bioRxivGenomicsPreprint
Biotic-response networks are an important organizer of the transcriptome in wild Arabidopsis thaliana populations
Ana Paula Leite MontalvãoiD, Kevin MurrayiD, Ilja BezrukoviD, Natalie BetziD, Lucas P. HenryiD, Paloma DurániD, Patrick BoppertiD, Martina KolbiD, TEAM PATHOCOM, Fabrice RouxiD, Joy M. BergelsoniD, Wei YuaniD, Detlef WeigeliD
Abstract
Extensive laboratory experimentation has revealed conserved molecular pathways controlling growth and stress responses in plants, yet how these programs operate in natural settings remains poorly understood. We investigated transcriptome organization in wild populations of Arabidopsis thaliana by sampling plants from 60 natural sites in Europe and North America across two seasons. Transcriptomes varied extensively among individuals and showed largely continuous rather than discrete structure across geography and season. Although disease and microbial colonization were common in the wild, wild transcriptomes did not simply recapitulate canonical laboratory stress signatures. Measured microbial infection, environmental, and phenotypic variables explained only a modest fraction of total expression variation, but infection-associated signals accounted for the largest share of the explainable component. Consistent with this, biotic-response networks defined in controlled laboratory experiments were well conserved in wild transcriptomes, whereas control and abiotic-response networks were substantially reorganized. Together, these results suggest that while core transcriptional modules remain recognizable across environments, regulatory relationships among modules differ markedly between laboratory and natural contexts. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/711176v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1c89152org.highwire.dtl.DTLVardef@bcd1acorg.highwire.dtl.DTLVardef@6975a2org.highwire.dtl.DTLVardef@1a9e2fe_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: Arabidopsis
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: benchmarking
- approach: statistical model
- Citations
- 2
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