Abstract:Carbon starvation is the current leading hypothesis of plant mortality mechanisms under drought stress; recently, it is also used to explain tree die-off in plant diseases. However, the molecular biology of the carbon starvation pathway is unclear. Here, using a punch inoculation system, we conducted transcriptome and physiological assays to investigate pathogen response in poplar stems at the early stages of
Botryosphaeria
and
Valsa
canker diseases. Transcriptome … Show more
“…It is possible that the growth of P. italicum exhibits a preference for glucose rather than fructose as an energy source. The impact of fungal pathogen on plant host transcriptome have widely revealed the altered gene expression patterns involved in defense and stress responses, cell wall structure, chloroplast biogenesis, carbon metabolism and transportation, and biosynthesis of secondary metabolites [ 38 , 39 ]. Figure 4.…”
Blue and green molds, the common phenotypes of post-harvest diseases in fruits, are mainly caused by
Penicillium
fungal species, including
P. italicum, P. digitatum,
and
P. expansum
. We sequenced and assembled the genome of a
P. italicum
strain, which contains 31,034,623 bp with 361 scaffolds and 627 contigs. The mechanisms underlying the evolution of host specificity among the analyzed
Penicillium
species were associated with the expansion of protein families, genome restructuring, horizontal gene transfer, and positive selection pressure. A dual-transcriptome analysis following the infection of Valencia orange
(Citrus sinensis)
by
P. italicum
resulted in the annotation of 9,307
P. italicum
genes and 24,591 Valencia orange genes. The pathogenicity of
P. italicum
may be due to the activation of effectors, including 51 small secreted cysteine-rich proteins, 110 carbohydrate-active enzymes, and 12 G protein-coupled receptors. Additionally, 211 metabolites related to the interactions between
P. italicum
and Valencia orange were identified by gas chromatography-time of flight mass spectrography, three of which were further confirmed by ultra-high performance liquid chromatography triple quadrupole mass spectrometry. A metabolomics analysis indicated that
P. italicum
pathogenicity is associated with the sphingolipid and salicylic acid signaling pathways. Moreover, a correlation analysis between the metabolite contents and gene expression levels suggested that
P. italicum
induces carbohydrate metabolism in Valencia orange fruits as part of its infection strategy. This study provides useful information regarding the genomic determinants that drive the evolution of host specificity in
Penicillium
species and clarifies the host-plant specificity during the infection of Valencia orange by
P. italicum
.
IMPORTANCE
P. italicum GL_Gan1, a local strain in Guangzhou, China, was sequenced. Comparison of the genome of
P. italicum
GL_Gan1 with other pathogenic
Penicillium
species,
P. digitatum
and
P. expansum
, revealed that the expansion of protein families, genome restructuring, HGT, and positive selection pressure were related to the host range expansion of the analyzed
Penicillium
species. Moreover, gene gains or losses might be associated with the speciation of these
Penicillium
species. In addition, the molecular basis of host-plant specificity during the infection of Valencia orange (
Citrus sinensis
) by
...
“…It is possible that the growth of P. italicum exhibits a preference for glucose rather than fructose as an energy source. The impact of fungal pathogen on plant host transcriptome have widely revealed the altered gene expression patterns involved in defense and stress responses, cell wall structure, chloroplast biogenesis, carbon metabolism and transportation, and biosynthesis of secondary metabolites [ 38 , 39 ]. Figure 4.…”
Blue and green molds, the common phenotypes of post-harvest diseases in fruits, are mainly caused by
Penicillium
fungal species, including
P. italicum, P. digitatum,
and
P. expansum
. We sequenced and assembled the genome of a
P. italicum
strain, which contains 31,034,623 bp with 361 scaffolds and 627 contigs. The mechanisms underlying the evolution of host specificity among the analyzed
Penicillium
species were associated with the expansion of protein families, genome restructuring, horizontal gene transfer, and positive selection pressure. A dual-transcriptome analysis following the infection of Valencia orange
(Citrus sinensis)
by
P. italicum
resulted in the annotation of 9,307
P. italicum
genes and 24,591 Valencia orange genes. The pathogenicity of
P. italicum
may be due to the activation of effectors, including 51 small secreted cysteine-rich proteins, 110 carbohydrate-active enzymes, and 12 G protein-coupled receptors. Additionally, 211 metabolites related to the interactions between
P. italicum
and Valencia orange were identified by gas chromatography-time of flight mass spectrography, three of which were further confirmed by ultra-high performance liquid chromatography triple quadrupole mass spectrometry. A metabolomics analysis indicated that
P. italicum
pathogenicity is associated with the sphingolipid and salicylic acid signaling pathways. Moreover, a correlation analysis between the metabolite contents and gene expression levels suggested that
P. italicum
induces carbohydrate metabolism in Valencia orange fruits as part of its infection strategy. This study provides useful information regarding the genomic determinants that drive the evolution of host specificity in
Penicillium
species and clarifies the host-plant specificity during the infection of Valencia orange by
P. italicum
.
IMPORTANCE
P. italicum GL_Gan1, a local strain in Guangzhou, China, was sequenced. Comparison of the genome of
P. italicum
GL_Gan1 with other pathogenic
Penicillium
species,
P. digitatum
and
P. expansum
, revealed that the expansion of protein families, genome restructuring, HGT, and positive selection pressure were related to the host range expansion of the analyzed
Penicillium
species. Moreover, gene gains or losses might be associated with the speciation of these
Penicillium
species. In addition, the molecular basis of host-plant specificity during the infection of Valencia orange (
Citrus sinensis
) by
...
“…Finally, the RF algorithm succeeded in eliciting diseases and pathogen response wavelengths, which are related to stomatal conductance activity. The relationship between plant-pathogen interaction and stomatal conductance was highlighted in past studies, e.g., downy mildew on cucumber leaves [69], carbon starvation in poplar stems caused by various fungi [70], and transpiration decrease in bean by anthracnose [56]. Although these studies corroborate our findings, it should be noted that the pathogen or the disease affected mostly photosynthesis within the plant, where attenuation in stomatal conductance is a secondary effect of the reaction of the plant to the stress in order to sustain photosynthetic activity.…”
Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 < 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra.
“…Carbon starvation is another proposed mechanism during necrotrophic fungal infection. Upon infection, downregulation of the genes encoding carbon and starch metabolism occurs, which leads to a reduction in carbon assimilation and transport (Li P. et al, 2019). The mycorrhizal colonization of banana plantlets alleviates the adverse effects due to the infection and salt stresses on photosynthesis.…”
Banana plants (Musa acuminata L.) are exposed to various biotic and abiotic stresses that affect their production worldwide. Banana plants respond to these stresses, but their responses to combined stresses are unique and differ from those to various individual stresses. This study reported the effects of the mycorrhizal colonization of banana roots and/or infection with root rot on the transcriptional expression of the responsive factor JERF3 and stress-responsive genes (POD, PR1, CHI, and GLU) under different salinity levels. Different transcriptional levels were recorded in response to the individual, dual, or triple treatments. All the applied biotic and abiotic stresses triggered the transcriptional expression of the tested genes when individually applied, but they showed different influences varying from synergistic to antagonistic when applied in combinations. The salinity stress had the strongest effect when applied in combination with the biotic stress and/or mycorrhizal colonization, especially at high concentrations. Moreover, the salinity level differentially affects the banana responses under combined stresses and/or mycorrhizal colonization in addition, the mycorrhizal colonization of banana plantlets improved their growth, photosynthesis, and nutrient uptake, as well as greatly alleviated the detrimental effects of salt and infection stresses. In general, the obtained results indicated that the responses of banana plantlets under the combined stresses are more complicated and differed from those under the individual stresses depending on the crosstalks between the signaling pathways.
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