Multi-omics provides functional insights and underscores practical challenges in assessing the composition and performance of a nitrifying microbial consortium
Notice bibliographique
Résumé
ABSTRACT Microbial consortia show promise for bioremediation of environmental pollution, but performance optimization and risk assessment remain challenging due to unculturable species and limitations of traditional biochemical and sequencing tools. This study demonstrates how a multi-omics approach can provide deeper insight into the performance and risks of using a model aerobic ammonia-oxidizing consortium under conditions representative of wastewater treatment. Long-read DNA sequencing recovered several high-quality genomes, revealing dominance by an unclassified Nitrosospira species with expected ammonia oxidation capabilities. Lower-abundance taxa with nitrogen cycling potential were also detected, though species-level identification was limited by poor taxonomic database representation. Multi-omics and nitrogen analyses showed shifts in community composition and nitrogen cycling activity when the consortium was grown along a redox gradient typical of wastewater. All cultures accumulated ammonia over 4 weeks, with only aerobic cultures reducing ammonia levels thereafter. The dominant Nitrosospira population declined in abundance and activity in aerobic cultures while shifting toward nitrogen reduction under anoxic conditions. This metabolic shift would not have been detected using amplicon sequencing alone. Multi-omics also supported risk assessment through detection of waterborne pathogens from the Legionella genus and other lineages harboring virulence genes resembling those from known pathogens. This study highlights the value of multi-omics for optimizing microbial consortia and assessing biosafety risks but also underscores challenges related to effective data analyses and the feasibility of risk assessment under realistic conditions. Addressing these challenges will be essential to support the broader adoption of multi-omics strategies by stakeholders working with microbial consortia across diverse environmental applications. IMPORTANCE Microbial consortia are increasingly used to advance a sustainable bioeconomy. Optimizing consortia for environmental applications and ensuring regulatory compliance remains challenging, largely due to reliance on culturing microbes with unknown physiology. In this study, we apply cutting-edge sequencing to a consortium designed for ammonia removal from wastewater. Long-read DNA sequencing enabled complete genome recovery and revealed that populations integral to nitrogen cycling are poorly represented in taxonomic databases. By integrating multi-omics with biochemical assays, we uncovered how environmental conditions drive off-target nitrogen reactions and the potential risks of exposure to pathogens carrying virulence genes. Our findings underscore how whole-community approaches provide insights that are not obtainable with traditional amplicon sequencing and biochemical analysis methods. However, our study also provides recommendations on how hurdles related to data integration and environmental representation must be addressed to support stakeholders adopting such approaches in the context of commercializing microbial consortia.
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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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».