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821.
Hirotaka Toh Takashi Nozawa Atsuko Minowa-Nozawa Miyako Hikichi Shintaro Nakajima Chihiro Aikawa 《Autophagy》2020,16(2):334-346
ABSTRACTAutophagy selectively targets invading bacteria to defend cells, whereas bacterial pathogens counteract autophagy to survive in cells. The initiation of canonical autophagy involves the PIK3C3 complex, but autophagy targeting Group A Streptococcus (GAS) is PIK3C3-independent. We report that GAS infection elicits both PIK3C3-dependent and -independent autophagy, and that the GAS effector NAD-glycohydrolase (Nga) selectively modulates PIK3C3-dependent autophagy. GAS regulates starvation-induced (canonical) PIK3C3-dependent autophagy by secreting streptolysin O and Nga, and Nga also suppresses PIK3C3-dependent GAS-targeting-autophagosome formation during early infection and facilitates intracellular proliferation. This Nga-sensitive autophagosome formation involves the ATG14-containing PIK3C3 complex and RAB1 GTPase, which are both dispensable for Nga-insensitive RAB9A/RAB17-positive autophagosome formation. Furthermore, although MTOR inhibition and subsequent activation of ULK1, BECN1, and ATG14 occur during GAS infection, ATG14 recruitment to GAS is impaired, suggesting that Nga inhibits the recruitment of ATG14-containing PIK3C3 complexes to autophagosome-formation sites. Our findings reveal not only a previously unrecognized GAS-host interaction that modulates canonical autophagy, but also the existence of multiple autophagy pathways, using distinct regulators, targeting bacterial infection.Abbreviations: ATG5: autophagy related 5; ATG14: autophagy related 14; ATG16L1: autophagy related 16 like 1; BECN1: beclin 1; CALCOCO2: calcium binding and coiled-coil domain 2; GAS: group A streptococcus; GcAV: GAS-containing autophagosome-like vacuole; LAMP1: lysosomal associated membrane protein 1; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MTORC1: mechanistic target of rapamycin kinase complex 1; Nga: NAD-glycohydrolase; PIK3C3: phosphatidylinositol 3-kinase catalytic subunit type 3; PtdIns3P: phosphatidylinositol-3-phosphate; PtdIns4P: phosphatidylinositol-4-phosphate; RAB: RAB, member RAS oncogene GTPases; RAB1A: RAB1A, member RAS oncogene family; RAB11A: RAB11A, member RAS oncogene family; RAB17: RAB17, member RAS oncogene family; RAB24: RAB24, member RAS oncogene family; RPS6KB1: ribosomal protein S6 kinase B1; SLO: streptolysin O; SQSTM1: sequestosome 1; ULK1: unc-51 like autophagy activating kinase 1; WIPI2: WD repeat domain, phosphoinositide interacting 2 相似文献
822.
Isamu Murakoshi Kyoko Fukuchi Joju Haginiwa Shigeru Ohmiya Hirotaka Otomasu 《Phytochemistry》1977,16(9):1460-1461
A new lupin alkaloid, N-(3,-oxobutyl)cytisine, was isolated from the aerial parts of Echinosophora koreensis. Its structure was determined by s 相似文献
823.
Isamu Murakoshi Eiji Kidoguchi Mohammad Ikram Mohammad Israr Nusrat Shafi Joju Haginiwa Shigeru Ohmiya Hirotaka Otomasu 《Phytochemistry》1982,21(6):1313-1315
The major alkaloids of Sophora mollis are (+)-sparteine and (?)-cytisine, and the minor ones are also of the sparteine-type (lupanine and 5,6-deh 相似文献
824.
Isamu Murakoshi Eiji Kidoguchi Joju Haginiwa Shigeru Ohmiya Kimio Higashiyama Hirotaka Otomasu 《Phytochemistry》1982,21(9):2379-2384
Two new lupin alkaloids, isokuraramine and (?)-7, 11-dihydromatrine, were isolated from the fresh flowers of Sophora flavescens along with 16 kno 相似文献
825.
Ayumu Yamashita Yuki Sakai Takashi Yamada Noriaki Yahata Akira Kunimatsu Naohiro Okada Takashi Itahashi Ryuichiro Hashimoto Hiroto Mizuta Naho Ichikawa Masahiro Takamura Go Okada Hirotaka Yamagata Kenichiro Harada Koji Matsuo Saori C. Tanaka Mitsuo Kawato Kiyoto Kasai Nobumasa Kato Hidehiko Takahashi Yasumasa Okamoto Okito Yamashita Hiroshi Imamizu 《PLoS biology》2020,18(12)
Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.Biomarkers for psychiatric disorders based on neuroimaging data have yet to be put to practical use. This study overcomes the problems of inter-site differences in fMRI data by using a novel harmonization method, thereby successfully constructing a generalizable brain network marker of major depressive disorder across multiple imaging sites. 相似文献