Multi-omics provides functional insights and underscores practical challenges in assessing the composition and performance of a nitrifying microbial consortium
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".