MitoEdit: a tool for targeted mitochondrial base editing

About

MitoEdit is a novel Python workflow designed for targeted base editing of the mitochondrial DNA (mtDNA). This tool streamlines the selection of optimal targeting windows for base editing, helping researchers efficiently target mtDNA mutations that are linked to various diseases. By enabling targeted base editing of the mitochondrial genome, MitoEdit will speed up the study of mtDNA-related diseases, help with preclinical drug testing, and enable therapeutic approaches to correct pathogenic mutations.

Overview

MitoEdit lets users input DNA sequences in text format, specify the target base position, and indicate the desired modification. The tool processes this information to identify candidate target windows, list the number and position of potential bystander edits, and find optimal flanking TALE sequences, where applicable. All the results are provided in a structured format including detailed logs to track progress.

Pipelines

The current MitoEdit workflow established four different pipelines based on the editing patterns observed in the following base editor systems:

1. Mok2020_G1333
2. Mok2020_G1397
3. Mok2022_G1397_DddA11
4. Cho_sTALEDs

A detailed description for each pipeline can be found in the README file in the pipelines folder.

Note: To use the evolved DddA6 variant from the Mok 2022 paper, you can use the Mok2020_G1397 pipeline.

TALE-NT Tool

The workflow uses the TALE-NT tool to identify optimal flanking TALE arrays around each candidate target window.

Quick Start

If you have all the necessary tools installed, you can use these commands to access MitoEdit:

For targeting the human mitochondrial DNA:

python mitocraft.py <position> <reference_base> <mutant_base>

Note: The position is based on the human mitochondrial genome sequence (NC_012920.1)

For targeting any other DNA sequence:

python mitocraft.py --input_file <input_DNA_file> <position> <reference_base> <mutant_base>

Prerequisites

• Python 3.x
• Required Python packages:
  • pandas
  • openpyxl
• Download and install Conda if not already installed. Follow the prompts to complete installation.

Installation

1. Clone the repository:

git clone https://github.com/Kundu-Lab/mitoedit.git
cd mitoedit

2. Set up the Conda environment:

• Use the provided environment.yml file to create the Conda environment:

conda env create -f environment.yml

• Alternatively, you can create the conda environment manually:

conda create -n run_talen_env python=2.7.18 biopython=1.70

Note: The Conda environment should be named run_talen_env for MitoEdit to correctly use the TALE-NT tool. There is no need to activate the conda environment, as the pipeline will automatically use run_talen_env if it is installed in Conda. This environment is specifically for the TALE-NT Tool.

What input parameters does MitoEdit require?

MitoEdit requires the following parameters:

Required Data Parameters:

1. Position: The position of the base to target (1-based index).

2. Reference Base: The original base at the specified position (A, T, C, or G).

3. Mutant Base: The desired base conversion (A, T, C, or G).

Optional Data Parameter:

Input File:

• The path to a file (.txt / .fasta) containing the DNA sequence.
• If not provided, MitoEdit will use the human mtDNA sequence from NCBI by default.

What does MitoEdit output?

MitoEdit generates the following outputs:

Output Directories

MitoEdit organizes the output files in the following directories:

• fasta: Contains the FASTA file of the 60bp sequence adjacent to the target base.
• pipeline_windows: Stores target windows generated from each individual pipeline.
• all_windows: Contains a combined list of target windows from all pipelines.
• talen: Stores the output from the TALE-NT tool.
• matching_output: Includes TALE sequences for applicable target windows.

• final_output: Contains the final consolidated output file.

Note: Check the final_output directory to see the final results. The other directories are stored under the running directory.

Output Files

Excel Files:

• {pipeline}_{position}.xlsx: Lists the target windows generated from each pipeline.
• all_windows_{position}.xlsx: Contains all target windows combined from all pipelines.
• matching_tales_{position}.xlsx: Summary of optimal flanking TALE sequences for each applicable target window.

• final_{position}.xlsx: Consolidated final result, including the target windows, position and number of bystander edits, and optimal flanking TALE sequences where applicable.

FASTA File:

• adjacent_bases_{position}.fasta: Contains the sequence adjacent to the target base, extending 30bp on each side.

TALE-NT File:

• TALENT_{position}.txt: Contains the output from TALE-NT Tool describing the optimal flanking TALE sequences possible.

Usage

• For a full list of parameters, use the --help flag from the command line.

python mitocraft.py --help
python mitocraft.py -h

• To run the tool, use the following commands:

python mitocraft.py <position> <reference_base> <mutant_base>
python mitocraft.py --input_file <DNA.txt> <position> <reference_base> <mutant_base>

Examples

To target the human mitochondrial DNA:

python mitocraft.py 11696 G A

Expected Output: When you run this command, MitoEdit generates an Excel file named final_11696.xlsx in the final_output directory. This file includes two spreadsheets: All_Windows and Bystander_Effect, with the first five rows from each shown below.

Note: The [ ] represents the target base and { } represent bystander edits.

1. All_Windows Sheet

Pipeline	Position	Reference Base	Mutant Base	Window Size	Window Sequence	Target Location	Number of bystanders	Position of Bystanders	Optimal Flanking TALEs	Flag CheckBystanderEffect
Mok2022_G1397_DddA11	11696	G	A	14bp	GCA[G]TCATT{C}TCAT	Position 4 from the 5' end	1	[11702]	FALSE	-
Mok2022_G1397_DddA11	11696	G	A	14bp	CGCA[G]TCATT{C}TCA	Position 5 from the 5' end	1	[11702]	FALSE	-
Mok2022_G1397_DddA11	11696	G	A	14bp	GCGCA[G]T{C}ATTCTC	Position 6 from the 5' end	1	[11698]	FALSE	-
Mok2022_G1397_DddA11	11696	G	A	14bp	GGCGCA[G]T{C}ATTCT	Position 7 from the 5' end	1	[11698]	FALSE	-

Note: If the column Flag CheckBystanderEffect=TRUE, you should manually check the results for potential amino acid changes caused by neighbouring bystanders on the same codon.

2. Bystanders_Effects Sheet

Bystander Position	Reference Base	Mutant Base	Location On Genome	Predicted Mutation Impact	SNV Type	AA Variant	Functional Impact	MutationAssessor Score
11698	C	T	Complex 1	Predicted Benign	synonymous SNV	V313V
11702	C	T	Complex 1	Predicted Pathogenic	nonsynonymous SNV	V315F	medium	3.44
11704	C	T	Complex 1	Predicted Benign	synonymous SNV	V315L

To target any other DNA sequence:

python mitocraft.py --input_file test.txt 33 G A

Expected Output: When using an input file, the generated Excel file will contain only one spreadsheet, similar to the following: (example taken from the file provided in the test file folder)

1. All_Windows Sheet

Pipeline	Position	Reference_Base	Mutant Base	Window Size	Window Sequence	Target Location	Number of bystanders	Position of Bystanders	Optimal Flanking TALEs	Flag_CheckBystanderEffect	LeftTALE1	RightTALE1	LeftTALE2	RightTALE2
Mok2020_G1397	33	G	A	14bp	TG{G}[G]A{G}AACT{C}TCT	Position 4 from the 5' end	3	[32, 35, 40]	FALSE	TRUE
Mok2020_G1397	33	G	A	14bp	CTG{G}[G]A{G}AACTCTC	Position 5 from the 5' end	2	[32, 35]	FALSE	TRUE
Mok2020_G1397	33	G	A	14bp	ACTG{G}[G]AGAACTCT	Position 6 from the 5' end	1	[32]	FALSE	TRUE
Mok2020_G1397	33	G	A	14bp	TACTG{G}[G]AGAACTC	Position 7 from the 5' end	1	[32]	TRUE	TRUE	T TACCCCCCACTATTAACC	TCTGTGCTAGTAACC A	T ACCCCCCACTATTAACC	TCTGTGCTAGTAACC A

Note: When optimal flanking TALE sequences are found, the sequence is added to the LeftTALE and RightTALE columns respectively. The impact of bystander edits is not provided when using an input DNA file.

Notes

• Input File Formatting: Ensure that your input file is correctly formatted, with the reference base matching the base at the specified position.
• Supported File Formats: MitoEdit can read .fasta and .txt formats for DNA sequences.
• Minimum Upload Sequence Length: The input file must contain at least 35 bases, covering the target base on either side, for accurate processing.
• Output File Name Conflicts: Check for existing output files with the same name before running MitoEdit to prevent overwriting and errors.
• Logging: MitoEdit logs its progress and any issues encountered during execution in logging_main.log.
• Species Support: While the tool is designed primarily for human mtDNA, other DNA sequences can also be uploaded and used.
• Modifying TALE-NT Workflow: If no matching flanking TALE sequences are identified, consider modifying the TALE-NT parameter by setting FILTER = 2. This will identify all TALE pairs targeting any base in the target window, not just those for the target base. For further information, refer to the TALE-NT FAQs.

How to Cite?

If you use MitoEdit in your research, please cite it as follows:

• {paper link!}

Contact

If you have any questions, please do not hesitate to contact me at:

• Devansh Shah: [email protected], [email protected]

License

This project is licensed under the MIT License. See the LICENSE file for details. You are free to use and modify it, but please give credit to the original authors.

Contributing

We welcome contributions! If you want to help improve MitoEdit, please fork the repository and submit a pull request.

TALE-NT License

Copyright (c) 2011-2015, Nick Booher [email protected] and Erin Doyle [email protected].

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE