This work is licensed under a Creative Commons Attribution 4.0 International License.
© Copyright 2022 Laboratory of Integrative Systems Physiology, EPFL, Switzerland
This is a Renku project, see https://renku.readthedocs.io for details.
This project is based on the following template: https://github.com/auwerxlab/renku-project-template/tree/master/R-renv (deb856b)
Authors: Alexis Rapin (alexis.rapin@epfl.ch), Adrienne Mottis (mottisadrienne@gmail.com)
This R project assesses the impact of two tetracycline (Tet) compounds, Doxycycline (Dox) and 9-TB, on the fecal microbial communities of 8 week-old female BALB/cN mice in a longitudinal manner both before and after infection with Influenza A virus (IFV).
Results were included in: Mottis A, Yang Li T, El Alam G, Rapin A, Katsyuba E, Liaskos D, D’Amico D, Harris NL, Grier MC, Mouchiroud L, Nelson ML, Auwerx J. Tetracycline-induced mitohormesis mediates disease tolerance against influenza. 2022. Accepted for publication in the Journal of Clinical Investigation.
Mitohormesis defines the increase in fitness mediated by adaptive responses to a mild mitochondrial stress. Tetracyclines inhibit not only bacterial but also mitochondrial translation, thus imposing a low level of mitochondrial stress to eukaryotic cells. The mitochondrial stress response induced by Dox and 9-TB improved survival and disease tolerance against lethal IFV infection.
The analysis carried on in this R project shows that while Dox affects the gut bacterial community, 9-TB has no detectable effect on it. This suggests the later compound may be used to induce mitohormesis and improve the response to viral infection while leaving the microbiome unaffected.
Mice were treated with Doxycycline (40 mpkd, IP), 9-TB (1 mpkd, IP) or vehicle (control) daily from day -3 pre Influenza A infection to day 6 post Influenza A infection.
Mice were inoculated intranasally with 175 PFU of the Influenza A virus (strain H1N1 PR8) in PBS.
DNA was extracted using the MagMAX Microbiome Ultra Nucleic Acid Isolation Kit (Thermo Fisher, Catalog#: A42358) using 100 mg of fecal sample for 800 µL of Lysis Buffer. Bead beating was performed for 5 min at 50 Hz. Lysate was centrifuged at 14,000 g for 2 min and 400-500 μL of supernatant was used in subsequent steps using a KingFisher Flex system (Thermo Fisher) following the manufacturer’s protocol. Extracted DNA quantified using the Qubit dsDNA Assay Kit (Thermo Fisher). Sequencing library was prepared with 100 ng of DNA per sample. Briefly: Shearing was performed on a Covaris LE200 system, end repair, A-tailing, ligation of adaptors and PCR were performed using the KAPA Hyper Prep Kit (Roche, Catalog#: 07962363001) with the following PCR program: 45 min at 98 deg C, 7 cycles including 15 min at 98 deg C, 30 min at 60 deg C and 30 min at 72 deg C, then finally 60 min at 72 deg C and holding 4 deg C until samples retrieval. Library concentration was measured using the Qubit dsDNA Assay Kit (Thermo Fisher) and fragment length was assessed on an Agilent TapeStation. The library was sequenced on a Illumina NovaSeq 6000 platform using paired-end 2x150 bp chemistry. Sequences were deposited on the European nucleotide archive (ENA) and are publicly available under accession number PRJEB52004.
Low quality bases and adapters were trimmed. Short reads (length shorter than 35 bp) and low-quality reads were removed. Host sequences were identified by mapping to the host reference genome with bowtie2, then removed. Taxonomy was assigned using the the Kraken2 sequence classifier with an in house developed microbial database including 27,165 reference genomes (spanning 9,471 bacteria, 1,854 fungi, 15,752 viruses, 88 parasites). Genus and species relative abundances in terms of reads per million (rpm) were estimated using Bracken.
The entire statistical analysis was done within R notebooks located in the notebooks/ directory. Briefly, the species composition of the bacterial communities was assessed using permutational multivariate analysis of variance (perMANOVA) based on the Bray-Curtis dissimilarity and 10,000 permutations. Samples similarities were further assessed using a non-metric multidimensional scaling (NMDS) analysis based on the Bray-Curtis dissimilarity. Bacterial species diversity was assessed in terms of Shannon diversity index (SDI) and richness and compared using Kruskal–Wallis one-way analysis of variance followed by post-hoc Wilcoxon tests with p-value adjusted for multiple comparison using the Holm-Bonferroni method.
- Raw whole metagenome sequencing FASTQ reads are available at ENA under accession number PRJEB52004.
You can clone this project from https://sv-renku-git.epfl.ch.
$ git clone https://sv-renku-git.epfl.ch/arapin/dox-9tb-mouse-metagenomic-analysis-01.git
If it is private, you would need a valid Gitlab token. Contact the authors if needed.
$ git clone https://< gitlab_token_name >:< gitlab_token >@sv-renku-git.epfl.ch/arapin/dox-9tb-mouse-metagenomic-analysis-01.git
You can also get an archive of this project from https://github.com.
$ git clone https://github.com/auwerxlab/dox-9tb-mouse-metagenomic-analysis-01.git
And from Zenodo:
- This is a Renku project, see https://renku.readthedocs.io for details
- R version 4.0.0
- renv R package
- See
renv.lockfor the complete list of R packages dependencies - See
Dockerfilefor more system requirements - Data (see below)
Data and associated metadata are located in the data directory and include.
- Description: Genus and species relative abundance data estimated with Braken, plus samples metadata.
- Source: Privately available on https://lispnas1.epfl.ch: archive/RAW/DNAseq/amottis_metagenomics_sequenta/report_table/:
$ scp arapin@lispsrv1.epfl.ch:archive/RAW/DNAseq/amottis_metagenomics_sequenta/report_table/*.xlsx data/metagenome/
- Files:
metagenome ├── C22001898LD01-< SampleID >.report_table.xlsx Table of taxa read counts in XLSX fomat (one file for each sample) ├── IFV_EPFL_20210817_run_samples.txt Samples metadata table in tab-delimited text format └── s1886g03001.Metagenomics_Analysis_Report.pdf Sequencing report, including description of report tables content
This is a Renku project and comes with a Dockerfile to build the required environment (see https://renku.readthedocs.io for details).
This project can also be used outside Renku, provided RStudio and dependencies are available (see below).
This project is setup to use the R renv package to manage dependencies and to keep required R libraries files on the docker image, instead of within a renv/ directory. This is why there is a symbolic renv link in the main directory in place of the usual renv/ directory.
To use renv outside Renku, simply remove the renv symbolic link (e.g. $ rm renv) before openning the project in RStudio or running > renv::activate().
Open This project in RStudio using the .Rproj file.
If needed, activate renv using > renv::activate() and install required R libraries using > renv::restore().
This project uses git lfs to track large files, see .gitattributes details and https://git-lfs.github.com/ for usage.
Run notebooks in notebooks/ sequencially.
Final figure panels and tables are assembled in the notebooks/figure_compilation.Rmd notebook.
Render notebooks using make:
$ make
render render all R notebooks located in notebooks/
commit run git commit with --no-verify option
. ├── data Data ├── figs Figures ├── notebooks R notebooks └── renv renv directory (set as a symbolic link in order to keep R libraries files on the docker image when using Renku) - not included in archive
- Langmead, B., Salzberg, S.L., 2012. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357–359. https://doi.org/10.1038/nmeth.1923
- Wood, D.E., Lu, J., Langmead, B., 2019. Improved metagenomic analysis with Kraken 2. Genome Biology 20, 257. https://doi.org/10.1186/s13059-019-1891-0
- Lu, J., Breitwieser, F.P., Thielen, P., Salzberg, S.L., 2017. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, e104. https://doi.org/10.7717/peerj-cs.104