DOI: 10.5061/dryad.mcvdnckch
title: Fair-weather friends: Priority determines disease outcomes in an agonistic multi-pathogen crop pathosystem

description: In both plant and animal pathosystems, the host is typically challenged by
 more than one pathogen simultaneously. Parastagonospora nodorum and
 Pyrenophora tritici-repentis frequently co-infect the same leaves, yet
 their interaction dynamics remain poorly understood due to limitations in
 species-specific pathogen quantification. We investigated how arrival
 order and host resistance affect disease outcomes during co-infection. We
 developed a duplex digital PCR assay targeting single copy α-tubulin genes
 to enable directly comparable biomass quantification of both pathogens.
 Field surveys and controlled sequential inoculation experiments were
 conducted across wheat cultivars with differential resistance to evaluate
 priority effects on pathogen proliferation and disease severity. Field
 surveys revealed a clear majority of symptomatic infections involved both
 pathogens, with individual pathogen biomass significantly elevated under
 co-infection, particularly in moderately resistant cultivars. Sequential
 inoculation experiments revealed asymmetric priority effects: P.
 tritici-repentis establishment facilitated subsequent P. nodorum
 colonisation and overcame host resistance, while P. nodorum priority
 consistently suppressed P. tritici-repentis regardless of host genotype.
 These asymmetric priority effects demonstrate that pathogen arrival order
 fundamentally alters disease dynamics and can overcome genetic resistance.
 Current resistance breeding strategies evaluating single-pathogen
 challenges may inadvertently select cultivars vulnerable to sequential
 co-infection, necessitating integrated disease complex approaches for
 durable resistance development.
description: # Fair-weather friends. Priority determines disease outcomes in an
 agonistic multi-pathogen crop pathosystem **# 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&gt;1 and Ptr_HEX&gt;1 (binary detection: values &gt;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&gt;1 and
 Ptr_HEX&gt;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&gt;1 and Ptr&gt;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](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 &gt;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](mailto: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.