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High-Resolution Imaging of Human Cancer Proteins Using Microprocessor Materials

ChemBioChem 2022年第17期23卷 e202200310-e202200310页

作者： Solares, Maria J. Jonaid, G.M. Luqiu, William Y. Berry, Samantha Khadela, Janki Liang, Yanping Evans, Madison C. Pridham, Kevin J. Dearnaley, William J. Sheng, Zhi Kelly, Deborah F. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Department of Biomedical Engineering Pennsylvania State University University Park 16802 PA United States Center for Structural Oncology Pennsylvania State University University Park 16802 PA United States Department of Electrical and Computer Engineering Duke University Durham 27708 NC United States Fralin Biomedical Research Institute at VTC Virginia Tech Roanoke 24016 VA United States

Mutations in tumor suppressor genes, such as Tumor Protein 53 (TP53), are heavily implicated in aggressive cancers giving rise to gain- and loss-of-function phenotypes. While individual domains of the p53 protein have been studied extensively, structural information for full-length p53 remains incomplete. Functionalized microprocessor chips (microchips) with properties amenable to electron microscopy permitted us to visualize complete p53 assemblies for the first time. The new structures revealed p53 in an inactive dimeric state independent of DNA binding. Residues located at the protein-protein interface corresponded with modification sites in cancer-related hot spots. Changes in these regions may amplify the toxic effects of clinical mutations. Taken together, these results contribute advances in technology and imaging approaches to decode native protein models in different states of activation. © 2022 Wiley-VCH GmbH.

关键词： cancer electron microscopy microchips p53, molecular modeling

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Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

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Plant Communications 2024年第8期5卷 1-17页

作者： Xiaotong Liu Daisuke Igarashi Rachel A.Hillmer Thomas Stoddard You Lu Kenichi Tsuda Chad L.Myers Fumiaki Katagiri Department of Plant and Microbial Biology University of Minnesota-Twin CitiesSt PaulMN 55108USA Department of Computer Science and Engineering University of Minnesota-Twin CitiesMinneapolisMN 55455USA Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin CitiesMinneapolisMN 55455USA Institute for Innovation Ajinomoto Co.Inc.KawasakiJapan State Key Laboratory of Agricultural Microbiology Hubei Hongshan LaboratoryHubei Key Lab of Plant PathologyCollege of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China Present address:225 S 6th St MinneapolisMN 55402USA Present address:Biotalys Inc.2520 Meridian Parkway Suite 480DurhamNC 27713USA Present address:Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan Medical SchoolAnn ArborMI 48109USA

Rapid plant immune responses in the appropriate cells are needed for effective defense against *** transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is *** reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in ***-peak time-course patterns are prevalent among thousands of upregulated *** implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each *** decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated *** demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to ***,the peak decomposition has extracted spatial information from the time-course *** echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered *** of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor *** discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

关键词： Arabidopsis Pseudomonas syringae plant immune response multi-compartment model WRKY transcription factors resilient immune signaling network

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Peer Teaching as bioinformatics Training Strategy: Incentives, Challenges, and Benefits

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Medical Reference Services Quarterly 2022年第1期41卷 13-25页

作者： Rahman, Nur-Taz Meyer, Caitlin Thakral, Durga Cai, Wesley L. Chen, Ann T. Obaid, Razib Garcia-Milian, Rolando Bioinformatics Support Program Research and Education Services Harvey Cushing/John Hay Whitney Medical Library Yale University New Haven CT United States Department of Dermatology Yale School of Medicine New Haven CT United States Department of Pathology Yale School of Medicine New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States RARAF Radiological Research Accelerator Facility Nevis Laboratory Columbia University Irvington NY United States

bioinformatics is essential for basic and clinical research. Peer-to-peer (P2P) teaching was used to respond to the bioinformatics training needs at a research-intensive institution. In addition to the data collected from the workshops, personal experiences of the teachers were used to understand incentives, challenges, and benefits of P2P teaching. Developing communication skills such as confidence in teaching, explaining complex concepts, and better understanding of topics benefited P2P teachers. Lack of time and classroom management were identified as major challenges. Hence, P2P teaching can be beneficial not only for bioinformatics trainees but also as a professional development opportunity for peer teachers. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： bioinformatics computational biology data education peer teaching training

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Improvement of Jakarta's air quality during large scale social restriction 2

Improvement of Jakarta's air quality during large scale soci...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Rahutomo, R. Purwandari, K. Sigalingging, J.W.C. Pardamean, B. Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather Data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Rationally designed antimicrobial peptides with high selectivity and efficiency against phytopathogenic Ralstonia solanecearum

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Science of the Total Environment 2025年 976卷 179354页

作者： Manik, Melvalia Cristin Darai, Nitchakan Rungrotmongkol, Thanyada Duan, Lian Harada, Ryuhei Shigeta, Yasuteru Hengphasatporn, Kowit Vangnai, Alisa S. Biotechnology Program Faculty of Science Chulalongkorn University Bangkok10330 Thailand Futuristic Science Research Center School of Science Walailak University Nakhon Si Thammarat80160 Thailand Drug and Cosmetics Excellence Center Walailak University Thasala Nakhon Si Thammarat80160 Thailand Program in Bioinformatics and Computational Biology Chulalongkorn University Bangkok10330 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Center of Excellence in Biocatalyst and Sustainable Biotechnology Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand

Ralstonia solanacearum, the causative agent of bacterial wilt, poses a global threat to agriculture, necessitating urgent and sustainable solutions as traditional methods lose efficacy. This study developed WRF-13, a synthetic antimicrobial peptide (AMP) designed to mimic natural AMPs, exhibiting potent antibacterial and anti-biofilm activity with high specificity against R. solanacearum. Mechanistic studies, including microscopy and computational analyses, demonstrated that WRF-13 disrupts the bacterial membrane. WRF-13 remained stable across a wide pH (5–8) and temperature (25–50 °C) range, essential for field applications, and showed no detectable toxicity to mammalian or plant cells at elevated concentrations. Greenhouse trials confirmed its efficacy in reducing bacterial wilt severity up to 65 %, highlighting its potential to protect crops from R. solanacearum infection. Overall, this study highlights WRF-13 as a targeted solution for managing bacterial wilt and advancing sustainable agriculture. © 2025 Elsevier B.V.

关键词： Antimicrobial peptides Bacterial wilt Crop protection In silico-peptide design Ralstonia solanacearum

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A FLOW ARTIST FOR HIGH-DIMENSIONAL CELLULAR DATA

arXiv

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arXiv 2023年

作者： MacDonald, Kincaid Bhaskar, Dhananjay Thampakkul, Guy Nguyen, Nhi Zhang, Joia Perlmutter, Michael Adelstein, Ian Krishnaswamy, Smita Department of Mathematics Yale University United States Department of Genetics Yale School of Medicine United States Department of Computer Science Yale University United States Department of Mathematics Pomona College United States Applied Mathematics Program Yale University United States Department of Statistics University of Washington United States Department of Mathematics Boise State University United States Computational Biology and Bioinformatics Program Yale University United States

We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured, including in high-throughput biology such as single-cell transcriptomics. Existing embedding techniques either do not utilize velocity information or embed the coordinates and velocities independently, i.e., they either impose velocities on top of an existing point embedding or embed points within a prescribed vector field. Here we present FlowArtist, a neural network that embeds points while jointly learning a vector field around the points. The combination allows FlowArtist to better separate and visualize velocity-informed structures. Our results, on toy datasets and single-cell RNA velocity data, illustrate the value of utilizing coordinate and velocity information in tandem for embedding and visualizing high-dimensional data. © 2023, CC BY.

关键词： RNA

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SeqMapPDB: A Standalone Pipeline to Identify Representative Structures of Protein Sequences and Mapping Residue Indices in Real-Time at Proteome Scale

arXiv

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arXiv 2022年

作者： Wang, Boshen Lei, Xue Tian, Wei Perez-Rathke, Alan Liang, Jie Tseng, Yan-Yuan Boinformatics Program Center for Bioinformatics and Quntitative Biology Richard and Loan Hill Department of Biomedical Engineering University of Illinois at Chicago Chicago60612 United States DMolecular Medicine and Genetics Wayne State University Detroit48201 United States

Motivation: 3D structures of proteins provide rich information for understanding their biochemical roles. Identifying the representative protein structures for protein sequences is essential for analysis of proteins at proteome scale. However, there are technical difficulties in identifying the representative structure of a given protein sequence and providing accurate mapping of residue indices. Existing databases of mapping between structures and sequences are usually static that are not suitable for studying proteomes with frequent gene model revisions. They often do not provide reliable and consistent representative structures that maximizes sequence coverage. Furthermore, proteins isomers are usually not properly resolved. Results: To overcome these difficulties, we have developed a computational pipeline called SeqMapPDB to provide high-quality representative PDB structures of given sequences. It provides mapping to structures that fully cover the sequences when available, or to the set of partial non-overlapping structural domains that maximally cover the query sequence. The residue indices are accurate mapped and isomeric proteins are resolved. SeqMapPDB is efficient and can rapidly carry out proteome-wide mapping to the selected version of reference genomes in real-time. Furthermore, SeqMapPDB provides the flexibility of a stand-alone pipeline for large scale mapping of in-house sequence and structure data. Availability: Our method is available at https://***/lianglabuic/seqmappdb with GNU GPL license. © 2022, CC BY.

关键词： Proteins

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jen-Chien Yu Chun-Chieh Yang John Reuben Gilbert Rou-Jun Liu Yen-Jen Oyang Meng-Han Yang Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan ROC Master Program in Statistics National Taiwan University Taipei Taiwan ROC Department of Computer Science & Information Engineering National Taiwan University Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset.

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Single-molecule localization microscopy of expanded mammalian centrioles unravels the ultrastructural constitution of distal appendages

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Biophysical journal 2023年第3S1期122卷 17a页

作者： Chang, Ting-Jui B Hsu, Jimmy C-C Yang, Tony Physics National Taiwan University Taipei City Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei City Taiwan Electrical Engineering National Taiwan University Taipei City Taiwan Electrical Engineering National Taiwan University Taipei City Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan

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Unsupervised Neural Tracing In Densely Labeled Multispectral Brainbow Images

Unsupervised Neural Tracing In Densely Labeled Multispectral...

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IEEE International Symposium on Biomedical Imaging

作者： Bin Duan Logan A Walker Douglas H Roossien Fred Y Shen Dawen Cai Yan Yan Illinois Institute of Technology Biophysics University of Michigan Bioinformatics Ball State University Neuroscience Graduate Program Cell and Developmental Biology Michigan Medicine

ISBN: (纸本)9781665412469;9781665429474

Recent advances in imaging technologies for generating large quantities of high-resolution 3D images, especially multispectral labeling technology such as Brainbow, permits unambiguous differentiation of neighboring neurons in a densely labeled brain. This enables, for the first time, the possibility of studying the connectivity between many neurons from a light microscopy image. The lack of reliable automated neuron morphology reconstruction, however, makes data analysis the bottleneck of extracting rich informatics in neuroscience. Supervoxel-based neuron segmentation methods have been proposed to solve this problem, however, previous approaches have been impeded by the large numbers of errors which arise in the final segmentation. In this paper, we present a novel unsupervised approach to trace neurons from multispectral Brainbow images, which prevents segmentation errors and tracing continuity errors using two innovations: First, we formulate a Gaussian mixture model-based clustering strategy to improve the separation of segmented color channels that provides accurate skeletons for the next steps. Then, a skeleton graph approach is proposed to allow the identification and correction of discontinuities in the neuron tree topology. We find that these innovations allow better performance over current state-of-the-art approaches, which results in more accurate neuron tracing results close to human expert annotation.

关键词： Image segmentation Technological innovation Three-dimensional displays Neuroscience Microscopy Neurons Morphology

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