Although currently several agronomic approaches of source-limitation have been set up to delay sugar accumulation in ripening grapes in the field , stomatal manipulation may be a favorable genetic strategy for the future, that deserves to be further explored also under combined environmental stress and in field trials. In our study, we further applied a water stress experiment to test if and how a reduced stomatal density can affect plant behavior in drought conditions. During a progressive reduction in soil water availability, significant differences in transpiration rate were observed in ‘Sugraone’ edited lines compared to WT only under moderate water stress . Yet, under severe water stress , some trends were observed in edited lines showing higher transpiration rate followed by Asat and gs maintenance. Notably, the reduction in gs and Asat during the dry-down was evident for WT plants while this was not significant for edited lines. This conservative behavior induced by reduced SD has been previously associated with a longer period of transpiration maintenance during drought, leading to a prolonged carbon assimilation respect to WT . In rice, lines over expressing the OsEPF1 gene had higher yield than WT when water-stressed at flowering stage confirming that water conservation during key-stages of yield formation may be desirable for yield maintenance . In addition, limiting plant transpiration could be an advantage for irrigated vineyards in terms of a reduction in water input demand . In lieu of an increasing number of grapevine growing regions where water resources will become limited , 25 liter pot genotypes with reduced stomatal density will require less irrigation water per area of cultivation, thereby increasing crop water productivity for farmers .

The ability of a genotype to produce different phenotypes as a function of environmental cues is known as phenotypic plasticity . Phenotypic plasticity is considered one of the main processes by which plants, as sessile organisms, can face and adapt to the spatio-temporal variation of environmental factors . Grapevine berries are characterized by high phenotypic plasticity and a genotype can present variability within berries, among berriesin a cluster, and among vines . Berry phenotypic traits, such as the content of sugars, acids, phenolic, anthocyanins, and flavor compounds, are the result of cultivar and environmental influences , and often strong G × E interactions . Although grapevine plasticity in response to environmental conditions and viticulture practices may provide advantages related to the adaptation of a cultivar to specific growing conditions, it may also cause irregular ripening and large inter-seasonal fluctuations , which are undesirable characteristics for wine making . Due to its complex nature, the study of phenotypic plasticity is challenging and the mechanisms by which the genes affecting plastic responses operate are poorly characterized . In fact it is often difficult to assess the performance of different phenotypes in different environments . It has been suggested that genetic and epigenetic regulation of gene expression might be at the basis of phenotypic plasticity through the activation of alternative gene pathways or multiple genes . Epigenetics has been proposed as crucial in shaping plant phenotypic plasticity, putatively explaining the rapid and reversible alterations in gene expression in response to environmental changes. This fine-tuning of gene expression can be achieved through DNA methylation, histone modifications and chromatin remodeling . Small non-coding RNAs are ubiquitous and adjustable repressors of gene expression across a broad group of eukaryotic species and are directly involved in controlling, in a sequence specific manner, multiple epigenetic phenomena such as RNA-directed DNA methylation and chromatin remodeling and might play a role in genotype by environment interactions.

In plants, small ncRNAs are typically 20–24 nt long RNA molecules and participate in a wide series of biological processes controlling gene expression via transcriptional and post-transcriptional regulation . Moreover, small RNAs have been recently shown to play an important role in plants environmental plasticity . Fruit maturation, the process that starts with fruit-set and ends with fruit ripening , has been largely investigated in fleshy fruits such as tomato and grapevine. These studies highlighted, among others, the vast transcriptomic reprogramming underlying the berry ripening process , the extensive plasticity of berry maturation in the context of a changing environment , and the epigenetic regulatory network which contributes to adjust gene expression to internal and external stimuli . In particular, small RNAs, and especially microRNAs , are involved, among others, in those biological processes governing fruit ripening . In this work, we assessed the role of small ncRNAs in the plasticity of grapevine berries development, by employing next-generation sequencing. We focused on two cultivars of Vitis vinifera, Cabernet Sauvignon, and Sangiovese, collecting berries at four different developmental stages in three Italian vineyards, diversely located. First, we described the general landscape of small RNAs originated from hotspots present along the genome, examining their accumulation according to cultivars, environments and developmental stages. Subsequently, we analyzed miRNAs, identifying known and novel miRNA candidates and their distribution profiles in the various samples. Based on the in silico prediction of their targets, we suggest the potential involvement of this class of small RNAs in GxE interactions. The results obtained provide insights into the complex molecular machinery that connects the genotype and the environment. A miRNA was considered as “expressed” only when the values of both biological replicates were greater than or equal to the threshold set at 10 TP4M. We defined a miRNA as “vineyard-, cultivar-, or stage-specific” when it was expressed only in a given vineyard, cultivar or one specific developmental stage.

Differentially expressed miRNAs were identified using the CLCbio Genomics Workbench using multiple comparison analysis. We loaded the total raw redundant reads from our 48 libraries in the CLCbio package and trimmed the adaptors, considering only reads between 18 and 34 nt. We annotated miRNAs against the user defined database, comprehending our set of 122 MIRNA loci and their corresponding mature sequences. For each library, the total counts of read perfectly mapping to the miRNA precursors was considered as the input of the expression analysis. Given the main focus of our work, we aimed at identifying miRNAs differentially expressed between the two cultivars in the same environment and developmental stage , or between the three vineyards in the same cultivar and in the same developmental stage . For this reason, we considered each developmental stage and we performed the Empirical Analysis of digital gene expression , an implementation of the “Exact Test” present in the EdgeR Bioconductor package, as implemented in CLCbio software and estimating tagwise dispersion with pairwise comparisons and setting the significance threshold to FDR-adjusted p ≤ 0.05. The normalized reads of all miRNAs identified in this study and also the cluster abundances obtained from the static clustering analysis were submitted to another adhoc normalization [log10 or log10 ] for correlation analysis. This normalization was chosen because of the enormous range of abundance values that produced a logunimodal distribution and may cause significant biases in the correlation analysis when performed using TP4M or HNA values. A unity was then added to the abundance value due to the presence of zero entries. After this addition, a value of zero still corresponds to zero of the log10 function, thus making consistent the comparisons among profiles. The dendrogram was generated using the function hclust and the Pearson correlation was calculated using the function cor in R, 25 liter plant pot based on the log10 or log10 values for miRNAs and sRNA-generating loci respectively. Pearson’s correlation coefficients were converted into distance coefficients to define the height of the dendrogram. Heat maps were produced using MeV based on TP4M values of miRNAs abundance. The Venn diagrams were produced using the function vennDiagram in R, based on the miRNA list for each cultivar, environment and developmental stage. Small RNA libraries were constructed and sequenced for 48 samples of grapevine berries . We obtained a total of 752,020,195 raw redundant reads . After adaptors trimming, 415,910,891 raw clean reads were recovered, ranging from 18 to 34 nt in length . Eliminating the reads mapping to rRNA, tRNA, snRNA, and snoRNA sequences, 199,952,950 reads represented by 20,318,708 distinct sequences, i.e., non-redundant sequences found in the 48 libraries , were perfectly mapped to the V. vinifera PN40024 reference genome .

Distinct peaks at 21- and 24-nt were observed in all the libraries. Consistent with previous reports in grapevine and other plant species , the 21- nt peak was the highest, comprising a higher proportion of redundant reads, whereas the 24-nt peak was less abundant. A few exceptions regarding the highest peak in the small RNA size profile were observed: Ric_SG_ps had the highest peak at 24- nt whereas Mont_CS_ps and Mont_SG_bc did not show a clear difference between the 21- and the 24-nt peak. Using the Pearson coefficients we observed a strong association between the replicates as indicated by the high coefficients . To facilitate access and utilization of these data, we have incorporated the small RNAs into a website . This website provides a summary of the library information, including samples metadata, mapped reads, and GEO accession numbers. It also includes pages for data analysis, such as quick summary of the abundances of annotated microRNAs from grapevine or other species. Small RNA-related tools are available, for example target prediction for user-specified small RNA sequences and matching criteria. Finally, and perhaps most importantly, a customized browser allows users to examine specific loci for the position, abundance, length, and genomic context of matched small RNAs; with this information, coupled with the target prediction output, users can develop and assess hypotheses about whether there is evidence for small RNA-mediated regulation of grapevine loci of interest.In order to investigate whether the overall distribution and accumulation of small RNA is affected by the interaction between different V. vinifera genotypes [Cabernet Sauvignon and Sangiovese ] and environments [Bolgheri , Montalcino and Riccione ], we investigated the regions in the grapevine genome from where a high number of small RNAs were being produced , by applying a proximity-based pipeline to group and quantify clusters of small RNAs as described by Lee et al. . The nuclear grapevine genome was divided in 972,413 adjacent, non-overlapping, fixed-size windows or clusters. To determine the small RNA cluster abundance, we summed the hits-normalized-abundance values of all the small RNAs mapping to each of the 500 bp clusters, for each library . To reduce the number of false positives, we considered a cluster as expressed when the cluster abundance was greater than the threshold for a given library, eliminating regions where few small RNAs were generated, possibly by chance. Libraries from bunch closure, representing green berries, and 19 ◦Brix representing ripened berries, where used in this analysis. From the 972,413 clusters covering the whole grapevine genome, 4408 were identified as expressed in at least one sample. As showed in Figure 1, CS-derived libraries have a higher number of expressed clusters when compared to SG-derived libraries of the same developmental stage and from the same vineyard. The exceptions were the Sangiovese green berries collected in Riccione and Sangiovese ripened berries collected in Montalcino, which have a higher number of expressed clusters than the respective CS ones. The two cultivars show a completely different small RNA profile across environments. When Cabernet berries were green, a higher number of sRNA-generating regions were found active in Bolgheri than in Montalcino and Riccione. Differently, ripened berries had the highest number of sRNAproducing regions expressed in Riccione, while Bolgheri and Montalcino show a similar level of expressed clusters . Sangiovese green berries instead show the highest number of active sRNA-generating regions in Riccione, and this number is twice the number found in Bolgheri and Montalcino that is similar. Ripened berries collected in Montalcino and Riccione show almost the same high level of sRNA-generating clusters, whereas those collected in Bogheri present a lower number . We also noted that when cultivated in Bolgheri, neither Cabernet Sauvignon or Sangiovese change dramatically the number of expressed clusters during ripening, while in Riccione Cabernet Sauvignon shows a 2-fold increase of sRNA producing clusters, which is not observed in Sangiovese. Next, the small RNA-generating clusters were characterized on the basis of the genomic regions where they map, i.e., genic, intergenic and transposable elements.