Dataset Information

Interleukin-22 ameliorates acute-on-chronic liver failure by reprogramming impaired regeneration pathways in mice.

ABSTRACT:

Background & aims

Acute-on-chronic liver failure (ACLF) is a clinical syndrome defined by liver failure on pre-existing chronic liver disease. It is often associated with bacterial infection and high short-term mortality. Experimental models that fully reproduce ACLF are lacking, so too are effective pharmacological therapies for this condition.

Methods

To mimic ACLF conditions, we developed a severe liver injury model by combining chronic injury (chronic carbon tetrachloride [CCl₄] injection), acute hepatic insult (injection of a double dose of CCl₄), and bacterial infection (intraperitoneal injection of bacteria). Serum and liver samples from patients with ACLF or acute drug-induced liver injury (DILI) were used. Liver injury and regeneration were assessed to ascertain the potential benefits of interleukin-22 (IL-22Fc) administration.

Results

This severe liver injury model recapitulated some of the key features of clinical ACLF, including acute-on-chronic liver injury, bacterial infection, multi-organ injury, and high mortality. Liver regeneration in this model was severely impaired because of a shift from the activation of the pro-regenerative IL-6/STAT3 pathway to the anti-regenerative IFN-γ/STAT1 pathway. The impaired IL-6/STAT3 activation was due to the inability of Kupffer cells to produce IL-6; whereas the enhanced STAT1 activation was due to a strong innate immune response and subsequent production of IFN-γ. Compared to patients with DILI, patients with ACLF had higher levels of IFN-γ but lower liver regeneration. IL-22Fc treatment improved survival in ACLF mice by reversing the STAT1/STAT3 pathway imbalance and enhancing expression of many antibacterial genes in a manner involving the anti-apoptotic protein BCL2.

Conclusions

Acute-on-chronic liver injury or bacterial infection is associated with impaired liver regeneration due to a shift from a pro-regenerative to an anti-regenerative pathway. IL-22Fc therapy reverses this shift and attenuates bacterial infection, thus IL-22Fc may have therapeutic potential for ACLF treatment.

Lay summary

A mouse model combining chronic liver injury, acute hepatic insult, and bacterial infection recapitulates some of the key features of acute-on-chronic liver failure (ACLF) in patients. Both fibrosis and bacterial infection contribute to the impaired regenerative capacity of the liver in patients with ACLF. Herein, we show that IL-22Fc therapy improves ACLF by reprogramming impaired regenerative pathways and attenuating bacterial infection. Thus, it may have therapeutic potential for patients with ACLF.

SUBMITTER: Xiang X

PROVIDER: S-EPMC7085428 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Interleukin-22 ameliorates acute-on-chronic liver failure by reprogramming impaired regeneration pathways in mice.

Xiang Xiaogang X Feng Dechun D Hwang Seonghwan S Ren Tianyi T Wang Xiaolin X Trojnar Eszter E Matyas Csaba C Mo Ruidong R Shang Dabao D He Yong Y Seo Wonhyo W Shah Vijay H VH Pacher Pal P Xie Qing Q Gao Bin B

Journal of hepatology 20191129 4

<h4>Background & aims</h4>Acute-on-chronic liver failure (ACLF) is a clinical syndrome defined by liver failure on pre-existing chronic liver disease. It is often associated with bacterial infection and high short-term mortality. Experimental models that fully reproduce ACLF are lacking, so too are effective pharmacological therapies for this condition.<h4>Methods</h4>To mimic ACLF conditions, we developed a severe liver injury model by combining chronic injury (chronic carbon tetrachloride [CCl ...[more]

PMID: 31786256

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Dataset Information

Interleukin-22 ameliorates acute-on-chronic liver failure by reprogramming impaired regeneration pathways in mice.

Background & aims

Methods

Results

Conclusions

Lay summary

Publications

Interleukin-22 ameliorates acute-on-chronic liver failure by reprogramming impaired regeneration pathways in mice.

Similar Datasets

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