DOI: 10.5061/dryad.jsxksn0fp
title: Data for: The relative impact of parental and current environment on plant transcriptomes depends on type of stress and genotype

description: 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.
description: <em>Parental and offspring
 generations</em> 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<em> transcriptome sequencing, assembly, and
 annotation</em> 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.  <em>Transcript quantification
 and differential expression analysis</em>
 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.
 <em>References</em> 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
description: # 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.