Blackcurrant fruit collected at six stages of development were assessed for changes in gene expression using custom whole transcriptome microarrays and for variation in metabolite content using a combination of liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry. Principal components analysis demonstrated that fruit development could be clearly defined according to their transcript or metabolite profiles. During early developmental stages, metabolite profiles were dominated by amino acids and tannins, whilst transcript profiles were enriched in functions associated with cell division, anatomical structure morphogenesis and cell wall metabolism. During mid fruit development, fatty acids accumulated and transcript profiles were consistent with seed and embryo development. At the later stages, sugars and anthocyanins accumulated consistent with transcript profiles that were associated with secondary metabolism. Transcript data also indicated active signaling during later stages of fruit development. A targeted analysis of signaling networks revealed a dynamic activation and repression of almost 60 different transcripts encoding transcription factors across the course of fruit development, many of which have been demonstrated as pivotal to controlling such processes in other species. Transcripts associated with cytokinin and gibberellin were highly abundant at early fruit development, whilst those associated with ABA and ethylene tended to be more abundant at later stages. The data presented here provides an insight into fruit development in blackcurrant and provides a foundation for further work in the elucidation of the genetic basis of fruit quality. Ribes nigrum L.) is a perennial bush native to northern Europe, Scandinavia, and the Russian federation , inter-simple-sequence repeat (ISSR) and microsatellite (SSR) markers . The firRipening of fruit is a dynamic and complex process that involves coordinated regulation of development, physiology and metabolism, leading to the production of a soft and edible fruit with desirable quality attributes . During Ribes fruits has not been previously undertaken.A large number of studies have adopted systems approaches to the understanding of fruit development, focussing on changes in metabolite content and transcript abundance to build integrated network models and highlight the key regulators in the coordination of fruit development. Examples of such work include studies undertaken in tomato , strawbeSeveral studies have been conducted concerning the nutritional profile of ripe blackcurrant fruit in relation to quality , howeverThe motivation for the present study was to increase knowledge regarding the metabolomic and transcriptional profiles of blackcurrant fruit during development. This provides key information regarding the underlying developmental processes and adds to the growing literature concerning development of other fruit species. We further identify key developmental stages associated with the accumulation of components that influence fruit quality. The data also provides improved annotation of blackcurrant transcripts.Fruit of cultivar Ben Finlay was collected at six stages of development from plants grown at the James Hutton Institute, Dundee, United Kingdom  under standard industry regimes for fertilizer and pest control in 2012. Fruit development was visually assessed as small green (<6 mm), large green (>6 mm), green–red, red–green, red and ripe  collecteFruit phenotyping was undertaken within 1 h of harvest on fresh fruit samples. Average fruit mass was estimated as 100 berry weight and average fruit diameter measured transversally across the broadest part of thirteen fruit. Fruit dry matter was estimated in a sample of 100 fruit that were weighed before and after lyophilisation. Fruit firmness was estimated by compression using a QTS-25 Texture Analyzer  following the manufacturer’s guidelines. The analyzer was fitted with a 25 Kg load cell to drive an aluminum probe of diameter 40 mm and length 80 mm using a trigger point at 0.049 Newtons [N] and probe speed of 30 mm/min to compress the fruit to a level of 20% deformation. Two parameters were determined. Work done expressed in Joules described the total amount of energy required to compress the fruit where a higher quantity of energy is required to compress a firmer fruit. Peak load (N) described the highest amount of force required to maintain constant movement of the probe and again a firmer fruit would require a greater peak load. Total soluble solids and pH were quantified in juice prepared from the different fruit stages as previously described . Fruit c2 and then extracted in 50% (v/v) methanol containing 1% (v/v) formic acid, 2 μM reserpine and 0.5 mM morin at a ratio of 10 ml g FW-1. Sugars, organic acids, total polyphenols and total anthocyanins were analyzed as previously described  and non-polar (non-adecanoic acid methyl ester 0.2 mg mL-1) standards were added with 0.75 mL distilled H2O, and extraction continued for a further 30 min as described. 6 mL of chloroform were added, and extraction continued for 30 min under increased agitation at 2500 rpm. Phase separation was achieved by the addition of a further 1.5 mL of water and centrifugation at 1000 g for 10 min. Following oximation, polar metabolites were converted to trimethylsilyl derivatives, while non-polar metabolites were subjected to methanolysis and trimethylsilylation. Polar metabolites primarily comprised amino acids, carbohydrates and organic acids while non-polar metabolites primarily comprised fatty acids, fatty alcohols and phytosterols. Metabolite profiles for the polar and non-polar fractions were acquired following separation of compounds on a DB5-MSTM column  using a Thermo Finnigan  DSQII GC/MS system as described (Gas chromatography/mass spectrometry (GC/MS) analysis was performed on extracts from three biological replicates per developmental stage, essentially as described by escribed . Data weRNA was extracted from developing fruit using the method previously described , with th1). Briefly, ∼80 million short (75 bp paired-end) Illumina reads were generated from RNA of blackcurrant developing bud tissue (2. Orientation of contigs were determined by BLASTx homology searches against known genes/proteins in the Arabidopsis genome (TAIR v10) or the Vitis vinifera genome database3, enabling defined orientation for ∼19,000 transcripts. In addition, ∼21,000 transcripts with unknown orientation were taken forward for array probe design. Single probes (60 mers) were designed to each transcript (one for each orientation for ambiguous sequences) using default parameters in eArray4 and formatted into a 60k array design.A custom Agilent gene expression microarray was designed using 2GS RNA-derived sequence data (array design A-MEXP_2372d tissue  and asse5 (accession # E-MTAB-6973). Briefly, a single-channel microarray design was utilized with all RNA samples labeled as cRNA with Cy3 fluorescent dye. A total of 18 microarrays were processed, consisting of 3 biological replicates for each of the 6 fruit developmental stages. RNA labeling and downstream microarray processing were performed as recommended in the One-Color Microarray-Based Gene Expression Analysis protocol  using the Low Input Quick Amp Labeling kit (Agilent). Following microarray scanning using an Agilent G2505B scanner, data were extracted from images using Feature Extraction (v.10.7.3.1) software. Intensity data and QC metrics were extracted using the recommended FE protocol (GE2_107_Sep09). Entire FE datasets for each array were loaded into GeneSpring (v.7.3) software for further analysis. Data were normalized using default single-channel settings: intensity values were set to a minimum of 0.01, data from each array were normalized to the 50th percentile of all measurements on the array and the signal from each probe was subsequently normalized to the median of its value across all arrays. Unreliable data flagged as absent in 2/3 replicate samples by the FE software were discarded. Statistical filtering of data was performed using ANOVA adjusted with Benjamini and Hochberg multiple testing correction.The experimental design and complete datasets are available at ArrayExpress-1. Quantitative RT-PCR was performed using the Roche Universal Probe Library as described (Ct-ΔΔ method (Supplementary Table S1.Reverse transcription of 1000 ng of RNA per sample was performed using the QuantiTect Reverse Transcription kit (Qiagen) using a combination of oligo d(T) and random hexamers as primers according to the manufacturer’s instructions. cDNA was diluted with sterile distilled water to a concentration of 10 ng μLescribed . The reat method  normalizt method  and the t method . Averager > 0.9), and therefore all data points were included in subsequent analysis.In order to understand dynamics of and between the metabolome and transcriptome, a series of statistical analyses were performed using both sets of data. MS data was presented as response ratios calculated as areas of peaks normalized to internal standard areas for metabolites acquired. Where compounds were detected using more than one method they were observed to be highly correlated  unless stated otherwise. An ANOVA P-value < 0.005) adjusted with Benjamini and Hochberg multiple testing correction. Transcript analysis was carried out in Genespring (v. 7.3) for transcriptomic data and Genstat for metabolomic data. All transcript data were log transformed as default in Genespring prior to analysis.For transcript data, statistical filtering of data was performed using ANOVA  software at parameters of 6 clusters and 100 iterations using Pearson’s Correlation as a similarity measure.A set of transcripts obtained by ANOVA Cluster analysis was performed for several sets of transcriptome data in order to obtain groups of probes linked depending on their expression patterns. Analysis was performed using GeneSpring (v. 7.3) using similarity measure of Pearson’s correlation with clustering algorithm of average linkage.P-value < 0.05 were analyzed further using cluster analysis. The data was expressed on a standard error of difference (SED) scale from the ANOVA output. Since the SEDs express the differences scaled by their variability, this standardization allows metabolites to be compared with respect to relative change both within and across technologies. Partitioning was based on Hierarchical Cluster Analysis (HCA) with complete linkage and Euclidean distance. Clusters were defined by assessing the compounds within the most similar clusters, initial cut off was 96% similarity, however, further manual adjustment was undertaken to group compounds that were clearly clustered based on visual assessment of the dendrogram. This procedure generated clusters which share the same pattern between developmental stages as defined by significant changes rather than absolute values.Analysis of Variance (ANOVA) was performed on each individual metabolite quantified in order to identify those with significant differences in response between the developmental stages. Only significant metabolites with a K-means analysis were analyzed for enrichment of Gene Ontology (GO) terms using the Term Enrichment Tool AgriGo 1.26  obtained from microarray expression study and all 194 metabolites obtained by several quality quantifying methods. PCA can be carried out using either the sample variance-covariance matrix or the sample correlation matrix: the former focuses on the most abundant metabolites, while the latter gives equal weight to all metabolites. Transcript data was log transformed and analyzed using variance-covariance matrix using Genstat. Metabolite data was checked for normal distribution and analyzed using a sample correlation matrix.Principal component analysis (PCA) was performed using the 1433 differentially expressed transcripts . Fruit fresh weight increased in a linear fashion from the small green  up to the green–red stage (77 DAF). This was followed by a pause in fresh weight gain to the red–green stage (89 days after flowering) then a rapid increase in fresh weight gain to the red fruit stage (95 days after flowering) and a more gradual increase to maturity (125 days after flowering). A similar pattern was observed in dry weight gain although there was a less pronounced cessation and subsequent increase in dry weight gain around the green–red to red stages of fruit development . Dry matter estimated in a sample of 100 fruit varied between 15% (w/w) in green–red fruit and 18% (w/w) in ripe fruit . Fruit diameter increased almost linearly between small green and green–red stage (77 days after flowering) after which only minor increases in fruit size were observed . Similarly, fruit firmness increased to the green–red stage after which there was a decline until fruit ripened .Fruit development took a total of 125 days from open flowers to ripe fruit and occurred over the period March 29– August 1, 2012. Fruit were collected at six developmental stages that cover the major developmental transitions . Juice soluble solids were constant in green fruit before exhibiting an increase to green–red fruit followed by a further decline and then an increase to a maximum in ripe fruit . Juice pH exhibited a steady decline to the red–green stage after which it was approximately constant around pH 3.25 .Consistent with the change from green to red to ripe fruit, a decline in photosynthetic pigment content was observed throughout development although small amounts of both chlorophyll a and chlorophyll b were still present in ripe fruit (P < 0.005) differentially expressed transcripts, or a broad range of primary and secondary metabolites including sugars, organic acids, amino acids, fatty acids, flavonols, and anthocyanins. For transcripts, the first two principal components accounted for 67 and 16%, respectively, of the total variation within the data and clearly separated individual fruit stages as well as clustering replicates of any one fruit stage . Metabolite profiles similarly discriminated fruit developmental stages and the first two principal components accounted for 38 and 18% of the variation, respectively . Green–red, red–green, and red fruit stages clustered more closely on the metabolite PCA plot  than on the transcript plot  indicating that metabolite profiles were more similar than transcript profiles in these fruit developmental stages. Nevertheless, the data presented here validates our fruit sampling protocol and indicates key differences in transcript and metabolite profiles at different stages of development.Principal components analysis was used to group fruit samples according to the profile of 1432 highly significant . Six clusters  were comprised of compounds that exhibited an overall decline in concentration during fruit maturation, while five clusters  were comprised of compounds that exhibited an overall increase in concentration during development. One cluster  was composed of compounds that were most abundant during mid-fruit ripening stages before falling back to levels similar to those in small green fruit in ripe fruit.To obtain an overview of the phytochemical changes that occur during fruit ripening, we used a combination of analytical techniques to examine the relative or absolute concentrations of a range of compounds that are relevant to fruit quality. Compounds quantified included sugars, organic acids, fatty acids and fatty alcohols, amino acids, flavonoids and anthocyanins. Initial data analysis was achieved by hierarchical clustering. Compounds that exhibited similar changes in concentration profile across development were grouped and 12 clusters (A–L) were identified (Supplementary Table S2). Clusters A and B, which were composed of compounds that exhibited a concentration peak in large green fruit, were dominated by the presence of amino acids and other amines. These included a range of plastid synthesized amino acids such as methionine, lysine, threonine, isoleucine, and phenylalanine (Supplementary Table S2). Cluster C, comprising compounds that were most abundant at the small green stage, contained a number of tannins and phenolic acids such as chlorogenic and cinnamic acids (Supplementary Table S2), while cluster D, that contained compounds that also showed a decline in abundance from small green fruit to ripening, also contained an epi-gallocatechin tannin, the polyphenol myrecetin-hexoside and also included a measure of total polyphenols estimated using a specific enzymatic method (Supplementary Table S2).Analysis of the composition of the different clusters revealed that in many cases they were composed of compounds that were metabolically related   .Supplementary Table S2). The concentration of these compounds declined between small and large green fruit before rising to a plateau in green–red, red–green and red fruit and then increasing steeply in ripe fruit. Compounds in cluster I exhibited a similar pattern, however, the concentration of compounds in this cluster were already elevated in red fruit. This cluster comprised a number of long chain fatty acids (C20 – C28) that are associated with cuticular waxes and found at high concentrations in deseeded blackcurrant pomace relative to seed or oil (Supplementary Table S2). Clusters J, K, and L contained compounds that exhibited significant increases in concentration toward the latter stages of ripening. These contained the major anthocyanins delphinidin- and cyanidin-rutinoside as well as other less abundant anthocyanins  and the flavanol kaempferol-hexoside-deoxyhexoside. Furthermore, a spectrophotometric measure of total anthocyanins localized to cluster K (Supplementary Table S2).Cluster H comprised the major monosaccharides glucose and fructose, as well as the minor sugars galactose and mannose (d or oil  and a nuP < 0.005 (Supplementary Table S3). Transcripts were clustered using the K-means algorithm producing six clusters exhibiting similar expression profiles . Clusters A, B, and C containing 241, 256, and 273 transcripts, respectively, exhibited a decline in transcript abundance over development. Transcripts in cluster D (141 transcripts) were present at low abundance in small green and ripe fruit, but were more abundant during mid-fruit development. Cluster E (209 transcripts) comprised transcripts that were least abundant at the large green and green–red stages, while transcripts in cluster F (312 transcripts) exhibited an increasing trend in abundance throughout the developmental period.To obtain an overview of significant changes in gene expression during fruit development we identified 1433 differentially expressed transcripts as estimated by analysis of variance, Supplementary Table S4). Cluster A contained 135 transcripts that had high confidence Arabidopsis homologs. Consistent with active cell division at the early stages of fruit development, transcripts associated with the cell cycle (GO:0007049) were the most significantly enriched in this cluster, with a little over 10% of the transcripts associated with this function compared with less than 1% of genes in the Arabidopsis genome being associated with the same function . These included transcripts  with homology to a range of cyclins  and cyclin dependent protein kinases . Consistent with active cell division, GO terms associated with DNA replication (GO:0006260) were also highly significantly overrepresented within this cluster and included transcripts with homology to Arabidopsis transcripts encoding MINICHROMOSOME MAINTENANCE 10 (comp12886/at2g20980) required for the initiation of DNA replication and TELOMERASE REVERSE TRANSCRIPTASE (comp20703/at5g16850).As a first interrogation of transcript data, GO term enrichment analysis was conducted on annotated transcripts present in the different clusters (Supplementary Table S4), where approximately 7% of significant annotated transcripts in this cluster were associated, compared with only 1.5% of genes in the Arabidopsis genome . These included a number of transcripts  with homology to Arabidopsis genes involved in red light signal transduction or response . Several of these genes are also known to be involved in auxin signaling and response (Supplementary Table S4). Other significant GO terms included cellular developmental processes (GO:0048869) and anatomical structure morphogenesis (GO:0009653). Included in the former were genes homologous to those encoding expansins (comp9500/at4g38400 and comp1051/at4g28250), as well as those required for pollen (comp6131/at2g19070) or embryo development (comp339/at5g23940). The latter included a MADS box transcription factor (comp7052) with homology to Arabidopsis AGL7 (at1g69120) with roles in floral meristem identity and the regulation of cytokinin levels , with more than 4.5% of the genes within this cluster associated, compared with less than 0.5% of genes within the Arabidopsis genome . These included genes encoding proteins with functions in cell wall loosening, such as expansins , pectate lyases  and a xyloglucan-xyloglucosyl transferase (comp55230/at1g10550). Another GO term that was significantly enriched was associated with enzyme linked receptor protein signaling pathway (GO:0007167) that was 10-fold more represented in cluster C transcripts than in the Arabidopsis genome . A role for various hormones in early fruit development was also implied by the finding that transcripts associated with response to hormone stimulus (GO:0009725) were significantly over-represented within cluster C . These included transcripts with roles in signaling or response to multiple hormones. Of particular interest was comp20486 with homology to Arabidopsis RGA-like 1 a DELLA protein known to interact with gibberellin receptors during fruit set . For example, 20% of the annotated transcripts were associated with anatomical structure development . A number of these transcripts were specifically associated with seed development and lipid accumulation and included a transcript (comp17985) with homology to oleosin 2 (at5g40420), a major protein found in seed oil bodies . In addition, cluster E was enriched in transcripts suggesting active metabolism, transport and compartmentation within the fruit. For example, transcripts associated with the biosynthesis of aromatic compounds (GO:0019438) and amino acid derivatives (GO:0042398) were significantly over represented. Transcripts associated with transport (GO:006810), specifically ATPase coupled transport (GO:0042686), were also significantly over represented.The most significantly overrepresented GO terms in Cluster E were associated with responses to stimuli, including both external stimuli (GO:0009605) such as wounding (GO:0009611) and endogenous stimuli (GO:0009719), including auxin (GO:0009733) and other hormones . Transcripts encoded a range of kinase classes, including leucine-rich repeat, as well as cysteine rich receptor-like kinases, calcium dependent kinases and a number of G-type lectin receptor-like kinases. One of the Ca-dependent protein kinases identified (comp27921) exhibited homology to CALCIUM DEPENDENT PROTEIN KINASE 1 (at5g04870) that is known to phosphorylate phenylalanine ammonia lyase, a key rate limiting enzyme of phenylpropanoid biosynthesis  and specifically protein phosphorylation  that are becoming increasingly recognized as essential for transport of flavonoids into the vacuole  and included 5 transcripts specifically associated with phenylpropanoid metabolism (GO:0009698). Interestingly, this group contained several transcripts  with homology to Arabidopsis glutathione  vacuole . The remSupplementary Table S5) which were subjected to cluster analysis performed in Genespring. Homologs grouped into four distinctive groups with expression peaking during mid-fruit ripening , during early fruit development (cluster B), at the later stages of development (cluster C) or at both early and late stages of development with reduced transcript abundance during the middle developmental stages (cluster D).In order to gain an insight of networks controlling fruit ripening, expression of transcription factor homologs from grape and Arabidopsis databases were investigated. Using this method we identified a total of 57 transcription factor homologs  were homologous to Arabidopsis transcripts encoding transcription factors controlling developmental functions such as mitosis, cell proliferation and differentiation, fruit set and development, and seed development. Interestingly, several of these transcription factors were associated with the process of endoreduplication, where chromosomal division and mitosis are uncoupled resulting in an increase in cell ploidy. Increased ploidy is commonly correlated with increased cell size , SHINE2 (at5g11190), and the MYB16 transcription factor (at5g15310) work in a coordinated fashion to induce genes required for cuticle synthesis  and homoWOX8 and WOX9 homeobox genes that are required for early embryo patterning and wrky2 mutants exhibit distorted embryo development . Arabidopsis lines containing a mutation in MEE68 exhibit an arrest of embryo development after a few cell divisions . Similarelopment . Severalelopment . A zinc elopment . Anotherelopment .Figure 6). A small number of transcripts with homology to transcription factors involved in developmental control were present in this group  although their Arabidopsis homologs  have not been reported to be directly involved in fruit development . The eight transcription factors identified in this group exhibited diverse functions, however, several transcription factors in group D had roles in cell fate and development. These included comp19178 and comp13730 that were homologous to Arabidopsis WOX4 (at1g46480) and WRKY23 (at2g47260), respectively. WOX4 has a function in the definition of the vascular stem cell niche . This transcription factor exhibits homology to an Arabidopsis tandem zinc finger transcription factor (at1g66810) that has roles in cell elongation via promotion of the expression of genes required for secondary cell wall biosynthesis and modification . These included three transcripts with homology to cytokinin oxidases involved in the degradation of cytokinins and two transcripts associated with cytokinin response (Supplementary Table S6A). The observation that cytokinin oxidases were already very highly expressed at the small green stage of fruit development is consistent with a role for cytokinin in fruit set. Furthermore, the data supports the suggestion that in blackcurrant increases in fruit size are driven by cell expansion rather than cell division where cytokinin degradation shortly after fruit set may inhibit cell division. Transcripts with homology to the Arabidopsis cytokinin sensors histidine kinase 2 (HK2) and HK3 were more highly expressed in the later stages of fruit development . In Arabidopsis roots both HK2 and HK3 transcripts are increased by a range of abiotic stresses . Thirty transcripts were highly expressed at the small green stage of development, these included a number of auxin-induced and small auxin-upregulated RNA (SAUR) and SAUR-like transcripts, as well as auxin efflux carriers with homology to transcripts encoding Arabidopsis PIN-FORMED (PIN) proteins, such as PIN1, PIN3, PIN6, and PIN 7. Early abundant transcripts also exhibited homology to a number of Arabidopsis transcripts encoding auxin biosynthetic or metabolic proteins. Approximately 20 transcripts were highly expressed at the later stages of fruit development during fruit maturation and ripening . A small number of these transcripts had homology to Arabidopsis transcripts encoding SAUR and other auxin responsive proteins (Supplementary Table S6B). There were also a number of transcripts with homology to Arabidopsis transcription factors, such as AUXIN RESPONSE FACTOR(ARF)5 with functions in embryo development and ARF1 which has been implicated in controlling aspects of maturation and senescence.Auxin and gibberellin are also known to play an interdependent role in fruit set and early development, where auxin is able to induce gibberellin biosynthesis . We idenFigure 7C). A transcript (comp7068) with homology to an Arabidopsis transcript encoding GA REQUIRING1  required for one of the plastid localized early steps of GA biosynthesis was highly abundant at the earliest stage of fruit development and then exhibited a second peak of abundance during mid-fruit ripening as seeds were developing and maturing . Two further biosynthetic transcripts encoding GA20 oxidases showed similar but distinct patterns of expression. GA20 oxidases are present in the cytosol and catalyze several steps in the biosynthesis of the immediate precursors of the active forms of gibberellin, GA1 and GA4 . The role of ethylene in ripening has been studied extensively. In climacteric fruit a burst of ethylene biosynthesis is characteristic of the ripening process . Those included a range of ethylene receptors ETR2 (comp6471/at3g23150), EIN4 (comp7284/at3g04580), and ERS1 (comp5338/at2g40940), having functions of ethylene sensing . These included comp9407, exhibiting homology to an Arabidopsis transcript (at4g19170) encoding CAROTENOID CLEAVAGE DIOXYGENASE4 (CCD4), a carotenoid cleavage dioxygenase with close homology to the 9-cis-epoxycarotenoid cleavage dioxygenases that catalyze a key step in ABA synthesis . A second transcript, comp4279, encoding a putative biosynthetic protein had homology to an Arabidopsis transcript encoding ABA DEFICIENT2  encoding an ABA aldehyde oxidase required for the final step of ABA biosynthesis .Alongside ethylene, ABA is a key regulator of fruit ripening in both climacteric and non-climacteric fruits with roles in the control of sugar accumulation, flavonoid biosynthesis and cell wall softening . Increasynthesis . In Arabynthesis  consisteynthesis . This trFigure 7E).Several transcripts encoding proteins required for ABA signal transduction were found to be significantly differentially abundant during fruit development. Two transcripts encoding putative PP2Cs, negative regulators of ABA signaling , were siFigure 7F and Supplementary Table S6F). Brassinosteroids play an important role in aspects of plant physiological development including cell elongation, division, vascular differentiation and flowering (Supplementary Table S6F). BR6OX2 might be particularly important as it has been demonstrated that VvBR6OX1 had a significant role in promotion of ripening in grape  included 11 blackcurrant probes homologous to Arabidopsis proteins associated with jasmonate biosynthesis and signaling. Jasmonate regulates a wide range of processes including plant development such as flowering, tuberization and fruit ripening, and response to environmental factors such as pest and pathogen attack, drought and wounding , has direct involvement in plant growth, flower induction, and uptake of ions, and it has a plant immune signaling function under pathogen challenge. However, it also affects ethylene biosynthesis and enhancement of plant photosynthetic pigments such as chlorophyll and carotenoids  . Each gene represented a divergent expression pattern of one K-means cluster  which are presented in the same order . Transcript abundance patterns were calculated along the course of berry development. The geNorm software indicated that UBC and TIP41-like reference genes were the most suitable for accurate normalization for blackcurrant. Comparison of gene expression profiles between microarray data and real-time qRT-PCR presented in Supplementary Figure S3 indicated clear correspondence of the two independent expression technologies.In order to validate the expression profiles generated using the microarray platform for Supplementary Figure S4). In the middle stages of fruit development, ascorbic acid is accumulated, seed fatty acids are formed, color appears and softening begins, indicating the beginning of a switch from growth to ripening stage . On ripening, major primary  and secondary metabolites are formed, coupled with further fruit softening and development of rich dark color due to anthocyanin biosynthesis, all in order to aid fruit attractiveness and seed dispersal . Overall, the data indicate the magnitude of the changes in fruit phytochemistry throughout blackcurrant fruit development. As such they indicate the utility of ripening fruit as a model for the association of gene expression with the accumulation of specific fruit phytochemicals providing opportunities for the functional characterization of gene products. Transcriptome analysis of developing blackcurrant fruit has provided insight into processes linked to signaling and regulation in fruit ripening. Major patterns of gene expression have been identified and suggest the presence of diverse processes taking part throughout different stages of fruit development. Gene ontology analysis has emphasized this observation by providing an overview of divergent significant terms underpinning the diverse patterns of expression of significantly changing transcripts. These, similar to previously indicated patterns in phenotypic analysis of developing fruit, could be generalized into three main periods of fruit development; early fruit growth (stages 1–2), middle stages linked to further development processes (stages 3–4), and third ripening stage, linked to secondary metabolism (stages 5–6). Early fruit development was dominated by enrichment of GO terms with functions in cell processes  such as cell cycle, regulation of cell cycle and cell wall modification. This was consistent with active growth at the early stages of fruit development and may represent a key event in fleshy fruits associated with expansion processes and endoreduplication . Cytokinin regulators  and oxidases (CKX), in line with tomato . As well as regulating developmental processes at early stages of ripening, some of those transcription factors may have important downstream regulatory functions in ripening, such as FRUITFULL which has functionality in the regulation of ethylene-independent aspects of tomato and bilberry fruit ripening . This may indicate that, similar to Arabidopsis, complex cross talk and interactions among embryo transcription factors and their target processes exist, and additionally in blackcurrant those processes are significant for the middle stages of fruit development.Gene Ontology terms of transcripts highly expressed in the middle stages of fruit development (stages 3 and 4) indicated strong representation of the terms associated with post-embryonic development and development of reproductive structures (seed). The importance of seed development processes in middle stages was also underpinned by the presence of transcription factors encoding proteins homologous to those with functions in seed and embryo development such as ZF-HD – MATERNAL EMBRYO ARREST, WRKY2 – where mutants manifest distorted embryo growth  and FUS3Figure 8). This is similar to grape and pepper, and may provide evidence that the ethylene-mediated signaling pathway is active during ripening of those non-climacteric fruits . The importance of ABA in fruit maturation is studied, for example, in some traits linked to the process such as color change. It has been indicated that in grape and strawberry, color change is strongly regulated by abscisic acid  prevailed. Also, homologs associated with jasmonate (at1g55020) and ethylene (ACC synthase) were upregulated. In hormone-targeted analysis abundance of ethylene related homologs was indeed recognized. This was seen by the presence of several ethylene receptor homologs  and was conceived and executed solely by JHI and James Hutton Limited staff. The manuscript was written without editorial comment from LRS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Lucozade Ribena Suntory (LRS) support a blackcurrant breeding programme at the James Hutton Institute for the development of cultivars to be used in the production of the blackcurrant cordial Ribena


However, visual assessment of the characteristic AD hypometabolic pattern relies on the expertise of the reader. Therefore, user-independent pipelines are preferred to evaluate the images and to classify the subjects. Moreover, glucose consumption is highly correlated with cerebral perfusion. Regional cerebral blood flow (rCBF) images can be derived from dynamic 11C-labelled Pittsburgh Compound B PET scans, which are also used for the assessment of the deposition of amyloid-β plaques on the brain, a fundamental characteristic of AD. The aim of this study was to explore whether these rCBF PIB images could be used for diagnostic purposes through the PMOD Alzheimer’s Discrimination Tool.In clinical practice, visual assessment of glucose metabolism images is often used for the diagnosis of Alzheimer’s disease (AD) through 2-[R1) and early PIB (ePIB) (20–130 s) uptake presented a good correlation when compared to FDG standardized uptake value ratio (SUVR), while ePIB (1–8 min) showed a worse correlation. All receiver operating characteristic curves exhibited a similar shape, with high area under the curve values, and no statistically significant differences were found between curves. However, R1 and ePIB (1–8 min) had the highest sensitivity, while FDG SUVR had the highest specificity.Both tracer relative cerebral flow  contains supplementary material, which is available to authorized users. This radiotracer evaluates the metabolism in the brain by measuring glucose consumption, allowing for the recognition of specific disease patterns. AD is characterized by a hypometabolic pattern that includes regions such as the precuneus, posterior cingulate cortex, posterior temporoparietal cortex, and medial temporal lobe  of all methods per group. (DOCX 14 kb)


Lupus glomerulopathies are classified into various histological patterns, which probably result from different pathophysiological origins. Podocyte injury can be demonstrated in lupus nephritis but its clinical relevance is far little appreciated and is often masked by proliferative lesions and inflammatory cell infiltrations. Two patterns of podocyte lesions may be considered, either occurring in the context of renal inflammation or reflecting podocyte dysfunction in non-proliferative and non-inflammatory glomerulopathies. This distinction remains elusive since no reliable biomarker discriminates between both entities. CMIP was recently found induced in some glomerular disease but its expression in different lupus nephritis classes has not been investigated. Twenty-four adult patients with lupus nephritis, including non-proliferative (n = 11) and proliferative (n = 13) glomerulopathies were analyzed. Clinical, biological and immunological data were compared with immunomorphological findings. We analyzed by quantitative and qualitative methods the expression of CMIP in different histological classes. We found CMIP abundance selectively increased in podocytes in class II and class V glomerulopathies, while in proliferative forms (class III and class IV), CMIP was rarely detected. CMIP was not expressed in cellular crescents, endothelial cells or mesangial cells. CMIP colocalized with some subsets of B and T cells within glomerular or interstitial mononuclear cell infiltrates but never with macrophages. Hematuria is rarely present in lupus glomerulopathies expressing CMIP. There was no correlation between classical immunological markers and CMIP expression. Thus, CMIP induction in lupus nephritis seems restricted to non-proliferative glomerulopathies and may define a specific pattern of podocyte injury. Systemic lupus erythematosous (SLE) is a chronic immune complex-mediated disease characterized by a disseminated inflammatory disease, which may affect multiple organs, including the kidney . The autThe pathogenesis processes underlying each type of histological lesion remain unclear . Given tThe prevalence of podocyte disease in SLE is not well known, neither its impact on the disease course. Nephrotic syndrome is usually thought to occur in SLE patients in association with immune aggregate deposition on the glomerular capillary wall, frequently accompanied by either endocapillary proliferation or necrosis. However, it can be observed in the absence of immune complex deposits on peripheral capillary walls. Such cases, although uncommon, have been described in association with mesangial lupus nephritis , exhibit foot process effacement and are considered as typical podocyte diseases like MCNS or focal glomerulosclerosis –8.CMIP (Cmaf-inducing protein) is a recently identified gene that encodes an 86 kDa protein. In physiological situations, CMIP is repressed by both WT1 and NF-kB, two major transcription factors in podocytes, which may account for its low levels or non-detection in normal glomeruli [on in vitro and in vivo studies suggest that CMIP induces podocyte signaling disorders and inhibits remodeling of cytoskeleton contributing to podocyte damages [lomeruli , 10. Evi damages –15.In the present work, we aimed to study whether CMIP could be expressed in lupus nephritis, and to determine whether its expression could be correlated with a particular pattern of lupus nephritis.All patients analyzed in this study met the criteria for SLE diagnosis based on clinical and laboratory analyses. Kidney biopsy was performed at the time of the onset of renal disease, except in four patients . Diagnosis of kidney disease was established by renal biopsy and pathological findings were categorized according to the ISN/RPN classification . ControlHuman kidney biopsies were processed for immunohistochemistry and immunofluorescence studies as described elsewhere . HistoloBecause many infiltrating cells express CMIP in some biopsies (classes III and IV), it was not possible to accurately quantify the abundance of CMIP transcript in podocytes on whole tissue biopsies, which required glomeruli isolation by laser capture microdissection. A series of 4 μm-thick sections were cut from frozen renal biopsy specimens using a Leica CM3050 cryostat at -20°C, and mounted onto slides coated with a thermoplastic membrane . Normal renal samples were supplied by our Department of Pathology, from patients undergoing nephrectomy for a polar kidney tumor and were considered as normal by pathologists. Cresyl violet staining was performed using a protocol from Zeiss Labs, Munich, Germany . Glomeru5’-CGTGTGCCTGGCTGCCATCTACTCCTGCTATG-3’; reverse: 5’-GACAGCGTGGCTTCCTGAGACACCAGGTC-3’). The internal control consisted of PCR amplification of 18S rRNA specific primers . Samples  were amplified in a 20 μl reaction mixture containing 0.5 mM of each primer and 1 X Quantitect Sybr Green PCR mix . Real-time qPCR reactions were performed as recommended by the manufacturer, in triplicate in a Light Cycler 480 apparatus . CMIP primers amplified a 176-bp sequence. PCR was initiated by denaturation at 95°C for 15 min, followed by 32 three-step cycles . A dissociation run (95°C for 5 sec followed by 65°C for one min) was performed at the end of PCR program allowing the generation of the melting curve. PCR conditions for 18S rRNA were similar except the annealing temperature (60°C) and the cycle number (n = 20). All PCR data were normalized to 18S rRNA expression, to control for variations in RT reactions. Cycle thresholds greater than 32 were excluded from analyses. Quantification of endogenous CMIP transcript was performed by the 2ΔΔCt method using ribosomal 18S rRNA normalization and results were expressed as -fold induction over values obtained from control samples.Real time quantitative polymerase chain reaction (qPCR) was performed using CMIP specific primers . Values of P < 0.05 were considered significant.The population studied includes 24 patients who fulfilled the ARA criteria. Kidney disease was categorized as follow: class II (6 patients); class III (7 patients); class IV (6 patients) and class V (5 patients). The clinical and biological characteristics at the time of biopsy are summarized in Extra renal manifestations include arthritis and skin disease (both classes), Raynaud’s syndrome (one class IV patient), central nervous system vasculitis (one class II patient and two class IV patients), acute pericarditis (one class III patient), cardiac valvular lesions (one class III patient), hematological disorders such as anemia, thrombopenia, (one class III patient and four class IV patients) and antiphospholipid antibody syndrome (one class IV patient). One class IV-patient received steroid (prednisone 10 mg/day) and plaquenil/ Hydroxychloroquine before kidney biopsy.Class II. We analyzed 112 glomeruli corresponding to six biopsies  four biopsy specimens were analyzed totalizing 30, 58, 32 and 48 glomeruli, respectively. Quantification of endogenous CMIP transcript was expressed as -fold induction over values obtained from control samples. While no apparent difference was observed in classes III/IV relative to controls . Quantification of the relative abundance of CMIP protein was determined for each biopsy in all glomeruli, except in those that were sclerotic. We found that protein expression was significantly increased  and consistent with the transcript level  was rarely observed in patients who exhibited increased CMIP abundance in podocytes. In classes III and IV, where proliferative and inflammatory lesions are prominent, CMIP was detected neither in podocytes nor in crescents regardless of proteinuria levels. Conversely, in class II and V biopsies, significant proteinuria was correlated with a strong induction of CMIP in podocytes. These data show that CMIP expression is not correlated with proteinuria in lupus nephritis but the variability in the cellular and subcellular distribution suggests that the pathophysiological mechanisms involved in different classes of lupus nephritis are likely distinct.Hematuria , which was initiated because of extrarenal manifestations including neurolupus (one case) and severe polyarthritis (two cases). In two cases (one class II and one class III) displaying a high nuclear abundance of CMIP in podocytes, the control of renal disease required intravenous cyclophosphamide in addition to steroid therapy with a favorable outcome. The lack of CMIP expression in podocytes of proliferative classes (III/IV) was associated with more aggressive therapy, including high doses of steroids (10/13), mycophenolate mofetil (9/13), cyclophosphamide (5/13) and methotrexate (1/13). Patients with class V biopsy were treated with moderate doses of steroids  except the patient who later developed a proliferative form (IV GA), requiring cyclophosphamide and Rituximab therapies. These results suggest that CMIP expression in the podocyte does not predict the course of SLE disease.One class II patient developed pulmonary hypertension and fatal acute stroke in the context of neurolupus, while kidney disease was in complete remission, four months after the onset of proteinuria. In another class II patient, glomerular disease evolved into class V glomerulonephritis. Among other patients with class II biopsy, three had a favorable outcome, while in one patient (N° 4), kidney disease progressed into class IV-glomerulonephritis within 53 months with a good response to therapy. We did not observe any worse renal course in patients with class III biopsy. In this group, one patient developed polyarthritis, while another presented subacute cutaneous SLE lesions. One patient with class V developed diffuse global proliferative lupus nephritis (class IV GA) within 44 months after the onset of renal disease.Lupus nephritis occurs in up to 60% of affected adults during the course of their disease. In a recent study, effacement of foot podocyte processes was reported in most lupus nephritis and did not appear to prejudge any class . Two difThe participation of podocyte in crescent formation remains a matter of debate. Early studies have found that most cells forming crescents do not express any podocyte markers, including CD10, podocalyxin, synaptopodin, glomerular epithelial protein 1, podocin, CD2AP, nephrin, and Wilms tumor antigen I (WT1), suggesting that podocytes are not involved in the formation of crescents , 20. ThiRecent studies have shown that parietal epithelial cells (PEC) predominate in crescents, based on the higher cell positivity for cytokeratin-8. Although the cell origin of crescent-forming cells is difficult to establish, animal models provide an experimental cue showing that selective PEC depletion induces remaining PECs to proliferate and form crescent .+ CD133+ proliferating cells constitute the major contingent of the hyperplastic lesions whereas podocytes included in these lesions do not proliferate [Renal progenitor cells are a subset of PEC defined by co-expression of CD24 and CD133 stem cell markers, which potentially give rise to tubular and podocyte cells [liferate . Given tCMIP expression was only detected in three out of six patients with class II biopsy, who remained free of relapse during the follow-up. One CMIP-negative-class II patient developed a more aggressive course (class IV). In a series analyzing the course of nineteen patients with class II biopsy, only eight patients achieved a complete remission, while nine patients were nonresponders and developed a higher-grade nephritis . AltogetCMIP gene in podocytes. We postulate that in these proliferative forms the release of proinflammatory mediators including tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß) and interleukin-6 (IL-6) may induce NF-κB activation in podocytes, which, in return, blocks transactivation of CMIP gene [We have recently shown that CMIP prevents translocation of RelA to the nuclear compartment, which results in inhibition of NF-κB activation . We haveMIP gene .We report for the first time the expression of CMIP in lupus nephritis. Our results suggest that induction of CMIP in lupus nephritis appears to be limited to non-proliferative forms and may define a specific pattern of podocyte injury. We did not find any evident correlation between classical immunological markers and CMIP expression. However, the latter is associated with a better renal course of the disease.


PIK3R1 and PIK3CB in gonadal fat and liver respectively. In contrast, maternal obesity during pregnancy up-regulated the insulin signalling genes IRS2, PIK3CB and SREBP1-c in skeletal muscle and perirenal fat, favouring insulin sensitivity. In conclusion exposure to maternal obesity during lactation programmes offspring adiposity and insulin resistance, overriding exposure to an optimal nutritional environment in utero, which cannot be alleviated by a nutritionally balanced post-weaning diet.Exposure to maternal obesity during early development programmes adverse metabolic health in rodent offspring. We assessed the relative contributions of obesity during pregnancy and suckling on metabolic health post-weaning. Wistar rat offspring exposed to control (C) or cafeteria diet (O) during pregnancy were cross-fostered to dams on the same  or alternate diet during suckling  and weaned onto standard chow. Measures of offspring metabolic health included growth, adipose tissue mass, and 12-week glucose and insulin concentrations during an intraperitoneal glucose tolerance test (ipGTT). Exposure to maternal obesity during lactation was a driver for reduced offspring weight post-weaning, higher fasting blood glucose concentrations and greater gonadal adiposity . Males displayed insulin resistance, through slower glucose clearance despite normal circulating insulin and lower mRNA expression of  Type-2 diabetes accounts for the majority of diabetes diagnoses, with the strongest risk factor for the disease being excess body fat2. Understanding the mechanisms which lead to the disease is therefore highly important. A hallmark characteristic is loss of function of normal circulating insulin, leading to insulin resistance3. Normal regulation of insulin binding to the insulin receptor, activates a series of downstream phosphorylation events, activation of insulin receptor substrate proteins (IRS2), type 1 A phosphatidylinositol 3-kinase (PI3K), and the serine/threonine-protein kinase AKT2, respectively. This leads to reduced hepatic glucose output4 and increased glucose uptake in skeletal muscle and adipose tissue5, and lipogenesis through downstream activation of sterol regulatory element binding protein 1-c (SREBP-1c)6. Dysregulation of critical components of the insulin signalling pathway is associated with the pathogenesis of insulin resistance and hyperglycaemia9.Annually, 5 million deaths and $673 billion in global healthcare expenditure are attributed to diabetes, with the prevalence predicted to rise substantially to over 10% of the world’s population by 204015. For example, male offspring of dams fed a high-fat diet throughout pregnancy and lactation, manifest reduced adipose tissue expression of the key genes within the insulin signalling pathway including insulin receptor, IRS1, p85α and P110ß  and AKT216, reduced hepatic expression of P85α17 and IRS118 and increased AKT expression in skeletal muscle19. To attempt to tease apart the important critical windows of programming, some studies have used cross-fostering, providing insight in to the effects of exposure during suckling alone on offspring metabolism21. However, high-fat diets often use large quantities of a narrow range of fatty acids and are less valuable as models of maternal obesity that reflect varied human overnutrition22.Small animal models of maternal obesity have provided robust evidence to suggest that metabolic disease can be programmed by adverse maternal nutrition during early-life, including abnormal glucose homeostasis and insulin signalling in offspring, insulin resistance, and obesity24. Many cafeteria diet protocols are still limited by the scope of variation in highly palatable foods offered to the animals, frequently with a panel of 6–12 foods26. We have developed a more varied protocol, retaining the effect of exposure to novel, palatable items and maximising the obesogenic impact. Using this approach, we demonstrated that cross-fostering was effective in separating the prenatal and postnatal effects of maternal obesity on rat offspring weight and adiposity pre-weaning, with exposure during suckling associated with greater adiposity in 2-week-old male offspring24. The aim of this present study was to establish the relative contributions of maternal obesity during pregnancy and lactation on offspring growth, adiposity, glucose tolerance and insulin resistance post-weaning.We have previously discussed the advantages of the cafeteria diet used as a model of maternal obesity in rats, importantly that it more closely reflects maternal obesity in humansP < 0.05). However, exposure to maternal obesity during lactation was associated with lighter offspring weight, which was more evident in females, with CO and OO offspring demonstrating lighter weights up to week 6 (P < 0.05). Offspring from CO, OC, and OO groups demonstrated some evidence of catch up growth thereafter through greater weight gain (P < 0.05). Fluctuations were seen in male offspring weights of the OO and OC groups, demonstrated by lighter weights at week eleven, compared to offspring exposed to a control diet during this period (P < 0.05). By week 12, there was no significant influence of maternal treatment on offspring weight.The effects of exposure to maternal obesity both during gestation and lactation on male and female offspring growth is demonstrated in Fig. P < 0.05). 12-week-old female offspring exposed to maternal obesity during lactation had 26% greater gonadal adiposity than offspring exposed to a control diet during this period (P = 0.030), with OO offspring demonstrating almost 30% greater adiposity compared to CC. No differences were seen between other dietary groups or at 4 weeks of age.Offspring gonadal and perirenal adipose fat pad mass was measured at 4 and 12 weeks of age (Table P = 0.031)   . Although CO and OO male offspring demonstrated elevated areas under the glucose curves (AUC), these differences did not achieve statistical significance  had significantly higher fasting blood glucose concentrations compared to offspring exposed to a control diet during this period (31) Fig. . Offspri47) Fig. . In fema24) Fig. . Beyond nce Fig. .Figure 2P < 0.05). As only male offspring exposed to maternal obesity during lactation demonstrated an altered glucose tolerance, measurements of insulin signalling mRNA expression focused on male rather than female tissues.There were no significant differences in plasma insulin concentrations between groups of offspring Fig. . Male ofPIK3CB mRNA expression in CO and OC male offspring by 37% and 23% respectively .Liver is an important tissue involved in the regulation of glucose homeostasis, therefore the mRNA expression of a panel of genes was measured to investigate if maternal diet during pregnancy or lactation disturbed this process. Liver mRNA expression was measured in male offspring at 4 and 12 weeks of age. Supplementary Table ely Fig.  . 4-week-old offspring demonstrated greater mRNA expression of IRS2 and PIK3R1 than at 12 weeks (P < 0.05). No differences were seen between dietary groups for the other target genes at 4 and 12 weeks . These effects were not seen in 12-week-old male offspring. SREBP-1c mRNA expression was 33% higher in offspring exposed to maternal obesity before birth across both age groups, compared to offspring exposed to a control diet during this period . No differences were seen between dietary groups for the other target genes at 4 and 12 weeks . Although not significant, there was a trend of reduced AKT2 mRNA expression in 4-week-old offspring (P = 0.056), and reduced IRS2 mRNA expression in 12-week-old offspring (P = 0.052), which was associated with maternal obesity exposure during lactation .Whilst perirenal adipose tissue signalling demonstrated an increase in insulin signalling mRNA expression associated with exposure to maternal obesity during gestation, gonadal adipose tissue demonstrated the opposite effect, highlighting a difference between the two depots. Supplementary Table 24. Using this approach, we identified that a highly varied, true cafeteria diet, comprising of 40 different food items, induced maternal obesity in female rats and exposure to maternal obesity during pregnancy was associated with reduced fetal growth, whereas exposure during lactation was associated with greater male offspring adiposity at 2 weeks of age24. Consistent with evidence from previous cross-fostering studies, these current findings show that exposure to maternal obesity during lactation was the main driver for significantly lighter weights from weaning27 and adverse metabolic function in offspring21. The findings also confirm that male offspring appear more susceptible to insulin resistance28 and females to adiposity in adulthood29. However, the present study was important because it assessed the effect of exposure to maternal obesity, induced through a highly varied cafeteria diet, rather than diets high in specific fatty acids or sugars, during critical windows of development.This study utilised a novel cafeteria-feeding regimen previously developed by the authors to maximise maternal obesity to assess the relative effects of maternal overfeeding during pregnancy and lactation on offspring growth, adiposity and metabolic functionin utero exposure. It cannot be ruled out that the lower protein composition of the maternal cafeteria diet contributed to the offspring phenotypes observed but it appears to have no effect on offspring stomach protein content or milk composition at birth30. Instead, higher milk concentrations of a range of nutrients are believed to influence offspring development31. This includes milk concentrations of the satiety adipokine leptin32, an important mediator of peripheral insulin sensitivity33. This warrants future investigation. Previous research provides some evidence to suggest maternal obesity, rather than the cafeteria diet itself, can exert independent programming effects on offspring growth and metabolism34. However, cross fostering to separate effects of maternal obesity and cafeteria diet on the phenotype of the offspring in the current study has shown it is unlikely that maternal obesity alone programmes postnatal growth and development.Only postnatal exposure to maternal obesity led to adiposity and insulin resistance, and was the main driver for reduced weights post-weaning, suggesting that the milk composition of the dams is key to these programming effects, which are independent of 37. Female offspring exposed to maternal obesity during lactation did display evidence of greater gonadal adiposity in adulthood. We have previously reported that 2 week-old male offspring exposed to maternal obesity during lactation alone demonstrated elevated perirenal adiposity24, but the present findings highlight this was lost by adulthood. This early transient visceral obesity phenotype has also been demonstrated in previous research27. These findings suggest that weaning onto a nutritionally balanced chow diet may alleviate the programming effects of reduced growth, and fat deposition in males but not females. Previous research also suggests that female offspring may be further susceptible to diet-induced obesity38. Further research would need to identify why these depot and sex-specific programming effects occur, such as the interaction of adverse maternal nutrition and offspring sex-hormones as well as the epigenome41. The mechanisms of elevated adiposity may be due to programming of adipocyte size, development, and pre-adipocyte differentiation42 with altered gene expression mediating these effects27.Smaller offspring, particularly females, caught up in weight in the later stages of life. This was evidenced by increased weight gain and lack of significance between groups. However, growth restriction in early-life followed by catch-up growth is associated with obesity and insulin resistance43. However, offspring did not demonstrate impaired glucose tolerance (IGT), defined in humans as raised blood glucose levels, two-hours following an oral GTT43. Glucose concentrations two-hours post-injection were below baseline values, which may be because animals were fasted overnight, as this can enhance insulin stimulated glucose uptake45. This indicates whilst offspring demonstrated hyperglycaemia and a pre-diabetic state, they did not appear clinically glucose intolerant. Male offspring exposed to maternal obesity during lactation did appear more susceptible to altered glucose homeostasis demonstrated by significantly greater peak glucose levels and slower clearance of the glucose load compared to offspring exposed to a control diet. However, no changes in insulin concentrations were observed between groups.To the best of our knowledge, this is the only study to investigate glucose handling using the cafeteria diet in combination with cross fostering. As a consequence, glucose rather than insulin tolerance tests were undertaken to gain a physiological overview of any changes in glucose homeostasis resulting from exposure to cafeteria diet, rather than identification of the detailed mechanisms behind any resulting phenotype. At 12 weeks of age, both male and female offspring exposed to maternal obesity during lactation demonstrated elevated fasting blood glucose levels compared to offspring exposed to a control diet. Although there is no classification in rats, this would indicate impaired fasting glucose (IFG) in humans46. Therefore, for future experiments extra time-points could be considered. However, in the early-stages of insulin resistance, blood glucose concentrations are elevated despite normal circulating insulin concentrations5, which is due to a suboptimal biological response to insulin and failure to transmit the insulin signal8. This suggests that male offspring exposed to maternal obesity during lactation displayed early signs of insulin resistance. Further investigation was carried out to establish the potential mechanisms of insulin resistance in male offspring, through the investigation of mRNA expression of key insulin signalling genes within the target tissues, that is liver, skeletal muscle and adipose tissue. There were clear differences between exposure to maternal obesity before birth and during lactation, with a gene expression profile favouring insulin resistance in offspring exposed to maternal obesity during lactation.There were only minor fluctuations in insulin concentrations throughout the time-course studied, but a peak in plasma insulin levels would usually be observedPIK3R1 mRNA expression was lower in gonadal adipose tissue of male offspring exposed to maternal obesity during lactation across the lifespan. PIK3R1 and PIK3CB genes code for the p85α regulatory and p110ß catalytic subunits of PI3K respectively, an important mediator of insulin signalling which activates downstream targets of the pathway, including AKT2, leading to glucose uptake47. Lower p85α protein expression in adipose tissue is associated with maternal high-fat feeding16, and maternal undernutrition during early-life48, suggesting it is a particular target for programming by adverse maternal nutrition. Male offspring exposed to maternal obesity during suckling also displayed a minor effect of lower gonadal mRNA expression of AKT2 at 4 weeks, and IRS2 at 12 weeks, an upstream activator of PI3K. Whilst white adipose tissue only accounts for a small amount of glucose uptake, it is susceptible to insulin resistance due to adverse maternal nutrition in offspring49. One limitation of the current study was that we only considered mRNA and not protein expression. If verified at the protein level the observed changes in insulin signalling may partly explain greater glucose concentrations in this group offspring.PIK3CB across the post-weaning lifespan. For OC offspring, this may be evidence of metabolic disturbance and abnormal glucose homeostasis may be present later in life50. Whereas CO offspring demonstrated a greater glucose response than other groups, suggesting the hepatic insulin resistance could be additive to the gonadal insulin resistance. In skeletal muscle and perirenal adipose tissue, cross-fostering highlighted the opposite effect in offspring exposed to maternal obesity during gestation, suggesting insulin sensitivity. In skeletal muscle, this was associated with greater mRNA expression of PIK3CB in OC offspring at 12 weeks and of IRS2 in OC and OO offspring across the lifespan. This may explain why male OC offspring demonstrated similar baseline blood glucose concentrations and AUC to CC, as well as lower peak concentrations, given that skeletal muscle is the major tissue involved in glucose uptake. Maternal obesity during gestation was also associated with greater perirenal adipose tissue mRNA expression of AKT2, and PIK3CB at 4 weeks, but these effects were lost at 12 weeks, whereas SREBP-1c was greater across the lifespan. This corresponds with findings that showed male offspring exposed to high-fat feeding before birth had elevated expression of SREBP-1c and the enhanced insulin signalling, thought to drive lipogenesis51. This suggests a profile favouring enhanced adiposity in male offspring exposed to maternal obesity in utero, which although was not seen by 12 weeks, may be seen later in life. In addition, early-insulin sensitivity could give rise to insulin resistance later in life, as seen in offspring exposed to low protein diets in utero who tend to demonstrate insulin resistance after one year of age52. Further study would be required to assess if hyperinsulinemia and insulin resistance was observed in older offspring. This research has highlighted a difference in response between the two visceral fat depots, supporting previous studies that suggest they are programmed differently dependent on the maternal nutritional insult and its timing53. Further research is required to understand why these programming mechanisms occur, such as the potential for epigenetic modifications throughout the lifespan54. However, our previous studies have suggested a limited role for DNA methylation in determining gene expression in offspring of cafeteria diet fed rats55.Male CO and OC offspring displayed signs of hepatic insulin resistance, through lower mRNA expression of in utero exposure to a normal maternal environment in programming for offspring obesity in females and insulin resistance across the lifespan in males, which cannot be alleviated by weaning onto a nutritionally balanced post-weaning diet. In contrast, exposure to maternal obesity during pregnancy was associated with insulin sensitivity in male offspring. This would warrant future investigation to understand the mechanisms behind the observed phenotypes and if altered insulin signalling is also seen in females, as well as the role of the epigenome. Whilst caution should be made in extrapolating these findings to humans, understanding the metabolic mechanisms of obesity and insulin resistance offers the potential target for nutritional intervention during breastfeeding.In summary, these findings highlight that exposure to maternal obesity during lactation can override genetics and 24. All animal procedures were performed in accordance with the Animals (Scientific Procedures) Act 1986 under Home Office licence and were approved by the Animal Ethics Committee of the University of Nottingham, UK. Briefly, animals were subjected to a controlled 12-hour light, 12-hour dark cycle, in conditions at 20–22 °C and 45% humidity. They were housed in plastic cages with wood-shavings and environmental stimuli and had ad libitum access to food and water. Virgin female Wistar rats , approximately 4-weeks-old  were randomly allocated to be fed either a control chow diet   or a cafeteria diet . The cafeteria diet consisted of a range of 40 highly palatable, energy-rich human foods, accompanying a chow diet. Animals were fed their respective diets for 8 weeks prior to mating through to weaning of offspring. At mating, all rats were housed with Wistar stud males and mating confirmed by the appearance of a semen plug. All offspring were cross-fostered within 96 hours of birth. Four groups of cross-fostered offspring were generated: offspring exposed to a chow diet during pregnancy cross-fostered to a chow-fed dam during lactation  or a cafeteria-fed dam , offspring exposed to a cafeteria diet during pregnancy cross-fostered to a chow-fed dam during lactation  or a cafeteria-fed dam . 3-week-old offspring were then weaned onto a chow diet and group housed with rats of the same sex. Offspring were weighed weekly (+/−3 days). Data in this paper focuses only on the outcomes for offspring aged 3–12 weeks. Data on the pregnant and lactating dams and the pre-weaning offspring are presented elsewhere24. Importantly, the dams to the offspring presented in this study consumed considerably more energy, fat, total carbohydrate and sugar and were heavier throughout pregnancy and lactation when fed the cafeteria diet. Relative to the control diet-fed dams adipose tissue mass was markedly greater, indicating marked obesity.Animal procedures including maternal feeding and cross-fostering have been previously reported56. Anaesthetic cream was applied to the tail 30 minutes before a baseline fasting blood glucose concentration was obtained by collecting a small blood sample from the superficial tail vein by removing a small section away from the tail tip using a blade. After 5 minutes, a glucose solution  was administered via an intraperitoneal injection . At 10, 30, 60, and 120 minutes post-glucose administration, blood was sampled by removing the scab from the tail tip and blood glucose concentration was determined instantly using a SD Codefree Blood Glucose Monitor and testing strips (SD Biosensor). Offspring were then culled, and tissue and plasma collected. Blood samples were collected in duplicate at each time point.At 12 weeks of age (+/−3 days), one male and one female per litter were fasted for approximately 16 hours overnight before determination of glucose tolerance using an intraperitoneal glucose tolerance test (ipGTT). The fasted state is argued to be advantageous for measuring glucose utilization2 asphyxia and cervical dislocation. Blood was collected in 1.3 mL EDTA tubes  during the ipGTT and post-cull using cardiac puncture, and was stored on ice until plasma could be separated on the same day by centrifugation for 10 min at 14,000 rpm. Plasma was removed from haematocrit using a pipette and stored in 1.5 mL tubes at −80 °C. Liver, gastrocnemius muscle, perirenal and gonadal adipose tissue was collected at 4 weeks of age (+/−3 days), from one male and one female (where possible). Tissue was collected from all remaining offspring at 12 weeks of age (+/−3 days). Gonadal and perirenal adipose tissue was weighed for analysis of adiposity. All tissue collected throughout the study was stored in 1.5 mL tubes snap frozen in liquid nitrogen before being stored at −80 °C. In preparation for RNA extraction, frozen tissue was then crushed in liquid nitrogen.Animals aged 4 or 12 weeks were culled using COPlasma samples from baseline, 30 minutes and 120 minutes during the ipGTT were used to measure insulin concentration with the Rat Insulin ELISA  as per manufacturer’s protocol. The absorbance was read at 450 nm using a Flurostar Optima . The concentration of the unknown samples was then calculated from the mean absorbance of the calibrators using as second order polynomial (quadratic) non-linear standard curve.56. RNA was extracted from 20 mg of liver per sample using the High Pure RNA Tissue Kit  as per the manufacturer’s protocol. RNA was extracted from 100 mg adipose tissue per sample, from the gonadal and perirenal fat depots, using the method as previously described57. RNA was extracted from gastrocnemius muscle using the RNeasy Fibrous Tissue Mini Kit , with the following modifications: 30 mg of skeletal muscle (frozen and crushed) was added into a 2 mL MagNA Lyser green bead tube  with 350 µL Buffer RLT. Tubes were vortexed for 10 seconds and then placed immediately in a MagNA Lyser  at 6500 rpm for 40 seconds. Tubes were then centrifuged for 3 minutes at 8000 rpm and at this point the standard manufacturer’s protocol applied. RNA concentrations were then diluted to 50 ng/µL by adding the correct amount of RNase-free water. Samples were then stored at −80 °C until required for cDNA synthesis. The method of determination of RNA concentration and quality, as well as cDNA synthesis has been previously described57.Male offspring aged 4 and 12 weeks were only used for these analyses as females did not exhibit any major differences in ipGTT response. Only offspring that did not undergo ipGTT at 12 weeks were used to measure insulin signalling in the fed state as it is the preferred method to measure insulin actionINSR) and downstream components IRS2, PIK3R1 and PIK3CB , AKT2 and SREBP-1c and were targeted because of their known differential expression within established models of developmental programming55. Primers were designed using a method previously reported23. Primer sequences were published elsewhere for AKT223, INSR55IRS2 and SREBP-1c50. Primer sequences can be seen in Table 57. Expression of the genes of interest was normalised to the housekeeping gene ATCB, which was unaltered in response to the dietary treatments.Insulin signalling target genes include: insulin receptor . All data was checked for normal distribution using the Shapiro-Wilk test of normality and normality plots. If data was not normally distributed, data was transformed using the appropriate method based on the skewness of the data. For analysis where duplicate or triplicate results were recorded, firstly the mean of the replicates was calculated. Area under the glucose curve was calculated for intraperitoneal glucose tolerance test data using GraphPad Prism . For the effect of maternal diet on offspring weight, this was assessed separately for males and females using a general linear model two-way ANOVA . For the effect of maternal diet on offspring body composition, glucose tolerance and plasma insulin, a general linear model three-way ANOVA was used . Two-way ANOVA was then used for males and females separately to highlight any specific sex effects . To determine the effect of maternal diet on male offspring mRNA expression for each gene of interest and each tissue type across all age groups, a three-way ANOVA was used . Two-way ANOVA was used to highlight any specific age effects . For offspring weights and 12-week-old offspring adiposity, where there was more than one offspring per litter, means per litter were identified and then statistical analysis was performed with this data. This study was approved by the Animal Ethics Committee of the University of Nottingham, UK. All animal procedures were performed in accordance with the Animals (Scientific Procedures) Act 1986 under Home Office licence.Supplementary Dataset 1


GROWTH-REGULATING FACTOR (GRF) genes that codes for transcription factors that promote organ growth. Here, we show that both Arabidopsis thaliana At-GRF2 and At-GRF3 genes resistant to miR396 activity (rGRF2 and rGRF3) increased organ size, but only rGRF3 can produce this effect without causing morphological defects. Furthermore, introduction of At-rGRF3 in Brassica oleracea can increase organ size, and when At-rGRF3 homologs from soybean and rice are introduced in Arabidopsis, leaf size is also increased. This suggests that regulation of GRF3 activity by miR396 is important for organ growth in a broad range of species. Plants harboring rGRF3 have larger leaves also under drought stress, a condition that stimulates miR396 accumulation. These plants also showed an increase in the resistance to virulent bacteria, suggesting that the size increment promoted by rGRF3 occurs without an obvious cost on plant defenses. Our findings indicate that rGRF3 can increase plant organ size under both normal and stress conditions and is a valuable tool for biotechnological applications.An increase in crop yield is essential to reassure food security to meet the accelerating global demand. Several genetic modifications can increase organ size, which in turn might boost crop yield. Still, only in a few cases their performance has been evaluated under stress conditions. MicroRNA miR396 repress the expression of  Multiple regulatory gene networks are known to participate in the morphogenesis of a leaf, although their precise role and interactions are unknown in many cases.The growth of plant organs is tightly controlled by their developmental program and the interaction with the environment. Leaves initiate as rod-like structures protruding from the shoot apical meristem, pass through different developmental stages and become a flat organ specialized in photosynthesis3. In this sense, the precise modification of IYG might increase crop yield and therefore they constitute a potential source of biotechnological applications. The GROWTH-REGULATING FACTORs (GRFs) genes code for a family of plant-specific transcription factors characterized by the presence of the WRC and QLQ protein domains, which have been involved in DNA-binding and protein-protein interaction, respectively9. In Arabidopsis thaliana, there are nine GRF coding genes (GRF1-9). Seven out of them harbor a target site for microRNA miR396. At early stages of leaf development, miR396 is expressed in the distal part of the leaf, restricting the expression of the GRFs to the proximal part, which is coincidental with the proliferative region of the organ13. At later stages of leaf development and after the stop of cell proliferation, miR396 is expressed throughout the organ repressing GRF expression in maturing organs12.Crop yield is a highly complex trait influenced by both external and internal factors. Intrinsic Yield Genes (IYG) have been defined as those genes that produce larger organs, such as leaves, roots or seeds, when mutated or ectopically expressedGRF module is present in a broad range of plants including angiosperms and gymnosperms15. In certain cases, the ectopic expression of the GRFs is sufficient to increase leaf size. In Arabidopsis, overexpression from the 35S promoter of At-GRF517, Brassica napus Bn-GRF218 and Brassica rapa Br-GRF819 promotes a moderate increase of leaf size.The miR396-GRFs have been generated with synonymous mutations in the miRNA target site to avoid the post-transcriptional repression mediated by miR39620. The observation that plants harboring these miR396-resistant versions of At-rGRF2 (rGRF2) or At-rGRF3 (rGRF3) have larger leaves with respect to wild type plants, indicates that miR396 normally restricts organ size through the repression of the genes coding for GRF transcription factors. In good agreement with these results, plants overexpressing miR39621 or single grf517 and multiple grf1 grf2 grf35 knock outs have smaller organs.Modified Oryza sativa Os-GRF1 caused pleiotropic defects, including curled leaves, delayed flowering and defects in carpel development4. Results in crops have also been variable. Overexpression of Zm-rGRF1 increased maize leaf size, while it also caused additional detrimental phenotypes such as large macrohairs covering the glumes and the ear rachis that reduced fertility15, while overexpression of Zm-GRF10, which lacks a transactivation domain, reduced maize leaf size22. Furthermore, high levels of At-GRF7 and At-GRF9 caused no major increase of Arabidopsis leaf size25. Interestingly, At-GRF7 has been implicated in the response of plants to osmotic stress23, while At-GRF9 has been claimed to be a growth repressor26. Furthermore, in certain organs and conditions, the GRFs can affect both cell number and size28.However, increased levels of the GRFs not always results in larger organs in Arabidopsis, as overexpression of per se suggests that they can act as IYG increasing plant organ size, and therefore they could be a valuable tool for biotechnological applications. Still, not all the GRFs have a positive impact on organ size, and some of them have even deleterious effects. Here, we characterized different members of the GRF family in Arabidopsis and found that the At-GRF3 gene decoupled from miR396 regulation consistently increase organ size in Arabidopsis thaliana, an ability that likely depends on the protein sequence of the transcription factor. We also show that a miR396-resistant GRF3 can increase leaf size, root length and seed size in transgenic Brassica oleracea. Furthermore, we found that plants expressing the rGRF3 transgene still have an increase in leaf size under mild drought stress and show enhanced resistance to certain plant pathogens. We conclude that GRF transcription factors similar to At-GRF3 can be used to increase plant organ size in Brassicaceae species without an obvious deleterious impact in plant fitness.The capacity of certain GRFs to increase leaf size GRFs in Arabidopsis , Glycine max (soybean) and Medicago truncatula, all the GRFs have a miR396-binding site  that consists of a noncoding RNA that binds to the miRNA and blocks its function and/or promotes its degradation30. We prepared a MIM396 harboring 9 sites that can act as a sponge for miR396 (9X-MIM396)  Fig. . First, ses Fig. . The areses Fig. . Furtherion Fig.  and a 40els Fig. , in agreels Fig. .Figure 2GRF caused different effects, e.g., while in Arabidopsis rGRF2 or rGRF3 expressed from their own promoter or 35S:GRF5 caused an increase of leaf size20, overexpression of GRF7 and GRF9 did not25. Furthermore, overexpression of certain GRFs from other species might also inhibit growth4. The combined observation that most of the GRFs are regulated by miR396 in angiosperms and that the developmental phenotype of 9X-MIM396 was an increase of leaf size, showed that the miR396-GRF module controls organ size in vivo. These results prompted us to focus on rGRF2 and rGRF3 in more detail, especially in the light of the potential biotechnological application to increase the size of leaves and other organs.Previous results have shown that the ectopic expression of rGRF2 or rGRF3  in well wattered pots MIR396B is expressed at low levels in the proximal end of leaf 3, that is still in a proliferative state  and pathogen content was quantified at 2, 3 and 5 days post infection (dpi). Interestingly, Pst titers were lower in rGRF3 than in wild type plants, and these differences were maintained also at late stages of infection (9 times difference at 5 dpi). This indicated a positive effect of GRF3 on the immune pathways responsible to counteract pathogen proliferation . It has been recently shown, both in Arabidopsis and maize, that miR396 levels are prematurely induced by UV-B in developing leaves. This induction causes the repression of the GRFs expression and a decrease of cell proliferation, leading to smaller leaves with fewer cells46.Microarray and RNA-seq studies have shown that MIR396 can respond to other abiotic stresses, such as aluminumGRF3 decoupled of miRNA regulation reduced their size upon mild drought stress treatment. Still, rGRF3 leaves were significantly larger than those from the control plants grown under drought stress. Therefore, At-rGRF3 is able to increase plant organ size under both optimal and adverse conditions.Plant expressing DREB2A, a master regulator of water limitation responses. At- GRF7 has been shown to act as a transcriptional repressor of DREB2A and other stress-responsive genes to avoid the detrimental effects of stress responses on cell proliferation and organ growth23.A particular role has been established for At-GRF7 in Arabidopsis in the context of stress responses. Mutants in this gene have higher expression levels of stress-responsive genes in developing organs, including MIM396 plants, with low miR396 activity, develop fast and strong immune responses and increased resistance to nectrotrophic and hemibiotrophic fungi, apparently by triggering defense priming47. Our results suggest that At-GRF3 could mediate similar responses as well. rGRF3 plants, in which miR396-mediated repression of GRF3 is avoided, presented increased resistance to virulent Pst suggesting a positive effect of this transcription factor on defenses against hemibiotrophs. Interestingly, our results indicate that rGFR3 improves immune responses at the same time that it boosts plant growth. This contrasts with many examples illustrating the costs of increasing defenses, where activation of immune pathways under non-stress conditions produces a fruitless diversion of energy that causes a negative effect on plant growth and fitness48. In turn, other plants with enhanced resistance and no fitness cost have also been described, including MIM396 itself47 and overexpressors of NPR149. Then, it is expected that rGRF3 plants may achieve a suitable balance to improve both stress response and growth under adverse conditions, however, we cannot exclude that GRF3 has different roles in these processes.Recently, it has been shown that GRF3-like transcription factors, even under stress conditions. It’s worth to mention, that the gene manipulations required are limited to only a few base pair substitutions in the properly chosen GRF, making possible to use modern gene editing technologies for the fast introduction of these traits in crop species with desired characteristics.In summary, our results indicate that it might be possible to increase plant size through the genetic engineering of Arabidopsis Brassica olaracea cultivation and transformation was performed as described in . For leaf analysis plants were grown on soil while for root analysis, surface-sterilized seeds were sown on solid medium containing 1X Murashige and Skoog salt mixture, 1% sucrose, 2.3 mM 2-(Nmorpholino) ethanesulfonic acid (pH 5.8) and 1% agar. In all cases plants were grown at 16 h light/8 h dark photoperiod at 23 °C and a light intensity of 100 μmol quanta m−2 s−1. Leaf and seed size and root length were measured using FIJI50. In all cases, photographs of rosettes, leaves, seeds or roots were taken next to a ruler that was used as a reference to convert pixels to the corresponding metric unit. Later, organ size or length was measured in pixels in Fiji and, finally, these values were converted to metric units using the conversion factor obtained.Arabidopsis of the Col-0 accession was used in all the experiments. See Supplementary Table http://www.promega.com). Next, first-strand cDNA synthesis was carried out using SuperScript III Reverse Transcriptase (Invitrogen) with the appropriate primers. PCR reactions were performed in a Mastercycler ep realplex thermal cycler  using SYBRGreen I  to monitor double-stranded (ds)DNA synthesis. Quantitative (q)PCR of each gene was carried out in at least three biological replicates, with technical duplicates for each biological replicate. After normalizing using PP2A (PROTEIN PHOSPHATASE 2A) for Arabidopsis51 or UBC (UBIQUITIN CONJUGATING ENZYME) for Brassica oleracea52 transcript levels relative to the selected control genotype or condition was determined for each sample. Primer sequences are given in Supplementary Table RNA was extracted using TRIzol reagent (Invitrogen) and 0.5–1.0 μg of total RNA was treated with RQ1 RNase-free DNase  oligonucleotide probe designed against miR396 or a probe against U6 snRNA as a loading and blotting control (5′-CTCGATTTATGCGTGTCATCCTTGC). Phosphorimaging were obtained with a Typhoon (GE) and the signal intensity of ach band were determined using the GelQuant. NET software (biochemlabsolutions.com). The values of the bands detected with the miR396 probe were first normalized to the signal obtained with the U6 snRNA probe and then relativized to the selected control genotype or condition.MiR396 accumulation was estimated by small RNA blots performed as described previously53.To visualize GUS reporter activity, transgenic plants were subjected to GUS staining and the staining intensity along the length of the leaf was estimated as described previously17. Palisade leaf cells were observed using differential interference contrast microscopy in a Olympus BH2 microscope. The density of palisade cells per unit area was determined, and the area of the leaf blade was divided by this value to calculate the total number of palisade cells in the sub epidermal layer. To determine the cell area, 20 palisade cells were measured in each leaf.To obtain paradermal views of palisade cells, leaves were fixed with formalin-acetic acid-alcohol and cleared with chloral hydrate solution  as described−1 dry soil for the mild drought treatment while the control condition was set at 2.19 g water g−1 dry soil. Once the seedlings were transferred to these pots they were placed on the automated phenotyping platform WIWAM34. The water content of the soil was kept constant until 8 DAS, after which it was lowered daily to 1.02 g water g−1 dry soil for the mild-drought-treated plants. This phenotyping platform is designed to monitor vegetative development as images of the rosette of each plant are taken daily until 19 DAS. Leaf 1 area was measured from these images to monitor organ growth during the experiment. Also, the plastochron length was estimated by registering the time when each consecutive leaf reached at least 1 mm in length.Plants were germinated on soil-filled 96-well plates after stratification at 4 °C in the dark. Seedlings were transferred to pots at 4 days after sowing (DAS). Before transfer, the relative water content of the pots was set at 1.19 g water gPseudomonas syringae pv. tomato DC3000 was determined using 4 weeks old plants. Bacterial suspensions (5.105 cfu/ml) were infiltrated into adult leaves using a syringe. Pathogen growth was determined as previously described54, using a set of six leaf-disks (4 mm2) of independent plants per sample.Susceptibility to 55 and PHYLIP56 softwares using the neighbor joining method. Bootstrap analysis was conducted to measure node robustness using 1000 replicates.Multiple sequence alignments and phylogenetic trees were obtained using the Clustal OmegaThe datasets generated during and analyzed during the current study are available from the corresponding author on request.Supplementary Info