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Record W3215009118 · doi:10.1101/2021.11.30.467465

Microbial cysteine degradation is a source of hydrogen sulfide in oxic freshwater lakes

2021· preprint· en· W3215009118 on OpenAlexfundno aff
Patricia Q. Tran, Samantha C. Bachand, Jacob C. Hotvedt, Kristopher Kieft, Elizabeth McDaniel, Katherine D. McMahon, Karthik Anantharaman

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaGreat Lakes Bioenergy Research CenterNational Institute of Food and AgricultureJoint Genome InstituteUniversity of Wisconsin-MadisonOffice of ScienceU.S. Department of AgricultureU.S. Department of EnergyNational Science Foundation
KeywordsGammaproteobacteriaBacteroidetesMetagenomicsProteobacteriaBiologyStenotrophomonasActinobacteriaCysteineSulfurBiogeochemical cycleEnvironmental chemistryChemistryEcologyBiochemistryGene16S ribosomal RNA

Abstract

fetched live from OpenAlex

Abstract The sulfur-containing amino acid cysteine is abundant in the environment including in freshwater lakes. Biological cysteine degradation can result in hydrogen sulfide (H 2 S), a toxic and ecologically relevant compound that is a central player in biogeochemical cycling in aquatic environments. Here, we investigated the ecological significance of cysteine in oxic freshwater lake environments, using isolated cultures, controlled growth experiments, and multi-omics. We screened bacterial isolates enriched from natural lake water for their ability to produce H 2 S when provided cysteine. In total, we identified 29 isolates that produced H 2 S and belonged to the phyla Bacteroidetes, Proteobacteria, and Actinobacteria . To understand the genomic and genetic basis for cysteine degradation and H 2 S production, we further characterized 3 freshwater isolates using whole-genome sequencing (using a combination of short-read and long-read sequencing), and quantitatively tracked cysteine and H 2 S levels over their growth ranges: Stenotrophomonas maltophilia (Gammaproteobacteria), Stenotrophomonas bentonitica (Gammaproteobacteria) and Chryseobacterium piscium (Bacteroidetes). We observed a decrease in cysteine and increase in H 2 S, and identified genes involved in cysteine degradation in all 3 genomes. Finally, to assess the presence of these organisms and genes in the environment, we surveyed a five-year time series of metagenomic data from the same isolation source (freshwater Lake Mendota, WI, USA) and identified their presence throughout the time series. Overall, our study shows that sulfur-containing amino acids can drive microbial H 2 S production in oxic environments. Future considerations of sulfur cycling and biogeochemistry in oxic environments should account for H 2 S accumulation from degradation of organosulfur compounds. Importance Hydrogen sulfide (H 2 S), a naturally occurring gas with biological origins, can be toxic to living organisms. In aquatic environments, H 2 S production typically originates from anoxic (lacking oxygen) environments such as sediments, or the bottom layers of thermally stratified lakes. However, the degradation of sulfur-containing amino acids such as cysteine, which all cells and life forms rely on, can be a source of ammonia and H 2 S in the environment. Unlike other approaches for biological H 2 S production such as dissimilatory sulfate reduction, cysteine degradation can occur in the presence of oxygen. Yet, little is known about how cysteine degradation influences sulfur availability and cycling in freshwater lakes. In our study, we identified diverse bacteria from a freshwater lake that can produce H 2 S in the presence of O 2 . Our study highlights the ecological importance of oxic H 2 S production in natural ecosystems and necessitates a change in our outlook of sulfur biogeochemistry.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.010
GPT teacher head0.198
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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