Evidence map›Paper›PMID 26863526›Full record

ArticlePloS one2016

Human Liver Infection in a Dish: Easy-To-Build 3D Liver Models for Studying Microbial Infection.

Debora B Petropolis, Daniela M Faust, Matthieu Tolle, Lise Rivière, Tanguy Valentin, Christine Neuveut, Nora Hernandez-Cuevas, Alexandre Dufour, Jean-Christophe Olivo-Marin, Nancy Guillen

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.4field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Review
  3. IncreasedLife (Basel, Switzerland) · 2023
    Article
  4. Review
  5. Review
  6. Review
  7. Unraveling the transcriptional determinants of liver sinusoidal endothelial cell specialization.American journal of physiology. Gastrointestinal and liver physiology · 2020
    Article
  8. Engineered Livers for Infectious Diseases.Cellular and molecular gastroenterology and hepatology · 2018
    Review
  9. Review
  10. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Debora B PetropolisCell Biology of Parasitism Unit, Inserm U786, BCI, Institut Pasteur, Paris, France.
Daniela M FaustCell Biology of Parasitism Unit, Inserm U786, BCI, Institut Pasteur, Paris, France.
Matthieu TolleCell Biology of Parasitism Unit, Inserm U786, BCI, Institut Pasteur, Paris, France.
Lise RivièreHepaciviruses and Innate Immunity Unit, Department of Virology, Institut Pasteur, Paris, France.
Tanguy ValentinCell Biology of Parasitism Unit, Inserm U786, BCI, Institut Pasteur, Paris, France.
Christine NeuveutHepaciviruses and Innate Immunity Unit, Department of Virology, Institut Pasteur, Paris, France.
Nora Hernandez-CuevasCell Biology of Parasitism Unit, Inserm U786, BCI, Institut Pasteur, Paris, France.
Alexandre DufourQuantitative Image Analysis, BCI, Institut Pasteur, Paris, France.
Jean-Christophe Olivo-MarinQuantitative Image Analysis, BCI, Institut Pasteur, Paris, France.
Nancy GuillenCell Biology of Parasitism Unit, Inserm U786, BCI, Institut Pasteur, Paris, France.
Institut Pasteur · FRInserm · FRDepartment of Virology · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human liver infection is a major cause of death worldwide, but fundamental studies on infectious diseases affecting humans have been hampered by the lack of robust experimental models that accurately reproduce pathogen-host interactions in an environment relevant for the human disease. In the case of liver infection, one consequence of this absence of relevant models is a lack of understanding of how pathogens cross the sinusoidal endothelial barrier and parenchyma. To fill that gap we elaborated human 3D liver in vitro models, composed of human liver sinusoidal endothelial cells (LSEC) and Huh-7 hepatoma cells as hepatocyte model, layered in a structure mimicking the hepatic sinusoid, which enable studies of key features of early steps of hepatic infection. Built with established cell lines and scaffold, these models provide a reproducible and easy-to-build cell culture approach of reduced complexity compared to animal models, while preserving higher physiological relevance compared to standard 2D systems. For proof-of-principle we challenged the models with two hepatotropic pathogens: the parasitic amoeba Entamoeba histolytica and hepatitis B virus (HBV). We constructed four distinct setups dedicated to investigating specific aspects of hepatic invasion: 1) pathogen 3D migration towards hepatocytes, 2) hepatocyte barrier crossing, 3) LSEC and subsequent hepatocyte crossing, and 4) quantification of human hepatic virus replication (HBV). Our methods comprise automated quantification of E. histolytica migration and hepatic cells layer crossing in the 3D liver models. Moreover, replication of HBV virus occurs in our virus infection 3D liver model, indicating that routine in vitro assays using HBV or others viruses can be performed in this easy-to-build but more physiological hepatic environment. These results illustrate that our new 3D liver infection models are simple but effective, enabling new investigations on infectious disease mechanisms. The better understanding of these mechanisms in a human-relevant environment could aid the discovery of drugs against pathogenic liver infection.

Indexed as

Cell Culture TechniquesCell Line, TumorCoculture TechniquesEntamoeba histolyticaHepatocytesHost-Parasite InteractionsHumansLiver Abscess, Amebic

Identifiers

PMID26863526
PMCPMC4749187
OpenAlexW2256240435

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.