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A direct gene transferring system for Oncidium orchids, a difficult crop for genetic transformation

Agriculture and Natural Resources 2018年第5期52卷 424-429页

作者： Niyomtham, Krittiya Bhinija, Kisana Huehne, Pattana S. Genetic Engineering and Bioinformatics Interdisciplinary Graduate Programs The Graduate School Kasetsart University Bangkok 10900 Thailand Laboratory of Biotechnology Chulabhorn Research Institute Bangkok 10210 Thailand Department of Genetics Faculty of Science Kasetsart University Bangkok 10900 Thailand

Oncidium orchids are susceptible to damage through viral infection caused by the Cymbidium mosaic virus (CymMV). Generating a virus-resistant plant is a major challenge. Unfortunately, gene transformation in Oncidium orchids is not easy. This study developed selection steps for a direct gene transferring system through particle bombardment. Optimized hygromycin screening was reported in the transformed protocorm-like bodies (PLBs) of two commercial Oncidium hybrids (Onc. Gower Ramsey and Onc. Sweet Sugar) with the RNAi construction of the coat protein gene of CymMV. The transgenic Onc. Sweet Sugar PLBs were regenerated on a modified Murashige and Skoog medium containing hygromycin. The effective selection system in exterminating non-transformed PLBs and the functional transgene in the transgenic lines was analyzed using reverse transcription polymerase chain reaction and quantitative polymerase chain reaction. The application of this study may help the gene transformation system of other orchid plant species. © 2018

关键词： Cymbidium mosaic virus Gene transfer Hygromycin Oncidium Particle bombardment

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Integrated synchrotron radiation-based fourier transform infrared (SR-FTIR) microscopy and tandem-mass spectrometry (LC-MS/MS) used to elucidate the apoptotic effect of chamuangone in A549 cells

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Biochemical and Biophysical Research Communications 2025年 764卷 151826页

作者： Wanaragthai, Panatda Yingchutrakul, Yodying Panichayupakaranant, Pharkphoom Vongsvivut, Jitraporn Aonbangkhen, Chanat Yang, Meng Chieh Maiuthed, Arnatchai Chanvorachote, Pithi Wood, Bayden R. Choowongkomon, Kiattawee Krobthong, Sucheewin Interdisciplinary Program of Genetic Engineering and Bioinformatics Kasetsart University Bangkok Thailand National Center for Genetic Engineering and Biotechnology NSTDA Pathum Thani 12120 Thailand Phytomedicine and Pharmaceutical Biotechnology Excellence Center Faculty of Pharmaceutical Sciences Prince of Songkla University Hat-Yai Thailand ANSTO-Australian Synchrotron 800 Blackburn Road Clayton Victoria Australia Center of Excellence in Natural Products Chemistry (CENP) Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Department of Anatomy Faculty of Medicine Srinakharinwirot University Bangkok 10110 Thailand Centre of Biopharmaceutical Science for Healthy Ageing Faculty of Pharmacy Mahidol University Bangkok 10400 Thailand Department of Pharmacology Faculty of Pharmacy Mahidol University Bangkok 10400 Thailand Center of Excellence in Cancer Cell and Molecular Biology Faculty of Pharmaceutical Sciences Chulalongkorn University Bangkok 10330 Thailand Department of Pharmacology and Physiology Faculty of Pharmaceutical Sciences Chulalongkorn University Bangkok 10330 Thailand Bio-spectroscopy Group School of Chemistry Monash University Clayton Victoria 3800 Australia Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand

Chamuangone, a natural compound extracted from Garcinia cowa leaves, has demonstrated potential in cancer therapeutics, but its effects on lung cancer cells remain unclear. This study investigates the apoptotic effects of Chamuangone on human lung adenocarcinoma cells (A549). The A549 cells were treated with Chamuangone, and the cytotoxic effects were evaluated using an MTT assay, revealing a dose-dependent inhibition of cell proliferation with an IC₅₀ value of 19.43 μM. Annexin V assays further confirmed that Chamuangone induces apoptosis in A549 cells, showing increased levels of late apoptosis with higher concentrations. Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microscopy provided insights into macromolecular changes, highlighting significant alterations in proteins, lipids, and nucleic acids. These structural changes in key cellular macromolecules were supported by proteomic analysis, which identified the upregulation of apoptosis-related proteins, including Peroxiredoxin-2 and Na⁺/H⁺ exchange regulatory cofactor NHE-RF1. Canonical pathway analysis indicated that Chamuangone affects oxidative phosphorylation and mitochondrial dysfunction, both crucial pathways for apoptosis. Additionally, upstream regulator analysis demonstrated significant inhibition of the epidermal growth factor receptor (EGFR), a key player in lung cancer progression. These findings suggest that Chamuangone triggers apoptosis through mitochondrial pathways and EGFR inhibition, positioning it as a promising therapeutic candidate for lung cancer treatment. © 2025 Elsevier Inc.

关键词： Epidermal growth factor receptor Garcinia cowa Mitochondrial dysfunction Oxidative phosphorylation Proteomics

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Serum proteomic approach to identifying differentially expressed proteins in effusive feline infectious peritonitis

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Scientific Reports 2025年第1期15卷 1-12页

作者： Wassamon Moyadee Jatuporn Rattanasrisomporn Sittiruk Roytrakul Janthima Jaresitthikunchai Narumon Phaonakrop Kiattawee Choowongkomon Sekkarin Ploypetch Natthasit Tansakul Amonpun Rattanasrisomporn Graduate Program in Animal Health and Biomedical Sciences Faculty of Veterinary Medicine Kasetsart University Bangkok Thailand Department of Companion Animal Clinical Sciences Faculty of Veterinary Medicine Kasetsart University Bangkok Thailand National Center for Genetic Engineering and Biotechnology National Science and Technology Development Agency Pathum Thani Thailand Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand Department of Clinical Sciences and Public Health Faculty of Veterinary Science Mahidol University Nakhon Pathom Thailand Department of Pharmacology Faculty of Veterinary Medicine Kasetsart University Bangkok Thailand Interdisciplinary of Genetic Engineering and Bioinformatics Graduate School Kasetsart University Bangkok Thailand

Feline infectious peritonitis (FIP) is a lethal, viral-induced immune-mediated disease that remains a challenge for diagnosis and treatment in cats. Proteomic profiling, which analyzes the protein content of biological samples, offers the potential to identify novel biomarkers that could improve the diagnosis and management of FIP. This study aims to assess the serum proteome and identify proteins that differentiate healthy cats from cats diagnosed with effusive FIP using liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). A total of 30 cats diagnosed with effusive FIP and 27 clinically normal cats were enrolled. Twenty-three proteins were significantly (p< 0.01, ≥ fivefold change in abundance) differentially expressed between cats with effusive FIP and controls. Among these, the P2X purinoceptor, DNA topoisomerase, Notch receptor 2, and cadherin-17 were identified as key proteins of interest in cats with effusive FIP. Our findings suggest that these differentially expressed proteins could serve as potential diagnostic biomarkers and therapeutic targets for FIP. However, further studies are needed to validate these findings and explore their potential applications.

关键词： Biomarkers Cats Coronavirus Feline infectious peritonitis Proteomics Serum

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Minimizing information loss reduces spiking neuronal networks to differential equations

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Journal of Computational Physics 2025年 537卷

作者： Jie Chang Zhuoran Li Zhongyi Wang Louis Tao Zhuo-Cheng Xiao Center for Bioinformatics School of Life Sciences National Laboratory of Protein Engineering and Plant Genetic Engineering Peking University Beijing 100871 China Center for Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 China Courant Institute of Mathematical Sciences New York University NY 10003 USA NYU-ECNU Institute of Mathematical Sciences New York University Shanghai Shanghai 200124 China NYU-ECNU Institute of Brain and Cognitive Science New York University Shanghai Shanghai 200124 China

Spiking neuronal networks (SNNs) are widely used in computational neuroscience, from biologically realistic modeling of local cortical networks to phenomenological modeling of the whole brain. Despite their prevalence, a systematic mathematical theory for finite-sized SNNs remains elusive, even for idealized homogeneous networks. The primary challenges are twofold: 1) the rich, parameter-sensitive SNN dynamics, and 2) the singularity and irreversibility of spikes. These challenges pose significant difficulties when relating SNNs to systems of differential equations, leading previous studies to impose additional assumptions or to focus on individual dynamic regimes. In this study, we introduce a Markov approximation of homogeneous SNN dynamics to minimize information loss when translating SNNs into ordinary differential equations. Our only assumption for the Markov approximation is the fast self-decorrelation of synaptic conductances. The system of ordinary differential equations derived from the Markov model effectively captures high-frequency partial synchrony and the metastability of finite-neuron networks produced by interacting excitatory and inhibitory populations. Besides accurately predicting dynamical statistics, such as firing rates, our theory also quantitatively captures the geometry of attractors and bifurcation structures of SNNs. Thus, our work provides a comprehensive mathematical framework that can systematically map parameters of single-neuron physiology, network coupling, and external stimuli to homogeneous SNN dynamics.

关键词： Spiking neural networks Markov models Coarse-graining Synchronization Metastability

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Dendritic Morphology Affects the Velocity and Amplitude of Back-propagating Action Potentials

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Neuroscience Bulletin 2022年第11期38卷 1330-1346页

作者： Wu Tian Luxin Peng Mengdi Zhao Louis Tao Peng Zou Yan Zhang State Key Laboratory of Membrane Biology College of Life SciencesPeking UniversityBeijing 100871China College of Chemistry and Molecular Engineering Synthetic and Functional Biomolecules CenterBeijing National Laboratory for Molecular SciencesKey Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of EducationPeking UniversityBeijing 100871China Center for Quantitative Biology Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China PKU-IDG/McGovern Institute for Brain Research Peking UniversityBeijing 100871China Center for Bioinformatics National Laboratory of Protein Engineering and Plant Genetic EngineeringSchool of Life SciencesPeking UniversityBeijing 100871China Peking-Tsinghua Center for Life Sciences Peking UniversityBeijing 100871China Chinese Institute for Brain Research(CIBR) Beijing 102206China Beijing Academy of Artifcial Intelligence Beijing 100084China

The back-propagating action potential(bpAP)is crucial for neuronal signal integration and synaptic plasticity in dendritic *** properties(velocity and amplitude)can be affected by dendritic *** to limited spatial resolution,it has been difficult to explore the specific propagation process of bpAPs along dendrites and examine the influence of dendritic morphology,such as the dendrite diameter and branching pattern,using patch-clamp *** taking advantage of Optopatch,an all-optical electrophysiological method,we made detailed recordings of the real-time propagation of bpAPs in dendritic *** found that the velocity of bpAPs was not uniform in a single dendrite,and the bpAP velocity differed among distinct dendrites of the same *** velocity of a bpAP was positively correlated with the diameter of the dendrite on which it *** addition,when bpAPs passed through a dendritic branch point,their velocity decreased *** to velocity,the amplitude of bpAPs was also positively correlated with dendritic diameter,and the attenuation patterns of bpAPs differed among different *** results from neuron models with different dendritic morphology corresponded well with the experimental *** findings indicate that the dendritic diameter and branching pattern significantly influence the properties of *** diversity among the bpAPs recorded in different neurons was mainly due to differences in dendritic *** results may inspire the construction of neuronal models to predict the propagation of bpAPs in dendrites with enormous variation in morphology,to further illuminate the role of bpAPs in neuronal communication.

关键词： Dendrite Action potential Backpropagation Synaptic integration

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Differences in action potential propagation speed and axon initial segment plasticity between neurons from Sprague-Dawley rats and C57BL/6 mice

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Zoological Research 2022年第4期43卷 615-633页

作者： Zhi-Ya Chen Luxin Peng Mengdi Zhao Yu Li Mochizuki Takahiko Louis Tao Peng Zou Yan Zhang State Key Laboratory of Membrane Biology College of Life SciencesPeking UniversityBeijing 100871China College of Chemistry and Molecular Engineering Synthetic and Functional Biomolecules CenterBeijing National Laboratory for Molecular SciencesKey Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of EducationPeking UniversityBeijing 100871China Center for Quantitative Biology Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China Center for Bioinformatics National Laboratory of Protein Engineering and Plant Genetic EngineeringSchool of Life SciencesPeking UniversityBeijing 100871China Peking-Tsinghua Center for Life Sciences Peking UniversityBeijing 100871China PKU-IDG/McGovern Institute for Brain Research Peking UniversityBeijing 100871China Beijing Academy of Artificial Intelligence Beijing 100084China Chinese Institute for Brain Research(CIBR) Beijing 102206China

Action potentials(APs)in neurons are generated at the axon initial segment(AIS).AP dynamics,including initiation and propagation,are intimately associated with neuronal excitability and neurotransmitter release *** learning and memory studies at the single-neuron level have relied on the use of animal models,most notably ***,we studied AP initiation and propagation in cultured hippocampal neurons from Sprague-Dawley(SD)rats and C57BL/6(C57)mice with genetically encoded voltage indicator(GEVI)-based voltage *** data showed that APs traveled bidirectionally in neurons from both species;forward-propagating APs(fpAPs)had a different speed than backpropagating APs(bpAPs).Additionally,we observed distinct AP propagation characteristics in AISs emerging from the somatic envelope compared to those originating from *** with rat neurons,mouse neurons exhibited higher bpAP speed and lower fpAP speed,more distally located ankyrin G(AnkG)in AISs,and longer Nav1.2 lengths in ***,during AIS plasticity,AnkG and Nav1.2 showed distal shifts in location and shorter lengths of labeled AISs in rat neurons;in mouse neurons,however,they showed a longer AnkG-labeled length and more distal Nav1.2 *** findings suggest that hippocampal neurons in SD rats and C57 mice may have different AP propagation speeds,different AnkG and Nav1.2 patterns in the AIS,and different AIS plasticity properties,indicating that comparisons between these species must be carefully considered.

关键词： Sprague-Dawley rats C57BL/6 mice Action potential Axon initial segment Plasticity

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Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms

arXiv

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arXiv 2025年

作者： Zhou, Junchi Wang, Haozhou Kato, Yoichiro Nampally, Tejasri Rajalakshmi, P. Balram, M. Katsura, Keisuke Lu, Hao Mu, Yue Yang, Wanneng Gao, Yangmingrui Xiao, Feng Chen, Hongtao Chen, Yuhao Li, Wenjuan Wang, Jingwen Yu, Fenghua Zhou, Jian Wang, Wensheng Hu, Xiaochun Yang, Yuanzhu Ding, Yanfeng Guo, Wei Liu, Shouyang Engineering Research Center of Plant Phenotyping Ministry of Education Jiangsu Collaborative Innovation Center for Modern Crop Production Academy for Advanced Interdisciplinary Studies Sanya Institute of Nanjing Agricultural University Nanjing Agricultural University Nanjing China Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan Department of Artificial Intelligence Indian Institute of Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Hyderabad India Institute of Biotechnology Professor Jayashankar Telangana Agricultural State University Hyderabad India Graduate School of Agriculture Kyoto University Kyoto Japan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Crop Genetic Improvement National Center of Plant Gene Research Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan China State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China the Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing China Center for Geospatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China School of Information and Electrical Engineering Shenyang Agricultural University Shenyang China Rice Research Institute Jilin Academy of Agricultural Sciences Changchun China Institute of Crop Sciences National Key Facility for Crop Gene Resources and Genetic Improvement Chinese Academy of Agricultural Sciences Beijing China Yuan Long Ping High-Tech Agriculture Co. Ltd. Changsha China

Developing computer vision–based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Among phenotyping tasks, distinguishing image components is a key prerequisite for characterizing plant growth and development at the organ scale, enabling deeper insights into eco-physiological processes. However, due to the fine structure of rice organs and complex illumination within the canopy, this task remains highly challenging, underscoring the need for a high-quality training dataset. Such datasets are scarce, both due to a lack of large, representative collections of rice field images and the time-intensive nature of annotation. To address this gap, we established the first comprehensive multi-class rice semantic segmentation dataset, RiceSEG. We gathered nearly 50,000 high-resolution, ground-based images from five major rice-growing countries (China, Japan, India, the Philippines, and Tanzania), encompassing over 6,000 genotypes across all growth stages. From these original images, 3,078 representative samples were selected and annotated with six classes (background, green vegetation, senescent vegetation, panicle, weeds, and duckweed) to form the RiceSEG dataset. Notably, the sub-dataset from China spans all major genotypes and rice-growing environments from the northeast to the south. Both state-of-the-art convolutional neural networks and transformer-based semantic segmentation models were used as baselines. While these models perform reasonably well in segmenting background and green vegetation, they face difficulties during the reproductive stage, when canopy structures are more complex and multiple classes are involved. These findings highlight the importance of our dataset for developing specialized segmentation models for rice and other crops. The RiceSEG dataset is publicly available at ***. © 2025, CC BY.

关键词： Semantic Segmentation

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Molecular Modelling, Docking and Dynamics Simulations of Solanum Lycopersicum Pad4 Using Eds1 Proteins

SSRN

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SSRN 2024年

作者： Tandon, Gitanjali Singh, Atul Sutradhar, Monoj Rajeshwar, J.R. Kaur, Sukhdeep Jaiswa, Sarika Iquebal, Mir Asif Rai, Anil Kumar, Dinesh Buddiga, Jamindar Ramteke, Pramod W. Singh, Satendra Centre for Agricultural Bioinformatics ICAR-Indian Agricultural Statistics Research Institute New Delhi11012 India Department of Agriculture Koneru Lakshmaiah Education Foundation AP Vijayawada India Department of Interdisciplinary Research and Discovery Koneru Lakshmaiah Education Foundation AP Vijayawada India Department of molecular biology and Genetic engineering RTM Nagpur University Nagpur India Department of Computational Biology and Bioinformatics JIBB Sam Higginbottom University of Agriculture Technology and Sciences Prayagraj India Indian Agricultural Research Institute India

As biotic stress is one of the major impediments to food production, it is important to decipher systemic immunity-related pathways and genes. Enhanced Disease Susceptibility 1 (EDS1) and Phytoalexin Lacking 4 (PAD4) are two important proteins that together have been found to be essential for the accumulation of salicylic acid (SA). This accumulation in turn controls the expression of pathogenesis-related (PR) proteins, which in turn activates defence pathways against pathogen attacks. A computational approach can predict major protein-protein interactions as well as major signaling pathways possessing key genes. In this study, we conducted 3D modeling and examined the interaction of EDS1 and PAD4 in tomatoes (Solanum lycopersicum), using a homology modeling approach. The predicted models were further validated and then subjected to protein-protein docking using HADDOCK. Docking analysis showed the presence of 13 hydrogen bonds and 5 hydrophobic interactions between the two proteins. Molecular dynamics simulations (MDSs) for 50ns were performed using GROMACS to observe the dynamic behavior of the proteins. During MDS analysis, various parameters including RMSD, potential energy and PCA were considered. Docking analysis revealed the presence of EDS1 and PAD4 complex in tomatoes, thus supporting the possibility that SA might be operating as a defense pathway. Such pathway genes can be directly targeted for further studies required for new variety development. © 2024, The Authors. All rights reserved.

关键词： Molecular docking

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Minimizing information loss reduces spiking neuronal networks to differential equations

arXiv

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arXiv 2024年

作者： Chang, Jie Li, Zhuoran Wang, Zhongyi Tao, Louis Xiao, Zhuo-Cheng Center for Bioinformatics National Laboratory of Protein Engineering and Plant Genetic Engineering School of Life Sciences Peking University Beijing100871 China Courant Institute of Mathematical Sciences New York University NY10003 United States Center for Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China NYU-ECNU Institute of Mathematical Sciences New York University Shanghai Shanghai200124 China NYU-ECNU Institute of Brain and Cognitive Science New York University Shanghai Shanghai200124 China

关键词： Markov processes

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The backpropagation algorithm implemented on spiking neuromorphic hardware

arXiv

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arXiv 2021年

作者： Renner, Alpha Sheldon, Forrest Zlotnik, Anatoly Tao, Louis Sornborger, Andrew Institute of Neuroinformatics University of Zurich ETH Zurich Zurich8057 Switzerland Los Alamos National Laboratory Los AlamosNM87545 United States Los Alamos National Laboratory Los AlamosNM87545 United States Center for Bioinformatics National Laboratory of Protein Engineering and Plant Genetic Engineering School of Life Sciences Peking University Beijing100871 China Center for Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China Los Alamos National Laboratory Los AlamosNM87545 United States

The capabilities of natural neural systems have inspired new generations of machine learning algorithms as well as neuromorphic very large-scale integrated (VLSI) circuits capable of fast, low-power information processing. However, it has been argued that most modern machine learning algorithms are not neurophysiologically plausible. In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware. In this study, we present a neuromorphic, spiking backpropagation algorithm based on synfire-gated dynamical information coordination and processing, implemented on Intel's Loihi neuromorphic research processor. We demonstrate a proof-of-principle three-layer circuit that learns to classify digits from the MNIST dataset. To our knowledge, this is the first work to show a Spiking Neural Network (SNN) implementation of the backpropagation algorithm that is fully on-chip, without a computer in the loop. It is competitive in accuracy with off-chip trained SNNs and achieves an energy-delay product suitable for edge computing. This implementation shows a path for using in-memory, massively parallel neuromorphic processors for low-power, low-latency implementation of modern deep learning applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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