Transcriptomic analysis of deceptively pollinated Arum maculatum (Araceae) reveals association between terpene synthase expression in floral trap chamber and species-specific pollinator attraction.
Mark A SzenteczkiAdrienne L GodschalxJérémy GauthierMarc GibernauSergio RasmannNadir AlvarezPublished in: G3 (Bethesda, Md.) (2022)
Deceptive pollination often involves volatile organic compound emissions that mislead insects into performing nonrewarding pollination. Among deceptively pollinated plants, Arum maculatum is particularly well-known for its potent dung-like volatile organic compound emissions and specialized floral chamber, which traps pollinators-mainly Psychoda phalaenoides and Psychoda grisescens-overnight. However, little is known about the genes underlying the production of many Arum maculatum volatile organic compounds, and their influence on variation in pollinator attraction rates. Therefore, we performed de novo transcriptome sequencing of Arum maculatum appendix and male floret tissue collected during anthesis and postanthesis, from 10 natural populations across Europe. These RNA-seq data were paired with gas chromatography-mass spectrometry analyses of floral scent composition and pollinator data collected from the same inflorescences. Differential expression analyses revealed candidate transcripts in appendix tissue linked to malodourous volatile organic compounds including indole, p-cresol, and 2-heptanone. In addition, we found that terpene synthase expression in male floret tissue during anthesis significantly covaried with sex- and species-specific attraction of Psychoda phalaenoides and Psychoda grisescens. Taken together, our results provide the first insights into molecular mechanisms underlying pollinator attraction patterns in Arum maculatum and highlight floral chamber sesquiterpene (e.g. bicyclogermacrene) synthases as interesting candidate genes for further study.
Keyphrases
- rna seq
- single cell
- gas chromatography mass spectrometry
- poor prognosis
- gas chromatography
- electronic health record
- genome wide
- big data
- gene expression
- genetic diversity
- palliative care
- long non coding rna
- risk assessment
- deep learning
- dna methylation
- single molecule
- artificial intelligence
- data analysis
- simultaneous determination