Work was conducted through 8 work packages (WP) and 13 deliverables (D). At the end of the MSCA project period, the fellow has completed, in some form, WP1-7 and is currently preparing manuscripts to complete WP-8 (publication of two manuscripts), and delivered D1, D2, D3-6, and D11-13. Deliverables 7-11 are in preparation and include; organization of a sustainable viticulture research symposium in Andalucia, Spain (D7), simple public summary reports for objectives 1 & 2 with infographics (D8), a YouTube video detailing our findings (D9), and at minimum two peer-reviewed scientific journal articles (D10). These deliverables will be completed after the project reporting period as the accumulation of large datasets has taken some time; metagenomics data for Objective 1 is still being sequenced, and microbiome community and function (metagenomics) data for Objective 2 was recently received and still being processed. See below for more details.
FUNVINE Research Objective 1: Global grapevine microbiome functional profiling
Global vineyard sampling, processing, metadata and metagenomic analyses:
Globally, grapevine leaf and rhizosphere soils were collected from ~40 vineyards across 16 countries to perform total DNA extractions and metagenomic sequencing to identify functional profiles for the above and belowground compartments of grapevines. From each sampling site (n = 242), we determined environmental data using available databases, for example various climate metrics were extracted for each site using the WorldClim database (Mean annual temperature, Mean annual precipitation, Maximum temperature, Precipitation seasonality, etc.). Bulk soils were collected from all sites to identify biogeochemical metrics across the global survey. These bulk soil analyses included: soil pH, C (total and mineral organic), N (total and available), P (available) and micronutrients, bulk density, texture, aggregate fractionation as well as grapevine leaf C (total), N (total) and micronutrients. Microbial respiration was measured to determine microbial activity, and enzymatic activity was calculated to determine the metabolic potential in each soil (C cycle: (i) β-1,4-glucosidase, β-xylosidase, β-D-cellobiohydrolase and α-1,4-glucosidase; N cycle: β-1,4-N-acetylglucosaminidase, and leucine-aminopeptidase; P cycle: phosphatase; S cycle: arylsulfatase), and ultimately all of these analyses will be paired with data of microbiome composition (GLOBAL VINE) as well as functional gene expression data from metagenomic sequencing (FUNVINE).
Main Achievements: Global surveys are complex endeavors; collection and analysis of all samples is laborious, and therefore this portion of the FUNVINE project will take some months to complete final analysis and manuscript preparation. To date, DNA from all soils and leaves has been sent to the sequencing facility to begin metagenomic sequencing. Further, we have processed bulk soils from each site for all other biogeochemical analyses as well as microbial respiration and enzymatic activity analysis, which determines the potential of the microbiome to process soil organic matter, nitrogen, phosphorus, or sulfur.
FUNVINE Research Objective 2: Experimental management of the grapevine microbiome under global change stress
Experimental design, sampling, analyses:
A controlled experiment was carried out using two grape varieties (Merlot and Garnacha), under factorial combinations of drought (intense and prolonged 2 week drought versus no drought) and fertilization (moderately high and no fertilization), and also received either no soil inoculation or inoculation of whole soils sourced from 22 vineyards across 8 countries in the EU. This experiment began in March of 2023 and was completed in November of 2023, with rootstocks of grapevines (i.e. first year planting) growing for a total of ~6 months in 18L pots prior to harvest of aboveground biomass (new growth) and soil sampling for biogeochemical analyses as well as microbiome analyses of either a) microbiome composition (targeted amplicon sequencing using 16S and ITS regions for arachael/bacterial or fungal community analyses, respectively) or total DNA sequencing for b) microbiome functional profiling using metagenomics. Analyses for each targeted set of analyses are summarized below:
Main Achievements:
Grapevine microbiome management under global change stress - COMPOSITION
Soil samples (n = 200) were collected at the end of the 2-week drought period for all pots and taken to the lab for DNA extraction, as well as soil chemical and metabolic potential analyses. DNA was extracted using Qiagen Powersoil Pro kit following manufacturer protocol, DNA was quantified using Qubit, normalized concentration to 30 ng/uL, and shipped to sequencing facility for 2 x 150bp sequencing on Illumina MiSeq. Bioinformatics were performed using QIIME2 (99% similarity with Greengenes database for 16S (archaea/bacteria) or 97% similarity with the UNITE database for ITS (fungi). Statistical analyses were performed in R. For soil chemical analyses, we performed total C, total N analyses (combustion CHN analysis), as well as available N and available P using fluorometric analyses (Mehlic extractions). Total C and N was also determined for all grapevine leaves using CHN analysis.
Grapevine microbiome management under global change stress - FUNCTION
A subset of soil samples (n = 74) were used to perform metagenomic analyses. All 22 soil inocula were sequenced, as well as 2 homogenized mixes of original background soils. The experimental samples were from one variety of grapevine (Merlot), under unfertilized conditions, which experienced drought or no drought and all inoculations (n = 22) or 3 controls (only background soils) for both drought or no-drought treatments. Metagenomics protocol: Total genomic DNA was determined on 1% agarose gel, fragmented at 300bp with Covaris M220, and paired-end libraries were constructed using the TruSeq DNA Sample Prep Kit. Bridge PCR was performed using HiSeq 3000/4000 PE Cluster Kit, and sequencing was performed with HiSeq 3000/4000 SBS Kits. Bioinformatics is being performed using the SAMSA2 pipeline and QIIME2. Functional genes are being annotated using the KEGG database, and statistical analyses will be performed using R.
