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Over-activation of microglia cells in the brain contributes to neurodegenerative processes promoted by the production of various neurotoxic factors including pro-inflammatory cytokines and nitric oxide. Recently, accumulating evidence has suggested that mitochondrial dynamics are an important constituent of cellular quality control and function. However, the role of mitochondrial dynamics in microglial activation is still largely unknown. In this study, we determined whether mitochondrial dynamics are associated with the production of pro-inflammatory mediators in lipopolysaccharide (LPS)-stimulated immortalization of murine microglial cells (BV-2) by a v-raf/v-myc carrying retrovirus (J2). Excessive mitochondrial fission was observed in lentivirus-transfected BV-2 cells stably expressing DsRed2-mito following LPS stimulation. Furthermore, mitochondrial localization of dynamin-related protein 1 (Drp1) (a key regulator of mitochondrial fission) was increased and accompanied by de-phosphorylation of Ser637 in Drp1.Interestingly, inhibition of LPS-induced mitochondrial fission and reactive oxygen species (ROS) generation by Mdivi-1 and Drp1 knock-down attenuated the production of pro-inflammatory mediators via reduced nuclear factor kappa-light-chainenhancer of activated B cells (NF-jB) and mitogen-activated protein kinase (MAPK) signaling. Our results demonstrated for the first time that mitochondrial fission regulates mitochondrial ROS production in activated microglial cells and influences the expression of pro-inflammatory mediators through the activation of NF-jB and MAPK. We therefore suggest that mitochondrial dynamics may be essential for understanding pro-inflammatory mediator expression in activated microglial cells. This could represent a new therapeutic approach for preventing neurodegenerative diseases. Keywords: Drp1, lipopolysaccharide, Mdivi-1, microglia, mitochondrial dynamics, neuroinflammation. Address correspondence and reprint requests to Dong-Seok Lee, College of Natural Sciences, Kyungpook National University, Daegu, Republic of Korea. E-mail: lee1@knu.ac.kr 1 These authors contributed equally to this work. Abbreviations used: CMVie, cytomegalovirus immediate early; Cox-2, cyclooxygenase-2; COXIV, cytochrome c oxidase; cPPT, central polypurine tract; DMEM, Dulbecco's modified Eagle's medium; Drp1, dynamin-related protein 1; DsRed2, discosoma sp. red fluorescent; ERK, extracellular signal-regulated kinase; FBS, fetal bovine serum; Fis1, mitochondrial fission 1; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; iNOS, inducible nitric oxide synthase; JNK, c-jun N-terminal kinase; LPS, lipopolysaccharide; LTR, long terminal repeat; MAPK, mitogen-activated protein kinase; Mdivi-1, 3-(2,4-dichloro-5-methoxyphenyl)-2,3-dihydro-2-thioxo-4(1H)-quinazolinone3-(2,4-dichloro-5-methoxyphenyl)-2-sulfanyl-4(3H)-quinazolinone; Mfn1, mitofusin 1; Mfn2, mitofusin 2; MOI, multiplicity of infection; MTS, mitochondrial targeting sequence; NF-jB, nuclear factor kappa-light-chainenhancer of activated B cells...

Alternative cell sources, such as three‐dimensional organoids and induced pluripotent stem cell–derived cells, might provide a potentially effective approach for both drug development applications and clinical transplantation. For example, the development of cell sources for liver cell–based therapy has been increasingly needed, and liver transplantation is performed for the treatment for patients with severe end‐stage liver disease. Differentiated liver cells and three‐dimensional organoids are expected to provide new cell sources for tissue models and revolutionary clinical therapies. However, conventional experimental methods confirming the expression levels of liver‐specific lineage markers cannot provide complete information regarding the differentiation status or degree of similarity between liver and differentiated cell sources. Therefore, in this study, to overcome several issues associated with the assessment of differentiated liver cells and organoids, we developed a liver‐specific gene expression panel (LiGEP) algorithm that presents the degree of liver similarity as a “percentage.” We demonstrated that the percentage calculated using the LiGEP algorithm was correlated with the developmental stages of in vivo liver tissues in mice, suggesting that LiGEP can correctly predict developmental stages. Moreover, three‐dimensional cultured HepaRG cells and human pluripotent stem cell–derived hepatocyte‐like cells showed liver similarity scores of 59.14% and 32%, respectively, although general liver‐specific markers were detected. Conclusion: Our study describes a quantitative and predictive model for differentiated samples, particularly liver‐specific cells or organoids; and this model can be further expanded to various tissue‐specific organoids; our LiGEP can provide useful information and insights regarding the differentiation status of in vitro liver models. (Hepatology 2017;66:1662–1674).

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