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Analytical strategies for studying stem cell metabolism

Frontiers in biology 2015年第2期10卷 141-153页

作者： James M. Arnold William T. Choi Arun Sreekumar Mirjana Maletic-Savatic Department of Molecular and Cell Biology Baylor College of Medicine Houston TX 77030 USA Program in Developmental Biology and Medical Scientist Training Program Baylor College of Medicine Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital Houston TX 77030 USA Departments of Pediatrics-Neurology and Neuroscience and Program in Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine Houston TX 77030 USA

Owing to their capacity for self-renewal and pluripotency, stem cells possess untold potential for revolutionizing the field of regenerative medicine through the development of novel therapeutic strategies for treating cancer, diabetes, cardiovascular and neurodegenerative diseases. Central to developing these strategies is improving our understanding of biological mechanisms responsible for governing stem cell fate and self-renewal. Increasing attention is being given to the significance of metabolism, through the production of energy and generation of small molecules, as a critical regulator of stem cell functioning. Rapid advances in the field of metabolomics now allow for in-depth profiling of stem cells both in vitro and in vivo, providing a systems perspective on key metabolic and molecular pathways which influence stem cell biology. Understanding the analytical platforms and techniques that are currently used to study stem cell metabolomics, as well as how new insights can be derived from this knowledge, will accelerate new research in the field and improve future efforts to expand our understanding of the interplay between metabolism and stem cell biology.

关键词： stem cell metabolism NMR mass spectrometry MRS flux analysis

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Redesign of high-affinity nonspecific nucleases with altered sequence preference

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Journal of the American Chemical Society 2009年第47期131卷 17345-17353页

作者： Wang, Yi-Ting Wright, Jon D. Doudeva, Lyudmila G. Jhang, Hua-Ci Lim, Carmay Yuan, Hanna S. Institute of Molecular Biology Academia Sinica Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan Institute of Chemical Biology and Molecular Biophysics Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structural Biology Department of Chemistry National Tsing Hua University HsinChu Taiwan Institute of Biochemistry and Molecular Biology National Taiwan University Taipei Taiwan

It is of crucial importance to elucidate the underlying principles that govern the binding affinity and selectivity between proteins and DNA. Here we use the nuclease domain of Colicin E7 (nColE7) as a model system to generate redesigned nucleases with improved DNA-binding affinities. ColE7 is a bacterial toxin, bearing a nonspecific endonuclease domain with a preference for hydrolyzing DNA phosphodiester bonds at the 3O-side after thymine and adenine;i.e., it prefers Thy and Ade at the -1 site. Using systematic computational screening, six nColE7 mutants were predicted to bind DNA with high affinity. Five of the redesigned single-point mutants were constructed and purified, and four mutants had a 3- to 5-fold higher DNA binding affinity than wild-type nColE7 as measured by fluorescence kinetic assays. Moreover, three of the designed mutants, D493N, D493Q, and D493R, digested DNA with an increased preference for guanine at +3 sites compared to the wild-type enzyme, as shown by DNA footprint assays. X-ray structure determination of the ColE7 mutant D493Q-DNA complex in conjunction with structural and free energy decomposition analyses provides a physical basis for the improved protein-DNA interactions: Replacing D493 at the protein-DNA interface with an amino acid residue that can maintain the native hydrogen bonds removes the unfavorable electrostatic repulsion between the negatively charged carboxylate and DNA phosphate groups. These results show that computational screening combined with biochemical, structural, and free energy analyses provide a useful means for generating redesigned nucleases with a higher DNA-binding affinity and altered sequence preferences in DNA cleavage. © 2009 American Chemical Society.

关键词： DNA

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Graph-based information diffusion method for prioritizing functionally related genes in protein-protein interaction networks 25

Graph-based information diffusion method for prioritizing fu...

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25th Pacific Symposium on Biocomputing, PSB 2020

作者： Pham, Minh Lichtarge, Olivier Integrative Molecular and Biomedical Sciences Graduate Program Department of Molecular and Human Genetics Baylor College of Medicine One Baylor Plaza HoustonTX77030 United States Departments of Molecular and Human Genetics Structural and Computational Biology and Molecular Biophysics Biochemistry and Molecular Biology and Pharmacology One Baylor Plaza HoustonTX77030 United States

Shortest path length methods are routinely used to validate whether genes of interest are functionally related to each other based on biological network information. However, the methods are computationally intensive, impeding extensive utilization of network information. In addition, nonweighted shortest path length approach, which is more frequently used, often treat all network connections equally without taking into account of confidence levels of the associations. On the other hand, graph-based information diffusion method, which employs both the presence and confidence weights of network edges, can efficiently explore large networks and has previously detected meaningful biological patterns. Therefore, in this study, we hypothesized that the graph-based information diffusion method could prioritize genes with relevant functions more efficiently and accurately than the shortest path length approaches. We demonstrated that the graph-based information diffusion method substantially differentiated not only genes participating in same biological pathways (p 0.0001) but also genes associated with specific human drug-induced clinical symptoms (p 0.0001) from random. Furthermore, the diffusion method prioritized these functionally related genes faster and more accurately than the shortest path length approaches (pathways: p = 2.7e-28, clinical symptoms: p = 0.032). These data show the graph-based information diffusion method can be routinely used for robust prioritization of functionally related genes, facilitating efficient network validation and hypothesis generation, especially for human phenotypespecific genes. © 2019 The Authors.

关键词： Genes

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Author Correction: DELE1 oligomerization promotes integrated stress response activation

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Nature structural & molecular biology 2025年第4期32卷 768页

作者： Jie Yang Kelsey R Baron Daniel E Pride Anette Schneemann Xiaoyan Guo Wenqian Chen Albert S Song Giovanni Aviles Martin Kampmann R Luke Wiseman Gabriel C Lander Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA USA. Department of Molecular Medicine Scripps Research La Jolla CA USA. Institute for Neurodegenerative Diseases and Department of Biochemistry and Biophysics University of California San Francisco CA USA. Department of Genetics and Genome Science University of Connecticut Health Center Farmington CT USA. Department of Molecular Medicine Scripps Research La Jolla CA USA. wiseman@scripps.edu. Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA USA. glander@scripps.edu.

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Transgenic mouse models for studying adult neurogenesis

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Frontiers in biology 2016年第3期11卷 151-167页

作者： Fatih Semerci Mirjana Maletic-Savatic Program in Developmental Biology Baylor College of Medicine Houston TX 77030 USA Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital Houston TX 77030 USA Department of Pediatrics-Neurology Department of Neuroscience and Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine Houston TX 77030 USA

The mammalian hippocampus shows a remarkable capacity for continued neurogenesis throughout life. Newborn neurons, generated by the radial neural stem cells （NSCs）, are important for learning and memory as well as mood control. During aging, the number and responses of NSCs to neurogenic stimuli diminish, leading to decreased neurogenesis and age-associatedcognitive decline and psychiatric disorders. Thus, adult hippocampal neurogenesis has garnered significant interest because targeting it could be a novel potential therapeutic strategy for these disorders. However, if we are to use nenrogenesis to halt or reverse hippocampal-related pathology, we need to understand better the core molecular machinery that governs NSC and their progeny. In this review, we summarize a wide variety of mouse models used in adult neurogenesis field, present their advantages and disadvantages based on specificity and efficiency of labeling of different cell types, and review their contribution to our understanding of the biology and the heterogeneity of different cell types found in adult neurogenic niches.

关键词： adult neurogenesis mouse models neural stem cells neuroprogenitors lineage tracing

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Phytohormone signaling pathway analysis method for comparing hormone responses in plant-pest interactions

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BMC Research Notes 2012年第1期5卷 1-7页

作者： Studham, Matthew E. MacIntosh, Gustavo C. Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States

Background: Phytohormones mediate plant defense responses to pests and pathogens. In particular, the hormones jasmonic acid, ethylene, salicylic acid, and abscisic acid have been shown to dictate and fine-tune defense responses, and identification of the phytohormone components of a particular defense response is commonly used to characterize it. Identification of phytohormone regulation is particularly important in transcriptome analyses. Currently there is no computational tool to determine the relative activity of these hormones that can be applied to transcriptome analyses in soybean. Findings: We developed a pathway analysis method that provides a broad measure of the activation or suppression of individual phytohormone pathways based on changes in transcript expression of pathway-related genes. The magnitude and significance of these changes are used to determine a pathway score for a phytohormone for a given comparison in a microarray experiment. Scores for individual hormones can then be compared to determine the dominant phytohormone in a given defense response. To validate this method, it was applied to publicly available data from previous microarray experiments that studied the response of soybean plants to Asian soybean rust and soybean cyst nematode. The results of the analyses for these experiments agreed with our current understanding of the role of phytohormones in these defense responses. Conclusions: This method is useful in providing a broad measure of the relative induction and suppression of soybean phytohormones during a defense response. This method could be used as part of microarray studies that include individual transcript analysis, gene set analysis, and other methods for a comprehensive defense response characterization. © 2012 Studham and MacIntosh;licensee BioMed Central Ltd.

关键词： Salicylic Acid Jasmonic Acid Soybean Cyst Nematode Soybean Aphid Salicylic Acid Pathway

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Refinement of NMR-determined protein structures with database derived distance constraints

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Journal of Bioinformatics and computational biology 2005年第6期3卷 1315-1329页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions. © Imperial College Press.

关键词： NMR protein structure refinement Protein structure database structural bioinformatics

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Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures

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细胞研究（英文版） 2021年第5期31卷 495-516页

作者： Lei Sun Kui Xu Wenze Huang Yucheng T.Yang Pan Li Lei Tang Tuanlin Xiong Qiangfeng Cliff Zhang MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological StructureCenter for Synthetic and Systems BiologySchool of Life SciencesTsinghua UniversityBeijing 100084China Tsinghua-Peking Center for Life Sciences Beijing 100084China Program in Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06520USA Department of Molecular Biophysics and Biochemistry Yale UniversityNew HavenCT 06520USA

Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation,and dynamically reflect specific cellular ***,presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures,and therefore do not capture their condition-dependent ***,after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types,we developed PrismNet,a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular *** results for 168 RBPs support its utility for both understanding CUP-seq results and largely extending such interaction data to accurately analyze additional cell ***,PrismNet employs an 'attention' strategy to computationally identify exact RBP-binding nucleotides,and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants(riboSNitches),which can link genetic diseases with dysregulated RBP *** rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions,with clear utility for understanding and treating human diseases.

关键词： Bioinformatics Mechanisms of disease molecular biology

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structural insights into TDP-43 in nucleic-acid binding and domain interactions

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Nucleic Acids Research 2009年第6期37卷 1799-1808页

作者： Kuo, Pan-Hsien Doudeva, Lyudmila G. Wang, Yi-Ting Shen, Che-Kun James Yuan, Hanna S. Institute of Bioinformatics and Structural Biology National Tsing Hua University Taipei Taiwan Institute of Molecular Biology National Taiwan University Taipei Taiwan Taiwan International Graduate Program Chemical Biology and Molecular Biophysics National Taiwan University Taipei Taiwan Graduate Institute of Biochemistry and Molecular Biology National Taiwan University Taipei Taiwan

TDP-43 is a pathogenic protein: its normal function in binding to UG-rich RNA is related to cystic fibrosis, and inclusion of its C-terminal fragments in brain cells is directly linked to frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS). Here we report the 1.65 Å crystal structure of the C-terminal RRM2 domain of TDP-43 in complex with a single-stranded DNA. We show that TDP-43 is a dimeric protein with two RRM domains, both involved in DNA and RNA binding. The crystal structure reveals the basis of TDP-43's TG/ UG preference in nucleic acids binding. It also reveals that RRM2 domain has an atypical RRM-fold with an additional β-strand involved in making protein-protein interactions. This self association of RRM2 domains produced thermal-stable RRM2 assemblies with a melting point greater than 85°C as monitored by circular dichroism at physiological conditions. These studies thus characterize the recognition between TDP-43 and nucleic acids and the mode of RRM2 self association, and provide molecular models for understanding the role of TDP-43 in cystic fibrosis and the neurodegenerative diseases related to TDP-43 proteinopathy. © 2009 The Author(s).

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