α-ketoglutarate suppresses immediate early gene expression in cancer cells.

Cancer cells exhibit increased glutamine consumption compared to normal cells, supporting cell survival and proliferation. Glutamine is converted to α-ketoglutarate (αKG), which then enters the tricarboxylic acid cycle to generate ATP. Recently, therapeutic modulation of glutamine metabolism has become an attractive metabolic anti-cancer strategy. However, how synergistic combination therapy is required to overcome glutamine metabolism drug resistance remains elusive. To address this issue, we first investigated the role of αKG in regulating gene expression in several cancer cell lines. Using RNA-seq analysis and histone modification screening, we demonstrated that αKG reduced the expression of the immediate early gene (IEG) in cancer cells in an H3K27 acetylation-dependent manner. Conversely, glutaminase (GLS) inhibitors induce IEG expression in cancer cells. Furthermore, we showed that siRNA knockdown of orphan nuclear receptor subfamily 4 group A member 1 (NR4A1) induces IEG expression. Notably, the NR4A1 agonist cytosporone B sensitizes GLS inhibitor resistance to cancer cell death. Together, these findings indicate that therapeutic targeting of IEG dysregulation by αKG can be a potentially effective anti-cancer therapeutic strategy for glutamine metabolism inhibitors.

ChREBP plays a pivotal role in the nutrient-mediated regulation of metabolic gene expression in brown adipose tissue.

AIMS: Carbohydrate-responsive element-binding protein (ChREBP) is a transcription factor that regulates several metabolic genes, including the lipogenic enzymes necessary for the metabolic conversion of carbohydrates into lipids. Although the crucial role of ChREBP in the liver, the primary site of de novo lipogenesis, has been studied, its functional role in adipose tissues, particularly brown adipose tissue (BAT), remains unclear. In this study, we investigated the role of ChREBP in BAT under conditions of a high-carbohydrate diet (HCD) and ketogenic diet (KD), represented by extremely low carbohydrate intake. MAIN METHODS: Using an adeno-associated virus and Cas9 knock-in mice, we rapidly generated Chrebp brown adipocyte-specific knock-out (B-KO) mice, bypassing the necessity for prolonged breeding by using the Cre-Lox system. KEY FINDINGS: We demonstrated that ChREBP is essential for glucose metabolism and lipogenic gene expression in BAT under HCD conditions in Chrebp B-KO mice. After nutrient intake, Chrebp B-KO attenuated the KD-induced expression of several inflammatory genes in BAT. SIGNIFICANCE: Our results indicated that ChREBP, a nutrient-sensing regulator, is indispensable for expressing a diverse range of metabolic genes in BAT.

Metformin Resistance Is Associated with Expression of Inflammatory and Invasive Genes in A549 Lung Cancer Cells.

Metformin, the most commonly used drug for type 2 diabetes, has recently been shown to have beneficial effects in patients with cancer. Despite growing evidence that metformin can inhibit tumor cell proliferation, invasion, and metastasis, studies on drug resistance and its side effects are lacking. Here, we aimed to establish metformin-resistant A549 human lung cancer cells (A549-R) to determine the side effects of metformin resistance. Toward this, we established A549-R by way of prolonged treatment with metformin and examined the changes in gene expression, cell migration, cell cycle, and mitochondrial fragmentation. Metformin resistance is associated with increased G1-phase cell cycle arrest and impaired mitochondrial fragmentation in A549 cells. We demonstrated that metformin resistance highly increased the expression of proinflammatory and invasive genes, including BMP5, CXCL3, VCAM1, and POSTN, using RNA-seq analysis. A549-R exhibited increased cell migration and focal adhesion formation, suggesting that metformin resistance may potentially lead to metastasis during anti-cancer therapy with metformin. Taken together, our findings indicate that metformin resistance may lead to invasion in lung cancer cells.

Device-Algorithm Co-Optimization for an On-Chip Trainable Capacitor-Based Synaptic Device with IGZO TFT and Retention-Centric Tiki-Taka Algorithm.

Analog in-memory computing synaptic devices are widely studied for efficient implementation of deep learning. However, synaptic devices based on resistive memory have difficulties implementing on-chip training due to the lack of means to control the amount of resistance change and large device variations. To overcome these shortcomings, silicon complementary metal-oxide semiconductor (Si-CMOS) and capacitor-based charge storage synapses are proposed, but it is difficult to obtain sufficient retention time due to Si-CMOS leakage currents, resulting in a deterioration of training accuracy. Here, a novel 6T1C synaptic device using only n-type indium gaIlium zinc oxide thin film transistor (IGZO TFT) with low leakage current and a capacitor is proposed, allowing not only linear and symmetric weight update but also sufficient retention time and parallel on-chip training operations. In addition, an efficient and realistic training algorithm to compensate for any remaining device non-idealities such as drifting references and long-term retention loss is proposed, demonstrating the importance of device-algorithm co-optimization.