B cell transcriptomes during EBV’s pre-latent phase

This page is an entry point to the rich biology of Epstein-Barr virus (EBV) and its preferred host cell. Here you will find cellular and viral genes and their regulation during the early time points after infection. We analyzed the transcriptomes of naïve B-lymphocytes from three different human donors at different stages.

You will be able to search for individual cellular or viral genes (“.t01”) and their expression levels during the pre-latent phase of viral infection from three donors at depicted time-points (day1, day2, day3, day4, day5, day8) and compare them with established, latently infected cells (day14) or B-lymphocytes prior to infection (day0). The gene list comprises all 55,955 cellular genes listed in the hg19 reference transcriptome as well as 50 EBV genes.

You can also look at and download six clusters of cellular genes. The clusters were identified and based on 11,860 cellular and viral genes that were recognized to be significantly regulated in the course of B-cell infection. After hierarchical clustering of these genes the clusters were named with an arbitrary color code. Each cluster combines genes with similar expression pattern identified in B cells during the pre-latent phase of EBV infection.

Notes

The gene list also contains entries that relate to the genetic composition of the recombinant wt/B95-8 EBV strain 2089 (Delecluse et al., 1998, Proc Natl Acad Sci U S A 95:8245–8250. doi: 10.1073/pnas.95.14.8245 ). Protein_A, Protein_B: The two genes refer to prokaryotic genes encoded by the mini-F factor backbone of the recombinant EBV strain. These genes are needed for DNA replication and partitioning of the plasmid in E.coli, only. They are under the control of prokaryotic promoters. Hygromycin, GFP: The genes are also located on the mini-F factor backbone and are ectopically expressed from the early promoter of SV40 and the immediate-early promoter of the human cytomegalovirus, respectively.

Information

Here you can search for individual genes and their normalized read counts in naive human B- lymphocytes from three different donors. Type a cellular or viral acronym of the gene name of your interest into the box on the right or select it from the (long) list you will find there. In the plot below, timepoints are displayed on the x-axis, normalized read counts are shown on the y-axis. Different bar colors indicate read counts from uninfected cells or cells infected for the given time intervals. Moving the cursor over the bars will reveal the individual read counts, the day of infection, and the donor. For example, day2_1, 80.9 refers to day 2 post infection, donor 1, and a normalized read count of 80.9. Clicking on the camera icon above the plot will generate an image in the png format.

If your particular gene of interest does not belong to any cluster you will see the term “N/A” indicating this fact and no image will appear in the neighboring window “Cluster Average Expression” on the right. The button “GeneCards” will provide a gene-specific link to “GeneCards®: The Human Gene Database” with a wealth of information of the particular gene for your convenience. (https://www.genecards.org/)

Controls

Cluster Average Expression

Gene Expression

Information

Here you can have a look at the primary data of the RNA-seq analysis after data normalization. In the box on the left, you can choose one of the six datasets, i.e. individual clusters that are classified by color codes (blue, brown, green, etc.). At your convenience, you can flip through the spread sheets, search for gene entries, or you can download the data.

Controls

Download

Impressum

Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt(GmbH)
Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
Phone: +49 89 3187-0
Website: https://www.helmholtz-muenchen.de/
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Prof. Dr. Wolfgang Hammerschmidt
Website: https://www.helmholtz-muenchen.de/agv/
email: hammerschmidt@helmholtz-muenchen.de


Dr. Antonio Scialdone
Website: https://www.helmholtz-muenchen.de/ies/research/physics-and-data-based-modelling-of-cellular-decision-making
email: antonio.scialdone@helmholtz-muenchen.de