# Monitor and steer the microbiota to protect grapevines against downy mildew

Corinne Vacher, Simon Labarthe

<figure><img src="image_1.png" /></figure>

To cite this version:

Corinne Vacher, Simon Labarthe. Monitor and steer the microbiota to protect grapevines against downy mildew. 2025 Annual Meeting of the VITAE project, PPR 'Growing and Protecting Differently', Mar 2025, Angers, France. hal-05300025

HAL Id: hal-05300025

https://hal.science/hal-05300025v1

Submitted on 6 Oct 2025

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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Monitor and steer the microbiota to protect grapevines against downy mildew

**Corinne Vacher**, INRAE, SAVE & **Simon Labarthe**, INRAE, Biogeco
# Grapevines are heavily treated

<figure><img src="image_3.png" /></figure>

80% of treatments are fungicides used to control downy mildew and powdery mildew

<caption>### Downy mildew</caption>

Plasmopara viticola
(oomycete)

<figure><img src="image_4.png" /><figcaption>### Powdery mildew</figcaption></figure>

Erysiphe necator
(fungus)

<figure><img src="image_5.png" /></figure>

**Image Source - Graph:** Simonovici & Caray. Agreste. 2023; modified by Olivier Nefti; **Diseased Leaves:** http://ephytia.inra.fr/fr/D/1757
# Towards zero-pesticide viticulture

<figure><img src="image_6.png" /></figure>

**Image authors**: Lea Cabrol-Froget, Corinne Vacher, Paola Fournier
# Microbiota-based management

<figure><img src="image_7.png" /></figure>

**MICROBIAL BIOINDICATORS**
of epidemic risk

**MICROBIAL BIOSOLUTIONS**
to control diseases

**RESIDENT MICROBIOTA MANAGEMENT**
through biodiversity management at the plot and landscape scales

**VARIETIES THAT ASSOCIATE WITH PROTECTIVE MICROBIOTA**
# Microbiota-based management

<figure><img src="image_8.png" /></figure>

**MICROBIAL BIOINDICATORS**
of epidemic risk

**MICROBIAL BIOSOLUTIONS**
to control diseases

**RESIDENT MICROBIOTA MANAGEMENT**
through biodiversity management at the plot and landscape scales

**VARIETIES THAT ASSOCIATE WITH PROTECTIVE MICROBIOTA**

Image authors: Lea Cabrol-Froget, Corinne Vacher, Paola Fournier
# Monitor the microbiota to predict risk of downy mildew epidemics

Corinne Vacher, INRAE, SAVE & Simon Labarthe, INRAE, Biogeco
<figure><img src="image_9.png" /></figure>

## Opinion

### The forecasting power of the microbiome

Sara Correa-Garcia,¹ Philippe Constant,¹ and Etienne Yergeau ¹,*,

Microbial communities **integrate** past and present ecosystem features (e.g. climate conditions, agricultural practices)

Microbial communities are directly **involved** in ecosystem processes (e.g. crop yield, protection against pathogens)

<figure><img src="image_10.png" /></figure>

Microbiome data can be used in forecasting models
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

Paola
Fournier
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

A

<figure><img src="image_17.png" /></figure>

B

<figure><img src="image_18.png" /></figure>
<figure><img src="image_19.png" /></figure>

Abundant fungi of the topsoil microbiota are good predictors of disease incidence and severity.

<figure><img src="image_20.png" /><figcaption>Paola Fournier</figcaption></figure>

Fournier et al. Environmental Microbiome. 2025
Cambon et al. Molecular Ecology. 2023 (NGB project)
<figure><img src="image_21.png" /></figure>

Foliar microbial communities cannot predict disease incidence and severity.

<figure><img src="image_22.png" /><figcaption>Paola Fournier</figcaption></figure>

Fournier et al. Environmental Microbiome. 2025
<figure><img src="image_23.png" /></figure>

Algorithms trained
with 2022 microbiota
data can be used to
predict disease
incidence and severity
in 2023.

<figure><img src="image_24.png" /><figcaption>Paola
Fournier</figcaption></figure>
<figure><img src="image_25.png" /></figure>

VITAE: metabarcoding
on pairs of parcels

Classical dimension reduction

Sample classification

<figure><img src="image_26.png" /></figure>

Diagnosis

Interpretation

<figure><img src="image_27.png" /></figure>

Signature
<figure><img src="image_28.png" /></figure>

VITAE: metabarcoding
on pairs of parcels

Optimized dimension reduction
for classification

<figure><img src="image_29.png" /></figure>

Optimal reduction basis
for classification
<figure><img src="image_30.png" /></figure>

VITAE: metabarcoding
on pairs of parcels

Selection of sentry
microorganisms for classification

<figure><img src="image_31.png" /></figure>

-> optimized q-PCR chip
<figure><img src="image_32.png" /></figure>

VITAE: metabarcoding
on pairs of parcels

Selection of sentry
microorganisms for classification

<figure><img src="image_33.png" /><figcaption>Yannis Ben
Bassite</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>Manon
Chargy</figcaption></figure>

<figure><img src="image_35.png" /></figure>

-> optimized q-PCR chip
# Microbiota-based management

<figure><img src="image_36.png" /></figure>

**MICROBIAL BIOINDICATORS**
of epidemic risk

**MICROBIAL BIOSOLUTIONS**
to control diseases

**RESIDENT MICROBIOTA MANAGEMENT**
through biodiversity management at the plot and landscape scales

**VARIETIES THAT ASSOCIATE WITH PROTECTIVE MICROBIOTA**

Image authors: Lea Cabrol-Froget, Corinne Vacher, Paola Fournier
# Steer the microbiota using biosolutions to strengthen protection against downy mildew

**Corinne Vacher**, INRAE, SAVE & **Simon Labarthe**, INRAE, Biogeco
# Few biocontrol products are available

## Les produits disponibles

<figure><img src="image_37.png" /></figure>

<table><thead><tr><th>Noms commerciaux</th><th>Catégorie</th><th>Principe actif</th><th>Mode d'action</th><th>Utilisable en AB</th></tr></thead><tbody><tr><td>LBG-01F34 / Etonan / Pertinan / Alucinan / Facinan</td><td>Substance naturelle</td><td>Phosphonate de potassium</td><td>Fongicide et SDP</td><td></td></tr><tr><td>Phytosarcan/ Kerala</td><td>Substance naturelle</td><td>Phosphonate de potassium</td><td>Fongicide et SDP</td><td></td></tr><tr><td>Fosika / Milfos</td><td>Substance naturelle</td><td>Phosphonate de potassium</td><td>Fongicide et SDP</td><td></td></tr><tr><td>Savial Forte / Mikonos/ Cuneb</td><td>Substance naturelle</td><td>Phosphonate de potassium</td><td>Fongicide et SDP</td><td></td></tr><tr><td>Ceraxel / BCPC358FC</td><td>Substance naturelle</td><td>Phosphonate de potassium</td><td>Fongicide et SDP</td><td></td></tr><tr><td>Redeli / Sirius / Fructial</td><td>Substance naturelle</td><td>Phosphonate de potassium</td><td>Fongicide et SDP</td><td></td></tr><tr><td>Messager/ Blason / Bastid / Bstim</td><td>Substance naturelle</td><td>COS-OGA</td><td>Stimulateur des défenses (SDP)</td><td>AB</td></tr><tr><td>Fytosave /Esdéaine</td><td>Substance naturelle</td><td>COS-OGA</td><td>Stimulateur des défenses (SDP)</td><td>AB</td></tr><tr><td>Romeo / Actileaf</td><td>Substance naturelle</td><td>Cerevisane (parois de levures)</td><td>Stimulateur des défenses (SDP)</td><td>AB</td></tr><tr><td>Essen'ciel/ Limocide/ Auran'ciel /Limoil</td><td>Substance naturelle</td><td>Huile essentielle d'orange</td><td>Fongicide</td><td>AB</td></tr><tr><td>Taegro*</td><td>Micro-organisme</td><td>Bacillus amyloliquefaciens s. FZB24</td><td>Antibiose et SDP</td><td>AB</td></tr></tbody></table>

<footnote>* Homologué mais non commercialisé pour cet usage</footnote>
# Synthetic Micobial Communities (SynComs)

Synthetic microbial communities (SynComs) are microbial consortia designed to **test hypotheses** and to mimic, to some extent, the role of a particular microbiome

<figure><img src="image_38.png" /></figure>

Useful tool to
**understand** plant-
**microbiome-pathogen**
interactions

Design commercial
**multi-strain biocontrol**
**products** for application
in the field
H1 : A SynCom composed of a combination of microbial species prevalent in the phyllosphere and species with known individual biocontrol effects is able to resist invasion of the leaf by the pathogen.

H2 : SynComs with higher alpha diversity exhibit greater biocontrol efficacy.

H3 : SynComs with higher phylogenetic diversity exhibit biocontrol efficacy.

H4 : Cross-kingdom SynComs exhibit greater biocontrol efficacy.

<figure><img src="image_39.png" /><figcaption>Valérie
Martin</figcaption></figure>

In collaboration with
**IRHS** (Marie Simonin) and **IGEPP** (Nathan Vannier)
# Experimental design

SynCom A (n=42): all isolates

SynCom B (n=22): Species with known biocontrol activity in literature

Four Modalities + Two Controls:

1. SynCom A only

2. SynCom A + mildew

3. SynCom B only

4. SynCom B + mildew

5. Mildew only

6. Saline only

320 discs per modality divided into 4 replicate plates

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

**Ongoing Analyses :**

- Quantification of disease symptoms

- qPCR Microfluidic chip

- Metabolomics

- Metatranscriptomics

<figure><img src="image_42.png" /><figcaption>Aarti
Jaswa</figcaption></figure>
<figure><img src="image_43.png" /></figure>

Notable observations:

* First visible mildew symptoms on T4

* Mildew development appeared to be slower on the plates with SynComs

<figure><img src="image_44.png" /><figcaption>Aarti
Jaswa</figcaption></figure>
<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /><figcaption>Aarti
Jaswa</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>Valérie
Martin</figcaption></figure>
<figure><img src="image_48.png" /><figcaption>Sthyve
Tatho</figcaption></figure>

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

Genomic data

Transcriptomic
data

Metabolomic
data

<figure><img src="image_51.png" /></figure>

=> Modelling plant-microbiota-pathogen
interaction networks
# Identifying microbial antagonists in the Syncom

<figure><img src="image_52.png" /></figure>
# Trophic interactions in the SynCom

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /><figcaption>Sthyve Tatho
PhD Mistic</figcaption></figure>

<figure><img src="image_55.png" /></figure>

FRANCE
2030
PROGRAMME DE RECHERCHE EN GÉNÉRATIONNELLE

anr®

## Goal : metabolism-informed inference

<figure><img src="image_56.png" /></figure>

Inference

<figure><img src="image_57.png" /></figure>

metabolic model

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

multi-omics

<figure><img src="image_60.png" /></figure>

individual
contributions,
trophic interactions

Individual
metabolism
# Perspectives (GetUp project)

<figure><img src="image_61.png" /></figure>

**MICROBIAL BIOINDICATORS**
of epidemic risk

**MICROBIAL BIOSOLUTIONS**
to control diseases

**RESIDENT MICROBIOTA**
**MANAGEMENT**
through biodiversity
management at the plot and
landscape scales

**VARIETIES THAT ASSOCIATE**
**WITH PROTECTIVE MICROBIOTA**
<figure><img src="image_62.png" /><figcaption>SAVE Microbiota Team
(+ BC2Grape)</figcaption></figure>

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>

**AGAP** (Patrice This)

**OENO** (Guilherme Martins, Isabelle Masneuf-Pomarède)

**BFP** (Thierry Candresse, Armelle Marais)

**Univ. Birmingham**
(Marine Cambon)

**IRHS** (Marie Simonin)

**IGEPP** (Nathan Vannier)

**Moët-Hennessy IFV**
**PGTB**
**MetaboHub**