Transcriptomic profiling of blood from autoimmune hepatitis patients reveals potential mechanisms with implications for management

Manganese Exacerbates ConA-Induced Liver Inflammation via the cGAS-STING Signaling Pathway

There is a potential association between the dysregulation of trace elements and impaired liver function. Elevated levels of manganese, an essential metal ion, have been observed in liver-related diseases, and excessive intake of manganese can worsen liver damage. However, the specific mechanisms underlying manganese-induced liver injury are not well understood. The aim of our study was to investigate the effects of excess manganese on autoimmune hepatitis (AIH) and elucidate its mechanisms. Our findings revealed that manganese exacerbates liver damage under ConA-induced inflammatory conditions. Transcriptomic and experimental data suggested that manganese enhances inflammatory signaling and contributes to the inflammatory microenvironment in the liver of AIH mice. Further investigations demonstrated that manganese exacerbates liver injury by activating the cGAS-STING signaling pathway and its downstream pro-inflammatory factors such as IFN[Formula: see text], IFN[Formula: see text], TNF[Formula: see text], and IL-6 in the liver of AIH mice. These results suggest that manganese overload promotes the progression of AIH by activating cGAS-STING-mediated inflammation, providing a new perspective for the treatment and prognosis of AIH.

Artificial intelligence for precision medicine in autoimmune liver disease

Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.

Early monocyte response following local ablation in hepatocellular carcinoma

Local ablative therapies are established treatment modalities in the treatment of early- and intermediate-stage hepatocellular carcinoma (HCC). Systemic effects of local ablation on circulating immune cells may contribute to patients' response. Depending on their activation, myeloid cells are able to trigger HCC progression as well as to support anti-tumor immunity. Certain priming of monocytes may already occur while still in the circulation. By using flow cytometry, we analyzed peripheral blood monocyte cell populations from a prospective clinical trial cohort of 21 HCC patients following interstitial brachytherapy (IBT) or radiofrequency ablation (RFA) and investigated alterations in the composition of monocyte subpopulations and monocytic myeloid-derived suppressor cells (mMDSCs) as well as receptors involved in orchestrating monocyte function. We discovered that mMDSC levels increased following both IBT and RFA in virtually all patients. Furthermore, we identified varying alterations in the level of monocyte subpopulations following radiation compared to RFA. (A) Liquid biopsy liquid biopsy of circulating monocytes in the future may provide information on the inflammatory response towards local ablation as part of an orchestrated immune response.