Oral N-acetylcysteine decreases IFN-γ production and ameliorates ischemia-reperfusion injury in steatotic livers

Abstract

Type 1 Natural Killer T-cells (NKT1 cells) play a critical role in mediating hepatic ischemia-reperfusion injury (IRI). Although hepatic steatosis is a major risk factor for preservation type injury, how NKT cells impact this is understudied. Given NKT1 cell activation by phospholipid ligands recognized presented by CD1d, we hypothesized that NKT1 cells are key modulators of hepatic IRI because of the increased frequency of activating ligands in the setting of hepatic steatosis. We first demonstrate that IRI is exacerbated by a high-fat diet (HFD) in experimental murine models of warm partial ischemia. This is evident in the evaluation of ALT levels and Phasor-Fluorescence Lifetime (Phasor-FLIM) Imaging for glycolytic stress. Polychromatic flow cytometry identified pronounced increases in CD45+CD3+NK1.1+NKT1 cells in HFD fed mice when compared to mice fed a normal diet (ND). This observation is further extended to IRI, measuring ex vivo cytokine expression in the HFD and ND. Much higher interferon-gamma (IFN-γ) expression is noted in the HFD mice after IRI. We further tested our hypothesis by performing a lipidomic analysis of hepatic tissue and compared this to Phasor-FLIM imaging using “long lifetime species”, a byproduct of lipid oxidation. There are higher levels of triacylglycerols and phospholipids in HFD mice. Since N-acetylcysteine (NAC) is able to limit hepatic steatosis, we tested how oral NAC supplementation in HFD mice impacted IRI. Interestingly, oral NAC supplementation in HFD mice results in improved hepatic enhancement using contrast-enhanced magnetic resonance imaging (MRI) compared to HFD control mice and normalization of glycolysis demonstrated by Phasor-FLIM imaging. This correlated with improved biochemical serum levels and a decrease in IFN-γ expression at a tissue level and from CD45+CD3+CD1d+ cells. Lipidomic evaluation of tissue in the HFD+NAC mice demonstrated a drastic decrease in triacylglycerol, suggesting downregulation of the PPAR-γ pathway.

Fluorescence Lifetime Nanoscopy of Liposomal Irinotecan Onivyde: From Manufacturing to Intracellular Processing

“…The graphical solution can be mathematically described in terms of intensity fraction F and characteristic lifetime τ of each species as per eq 7.a, whereas the molar fraction of each species can be computed as per eq 7.b as a function of the molar extinction coefficient ε and quantum yield (QY). Equation 7.c is a simplified version of (7.b), based on well-documented considerations reported in the literature. ,− τ = F a τ a + F b τ b X a = ε b Q normalY normalb / ε a Q normalY normala · F a 1 + F a · ( ε normalb normalQ Y b / ε normala normalQ Y a − 1 ) X a = τ b / τ a · F a 1 + F a · ( τ normalb / τ normala − 1 ) …”

“…The graphical solution can be mathematically described in terms of intensity fraction F and characteristic lifetime τ of each species as per eq 7.a, whereas the molar fraction of each species can be computed as per eq 7.b as a function of the molar extinction coefficient ε and quantum yield (QY). Equation 7.c is a simplified version of (7.b), based on well-documented considerations reported in the literature. ,− …”

Riluzole suppresses growth and enhances response to endocrine therapy in ER+ breast cancer

“…The transformed data were then plotted in the phasor plot so that the data from each pixel is transformed to a point in the phasor plot [45–47]. The fractional intensity distribution between free and protein-bound NADH was calculated based on a two-component analysis of the phasor plot [38,48] and then converted to concentration ratio based on the quantum yield of the two species [49]. The higher free/bound NADH ratio is representative of increased glycolysis [43].…”

Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring

“…It subsequently declines in old age ( Simon et al, 2015 ). Beyond the effects of these genes, patients may have several transcriptomic ( Furlan et al, 2020 ), proteomic ( Cohen Freue et al, 2013 ; Levitsky et al, 2013 ), metabolomic [reviewed in Supplementary Table S1 of McCune et al (2021a) ], and lipidomic ( Liggett et al, 2022 ) characteristics that could influence the effectiveness and toxicity of transplantation and/or sirolimus. For example, metabolomics can offer discoveries yielding new insights into how metabolites (here, endogenous, not sirolimus metabolites) influence gut physiology, organ function, and immune function ( Wishart, 2019 ).…”