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SummaryDue to climate warming, recently deglaciated glacier forefields create virtually uninhabited substrates waiting for initial colonization of bacteria, fungi and plants and serve as an ideal ecosystem for studying transformations in community composition and diversity over time and the interactions between taxonomic groups.In this study, we investigated the composition and diversity of bacteria, and fungi, plants and environmental factors (pH, temperature, plot age and soil nutrients) along a 1.5km glacier forefield. We used random forest analysis to detect how well the composition and diversity of taxonomic groups and environmental factors can be mutually predicted.Community composition and diversity of taxonomic groups predicted each other more accurately than environmental factors predicted the taxonomic groups; within the taxonomic groups bacteria and fungi predicted each other best and the taxa’s composition was better predicted than diversity indices. Additionally, accuracy of prediction among taxonomic groups and environmental factors considerably varied along the successional gradient.Although our results are no direct indication of interactions between the taxa investigated and the environmental conditions, the accurate predictions among bacteria, fungi, and plants do provide insights into the concerted community assembly of different taxa in response to changing environments along a successional gradient.

Community assembly is a result of dispersal, abiotic and biotic characteristics of the habitat as well as stochasticity. A direct comparison between the assembly of microbial and ‘macrobial’ organisms is hampered by the sampling of these communities in different studies, at different sites, or on different scales. In a glacier forefield in the Austrian Alps, we recorded the soil and plant microbiome (bacteria and fungi) and plants that occurred in the same landscape and in close proximity in the same plots. We tested five predictions deduced from assembly processes and revealed deviating patterns of assembly in these community types. In short, microbes appeared to be less dispersal limited than plants, soil microbes and plants strongly responded to abiotic factors whereas the leaf microbiome was plant species-specific and well buffered from environmental conditions. The observed differences in community assembly processes may be attributed to the organisms’ dispersal abilities, the exposure of the habitats to airborne propagules, and habitat characteristics. The finding that assembly is conditional to the characteristics of the organisms, the habitat, and the spatial scale under consideration is thus central for our understanding about the establishment and the maintenance of biodiversity.

Community assembly is a result of dispersal, abiotic and biotic characteristics of the habitat as well as stochasticity. A direct comparison between the assembly of microbial and ‘macrobial’ organisms is hampered by the sampling of these communities in different studies, at different sites or on different scales. In a glacier forefield in the Austrian Alps, we recorded the soil and plant microbiome (bacteria and fungi) and plants that occurred in the same landscape and in close proximity in the same plots. We tested five predictions deduced from assembly processes and revealed deviating patterns of assembly in these community types. In short, microbes appeared to be less dispersal limited than plants and soil microbes, and plants strongly responded to abiotic factors whereas the leaf microbiome was plant species specific and well buffered from environmental conditions. The observed differences in community assembly processes may be attributed to the organisms’ dispersal abilities, the exposure of the habitats to airborne propagules and habitat characteristics. The finding that assembly is conditional to the characteristics of the organisms, the habitat and the spatial scale under consideration is thus central for our understanding about the establishment and the maintenance of biodiversity., A comparison between bacterial and fungal communities associated with leaves or soil and plant communities suggests different assembly processes shaped by characteristics of the organisms and the habitat.

Circadian clock; Constitutive metabolome; Machine learning Reloj circadiano; Metaboloma constitutivo; Aprendizaje automático Rellotge circadià; Metaboloma constitutiu; Aprenentatge automàtic Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, Deficiens nicotianoides and compacta affecting floral identity and RNAi:AmLHY targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites. This research was funded by Ministerio de Ciencia, Innovación y Universidades and FEDER grant numbers BFU2017-88300-C2-1R to M.E.-C. and J.W.; BFU2017-88300-C2-2R to P.J.N.; and a PhD contract by the Ministerio de Educación Cultura y Deporte FPU13/03606 to V.R.-H.

Narcissus flowers are used as cut flowers and to obtain high quality essential oils for the perfume industry. As a winter crop in the Mediterranean area, it flowers at temperatures ranging between 10 and 15°C during the day and 3–10°C during the night. Here we tested the impact of different light and temperature conditions on scent quality during post-harvest. These two types of thermoperiod and photoperiod. We also used constant darkness and constant temperatures. We found that under conditions of 12:12 Light Dark and 15-5°C, Narcissus emitted monoterpenes and phenylpropanoids. Increasing the temperature to 20°-10°C in a 12:12 LD cycle caused the loss of cinnamyl acetate and emission of indole. Under constant dark, there was a loss of scent complexity. Constant temperatures of 20°C caused a decrease of scent complexity that was more dramatic at 5°C, when the total number of compounds emitted decreased from thirteen to six. Distance analysis confirmed that 20°C constant temperature causes the most divergent scent profile. We found a set of four volatiles, benzyl acetate, eucalyptol, linalool, and ocimene that display a robust production under differing environmental conditions, while others were consistently dependent on light or thermoperiod. Scent emission changed significantly during the day and between different light and temperature treatments. Under a light:dark cycle and 15-5°C the maximum was detected during the light phase but this peak shifted toward night under 20-10°C. Moreover, under constant darkness the peak occurred at midnight and under constant temperature, at the end of night. Using Machine Learning we found that indole was the volatile with a highest ranking of discrimination followed by D-limonene. Our results indicate that light and temperature regimes play a critical role in scent quality. The richest scent profile is obtained by keeping flowers at 15°-5°C thermoperiod and a 12:12 Light Dark photoperiod.

Narcissus flowers are used as cut flowers and to obtain high quality essential oils for the perfume industry. As a winter crop in the Mediterranean area, it flowers at temperatures ranging between 10 and 15°C during the day and 3–10°C during the night. Here we tested the impact of different light and temperature conditions on scent quality during post-harvest. These two types of thermoperiod and photoperiod. We also used constant darkness and constant temperatures. We found that under conditions of 12:12 Light Dark and 15-5°C, Narcissus emitted monoterpenes and phenylpropanoids. Increasing the temperature to 20°-10°C in a 12:12 LD cycle caused the loss of cinnamyl acetate and emission of indole. Under constant dark, there was a loss of scent complexity. Constant temperatures of 20°C caused a decrease of scent complexity that was more dramatic at 5°C, when the total number of compounds emitted decreased from thirteen to six. Distance analysis confirmed that 20°C constant temperature causes the most divergent scent profile. We found a set of four volatiles, benzyl acetate, eucalyptol, linalool, and ocimene that display a robust production under differing environmental conditions, while others were consistently dependent on light or thermoperiod. Scent emission changed significantly during the day and between different light and temperature treatments. Under a light:dark cycle and 15-5°C the maximum was detected during the light phase but this peak shifted toward night under 20-10°C. Moreover, under constant darkness the peak occurred at midnight and under constant temperature, at the end of night. Using Machine Learning we found that indole was the volatile with a highest ranking of discrimination followed by D-limonene. Our results indicate that light and temperature regimes play a critical role in scent quality. The richest scent profile is obtained by keeping flowers at 15°-5°C thermoperiod and a 12:12 Light Dark photoperiod.

Data analysis in non-targeted metabolomics is extremely time consuming. Genetic factors and environmental cues affect the composition and quantity of present metabolites i.e. the constitutive and non-constitutive metabolites. We developed gcProfileMakeR, an R package that uses standard output files from GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR produces three outputs: a core or constitutive metabolome, a second list of compounds with high quality matches that is non-constitutive and a third set of compounds with low quality matching to MS libraries. As a proof of concept, we defined the floral scent emission of Antirrhinum majus using wild type plants, the floral identity mutants deficiens and compacta as well as RNAi lines of AmLHY. Loss of petal identity was accompanied by appearance of aldehydes typical of green leaf volatile profiles. Decreased levels of AmLHY caused a major increase in volatile complexity, and activated the synthesis of benzyl acetate, absent in WT. Furthermore, some volatiles emitted in a gated fashion in WT such as methyl 3,5-dimethoxybezoate or linalool became constitutive. Using sixteen volatiles of the constitutive profile, all genotypes were classified by Machine Learning with 0% error. gcProfileMakeR may thus help define core and pan-metabolomes. It enhances the quality of data reported in metabolomic profiles as text outputs rely on CAS numbers. This is especially important for FAIR data implementation.One sentence summarygcProfileMakeR allows the automatic annotation of the core metabolome and non-constitutive metabolites, increasing speed and accuracy of non-targeted metabolomics.

The Antirrhinum genus has a considerable complexity in the scent profiles produced by different species. We have analyzed the genetic differences between A. majus and A. linkianum, two species divergent in the emission of methyl benzoate, methyl cinnamate, acetophenone, and ocimene. The genetic analysis showed that all compounds segregated in a Mendelian fashion attributable to one or two loci with simple or epistatic interactions. Several lines lacked methyl benzoate, a major Volatile Organic Compound emitted by A. majus but missing in A. linkianum. Using a candidate gene approach, we found that the BENZOIC ACID CARBOXYMETHYL TRANSFERASE from A. linkianum appeared to be a null allele as we could not detect mRNA expression. The coding region did not show significant differences that could explain the loss of expression. The intron-exon boundaries was also conserved indicating that there is no alternative splicing in A. linkianum as compared to A. majus. However, it showed multiple polymorphisms in the 5′ promoter region including two insertions, one harboring an IDLE MITE transposon with additional sequences with high homology to the PLENA locus and a second one with somewhat lower homology to the regulatory region of the VENOSA locus. It also had a 778 bp deletion as compared to the A. majus BAMT promoter region. Our results show that the differences in scent emission between A. majus and A. linkianum may be traced back to single genes involved in discrete biosynthetic reactions such as benzoic acid methylation. Thus, natural variation of this complex trait maybe the result of combinations of wild type, and loss of function alleles in different genes involved in discrete VOCs biosynthesis. Furthermore, the presence of active transposable elements in the genus may account for rapid evolution and instability, raising the possibility of adaptation to local pollinators.

The suitability of twelve (three commercial and nine local) accessions of purslane for the ready-to-eat market was evaluated by molecular, morphological, agronomical and biochemical analyses. Accessions were classified by their different seed coat characteristics using a scanning microscope. Molecular analysis was carried out using the ISSR technique with 10 primers, producing a total of 141 DNA bands, 85 of which were polymorphic among the accessions. A dendrogram drawn on the basis of a similarity matrix using the UPGMA algorithm revealed that the 60 samples of purslane plants could be classified into two major clusters at a Jaccard's genetic similarity index of 0.32, confirming that genetic variation was very high among and within accessions. An analysis of the morphological, agronomical and biochemical traits was a less efficient way of grouping the accessions due to the high inter-population variability observed for the analyzed traits used. Among all accessions, CM 13-00809 presented a high yield, a high dry matter content, high potassium level, low oxalate level and an attractive green colour of the stem and leaves, which, taken together, make it an attractive candidate for consumption as a ready-to-eat product. In addition, the variations that exist in the desired traits within and among accessions in the germplasm could give rise to a breeding programme to develop Portulaca oleracea cultivars as a high quality, minimally processed product.