Production and detection of HIV-Gag particles produced by Saccharomyces cerevisiae

Description

dataset description

doi: 10.70124/kzk0s-kdq60

The following description is used to give a comprehensive overview of the data structure and data management generated in a set of experiments designed to answer the following questions:

1. can we establish a robust production and purification system for HIV-Gag virus-like particles (VLPs) in the yeast Saccharomyces cerevisiae?
2. can we also show the same results with an already described modified version called Gag:sGFP?

important abbreviations:

Gag - group-specific antigen, a core structural protein-complex found in some viruses in different variants.

sGFP - superfolder green fluorescent protein, a common reporter used in biochemical and microbiological experiments which matures quickly after expression and is suitable for strong overexpression systems.

HIV - human immunodeficiency virus, an immune system compromising virus specific to humans.

virus-like particle - also called VLP; a non-infectious particle that resembles a mature virus but lacks key features like the ability to self-reproducet or other virulent factors. In the case of the HIV-Gag it mainly consist of the Gag protein, which self-assembles at the host cell wall and is released as a spherical structure.

The data in this dataset is generated, collected, and analyzed by Adrian Köber.

Context and methodology

• This project mainly involves methods from the fields of biochemistry and microbiology. It includes SDS-PAGE, Western Blotting, and fluorescence in 96-well format, and general cultivation methods for yeast.
• This dataset serves the main purpose of showing the robustness of the laboratory procedure which is used to generate and purify the VLPs, which is built on two main publications.
• This dataset was created as a part of my Ph.D. thesis project and resolves around a center point of the underlying laboratory procedures.

Technical details

• The general structure of the dataset will include the raw data from fluorescence measurements in .csv format with appropriate sample tags to identify the samples, possible dilutions, used volumes, or other relevant information. Pictures are saved as .tiff in an unaltered version. There will be descriptive metadata or README files for each dataset and a general README file for the overall process description, which also includes a detailed protocol for the method with the aim of providing an in-depth manual to redo the experiments themselves.
• The analyzed and annotated data will also be provided in a .csv or .tiff format appropriate to the data structure; e.g. tabular data -> .csv
• The dataset will include a three-layered folder structure with a main folder containing the sub-folders for the raw and analyzed data, which in turn include folders for the different experiment parts. The naming convention is described in the appended metadata file, in short: main folder: experiment ID, sub-folder: experiment IDraw/anaylzed, internal folder: experiment ID_raw_date.
• The aim is to have no proprietary software needed to open or evaluate the data itself. Data will be processed in Excel, Powerpoint, or Texteditor to the stated level of detail and then converted to a non-proprietary file format (.csv; .tiff; .xml; .rtf)
• There will be also proprietary data formats like .xlsx and .scn, which can be used, if possible.
• The dataset includes a general metadata description/documentation in .rtf format which will include the necessary details for the procedure, data analysis, and data provenance.

Further details

• To re-do the experiments it will be necessary to have access to the correct S. cerevisiae strains, which are owned by the group of Matthias Steiger (TU Wien, E166-5-2) and are located in the BH building of the Campus Getreidemarkt at the Gumpendorfer Straße 1A, 1060 Wien, Austria. For that please contact adrian.koeber@tuwien.ac.at or matthias.steiger@tuwien.ac.at
• The genetic construct maps will also be provided in an open-source format with annotations to discern the crucial genetic components.
• If there are questions concerning the re-use of the data please contact adrian.koeber@tuwien.ac.at or matthias.steiger@tuwien.ac.at.

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