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Avocado genome assemblies, annotations and additional data created for the avocado resequencing project. This data is of the Gwen variety of avocado. Files containing the gz extension require decompression using gunzip. Description of Files: gwen.contigs.to.scaffolds.geneticmap.gff.gz This is the annotation file for creating the scaffolds from contigs. It also contains the strand information demoted by +, -, *. asterisk denotes unknown orientation. gwen.hapsolo.contigs.gene.annotation.genes.b2g.xlsx This contains the Blast2GO results of the Gwen C+H (HapSolo) genome assembly for each gene present in the annotation. Some genes may not have Blast2GO results. gwen.hapsolo.contigs.gene.annotation.gff3.gz This is the complete gene annotation for the Gwen C+H (HapSolo) contig assembly in GFF3 format gwen.hapsolo.contigs.primary.fasta.gz This is the Gwen C+H primary contigs assembly gwen.hapsolo.contigs.secondary.fasta.gz This is the Gwen C+H secondary contigs assembly gwen.hapsolo.contigs.TE.annotation.gff.gz This is the complete TE annotation for the Gwen C+H (HapSolo) contig assembly in GFF format gwen.scaffolds.10Ns.noU.fasta.gz This is the Gwen scaffolded genome assembly submitted to NCBI. All 12 scaffolds were merged using the contigs to scaffold annotation, and oriented by strand. The merged contigs are separated by 10 consecutive N's. The unplaced contigs were not scaffolded and remain as contigs carried over from the C+H assembly. gwen.scaffolds.20kNs.fasta.gz This is the Gwen scaffolded assembly used for all of our analyses in our publication. All 12 scaffolds were merged using the contigs to scaffold annotation, and oriented by strand. The merged contigs are separated by 20,000 consecutive N's. The unplaced contigs were scaffolded into a scaffold named ChrU, where U signifies unplaced contigs carried over from the C+H assembly. gwen.scaffolds.20kNs.gene.annotation.genes.b2g.xlsx This contains the Blast2GO results of the Gwen scaffolded genome assembly for each gene present in the annotation. Some genes may not have Blast2GO results. 20kNs signifies the scaffold assembly in which this annotation belongs to (the genome assembly with contigs separated by 20,000 N's). gwen.scaffolds.20kNs.gene.annotation.gff3.gz This is the complete gene annotation for the Gwen scaffolded genome assembly in GFF3 format. All unplaced contigs are placed into ChrU, where U signifies unplaced contigs carried over from the C+H assembly.. All contigs that were merged to create scaffolds contain a separation by 20,000 consecutive N's. gwen.scaffolds.20kNs.TE.annotation.gff.gz This is the complete TE annotation for the Gwen scaffolded genome assembly in GFF format. All unplaced contigs are placed into ChrU, where U signifies unplaced contigs carried over from the C+H assembly. All contigs that were merged to create scaffolds contain a separation by 20,000 consecutive N's., E. Solares was supported by an NSF pre-doctoral fellowship and a UC Presidential Postdoctoral Fellowship. This work was supported in part by a USDA grant #2020-04640 to M.L. Arpaia.

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Additional file 1.

The presence of naturally-occurring color mutants in plants has permitted the identification of many regulatory genes implicated in the synthesis of discrete metabolic compounds, mostly anthocyanins and carotenoids. Conversely, transcription factors that coordinate more than one specialized metabolic pathway seem challenging to screen from a forward genetics’ perspective. We explored the relationship between different branches of the phenylpropanoid and isoprenoid pathways while examining an infrequent berry skin color variegation in grapevine. Red and white berry skin sections were compared at the genetic, transcriptomic and metabolomic levels showing that, as in most cultivated white grape varieties, the uncolored skin section convened the non-functional alleles of the anthocyanin regulators MYBA1 and MYBA2, explaining the lack of pigments. In contrast, light-responsive flavonols and monoterpenes increased in anthocyanin-depleted areas. We disclosed an enrichment of the flavonol, terpene and carotenoid pathways among up-regulated genes from white-skin sections, accompanied by increased expressions of flavonol regulators and the still uncharacterized MYB24 gene. We used DAP-seq to examine the in vitro binding of affinity-purified MYB24 protein to genomic DNA and demonstrated its binding in the promoter regions of terpene (22) and carotenoid (6) genes, in addition to more than 30 photosynthesis and light-response genes, including the flavonol-regulator HY5 homologue (HYH). We confirmed the activation of TPS35 and HYH promoter:luciferase reporters in the presence of MYB24 and the grape bHLH MYC2, all of which correlate in their higher expression in white skin variegated sections. The integration of several datasets allowed to define a list of high confidence targets, suggesting MYB24 as a modulator of light responses including the synthesis of flavonoids (flavonols) and isoprenoids (terpenes, and putatively carotenoids). The correspondence between MYB24 and monoterpenes in all conditions surveyed implies that this regulatory network is broadly triggered towards berry ripening, and that the absence of anthocyanin sunscreens accelerates its activation most likely in a dose-dependent manner due to increased radiation exposure.

The control of plant specialised metabolism is exerted by transcription factors and co-regulators acting on cis-regulatory DNA sequences of pathway-structural genes, determining when, where, and how metabolites are accumulated. A particularly interesting case for studying the transcriptional control of metabolism is represented by stilbenoids, produced within the phenylpropanoid pathway, as their ability to inhibit infection by coronaviruses MERS-CoV and SARS-CoV has been recently demonstrated in vitro. Integrative omic studies in grapevine (Vitis vinifera L.), including gene co-expression networks, have previously highlighted several transcription factors (TFs) from different gene families as potential modulators of stilbenoid accumulation, offering an ideal framework for gene function characterisation using genome-wide approaches. In the context of non-model plant species, DNA affinity purification sequencing (DAP-Seq) results a novel and potentially powerful tool for the analysis of novel uncharacterised regulators, however, it has not yet been applied in fruit crops. Accordingly, we tested as a proof-of-concept the binding of two previously characterised R2R3-MYB TFs to their known targets of the stilbene pathway, MYB14 and MYB15, obtaining 5,222 and 4,502 binding events assigned to 4,038 and 3,645 genes for each TF, respectively. Bound genes (putative targets) were overlapped with aggregated gene centred co-expression networks resulting in shared and exclusive High Confidence Targets (HCTs) suggesting a high, but not complete, redundancy. Our results show that in addition to the previously known but few STS targets, these regulators bind to almost half of the complete STS family in addition to other phenylpropanoid- and stilbenoid-related genes. We also suggest they are potentially involved in other processes such as the circadian rhythm or the synthesis of biotin. We searched the activated transcriptomes of transiently MYB15-overexpressing grapevine plants and observed a large activation of its high confidence targets, validating our methodological approach. Our results also show that MYB15 seems to play a role in regulating other stilbenoid-related TFs such as WRKY03., This work was supported by Grant PGC2018-099449-A-I00 and by the Ramón y Cajal program grant RYC-2017-23645, both awarded to J.T.M. and to the FPI scholarship PRE2019-088044 granted to L.O. from the Ministerio de Ciencia, Innovaci´on y Universidades (MCIU, Spain), Agencia Estatal de Investigaci´on (AEI, Spain), and Fondo Europeo de Desarrollo Regional (FEDER, European Union). C.Z. is supported by China Scholarship Council (CSC) no. 201906300087. This article is based upon work from COST Action CA 17111 INTEGRAPE, supported by COST (European Cooperation in Science and Technology). Data has been treated and uploaded in public repositories according to the FAIR principles.

Additional file 2: Figure S1. Genetic clustering of all samples (including hybrid samples) detected by the structure analyses from K = 2 to K = 10. Figure S2. Consensus maximum likelihood phylogenetic tree created with IQtree using 15,893 informative sites found by SNPphylo. Figure S3. Clock calibrated phylogenetic tree created with BEAST using 15,893 informative sites found by SNPphylo. Figure S4. Results of the Fbranch statistic using species tree models. Figure S5. Pairs of species that show evidence of introgression present evidence of overlap at three periods: present, Holocene, Pleistocene. Figure S6. Pairs of species that do not show evidence of introgression present no or low overlap at three periods: present, Holocene, Pleistocene. Figure S7. Pairs of species that do not show evidence of introgression but have overlapping distributions at three periods: present, Holocene, Pleistocene. Figure S8. Genomic size distribution of the windows used to calculate the ABBA-BABA statistics per trio. Figure S9. Distribution the fdM statistic values across windows per trio. Figure S10. Distribution the fd statistic values across windows per trio. Figure S11. Manhattan plot of all the middle point value all windows with fdM values for all nine trios. Figure S12. Scatterplot showing correlations between the fdM and fd statistics across all windows in the genome. Figure S13. Mean gene density of across the whole genome and within the pIRs of all the trios. Figure S14. Average weights of phylogenetic relationships in the pIRs. SNPs from pIRs for each trio were split in 10Kb windows to create a phylogenetic tree for each window. Figure S15. Principal component variance LFMM association analyses. Figure S16. QQ plots showing the logarithm of the p-values for a linear regression model, the LFMM model using 7 LF and without controlling for a genome inflation factor and controlling for a genome inflation factor. Figure S17. Manhattan plot of LFMM association results for the concentration of X. fastidiosa. The red lines indicate the threshold at an FDR < 0.05. Figure S18. QQ plots showing the logarithm of the p-values for a linear regression model (gray), and LFMM models using 6-12 LF and controlling for a genome inflation factor. Figure S19. Number of associated candidate SNPs per bioclimatic variables per species.

Additional file 3. Review history.

Microbiomes are integral to viticulture and winemaking – collectively termed winegrowing – where diverse fungi and bacteria can exert positive and negative effects on grape health and wine quality. Wine is a fermented natural product, and the vineyard serves as a key point of entry for quality-modulating microbiota, particularly in wine fermentations that are conducted without the addition of exogenous yeasts. Thus, the sources and persistence of wine-relevant microbiota in vineyards critically impact its quality. Site-specific variations in microbiota within and between vineyards may contribute to regional wine characteristics. This includes distinctions in microbiomes and microbiota at the strain level, which can contribute to wine flavor and aroma, supporting the role of microbes in the accepted notion of terroir as a biological phenomenon. Little is known about the factors driving microbial biodiversity within and between vineyards, or those that influence annual assembly of the fruit microbiome. Fruit is a seasonally ephemeral, yet annually recurrent product of vineyards, and as such, understanding the sources of microbiota in vineyards is critical to the assessment of whether or not microbial terroir persists with inter-annual stability, and is a key factor in regional wine character, as stable as the geographic distances between vineyards. This review examines the potential sources and vectors of microbiota within vineyards, general rules governing plant microbiome assembly, and how these factors combine to influence plant-microbe interactions relevant to winemaking., Frontiers in Microbiology, 12, ISSN:1664-302X

Additional file 1: Table S1. Statistics of the V. arizonica b40-14 genome assembly. Table S2. Summary table of the BUSCO genes of both haplotypes detected in the V. arizonica genome assembly. Table S3. Accession identifiers and geographical coordinates of the 130 accessions used in this study. Table S4. Chromosome size, raw number of predicted SNPs and number of SNPs after filtering across all 130 samples. Table S5. Genome-wide calculation of nucleotide diversity (휋) within per species. Table S6. Results from genome-wide estimates of introgressions across all combination of trios tested. Table S7. GO terms significantly enriched (p

Figure S3. Scatterplots showing the correlation of relative abundance of fungal taxa detected using GTAA and BITS, and whole-genome shotgun metagenomics results with multiple taxonomy classifiers. The genus abundances resulting from the QIIME pipeline with Mothur, BLASTn, and UCLUST, were compared to the metagenomics values. Mothur results were also compared to the other classifying methods. R values correspond to Pearsonâ s correlation coefficients. (PDF 634 kb)

Figure S1. Scatterplots showing the correlation between expected relative abundance based on how mock communities were prepared and the observed relative abundance of fungal taxa detected in the mock communities using the GTAA and BITS primers. The genus abundances resulting from the QIIME pipeline with Mothur, BLASTn and UCLUST taxonomy classifiers, as well as direct BLASTn of the reads to the custom database created in this study, were compared to the expected values. R values correspond to Pearsonâ s correlation coefficients. (PDF 643 kb)

Figure S2. Scatterplots showing the correlation between expected relative abundances and observed values using GTAA and BITS primers for individual fungal taxa in the mock communities. R values correspond to Pearsonâ s correlation coefficients. (PDF 286 kb)