Characterizing the Rhizosphere Microbial Community of Food Crops in a Changing Climate
Notice bibliographique
Résumé
In recent years, there have been increased occurrences of food-borne illness in Canada, which is not directly associated with animal-related products but is caused by the increasing prevalence of pathogenic outbreaks in fresh produce. This can have health and economic impacts on the agriculture industry in Canada. As such, it is essential to better understand the plant-microbe interactions of crops grown in Canada and determine how climate change may alter the relationship. Plants can promote the growth of beneficial bacteria in the rhizosphere, which can create protective competition between beneficial and pathogenic bacteria. Changes in the environment of the rhizosphere can lead to an imbalance in the community. They may allow pathogenic bacteria to infect plant tissue or become incorporated into the rhizosphere community, where the pathogenic bacteria can redistribute to the surrounding environment. Not only do different plant species promote the growth of different bacterial community compositions, but abiotic factors such as temperature, nutrient levels, and humidity also impact community profiles. By better understanding the microbial community dynamics under changing environmental conditions, we could promote the growth of diverse and abundant microbial communities to increase plant resilience against pathogenic bacteria and climate change, thereby supporting healthy crops for optimal food output. Using Community Level Physiological Profiling (CLPP) and Denaturing Gradient Gel Electrophoresis (DGGE) to assess the functional and structural fingerprint of the bacterial community, the metabolic fingerprints of microbial communities associated with kale, broccoli, cabbage, and spinach at two locations and temperatures were analysed. The crops were shown to have distinct metabolic and genetic communities that shifted with temperature increases, location, and across species. The microbial community of crops shared similar metabolic profiles in the rhizoplane regardless of species and cultivar but showed distinct groupings in the genetic profiles. As nutrient levels increased there was a loss of differentiation between the bulk soil and rhizosphere communities metabolically as the nutrients increased microbial activity in the bulk soil to rhizosphere levels. The increased temperature caused similar loss of metabolic distinction, as the stress on the plants caused the release of similar metabolites into the rhizoplane, increasing metabolic activity, diversity, and similarity between all crop microbial communities. Temperature caused similarity to increase genetically between rhizoplane fractions of the different crops. Potential foodborne pathogen presence was mostly unaffected by temperature and location, with the greatest difference occurring between fraction types. Overall, this research showed changes in metabolic and genetic profiles of microbial communities associated with Canadian food crops under different conditions in rising temperatures.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».