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New candidate genes potentially involved in Zika virus teratogenesis

Computers in Biology and Medicine 2024年 173卷 108259-108259页

作者： Ferrão Maciel-Fiuza, Miriãn Rengel, Bruna Duarte Wachholz, Gabriela Elis do Amaral Gomes, Julia de Oliveira, Maikel Rosa Kowalski, Thayne Woycinck Roehe, Paulo Michel Luiz Vianna, Fernanda Sales Schüler-Faccini, Lavínia Mayer, Fabiana Quoos Varela, Ana Paula Muterle Fraga, Lucas Rosa Graduate Program in Genetics and Molecular Biology Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Instituto Nacional de Genética Médica Populacional Porto Alegre Brazil Genomics Medicine Laboratory Center of Experimental Research Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Department of Morphological Sciences Institute of Health Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Medicine: Medical Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Teratogen Information System Medical Genetics Service Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Bioinformatics Core Hospital de Clínicas de Porto Alegre Porto Alegre Brazil Centro Universitário CESUCA Cachoeirinha Brazil Department of Microbiology Immunology and Parasitology Institute of Health Sciences Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Molecular and Cellular Biology Biotechnology Center Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Graduate Program in Biosciences Universidade Federal de Ciências da Saúde de Porto Alegre Porto Alegre Brazil

Despite efforts to elucidate Zika virus (ZIKV) teratogenesis, still several issues remain unresolved, particularly on the molecular mechanisms behind the pathogenesis of Congenital Zika Syndrome (CZS). To answer this question, we used bioinformatics tools, animal experiments and human gene expression analysis to investigate genes related to brain development potentially involved in CZS. Searches in databases for genes related to brain development and CZS were performed, and a protein interaction network was created. The expression of these genes was analyzed in a CZS animal model and secondary gene expression analysis (DGE) was performed in human cells exposed to ZIKV. A total of 2610 genes were identified in the databases, of which 1013 were connected. By applying centrality statistics of the global network, 36 candidate genes were identified, which, after selection resulted in nine genes. Gene expression analysis revealed distinctive expression patterns for PRKDC, PCNA, ATM, SMC3 as well as for FGF8 and SHH in the CZS model. Furthermore, DGE analysis altered expression of ATM, PRKDC, PCNA. In conclusion, systems biology are helpful tools to identify candidate genes to be validated in vitro and in vivo. PRKDC, PCNA, ATM, SMC3, FGF8 and SHH have altered expression in ZIKV-induced brain malformations. © Elsevier Ltd

关键词： Viruses

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Antibodies from dengue patients with prior exposure to Japanese encephalitis virus are broadly neutralizing against Zika virus(vol 7, 15, 2024)

COMMUNICATIONS BIOLOGY

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COMMUNICATIONS BIOLOGY 2024年第1期7卷 15页

作者： Salem, Gielenny M. Galula, Jedhan Ucat Wu, Shang-Rung Liu, Jyung-Hurng Chen, Yen-Hsu Wang, Wen-Hung Wang, Sheng-Fan Song, Cheng-Sheng Chen, Fan-Chi Abarientos, Adrian B. Chen, Guan-Wen Wang, Cheng-, I Chao, Day-Yu Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung City 402 Taiwan. Institute of Oral Medicine School of Dentistry College of Medicine National Cheng Kung University Tainan City 701 Taiwan. Graduate Institute of Genomics and Bioinformatics College of Life Sciences National Chung Hsing University Taichung City 40227 Taiwan. School of Medicine College of Medicine National Sun Yat-Sen University Kaohsiung City 80424 Taiwan. Center for Tropical Medicine and Infectious Disease Research Kaohsiung Medical University Kaohsiung City 80708 Taiwan. Doctoral Program in Microbial Genomics National Chung Hsing University and Academia Sinica Taichung City 402 Taiwan. Singapore Immunology Network Agency for Science Technology and Research (A*STAR) 8A Biomedical Grove Immunos Singapore 138648 Singapore. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung City 402 Taiwan. dychao@nchu.edu.tw.

Exposure to multiple mosquito-borne flaviviruses within a lifetime is not uncommon; however, how sequential exposures to different flaviviruses shape the cross-reactive humoral response against an antigen from a different serocomplex has yet to be explored. Here, we report that dengue-infected individuals initially primed with the Japanese encephalitis virus (JEV) showed broad, highly neutralizing potencies against Zika virus (ZIKV). We also identified a rare class of ZIKV-cross-reactive human monoclonal antibodies with increased somatic hypermutation and broad neutralization against multiple flaviviruses. One huMAb, K8b, binds quaternary epitopes with heavy and light chains separately interacting with overlapping envelope protein dimer units spanning domains I, II, and III through cryo-electron microscopy and structure-based mutagenesis. JEV virus-like particle immunization in mice further confirmed that such cross-reactive antibodies, mainly IgG3 isotype, can be induced and proliferate through heterologous dengue virus (DENV) serotype 2 virus-like particle stimulation. Our findings highlight the role of prior immunity in JEV and DENV in shaping the breadth of humoral response and provide insights for future vaccination strategies in flavivirus-endemic countries.

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The cell surface marker CD36 selectively identifies matured,mitochondria-rich hPSC-cardiomyocytes

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细胞研究（英文版） 2020年第7期30卷 626-629页

作者： Ellen Ngar-Yun Poon Xiao-ling Luo Sarah E.Webb Bin Yan Rui Zhao Stanley Chun Ming Wu Yong Yang Peng Zhang Huajun Bai Jiaofang Shao Ching Man Chan Godfrey Chi-Fung Chan Suk Ying Tsang Rebekah L.Gundry Huang-Tian Yang Kenneth R.Boheler Department of Medicine and Therapeutics and Centre for Cardiovascular Genomics and Medicine The Chinese University of Hong Kong (CUHK) HKSAR China CAS Key Laboratory of Tissue Microenvironment and Tumor Laboratory of Molecular CardiologyShanghai Institute of Nutrition and Health University of Chinese Academy of Sciences (CAS) CAS Shanghai China Division of Life Science and State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology HKSAR China Department of Computer Science The University of Hong Kong (HKU) HKSAR China Intervention and Cell Therapy Center Peking University Shenzhen Hospital Shenzhen China School of Life Sciences CUHK HKSAR China Department of Paediatrics and Adolescent Medicine HKU HKSAR China School of Biomedical Sciences HKU HKSAR China SUSTech Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology (SUSTech) Shenzhen China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical University Nanjing Jiangsu China State Key Laboratory of Agrobiotechnology CUHK HKSAR China Department of Biochemistry and Center for Biomedical Mass Spectrometry Research Medical College of Wisconsin Milwaukee WIUSA CardiOmics Program Center for Heart and Vascular Research Division of Cardiovascular Medicine and Department of Cellular and Integrative Physiology University of Nebraska Medical Center Omaha NE USA Institute for Stem Cell and Regeneration Medicine CASBeijing China Division of Cardiology Department of Medicineand Department of Biomedical Engineering The Johns Hopkins University Baltimore MD USA

Dear Editor,Human pluripotent stem cell (hPSC)-derived cardiomyocytes (CMs) are of significant translational value to in vitro studies of human cardiac development, drug and cardiotoxicity testing and cardiac disease *** of hPSCs to CMs,however, yields mixed cultures of atrial-, ventricular-, and pacemaker-like cells as well as non-CMs in variable proportions.1

关键词： Stem-cell differentiation Stem cells

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Combined allelic dosage of Nfia and Nfib regulates cortical development

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Brain and neuroscience advances 2017年 1卷 2398212817739433页

作者： Jens Bunt Jason M Osinski Jonathan Wc Lim Diana Vidovic Yunan Ye Oressia Zalucki Timothy R O'Connor Lachlan Harris Richard M Gronostajski Linda J Richards Michael Piper The Queensland Brain Institute The University of Queensland Brisbane QLD Australia. Department of Biochemistry Program in Genetics Genomics and Bioinformatics Center of Excellence in Bioinformatics and Life Sciences State University of New York at Buffalo Buffalo New York USA. The School of Biomedical Sciences Faculty of Medicine The University of Queensland Brisbane QLD Australia. School of Chemical and Molecular Biosciences and Institute for Molecular Bioscience The University of Queensland Brisbane QLD Australia.

BACKGROUND:Nuclear factor I family members nuclear factor I A and nuclear factor I B play important roles during cerebral cortical development. Nuclear factor I A and nuclear factor I B regulate similar biological processes, as their expression patterns, regulation of target genes and individual knockout phenotypes overlap. We hypothesised that the combined allelic loss of and would culminate in more severe defects in the cerebral cortex than loss of a single member. METHODS:We combined immunofluorescence, co-immunoprecipitation, gene expression analysis and immunohistochemistry on knockout mouse models to investigate whether nuclear factor I A and nuclear factor I B function similarly and whether increasing allelic loss of and caused a more severe phenotype. RESULTS:We determined that the biological functions of nuclear factor I A and nuclear factor I B overlap during early cortical development. These proteins are co-expressed and can form heterodimers . Differentially regulated genes that are shared between and knockout mice are highly enriched for nuclear factor I binding sites in their promoters and are associated with neurodevelopment. We found that compound heterozygous deletion of both genes resulted in a cortical phenotype similar to that of single homozygous or knockout embryos. This was characterised by retention of the interhemispheric fissure, dysgenesis of the corpus callosum and a malformed dentate gyrus. Double homozygous knockout of and resulted in a more severe phenotype, with increased ventricular enlargement and decreased numbers of differentiated glia and neurons. CONCLUSION:In the developing cerebral cortex, nuclear factor I A and nuclear factor I B share similar biological functions and function additively, as the combined allelic loss of these genes directly correlates with the severity of the developmental brain phenotype.

关键词： Nuclear factor I corpus callosum cortical development hippocampus neocortex nuclear factor I A nuclear factor I B

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Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning

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Nature 2025年 2025 Apr 22页

作者： Rachel A Silverstein Nahye Kim Ann-Sophie Kroell Russell T Walton Justin Delano Rossano M Butcher Martin Pacesa Blaire K Smith Kathleen A Christie Leillani L Ha Ronald J Meis Aaron B Clark Aviv D Spinner Cicera R Lazzarotto Yichao Li Azusa Matsubara Elizabeth O Urbina Gary A Dahl Bruno E Correia Debora S Marks Shengdar Q Tsai Luca Pinello Suk See De Ravin Qin Liu Benjamin P Kleinstiver PhD Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Department of Pathology Massachusetts General Hospital Boston MA USA. Department of Pathology Harvard Medical School Boston MA USA. Institute of Pharmacy and Molecular Biotechnology (IPMB) Faculty of Engineering Sciences Heidelberg University Heidelberg Germany. Department of Biomedical Informatics Harvard Medical School Boston MA USA. Molecular Pathology Unit Massachusetts General Hospital Charlestown MA USA. Krantz Family Center for Cancer Research Massachusetts General Hospital Charlestown Charlestown MA USA. Broad Institute of Harvard and MIT Cambridge MA USA. Ocular Genomics Institute Massachusetts Eye and Ear Boston MA USA. Department of Ophthalmology Harvard Medical School Boston MA USA. Laboratory of Protein Design and Immunoengineering École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics Lausanne Switzerland. CELLSCRIPTTM Madison WI USA. Department of Systems Biology Harvard Medical School Boston MA USA. Department of Hematology St. Jude Children's Research Hospital Memphis TN USA. Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda MD USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Massachusetts General Hospital Boston MA USA. bkleinstiver@mgh.harvard.edu. Department of Pathology Harvard Medical School Boston MA USA. bkleinstiver@mgh.harvard.edu.

Engineering and characterizing proteins can be time-consuming and cumbersome, motivating the development of generalist CRISPR-Cas enzymes to enable diverse genome editing applications. However, such enzymes have caveats such as an increased risk of off-target editing. To enable scalable reprogramming of Cas9 enzymes, here we combined high-throughput protein engineering with machine learning (ML) to derive bespoke editors more uniquely suited to specific targets. Via structure/function-informed saturation mutagenesis and bacterial selections, we obtained nearly 1,000 engineered SpCas9 enzymes and characterized their protospacer-adjacent motif (PAM) requirements to train a neural network that relates amino acid sequence to PAM specificity. By utilizing the resulting PAM ML algorithm (PAMmla) to predict the PAMs of 64 million SpCas9 enzymes, we identified efficacious and specific enzymes that outperform evolution-based and engineered SpCas9 enzymes as nucleases and base editors in human cells while reducing off-targets. An in silico directed evolution method enables user-directed Cas9 enzyme design, including for allele-selective targeting of the RHO P23H allele in human cells and mice. Together, PAMmla integrates ML and protein engineering to curate a catalog of SpCas9 enzymes with distinct PAM requirements, and motivates the use of efficient and safe bespoke Cas9 enzymes instead of generalist enzymes for various applications.

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Meeting abstracts from the Annual Conference on Hereditary Cancers 2015 Abstracts

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HEREDITARY CANCER IN CLINICAL PRACTICE 2017年第SUPPL 1期15卷 17-17页

作者： [Anonymous] Cancer Genetics Laboratory Peter MacCallum Cancer Centre East Melbourne Victoria Australia Department of Oncology University of Melbourne Melbourne Victoria Australia The University of Newcastle and Hunter Medical Research Institute Newcastle Australia Division of Genetics Hunter Area Pathology Service Newcastle Australia Hereditary Cancer Program BC Cancer Agency Vancouver Canada European Centre for Bioinformatics and Genomics Institute of Bioorganic Chemistry Polish Academy of Sciences Poznan Poland Department of Genetics and Pathology Pomeranian Medical University Szczecin Poland Oncology Institute Riga Stradins University Riga Latvia Center for Translational Research and Molecular Biology of Cancer Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch Gliwice Poland Regional Oncology Center Opole Poland Department of Genetics and Pathology International Hereditary Cancer Center Pomeranian Medical University Szczecin Poland Department of Oncology Disctict Specialist Hospital Świdnica Poland Department of Nuclear Medicine and Endocrine Oncology Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch Gliwice Poland Genomic Medicine Medical University of Warsaw Warsaw Poland III Department of Radiotherapy and Chemotherapy Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology Gliwice Branch Gliwice Poland Institute of Human Genetics Polish Academy of Sciences Poznan Poland Department of Genetics Holycross Cancer Centre Kielce Poland Read – Gene S.A. Grzepnica Poland Department of Ophthalmology Pomeranian Medical University Szczecin Poland

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The LSC17 Leukemic Stem Cell Signature Predicts Outcome in Pediatric Acute Myeloid Leukemia

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BLOOD 2017年 130卷 24-24页

作者： Smith, Jenny L. Ries, Rhonda E. Kolb, Anders Alonzo, Todd A. Gerbing, Robert B. Ma, Yussanne Marra, Marco A. Bolouri, Hamid Meshinchi, Soheil Clinical Research Division Fred Hutchinson Cancer Research Center Seattle WA Applied Bioinformatics and Genomics Program University of Oregon Eugene OR Nemours Center for Cancer and Blood Disorders Alfred I. duPont Hospital for Children Wilmington DE Children's Oncology Group Monrovia CA Univ. of Southern California Keck School of Med. Monrovia CA Department of Preventive Medicine University of Southern California Los Angeles CA Canada's Michael Smith Genome Sciences Centre British Columbia Cancer Agency Vancouver Canada Human Biology Division Fred Hutchinson Cancer Research Center Seattle Department of Pediatrics University of Washington Seattle Clinical Research Division Fred Hutchinson Cancer Rsrch. Ctr. Seattle WA

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Correction: Chromatin architecture in addiction circuitry identifies risk genes and potential biological mechanisms underlying cigarette smoking and alcohol use traits (vol 27, pg 3085, 2022)

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MOLECULAR PSYCHIATRY 2022年第10期27卷 4368-4368页

作者： Sey, Nancy Y. A. Hu, Benxia Iskhakova, Marina Lee, Sool Sun, Huaigu Shokrian, Neda Ben Hutta, Gabriella Marks, Jesse A. Quach, Bryan C. Johnson, Eric O. Hancock, Dana B. Akbarian, Schahram Won, Hyejung UNC Neuroscience Center University of North Carolina Chapel Hill NC 27599 USA Department of Genetics University of North Carolina Chapel Hill NC 27599 USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY 10029 USA Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY 10029 USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York NY 10029 USA GenOmics Bioinformatics and Translational Research Center Biostatistics and Epidemiology Division RTI International Research Triangle Park Chapel Hill NC 27709 USA Fellow Program RTI International Research Triangle Park Chapel Hill NC 27709 USA

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Emergence of ceftazidime-avibactam resistance through in-vivo bla KPC-2 to bla KPC-33 conversion during treatment of ST11 Klebsiella pneumoniae associated infections

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International Journal of Antimicrobial Agents 2024年第2期64卷

作者： Yi-Chyi Lai Luo-Wei Lin Yu-Lin Lee Department of Internal Medicine Chung Shan Medical University Hospital Taichung Taiwan Department of Microbiology and Immunology School of Medicine Chung Shan Medical University Taichung Taiwan PhD Program in Medical Biotechnology Institute of Genomics and Bioinformatics National Chung-Hsing University Taichung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan

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