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28. STAT3-dependent reactive astrogliosis in the spinal dorsal horn underlies chronic itch

Author

Shiratori-Hayashi, Miho, Koga, Keisuke, Tozaki-Saitoh, Hidetoshi, Kohro, Yuta, Toyonaga, Honami, Yamaguchi, Chiharu, Hasegawa, Ayumi, Nakahara, Takeshi, Hachisuka, Junichi, Akira, Shizuo, Okano, Hideyuki, Furue, Masutaka, Inoue, Kazuhide, and Tsuda, Makoto

Abstract

An itch (pruritus) is an unpleasant sensation that elicits a scratch reflex or a desire to scratch the affected skin region (1,8), and scratching can transiently relieve such itching sensations. [...], Chronic itch is an intractable symptom of inflammatory skin diseases, such as atopic and contact dermatitis (1-3). Recent studies have revealed neuronal pathways selective for itch (4-8), but the mechanisms by which itch turns into a pathological chronic state are poorly understood. Using mouse models of atopic and contact dermatitis, we demonstrate a long-term reactive state of astrocytes in the dorsal horn of the spinal segments that corresponds to lesioned, itchy skin. We found that reactive astrogliosis depended on the activation of signal transducer and activator of transcription 3 (STAT3). Conditional disruption of astrocytic STAT3 suppressed chronic itch, and pharmacological inhibition of spinal STAT3 ameliorated the fully developed chronic itch. Mice with atopic dermatitis exhibited an increase in scratching elicited by intrathecal administration of the itch-inducer gastrin-releasing peptide (GRP) (4), and this enhancement was normalized by suppressing STAT3-mediated reactive astrogliosis. Moreover, we identified lipocalin-2 (LCN2) as an astrocytic STAT3-dependent upregulated factor that was crucial for chronic itch, and we demonstrated that intrathecal administration of LCN2 to normal mice increased spinal GRP-evoked scratching. Our findings indicate that STAT3-dependent reactive astrocytes act as critical amplifiers of itching through a mechanism involving the enhancement of spinal itch signals by LCN2, thereby providing a previously unrecognized target for treating chronic itch.

Published
2015

43. Ifn-[gamma] receptor signaling mediates spinal microglia activation driving neuropathic pain

Author

Tsuda, Makoto, Masuda, Takahiro, Kitano, Junko, Shimoyama, Hiroshi, Tozaki-Saitoh, Hidetoshi, and Inoue, Kazuhide

Subjects
Interferon -- Properties, Neuralgia -- Diagnosis, Tyrosine -- Properties, Neuroglia -- Properties, Science and technology
Abstract

Neuropathic pain, a highly debilitating pain condition that commonly occurs after nerve damage, is a reflection of the aberrant excitability of dorsal horn neurons. This pathologically altered neurotransmission requires a communication with spinal microglia activated by nerve injury. However, how normal resting microglia become activated remains unknown. Here we show that in naive animals spinal microglia express a receptor for the cytokine IFN-[gamma] (IFN-[gamma]R) in a cell-type-specific manner and that stimulating this receptor converts microglia into activated cells and produces a long-lasting pain hypersensitivity evoked by innocuous stimuli (tactile allodynia, a hallmark symptom of neuropathic pain). Conversely, ablating IFN-[gamma]R severely impairs nerve injury-evoked microglia activation and tactile allodynia without affecting microglia in the contralateral dorsal horn or basal pain sensitivity. We also find that IFN-[gamma]-stimulated spinal microglia show up-regulation of Lyn tyrosine kinase and purinergic [P2X.sub.4] receptor, crucial events for neuropathic pain, and genetic approaches provide evidence linking these events to IFN-[gamma]R-dependent microglial and behavioral alterations. These results suggest that IFN-[gamma]R is a key element in the molecular machinery through which resting spinal microglia transform into an activated state that drives neuropathic pain. allodynia | cytokine | glia | Lyn tyrosine kinase | purinergic receptor

Published
2009

45. Additional file 3 of Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models

Author

Urago, Yuka, Okamoto, Hiroyuki, Kaneda, Tomoya, Murakami, Naoya, Kashihara, Tairo, Takemori, Mihiro, Nakayama, Hiroki, Iijima, Kotaro, Chiba, Takahito, Kuwahara, Junichi, Katsuta, Shouichi, Nakamura, Satoshi, Chang, Weishan, Saitoh, Hidetoshi, and Igaki, Hiroshi

Subjects
ComputerApplications_COMPUTERSINOTHERSYSTEMS
Abstract

Additional file 3. Supplementary Table 2: The relative error between manual and automatic delineation volume at each organ.

Published
2021
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46. Additional file 4 of Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models

Author

Urago, Yuka, Okamoto, Hiroyuki, Kaneda, Tomoya, Murakami, Naoya, Kashihara, Tairo, Takemori, Mihiro, Nakayama, Hiroki, Iijima, Kotaro, Chiba, Takahito, Kuwahara, Junichi, Katsuta, Shouichi, Nakamura, Satoshi, Chang, Weishan, Saitoh, Hidetoshi, and Igaki, Hiroshi

Abstract

Additional file 4. Supplementary Table 3: The Pearson's correlation coefficient between three similarity indexes and volumes at each site of head and neck cancer patients.

Published
2021
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47. Additional file 2 of Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models

Author

Urago, Yuka, Okamoto, Hiroyuki, Kaneda, Tomoya, Murakami, Naoya, Kashihara, Tairo, Takemori, Mihiro, Nakayama, Hiroki, Iijima, Kotaro, Chiba, Takahito, Kuwahara, Junichi, Katsuta, Shouichi, Nakamura, Satoshi, Chang, Weishan, Saitoh, Hidetoshi, and Igaki, Hiroshi

Abstract

Additional file 2. Supplementary Table 1: The Pearson's correlation coefficient r of volume and metrics in the bladder and rectum for the atlas-based segmentation and the AI-based segmentation.

Published
2021
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48. Additional file 1 of Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models

Author

Urago, Yuka, Okamoto, Hiroyuki, Kaneda, Tomoya, Murakami, Naoya, Kashihara, Tairo, Takemori, Mihiro, Nakayama, Hiroki, Iijima, Kotaro, Chiba, Takahito, Kuwahara, Junichi, Katsuta, Shouichi, Nakamura, Satoshi, Chang, Weishan, Saitoh, Hidetoshi, and Igaki, Hiroshi

Subjects
Data_FILES
Abstract

Additional file 1. Supplementary Figure 1: The workflow of the atlas-based segmentation.

Published
2021
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