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Johannes B Goll

Publications and source records attributed to Johannes B Goll.

2 recordsLinked to original sources

Transcriptomic and proteomic signatures following AS03-adjuvanted Influenza A/H7N9 vaccine.

INTRODUCTION: Vaccines targeting avian influenza virus A/H7N9 are poorly immunogenic. While the immune responses can be improved with oil-in-water emulsion adjuvants such as Adjuvant System 03 (AS03), the cellular mechanisms underpinning the adjuvant effect are incompletely characterized and poorly understood. METHODS: We enrolled 30 healthy adult participants and used RNA sequencing and quantitative proteomics to characterize the response to two doses of the influenza A/H7N9 vaccine, with and without AS03, in six immune cell types. These responses were compared to those seen after administration of an unadjuvanted seasonal in uenza A/H3N2 variant vaccine to identify signatures unique to adjuvanted influenza vaccines and correlated with later antibody responses. Transcriptomic and proteomic analyses revealed that. RESULTS: AS03-adjuvanted vaccine was associated with upregulation of immune pathways in innate immune cells within 24h following vaccination for phagocytosis, antigen presentation and processing, inflammasome activation, NK-cell mediated cytotoxicity, IgA production, and interferon-response pathways. Moreover, while major histocompatibility complex (MHC I and II) upregulation was observed across multiple immune cell types, MHCII gene transcription was also increased in the neutrophil compartment, generating the hypothesis that neutrophils may play a more important role in antigen presentation than previously understood. DISCUSSION: Taken together, these data provide a more complete mechanistic understanding of oil-in-water adjuvants and their role in enhancing the immune response for pandemic influenza preparedness. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT02921997?term=NCT02921997&viewType, idientifier NCT02921997.

Adult

RP-REP Ribosomal Profiling Reports: an open-source cloud-enabled framework for reproducible ribosomal profiling data processing, analysis, and result reporting.

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user's own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6 h, 12 h, 24 h, and 40 h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

AMI