We previously reported that liver natural killer (NK) and NKT cells play a critical role in mouse model of acetaminophen (APAP)-induced liver injury by producing interferon gamma (IFN-γ) and modulating chemokine production and subsequent recruitment of neutrophils into the liver. In this report, we examined the role of neutrophils in the progression of APAP hepatotoxicity. C57BL/6 mice were given an intraperitoneal toxic dose of APAP (500 mg/kg), which caused severe acute liver injury characterized by significant elevation of serum ALT, centrilobular hepatic necrosis, and increased hepatic inflammatory cell accumulation. Flow cytometric analysis of isolated hepatic leukocytes demonstrated that the major fraction of increased hepatic leukocytes at 6 and 24 hours after APAP was neutrophils (Mac-1⁺Gr-1⁺). Depletion of neutrophils by in vivo treatment with anti-Gr-1 antibody (RB6-8C5) significantly protected mice against APAP-induced liver injury, as evidenced by markedly reduced serum ALT levels, centrilobular hepatic necrosis, and improved mouse survival. The protection was associated with decreased FasL-expressing cells, cytotoxicity against hepatocytes, and respiratory burst in hepatic leukocytes. In intracellular adhesion molecule (ICAM)-1–deficient mice, APAP caused markedly reduced liver injury when compared with wild-type mice. The marked protection in ICAM-1–deficient mice was associated with decreased accumulation of neutrophils in the liver. Hepatic GSH depletion and APAP-adducts showed no differences among the antibody-treated, ICAM-1–deficient, and normal mice. In conclusion, accumulated neutrophils in the liver contribute to the progression and severity of APAP-induced liver injury. (HEPATOLOGY 2006;43:1220–1230.)

Acetaminophen (APAP) hepatotoxicity is currently the single most important cause of acute liver failure in the US, and is associated with a significant number of deaths. The toxic response to APAP is triggered by a highly reactive metabolite N-acetyl-p-benzoquinone-imine. Following the hepatocellular initiation events, such as glutathione depletion and covalent binding, intracellular stress simultaneously activates signal transduction and transcription factor pathways that are protective or toxic (directly or through sensitisation). Subsequently, pro- and anti-inflammatory cascades of the innate immune system are simultaneously activated, the balance of which plays a major role in determining the progression and severity of APAP-induced hepatotoxicity. The threshold and susceptibility to APAP hepatotoxicity is determined by the interplay of injury promoting and inhibiting events downstream of the initial production of toxic metabolite. The environmental and genetic control of these intracellular and intercellular responses to toxic metabolites may be of critical importance in determining susceptibility to APAP hepatotoxicity and presumably idiosyncratic drug hepatotoxicity.

Receptor-interacting protein kinase (RIPK)1 has an essential role in the signaling pathways triggered by death receptors through activation of NF-κB and regulation of caspase-dependent apoptosis and RIPK3/mixed lineage kinase domain-like protein (MLKL)-mediated necroptosis. We examined the effect of RIPK1 antisense knockdown on immune-mediated liver injury in C57BL/6 mice caused by α-galactosylceramide (αGalCer), a specific activator for invariant NKT cells. We found that knockdown of RIPK1 markedly exacerbated αGalCer-mediated liver injury and induced lethality. This was associated with increased hepatic inflammation and massive apoptotic death of hepatocytes, as indicated by TUNEL staining and caspase-3 activation. Pretreatment with zVAD.fmk, a pan-caspase inhibitor, or neutralizing Abs against TNF, almost completely protected against the exacerbated liver injury and lethality. Primary hepatocytes isolated from RIPK1-knockdown mice were sensitized to TNF-induced cell death that was completely inhibited by adding zVAD.fmk. The exacerbated liver injury was not due to impaired hepatic NF-κB activation in terms of IκBα phosphorylation and degradation in in vivo and in vitro studies. Lack of RIPK1 kinase activity by pretreatment with necrostatin-1, a RIPK1 kinase inhibitor, or in the RIPK1 kinase-dead knock-in (RIPK1^D138N) mice did not exacerbate αGalCer-mediated liver injury. Furthermore, RIPK3-knockout and MLKL-knockout mice behaved similarly as wild-type control mice in response to αGalCer, with or without knockdown of RIPK1, excluding a switch to RIPK3/MLKL-mediated necroptosis. Our findings reveal a critical kinase-independent platform role for RIPK1 in protecting against TNF/caspase-dependent apoptosis of hepatocytes in immune-mediated liver injury.

Liver disease carries significant healthcare burden and frequently requires a combination of blood tests, imaging, and invasive liver biopsy to diagnose. Distinguishing between inflammatory liver diseases, which may have similar clinical presentations, is particularly challenging. In this study, we implemented a machine learning pipeline for the identification of diagnostic gene expression biomarkers across several alcohol-associated and non-alcohol-associated liver diseases, using either liver tissue or blood-based samples. We collected peripheral blood mononuclear cells (PBMCs) and liver tissue samples from participants with alcohol-associated hepatitis (AH), alcohol-associated cirrhosis (AC), non-alcohol-associated fatty liver disease, chronic HCV infection, and healthy controls. We performed RNA sequencing (RNA-seq) on 137 PBMC samples and 67 liver tissue samples. Using gene expression data, we implemented a machine learning feature selection and classification pipeline to identify diagnostic biomarkers which distinguish between the liver disease groups. The liver tissue results were validated using a public independent RNA-seq dataset. The biomarkers were computationally validated for biological relevance using pathway analysis tools. Utilizing liver tissue RNA-seq data, we distinguished between AH, AC, and healthy conditions with overall accuracies of 90% in our dataset, and 82% in the independent dataset, with 33 genes. Distinguishing 4 liver conditions and healthy controls yielded 91% overall accuracy in our liver tissue dataset with 39 genes, and 75% overall accuracy in our PBMC dataset with 75 genes. Our machine learning pipeline was effective at identifying a small set of diagnostic gene biomarkers and classifying several liver diseases using RNA-seq data from liver tissue and PBMCs. The methodologies implemented and genes identified in this study may facilitate future efforts toward a liquid biopsy diagnostic for liver diseases. Distinguishing between inflammatory liver diseases without multiple tests can be challenging due to their clinically similar characteristics. To lay the groundwork for the development of a non-invasive blood-based diagnostic across a range of liver diseases, we compared samples from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, and non-alcohol-associated fatty liver disease. We used a machine learning computational approach to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to discover a small set of gene biomarkers for effective diagnosis of these liver diseases.