Multi-omics highlights challenges in assessing the composition and performance of microbial consortia for commercial applications
Bibliographic record
Abstract
Abstract The use of commercial microbial consortia in bioremediation is a promising method for addressing environmental pollution. These consortia are comprised of complex communities that include unculturable species that make it challenging to optimize consortia performance and carry out risk assessments for regulatory purposes. In this study, we provide a framework for using multi-omics to monitor the composition and performance of an aerobic ammonia oxidizing consortium in development for wastewater treatment. Long-read sequencing showed the consortium was dominated by an unclassified Nitrosospira species with the capacity for ammonia oxidation with many lower abundance taxa displaying the potential for denitrification. Considerable shifts in community composition and nitrogen cycling occurred when the consortium was grown along a redox gradient representative of wastewater for eight weeks. All aerobic and anaerobic cultures produced ammonia during the first four weeks and only aerobic cultures decreased ammonia concentrations after that time. Shotgun metagenomic sequencing showed the key ammonia oxidizing Nitrosospira sp. population decreased in abundance in aerobic cultures yet remained dominant in anaerobic cultures. Shotgun metatranscriptomic sequencing revealed that aerobic cultures decreased ammonia oxidation activity during the incubation and taxa that precluded detection in the starting material likely contributed to denitrification in anaerobic cultures. Metatranscriptomics showed that deamination of amino acids was an unexpected contributor to ammonia production that could negatively affect consortium performance. This study highlights how multi-omics provides insights that can be used to optimize performance and carry out risks assessments for consortia applied in different environmental settings. Importance The use of microbial consortia from diverse environments is gaining traction in terms of advancing a more sustainable bioeconomy. Optimizing consortia for different applications and ensuring they are compliant with environmental regulations is difficult because current practices rely on growing microbes with unknown physiological requirements. In this study we apply leading-edge sequencing approaches to develop a framework that addresses these challenges using a consortium in development for ammonia removal from wastewater. We demonstrate that long-read DNA sequencing provides complete genome assemblies and functional insights into key populations involved in ammonia removal that are poorly represented in taxonomic databases. We show that coupling DNA to RNA sequencing provides valuable information on changes in composition and metabolic activity that can occur under environmentally representative conditions for wastewater. Ultimately, our approach serves as an example of cutting-edge genomics applications for stakeholders to consider in developing microbial consortia for safe and effective use across diverse applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".