Biology subjects

Fernandes, J. M. C.

Publications and source records attributed to Fernandes, J. M. C..

3 recordsLinked to original sources

bioRxiv2023

Pyricularia Populations are Mostly Host-Specialized with Limited Reciprocal Cross-Infection Between Wheat and Endemic Grasses in Minas Gerais, Brazil

Wheat blast, caused by Pyricularia oryzae Triticum (PoT), is an emergent threat to wheat production. Current understanding of the evolution and population biology of the pathogen and epidemiology of the disease has been based on phylogenomic studies that compared the wheat blast pathogen with isolates collected from grasses that were invasive to Brazilian wheat fields. Genetic similarity between isolates from wheat and grasses lead to the conclusion that significant cross-infection occurs, especially on signalgrass (Urochloa spp.); and this in turn prompted speculation that its widespread use as forage is a key driver of the diseases epidemiology. We reanalyzed data from those studies and found that all but one of the isolates from non-wheat hosts were members of PoT and the related Lolium-adapted lineage (PoL1), which meant that the Pyricularia populations typically found on endemic grasses had not yet been sampled. To address this shortcoming, we performed a comprehensive sampling of blast lesions in wheat crops and endemic grasses found in and away from wheat fields in Minas Gerais. A total 1,368 diseased samples were collected (976 leaves of wheat and grasses and 392 wheat heads) which yielded a working collection of 564 Pyricularia isolates. We show that, contrary to earlier implications, PoT was rarely found on endemic grasses and, conversely, members of grass-adapted populations were rarely found on wheat. Instead, most populations were host-specialized with constituent isolates usually grouping according to their host-of-origin. With regard to the dominant role proposed for signalgrass in wheat blast epidemiology, we found only one PoT member in 67 isolates collected from signalgrass grown away from wheat fields, and only three members of Urochloa-adapted populations among hundreds of isolates from wheat. Cross-inoculation assays on wheat and a signalgrass used in pastures (U. brizantha) suggested that the limited cross-infection observed in the field may be due to innate compatibility differences. Whether or not the observed level of cross-infection would be sufficient to provide an inoculum reservoir, or serve as a bridge between wheat growing regions, is questionable and, therefore, deserves further investigation.

microbiology↗

bioRxiv2022

A Re-evaluation of phylogenomic data reveals that current understanding in wheat blast population biology and epidemiology is obfuscated by oversights in population sampling

Wheat blast, caused by the Triticum lineage of Pyricularia oryzae (PoT), is a serious disease that first emerged in Brazil and quickly spread to neighboring countries. The recent appearance of this disease in Bangladesh and Zambia highlights a need to understand the population biology and epidemiology of the disease so as to mitigate pandemic outbreaks. Current knowledge in these areas is largely based on analyses of wheat blast isolates collected in Brazil, and their comparison with isolates from non-wheat, endemic grasses. Those studies concluded that wheat blast is caused by a highly diverse P. oryzae population that lacks host specificity and, as a result, undergoes extensive gene flow with populations infecting non-wheat hosts. Additionally, based on genetic similarity between wheat blast and isolates infecting Urochloa species, it was proposed that the disease originally emerged via a host jump from this grass, and the widespread use of Urochloa as a pasture grass likely plays a central role in wheat blast epidemiology. Inconsistencies with earlier phylogenetic studies prompted us to re-analyze the Brazilian data in the context of a comprehensive, global, phylogenomic dataset. We now show that the seminal studies failed to sample the P. oryzae populations normally found on endemic grasses and, instead, repeatedly sampled PoT and P. oryzae Lolium (PoL) members that happened to be present in these hosts. The resulting lack of accurate and representative information about the grass-infecting populations in Brazil means that current conclusions about wheat blasts evolution, population biology and epidemiology are unsubstantiated and could be equivocal.

microbiology↗

bioRxiv2021

INCORPORATING A DYNAMIC GENE-BASED PROCESS MODULE INTO A CROP SIMULATION MODEL

Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as "genetic coefficients." These GSPs are estimated using phenotypic observations and may not represent "true" genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study conducted in 2011 and 2012 included 187 recombinant inbred lines (RILs) from a bi-parental bean family to measure the effects of quantitative trait loci (QTL), environment (E), and QTLxE interactions across five sites. The dynamic mixed linear model from Vallejos et al. (2020) was modified in this study to create a dynamic module that was then integrated into the CSM-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic makeup of each genotype being simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and GxE effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.

genomics↗