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Record W3203965477 · doi:10.1016/j.heliyon.2021.e08131

Prediction of bacterial functional diversity in clay microcosms

2021· article· en· W3203965477 on OpenAlexafffundabout
Alexander Grigorʼyan, Daphne R. Jalique, S. Stroes-Gascoyne, Gideon Wolfaardt, Peter Keech, Darren R. Korber

Bibliographic record

VenueHeliyon · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsNuclear Waste Management OrganizationToronto Metropolitan UniversityLallemand (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaNuclear Waste Management Organization
KeywordsFirmicutesMicrocosmProteobacteriaMicroorganismMicrobial population biologyBiologyMicrobial consortiumMicrobiology16S ribosomal RNAEnvironmental chemistryChemistryBacteriaEcology

Abstract

fetched live from OpenAlex

Microorganisms in clay barriers could affect the long-term performance of waste containers in future deep geological repositories (DGR) for used nuclear fuel through production of corrosive metabolites (e.g., sulfide), which is why clay materials are highly compacted: to reduce both physical space and access to water for microorganisms to grow. However, the highly compacted nature of clays and the resulting low activity or dormancy of microorganisms complicate the extraction of biomarkers (i.e., PLFA, DNA etc.) from such barriers for predictive analysis of microbial risks. In order to overcome these challenges, we have combined culture- and 16S rRNA gene amplicon sequencing-based approaches to describe the functional diversity of microorganisms in several commercial clay products, including two different samples of Wyoming type MX-80 bentonite (Batch 1 and Batch 2), the reference clay for a future Canadian DGR, and Avonlea type Canaprill, a clay sample for comparison. Microorganisms from as-received bentonites were enriched in anoxic 10% w/v clay microcosms for three months at ambient temperature with addition of 10% hydrogen along with presumable indigenous organics and sulfate in the clay. High-throughput sequencing of 16S rRNA gene fragments indicated a high abundance of Gram-positive bacteria of the phylum Firmicutes (82%) in MX-80 Batch 1 incubations. Bacterial libraries from microcosms with MX-80 Batch 2 were enriched with Firmicutes (53%) and Chloroflexi (43%). Firmicutes also significantly contributed (<15%) to the bacterial community in Canaprill clay microcosm, which was dominated by Gram-negative Proteobacteria (>70%). Sequence analysis revealed presence of the bacterial families Peptostreptococcaceae, Clostridiaceae, Peptococcaceae, Bacillaceae, Enterobacteriaceae, Veillonellaceae, Tissierellaceae and Planococcaceae in MX-80 Batch 1 incubations; Bacillaceae, along with unidentified bacteria of the phylum Chloroflexi, in MX-80 Batch 2 clay microcosms, and Pseudomonadaceae, Hydrogenophilaceae, Bacillaceae, Desulfobacteraceae, Desulfobulbaceae, Peptococcaceae, Pelobacteraceae, Alcaligenaceae, Rhodospirillaceae in Canaprill microcosms. Exploration of potential metabolic pathways in the bacterial communities from the clay microcosms suggested variable patterns of sulfur cycling in the different clays with the possible prevalence of bacterial sulfate-reduction in MX-80 bentonite, and probably successive sulfate-reduction/sulfur-oxidation reactions in Canaprill microcosms. Furthermore, analysis of potential metabolic pathways in the bentonite enrichments suggested that bacteria with acid-producing capabilities (i.e., fermenters and acetogens) together with sulfide-producing prokaryotes might perhaps contribute to corrosion risks in clay systems. However, the low activity or dormancy of microorganisms in highly compacted bentonites as a result of severe environmental constraints (e.g., low water activity and high swelling pressure in the confined bentonite) in situ would be expected to largely inhibit bacterial activity in highly compacted clay-based barriers in a future DGR.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.201

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 studies0.0000.000
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 head0.023
GPT teacher head0.229
Teacher spread0.206 · 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

Citations6
Published2021
Admission routes3
Has abstractyes

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