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Record W2728814758 · doi:10.3389/fmicb.2017.01212

Nutrient Acquisition and the Metabolic Potential of Photoferrotrophic Chlorobi

2017· article· en· W2728814758 on OpenAlexafffund
Katharine J. Thompson, Rachel L. Simister, Aria S Hahn, Steven Hallam, Sean A. Crowe

Bibliographic record

VenueFrontiers in Microbiology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaAgouron Institute
KeywordsNutrientBiologyCommunicationPsychologyBiochemical engineeringFood scienceEcologyEngineering

Abstract

fetched live from OpenAlex

Anoxygenic photosynthesis evolved prior to oxygenic photosynthesis and harnessed energy from sunlight to support biomass production on the early Earth. Models that consider the availability of electron donors predict that anoxygenic photosynthesis using Fe(II), known as photoferrotrophy, would have supported most global primary production before the proliferation of oxygenic phototrophs at approximately 2.3 billion years ago. These photoferrotrophs have also been implicated in the deposition of banded iron formations, the world's largest sedimentary iron ore deposits that formed mostly in late Archean and early Proterozoic Eons. In this work we present new data and analyses that illuminate the metabolic capacity of photoferrotrophy in the phylum Chlorobi. Our laboratory growth experiments and biochemical analyses demonstrate that photoferrotrophic Chlorobi are capable of assimilatory sulfate reduction and nitrogen fixation under sulfate and nitrogen limiting conditions, respectively. Furthermore, the evolutionary histories of key enzymes in both sulfur (CysH and CysD) and nitrogen fixation (NifDKH) pathways are convoluted; protein phylogenies, however, suggest that early Chlorobi could have had the capacity to assimilate sulfur and fix nitrogen. We argue, then, that the capacity for photoferrotrophic Chlorobi to acquire these key nutrients enabled them to support primary production and underpin global biogeochemical cycles in the Precambrian.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.187
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2017
Admission routes2
Has abstractyes

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