# Data from: Lenzo_et_al_dryad.tar.gz

Description

This dataset contains data and analysis scripts for all figures presented in Lenzo et al. (2025). The study investigates co-infection dynamics between Parastagonospora nodorum and Pyrenophora tritici-repentis, the causal agents of septoria nodorum blotch (SNB) and tan spot (TS) of wheat. Using a novel digital PCR-based quantification method targeting the α-tubulin gene, this work reveals asymmetric priority effects where pathogen arrival order determines infection outcomes and can overcome host resistance. Field surveys revealed up to two in three symptomatic infections involved both pathogens, with controlled experiments demonstrating that P. tritici-repentis establishment facilitates subsequent P. nodorum colonization and breakdown of host resistance, while P. nodorum priority establishment suppresses P. tritici-repentis colonization regardless of host genotype.

File Structure

The repository is organized by figure number, with each directory containing the data and scripts used to generate the corresponding figure(s) in the manuscript:

1_ddPCR_IS (Figure 1)

Digital droplet PCR in silico primer/probe design and validation

- 1C/: BLAST analysis of α-tubulin primers and probes against pan-genomes

  - data/: Pn_aT_detailed_results.csv, Ptr_aT_detailed_results.csv - Standard BLAST tabular format (see NCBI BLAST+ documentation for full column descriptions)

  - scripts/: db.py - BLAST database creation; blastn.py - nucleotide BLAST searches; plot.r - visualization of % identity by region

- 1D/: Cross-reactivity and specificity analysis

  - data/: primer_blast.txt - Raw BLAST output for primer sequences against NCBI RefSeq fungi database (standard BLAST tabular format); probe_blast.txt - Raw BLAST output for probe sequences against NCBI RefSeq fungi database (standard BLAST tabular format); viz_matrix.csv - organism (species name), forward_primer (% identity for forward primer match, 0-100), reverse_primer (% identity for reverse primer match, 0-100), probe_Pn_FAM (% identity for P. nodorum probe, 0-100), probe_Ptr_HEX (% identity for P. tritici-repentis probe, 0-100). Values of 0 indicate no significant match detected

  - scripts/: proccess.py - BLAST filtering and data processing; plot.r - cross-reactivity heatmap generation

2_dPCR_IV (Figure 2)

Digital droplet PCR in vitro biomass modeling and assay validation

- 2A/: DNA yield per unit fungal biomass analysis

  - data/: biomass.csv - Isolate (pathogen species: Pn = P. nodorum, Ptr = P. tritici-repentis), Tissue (freeze-dried mycelial mass in mg), DNA (extracted DNA concentration in μg/μL)

  - scripts/: biomass.r - Linear regression analysis deriving DNA-to-tissue ratios

- 2B/: dPCR calibration curves and sensitivity testing

  - data/: DNA_Curve.csv - Type (species being quantified: Pn or Ptr), Pn_ng/ul (P. nodorum DNA input concentration in ng/μL), Ptr_ng/ul (P. tritici-repentis DNA input concentration in ng/μL), Pn_Copies/ul (measured P. nodorum target copies per μL from dPCR), Ptr Copies/ul (measured P. tritici-repentis target copies per μL from dPCR); Raw_ddPCR.csv - Bio-Rad QuantaSoft Analysis Pro v1.0.596 standard output

  - scripts/: ddPCR_curve.r - Calibration curve analysis (copy number vs DNA input)

- 2C/: Specificity testing against non-target organisms

  - data/: figure_graphs.csv - Cross-reactivity test results (presence/absence of amplification with non-target DNA)

  - scripts/: off_target.r - Visualization of probe specificity

3_Biomass (Figure 3)

Correlation between visual disease symptoms and pathogen biomass

- 3B/: Controlled infection time-course (10 days post-infection)

  - data/: invitro.csv - Sample (sample identifier), Line (wheat cultivar: Axe or Scepter with resistance rating), Chlorosis (proportion of leaf area with chlorosis, 0-1 scale where 1 = 100%), Necrosis (proportion of leaf area with necrosis, 0-1 scale), Total.Disease (sum of chlorosis and necrosis), pathogen1 (P. tritici-repentis biomass in μg estimated from dPCR), pathogen2 (P. nodorum biomass in μg estimated from dPCR). Values of 0 indicate pathogen not detected or no symptoms

  - scripts/: scoringmethod.r - Linear regression models (disease symptoms vs pathogen biomass)

4_5_Field_Data (Figures 4 & 5)

Field surveys examining co-infection dynamics and host resistance breakdown

- Field Data/:

  - Figure 4: Endemic field site (Northam) with naturally occurring infections on four cultivars

  - Figure 5: Manipulated field trial (South Perth) with different inoculum sources to investigate priority effects

  - data/: field_biomass_data.csv - Week (sampling week), Cultivar (wheat cultivar code), Site (field site: 1=Northam, 4=South Perth), Replicate (biological replicate), Code (unique sample ID), Raw_Pn_FAM and Raw_Ptr_HEX (raw dPCR copy numbers in copies/µL), Pn_FAM>1 and Ptr_HEX>1 (binary detection: values >1 = detected), Input DNA (total DNA concentration in ng/µL), 1/DNA (reciprocal for normalization), Normalised Pn_FAM and Normalised Ptr_HEX (copy numbers normalized to 1 ng/µL), Pn_Biomass and Ptr_Biomass (pathogen biomass in μg), Log10_Pn_Biomass and Log10_Ptr_Biomass (log-transformed biomass), Necrosis_Percent (proportion of leaf area with necrosis, 0-1 scale); Field_Master_Data.xlsx - Master dataset with 4 sheets:

    - Legend: Cultivar (wheat cultivar codes and names), Site (field site identifiers and descriptions), unnamed columns (supplementary metadata)

    - Raw ddPCR: Bio-Rad QuantaSoft standard output. Plate (plate ID), Well (well position), Week (sampling week), Date (sampling date), Cultivar (wheat cultivar), Site (field location: Northam or South Perth), Replicate (biological replicate number), Code (unique sample identifier), Target (pathogen species: Pn_FAM or Ptr_HEX), Conc (copies/µL) (measured target concentration from dPCR), Status (quality control status), Experiment/SampleType/TargetType/Supermix/DyeName(s) (instrument parameters), Accepted Droplets/Positives/Negatives (droplet counts from dPCR analysis)

    - Biomass Model: Week (sampling week), Date (sampling date), Cultivar (wheat cultivar), Site (field location), Replicate (biological replicate), Code (unique sample ID), Raw_Pn_FAM and Raw_Ptr_HEX (copies/µL) (raw dPCR copy numbers), Pn_FAM>1 and Ptr_HEX>1 (binary detection: 1=detected, 0=not detected), Input DNA (ng/µL) (total DNA concentration measured by fluorometry), 1/DNA (reciprocal of DNA concentration for normalization), Normalised Pn_FAM and Normalised Ptr_HEX (copy numbers normalized to 1 ng/µL total DNA), Pn_Biomass (µg) and Ptr_Biomass (µg) (estimated pathogen biomass using calibration factors), Log10 Pn_Biomass and Log10 Ptr_Biomass (log10-transformed biomass for statistical analysis)

    - LeafScan Data: Code (sample identifier), Date (sampling date), Week (sampling week), Line (wheat cultivar), Treatment (field treatment/inoculum source), Rep (biological replicate), Total (total image area in pixels), Leaf (healthy leaf area in pixels), Chlor (chlorotic area in pixels), Necro (necrotic area in pixels), L% (percentage healthy leaf area), C% (percentage chlorotic area), N% (percentage necrotic area)

  - scripts/: biomass_by_line.r - Statistical comparisons of pathogen biomass by cultivar and co-infection status; normality.r - Normality testing for data distributions

6_Sequential_Data (Figure 6)

Controlled sequential co-infection experiments revealing asymmetric priority effects (72-hour staggered infections)

- Treatments: simultaneous, Ptr→Pn, Pn→Ptr, single pathogen controls; Cultivars: Axe, Scepter

- data/Sequential_Master_Data.xlsx: Master dataset with 3 sheets:

  - Raw ddPCR: Bio-Rad QuantaSoft standard output. Well (well position on plate), Treatment (sequential inoculation treatment: 1=simultaneous, 2=Ptr→Pn, 3=Pn→Ptr, 4=single pathogen controls), Line (wheat cultivar: Axe or Scepter), Replicate (biological replicate number), Timepoint (days post-secondary inoculation), Target (pathogen species: Pn_FAM or Ptr_HEX), Conc(copies/µL) (measured target concentration from dPCR), Status (quality control status), Experiment/SampleType/TargetType/Supermix/DyeName(s) (instrument parameters), Accepted Droplets/Positives/Negatives (droplet counts from dPCR analysis)

  - Biomass Modelling: Primary (first pathogen inoculated: Pn, Ptr, or None), Secondary (second pathogen inoculated: Pn, Ptr, or None), Treament (treatment code 1-4), Line  (wheat cultivar, note trailing space in column name), Time (days post-secondary inoculation), Replicate (biological replicate), Code (unique sample identifier), Plate (plate ID), Well (well position), Qubit (total DNA concentration measured by fluorometry in ng/µL), Total (total DNA amount), 1/Qubit (reciprocal of DNA concentration for normalization), Total DNA (total DNA recovered), Input (2) (DNA input for dPCR reaction), Pn_Raw and Ptr_Raw (raw dPCR copy numbers in copies/µL), Pn>1 and Ptr>1 (binary detection: 1=detected, 0=not detected), Pn 1ng and Ptr 1ng (copy numbers normalized to 1 ng/µL total DNA), Pn_Biomass and Ptr_Biomass (estimated pathogen biomass in µg using calibration factors), log10(Pn Biomass) and log10(Ptr_Biomass (log10-transformed biomass for statistical analysis, note inconsistent closing parenthesis in column names)

  - Leaf Scan Results: Primary (first pathogen inoculated), Secondary (second pathogen inoculated), Treament (treatment code), Line  (wheat cultivar), Time (days post-secondary inoculation), Replicate (biological replicate), Code (sample identifier), Plate (plate ID), Well (well position), healthy_area (healthy leaf tissue in pixels), chlorosis_area (chlorotic leaf tissue in pixels), necrosis_area (necrotic leaf tissue in pixels), leaf_area (total leaf area in pixels), N% (percentage necrotic area), C% (percentage chlorotic area)

- data/day_10 with log xform.csv - Treatment (inoculation treatment code: 1=simultaneous, 2=Ptr→Pn, 3=Pn→Ptr, 4=single pathogen), Line (wheat cultivar: Axe or Scepter), Time (days post-secondary inoculation), Necrosis (proportion of leaf area with necrosis, 0-1 scale), Pn (P. nodorum biomass in μg), Ptr (P. tritici-repentis biomass in μg), log_Pn and log_Ptr (log10-transformed biomass values for statistical analysis). Values of 0 indicate pathogen not detected

- data/plots.csv - Type (infection type: Single or Co-Inf), Treatment (inoculation sequence), Line (cultivar), N_mean and N_SE (mean necrosis % and standard error), Pn_M and Pn_SE (mean P. nodorum biomass in μg and standard error), Pn_Group (Tukey HSD grouping), Ptr_M and Ptr_SE (mean P. tritici-repentis biomass in μg and standard error), Ptr_Group (Tukey HSD grouping). Summary statistics for figure generation

- data/simple_anova_results.txt - Plain text output from one-way ANOVA with Tukey's HSD post-hoc tests performed in R

- scripts/: anova2.r - One-way ANOVA with Tukey's HSD post-hoc tests; plot.ba.r - Biomass visualization and figure generation

Data Notes

- CSV/XLSX files: All biomass measurements in μg unless otherwise specified; proportional disease metrics use 0-1 scale

- Empty cells: Indicate pathogen not detected, samples not collected, or measurements not applicable

- ddPCR outputs: Raw ddPCR sheets are standard Bio-Rad QuantaSoft Analysis Pro v1.0.596 format (see software documentation)

- Python scripts (.py): BLAST analysis and data processing (directories 1C, 1D)

- R scripts: Statistical analyses and figure generation (in scripts/ subdirectories)

Usage

Each subdirectory contains:

1. data/ folder with input data files

2. scripts/ folder with analysis code to reproduce figures

To reproduce analyses:

1. Navigate to the appropriate figure directory

2. Run scripts in the scripts/ folder using the data from the data/ folder

3. Refer to individual script headers for dependencies and execution order

Software Requirements

Python 3.x

Required for BLAST analysis, data processing, and image analysis (directories 1_ddPCR_IS)

- biopython - BLAST database creation and sequence analysis

- opencv (cv2) - Image processing and HSV-based pixel classification

- Standard libraries: pandas, numpy

R (version 4.5.0 or higher)

Required for all statistical analyses and figure generation

- Base R functions: lm(), t.test(), aov(), TukeyHSD()

- Statistical testing includes: linear regression, Student's t-tests, one-way ANOVA

- Data transformation: log10 transformation for biomass normalization

Additional Software

- NCBI BLAST+ - Nucleotide BLAST for in silico primer/probe validation (directory 1_ddPCR_IS)

- ImageJ - Manual leaf image extraction and cataloging (directory 3_Biomass)

- Bio-Rad QuantaSoft Analysis Pro v1.0.596 - Digital droplet PCR data analysis and Poisson distribution correction

Image Analysis Tool

The custom Python tool LeafScan for automated HSV-based disease quantification is available at: https://github.com/LeonLenzo/leaf-scan

Methods Summary

Digital PCR Assay Design

The duplex dPCR assay targets a 185-187 bp region of the α-tubulin gene using:

- Universal primers: aT_F and aT_R (amplify both species)

- Species-specific probes: Pn_FAM (P. nodorum) and Ptr_HEX (P. tritici-repentis)

- Single-copy locus validated across 179 P. nodorum and 67 P. tritici-repentis genome assemblies

Biomass Conversion

Pathogen biomass estimated from dPCR copy numbers using integrated calibration:

- P. tritici-repentis: 1.3 μg biomass per target copy

- P. nodorum: 3.4 μg biomass per target copy

Wheat Cultivars and Resistance Ratings

Field trials used four wheat cultivars with differential disease resistance:

- Axe: Susceptible-Very Susceptible (SVS) to both SNB and TS

- Scepter: Moderately Resistant-Moderately Susceptible (MRMS) to both

- Emu Rock: Variable resistance

- Yitpi: Variable resistance

Field Sites (Western Australia, 2022)

1. Northam disease nursery (31°32'23.4"S 116°42'17.3"E) - Endemic site, no fungicide >10 years

2. South Perth (-31.991046, 115.888172) - Manipulated inoculum plots with different stubble treatments

Citation

If you use this data, please cite:

Lenzo, L., John, E., Bradley, J., Thomas, G., Bennett, D., and Tan, K.-C. (2025). Fair-weather friends: Unequal partnerships between Parastagonospora nodorum and Pyrenophora tritici-repentis define disease dynamics in wheat [DOI to be added upon publication]

Contact

For questions about this dataset, please contact:

Leon Lenzo

Centre for Crop and Disease Management

School of Molecular and Life Sciences

Curtin University, Perth, Western Australia, 6102

Corresponding author: Kar-Chun Tan

Email: Kar-Chun.Tan@curtin.edu.au

License

This dataset is released under CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, consistent with Dryad's default licensing policy.