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Biology subjects

Arakawa, S.

Publications and source records attributed to Arakawa, S..

2 recordsLinked to original sources

Branched-chain amino acid metabolism is a crucial modulator of cellular senescence

Cellular senescence is a complex stress response that results in the permanent arrest of cell proliferation. The accumulation of senescent cells occurs during aging in living organisms, and contributes to tissue dysfunction. Although there are growing lines of evidence that various metabolic changes occur in senescent cells, the link between cellular metabolism and senescence is not yet fully understood. In this study, we demonstrate that alterations in the metabolism of branched-chain amino acids (BCAAs) play a crucial role in establishing cellular senescence. Furthermore, we identified mitochondrial BCAA transamination as a crucial step in this process. Our findings show that various types of cellular stress lead to a reduction in the expression of BCAA aminotransferase 2 (BCAT2), one of the BCAA catabolic enzymes, resulting in decreased catabolism of BCAAs and reduced synthesis of glutamate. The reduction of BCAA catabolites, together with the consequent limitation in glutathione production from glutamate, triggers cellular senescence. Furthermore, we demonstrate that a reduction in BCAT2 levels alone is sufficient to induce cellular senescence, both in cultured cells and in mice. Additionally, our results demonstrate that aging alters BCAA metabolism in both mice and humans. Our findings provide new insights into the metabolic mechanisms underlying cellular senescence, with a particular focus on the role of BCAAs.

cell biology↗

Artificial Intelligence Enables the Label-Free Identification of Chronic Myeloid Leukemia Cells with Mitochondrial Morphological Alterations

Long-term tyrosine kinase inhibitor (TKI) treatment for patients with chronic myeloid leukemia (CML) causes various adverse events. Achieving a deep molecular response (DMR) is necessary for discontinuing TKIs and attaining treatment-free remission. Thus, early diagnosis is crucial as a lower DMR achievement rate has been reported in high-risk patients. Therefore, we attempted to identify CML cells using a novel technology that combines artificial intelligence (AI) with flow cytometry and investigated the basis for AI- mediated identification. Our findings indicate that BCR-ABL1-transduced cells and leukocytes from patients with CML showed significantly fragmented mitochondria and decreased mitochondrial membrane potential. Additionally, BCR-ABL1 enhanced the phosphorylation of Drp1 via the mitogen-activated protein kinase pathway, inducing mitochondrial fragmentation. Finally, the AI identified cell line models and patient leukocytes that showed mitochondrial morphological changes. Our study suggested that this AI- based technology enables the highly sensitive detection of BCR-ABL1-positive cells and early diagnosis of CML.

cancer biology↗