Deep proteomics network and machine learning analysis of human cerebrospinal fluid in Japanese encephalitis virus infection
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X. de Lamballerie | P. Newton | M. Mayxay | I. Vendrell | R. Fischer | N. Zitzmann | A. Myall | B. Kessler | L. Turtle | B. Pastorino | A. Dubot-Pérès | A. Chanthongthip | Onanong Sengvilaipaseuth | S. Rattanavong | M. Vongsouvath | Darragh P. O’Brien | T. Bharucha | Abhinav Kumar | B. Gangadharan | Ooyanong Phonemixay | N. Ayhan | L. Turtle | Bevin Gangadharan | X. D. de Lamballerie | Nicole Zitzmann | Benedikt M. Kessler | P. Newton | R. Fischer | DP O’Brien | Xavier de Lamballerie
[1] K. Bleakley,et al. Childhood encephalitis in the Greater Mekong region (the SouthEast Asia Encephalitis Project): a multicentre prospective study , 2022, The Lancet. Global health.
[2] X. de Lamballerie,et al. Immunoglobulin M seroneutralization for improved confirmation of Japanese encephalitis virus infection in a flavivirus-endemic area , 2022, Transactions of the Royal Society of Tropical Medicine and Hygiene.
[3] Hui-Ru Ma,et al. Proteomic landscape subtype and clinical prognosis of patients with the cognitive impairment by Japanese encephalitis infection , 2022, Journal of Neuroinflammation.
[4] M. Affolter,et al. Proteomics of human biological fluids for biomarker discoveries: technical advances and recent applications , 2022, Expert review of proteomics.
[5] Anthony J. Cesnik,et al. MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses. , 2022, Journal of proteome research.
[6] David T. Williams,et al. The Ecology and Evolution of Japanese Encephalitis Virus , 2021, Pathogens.
[7] W. Tong,et al. FUSE binding protein FUBP3 is a potent regulator in Japanese encephalitis virus infection , 2021, Virology Journal.
[8] A. Basu,et al. Involvement of RIG-I Pathway in Neurotropic Virus-Induced Acute Flaccid Paralysis and Subsequent Spinal Motor Neuron Death , 2021, mBio.
[9] Huanchun Chen,et al. Japanese Encephalitis Virus NS1′ Protein Interacts with Host CDK1 Protein to Regulate Antiviral Response , 2021, Microbiology spectrum.
[10] Chiou-Feng Lin,et al. Polarization of Type 1 Macrophages Is Associated with the Severity of Viral Encephalitis Caused by Japanese Encephalitis Virus and Dengue Virus , 2021, Cells.
[11] Yuzhen Niu,et al. The 5′ and 3′ Untranslated Regions of the Japanese Encephalitis Virus (JEV): Molecular Genetics and Higher Order Structures , 2021, Frontiers in Microbiology.
[12] T. Dhole,et al. Differential expression of circulating microRNAs in serum: Potential biomarkers to track Japanese encephalitis virus infection , 2021, Journal of medical virology.
[13] Fang-lin Zhang,et al. Axl-/- neurons promote JEV infection by dampening the innate immunity. , 2021, Virus research.
[14] J. Lian,et al. Integrated Metabolomics and Transcriptomics Analyses Reveal Metabolic Landscape in Neuronal Cells during JEV Infection , 2021, Virologica Sinica.
[15] Rajni Kant,et al. Association of single nucleotide polymorphisms in the CD209, MMP9, TNFA and IFNG genes with susceptibility to Japanese encephalitis in children from North India. , 2021, Gene.
[16] A. Yadav,et al. Proteomic landscape of Japanese encephalitis virus-infected fibroblasts. , 2021, The Journal of general virology.
[17] S. Vrati,et al. Development and characterization of an animal model of Japanese encephalitis virus infection in adolescent C57BL/6 mouse , 2021, Disease models & mechanisms.
[18] Maximilian T. Strauss,et al. Artificial intelligence for proteomics and biomarker discovery. , 2021, Cell systems.
[19] Ankit Halder,et al. Recent advances in mass-spectrometry based proteomics software, tools and databases. , 2021, Drug discovery today. Technologies.
[20] Xinze Liu,et al. Proteomic analyses identify intracellular targets for Japanese encephalitis virus nonstructural protein 1 (NS1). , 2021, Virus research.
[21] Rashmi Kumar,et al. Association of interleukin-6 (174 G/C) and interleukin-12B (1188 A/C) gene polymorphism with expression and risk of Japanese encephalitis disease in North Indian population , 2021, Journal of Neuroimmunology.
[22] A. Barrett,et al. The future of Japanese encephalitis vaccination: expert recommendations for achieving and maintaining optimal JE control , 2021, NPJ vaccines.
[23] J. Xia,et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights , 2021, Nucleic Acids Res..
[24] K. Chu,et al. Clinical Outcomes of Japanese Encephalitis after Combination Treatment of Immunoglobulin, Ribavirin, and Interferon-α2b , 2021, Journal of clinical neurology.
[25] Sean M. Moore,et al. The current burden of Japanese encephalitis and the estimated impacts of vaccination: Combining estimates of the spatial distribution and transmission intensity of a zoonotic pathogen , 2021, medRxiv.
[26] Andrew R. Jones,et al. An OMICs-based meta-analysis to support infection state stratification , 2021, Bioinform..
[27] R. Tao,et al. Screening and verification of potential gene targets in esophageal carcinoma by bioinformatics analysis and immunohistochemistry , 2021, Annals of translational medicine.
[28] Jing Ye,et al. Pathogenicity and virulence of Japanese encephalitis virus: Neuroinflammation and neuronal cell damage , 2021, Virulence.
[29] P. Newton,et al. Outcome of Japanese Encephalitis Virus (JEV) Infection in Pediatric and Adult Patients at Mahosot Hospital, Vientiane, Lao PDR , 2020, The American journal of tropical medicine and hygiene.
[30] R. Jia,et al. The Dual Regulation of Apoptosis by Flavivirus , 2020, Frontiers in Microbiology.
[31] A. Brault,et al. Persistence of IgM Antibodies after Vaccination with Live Attenuated Japanese Encephalitis Vaccine , 2020, The American journal of tropical medicine and hygiene.
[32] R. Roy,et al. Life cycle process dependencies of positive-sense RNA viruses suggest strategies for inhibiting productive cellular infection , 2020, bioRxiv.
[33] Paul Theodor Pyl,et al. Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis , 2020, bioRxiv.
[34] Thomas Yu,et al. DreamAI: algorithm for the imputation of proteomics data , 2020, bioRxiv.
[35] Olga Vitek,et al. MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures , 2020, Molecular & Cellular Proteomics.
[36] U. K. Misra,et al. Impaired Autophagy Flux is Associated with Proinflammatory Microglia Activation Following Japanese Encephalitis Virus Infection , 2020, Neurochemical Research.
[37] C. Pineau,et al. Exploration of human cerebrospinal fluid: A large proteome dataset revealed by trapped ion mobility time-of-flight mass spectrometry , 2020, Data in brief.
[38] Dana Pascovici,et al. A workflow for rapidly extracting biological insights from complex, multi-condition proteomics experiments with WGCNA and PloGO2. , 2020, Journal of proteome research.
[39] M. Gale,et al. Flavivirus Nonstructural Protein NS5 Dysregulates HSP90 to Broadly Inhibit JAK/STAT Signaling , 2020, Cells.
[40] E. Gould,et al. A need to raise the bar — A systematic review of temporal trends in diagnostics for Japanese encephalitis virus infection, and perspectives for future research , 2020, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.
[41] Dong-ying Fan,et al. Transcriptomic Analysis Suggests the M1 Polarization and Launch of Diverse Programmed Cell Death Pathways in Japanese Encephalitis Virus-Infected Macrophages , 2020, Viruses.
[42] A. Mantovani,et al. Determination of pentraxin 3 levels in cerebrospinal fluid during central nervous system infections , 2019, European Journal of Clinical Microbiology & Infectious Diseases.
[43] Yasset Perez-Riverol,et al. The ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics , 2019, Nucleic Acids Res..
[44] P. Newton,et al. Mass spectrometry-based proteomic techniques to identify cerebrospinal fluid biomarkers for diagnosing suspected central nervous system infections. A systematic review , 2019, The Journal of infection.
[45] Christoph B. Messner,et al. DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput , 2019, Nature Methods.
[46] J. Schoggins. Interferon-Stimulated Genes: What Do They All Do? , 2019, Annual review of virology.
[47] Meng Wang,et al. RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data , 2019, Bioinform..
[48] Jing Wang,et al. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs , 2019, Nucleic Acids Res..
[49] P. Newton,et al. Management of Central Nervous System Infections, Vientiane, Laos, 2003–2011 , 2019, Emerging infectious diseases.
[50] F. Dehghani,et al. The Cytoskeleton—A Complex Interacting Meshwork , 2019, Cells.
[51] Olga Vitek,et al. Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time. , 2019, Journal of proteome research.
[52] Damian Szklarczyk,et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets , 2018, Nucleic Acids Res..
[53] Jürgen Cox,et al. A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis , 2018, bioRxiv.
[54] T. Solomon,et al. Japanese encephalitis — the prospects for new treatments , 2018, Nature Reviews Neurology.
[55] P. Newton,et al. Development of an improved RT-qPCR Assay for detection of Japanese encephalitis virus (JEV) RNA including a systematic review and comprehensive comparison with published methods , 2018, PloS one.
[56] Matthias Mann,et al. Revisiting biomarker discovery by plasma proteomics , 2017, Molecular systems biology.
[57] Tianbing Ding,et al. Heat shock protein 90β in the Vero cell membrane binds Japanese encephalitis virus , 2017, International journal of molecular medicine.
[58] M. Bloom,et al. BiP: Master Regulator of the Unfolded Protein Response and Crucial Factor in Flavivirus Biology , 2017, The Yale journal of biology and medicine.
[59] Anushya Muruganujan,et al. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements , 2016, Nucleic Acids Res..
[60] A. Barabasi,et al. Network science , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[61] A. Butte,et al. Cross-tissue Analysis of Gene and Protein Expression in Normal and Cancer Tissues , 2016, Scientific Reports.
[62] P. Newton,et al. How many patients with anti-JEV IgM in cerebrospinal fluid really have Japanese encephalitis? , 2015, The Lancet. Infectious diseases.
[63] M. Diamond,et al. Innate immune interactions within the central nervous system modulate pathogenesis of viral infections. , 2015, Current opinion in immunology.
[64] Kar Yue Foo,et al. Interaction between Flavivirus and Cytoskeleton during Virus Replication , 2015, BioMed research international.
[65] P. Newton,et al. Blood–Brain Barrier Function and Biomarkers of Central Nervous System Injury in Rickettsial versus Other Neurological Infections in Laos , 2015, The American journal of tropical medicine and hygiene.
[66] A. Basu,et al. Cerebrospinal Fluid Biomarkers of Japanese Encephalitis , 2015, F1000Research.
[67] G. von Heijne,et al. Tissue-based map of the human proteome , 2015, Science.
[68] A. Kipar,et al. Neuropathogenesis of Japanese Encephalitis in a Primate Model , 2014, PLoS neglected tropical diseases.
[69] S. Granjeaud,et al. Cerebrospinal Fluid Biomarker Candidates Associated with Human WNV Neuroinvasive Disease , 2014, PloS one.
[70] Nuno A. Fonseca,et al. Expression Atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments , 2013, Nucleic Acids Res..
[71] U. Misra,et al. Matrix metalloproteinases and their tissue inhibitors in serum and cerebrospinal fluid of children with Japanese encephalitis virus infection , 2013, Archives of Virology.
[72] Harini Sooryanarain,et al. Activated CD56(+) lymphocytes (NK+NKT) mediate immunomodulatory and anti-viral effects during Japanese encephalitis virus infection of dendritic cells in-vitro. , 2012, Virology.
[73] B. Biggerstaff,et al. Evaluation of three commercially available Japanese encephalitis virus IgM enzyme-linked immunosorbent assays. , 2010, The American journal of tropical medicine and hygiene.
[74] Witold R. Rudnicki,et al. Feature Selection with the Boruta Package , 2010 .
[75] William Stafford Noble,et al. Semi-supervised learning for peptide identification from shotgun proteomics datasets , 2007, Nature Methods.
[76] Joydeep Ghosh,et al. Japanese encephalitis virus infection decrease endogenous IL-10 production: Correlation with microglial activation and neuronal death , 2007, Neuroscience Letters.
[77] R. Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[78] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[79] Ashutosh Kumar Singh,et al. Secretion of the chemokine interleukin-8 during Japanese encephalitis virus infection. , 2000, Journal of medical microbiology.
[80] Jessen T. Havill,et al. Networks , 1995, Discovering Computer Science.
[81] J. Yates,et al. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.
[82] D. Burke,et al. Kinetics of IgM and IgG responses to Japanese encephalitis virus in human serum and cerebrospinal fluid. , 1985, The Journal of infectious diseases.
[83] Horace K. Gifpen. The American Journal of Tropical Medicine and Hygiene , 1952 .
[84] OUP accepted manuscript , 2022, Transactions of the Royal Society of Tropical Medicine and Hygiene.
[85] T. Solomon,et al. Japanese encephalitis virus infection. , 2014, Handbook of clinical neurology.
[86] P. Lu,et al. A specific and sensitive antigen capture assay for NS1 protein quantitation in Japanese encephalitis virus infection. , 2012, Journal of virological methods.
[87] A. Allam,et al. Lipocalin-2 as a marker of bacterial infections in chronic liver disease: a study in Egyptian children. , 2011, The Egyptian journal of immunology.
[88] N. Gulbahce,et al. Network medicine: a network-based approach to human disease , 2010, Nature Reviews Genetics.
[89] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.
[90] Xianrang Song,et al. Maturation of a central , 1996 .