fates, but the observation of equivalent expression of TFs among differentially fated precursor ... 详细信息

Dynamic changes in the expression of transcription factors (TFs) can influence the specification of distinct CD8⁺ T cell fates, but the observation of equivalent expression of TFs among differentially fated precursor cells suggests additional underlying mechanisms. Here we profiled the genome-wide histone modifications, open chromatin and gene expression of naive, terminal-effector, memory-precursor and memory CD8⁺ T cell populations induced during the in vivo response to bacterial infection. Integration of these data suggested that the expression and binding of TFs contributed to the establishment of subset-specific enhancers during differentiation. We developed a new bioinformatics method using the PageRank algorithm to reveal key TFs that influence the generation of effector and memory populations. The TFs YY1 and Nr3c1, both constitutively expressed during CD8⁺ T cell differentiation, regulated the formation of terminal-effector cell fates and memory-precursor cell fates, respectively. Our data define the epigenetic landscape of differentiation intermediates and facilitate the identification of TFs with previously unappreciated roles in CD8⁺ T cell differentiation.

关键词： Epigenetics in immune cells Immunological memory

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Computational approaches to understanding protein aggregation in neurodegeneration

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Journal of Molecular Cell biology 2014年第2期8卷 104-115页

作者： Rachel L. Redler David Shirvanyants Onur Dagliyan Feng Ding Doo Nam Kim Pradeep Kota Elizabeth A. Proctor Srinivas Ramachandran Arpit Tandon Nikolay V. Dokholyan Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Program in Cellular and Molecular Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Curriculum in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel Hill NC USA Center for Computational and Systems Biology University of NorthCarolina at Chapel Hill chapel Hill NC USA Department of Physics and Astronomy Ctemson University Ctemson SC USA Cellular Growth Mechanisms Section Laboratory of Cell and Developmental Signaling Frederick National Laboratory for Cancer Research Frederick MD USA Division of Basic Sciences Fred Hutchison Cancer Research Center Seatt{e WA USA

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer＇s disease, Parkinson＇s disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggre- gation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute signifi- cantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both aU-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.

关键词： protein aggregation molecular dynamics protein folding neurodegeneration

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Predicting regional somatic mutation rates using DNA motifs

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PLoS Computational biology 2023年第10 October期19卷 e1011536-e1011536页

作者： Liu, Cong Wang, Zengmiao Wang, Jun Liu, Chengyu Wang, Mengchi Ngo, Vu Wang, Wei Department of Chemistry and Biochemistry University of California San Diego La Jolla CA United States State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health Faculty of Geographical Science Beijing Normal University Beijing China Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA United States

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-related regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature. © 2023 Liu et al.

关键词： Neural network models

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