Phenotyping of host-pathogen biological interactions
Cross-compatibility among rice / P. oryzae paired samples was
assessed through cross inoculation experiments. One rice accession
(HO-Q-F16) could not be included since seeds were lacking, and the
corresponding P. oryzae isolate (CH1866) was also excluded. The
remaining 45 rice accessions were separated in two trays, each tray
containing 22 or 23 accessions plus the two highly susceptible rice
accessions CO39 and Maratelli
(Gallet et al., 2016)
used as positive controls, with six rice seeds sown per variety (i.e.
144 or 150 plants per tray). As many batches as P. oryzaeisolates (i.e. 45) of such two trays were prepared (i.e. 90 trays in
total). Trays were inoculated four weeks after sowing when plants had
4-5 leaves, each batch of two trays was inoculated with a singleP. oryzae isolate. The fungal inocula were composed of conidia
suspensions at 50,000 spores/ml and 25,000 spores/ml for the first and
second repetition of the experiment, respectively. Spore suspensions
were supplemented with 0.5% gelatin
(Gallet et al., 2016).
An inoculation corresponded to the spraying of the spore suspension of
one particular isolate on one batch of two trays. All inoculations were
performed at the same date. Seven days after inoculation, symptoms were
read on four plants for each blast genotype × rice genotype interaction;
the four corresponding leaves were glued on sticky papers and scanned
for subsequent scoring of symptoms. The experimental design was repeated
twice. Twelve other rice accessions were removed from the analysis
because they were difficult to multiply, did not grow well in our
controlled conditions, could not be assigned to any rice genetic cluster
(BJ-Q-B06, Fig. 3 left panel), or were of modern origin (HongYang
accessions). We thus ended with a matrix of 33*33 rice / P.
oryzae paired samples with complete results.
Qualitative interactions were noted on each leaf qualitative scale of
1-6: scores 1-2 corresponding to incompatible reactions showing no
symptoms, scores 3-6 corresponding to compatible reactions
(Gallet et al., 2014).
The percentage of compatible / incompatible reactions for each
interaction was estimated by counting the total number of compatible /
incompatible leaves among the total number of leaves scored over the two
experimental repetitions. When less than four leaves were available in
total, the data was considered as missing. We verified that the
qualitative scores of the two independent replications were positively
correlated (Supplementary Information SI3, Fig. SI3.2). To obtain
quantitative measure of host-pathogen compatibility phenotype, the
scanned images were analysed with Ebimage package implemented
in R statistical environment
(Pau, Fuchs, Sklyar,
Boutros, & Huber, 2010). In-house scripts were used for calibration and
image analyses (https://github.com/sravel/LeAFtool). Briefly,
calibrations were made according to discriminant analysis of RGB
composition of pixels chosen and classified by the user as lesion, leaf
and background, and the resulting discriminant functions were used to
assign pixels of the entire image to these three categories. Statistical
analyses were performed using the nlme package in R
environment. The studied variable was the percentage of diseased leaf
area. After log-transformation of this variable (y = log(x + 0.15)), we
performed a two-step analysis. First we performed an ANOVA considering
only the positive controls to evaluate the respective effects of the
following factors: “repetition”, “tray”, “P. oryzaeisolate”, “rice accession”, and the interaction between the last two.
We obtained significant effects for fungal isolate (F = 5.13, P =
2.4e-10, df = 32) and rice landrace (F = 416.9, P
< 10-16, df = 1); the effect of tray was
significant (F = 1.7, P = 0.003, df = 98) but neglectable compared to
the effect of experimental replicate (F = 181.7, P <
10-16, df = 1), and was therefore ignored in
subsequent data analyses. We then analysed the log-transformed variable
for the rest of the matrix (excluding the positive controls) using an
ANOVA considering two factors: “repetition” and “combination”
(corresponding to each P. oryzae isolate × rice accession
combination). Heatmaps of the adjusted value of the log-transformed
variable were drawn using ggplot2 package in R environment.
We analyzed nestedness and modularity of the quantitative interaction
matrix following Moury et al. (2021). Nestedness and modularity are
quantitative properties of matrices that reveal non-random distribution
of links between rows and columns. Nestedness measures the tendency of
hierarchical organization between lines and columns. Modularity measures
the tendency of such matrices to be organized in different modules, with
highest probability of strong interactions between members of the same
module. Briefly, quantitative trait values were transformed into
integers from 0 to 9, by defining ten intervals with equal sizes
spanning the range of quantitative values, so that “0” and “9”
grades correspond to the minimal and maximal trait value, respectively.
We used the Wine algorithm (Galeano et al., 2009) for
nestedness estimation, and the spinglass algotithm (Newman and Girvan,
2004) for modularity estimation, since these methods were shown to be
the most statistically powerful (Moury et al. 2021). The significance of
both estimates was assessed by generating 100 random matrices using the
null models R1 (random matrices generated row by row ensuring that the
total sum of the cells and the number of zero-valued cells are the same
as in the actual matrix) and R2 (random matrices generated row by row by
shuffling the cell values of the actual matrix), since these two null
models were shown to exhibit the lowest false negative rates (Moury et
al. 2021).