ABSTRACT
BACKGROUND: The steady world population growth and the current climate emergency crisis demand the development of sustainable methods to increase crop performance and resilience to the abiotic and biotic stresses produced by global warming. Microalgal extracts are being established as sustainable sources to produce compounds that improve agricultural yield, concurrently contributing during their production process to atmospheric CO2 abatement through the photosynthetic activity of microalgae. RESULTS: In the present study, we characterize the transcriptomic response in the model plant Arabidopsis thaliana and the plant of horticultural interest Solanum lycopersicum to the foliar application of a microalgae-based commercial preparation LRM™ (AlgaEnergy, Madrid, Spain). The foliar spray of LRM™ has a substantial effect over both transcriptomes potentially mediated by various compounds within LRM™, including its phytohormone content, activating systemic acquired resistance, possibly mediated by salicylic acid biosynthetic processes, and drought/heat acclimatization, induced by stomatal control and wax accumulation during cuticle development. Specifically, the agronomic improvements observed in treated S. lycopersicum (tomato) plants include an increase in the number of fruits, an acceleration in flowering time and the provision of higher drought resistance. The effect of LRM™ foliar spray in juvenile and adult plants was similar, producing a fast response detectable 2 h from its application that was also maintained 24 h later. CONCLUSION: The present study improves our knowledge on the transcriptomic effect of a novel microalgal extract on crops and provides the first step towards a full understanding of the yield and resistance improvement of crops. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
ABSTRACT
BACKGROUND: Microalgae are emerging as promising sustainable sources for biofuels, biostimulants in agriculture, soil bioremediation, feed and human nutrients. Nonetheless, the molecular mechanisms underpinning microalgae physiology and the biosynthesis of compounds of biotechnological interest are largely uncharacterized. This hinders the development of microalgae full potential as cell-factories. The recent application of omics technologies into microalgae research aims at unraveling these systems. Nevertheless, the lack of specific tools for analysing omics raw data generated from microalgae to provide biological meaningful information are hampering the impact of these technologies. The purpose of ALGAEFUN with MARACAS consists in providing researchers in microalgae with an enabling tool that will allow them to exploit transcriptomic and cistromic high-throughput sequencing data. RESULTS: ALGAEFUN with MARACAS consists of two different tools. First, MARACAS (MicroAlgae RnA-seq and Chip-seq AnalysiS) implements a fully automatic computational pipeline receiving as input RNA-seq (RNA sequencing) or ChIP-seq (chromatin immunoprecipitation sequencing) raw data from microalgae studies. MARACAS generates sets of differentially expressed genes or lists of genomic loci for RNA-seq and ChIP-seq analysis respectively. Second, ALGAEFUN (microALGAE FUNctional enrichment tool) is a web-based application where gene sets generated from RNA-seq analysis as well as lists of genomic loci from ChIP-seq analysis can be used as input. On the one hand, it can be used to perform Gene Ontology and biological pathways enrichment analysis over gene sets. On the other hand, using the results of ChIP-seq data analysis, it identifies a set of potential target genes and analyses the distribution of the loci over gene features. Graphical representation of the results as well as tables with gene annotations are generated and can be downloaded for further analysis. CONCLUSIONS: ALGAEFUN with MARACAS provides an integrated environment for the microalgae research community that facilitates the process of obtaining relevant biological information from raw RNA-seq and ChIP-seq data. These applications are designed to assist researchers in the interpretation of gene lists and genomic loci based on functional enrichment analysis. ALGAEFUN with MARACAS is publicly available on https://greennetwork.us.es/AlgaeFUN/ .
