Metabarcode and transcriptome datasets of Pinus sylvestris to assess fungal phyllosphere and disease dynamics.

This paper presents a comprehensive dataset of ITS2 metabarcoding and RNA-seq profiles from 200 and 48 *Pinus sylvestris* genotypes, respectively, to investigate how host genotype influences foliar fungal communities and disease susceptibility in the context of Dothistroma needle blight.

The Gist

Imagine a pine tree as a bustling city, where the leaves are the neighborhoods and the tiny fungi living on them are the residents. This paper is like a detailed census and a set of video recordings taken to understand how these fungal residents interact with the tree and how they react when a specific "invader" arrives.

The invader in question is a nasty fungus called Dothistroma septosporum, which causes a disease known as Dothistroma needle blight (DNB). Think of this disease as a destructive storm that can wipe out parts of the city (the tree's needles). The researchers wanted to know: Does the tree's own "family history" (its genetics) change who lives on its leaves, and does that change how badly the storm hits?

To find out, the scientists went to a giant outdoor experiment in southern Scotland. This wasn't just a random forest; it was a carefully organized "family reunion" of 200 different pine tree families (genotypes), each with different natural defenses against the storm.

The researchers took two main types of "snapshots" of these trees over time:

1. The Fungal Roll Call (Metabarcoding): They took samples from 200 different trees at three different times. It's like taking a headcount of every fungal resident on the leaves to see which families were living there and how the neighborhood changed as the disease season progressed.
2. The Tree's Diary (Transcriptome/RNAseq): For 48 of those trees, they went deeper. Instead of just counting residents, they read the tree's "diary" (its genetic instructions) to see what the tree was actually doing and thinking when the disease showed up. This was done at two different times to catch the tree's reaction in real-time.

The team used very strict rules and double-checked their work (using "negative and positive controls") to make sure they weren't accidentally counting dust or mistakes as real data. They treated the trees like a scientific laboratory, capturing the exact moments when the disease started and how it spread.

The Bottom Line:
This paper doesn't claim to have found a cure or a new way to save forests yet. Instead, it simply provides a massive, high-quality library of data (available online for anyone to use) that records:

• How the fungal communities on pine needles change over time.
• How different tree families react to the disease at a genetic level.
• Whether a tree's family background influences its neighborhood of fungi and its ability to fight off the blight.

Think of this dataset as the raw footage and the census data that other scientists can now use to solve the mystery of why some pine trees survive the storm while others don't.

Technical Summary

Technical Summary

Problem Statement
The paper addresses the critical need to understand how host microbiome interactions influence tree disease, specifically within the context of forest resilience. It focuses on Pinus sylvestris (Scots pine) and its susceptibility to Dothistroma needle blight (DNB), a globally significant foliar disease caused by the fungus Dothistroma septosporum. The central hypothesis posits that host genotype shapes foliar microbial communities and their interactions, thereby directly influencing disease outcomes.

Methodology
To investigate this, the authors utilized a progeny provenance field trial located in southern Scotland, which represents a broad spectrum of disease susceptibilities among the host genotypes. The study employed a multi-omics approach combining metabarcoding and transcriptomics:

• Sampling Design: Foliar samples were collected from 200 genotypes for ITS2 metabarcoding and 48 genotypes for RNA sequencing (RNAseq). Sampling occurred across three timepoints for the metabarcoding data and two timepoints for the transcriptomic data, capturing key stages of pathogen exposure and disease progression.
• Laboratory Protocols: The study utilized both standardised and bespoke protocols for nucleotide extraction, sequencing, and quality control. Rigorous quality assurance was maintained through the inclusion of multiple negative and positive controls.
• Data Types: The resulting datasets include ITS2 metabarcoding data to profile fungal communities and RNAseq data to assess host transcriptional responses.

Key Contributions and Results
The primary contribution of this work is the generation and public release of a comprehensive dataset linking host genotype, foliar fungal community dynamics, and disease progression. The dataset comprises:

• ITS2 metabarcoding samples from 200 Pinus sylvestris genotypes.
• RNAseq samples from 48 genotypes.
• Temporal data covering the progression of DNB.

The data enables the analysis of temporal dynamics within foliar fungal communities and the transcriptional responses of the host. It specifically facilitates the investigation of genotype-dependent variation in disease susceptibility and the interplay between the host and its microbiome.

Significance
The paper claims that these datasets, made available in the European Nucleotide Archive under project accession PRJEB88228, provide a foundational resource for researchers. They enable the analysis of how host genotype shapes microbial interactions and disease outcomes in Pinus sylvestris. By offering standardized and controlled data across multiple timepoints and genotypes, the work supports further investigation into the mechanisms driving forest resilience against foliar diseases like DNB.