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Temperature impacts plant immunity and growth but how temperature intersects with endogenous pathways remains unclear. Here we uncover variation between Arabidopsis thaliana natural accessions in response to two non-stress temperatures (22°C and 16°C) affecting accumulation of the thermoresponsive stress hormone salicylic acid (SA) and plant growth...
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As a plant-specific transcription factor, the NAC (NAM, ATAF1/2 and CUC2) domain protein plays an important role in plant growth and development, as well as stress resistance. Based on the genomic data of the cacao tree, this study identified 102 cacao NAC genes and named them according to their location within the genome. The phylogeny of the prot...
Lignin, a major component of the secondary cell wall, is important for plant growth and development. Moreover, lignin plays a pivotal role in plant innate immunity. Lignin is readily deposited upon pathogen infection and functions as a physical barrier that limits the spread of pathogens. In this study, we show that an Arabidopsis MYB transcription...
Open or accessible regions of the genome are the primary positions of binding sites for transcription factors and chromatin regulators. Transposase-accessible chromatin sequencing (ATAC-seq) can probe chromatin accessibility in the intact nucleus. Here, we describe a protocol to generate ATAC-seq libraries from fresh Arabidopsis thaliana tissues an...
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... Highthroughput phenotyping tools, combined with the production of new adapted genomic resources using new sequencing technologies and genome-wide association (GWA) mapping approaches, has provided the opportunity to consider and explore more broadly the genetic diversity of the plant response to specific traits. The development of such strategies on model plants and crop species has already demonstrated their great potential with regards to the identification of genes underlying QDR to bacteria, fungi and oomycetes (French et al., 2016;Bartoli & Roux, 2017;Bruessow et al., 2019). Because of the durable nature and the broad spectrum resistance conferred by QDR genes Roux et al., 2014;French et al., 2016), it has become relevant to use such strategies to uncover thermoresilient resistance mechanisms or to consider other climate parameters in the changing environment. ...
In their natural environment, plants are exposed to biotic or abiotic stresses that occur sequentially or simultaneously. Plant responses to these stresses have been studied widely and have been well characterised in simplified systems involving single plant species facing individual stress. Temperature elevation is a major abiotic driver of climate change and scenarios have predicted an increase in the number and severity of epidemics. In this context, here we review the available data on the effect of heat stress on plant–pathogen interactions. Considering 45 studies performed on model or crop species, we discuss the possible implications of the optimum growth temperature of plant hosts and pathogens, mode of stress application and temperature variation on resistance modulations. Alarmingly, most identified resistances are altered under temperature elevation, regardless of the plant and pathogen species. Therefore, we have listed current knowledge on heat‐dependent plant immune mechanisms and pathogen thermosensory processes, mainly studied in animals and human pathogens, that could help to understand the outcome of plant–pathogen interactions under elevated temperatures. Based on a general overview of the mechanisms involved in plant responses to pathogens, and integrating multiple interactions with the biotic environment, we provide recommendations to optimise plant disease resistance under heat stress and to identify thermotolerant resistance mechanisms.
Erythrina velutina is a Brazilian native tree of the Caatinga (a unique semiarid biome). It is widely used in traditional medicine showing anti-inflammatory and central nervous system modulating activities. The species is a rich source of specialized metabolites, mostly alkaloids and flavonoids. To date, genomic information, biosynthesis, and regulation of flavonoids remain unknown in this woody plant. As part of a larger ongoing research goal to better understand specialized metabolism in plants inhabiting the harsh conditions of the Caatinga, the present study focused on this important class of bioactive phenolics. Leaves and seeds of plants growing in their natural habitat had their metabolic and proteomic profiles analyzed and integrated with transcriptome data. As a result, 96 metabolites (including 43 flavonoids) were annotated. Transcripts of the flavonoid pathway totaled 27, of which EvCHI, EvCHR, EvCHS, EvCYP75A and EvCYP75B1 were identified as putative main targets for modulating the accumulation of these metabolites. The highest correspondence of mRNA vs. protein was observed in the differentially expressed transcripts. In addition, 394 candidate transcripts encoding for transcription factors distributed among the bHLH, ERF, and MYB families were annotated. Based on interaction network analyses, several putative genes of the flavonoid pathway and transcription factors were related, particularly TFs of the MYB family. Expression patterns of transcripts involved in flavonoid biosynthesis and those involved in responses to biotic and abiotic stresses were discussed in detail. Overall, these findings provide a base for the understanding of molecular and metabolic responses in this medicinally important species. Moreover, the identification of key regulatory targets for future studies aiming at bioactive metabolite production will be facilitated.
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.
