Wos İndeksli Açık & Kapalı Erişimli Yayınlar

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    Characterization of local SARS-CoV-2 isolates and pathogenicity in IFNAR(-/-) mice
    (2020) Hanifehnezhad, Alireza; Kehribar, Ebru Sahin; Oztop, Sidika; Sheraz, Ali; Kasirga, Serkan; Ergunay, Koray; Onder, Sevgen; Yilmaz, Erkan; Engin, Doruk; Oguzoglu, T. Cigdem; Seker, Urartu Ozgur Safak; Yilmaz, Engin; Ozkul, Aykut; 0000-0001-5653-6080; 33015402; AAJ-7911-2020
    The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recently a global pandemic with unprecedented public health, economic and social impact. The development of effective mitigation strategies, therapeutics and vaccines relies on detailed genomic and biological characterization of the regional viruses. This study was carried out to isolate SARS-CoV-2 viruses circulating in Anatolia, and to investigate virus propagation in frequently-used cells and experimental animals. We obtained two SARS-CoV-2 viruses from nasopharngeal swabs of confirmed cases in Vero E6 cells, visualized the virions using atomic force and scanning electron microscopy and determined size distribution of the particles. Viral cytopathic effects on Vero E6 cells were initially observed at 72 h post-inoculation and reached 90% of the cells on the 5th day. The isolates displayed with similar infectivity titers, time course and infectious progeny yields. Genome sequencing revealed the viruses to be well-conserved, with less than 1% diversity compared to the prototype virus. The analysis of the viral genomes, along with the available 62 complete genomes from Anatolia, showed limited diversity (up to 0.2% on deduced amino acids) and no evidence of recombination. The most prominent sequence variation was observed on the spike protein, resulting in the substitution D614G, with a prevalence of 56.2%. The isolates produced non-fatal infection in the transgenic type I interferon knockout (IFNAR(-/-)) mice, with varying neutralizing antibody titers. Hyperemia, regional consolidation and subpleural air accumulation was observed on necropsy, with similar histopathological and immunohistochemistry findings in the lungs, heart, stomach, intestines, liver, spleen and kidneys. Peak viral loads were detected in the lungs, with virus RNA present in the kidneys, jejunum, liver, spleen and heart. In conclusion, we characterized two local isolates, investigated in vitro growth dynamics in Vero E6 cells and identified IFNAR-/- mice as a potential animal model for SARS-CoV-2 experiments.
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    Systematic identification of cancer-specific MHC-binding peptides with RAVEN
    (2018) Ozen, Ozlem; Baldauf, Michaela C.; Gerke, Julia S.; Kirschner, Andreas; Blaeschke, Franziska; Effenberger, Manuel; Schober, Kilian; Rubio, Rebeca Alba; Kanaseki, Takayuki; Kiran, Merve M.; Dallmayer, Marlene; Musa, Julian; Akpolat, Nurset; Akatli, Ayse N.; Rosman, Fernando C.; Sugita, Shintaro; Hasegawa, Tadashi; Sugimura, Haruhiko; Baumhoer, Daniel; Knott, Maximilian M. L.; Sannino, Giuseppina; Marchetto, Aruna; Li, Jing; Busch, Dirk H.; Feuchtinger, Tobias; Ohmura, Shunya; Orth, Martin F.; Thiel, Uwe; Kirchner, Thomas; Gruenewald, Thomas G. P.; 30228952
    Immunotherapy can revolutionize anti-cancer therapy if specific targets are available. Immunogenic peptides encoded by cancer-specific genes (CSGs) may enable targeted immunotherapy, even of oligo-mutated cancers, which lack neo-antigens generated by protein-coding missense mutations. Here, we describe an algorithm and user-friendly software named RAVEN (Rich Analysis of Variable gene Expressions in Numerous tissues) that automatizes the systematic and fast identification of CSG-encoded peptides highly affine to Major Histocompatibility Complexes (MHC) starting from transcriptome data. We applied RAVEN to a dataset assembled from 2,678 simultaneously normalized gene expression microarrays comprising 50 tumor entities, with a focus on oligo-mutated pediatric cancers, and 71 normal tissue types. RAVEN performed a transcriptome-wide scan in each cancer entity for gender-specific CSGs, and identified several established CSGs, but also many novel candidates potentially suitable for targeting multiple cancer types. The specific expression of the most promising CSGs was validated in cancer cell lines and in a comprehensive tissue-microarray. Subsequently, RAVEN identified likely immunogenic CSG-encoded peptides by predicting their affinity to MHCs and excluded sequence identity to abundantly expressed proteins by interrogating the UniProt protein-database. The predicted affinity of selected peptides was validated in T2-cell peptide-binding assays in which many showed binding-kinetics like a very immunogenic influenza control peptide. Collectively, we provide an exquisitely curated catalogue of cancer-specific and highly MHC-affine peptides across 50 cancer types, and a freely available software (https://github.com/JSGerke/RAVENsoftware) to easily apply our algorithm to any gene expression dataset. We anticipate that our peptide libraries and software constitute a rich resource to advance anti-cancer immunotherapy.