MétaCan
Menu
← Back to cohort
Record W4220996375 · doi:10.1101/2022.03.28.486083

GLOBAL HOST RESPONSES TO THE MICROBIOTA AT SINGLE CELL RESOLUTION IN GNOTOBIOTIC ZEBRAFISH

2022· preprint· en· W4220996375 on OpenAlexaff
Michelle S. Massaquoi, Garth Kong, Daisy Chilin, Mary Kristina Hamilton, Ellie Melançon, Judith S Eisen, Karen Guillemin

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Institutes of HealthUniversity of Oregon
KeywordsBiologyZebrafishCell typeImmune systemTranscriptomeCell biologyProgenitor cellCellGut floraGene expressionGeneStem cellImmunologyGenetics

Abstract

fetched live from OpenAlex

Abstract Resident microbes are a feature of vertebrate animals that influence diverse aspects of their biology from tissue development to metabolism. Here we describe transcriptional responses to the microbiota across all the cells of a model developing vertebrate, the larval zebrafish. By performing single cell transcriptomic analysis of whole germ free and conventionalized larvae, we show that the impacts of the microbiota are sensed by all major organ systems but that responses are highly specific to different cell types. The presence of microbiota stimulates the expansion of progenitor-like cells in epithelial tissues and increases proliferation gene expression in progenitor-like cell populations of the immune and nervous systems. Across many cell types, including enterocytes, immune cells, and neurons, the microbiota upregulates expression of genes involved in microbial responses, cell type-specific activities, and cell type-specific deployment of ATP metabolism genes. These combined transcriptional patterns demonstrate how the microbiota simultaneously modulate cellular immune and metabolic programs. The impacts of the microbiota on tissue development are illustrated by the exocrine pancreas, which in the absence of the microbiota is smaller and composed of uniformly differentiated acinar cells. The presence of the microbiota results in exocrine pancreas enlargement and heterogeneous cellular expression of digestive enzyme and secretion genes, demonstrating how the microbiota promotes plasticity in tissue development and function. This single cell transcriptional dataset demonstrates the impacts of the microbiota on vertebrate development across the body and provides a foundation for dissecting cell type specific responses to microbial consortia members or molecules. Summary Animal development proceeds in the presence of intimate microbial associations, but the extent to which different host cells across the body respond to resident microbes remains to be fully explored. Using the vertebrate model organism, the larval zebrafish, we assessed transcriptional responses to the microbiota across the entire body at single cell resolution. We find that cell types across the body, not limited to tissues at host-microbe interfaces, respond to the microbiota. Responses are cell-type specific, but across many tissues the microbiota enhances cell proliferation, increases metabolism, and stimulates a diversity of cellular activities, revealing roles for the microbiota in promoting developmental plasticity. This work provides a resource for exploring transcriptional responses to the microbiota across all cell types of the vertebrate body and generating new hypotheses about the interactions between vertebrate hosts and their microbiota.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.226
Teacher spread0.214 · 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 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicEpigenetics and DNA Methylation→French-language works237,207→