Human Hepatocytes and Differentiated Adult-Derived Human Liver Stem/Progenitor Cells Display<i>In Vitro</i>Immunosuppressive Properties Mediated, at Least in Part, through the Nonclassical HLA Class I Molecule HLA-G
Bibliographic record
Abstract
One of the main challenges in liver cell therapy (LCT) is the induction of a tolerogenic microenvironment to promote graft acceptance in the recipient. Little is known about the immunomodulatory potential of the hepatic cells used in liver cell therapy. In this work, we wanted to evaluate the immunosuppressive properties of human hepatocytes and adult-derived human liver stem/progenitor cells (ADHLSCs), as well as the potential involvement of the immunomodulatory molecule HLA-G. We demonstrated that both cell types were capable of inhibiting the proliferative response of PBMCs to an allogenic stimulus and that the immune inhibitory potential of ADHLSCs, although lower than that of hepatocytes, increased after hepatogenic differentiation. We demonstrated that liver cells express HLA-G and that the immune inhibition pattern was clearly associated to its expression. Interestingly, HLA-G expression increased after the third step of differentiation, wherein oncostatin M (OSM) was added. A 48 hr treatment with OSM was sufficient to induce HLA-G expression in ADHLSCs and result in immune inhibition. Surprisingly, blocking HLA-G partially reversed the immune inhibition mediated by hepatocytes and differentiated ADHLSCs, but not that of undifferentiated ADHLSCs, suggesting that additional immune inhibitory mechanisms may be used by these cells. In conclusion, we demonstrated that both hepatocytes and ADHLSCs present immunomodulatory properties mediated, at least in part, through HLA-G, which can be upregulated following hepatogenic differentiation or liver cell pretreatment with OSM. These observations open up new perspectives for the induction of tolerance following LCT and for potential therapeutic applications of these liver cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".