Foliar mycobiome remains unaltered under urban air-pollution but differentially express stress-related genes.
Valeria Stephany Flores-AlmarazCamille TruongDiana Hernández-OaxacaVerónica Reyes-GalindoAlicia Mastretta-YanesJuan Pablo Jaramillo-CorreaRodolfo Salas-LizanaPublished in: Microbial ecology (2024)
Air pollution caused by tropospheric ozone contributes to the decline of forest ecosystems; for instance, sacred fir, Abies religiosa (Kunth) Schltdl. & Cham. forests in the peri-urban region of Mexico City. Individual trees within these forests exhibit variation in their response to ozone exposure, including the severity of visible symptoms in needles. Using RNA-Seq metatranscriptomic data and ITS2 metabarcoding, we investigated whether symptom variation correlates with the taxonomic and functional composition of fungal mycobiomes from needles collected in this highly polluted area in the surroundings of Mexico City. Our findings indicate that ozone-related symptoms do not significantly correlate with changes in the taxonomic composition of fungal mycobiomes. However, genes coding for 30 putative proteins were differentially expressed in the mycobiome of asymptomatic needles, including eight genes previously associated with resistance to oxidative stress. These results suggest that fungal communities likely play a role in mitigating the oxidative burst caused by tropospheric ozone in sacred fir. Our study illustrates the feasibility of using RNA-Seq data, accessible from global sequence repositories, for the characterization of fungal communities associated with plant tissues, including their gene expression.
Keyphrases
- rna seq
- particulate matter
- air pollution
- climate change
- single cell
- gene expression
- hydrogen peroxide
- oxidative stress
- cell wall
- electronic health record
- genome wide
- big data
- heavy metals
- sleep quality
- bioinformatics analysis
- dna damage
- risk assessment
- high frequency
- artificial intelligence
- machine learning
- genome wide identification
- induced apoptosis
- single molecule
- deep learning
- genome wide analysis
- heat shock