The soil microbiome of Lolium perenne L. depends on host genotype, is modified by nitrogen level and varies across season.

Genotype by environment interactions (G × E) are frequently observed in herbage production. Understanding the underlying biological mechanisms is important for achieving stable and predictive outputs across production environments. The microbiome is gaining increasing attention as a significant contributing factor to G × E. Here, we focused on the soil microbiome of perennial ryegrass (Lolium perenne L.) grown under field conditions and investigated the soil microbiome variation across different ryegrass varieties to assess whether environmental factors, such as seasonality and nitrogen levels, affect the microbial community. We identified bacteria, archaea, and fungi operational taxonomic units (OTUs) and showed that seasonality and ryegrass variety were the two factors explaining the largest fraction of the soil microbiome diversity. The strong and significant variety-by-treatment-by-seasonal cut interaction for ryegrass dry matter was associated with the number of unique OTUs within each sample. We identified seven OTUs associated with ryegrass dry matter variation. An OTU belonging to the Solirubrobacterales (Thermoleophilales) order was associated with increased plant biomass, supporting the possibility of developing engineered microbiomes for increased plant yield. Our results indicate the importance of incorporating different layers of biological data, such as genomic and soil microbiome data to improve the prediction accuracy of plant phenotypes grown across heterogeneous environments.

Corrigendum: Methylome changes in Lolium perenne associated with long-term colonisation by the endophytic fungus Epichloë sp. LpTG-3 strain AR37.

[This corrects the article DOI: 10.3389/fpls.2023.1258100.].

Methylome changes in Lolium perenne associated with long-term colonisation by the endophytic fungus Epichloë sp. LpTG-3 strain AR37.

Epichloë spp. often form mutualistic interactions with cool-season grasses, such as Lolium perenne. However, the molecular mechanisms underlying this interaction remain poorly understood. In this study, we employed reduced representation bisulfite sequencing method (epiGBS) to investigate the impact of the Epichloë sp. LpTG-3 strain AR37 on the methylome of L. perenne across multiple grass generations and under drought stress conditions. Our results showed that the presence of the endophyte leads to a decrease in DNA methylation across genomic features, with differentially methylated regions primarily located in intergenic regions and CHH contexts. The presence of the endophyte was consistently associated with hypomethylation in plants across generations. This research sheds new light on the molecular mechanisms governing the mutualistic interaction between Epichloë sp. LpTG-3 strain AR37 and L. perenne. It underscores the role of methylation changes associated with endophyte infection and suggests that the observed global DNA hypomethylation in L. perenne may be influenced by factors such as the duration of the endophyte-plant association and the accumulation of genetic and epigenetic changes over time.

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data.

BACKGROUND: Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce. RESULTS: Our results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors. CONCLUSIONS: The utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions.

Fine-Mapping and Comparative Genomic Analysis Reveal the Gene Composition at the S and Z Self-incompatibility Loci in Grasses.

Self-incompatibility (SI) is a genetic mechanism of hermaphroditic plants to prevent inbreeding after self-pollination. Allogamous Poaceae species exhibit a unique gametophytic SI system controlled by two multi-allelic and independent loci, S and Z. Despite intense research efforts in the last decades, the genes that determine the initial recognition mechanism are yet to be identified. Here, we report the fine-mapping of the Z-locus in perennial ryegrass (Lolium perenne L.) and provide evidence that the pollen and stigma components are determined by two genes encoding DUF247 domain proteins (ZDUF247-I and ZDUF247-II) and the gene sZ, respectively. The pollen and stigma determinants are located side-by-side and were genetically linked in 10,245 individuals of two independent mapping populations segregating for Z. Moreover, they exhibited high allelic diversity as well as tissue-specific gene expression, matching the expected characteristics of SI determinants known from other systems. Revisiting the S-locus using the latest high-quality whole-genome assemblies revealed a similar gene composition and structure as found for Z, supporting the hypothesis of a duplicated origin of the two-locus SI system of grasses. Ultimately, comparative genomic analyses across a wide range of self-compatible and self-incompatible Poaceae species revealed that the absence of a functional copy of at least one of the six putative SI determinants is accompanied by a self-compatible phenotype. Our study provides new insights into the origin and evolution of the unique gametophytic SI system in one of the largest and economically most important plant families.

Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models.

Joint modeling of correlated multienvironment and multiharvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within-season multiple measurements of forage perennial ryegrass (Lolium perenne L.) traits in a reaction-norm model setup that additionally accounts for genotype × environment (G × E) interactions. Genetic parameters and accuracy of genomic estimated breeding value (gEBV) predictions were investigated by fitting three genomic random regression models (gRRMs) using Legendre polynomial functions to the data. Genomic DNA sequencing of family pools of diploid perennial ryegrass was performed using DNA nanoball-based technology and yielded 56,645 single-nucleotide polymorphisms, which were used to calculate the allele frequency-based genomic relationship matrix. Biomass yield's estimated additive genetic variance and heritability values were higher in later harvests. The additive genetic correlations were moderate to low in early measurements and peaked at intermediates with fairly stable values across the environmental gradient except for the initial harvest data collection. This led to the conclusion that complex (G × E) arises from spatial and temporal dimensions in the early season with lower reranking trends thereafter. In general, modeling the temporal dimension with a second-order orthogonal polynomial improved the accuracy of gEBV prediction for nutritive quality traits, but no gain in prediction accuracy was detected for dry matter yield (DMY). This study leverages the flexibility and usefulness of gRRM models for perennial ryegrass breeding and can be readily extended to other multiharvest crops.

Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare.

Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72-0.91) than with genomic models alone (0.55-0.86). The correlation between predictions and phenotypes varied from 0.17-0.28 for control plants and 0.23-0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.

Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.

The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE × treatment (GEControl and GEDrought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits.

Chromosome-scale assembly and annotation of the perennial ryegrass genome.

BACKGROUND: The availability of chromosome-scale genome assemblies is fundamentally important to advance genetics and breeding in crops, as well as for evolutionary and comparative genomics. The improvement of long-read sequencing technologies and the advent of optical mapping and chromosome conformation capture technologies in the last few years, significantly promoted the development of chromosome-scale genome assemblies of model plants and crop species. In grasses, chromosome-scale genome assemblies recently became available for cultivated and wild species of the Triticeae subfamily. Development of state-of-the-art genomic resources in species of the Poeae subfamily, which includes important crops like fescues and ryegrasses, is lagging behind the progress in the cereal species. RESULTS: Here, we report a new chromosome-scale genome sequence assembly for perennial ryegrass, obtained by combining PacBio long-read sequencing, Illumina short-read polishing, BioNano optical mapping and Hi-C scaffolding. More than 90% of the total genome size of perennial ryegrass (approximately 2.55 Gb) is covered by seven pseudo-chromosomes that show high levels of collinearity to the orthologous chromosomes of Triticeae species. The transposon fraction of perennial ryegrass was found to be relatively low, approximately 35% of the total genome content, which is less than half of the genome repeat content of cultivated cereal species. We predicted 54,629 high-confidence gene models, 10,287 long non-coding RNAs and a total of 8,393 short non-coding RNAs in the perennial ryegrass genome. CONCLUSIONS: The new reference genome sequence and annotation presented here are valuable resources for comparative genomic studies in grasses, as well as for breeding applications and will expedite the development of productive varieties in perennial ryegrass and related species.

Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.

The majority of forage grass species are obligate outbreeders. Their breeding classically consists of an initial selection on spaced plants for highly heritable traits such as disease resistances and heading date, followed by familial selection on swards for forage yield and quality traits. The high level of diversity and heterozygosity, and associated decay of linkage disequilibrium (LD) over very short genomic distances, has hampered the implementation of genomic selection (GS) in these species. However, next generation sequencing technologies in combination with the development of genomic resources have recently facilitated implementation of GS in forage grass species such as perennial ryegrass (Lolium perenne L.), switchgrass (Panicum virgatum L.), and timothy (Phleum pratense L.). Experimental work and simulations have shown that GS can increase significantly the genetic gain per unit of time for traits with different levels of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associated with an increase in the genetic variance used for selection. Nevertheless, several factors should be taken into account for the successful implementation of GS in forage grasses. For example, it has been shown that the level of relatedness between the training and the selection population is particularly critical when working with highly structured meta-populations consisting of several genetic groups. A sufficient number of markers should be used to estimate properly the kinship between individuals and to reflect the variability of major QTLs. It is also important that the prediction models are trained for relevant environments when dealing with traits with high genotype × environment interaction (G × E). Finally, in these outbreeding species, measures to reduce inbreeding should be used to counterbalance the high selection intensity that can be achieved in GS.

A comparison of shared patterns of differential gene expression and gene ontologies in response to water-stress in roots and leaves of four diverse genotypes of Lolium and Festuca spp. temperate pasture grasses.

Ryegrasses (Lolium spp.) and fescues (Festuca spp.) are closely related and widely cultivated perennial forage grasses. As such, resilience in the face of abiotic stresses is an important component of their traits. We have compared patterns of differentially expressed genes (DEGs) in roots and leaves of two perennial ryegrass genotypes and a single genotype of each of a festulolium (predominantly Italian ryegrass) and meadow fescue with the onset of water stress, focussing on overall patterns of DEGs and gene ontology terms (GOs) shared by all four genotypes. Plants were established in a growing medium of vermiculite watered with nutrient solution. Leaf and root material were sampled at 35% (saturation) and, as the medium dried, at 15%, 5% and 1% estimated water contents (EWCs) and RNA extracted. Differential gene expression was evaluated comparing the EWC sampling points from RNAseq data using a combination of analysis methods. For all genotypes, the greatest numbers of DEGs were identified in the 35/1 and 5/1 comparisons in both leaves and roots. In total, 566 leaf and 643 root DEGs were common to all 4 genotypes, though a third of these leaf DEGs were not regulated in the same up/down direction in all 4 genotypes. For roots, the equivalent figure was 1% of the DEGs. GO terms shared by all four genotypes were often enriched by both up- and down-regulated DEGs in the leaf, whereas generally, only by either up- or down-regulated DEGs in the root. Overall, up-regulated leaf DEGs tended to be more genotype-specific than down-regulated leaf DEGs or root DEGs and were also associated with fewer GOs. On average, only 5-15% of the DEGs enriching common GO terms were shared by all 4 genotypes, suggesting considerable variation in DEGs between related genotypes in enacting similar biological processes.

Genomic selection in American mink (Neovison vison) using a single-step genomic best linear unbiased prediction model for size and quality traits graded on live mink.

Genomic selection relies on single-nucleotide polymorphisms (SNPs), which are often collected using medium-density SNP arrays. In mink, no such array is available; instead, genotyping by sequencing (GBS) can be used to generate marker information. Here, we evaluated the effect of genomic selection for mink using GBS. We compared the estimated breeding values (EBVs) from single-step genomic best linear unbiased prediction (SSGBLUP) models to the EBV from ordinary pedigree-based BLUP models. We analyzed seven size and quality traits from the live grading of brown mink. The phenotype data consisted of ~20,600 records for the seven traits from the mink born between 2013 and 2016. Genotype data included 2,103 mink born between 2010 and 2014, mostly breeding animals. In total, 28,336 SNP markers from 391 scaffolds were available for genomic prediction. The pedigree file included 29,212 mink. The predictive ability was assessed by the correlation (r) between progeny trait deviation (PTD) and EBV, and the regression of PTD on EBV, using 5-fold cross-validation. For each fold, one-fifth of animals born in 2014 formed the validation set. For all traits, the SSGBLUP model resulted in higher accuracies than the BLUP model. The average increase in accuracy was 15% (between 3% for fur clarity and 28% for body weight). For three traits (body weight, silky appearance of the under wool, and guard hair thickness), the difference in r between the two models was significant (P < 0.05). For all traits, the regression slopes of PTD on EBV from SSGBLUP models were closer to 1 than regression slopes from BLUP models, indicating SSGBLUP models resulted in less bias of EBV for selection candidates than the BLUP models. However, the regression coefficients did not differ significantly. In conclusion, the SSGBLUP model is superior to conventional BLUP model in the accurate selection of superior animals, and, thus, it would increase genetic gain in a selective breeding program. In addition, this study shows that GBS data work well in genomic prediction in mink, demonstrating the potential of GBS for genomic selection in livestock species.

Genome-Wide Association Mapping of Crown and Brown Rust Resistance in Perennial Ryegrass.

A population of 239 perennial ryegrass (Lolium perenne L.) genotypes was analyzed to identify marker-trait associations for crown rust (Puccinia coronata f. sp. lolii) and brown rust (Puccinia graminis f. sp. loliina) resistance. Phenotypic data from field trials showed a low correlation (r = 0.17) between the two traits. Genotypes were resequenced, and a total of 14,538,978 SNPs were used to analyze population structure, linkage disequilibrium (LD), and for genome-wide association study. The SNP heritability (h2SNP) was 0.4 and 0.8 for crown and brown rust resistance, respectively. The high-density SNP dataset allowed us to estimate LD decay with the highest possible precision to date for perennial ryegrass. Results showed a low LD extension with a rapid decay of r2 value below 0.2 after 520 bp on average. Additionally, QTL regions for both traits were detected, as well as candidate genes by applying Genome Complex Trait Analysis and Multi-marker Analysis of GenoMic Annotation. Moreover, two significant genes, LpPc6 and LpPl6, were identified for crown and brown rust resistance, respectively, when SNPs were aggregated to the gene level. The two candidate genes encode proteins with phosphatase activity, which putatively can be induced by the host to perceive, amplify and transfer signals to downstream components, thus activating a plant defense response.

The cytosine methylation landscape of spring barley revealed by a new reduced representation bisulfite sequencing pipeline, WellMeth.

Patterns and level of cytosine methylation vary widely among plant species and are associated with genome size as well as the proportion of transposons and other repetitive elements in the genome. We explored epigenetic patterns and diversity in a representative proportion of the spring barley (Hordeum vulgare L.) genome across several commercial and historical cultivars. This study adapted a genotyping-by-sequencing (GBS) approach for the detection of methylated cytosines in genomic DNA. To analyze the data, we developed WellMeth, a complete pipeline for analysis of reduced representation bisulfite sequencing. WellMeth enabled quantification of context-specific DNA methylation at the single-base resolution as well as identification of differentially methylated sites (DMCs) and regions (DMRs). On average, DNA methylation levels were significantly higher than what is commonly observed in many plants species, reaching over 10-fold higher levels than those in Arabidopsis thaliana (L.) Heynh. in the CHH methylation. Preferential methylation was observed within and at the edges of long-terminal repeats (LTR) retrotransposons Gypsy and Copia. From a pairwise comparison of cultivars, numerous DMRs could be identified of which more than 5,000 were conserved within the analyzed set of barley cultivars. The subset of regions overlapping with genes showed enrichment in gene ontology (GO) categories associated with chromatin and cellular structure and organization. A significant correlation between genetic and epigenetic distances suggests that a considerable portion of methylated regions is under strict genetic control in barley. The data presented herein represents the first step in efforts toward a better understanding of genome-level structural and functional aspects of methylation in barley.

Fungal Endophyte Colonization Patterns Alter Over Time in the Novel Association Between Lolium perenne and Epichloë Endophyte AR37.

Infection of the pasture grass Lolium perenne with the seed-transmitted fungal endophyte Epichloë festucae enhances its resilience to biotic and abiotic stress. Agricultural benefits of endophyte infection can be increased by generating novel symbiotic associations through inoculating L. perenne with selected Epichloë strains. Natural symbioses have coevolved over long periods. Thus, artificial symbioses will probably not have static properties, but symbionts will coadapt over time improving the fitness of the association. Here we report for the first time on temporal changes in a novel association of Epichloë strain AR37 and the L. perenne cultivar Grasslands Samson. Over nine generations, a seed maintenance program had increased the endophyte seed transmission rates to > 95% (from an initial 76%). We observed an approximately fivefold decline in endophyte biomass concentration in vegetative tissues over time (between generations 2 and 9). This indicates strong selection pressure toward reducing endophyte-related fitness costs by reducing endophyte biomass, without compromising the frequency of endophyte transmission to seed. We observed no obvious changes in tillering and only minor transcriptomic changes in infected plants over time. Functional analysis of 40 plant genes, showing continuously decreasing expression over time, suggests that adaptation of host metabolism and defense mechanisms are important for increasing the fitness of this association, and possibly fitness of such symbioses in general. Our results indicate that fitness of novel associations is likely to improve over time and that monitoring changes in novel associations can assist in identifying key features of endophyte-mediated enhancement of host fitness.

Insights into the evolution of symbiosis gene copy number and distribution from a chromosome-scale Lotus japonicus Gifu genome sequence.

Lotus japonicus is a herbaceous perennial legume that has been used extensively as a genetically tractable model system for deciphering the molecular genetics of symbiotic nitrogen fixation. Our aim is to improve the L. japonicus reference genome sequence, which has so far been based on Sanger and Illumina sequencing reads from the L. japonicus accession MG-20 and contained a large fraction of unanchored contigs. Here, we use long PacBio reads from L. japonicus Gifu combined with Hi-C data and new high-density genetic maps to generate a high-quality chromosome-scale reference genome assembly for L. japonicus. The assembly comprises 554 megabases of which 549 were assigned to six pseudomolecules that appear complete with telomeric repeats at their extremes and large centromeric regions with low gene density. The new L. japonicus Gifu reference genome and associated expression data represent valuable resources for legume functional and comparative genomics. Here, we provide a first example by showing that the symbiotic islands recently described in Medicago truncatula do not appear to be conserved in L. japonicus.

Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics.

Near infrared spectroscopy (NIRS) is an accurate, fast and nondestructive technique whose use in predicting forage quality has become increasingly relevant in recent decades. Epichloë-infected grass varieties are commonly used in areas with high pest pressure due to their better performances compared to endophyte-free varieties. The insect resistance of Epichloë-infected grasses has been associated with four main groups of endophyte secondary metabolites: ergot alkaloids, indole-diterpenes, lolines and peramine. Concentrations of these alkaloids are usually measured with high performance liquid chromatography or gas chromatography analysis, which are accurate methods but relatively expensive and laborious. In this paper, we developed a rapid method based on NIRS to detect and quantify loline alkaloids in wild accessions of Schedonorus pratensis infected with the fungal endophyte Epichloë uncinata. The quantitative NIR equations obtained by modified partial least squares algorithm had coefficients of correlation of 0.90, 0.78, 0.85, 0.90 for N-acetylloline, N-acetylnorloline and N-formylloline and the sum of the three, respectively. The acquired NIR spectra were also used for developing an equation to predict in planta fungal biomass with a coefficient of correlation of 0.75. These results showed that the use of NIRS and chemometrics allows the quantification of loline alkaloids and mycelial biomass in a heterogeneous set of endophyte-infected meadow fescue samples.

The Mitogenome of Norway Spruce and a Reappraisal of Mitochondrial Recombination in Plants.

Plant mitogenomes can be difficult to assemble because they are structurally dynamic and prone to intergenomic DNA transfers, leading to the unusual situation where an organelle genome is far outnumbered by its nuclear counterparts. As a result, comparative mitogenome studies are in their infancy and some key aspects of genome evolution are still known mainly from pregenomic, qualitative methods. To help address these limitations, we combined machine learning and in silico enrichment of mitochondrial-like long reads to assemble the bacterial-sized mitogenome of Norway spruce (Pinaceae: Picea abies). We conducted comparative analyses of repeat abundance, intergenomic transfers, substitution and rearrangement rates, and estimated repeat-by-repeat homologous recombination rates. Prompted by our discovery of highly recombinogenic small repeats in P. abies, we assessed the genomic support for the prevailing hypothesis that intramolecular recombination is predominantly driven by repeat length, with larger repeats facilitating DNA exchange more readily. Overall, we found mixed support for this view: Recombination dynamics were heterogeneous across vascular plants and highly active small repeats (ca. 200 bp) were present in about one-third of studied mitogenomes. As in previous studies, we did not observe any robust relationships among commonly studied genome attributes, but we identify variation in recombination rates as a underinvestigated source of plant mitogenome diversity.

Transcriptome profiling and environmental linkage to salinity across Salicornia europaea vegetation.

BACKGROUND: Salicornia europaea, a succulent obligatory halophyte is the most salt-tolerant plant species in the world. It survives salt concentrations of more than 1 M. Therefore, it is a suitable model plant to identify genes involved in salt tolerance mechanisms that can be used for the improvement of crops. The changes in a plant's gene expression in response to abiotic stresses may depend on factors like soil conditions at the site, seasonality, etc. To date, experiments were performed to study the gene expression of S. europaea only under controlled conditions. Conversely, the present study investigates the transcriptome and physicochemical parameters of S. europaea shoots and roots from two different types of saline ecosystems growing under natural conditions. RESULTS: The level of soil salinity was higher at the naturally saline site than at the anthropogenic saline site. The parameters such as ECe, Na+, Cl-, Ca+, SO42- and HCO3- of the soils and plant organs significantly varied according to sites and seasons. We found that Na+ mainly accumulated in shoots, whereas K+ and Ca2+ levels were higher in roots throughout the growing period. Moreover, changes in S. europaea gene expression were more prominent in seasons, than sites and plant organs. The 30 differentially expressed genes included enzymes for synthesis of S-adenosyl methionine, CP47 of light-harvesting complex II, photosystem I proteins, Hsp70 gene, ATP-dependent Clp proteases, ribulose bisphosphate carboxylase/oxygenase (Rubisco), phenylalanine ammonia-lyase (PAL), cytochrome c oxidase (COX) and ATP synthase. CONCLUSION: The comparisons made based on two seasons, plant organs and two different sites suggest the importance of seasonal variations in gene expression of S. europaea. We identify the genes that may play an important role in acclimation to season-dependent changes of salinity. The genes were involved in processes such as osmotic adjustment, energy metabolism and photosynthesis.