Mitochondrial retrograde signaling reports mitochondrial status to the nucleus. However, there is a lack of understanding of how the nucleus capture mitochondrial status in dynamics and information processing. It is a complicated biochemical reaction that occurs in most eukaryotic organisms. In this repository, we focus on the RTG pathway in yeast. This pathway is the simplest retrograde signaling pathway that has been investigated thoroughly. Data are collected from [1] and [2] (See src/data/). This repository aims to compose known protein interactions and nucleus relocation that fulfills all known responses of the yeast RTG pathway. Monte-Carlo approach is used to solve this Boolean satisfiability problem, and the parameter searching/ simulation/ threading is facilitated by DifferentialEquations.jl [3].
This repository is a Julia package. To use this function, one needs to install Julia v1.7+ first (https://julialang.org/).
Use the following script to install this package in Julia REPL.
using Pkg
Pkg.add(url="https://github.com/stevengogogo/RetroSignalModel.jl#master")https://stevengogogo.github.io/RetroSignalModel.jl/dev/
See folder src/data
Data:
solution_rtgM4.csv
This file is generated and modified from scripts/find_valid_solutions.jl. All solutions are corresponding to the knockout experiments of [1] and [2] with the conditions in boolean_table_RTG13.csv. The solutions are stored in solution_rtgM4.csv.
Data: boolean_table_RTG13.csv
This folder contains summarized responses of mitochondrial retrograde signaling in yeast.
| Standard Name | Variable Name | Details |
|---|---|---|
| RTG1 | rtg1 |
https://www.yeastgenome.org/locus/S000005428 |
| RTG2 | rtg2 |
https://www.yeastgenome.org/locus/S000005428 |
| RTG3 | rtg3 |
https://www.yeastgenome.org/locus/S000000199 |
| Mks1 | mks1 |
https://www.yeastgenome.org/locus/S000005020 |
In [1] and [2], RTG response is observed via GFP tags on either RTG1 or RTG3. In wild-type, mitochondrial damage can cause these proteins to accumulate in the nucleus, resulting in the intensified brightness of the nucleus region observed by fluorescent microscopy. As shown in boolean_table_RTG13.csv, the responses are categorized in binary results: whether GFP is accumulated in the nucleus in a given condition. Based on [1] and [2], there are 20 reactions listed in the table.
For example, the following is one of the conditions mentioned in [1]:
| Rtg1 | Rtg2 | Rtg3 | s | Mks | gfp | Trans2Nuc |
|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 1 | 1 | rtg3 | 1 |
Under the columns of Rtg1, Rtg2, Rtg3 and Mks, 0 means that the given protein is suppressed by knockout. On the other hand, 1 represent an expression of wild type. Also, 1 in s represent mitochondrial dysfunction, and 0 means the absence of mitochondrial damage. The gfp column describes the location of GFP tag. In this example, GFP tag is on Rtg3. As known in [1], Rtg3-GFP translocates to the nucleus under this condition. Therefore, Trans2Nuc is marked as 1, which means the GFP tags nucleus translocation happens.
There are 20 reactions summarized in the table. Some conditions are yet to be explored; some are from [1] (Sekito et al. 2000) or [2] (Sekito et al. 2002). Missing conditions are labeled with NA.
| Line Number | Reference |
|---|---|
| 2 | NA |
| 3 | [1] |
| 4 | [1] |
| 5 | [1] |
| 6 | [1] |
| 7 | [1] |
| 8 | [1] |
| 9 | [1] |
| 10 | NA |
| 11 | NA |
| 12 | [1] |
| 13 | [1] |
| 14 | [1] |
| 15 | [1] |
| 16 | [1] |
| 17 | [1] |
| 18 | [2] |
| 19 | [2] |
| 20 | [2] |
| 21 | [2] |
Data: RNAseq_RTG_expression.csv
Raw data and analysis: RNAseq_RTG_expression
This figure is produced by RNAseq_RTG_expression/analysis_RNA-Seq.ipynb
Whole-genome RNA sequencing was performed under normal conditions and osmotic stress [4] (GEO database's access number: GSE102475). This paper is selected because all RTG-related gene expressions are covered in this research, and this paper also provides stress condition that is informative to investigate the range of expressions of RTG elements.
The original data is downloaded and stored at Single Cell RNAseq_yeast_GSE102475.xlsx [4].
By using the differential gene expression [5], the relative expressions of Rtg1, Rtg2, Rtg3, Bmh1, Mks1 and Cit2 are analyzed by Discrete distributional differential expression [5] (D3E). Noted that Cit2 gene is used as an indicator of RTG response, while this procedure aims to get the relative expressions between RTG components rather than the exact RTG response. Zeros in RNA readouts from [4] were removed and filtered by genes of interest. The filtered data is summarized in the txt file called SingleCellRNAseq_yeast_GSE102475_LabelSep.txt. Further, differential gene expression analysis is done by D3E method by the following command
python D3ECmd.py SingelCellRNAseq_yeast_GSE102475_LabelSep.txt SingelCellRNAseq_yeast_GSE102475_LabelSep.out Stressed Unstressed -m 1 -t 0 -z 0 -n 1 -v
the installation details can be found at https://github.com/hemberg-lab/D3E [5]. Alternatively, the filtered RNA seq data can be processed by D3E online service (https://www.sanger.ac.uk/sanger/GeneRegulation_D3E/).
The mean expression values (mu1 in analysis_RNA-Seq.ipynb) are summarized in RNAseq_RTG_expression.csv that contains the relative expression levels of RTG components under normal and stressed conditions.
| File | Description |
|---|---|
| RNAseq_RTG_expression.csv | Differential gene expression of RTG elements |
| analysis_RNA-Seq.ipynb | Visualization of D3E processed differential gene expressions |
| get_RTG-Expression-Table_zero-removed.ipynb | Remove zeros in RNA-seq data and select genes of interest |
| SingleCellRNAseq_yeast_GSE102475_LabelSep.txt | RNA seq data of genes of interest (zeros are removed) |
| d3e_SC_resp_RtgGenes_GSE102475.csv | Differential gene expression (processed by https://www.sanger.ac.uk/sanger/GeneRegulation_D3E/) |
The following steps are needed to execute Jupyter notebooks under src/data/RNAseq_RTG_expression/. Noted that those notebooks are written in Python 3.7.
- Install Python 3.7 or above (https://www.python.org/)
- Install pip3 (https://pip.pypa.io/en/stable/)
- Install Python packages
pip3 install matplotlib pip3 install pandas pip3 install numpy pip3 install seaborn
- Sekito, Takayuki, Janet Thornton, and Ronald A. Butow. "Mitochondria-to-nuclear signaling is regulated by the subcellular localization of the transcription factors Rtg1p and Rtg3p." Molecular biology of the cell 11.6 (2000): 2103-2115. URL: https://doi.org/10.1091/mbc.11.6.2103
- Sekito, Takayuki, Zhengchang Liu, Janet Thornton, and Ronald A. Butow. “RTG-Dependent Mitochondria-to-Nucleus Signaling Is Regulated by MKS1 and Is Linked to Formation of Yeast Prion [URE3].” Molecular Biology of the Cell 13, no. 3 (March 2002): 795–804. https://doi.org/10.1091/mbc.01-09-0473.
- Rackauckas, Christopher, and Qing Nie. “DifferentialEquations.Jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia.” Journal of Open Research Software 5, no. 1 (May 25, 2017): 15. https://doi.org/10.5334/jors.151.
- Gasch, Audrey P., et al. "Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress." PLoS biology 15.12 (2017): e2004050. URL: https://doi.org/10.1371/journal.pbio.2004050
- Delmans, Mihails, and Martin Hemberg. "Discrete distributional differential expression (D3E)-a tool for gene expression analysis of single-cell RNA-seq data." BMC bioinformatics 17.1 (2016): 1-13. URL: https://doi.org/10.1186/s12859-016-0944-6