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Seed germination is the essential first step in crop establishment, and can be severely affected by salinity stress which can inhibit essential metabolic processes during the germination process. Salt stress during seed germination can trigger lipid-dependent signalling cascades that activate plant adaptation processes, lead to changes in membrane fluidity to help resist the stress, and cause secondary metabolite responses due to increased oxidative stress. In germinating barley ( Hordeum vulgare ), knowledge of the changes in spatial distribution of lipids and other small molecules at a cellular level in response to salt stress is limited. In this study, mass spectrometry imaging (MSI), liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS), inductively coupled plasma mass spectrometry (ICP-MS), and X-ray fluorescence (XRF) were used to determine the spatial distribution of metabolites, lipids and a range of elements, such as K+ and Na+, in seeds of two barley genotypes with contrasting germination phenology (Australian barley varieties Mundah and Keel). We detected and tentatively identified more than 200 lipid species belonging to seven major lipid classes (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, prenol lipids, sterol lipids, and polyketides) that differed in their spatial distribution based on genotype (Mundah or Keel), time post-imbibition (0 to 72 h), or treatment (control or salt). We found a tentative flavonoid was discriminant in post-imbibed Mundah embryos under saline conditions, and a delayed flavonoid response in Keel relative to Mundah. We further employed MSI-MS/MS and LC-QToF-MS/MS to explore the identity of the discriminant flavonoid and study the temporal pattern in five additional barley genotypes. ICP-MS was used to quantify the elemental composition of both Mundah and Keel seeds, showing a significant increase in Na+ in salt treated samples. Spatial mapping of elements using µ-XRF localized the elements within the seeds. This study integrates data obtained from three mass spectrometry platforms together with µ-XRF to yield information on the localization of lipids, metabolites and elements improving our understanding of the germination process under salt stress at a molecular level.

One of the most important environmental factors impacting crop plant productivity is soil salinity. Fungal endophytes have been characterised as biocontrol agents that help in plant productivity and induce resistance responses to several abiotic stresses, including salinity. In the salt-tolerant cereal crop barley ( Hordeum vulgare L.), there is limited information about the metabolites and lipids that change in response to inoculation with fungal endophytes in saline conditions. In this study, gas chromatography coupled to mass spectrometry (GC–MS) and LC–electrospray ionisation (ESI)–quadrupole–quadrupole time of flight (QqTOF)-MS were used to determine the metabolite and lipid changes in two fungal inoculated barley genotypes with differing tolerance levels to saline conditions. The more salt-tolerant cultivar was Vlamingh and less salt tolerant was Gairdner. Trichoderma harzianum strain T-22 was used to treat these plants grown in soil under control and saline (200 mM NaCl) conditions. For both genotypes, fungus-colonised plants exposed to NaCl had greater root and shoot biomass, and better chlorophyll content than non-colonised plants, with colonised-Vlamingh performing better than uninoculated control plants. The metabolome dataset using GC–MS consisted of a total of 93 metabolites of which 74 were identified in roots of both barley genotypes as organic acids, sugars, sugar acids, sugar alcohols, amino acids, amines, and a small number of fatty acids. LC-QqTOF-MS analysis resulted in the detection of 186 lipid molecular species, classified into three major lipid classes—glycerophospholipids, glycerolipids, and sphingolipids, from roots of both genotypes. In Cultivar Vlamingh both metabolites and lipids increased with fungus and salt treatment while in Gairdner they decreased. The results from this study suggest that the metabolic pathways by which the fungus imparts salt tolerance is different for the different genotypes.

A proteomic examination of Sinorhizobium meliloti strain 1021 was undertaken using a combination of 2-D gel electrophoresis, peptide mass fingerprinting, and bioinformatics. Our goal was to identify (i) putative symbiosis- or nutrientstress-specific proteins, (ii) the biochemical pathways active under different conditions, (iii) potential new genes, and (iv) the extent of posttranslational modifications of S. meliloti proteins. In total, we identified the protein products of 810 genes (13.1% of the genome's coding capacity). The 810 genes generated 1,180 gene products, with chromosomal genes accounting for 78% of the gene products identified (18.8% of the chromosome's coding capacity). The activity of 53 metabolic pathways was inferred from bioinformatic analysis of proteins with assigned Enzyme Commission numbers. Of the remaining proteins that did not encode enzymes, ABC-type transporters composed 12.7% and regulatory proteins 3.4% of the total. Proteins with up to seven transmembrane domains were identified in membrane preparations. A total of 27 putative nodulespecific proteins and 35 nutrient-stress-specific proteins were identified and used as a basis to define genes and describe processes occurring in S. meliloti cells in nodules and under stress. Several nodule proteins from the plant host were present in the nodule bacteria preparations. We also identified seven potentially novel proteins not predicted from the DNA sequence. Post-translational modifications such as N-terminal processing could be inferred from the data. The posttranslational addition of UMP to the key regulator of nitrogen metabolism, PII, was demonstrated. This work demonstrates the utility of combining mass spectrometry with protein arraying or separation techniques to identify candidate genes involved in important biological processes and niche occupations that may be intransigent to other methods of gene expression profiling.