# Network-informed design of resilient microbial consortia with a biocontrol activity

Corinne Vacher

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## To cite this version:

Corinne Vacher. Network-informed design of resilient microbial consortia with a biocontrol activity. Journées annuelles du Consortium Biocontrôle, Nov 2023, Angers, France. hal-05297882

HAL Id: hal-05297882

https://hal.inrae.fr/hal-05297882v1

Submitted on 5 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
> Network-informed design of resilient microbial consortia with a biocontrol activity

Corinne VACHER (INRAE-Bordeaux)

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RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

INRAE

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Peredo & Simons. Frontiers Microb. 2018
**BCMicrobiome (2018-22)** - Learning microbial networks from metabarcoding data: application to biological control

PI: Corinne VACHER & David BOHAN (INRAE)

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**WheatSimpCom (2023-25)** - Identifying microbial candidates and designing a simplified community for biocontrol in wheat

PI: Qassim ESMAEEL (Univ. Reims)

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**VITAE (2020-26)** - Growing grapevine without pesticide

PI: François DELMOTTE (INRAE) & Hervé HANNIN (Institut Agro)

**WP1 - Microbiome management** (Corinne VACHER & Simon LABARTHE)

**WP2 - Biocontrol development** (Marielle ADRIAN)

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CULTIVER
PROTÉGER
autrement

**MISTIC (2022-27)** - Microbiomes of cultivated plants and ICT

PI: David SHERMAN (INRIA) & Pierre ABAD (INRAE)

**WP1 - Metabolic networks** (Clémence FRIOUX)

**WP2 - Digital twins** (Simon LABARTHE)

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Agroécologie
et Numérique

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# Plant-pathogen-microbiome interactions under climate change

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# How to identify key microbes and disentangle interaction mechanisms?

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Current Opinion in Microbiology

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# How to identify key microbes and disentangle interaction mechanisms?

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Current Opinion in Microbiology

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# Objectives of the BCMicrobiome project (2018-22)

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1. Develop theoretical methods to reconstruct microbial networks from metabarcoding data

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* Go beyond co-occurrence networks

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* Infer interaction types

2. Use these methods to elucidate the pathobiome of two major plant pathogens

<figure><img src="image_20.png" /><figcaption>Plasmopara viticola (downy mildew)</figcaption></figure>

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Metagenomic Next-Generation Sequencing (mNGS)
Data Reveal the Phyllosphere Microbiome of Wheat
Plants Infected by the Fungal Pathogen
*Zymoseptoria tritici*

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Leaf Microbiome Data for European Cultivated
Grapevine (Vitis vinifera) During Downy Mildew
(Plasmopara viticola) Epidemics in Three Wine-
Producing Regions in France

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Unravelling the web of dark
interactions: Explainable
inference of the diversity of
microbial interactions

CONSORTIUM
**biocontrôle**
BCMicrobiome

<figure><img src="image_26.png" /><figcaption>Didac Barroso-Bergadà,
UMR Agroécologie</figcaption></figure>

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INRAO
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# Metagenomic Next-Generation Sequencing (mNGS)
## Data Reveal the Phyllosphere Microbiome of Wheat
## Plants Infected by the Fungal Pathogen
### Zymoseptoria tritici

CONSORTIUM
**biocontrôle**
BCMicrobiome

nytobiome

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**Phytobiomes Journal**

@PhytobiomesJ · Follow

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The most read paper from 2023 thus far! Didac Barroso-Bergadà et al. provide fungal and bacterial #metabarcoding datasets obtained by sampling #wheat leaves with and without symptoms caused by #Zymoseptoriatritici.

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Frédéric Suffert et Valérie Laval, BIOGER

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INRAO

Microbial networks for biocontrol

15 nov. 2023 /Angers / Corinne VACHER
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# Leaf Microbiome Data for European Cultivated Grapevine (Vitis vinifera) During Downy Mildew (Plasmopara viticola) Epidemics in Three Wine-Producing Regions in France

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**biocontrôle**
BCMicrobiome

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INRAO

Microbial networks for biocontrol

15 nov. 2023 / Angers / Corinne VACHER
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# Unravelling the web of dark interactions: Explainable inference of the diversity of microbial interactions

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https://github.com/didacb/InfIntE

## What is InflIntE?

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InflIntE stands for Inference of Interactions using Explainable machine learning. This package uses abundance data to directly infer ecological interactions using PyGol, an Abductive/Inductive logic program, classified by their interaction type.

## Table of contents

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1. Installation

2. Example Data

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Wheat dataset

3. Interaction Inference

```R
# Take values from abduced effects dataframe
interactions <- abduced Effects[paste0(abduced Effects[, 1], abduced Effects[, 2])
  %in% paste0(interactions[, 1], interactions[, 2]), ]
```

```R
# Classify and give back original names
interactions <- classify_interactions(interactions)
interactions <- return_names(interactions, otu_data$otu_names)
```

R code

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Toy example

* To be continued in WheatSimpCom

* Partial resequencing to improve taxonomy (rpoD, gyrB)

* Experimental validation
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Proof of concept of the BCMicrobiome project
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# Unravelling the web of dark interactions: Explainable inference of the diversity of microbial interactions

## Test on simulated data

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## Application to real data (grapevine)

* 12 fungal species which might compete with *P. viticola*

* 5 species included in VITAE for experimental validation

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vitae

Cultiver la vigne sans pesticides

BC2GRPE
BioControl to Grape

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Select key microbes

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SynCom evaluation and monitoring

Biocontrol consortia

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Microbial dynamics prediction

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Culturomics

Large-scale sampling

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Metabarcoding and ecological inference

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INRAE

Microbial networks for biocontrol

15 nov. 2023 /Angers / Corinne VACHER

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vitae
Cultiver la vigne sans pesticides

Large-scale sampling

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INRAE

Microbial networks for biocontrol

15 nov. 2023 /Angers / Corinne VACHER

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Culturomics

2020-23

Metabarcoding and
ecological inference

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Select key
microbes

BC2GRPE
BioControl to Grape

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SynCom evaluation
and monitoring

Biocontrol
consortia

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2024-26

Microbial dynamics
prediction

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MISTIC

Large-scale sampling

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Culturomics and
functional inference

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Metabarcoding and
ecological inference

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INRAE

Microbial networks for biocontrol

15 nov. 2023 /Angers / Corinne VACHER

Select key
microbes

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SynCom evaluation
and monitoring

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BC2GRPE
BioControl to Grape

Biocontrol
consortia

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Digital twin

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## Key applied perspectives

* **Screen key microbial taxa (BCA, hub taxa) using network inference**
  *Output of BCMicrobiome/WheatSimpCom*
  *Collab. INRIA*

* **Monitor key taxa in environmental reservoirs using molecular tools**
  *Collab. BC2grape/IAGE*

* **Control plant immunity to favor the recruitment of key taxa**
  *Collab. M. Adrian, WP2 VITAE*

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Adapted from Bettenfeld et al. *J. Adv. Res.* 2022

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> Projects members

**ISVV (Bordeaux)**

UMR SAVE
UMR BFP
UMR OENO
UMT SEVEN (IFV)
BC2GRAPE

GRAPEVINE

**IUVV (Dijon)**

UMR Agroécologie (M. Adrian)

**Montpellier**

UMR AGAP

**Univ. Cornell, USA**

Lance Cadle-Davidson (USDA)

MICROBIAL
NETWORKS

**Bordeaux**

INRIA (Pléïade)
UMR SAVE

**Dijon**

UMR Agroécologie
(D. Bohan)

**Saclay**

UMR MIA
UMR BIOGER

**Bordeaux**

PGTB
BC2GRAPE

MOLECULAR
TOOLS

**Montpellier**

IAGE

**Saclay**

UMR BIOGER

**Reims**

UMR RIBP

WHEAT

**Lyon**

UMR LEM

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Many thanks for your attention and support

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Corinne VACHER (**corinne.vacher@inrae.fr**)

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