Ecological Interactions of Cyanobacteria and Heterotrophs Enhances the Robustness of Cyanobacterial Consortium for Carbon Sequestration
Bibliographic record
Abstract
Lack of robustness is a major barrier to foster a sustainable cyanobacterial biotechnology. Use of cyanobacterial consortium increases biodiversity, which provides functional redundancy and prevents invading species from disrupting the production ecosystem. Here we characterized a cyanobacterial consortium enriched from microbial mats of alkaline soda lakes in BC, Canada, at high pH and alkalinity. This consortium has been grown in open laboratory culture for 4 years without crashes. Using shotgun metagenomic sequencing, 29 heterotrophic metagenome-assembled-genomes (MAGs) were retrieved and were assigned to Bacteroidota, Alphaproteobacteria , Gammaproteobacteria , Verrucomicrobiota , Patescibacteria , Planctomycetota , and Archaea. In combination with metaproteomics, the overall stability of the consortium was determined under different cultivation conditions. Genome information from each heterotrophic population was investigated for six ecological niches created by cyanobacterial metabolism and one niche for phototrophy. Genome-resolved metaproteomics with stable isotope probing using 13 C-bicarbonate (protein/SIP) showed tight coupling of carbon transfer from cyanobacteria to the heterotrophic populations, specially Wenzhouxiangella. The community structure was compared to a previously described consortium of a closely related cyanobacteria, which indicated that the results may be generalized. Productivity losses associated with heterotrophic metabolism were relatively small compared to other losses during photosynthesis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".