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DOI： 10.1007/s12013-023-01165-w

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Lipid droplets (LDs) are organelles that store excess lipids and provide fatty acids for energy production during starvation. LDs are also essential for cellular maintenance, but excessive accumulation of LDs triggers various cancers in addition to metabolic diseases such as diabetes. In this study, we aimed to develop a strategy to identify new genes that reduces accumulation of LDs in cancer cells using an RNA interference (RNAi) screening system employing artificial sequence-enriched shRNA libraries. Monitoring LDs by fluorescent activated cell sorting, the subsequently collected cumulative LDs cells, and shRNA sequence analysis identified a clone that potentially functioned to accumulate LDs. The clone showed no identical sequence to human Refseq. It showed very similar sequence to seven genes by allowing three mismatches. Among these genes, we identified the mediator complex subunit 6 (MED6) gene as a target of this shRNA. Silencing of MED6 led to an increase in LD accumulation and expression of the marker genes, PLIN2 and DGAT1, in fatty cells. MED6 is a member of the mediator complex that regulates RNA polymerase II transcription through transcription factor II. Some mediator complexes play important roles in both normal and pathophysiological transcription processes. These results suggest that MED6 transcriptionally regulates the genes involved in lipid metabolism and suppresses LD accumulation.

DOI： 10.1002/biof.2019

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Stress response is an inherent mechanism in the endoplasmic reticulum (ER). The inducers of ER cause a specific cascade of reactions, leading to gene expression. Transmembrane protein 117 (TMEM117) is in the ER and plasma membrane. In our previous study, TMEM117 protein expression was found to be decreased by an ER stress inducer. However, the mechanism underlying this decrease in TMEM117 protein expression remains unclear. This study aimed to elucidate the mechanism underlying the decrease in TMEM117 protein expression during ER stress and identify the unfolded protein response (UPR) pathway related to decreased TMEM117 protein expression. We showed that the gene expression levels of TMEM117 were decreased by ER stress inducers and were regulated by PKR-like ER kinase (PERK), indicating that TMEM117 protein expression was regulated by the signaling pathway. Surprisingly, gene knockdown of activating transcription factor 4 (ATF4) downstream of PERK did not affect the gene expression of TMEM117. These results suggest that TMEM117 protein expression during ER stress is transcriptionally regulated by PERK but not by ATF4. TMEM117 has a potential to be a new therapeutic target against ER stress-related diseases.

DOI： 10.1007/s12013-023-01150-3

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The anti-inflammatory drug celecoxib, the only inhibitor of cyclooxygenase-2 (COX-2) with anticancer activity, is used to treat rheumatoid arthritis and can cause endoplasmic reticulum (ER) stress by inhibiting sarco/ER Ca2 +-ATPase activity in cancer cells. This study aimed to investigate the correlation between celecoxib-induced ER stress and the effects of celecoxib against cell death signaling. Treatment of human colon cancer HCT116 cells with celecoxib reduced their viability and resulted in a loss of mitochondrial membrane potential ([Formula: see text]). Additionally, celecoxib treatment reduced the expression of genes involved in mitochondrial biogenesis and metabolism such as mitochondrial transcription factor A (TFAM) and uncoupling protein 2 (UCP2). Furthermore, celecoxib reduced transmembrane protein 117 (TMEM117), and RNAi-mediated knockdown of TMEM117 reduced TFAM and UCP2 expressions. These results suggest that celecoxib treatment results in the loss of [Formula: see text] by reducing TMEM117 expression and provide insights for the development of novel drugs through TMEM117 expression.

DOI： 10.1007/s00210-023-02399-4

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Artificial intelligence (AI) technology for image recognition has the potential to identify cancer stem cells (CSCs) in cultures and tissues. CSCs play an important role in the development and relapse of tumors. Although the characteristics of CSCs have been extensively studied, their morphological features remain elusive. The attempt to obtain an AI model identifying CSCs in culture showed the importance of images from spatially and temporally grown cultures of CSCs for deep learning to improve accuracy, but was insufficient. This study aimed to identify a process that is significantly efficient in increasing the accuracy values of the AI model output for predicting CSCs from phase-contrast images. An AI model of conditional generative adversarial network (CGAN) image translation for CSC identification predicted CSCs with various accuracy levels, and convolutional neural network classification of CSC phase-contrast images showed variation in the images. The accuracy of the AI model of CGAN image translation was increased by the AI model built by deep learning of selected CSC images with high accuracy previously calculated by another AI model. The workflow of building an AI model based on CGAN image translation could be useful for the AI prediction of CSCs.

DOI： 10.3390/ijms24065323

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