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Record W4407528086 · doi:10.1128/msphere.00789-24

Characterization of diet-linked amino acid pool influence on <i>Fusobacterium</i> spp. growth and metabolism

2025· article· en· W4407528086 on OpenAlexafffund
Avery Robinson, Sarah J. Vancuren, Massimo F. Marcone, Emma Allen‐Vercoe

Bibliographic record

VenuemSphere · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Guelph
FundersNational Institute of Dental and Craniofacial ResearchCanadian Institutes of Health ResearchCancer Research UKNational Institutes of HealthCase Western Reserve University
KeywordsFusobacteriumAmino acidFusobacteriaBiologyMicrobiologyBacteriaBiochemistryFood scienceBacteroides16S ribosomal RNAGenetics

Abstract

fetched live from OpenAlex

ABSTRACT The genus Fusobacterium contains multiple proteolytic opportunistic pathogens that have been increasingly linked to colorectal cancer (CRC). “Oncomicrobes” such as these fusobacterial species within the gut microbiota may contribute to CRC onset and/or progression. Protein-rich diets may both directly increase CRC risk and enrich for proteolytic oncomicrobes, including Fusobacterium spp. Individual food substrates vary in amino acid content, and released amino acid content that is not absorbed in the small intestine may influence the growth of colonic proteolytic fermenters. Fusobacteria such as Fusobacterium spp. are known to preferentially metabolize certain amino acids. As such, some foods may better support the growth of these species within the colonic environment than others. To explore this, in this study, we created free amino acid pools (FAAPs) to represent proportions of amino acids in major proteins of three common dietary protein sources (soy, beef, and bovine milk). Growth curves were generated for 39 Fusobacterium spp. strains cultured in a dilute medium supplemented with each of the three FAAPs. Thereafter, amino acid use by 31 of the 39 Fusobacterium spp. strains in each FAAP treatment was assessed. FAAP supplementation increased growth metrics of all Fusobacterium spp. strains tested; however, the strains varied greatly in terms of the FAAP(s) generating the greatest increase in growth. Furthermore, the amino acid utilization strategy was highly variable between strains of Fusobacterium spp. Neither growth metrics nor amino acid utilization could be explained by species classification of Fusobacterium spp. strains. This report expands upon the previous knowledge of fusobacterial amino acid metabolism and indicates that proteolytic oncomicrobial activity should be assessed in the context of available protein sources. IMPORTANCE Fusobacterium spp. including F. animalis , F. nucleatum , F. vincentii , and F. polymorphum are common oral commensals with emerging importance in diseases across multiple body sites, including CRC. CRC lesions associated with fusobacteria tend to result in poorer prognosis and increased disease recurrence. While Fusobacterium spp. are thought to colonize after tumorigenesis, little is known about the factors that facilitate this colonization. Protein-rich diets yielding readily metabolized free amino acids within the colon may promote the growth of proteolytic fermenters such as fusobacteria. Here, we show that variable concentrations of free amino acids within pools that represent different dietary protein sources differentially influence fusobacterial growth, including CRC-relevant strains of Fusobacterium spp. This work highlights the high degree of variation in fusobacterial amino acid utilization patterns and suggests differing proportions of dietary amino acids that reach the colon could influence fusobacterial growth.

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 score0.352
Threshold uncertainty score0.451

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 head0.004
GPT teacher head0.225
Teacher spread0.221 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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