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IVES 9 IVES Conference Series 9 OIV 9 OIV 2024 9 Orals - Viticulture, table grapes, dried grapes and unfermented grape products 9 OmicBots – An innovative and intelligent multi-omics platform facing wine sector challenges

OmicBots – An innovative and intelligent multi-omics platform facing wine sector challenges

Abstract

To address emerging competition and challenges, wine producers globally rely on Precision Viticulture (PV) solutions to improve vineyard management. However, traditional PV methods depend on multispectral sensor data which often lack in grapevine physiological insights. The Omicbots project aims to develop an automated platform that combines low-cost robotic sensors with artificial intelligence and systems biology (in silico) to better understand how the physiology and metabolism of grapevines are influenced by the interaction of Genotype, Environment, and Management (GxExM). By integrating multi-omics approaches with smart sensing technology, this project intends to bridge the gap between vineyard mapping and plant phenomics attributes. As a key partner in this project and a leader in the grape-wine industry, ADVID / CoLAB VINES & WINES team has established experimental plots in two distinct locations within the Douro Region. Data was collected from these plots, for two years, involving two grapevine varieties and varying irrigation and sunlight exposure conditions. Leaf and grape samples were collected and used for transcriptional analyses, focusing on genes associated with different grapevine biosynthetic pathways. This study showed that UFGT, PAL1, and CHS3 genes respond differently through growth stages and irrigation levels, highlighting their sensitivity to water stress and the need for optimal irrigation for grapevine productivity. RCA, LBCY2, and CHLG3 in leaves indicate adaptive responses to water availability, with varying sensitivity among genes. Sunlight exposure significantly promotes gene expression, particularly for light-sensitive genes like PAL1 and CHS genes.

Omicbots – une plateforme multi-omique innovante et intelligente pour répondre aux défis du secteur vitivinicole

Pour répondre à la concurrence et aux défis émergents, les producteurs de vin du monde entier s’appuient sur des solutions de viticulture de précision (VP) pour augmenter la productivité, améliorer la qualité, renforcer la rentabilité et réduire l’impact des vignobles sur l’environnement. Les méthodes vp actuelles utilisent surtout des données de capteurs multispectraux provenant de différentes plateformes (satellites ou installations dans les vignobles). Cependant, ces applications utilisent généralement des stratégies d’analyse de données qui manquent de support physiologique pour la vigne. Dans ce contexte, ce travail vise à développer une plateforme automatique qui intègre des capteurs robotiques à faible coût avec l’intelligence artificielle et la biologie des systèmes (in silico), pour explorer les voies métaboliques de la vigne, afin de comprendre comment la physiologie/métabolisme de la vigne est influencée par l’interaction génotype x environnement x « management » (gxexm).  L’intégration d’approches multi-omiques, comprenant des analyses phénomiques, génomiques, métabolomiques et transcriptomiques avec l’utilisation de la détection intelligente, en tenant compte des phénotypes interdépendants, des métabolites et des gènes connexes, peut combler le fossé entre la cartographie des vignobles et les attributs phénomiques des plantes. Dans cette étude, des parcelles expérimentales ont été établies dans deux localisations distincts de la région du douro, connue pour sa vulnérabilité au changement climatique. La collecte de données sur le terrain s’est déroulée sur deux ans, avec deux variétés de vigne et différentes conditions d’irrigation et d’exposition à la lumière du soleil. L’acquisition de données spectrales sur le terrain a été suivie d’un échantillonnage de feuilles et de raisins pour des analyses transcriptionnelles ciblant les gènes liés aux différentes voies de biosynthèse de la vigne. Les données collectées ont apporté des informations précieuses sur la réponse de la vigne à l’interaction gxexm. Cet ensemble de données sera également utilisé pour développer et améliorer une nouvelle plateforme de criblage des plantes qui permettra à l’industrie de la vigne et du vin de disposer d’une solution technologique vp innovante afin d’améliorer la prise de décision dans la gestion des vignobles et d’accroître la durabilité et la productivité.

Remerciements : recherche financée par des fonds nationaux de la fct – fondation pour la science et la technologie du portugal, dans le cadre du projet ptdc/asp-hor/1338/2021. L. Pereira et c. Barbosa ont également bénéficié d’un supporte financier du colab vines&wines (programme de financement re-c05-i02 – mission interface nº 01/c05-i02/2022).

Omicbots – una plataforma multiómica innovadora e inteligente para responder a los retos del sector vitivinícola

Para responder a la competencia y los retos emergentes, los productores de vino de todo el mundo recurren a soluciones de viticultura de precisión (VP) para impulsar la productividad, mejorar la calidad, aumentar la rentabilidad y reducir el impacto ambiental de los viñedos. Los métodos actuales de vp utilizan predominantemente datos de sensores multiespectrales procedentes de varias plataformas (satélites o instalaciones en viñedos). Sin embargo, estas aplicaciones generalmente emplean estrategias de análisis de datos que carecen de soporte fisiológico de la vid. En este contexto, este trabajo tiene como objetivo desarrollar una plataforma automática que integre sensores robóticos de bajo costo con inteligencia artificial y biología de sistemas (in silico), para explorar las vías metabólicas de la vid, y comprender mejor cómo la fisiología/metabolismo de la vid está determinada por la interacción genotipo x ambiente x manejo (gxexm). La integración de enfoques multi-ómicos, que incluyen estudios fenómicos, genómicos, metabolómicos y transcriptómicos con el uso de sensores inteligentes, considerando fenotipos interdependientes, metabolitos y genes relacionados, puede cerrar la brecha entre el mapeo de viñedos y los atributos fenómicos de las plantas. En este estudio se establecieron parcelas experimentales en dos localizaciones distintas de la región del douro, conocida por su vulnerabilidad al cambio climático. La recogida de datos en campo comprendió dos años, con dos variedades de vid y diferentes condiciones de riego y exposición a la luz solar. La adquisición de datos espectrales en el campo fue seguida de un muestreo de hojas y uvas para análisis transcripcionales dirigidos a genes relacionados con diferentes vías biosintéticas de la vid. Los datos recogidos proporcionaron información valiosa sobre la respuesta de la vid a la interacción gxexm. Este conjunto de datos también se utilizará para desarrollar y mejorar una nueva plataforma de cribado de plantas que proporcionará al sector vitivinícola una innovadora solución de vp para mejorar la toma de decisiones en la gestión de viñedos y así potenciar la sostenibilidad y la productividad.

Agradecimientos: investigación financiada por fondos nacionales por la fct – fundación portuguesa para la ciencia y la tecnología, en el proyecto ptdc/asp-hor/1338/2021. L. Pereira e c. Barbosa fueran también apoyadas financieramente pelo colab vines&wines (programa de financiamento re-c05-i02 – missão interface nº 01/c05-i02/2022).

DOI:

Publication date: November 18, 2024

Issue: OIV 2024

Type: Article

Authors

Leonor Pereira¹, Catarina Barbosa¹, Igor Gonçalves¹, Ana Morais¹, Rui Soares², Maria Manuel Maia³, Rafael Mendes⁴, Igor Portis⁴,⁵, Renan Tosin⁴, Rui Martins⁵, Filipe Santos⁵, Conceição Santos⁴, Mário Cunha⁴,

¹ ADVID – Associação para o Desenvolvimento da Viticultura Duriense | COLAB Vines & Wines, Vila Real, Portugal
² Real Companhia Velha, Portugal
³ Sociedade Vinícola Terras de Valdigem, SA, Portugal
⁴ FCUP – Faculdade de Ciências da Universidade do Porto, Porto, Portugal
⁵ INESC TEC – Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Porto, Portugal

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Full papers OIV 2024 | IVES Conference Series | OIV | OIV 2024

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