Dynamic metabolic and molecular changes during seasonal shrinking in Sorex araneus
Data files
Oct 05, 2023 version files 86.44 MB
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README.md
6.62 KB
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Supplemental1_SampleData.xlsx
21.83 KB
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Supplemental2_Liver.xlsx
33.29 MB
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Supplemental3_Hippocampus.xlsx
35.08 MB
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Supplemental4_Cortex.xlsx
18.04 MB
Apr 15, 2025 version files 22.84 MB
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README.md
6.96 KB
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Supplemental_Liver.xlsx
22.81 MB
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Supplemental1_SampleData.xlsx
17.61 KB
Abstract
To meet the challenge of wintering in place many high-latitude small mammals reduce energy demands through hibernation. In contrast, short-lived Eurasian common shrews, Sorex araneus, remain active and shrink, including energy-intensive organs in winter, regrowing in spring in an evolved strategy called Dehnel’s phenomenon. How this size change is linked to metabolic and regulatory changes to sustain their high metabolism is unknown. We analyzed metabolic, proteomic, and gene expression profiles spanning the entirety of Dehnel’s seasonal cycle in wild shrews. We show regulatory changes to oxidative phosphorylation and increased fatty acid metabolism during autumn-to-winter shrinkage, as previously found in hibernating species. But in shrews we also found upregulated winter expression of genes involved in gluconeogenesis: the biosynthesis of glucose from non-carbohydrate substrates. Co-expression models revealed changes in size and metabolic gene expression interconnect via FOXO signaling, whose overexpression reduces size and extends lifespan in many model organisms. We propose that while shifts in gluconeogenesis meet the challenge posed by high metabolic rate and active winter lifestyle, FOXO signaling is central to Dehnel’s phenomenon, with spring downregulation limiting lifespan in these shrews.
https://doi.org/10.5061/dryad.pc866t1w3
Scripts and code to reproduce RNAseq analysis for looking at changes in expression through Dehnel’s phenomenon in the liver. These analysis will help to understand both size and metabolic changes underpinning Dehnel’s phenomenon.
Change Log
From the previous version, this data set now includes gene set enrichments using fgsea, uses sex as a covariate in DESeq2 analyses, and has removed our brain data.
Description of the data and file structure
The first data table, Supplemental1_SampleData, consists of the phenotypes of the 24 individual shrews samples and there sequencing meta data.
Sheet 1 (Data)
- Rows =individuals
- Columns = Individual (#), Season , Sexual Maturity, Stage, Date, Sex, Body Mass, Brain Mass (g), Liver Mass (g), Spleen Mass (g), Heart Mass (g), Stage BoM Average (g), Stage BrM Average (g), Stage LM Average (g), Stage SM Average (g), Stage HM Average (g), Liver ID, Liver RIN, Liver Reads prefilter, Liver Reads postfilter, Liver filter difference, Liver Pseudo aligned Percent
Sheet 2 (T-tests)
- individual T-test between each stage. Highlighted values are significant (p<0.05)
Next, we will have to access the quality of our RNA-seq data, filter low quality reads and trim adapters, map to the transcriptome and quantify abundance. Then we ran analyses dependent on each tissue 1) analyze differential expression between stages of Dehnel’s phenomenon using DESeq2, 2) characterize temporal patterns in expression using TCSeq, and 3) build gene correlation networks and identify correlation between network structure and traits. Throughout the analysis, we will look at resultant genes and test whether they enrich KEGG pathways using DAVID Functional Enrichment Tools. Data and results for each tissue can be found in these three files Supplemental2_Liver, Supplemental3_Hippocampus, Supplemental4_Cortex, with sheet patterns listed below.
Sheet 1 - Gene Counts (counts)
- Rows = Genes
- Columns = Samples
Sheet 2 - DESeq2 Results (Summer vs Winter)
- Rows = Genes
- Columns = means , log-fold changes, p-values
Sheet 3 - DESeq2 Results (Winter vs Spring)
- Rows = Genes
- Columns = means , log-fold changes, p-values
Sheet 4 - fgsea (Summer vs Winter)
- Rows = Pathways
- Columns = pvalue, adjusted pvalue, error, enrichment score (no units), normalized enrichment score (no units), size (# of genes), leading edges (gene names)
Sheet 5 - fgsea (Winter vs Spring)
- Rows = Pathways
- Columns = pvalue, adjusted pvalue, error, enrichment score (no units), normalized enrichment score (no units), size (# of genes), leading edges (gene names)
Sheet 6 - WGCNA Module Membership
- Rows = Genes
- Columns = modules, memberships, p-values
Sheet 7 - WGCNA Trait to Module Correlations
- Rows = Modules
- Columns = Traits Correlations, pvalues
Sheet 8 - DAVID Gene Enrichment (Red Module)
* Rows = Pathways
* Columns = count of genes tested, total hits, percent hits, p-values, genes, list total, pop hits, pop totals, fold enrichment, Bonferroni correction, Benjamin correction, FDR
Sheet 9 - DAVID Gene Enrichment (Grey60 Module)
* Rows = Pathways
* Columns = count of genes tested, total hits, percent hits, p-values, genes, list total, pop hits, pop totals, fold enrichment, Bonferroni correction, Benjamin correction, FDR
Code/Software
RNA-seq analyses require alignment to a reference and quantification of reads. The genome and original unfiltered reads can be downloaded as described below. However, these steps could be skipped when reproducing, as count data has been saved in ./data/TISSUE/GeneCounts.
The reference (mSorAra2; GCF_027595985.1) can be download from straight from NCBI, or using the code below.
mkdir ./data/ref/
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/027/595/985/GCF_027595985.1_mSorAra2.pri/GCF_027595985.1_mSorAra2.pri_genomic.gff.gz ./data/ref/
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/027/595/985/GCF_027595985.1_mSorAra2.pri/GCF_027595985.1_mSorAra2.pri_rna.fna.gz ./data/ref/
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/027/595/985/GCF_027595985.1_mSorAra2.pri/GCF_027595985.1_mSorAra2.pri_protein.gpff.gz ./data/ref/
gunzip ../ref/GCF_027595985.1_mSorAra2.pri_rna.fna.gz
RNA-seq data from this project can also be found on NCBI Sequencing Read Archive, . The list of samples and associated accession numbers can be found in the data folder. These can be downloaded manually, or using the getter.sh script with the help of sratoolkit (https://github.com/ncbi/sra-tools). Note, all scripts are meant to be ran from the scripts folder with indirect paths contained in this git.
bash get_rawseq.sh
Quality control, filtering, trimming
Again, these scripts can be skipped if reproducing from counts. If not proceed! Here we will trim adapters from our reads and remove low quality reads using default settings and fastp. Will need to download fastp to your local environment (https://github.com/OpenGene/fastp).
bash fastp.sh
Mapping and quantification
Reads that have went through quality control are then mapped to the reference transcriptome and quantified using pseudoalignment. This method does not directly map reads to the genome, but can infer counts despite similarities between different coding regions (https://pachterlab.github.io/kallisto/about).
bash kallisto.sh
Note: This will create new transcript abundances separate ffrom the ones used in this analysis. Further scripts will use the ones I generated ./data/TISSUE/TranscriptAbundances and naming convention, but feel free to update the paths in the scripts with the ones you generated.
Analyses
Each analysis was conducted using the R code below for each tissue type. For best results, run in RStudio, as each matrix and figure is not set to print out in a best attempt to not overwrite results. If this is your desired outcome, edit code to include saving.
R Dehnel_Liver.R
DAVID Geneset Enrichment and MetaboAnalyst5.0
Both the above programs were done online at the below links. In a perfect world these should be scripted, however, due to conflicts in packages and Rversions they were not.https://www.metaboanalyst.ca https://david.ncifcrf.gov/summary.jsp