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Data for: The relative impact of parental and current environment on plant transcriptomes depends on type of stress and genotype

Cite this dataset

Earley, Timothy et al. (2023). Data for: The relative impact of parental and current environment on plant transcriptomes depends on type of stress and genotype [Dataset]. Dryad. https://doi.org/10.5061/dryad.jsxksn0fp

Abstract

Through developmental plasticity, an individual organism integrates influences from its immediate environment with those due to the environment of its parents. While both effects on phenotypes are well documented, their relative impact has been little studied in natural systems, especially at the level of gene expression. We examined this issue in four genotypes of the annual plant Persicaria maculosa by varying two key resources light and soil moisture in both generations. Transcriptomic analyses showed that the relative effects of parent and offspring environment on gene expression (i.e., the number of differentially expressed transcripts, DETs) varied both for the two types of resource stress and among genotypes. For light, immediate environment induced more DETs than parental environment for all genotypes (although the precise proportion of parental versus immediate DETs varied among genotypes). By contrast, the relative effect of soil moisture varied dramatically among genotypes, from 8-fold more DETs due to parental than immediate conditions to 10-fold fewer. These findings provide evidence at the transcriptome level that the relative impacts of parental and immediate environment on the developing organism may depend on the environmental factor and vary strongly among genotypes, providing potential for the interplay of these developmental influences to evolve.

README: The Relative Impact of Parental and Current Environment on Plant Transcriptomes Depends on Type of Stress and Genotype

Earley, Feiner, Alvarez, Coolon, and Sultan


Experimental data and code for Earley et al. "The Relative Impact of Parental and Current Environment on Plant Transcriptomes Depends on Type of Stress and Genotype." Please refer to this manuscript for detailed methodology used to generate this data.

Description of the data and file structure

The data in this repository pertain to the differential expression analysis of RNA-seq data. The data folder contains all necessary sample information and outputs for the differential expression analysis. The sub-folder Counts contains kallisto quant outputs, data quantifying the abundance of each transcript in each sample. The sub-folder GO within data contains all all output files for the gene ontology analysis. The sub-folder venn-diagrams within data contains all output files for the Venn diagram visualization and overlap analysis. The sub-folder WGCNA within data contains all output files for the co-expression network analysis. Pmaculosa_Trinity_filter.fasta is the final transcriptome assembly used to quantify all transcripts.

Sharing/Access information

Data was derived from:

  • RNA-seq reads, obtainable at the NCBI Sequence Read Archive, accession PRJNA886646

Code/Software

All analysis scripts provided in this repository were written/run in R (version 3.6.2). Refer to the repository Methods and the primary manuscript for detailed list of program versions used in these analyses. File countable.R should be run first to generate a count table of transcript quantities for each sample. File sleuth_diff_expr.R should be run next to determine the differentially expressed transcripts. Files topGO.R, venndiagram_script.R, and WGCNA_analysis.R can all then be run in any order to obtain more detailed results on the differential expression output.

Methods

Parental and offspring generations

We studied 4 genetic lines of Persicaria maculosa, an annual generalist plant of allotetraploid origin (Kim et al., 2008). Achenes from each of four experimental genotypes (MHF1, NAT1, NAT2, and TP2) were grown to reproductive maturity in one of three randomly assigned greenhouse treatments: full sun with moist soil (“High Light/Moist”), full sun with dry soil (“Dry”), or simulated shade with moist soil (“Shade”). Note that the parental High Light/Moisture treatment provided a stress-free (“control”) comparison for both the parental Dry and the parental Shade treatments.

Mature achenes from one (self-fertilized) parent plant for each genotype × Parent treatment combination were germinated on petri plates and transplanted into pots (3 replicate seedings per pot). Experimental pots (4 genotypes × 5 [Parent treatment × Offspring treatment] combinations × 3 replicates = 60 pots) were raised in a randomized complete block design in a Conviron E2 growth chamber (Controlled Environments, Winnipeg, Canada) in one of 5 Parent treatment × Offspring treatment combinations—Parent High Light/Moist × Offspring High Light/Moist; Parent Shade × Offspring Shade; Parent Dry × Offspring Dry; Parent High Light/Moist × Offspring Shade; Parent High Light/Moist × Offspring Dry. 11-12 d post-transplant, leaf tissue from each replicate pot of 3 seedlings was harvested, pooled and flash frozen for RNA extraction (Promega SV Total RNA Isolation System Kit, Promega Corporation, Madison, WI, USA).

De novo transcriptome sequencing, assembly, and annotation

We submitted all 60 RNA samples to the National Genomics Infrastructure (NGI) at Uppsala University, Uppsala, Sweden for RNA sequencing. Libraries were prepared for each sample using an Illumina TruSeq Stranded mRNA with Poly-A selection Library Prep kit (Illumina, San Diego, CA, USA), which were subsequently paired-end sequenced (2×150) on an Illumina NovaSeq 6000 platform utilizing an S1 flow cell. In addition to the short read sequences, we submitted a pool of 5 samples from genotype TP2 representing all 5 Parent/Offspring treatment combinations for long read sequencing following PacBio’s Iso-Seq protocol (Pacific Biosciences of California Inc., Menlo Park, CA, USA) using a PacBio Sequel sequencing platform at the NGI, Uppsala, Sweden.

Because no reference genome of P. maculosa was available, we assembled the Illumina short read and PacBio long read data into a de novo transcriptome using Trinity software (version 2.8.4; Grabherr et al., 2011), following the protocol in Feiner et al. (Feiner et al., 2018), with minor changes made to optimize for this data set. Trinity assembled 48,022 transcripts, representing 33,828 predicted genes. The N50 for the transcriptome was 1,938 nucleotides (nt), with a median contig length of 1,015 nt and a mean contig length of 1,322.12 nt. We annotated the transcriptome using Trinotate (version 3.2.0; Bryant et al., 2017), a software suite that makes use of a variety of other annotation tools. In brief, TransDecoder (version 5.5.0, https://github.com/TransDecoder/TransDecoder) generated putative amino acid sequences, and BLASTx and BLASTp (BLAST+ version 2.9.0; Camacho et al., 2009) were used to search nucleic and amino acid sequences against the UniProtKB/Swiss-Prot database (retrieved December 19, 2019). A list of gene ontology (GO) terms for each transcript was generated based on the BLAST matches. 

Transcript quantification and differential expression analysis

Transcript abundances were quantified with kallisto quant using default settings (Bray et al., 2016), and transcripts with low expression were discarded from the subsequent analyses. Complete descriptions can be found in the primary manuscript and the scripts contained within this respository.

References

Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 34, 525. doi:10.1038/nbt.3519

Bryant, D. M., Johnson, K., DiTommaso, T., Tickle, T., Couger, M. B., Payzin-Dogru, D., . . . Whited, J. L. (2017). A Tissue-Mapped Axolotl De Novo Transcriptome Enables Identification of Limb Regeneration Factors. Cell Reports, 18(3), 762-776. doi:10.1016/j.celrep.2016.12.063

Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: architecture and applications. BMC Bioinformatics, 10, 421. doi:10.1186/1471-2105-10-421

Feiner, N., Rago, A., While, G. M., & Uller, T. (2018). Developmental plasticity in reptiles: Insights from temperature-dependent gene expression in wall lizard embryos. Journal of Experimental Zoology Part A: Ecological and Integrative Physiology, 329(6-7), 351-361. doi:10.1002/jez.2175

Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., . . . Regev, A. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29, 644. doi:10.1038/nbt.1883

Kim, S.-T., Sultan, S. E., & Donoghue, M. J. (2008). Allopolyploid speciation in Persicaria (Polygonaceae): Insights from a low-copy nuclear region. Proceedings of the National Academy of Sciences, 105(34), 12370-12375. doi:10.1073/pnas.0805141105

Funding

John Templeton Foundation, Award: 60501

European Research Council, Award: 948126

Swedish Research Council, Award: 2020-03650

John Templeton Foundation, Award: 61369