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Record W4213024474 · doi:10.1093/jcag/gwab049.000

A1 USING HUMAN NEONATAL ORGANOIDS TO EXPLORE GUT-IMMUNE SYSTEM INTERACTIONS OF THE NEONATAL INTESTINE

2022· article· en· W4213024474 on OpenAlexaff
Joannie M. Allaire, Z Sharafianardakani, Brenda T. Poon, H. M. Piper, Kenneth A. Jacobson, Denis Lavoie, Bruce A. Vallance

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsImmune systemNecrotizing enterocolitisOrganoidBiologyImmunologyIntestinal epitheliumIntestinal mucosaGut floraMucosal immunologyCell biologyImmunityEpitheliumMedicineInternal medicineGenetics

Abstract

fetched live from OpenAlex

Abstract Background Necrotizing Enterocolitis (NEC) affects around 10% of preterm babies and is one of the leading causes of death for newborns. NEC is characterized by exaggerated inflammation of the intestinal mucosa, possibly triggered by aberrant exposure to gut microbes, leading to hypoxic conditions and the death of intestinal tissues. It has been hypothesized that NEC develops when the immature intestine (epithelium and immune system) is unable to properly balance these new microbial interactions. To date, NEC is poorly understood and due to the difficulty of modeling the human neonatal intestine, few therapeutic options are available. Intestinal epithelial cells (IEC) are important players in promoting beneficial host-microbe interactions in the gut, being the primary barrier that separates the host’s mucosal immune system from luminal microbiota, as well as key players in mediating signaling between microbes and the host. Based on their location, IEC are also subject to injury associated with maladaptive immune responses against gut microbes. Many studies have shown that immune cells (such as T helper 17 cells) interact with IEC to promote gut health and function. These interactions include educating IEC on how to respond to, and fight pathogenic microbes, yet also remain tolerant to commensal microbes. Aims This project seeks to develop an in vitro human neonatal intestinal organoid model to study developmental changes in IEC and their functional interactions with neonatal Th17 cells. Methods 3D organoids were established from human neonatal intestinal biopsies and then co-cultured with the supernatant of differentiated Th17 cells or with recombinant cytokines, IL-17 and IL-22. Changes in barrier function, cell proliferation, production of mucins and anti-microbial peptides (AMP) were analyzed by qPCR and immunostaining. Results Using 3D neonatal organoids we observed that the supernatants from neonatal Th17 cells (containing IL-17, IL-22 etc.) promoted the proliferation, differentiation and barrier function of the neonatal epithelium. By using specific recombinant cytokine (IL22, IL17) and neutralizing IL-22 antibodies in parallel, we demonstrated that the high levels of IL-22 produced by neonatal Th17 cells specifically induced proliferation of IEC, AMP and mucus production as compared to control media treated organoids, as shown by increases in Ki67, Reg3γ and Muc2 markers. Conclusions This experimental model mimicking the neonatal intestinal environment can be used to study interactions between neonatal IEC and immune cells. Our findings can provide clinically relevant information and clues to how developmental changes in the newborn intestine can influence susceptibility to NEC while demonstrating our development of a simple, yet accurate and clinically applicable model of the neonatal gut. Funding Agencies CAG, CIHRBCCHRI, CCC, C.H.I.L.D. Fdn

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.277
Teacher spread0.252 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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