Last updated: 2021-03-04

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Knit directory: liver-disease-atlas/

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Transcriptomic cross-species analysis of chronic liver disease reveals a consistent regulation pattern between humans and mice

The results and the analysis scripts presented on this website ensures the reproducibility of all bioinformatics related findings presented in “Transcriptomic cross-species analysis of chronic liver disease reveals a consistent regulation pattern between humans and mice”. Please see below to get more information about the individual analysis and some tips on how to reproduce the results.

Abstract

Background and aims

Mouse models are frequently used to study chronic liver diseases (CLD). To assess their translational relevance, we quantified the similarity of commonly used mouse models to human CLD based on transcriptome data.

Methods

Gene expression data from 372 patients were compared to data from acute and chronic mouse models comprising 227 mice, and additionally to nine published gene sets of chronic mouse models. Genes consistently altered in humans and mice were mapped to liver cell types based on single-cell RNA-sequencing data and validated by immunostaining.

Results

Considering the top differentially expressed genes, the similarity between humans and mice varied among the mouse models and depended on the period of damage induction. The highest recall (0.4) and precision (0.33) were observed for the model with 12 months damage induction by CCl4 and by a Western type damage, respectively. Genes consistently upregulated between the chronic CCl4 model and human CLD were enriched in inflammatory and developmental processes, and mapped to cholangiocytes, macrophages, endothelial and mesenchymal cells. Downregulated genes were enriched in metabolic processes and mapped to hepatocytes. Immunostaining confirmed the regulation of selected genes and their cell type specificity. Genes that were upregulated in both acute and chronic models showed higher recall and precision with respect to human CLD than exclusively acute or chronic genes.

Conclusion

Similarly regulated genes in human and mouse CLD were identified. Despite major interspecies differences, mouse models detected 40% of the genes significantly altered in human CLD. The translational relevance of individual genes can be assessed at https://saezlab.shinyapps.io/liverdiseaseatlas/.

Analysis

The tab Mouse models contains Rmarkdown scripts to analyze and characterize the transcriptomic profiles of acute and chronic liver disease mouse models. These analyses comprised

  • Normalization
  • PCA analysis
  • Differential gene expression analysis
  • Time series clustering and characterization (if applicable)

The tab Patient cohorts contains Rmarkdown scripts to analyze and characterize the transcriptomic profiles of patient cohorts suffering from various chronic liver disease etiologies. These analyses comprised:

  • Normalization
  • PCA analysis
  • Differential gene expression analysis

The tab Meta analysis contains Rmarkdown scripts to integrate acute and chronic mouse models with patient cohorts.

  • Chronic vs. acute
    • Identification of exclusively and commonly- regulated genes of chronic and acute disease in mice.
  • Mouse vs. human
    • Identification of consistently regulated genes in the chronic CCl4[mouse-chronic-ccl4.html] mouse model and patients.
    • Quantification of the similarity of the in total 12 chronic mouse models with the different human patient cohorts based on precision and recall.

The tab Figures contains Rmarkdown scripts to generate the figures used in the manuscript.

How can I reproduce the analysis?

We have used the workflowr package to organize the analysis scripts within this project so please familiarize yourself with its concept.

  1. Clone the repository from https://github.com/saezlab/liver-disease-atlas which automatically provides you with all analysis scripts and the correct directory structure.
  2. You need to install all packages that are required for the analyses. The package renv allows you to easily install the packages with the correct versions:
install.packages("renv")
renv::restore()

  1. The GitHub repository contains only the analysis code and same small objects. All raw data is deposited at Zenodo DOI. Download the zipped data folder unzip it and replace the existing data folder at the root level of the R-project.

  2. Run all analyses by running

install.packages("workflowr")
workflowr::wflow_build(republish = TRUE)

All intermediate and final results will be saved in the output folder. Please make sure to run all analyses in the specified order as downstream analyses depend on prior generated results.

How to cite?

Holland CH, Ramirez Flores RO, Myllys M, Hassan R, Edlund K, Hofmann U, Marchan R, Cadenas C, Reinders J, Hoehme S, Seddek A, Dooley S, Keitel V, Godoy P, Begher-Tibbe B, Trautwein C, Rupp C, Mueller S, Longerich T, Hengstler JG#, Saez-Rodriguez J#, Ghallab A#. “Transcriptomic cross-species analysis of chronic liver disease reveals a consistent regulation pattern between humans and mice.” In preparation. 2021.

#Shared senior authorship


sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      rstudioapi_0.13 whisker_0.4     knitr_1.30     
 [5] magrittr_2.0.1  R6_2.5.0        rlang_0.4.9     stringr_1.4.0  
 [9] tools_4.0.2     xfun_0.19       git2r_0.27.1    htmltools_0.5.0
[13] ellipsis_0.3.1  rprojroot_2.0.2 yaml_2.2.1      digest_0.6.27  
[17] tibble_3.0.4    lifecycle_0.2.0 crayon_1.3.4    later_1.1.0.1  
[21] vctrs_0.3.6     promises_1.1.1  fs_1.5.0        glue_1.4.2     
[25] evaluate_0.14   rmarkdown_2.6   stringi_1.5.3   compiler_4.0.2 
[29] pillar_1.4.7    httpuv_1.5.4    renv_0.12.3     pkgconfig_2.0.3