The Caryopsis of Red-Grained Rice Has Enhanced Resistance to Fungal Attack

Epiphytic and Endophytic Fungi Colonizing Seeds of Two Poaceae Weed Species and Fusarium spp. Seed Degradation Potential In Vitro

Fungi colonizing the surface and endosphere of two widespread Poaceae weed species, Avena fatua and Echinochloa crus-galli, were isolated to compare the taxonomic composition between the plant species, location, and year of the seed collection. The seed-degrading potential of Fusarium isolated from the seeds was tested by inoculating seeds of E. crus-galli with spore suspension. Molecular identification of epiphytic and endophytic fungal genera was performed by sequencing the ITS region of rDNA. Endophytes comprised of significantly lower fungal richness compared to epiphytes. A significant taxonomic overlap was observed between the endosphere and seed surface. The most abundant genera were Alternaria, Fusarium, Cladosporium, and Sarocladium. Analysis of similarities and hierarchical clustering showed that microbial communities were more dissimilar between the two plant species than between the years. Fusarium isolates with a high potential to infect and degrade E. crus-galli seeds in laboratory conditions belong to F. sporotrichioides and F. culmorum.

Measurements of Antibacterial Activity of Seed Crude Extracts in Cultivated Rice and Wild Oryza Species

Seeds are continuously exposed to a wide variety of microorganisms in the soil. In addition, seeds contain large amounts of carbon and nitrogen sources that support initial growth after germination. Thus, seeds in the soil can easily promote microbial growth, and seeds are susceptible to decay. Therefore, seed defense against microorganisms is important for plant survival. Seed-microbe interactions are also important issues from the perspective of food production, in seed quality and shelf life. However, seed-microbe interactions remain largely unexplored. In this study, we established a simple and rapid assay system for the antibacterial activity of rice seed crude extracts by colorimetric quantification methods by the reduction of tetrazolium compound. Using this experimental system, the diversity of effects of rice seed extracts on microbial growth was analyzed using Escherichia coli as a bacterial model. We used collections of cultivated rice, comprising 50 accessions of Japanese landraces, 52 accessions of world rice core collections, and of 30 wild Oryza accessions. Furthermore, we attempted to find genetic factors responsible for the diversity by genome-wide association analysis. Our results demonstrate that this experimental system can easily analyze the effects of seed extracts on bacterial growth. It also suggests that there are various compounds in rice seeds that affect microbial growth. Overall, this experimental system can be used to clarify the chemical entities and genetic control of seed-microbe interactions and will open the door for understanding the diverse seed-microbe interactions through metabolites.

Basic Features of the Analysis of Germination Data with Generalized Linear Mixed Models

Germination data are discrete and binomial. Although analysis of variance (ANOVA) has long been used for the statistical analysis of these data, generalized linear mixed models (GzLMMs) provide a more consistent theoretical framework. GzLMMs are suitable for final germination percentages (FGP) as well as longitudinal studies of germination time-courses. Germination indices (i.e., single-value parameters summarizing the results of a germination assay by combining the level and rapidity of germination) and other data with a Gaussian error distribution can be analyzed too. There are, however, different kinds of GzLMMs: Conditional (i.e., random effects are modeled as deviations from the general intercept with a specific covariance structure), marginal (i.e., random effects are modeled solely as a variance/covariance structure of the error terms), and quasi-marginal (some random effects are modeled as deviations from the intercept and some are modeled as a covariance structure of the error terms) models can be applied to the same data. It is shown that: (a) For germination data, conditional, marginal, and quasi-marginal GzLMMs tend to converge to a similar inference; (b) conditional models are the first choice for FGP; (c) marginal or quasi-marginal models are more suited for longitudinal studies, although conditional models lead to a congruent inference; (d) in general, common random factors are better dealt with as random intercepts, whereas serial correlation is easier to model in terms of the covariance structure of the error terms; (e) germination indices are not binomial and can be easier to analyze with a marginal model; (f) in boundary conditions (when some means approach 0% or 100%), conditional models with an integral approximation of true likelihood are more appropriate; in non-boundary conditions, (g) germination data can be fitted with default pseudo-likelihood estimation techniques, on the basis of the SAS-based code templates provided here; (h) GzLMMs are remarkably good for the analysis of germination data except if some means are 0% or 100%. In this case, alternative statistical approaches may be used, such as survival analysis or linear mixed models (LMMs) with transformed data, unless an ad hoc data adjustment in estimates of limit means is considered, either experimentally or computationally. This review is intended as a basic tutorial for the application of GzLMMs, and is, therefore, of interest primarily to researchers in the agricultural sciences.