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 PMID:37221008  

Beyond the reference: gene expression variation and transcriptional response to RNA interference in Caenorhabditis elegans.

Avery Davis Bell | Han Ting Chou | Francisco Valencia | Annalise B Paaby
G3 (Bethesda, Md.) | 2023

Though natural systems harbor genetic and phenotypic variation, research in model organisms is often restricted to a reference strain. Focusing on a reference strain yields a great depth of knowledge but potentially at the cost of breadth of understanding. Furthermore, tools developed in the reference context may introduce bias when applied to other strains, posing challenges to defining the scope of variation within model systems. Here, we evaluate how genetic differences among 5 wild Caenorhabditis elegans strains affect gene expression and its quantification, in general and after induction of the RNA interference (RNAi) response. Across strains, 34% of genes were differentially expressed in the control condition, including 411 genes that were not expressed at all in at least 1 strain; 49 of these were unexpressed in reference strain N2. Reference genome mapping bias caused limited concern: despite hyperdiverse hotspots throughout the genome, 92% of variably expressed genes were robust to mapping issues. The transcriptional response to RNAi was highly strain- and target-gene-specific and did not correlate with RNAi efficiency, as the 2 RNAi-insensitive strains showed more differentially expressed genes following RNAi treatment than the RNAi-sensitive reference strain. We conclude that gene expression, generally and in response to RNAi, differs across C. elegans strains such that the choice of strain may meaningfully influence scientific inferences. Finally, we introduce a resource for querying gene expression variation in this dataset at https://wildworm.biosci.gatech.edu/rnai/.

Pubmed ID: 37221008

Research resources used in this publication

None found

Antibodies used in this publication

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Associated grants

  • Agency: NIGMS NIH HHS, United States
    Id: R35 GM119744
  • Agency: NIH HHS, United States
    Id: R35 GM119744

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This is a list of tools and resources that we have found mentioned in this publication.


Gene Set Enrichment Analysis (tool)

RRID:SCR_003199

Software package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.

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STAR (tool)

RRID:SCR_004463

Software performing alignment of high-throughput RNA-seq data. Aligns RNA-seq reads to reference genome using uncompressed suffix arrays.

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Trimmomatic (tool)

RRID:SCR_011848

Software Java pipeline for trimming tasks for Illumina paired end and single ended data. Flexible Trimmer for Illumina Sequence Data. Pair aware preprocessing tool optimized for Illumina next generation sequencing data. Includes several processing steps for read trimming and filtering. Operating systems Unix/Linux, Mac OS, Windows.

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ggplot2 (tool)

RRID:SCR_014601

Open source software package for statistical programming language R to create plots based on grammar of graphics. Used for data visualization to break up graphs into semantic components such as scales and layers.

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DESeq2 (tool)

RRID:SCR_015687

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

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