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Peromyscus WGCNA supplement

Citation

Balderrama-Gutierrez, Gabriela; Milovic, Ana; Mortazavi, Ali; Barbour, Alan (2021), Peromyscus WGCNA supplement, Dryad, Dataset, https://doi.org/10.7280/D1B38G

Abstract

P. leucopus also (deer mouse) is a known reservoir for infectious disease such as lyme disease and it is able to survive infections that other rodent models cannot. In this paper we explore P. lecucopus transcriptomic response after LPS stimulus in blood, liver and spleen using RNA-seq and compare it to M. musculus response. When comparing gene response to LPS from different species and tissues, neutrophil associated terms are enriched in the P. leucopus response, while mouse response is enriched for cytokine signaling. To compare intra species and tissue gene patterns, we identified gene networks that were associated with specie specific upregulation response to LPS. Interestingly majority of the genes that were upregulated in P. leucopus response originated from blood, whereas most of the M. musculus genes originated from liver. Both of these gene modules uphold the Neutrophil associated pathways for P. leucopus and cytokine mediated response for mouse that originated from independent analysis. We characterized and identify key genes for LPS response in P. leucopus and M. musculus, additionally we identify biological processes characteristic for each specie that pointed to inflammation, oxidative stress, phagocytosis, and metabolism. Thanks to its robust immune response and alternative pathways, P. leucopus stands out as a novel model to evaluate a broader range of infectious diseases.

Methods

After performing RNA-seq on three different tissues, We used WGCNA {Langfelder, 2008 #1404} to identify densely interconnected genes (modules) for the 6 datasets across species and tissues by building a matrix of gene expression with genes TPM >1 in one or more individual. We selected a power (b) of 13 for a soft threshold for the weighted network and specified a minimum of 100 genes per module. To merge modules with similar gene expression profiles, we carried out a dynamic tree cut, with an eigengene dissimilarity threshold of 0.2 that generated the final eigengene profiles, where eigengene is the first principal component of module expression matrix {Foroushani, 2017 #1406}. The inferred modules were distinguished by different color names, e.g. cyan.