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Record W4281905638 · doi:10.1186/s40168-022-01270-7

Linking gastrointestinal microbiota and metabolome dynamics to clinical outcomes in paediatric haematopoietic stem cell transplantation

2022· article· en· W4281905638 on OpenAlexaff
Gintare Vaitkute, Gordana Panić, Dagmar Alber, Intan Faizura-Yeop, Elaine Cloutman-Green, Jonathan R. Swann, Paul Veys, Joseph F. Standing, Nigel Klein, Mona Bajaj‐Elliott

Bibliographic record

VenueMicrobiome · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsInstitute of Infection and Immunity
FundersBiotechnology and Biological Sciences Research CouncilNIHR Great Ormond Street Hospital Biomedical Research CentreGreat Ormond Street Hospital CharityMedical Research CouncilNational Institute for Health and Care Research
KeywordsBiologyTransplantationMetabolomeLachnospiraceaeMicrobiomeHematopoietic stem cell transplantationImmunologyInternal medicineMedicineBioinformaticsBacteriaFirmicutesGeneticsMetabolomics

Abstract

fetched live from OpenAlex

BACKGROUND: Haematopoietic stem cell transplantation is a curative procedure for a variety of conditions. Despite major advances, a plethora of adverse clinical outcomes can develop post-transplantation including graft-versus-host disease and infections, which remain the major causes of morbidity and mortality. There is increasing evidence that the gastrointestinal microbiota is associated with clinical outcomes post-haematopoietic stem cell transplantation. Herein, we investigated the longitudinal dynamics of the gut microbiota and metabolome and potential associations to clinical outcomes in paediatric haematopoietic stem cell transplantation at a single centre. RESULTS: On admission (baseline), the majority of patients presented with a different gut microbial composition in comparison with healthy control children with a significantly lower alpha diversity. A further, marked decrease in alpha diversity was observed immediately post-transplantation and in most microbial diversity, and composition did not return to baseline status whilst hospitalised. Longitudinal trajectories identified continuous fluctuations in microbial composition, with the dominance of a single taxon in a significant proportion of patients. Using pam clustering, three clusters were observed in the dataset. Cluster 1 was common pre-transplantation, characterised by a higher abundance of Clostridium XIVa, Bacteroides and Lachnospiraceae; cluster 2 and cluster 3 were more common post-transplantation with a higher abundance of Streptococcus and Staphylococcus in the former whilst Enterococcus, Enterobacteriaceae and Escherichia predominated in the latter. Cluster 3 was also associated with a higher risk of viraemia. Likewise, further multivariate analysis reveals Enterobacteriaceae, viraemia, use of total parenteral nutrition and various antimicrobials contributing towards cluster 3, Streptococcaceae, Staphylococcaceae, Neisseriaceae, vancomycin and metronidazole contributing towards cluster 2. Lachnospiraceae, Ruminococcaceae, Bifidobacteriaceae and not being on total parenteral nutrition contributed to cluster 1. Untargeted metabolomic analyses revealed changes that paralleled fluctuations in microbiota composition; importantly, low faecal butyrate was associated with a higher risk of viraemia. CONCLUSIONS: These findings highlight the frequent shifts and dominations in the gut microbiota of paediatric patients undergoing haematopoietic stem cell transplantation. The study reveals associations between the faecal microbiota, metabolome and viraemia. To identify and explore the potential of microbial biomarkers that may predict the risk of complications post-HSCT, larger multi-centre studies investigating the longitudinal microbial profiling in paediatric haematopoietic stem cell transplantation are warranted. Video abstract.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.269
Teacher spread0.258 · 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 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

Citations19
Published2022
Admission routes1
Has abstractyes

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