Viral diversity and dynamics and CRISPR-Cas-mediated immunity in a robust alkaliphilic cyanobacterial consortium
Bibliographic record
Abstract
ABSTRACT In many industries, from food to biofuels, contamination of production systems with predators is a costly problem and requires the maintenance of sterile operating conditions. In this study, we look at the robustness of one such alkaliphilic consortium, comprised largely of a cyanobacterium Candidatus Phormidium alkaliphilum, to viral predation. This consortium has existed without a community crash for several years in laboratory and pilot-scale environments. We look at clustered regularly interspaced short palindromic repeat (CRISPR)-Cas systems and viral dynamics in this consortium at four conditions using metagenomic analyses. Results show that while there are active viral members in this community, viral predation of the cyanobacteria is low and does not affect the community dynamics. The multiple CRISPR arrays within the Phormidium were found to be static following the initial lab establishment of consortium. Multiple cryptic CRISPR-Cas systems were detected with uncertain viral protection capacity. Our results suggest that the dynamics of potential viruses and CRISPR-Cas-mediated immunity likely play an important role in the initial establishment of consortia and may continue to support the functional robustness of engineered microbial communities throughout biotechnology applications. IMPORTANCE Biotechnology applications utilizing the function of microbial communities have become increasingly important solutions as we strive for sustainable applications. Although viral infections are known to have a significant impact on microbial turnover and nutrient cycling, viral dynamics have remained largely overlooked in these engineered communities. Predatory perturbations to the functional stability of these microbial biotechnology applications must be investigated in order to design more robust applications. In this study, we closely examine virus-microbe dynamics in a model microbial community used in a biotechnology application. Our findings suggest that viral dynamics change significantly with environmental conditions and that microbial immunity may play an important role in maintaining functional stability. We present this study as a comprehensive template for other researchers interested in exploring predatory dynamics in engineered microbial communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".