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Rapid identification and monitoring of cooking oil fume-based toxic volatile organic aldehydes in lung tissue for predicting exposure level and cancer risks

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Chemosphere 2023年第1期339卷 139704页

作者： Prakasham, Karthikeyan Gurrani, Swapnil Wu, Chia-Fang Wu, Ming-Tsang Hsieh, Tusty-Jiuan Peng, Chiung-Yu Huang, Po-Chin Krishnan, Anbarasu Tsai, Pei-Chien Lin, Yu-Chia Tsai, Bongee Lin, Yuan-Chung Ponnusamy, Vinoth Kumar Kaohsiung City807 Taiwan Kaohsiung City807 Taiwan International Master Program of Translational Medicine College of Engineering and Science National United University Miaoli Taiwan Kaohsiung City807 Taiwan Department of Public Health College of Health Sciences Kaohsiung Medical University Kaohsiung City807 Taiwan Miaoli County 35053 Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Kaohsiung City807 Taiwan Corporation No.392 Yucheng Rd. Zuoying District. Kaohsiung City813 Taiwan Center for Emerging Contaminants Research National Sun Yat-Sen University Kaohsiung City804 Taiwan Institute of Environmental Engineering National Sun Yat-Sen University Kaohsiung City804 Taiwan Kaohsiung Medical University Kaohsiung City807 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Department of Family Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan

Cooking oil fumes (COFs) comprised of a mixture of cancer-causing volatile organic aldehydes (VOAs), particularly trans, trans-2,4-decadienal (t,t-DDE), 4-hydroxy-hexenal (4-HHE), and 4-hydroxy-nonenal (4-HNE). Monitoring toxic VOAs levels in people exposed to different cooking conditions is vital to predicting the cancer risk. For this purpose, we developed a fast tissue extraction (FaTEx) technique combined with UHPLC-MS/MS to monitor three toxic VOAs in mice lung tissue samples. FaTEx pre-treatment protocol was developed by combining two syringes for extraction and clean-up process. The various procedural steps affecting the FaTEx sample pre-treatment process were optimized to enhance the target VOAs’ extraction efficiency from the sample matrix. Under the optimal experimental conditions, results exhibit good correlation coefficient values > 0.99, detection limits were between 0.5–3 ng/g, quantification limits were between 1–10 ng/g, and the matrix effect was © 2023 Elsevier Ltd

关键词： Diseases

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Titania Nanosheets Generates Peroxynitrite for S-Nitrosylation and Enhanced p53 Function in Lung Cancer Cells

Research Square

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Research Square 2021年

作者： Soonnarong, Rapeepun Tungsukruthai, Sucharat Nutho, Bodee Rungrotmongkol, Thanyada Vinayanuwattikun, Chanida Maluangnont, Tosapol Chanvorachote, Pithi Interdisciplinary Program of Pharmacology Graduate School Chulalongkorn University Bangkok Thailand Cell-based Drug and Health Products Development Research Unit Chulalongkorn University Bangkok Thailand Departments of Pharmacology and Physiology Faculty of Pharmaceutical Sciences Bangkok Thailand Department of Pharmacology Faculty of Science Mahidol University Bangkok Thailand Biocatalyst and Environmental Biotechnology Reseach Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok Thailand Division of Medical Oncology Department of Medicine Chulalongkorn University Bangkok Thailand College of Materials Innovation and Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

Background: Metal oxide nanomaterials are increasingly being exploited in cancer therapy thanks to their unique properties, which can enhance the efficacy of current cancer therapies. However, the nanotoxicity and mechanism of Ti0.8O2 nanosheets for specific site-targeting strategies in NSCLC have not yet been investigated. Methods: The effects of Ti0.8O2 nanosheets on cytotoxicity in NSCLC cells and normal cells were examined. The apoptosis characteristics, including condensed and fragmented nuclei, as assessed by positive staining with annexin V. The cellular uptake of the nanosheets and the induction of stress fiber were assessed via transmission electron microscopy (TEM) and scanning electron microscopy (SEM) analyses, respectively. We also evaluated the expression of protein in death mechanism to identify the molecular mechanisms behind the toxicity of these cells. We investigated the relationship between S-nitrosylation and the increase in p53 stability by molecular dynamics. Results: Ti0.8O2 nanosheets caused cytotoxicity in several lung cancer cells, but not in normal cells. The nanosheets could enter lung cancer cells and exert an apoptosis induction. Results for protein analysis further indicated the activation of p53, increased Bax, decreased Bcl-2 and Mcl-1, and activation of caspase-3. The nanosheets also exhibited a substantial apoptosis effect in drug-resistant metastatic primary lung cancer cells, and it was found that the potency of the nanosheets was dramatically higher than that of cisplatin and etoposide. In terms of their mechanism of action, we found that the mode of apoptosis induction was through the generation of cellular ONOO− mediated the S-nitrosylation of p53 at C182. Molecular dynamics analysis further showed that the S-nitrosylation of one C182 stabilized the p53 dimer. Consequently, this nitrosylation of the protein led to an upregulation of p53 through its stabilization. Conclusions: Taking all the evidence together, we provided info

关键词： Molecular dynamics

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FRI076 Colchicine Treatment Effects On The Gut Microbiome In Adults With Metabolic Syndrome, Insulin Resistance, And High C-reactive Protein (CRP): A Secondary Analysis Of A Randomized, Placebo-controlled Clinical Trial

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Journal of the Endocrine Society 2023年第SUPPLEMENT_1期7卷

作者： Celine M Kisimba Jack L Donahue Krishna Karthik Chivukula Poorani Subramanian Shreni D Mistry Anna Wolska Alan T Remaley Jack Adam Yanovski Andrew Paul Demidowich Section on Growth and Obesity Division of Intramural Research National Institute of Child Health and Human Development NIH Bethesda MD USA Bioinformatics and Computational Biosciences Branch Office of Cyber Infrastructure and Computational Biology National Institute of Allergy and Infectious Diseases NIH Bethesda MD USA NIAID Microbiome Program National Institute of Allergy and Infectious Diseases NIH Bethesda MD USA Lipoprotein Metabolism Laboratory Translational Vascular Medicine Branch National Heart Lung and Blood Institute NIH Bethesda MD USA Division of Endocrinology Diabetes and Metabolism Johns Hopkins Medicine Columbia MD USA

Disclosure: C.M. Kisimba:None.J.L. Donahue:None.K.K. Chivukula:None.P. Subramanian:None.S.D. Mistry:None.A. Wolska:None.A.T. Remaley:None.J.A. Yanovski:None.A.P. Demidowich:None.

Disclosure: C.M. Kisimba: None. J.L. Donahue: None. K.K. Chivukula: None. P. Subramanian: None. S.D. Mistry: None. A. Wolska: None. A.T. Remaley: None. J.A. Yanovski: None. A.P. Demidowich: None.

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Weight, habitual fibre intake, and microbiome composition predict tolerance to fructan supplementation

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Journal of Plant Foods 2024年第6期75卷

作者： Jeffrey Letourneau Benjamin C. Neubert Diana Dayal Verónica M. Carrion Heather K. Durand Eric P. Dallow Sharon Jiang Michelle Kirtley Geoffrey S. Ginsburg P. Murali Doraiswamy Lawrence A. David a Department of Molecular Genetics and Microbiology Duke University School of Medicine Durham NC USA b Program in Computational Biology and Bioinformatics Duke University School of Medicine Durham NC USA c School of Medicine University of North Carolina Chapel Hill NC USA d Duke Office of Clinical Research Duke University Durham NC USA e Duke Center for Applied Genomics and Precision Medicine Duke University Health System Durham NC USAf Department of Medicine Duke University School of Medicine Durham NC USA e Duke Center for Applied Genomics and Precision Medicine Duke University Health System Durham NC USAf Department of Medicine Duke University School of Medicine Durham NC USAg Department of Psychiatry Duke University School of Medicine Durham NC USAh Duke Microbiome Center Duke University School of Medicine Durham NC USA a Department of Molecular Genetics and Microbiology Duke University School of Medicine Durham NC USAb Program in Computational Biology and Bioinformatics Duke University School of Medicine Durham NC USAh Duke Microbiome Center Duke University School of Medicine Durham NC USA

Fructans are commonly used as dietary fibre supplements for their ability to promote the growth of beneficial gut microbes. However, fructan consumption has been associated with various dosage-dependent side effects. We characterised side effects in an exploratory analysis of a randomised trial in healthy adults (n= 40) who consumed 18 g/day inulin or placebo. We found that individuals weighing more or habitually consuming higher fibre exhibited the best tolerance. Furthermore, we identified associations between gut microbiome composition and host tolerance. Specifically, higher levels ofChristensenellaceae R-7 groupwere associated with gastrointestinal discomfort, and a machine-learning-based approach successfully predicted high levels of flatulence, with[Ruminococcus] torques groupand (Oscillospiraceae)UCG-002 *** as key predictive taxa. These data reveal trends that can help guide personalised recommendations for initial inulin dosage. Our results support prior ecological findings indicating that fibre supplementation has the greatest impact on individuals whose baseline fibre intake is lowest.

关键词： Microbiome inulin dietary fibre gastroenterology bioinformatics

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A meta-analysis of Boolean network models reveals design principles of gene regulatory networks

arXiv

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arXiv 2020年

作者： Kadelka, Claus Butrie, Taras-Michael Hilton, Evan Kinseth, Jack Schmidt, Addison Serdarevic, Haris Department of Mathematics Iowa State University AmesIA50011 United States Department of Aerospace Engineering Iowa State University AmesIA50011 United States Department of Computer Science Iowa State University AmesIA50011 United States Bioinformatics and Computational Biology Program Iowa State University AmesIA50011 United States

Gene regulatory networks (GRNs) play a central role in cellular decision-making. Understanding their structure and how it impacts their dynamics constitutes thus a fundamental biological question. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data is sparse. We assembled the largest repository of expert-curated Boolean GRN models. A meta-analysis of this diverse set of models reveals several design principles. GRNs exhibit more canalization, redundancy and stable dynamics than expected. Moreover, they are enriched for certain recurring network motifs. This raises the important question why evolution favors these design mechanisms. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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Inhibition of topoisomerase IIα and induction of DNA damage in cholangiocarcinoma cells by altholactone and its halogenated benzoate derivatives

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Biomedicine and Pharmacotherapy 2020年 127卷 110149-110149页

作者： Kitdumrongthum, Sarunya Reabroi, Somrudee Suksen, Kanoknetr Tuchinda, Patoomratana Munyoo, Bamroong Mahalapbutr, Panupong Rungrotmongkol, Thanyada Ounjai, Puey Chairoungdua, Arthit Toxicology Graduate Program Faculty of Science Mahidol University Bangkok Thailand Excellent Center for Drug Discovery (ECDD) Mahidol University Bangkok Thailand Department of Pharmacology Faculty of Science Mahidol University Bangkok Thailand Department of Physiology Faculty of Science Mahidol University Bangkok Thailand Department of Chemistry Faculty of Science Mahidol University Bangkok Thailand Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok Thailand Department of Biology Faculty of Science Mahidol University Bangkok Thailand

Topoisomerase IIα enzyme (Topo IIα) plays a critical function in DNA replication process and is considered to be a promising target of anti-cancer drugs. In the present study, we reported that the altholactone derivatives modified by adding a halogenated benzoate group showed greater inhibitory activity on Topo IIα enzyme in cell-free system concomitant with cytotoxicity against the CCA cell lines (KKU-M055 and KKU-M213) than those of the parent altholactone. However, the cytotoxic activities of four halogenated benzoate altholactone derivatives including iodo-, fluoro-, chloro-, and bromobenzoate derivatives (compound 1, 2, 3, and 4, respectively) were of equal potency. The fluorobenzoate derivative (compound 2) was chosen for investigating the underlying mechanism in CCA cells. Compound 2 arrested CCA cell cycle at sub G1 phase and induced apoptotic cell death. It markedly inhibited Topo IIα protein expression in both KKU-M055 and KKU-M213 cells, which was accompanied by DNA double-strand breaks demonstrated by an increase in phosphorylated H2A.X protein. Interestingly, KKU-M055 cells, which express higher Topo IIα mRNA compared to KKU-M213 cells, showed greater sensitivity to the compound, indicating the selectivity of the compound to Topo IIα enzyme. By computational docking analysis, the binding affinity of altholactone (-52.5 kcal/mol) and compound 2 (-56.7 kcal/mol) were similar to that of the Topo II poison salvicine (-53.7 kcal/mol). The aromatic moiety of both altholactones embedded in a hydrophobic pocket of Topo II ATPase domain. In addition, compound 2 induced the formation of linear DNA in Topo II-mediated cleavage assay. Collectively, our results demonstrate that the addition of fluorobenzoyl group to altholactone enhances potency and selectivity to inhibit type IIα topoisomerases. Atholactone and fluorobenzoate derivative act as Topo II cleavage complexes stabilizing compounds or Topo II poisons preferentially through binding at ATPase domain of Topo

关键词： Altholactone Cholangiocarcinoma Halogenated benzoate Topoisomerase IIα

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Use Symmetry to Elucidate the Roles of Global Shape and Local Interactions in Protein Dynamics and Cooperativity

arXiv

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arXiv 2019年

作者： Song, Guang Department of Computer Science Iowa State University AmesIA50011 United States Program of Bioinformatics and Computational Biology Iowa State University AmesIA50011 United States

Shape had been intuitively recognized to play a dominant role in determining the global motion patterns of bio-molecular assemblies. However, it is not clear exactly how shape determines the motion patterns. What about the local interactions that hold a structure together to a certain shape? The contributions of global shape and local interactions usually mix together and are difficult to tease part. In this work, we use symmetry to elucidate the distinct roles of global shape and local interactions in protein dynamics. Symmetric complexes provide an ideal platform for this task since in them the effects of local interactions and global shape are separable, allowing their distinct roles to be identified. Our key findings based on symmetric assemblies are: (i) the motion patterns of each subunit are determined primarily by intra-subunit interactions (IRSi), and secondarily by inter-subunit interactions (IESi);(ii) the motion patterns of the whole assembly are fully dictated by the global symmetry/shape and have nothing to do with local iESi or IRSi. This is followed by a discussion on how the findings may be generalized to complexes in any shape, with or without symmetry. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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A large-scale binding and functional map of human RNA-binding proteins (vol 583, pg 711, 2020)

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NATURE 2021年第7842期589卷 E5-E5页

作者： Van Nostrand, Eric L. Freese, Peter Pratt, Gabriel A. Wang, Xiaofeng Wei, Xintao Xiao, Rui Blue, Steven M. Chen, Jia-Yu Cody, Neal A. L. Dominguez, Daniel Olson, Sara Sundararaman, Balaji Zhan, Lijun Bazile, Cassandra Bouvrette, Louis Philip Benoit Bergalet, Julie Duff, Michael O. Garcia, Keri E. Gelboin-Burkhart, Chelsea Hochman, Myles Lambert, Nicole J. Li, Hairi McGurk, Michael P. Nguyen, Thai B. Palden, Tsultrim Rabano, Ines Sathe, Shashank Stanton, Rebecca Su, Amanda Wang, Ruth Yee, Brian A. Zhou, Bing Louie, Ashley L. Aigner, Stefan Fu, Xiang-Dong Lecuyer, Eric Burge, Christopher B. Graveley, Brenton R. Yeo, Gene W. Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Institute for Genomic Medicine University of California San Diego La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Medical Research Institute Wuhan University Wuhan China Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Institut de Recherches Cliniques de Montréal (IRCM) Montreal Quebec Canada Department of Biochemistry and Molecular Medicine Université de Montréal Montreal Quebec Canada Division of Experimental Medicine McGill University Montreal Quebec Canada Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA

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Correction: The molecular identity of the TLQP-21 peptide receptor

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Cellular and molecular life sciences : CMLS 2023年第3期80卷 66页

作者： Bhavani S Sahu Megin E Nguyen Pedro Rodriguez Jean Pierre Pallais Vinayak Ghosh Maria Razzoli Yuk Y Sham Stephen R Salton Alessandro Bartolomucci National Brain Research Centre NH-8 Manesar Gurugram Haryana 122052 India. Department of Integrative Biology and Physiology University of Minnesota 2231 6th St. SE Minneapolis MN 55455 USA. Bioinformatics and Computational Biology Program University of Minnesota Minneapolis USA. Departments of Neuroscience and Geriatrics and Palliative Medicine Friedman Brain Institute Icahn School of Medicine at Mount Sinai One Gustave L. Levy Place New York NY 10029 USA. Department of Integrative Biology and Physiology University of Minnesota 2231 6th St. SE Minneapolis MN 55455 USA. abartolo@umn.edu.

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