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Your search keyword '"Yoshida, Kosuke"' showing total 22 results
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22 results on '"Yoshida, Kosuke"'

1. Prenatal inflammation impairs early CD11c-positive microglia induction and delays myelination in neurodevelopmental disorders.

Author

Fuma, Kazuya, Iitani, Yukako, Imai, Kenji, Ushida, Takafumi, Tano, Sho, Yoshida, Kosuke, Yokoi, Akira, Miki, Rika, Kidokoro, Hiroyuki, Sato, Yoshiaki, Hara, Yuichiro, Ogi, Tomoo, Nomaki, Kohei, Tsuda, Makoto, Komine, Okiru, Yamanaka, Koji, Kajiyama, Hiroaki, and Kotani, Tomomi

Abstract

Histological chorioamnionitis (HCA) is a form of maternal immune activation (MIA) linked to an increased risk of neurodevelopmental disorders in offspring. Our previous study identified neurodevelopmental impairments in an MIA mouse model mimicking HCA. Thus, this study investigated the role of CD11c+ microglia, key contributors to myelination through IGF-1 production, in this pathology. In the mouse model, the CD11c+ microglial population was significantly lower in the MIA group than in the control group on postnatal day 3 (PN3d). Furthermore, myelination-related protein levels significantly decreased in the MIA group at PN8d. In humans, preterm infants with HCA exhibited higher IL-6 and IL-17A cord-serum levels and lower IGF-1 levels than those without HCA, followed by a higher incidence of delayed myelination on magnetic resonance imaging at the term-equivalent age. In silico analysis revealed that the transient induction of CD11c+ microglia during early development occurred similarly in mice and humans. Notably, a lack of high CD11c+ microglial population has been observed in children with neurodevelopmental disorders. This study reports impaired induction of CD11c+ microglia during postnatal development in a mouse model of MIA associated with delayed myelination. Our findings may inform strategies for improving outcomes in infants with HCA.CD11c-positive microglia, which have an important role in myelination through IGF-1 production during early development, are poorly induced in infants with histological chorioamnionitis and this may be involved in the development of neurodevelopmental impairments. [ABSTRACT FROM AUTHOR]

Published
2025
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2. Chemotherapeutic hormesis induced by the tumor microenvironment in refractory ovarian cancer.

Author

Chang, Xuboya, Tamauchi, Satoshi, Nakagawa, Atsushi, Xinyuan, Wang, Yoshida, Kosuke, Yokoi, Akira, Yoshikawa, Nobuhisa, and Kajiyama, Hiroaki

Subjects
TREATMENT effectiveness, MEDICAL sciences, CANCER chemotherapy, CANCER cell proliferation, SPINDLE apparatus
Abstract

Advanced ovarian cancer often presents with multiple lesions exhibiting varying responses to chemotherapy, highlighting the critical influence of the tumor microenvironment (TME). This study investigates the phenomenon of chemotherapeutic hormesis, wherein low doses of chemotherapeutic agents, such as cisplatin (CDDP) and paclitaxel (PTX), paradoxically stimulate rather than inhibit cancer cell proliferation. Our findings indicate that NOS3 ovarian cancer cells, particularly drug-resistant variants, exhibit enhanced proliferation when exposed to low concentrations of these drugs. This effect is further amplified under hypoxic conditions, suggesting that the TME plays a pivotal role in modulating chemotherapeutic outcomes. Mechanistically, low-dose CDDP upregulates pathways involved in cell cycle progression, specifically the G2/M checkpoint and mitotic spindle formation, accelerating rather than arresting the cell cycle. Furthermore, the activation of the reactive oxygen species (ROS) pathway and increased glutathione levels indicate increased cellular response to oxidative stress, further contributing to cell survival and proliferation. These findings challenge traditional treatment strategies that prioritize the maximization of drug dosage, suggesting that a more nuanced approach considering the influence of the TME and the potential for hormesis could improve therapeutic outcomes. Understanding the mechanisms driving chemotherapeutic hormesis is essential for developing more effective treatments for refractory ovarian cancer. Future research should focus on mitigating the impact of hormesis to enhance the efficacy of chemotherapy in resistant cancer types. [ABSTRACT FROM AUTHOR]

Published
2025
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3. Understanding the impact of spatial immunophenotypes on the survival of endometrial cancer patients through the ProMisE classification.

Author

Hattori, Satomi, Yoshikawa, Nobuhisa, Liu, Wenting, Matsukawa, Tetsuya, Kubokawa, Mei, Yoshida, Kosuke, Yoshihara, Masato, Tamauchi, Satoshi, Ikeda, Yoshiki, Yokoi, Akira, Shimizu, Yusuke, Niimi, Kaoru, and Kajiyama, Hiroaki

Subjects
MEDICAL sciences, MYC oncogenes, ENDOMETRIAL cancer, TUMOR-infiltrating immune cells, IMMUNOSTAINING
Abstract

Objectives: We focused on how the immunophenotypes based on the distribution of CD8-positive tumor-infiltrating lymphocytes (TILs) relate to the endometrial cancer (EC) molecular subtypes and patients' prognosis. Patients and methods: Two cohorts of EC patients (total n = 145) were analyzed and categorized using the Molecular Risk Classifier for Endometrial cancer (ProMisE): POLEmut (POLE mutation), MMRd (mismatch repair deficiency), NSMP (no specific molecular profile), and p53abn (p53 abnormality). CD8-positive TILs, within the central tumor and the invasive margin, were examined by using immunohistochemical staining and advanced image-analysis software. It was investigated whether these immunophenotypes correlate with the molecular subtypes and patients' survival. RNA-sequencing (RNA-seq) was used to explore tumor-derived factors influencing these immunophenotypes. Results: Three distinct immunophenotypes (inflamed, excluded, and desert) based on the CD8-positive TIL patterns were identified in EC patients. Notably, the inflamed phenotype was most frequently observed in the POLEmut and MMRd subtypes, while the desert phenotype was predominant in the NSMP subtype; however, other immunophenotypes were also observed. All p53abn subtype showed the non-inflamed (excluded or desert) phenotype. The prognosis was markedly poorer in the patients with the non-inflamed phenotype than in those with the inflamed phenotype. The RNA-seq analysis showed that the expression of MYC target genes and type-1 interferon response genes was enriched in the non-inflamed phenotype in MMRd and NSMP subtypes, respectively. Conclusion: Evaluating not only the molecular classification but also the immunophenotype may lead to more personalized immunotherapy in EC and elucidating the mechanisms that underlie the formation of the three immunophenotypes could lead to the discovery of new immunotherapy targets. [ABSTRACT FROM AUTHOR]

Published
2025
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4. Safety assessments and clinical features of PARP inhibitors from real-world data of Japanese patients with ovarian cancer.

Author

Uekusa, Ryosuke, Yokoi, Akira, Watanabe, Eri, Yoshida, Kosuke, Yoshihara, Masato, Tamauchi, Satoshi, Shimizu, Yusuke, Ikeda, Yoshiki, Yoshikawa, Nobuhisa, Niimi, Kaoru, Suzuki, Shiro, and Kajiyama, Hiroaki

Subjects
JAPANESE people, OVARIAN cancer, CANCER patients, SEROUS fluids, ADP-ribosylation, HOMOLOGOUS recombination, POLY(ADP-ribose) polymerase
Abstract

Poly (ADP-ribose) polymerase inhibitors have been increasingly used in ovarian cancer treatment. However, the real-world safety data of these drugs in Japanese patients are limited. This retrospective study included 181 patients with ovarian cancer who received olaparib or niraparib at two independent hospitals in Japan between May 2018 and December 2022. Clinical information and blood sampling data were collected. Regarding patient backgrounds, the olaparib group had higher proportions of patients with serous carcinoma, BRCA positivity, homologous recombination deficiency, and those receiving maintenance therapy after recurrence treatment than the niraparib group. Regarding toxicity properties, the most common reasons for discontinuation in the olaparib group were anemia, fatigue, and nausea, while the reason in the niraparib was thrombocytopenia. Thrombocytopenia caused by niraparib treatment occurred earlier than anemia caused by olaparib treatment. Patients with a low body mass index or who had undergone several previous treatment regimens were more likely to discontinue treatment within the first 3 months. Although we analyzed blood collection data, predicting treatment interruptions due to blood toxicity was challenging. In this study, we revealed the characteristics of patients and the timing of interruptions for each drug, highlighting the importance of carefully managing adverse effects. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Mean platelet volume as a potential biomarker for survival outcomes in ovarian clear cell carcinoma.

Author

Yoshikawa, Nobuhisa, Matsukawa, Tetsuya, Hattori, Satomi, Iyoshi, Shohei, Yoshida, Kosuke, Yoshihara, Masato, Tamauchi, Satoshi, Shimizu, Yusuke, Ikeda, Yoshiki, Yokoi, Akira, Niimi, Kaoru, Kawai, Michiyasu, and Kajiyama, Hiroaki

Subjects
MEAN platelet volume, SURVIVAL rate, PROGRESSION-free survival, PROPORTIONAL hazards models, RANDOM forest algorithms
Abstract

Objective: This study aimed to explore the prognostic value of mean platelet volume (MPV) in patients with ovarian clear cell carcinoma (OCCC) and evaluate the predictive performance of a random forest model incorporating MPV and other key clinicopathological factors. Methods: A total of 204 patients with OCCC treated between January 2004 and December 2019 were retrospectively analyzed. Clinicopathological characteristics and preoperative laboratory data were collected, and survival outcomes were evaluated using the Kaplan–Meier method and Cox proportional hazards models. An optimal MPV cutoff was determined by receiver operating characteristic (ROC) curve analysis. A random forest model was then constructed using the identified independent prognostic factors, and its predictive performance was evaluated. Results: The ROC analysis identified 9.3 fL as the MPV cutoff value for predicting 2-year survival. The MPV-low group had lower 5-year overall survival and progression-free survival rates than the MPV-high group (p = 0.003 and p = 0.034, respectively). High MPV emerged as an independent prognostic factor (p = 0.006). The random forest model, incorporating the FIGO stage, residual tumors, peritoneal cytology, and MPV, demonstrated robust predictive performance (area under the curve: 0.905). Conclusion: MPV is a promising prognostic indicator in OCCC. Lower MPV correlated with worse survival rates, advocating its potential utility in refining patient management strategies. The commendable predictive performance of the random forest model, integrating MPV and other significant prognostic factors, suggests a pathway toward enhanced survival prediction, thereby warranting further research. [ABSTRACT FROM AUTHOR]

Published
2023
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7. Spatial exosome analysis using cellulose nanofiber sheets reveals the location heterogeneity of extracellular vesicles.

Author

Yokoi, Akira, Yoshida, Kosuke, Koga, Hirotaka, Kitagawa, Masami, Nagao, Yukari, Iida, Mikiko, Kawaguchi, Shota, Zhang, Min, Nakayama, Jun, Yamamoto, Yusuke, Baba, Yoshinobu, Kajiyama, Hiroaki, and Yasui, Takao

Subjects
EXTRACELLULAR vesicles, RNA sequencing, EXOSOMES, CELLULOSE, NON-coding RNA
Abstract

Extracellular vesicles (EVs), including exosomes, are recognized as promising functional targets involved in disease mechanisms. However, the intravital heterogeneity of EVs remains unclear, and the general limitation for analyzing EVs is the need for a certain volume of biofluids. Here, we present cellulose nanofiber (CNF) sheets to resolve these issues. We show that CNF sheets capture and preserve EVs from ~10 μL of biofluid and enable the analysis of bioactive molecules inside EVs. By attaching CNF sheets to moistened organs, we collect EVs in trace amounts of ascites, which is sufficient to perform small RNA sequence analyses. In an ovarian cancer mouse model, we demonstrate that CNF sheets enable the detection of cancer-associated miRNAs from the very early phase when mice did not have apparent ascites, and that EVs from different locations have unique miRNA profiles. By performing CNF sheet analyses in patients, we identify further location-based differences in EV miRNA profiles, with profiles reflecting disease conditions. We conduct spatial exosome analyses using CNF sheets to reveal that ascites EVs from cancer patients exhibit location-dependent heterogeneity. This technique could provide insights into EV biology and suggests a clinical strategy contributing to cancer diagnosis, staging evaluation, and therapy planning. Extracellular vesicles (EVs) are known to be heterogeneous intravitally. Here, we present cellulose nanofiber sheets which capture EVs with high purity from trace volumes of biofluids and enable analysis of location-dependent heterogeneity to enhance understanding of pathological mechanisms and enable diagnostic readouts. [ABSTRACT FROM AUTHOR]

Published
2023
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8. Unclear tumor border in magnetic resonance imaging as a prognostic factor of squamous cell cervical cancer.

Author

Sato, Mamiko, Tamauchi, Satoshi, Yoshida, Kosuke, Yoshihara, Masato, Ikeda, Yoshiki, Yoshikawa, Nobuhisa, and Kajiyama, Hiroaki

Subjects
SQUAMOUS cell carcinoma, MAGNETIC resonance imaging, PROGNOSIS, LYMPHATIC metastasis, CANCER relapse
Abstract

Magnetic resonance imaging (MRI) is used for pretreatment staging in cervical cancer. In the present study, we used pretreatment images to categorize operative cases into two groups and evaluated their prognosis. A total of 53 cervical cancer patients with squamous cell carcinoma who underwent radical hysterectomy were included in this study. Based on MRI, the patients were classified into two groups, namely clear and unclear tumor border. For each patient, the following characteristics were evaluated: overall survival; recurrence-free survival; lymph node metastasis; lymphovascular space invasion; and pathological findings, including immunohistochemical analysis of vimentin. The clear and unclear tumor border groups included 40 and 13 patients, respectively. Compared with the clear tumor border group, the unclear tumor border group was associated with higher incidence rates of recurrence (3/40 vs. 3/13, respectively), lymphovascular space invasion (24/40 vs. 13/13, respectively), lymph node metastasis (6/40 vs. 10/13, respectively), and positivity for vimentin (18/40 vs. 10/13, respectively). Despite the absence of significant difference in recurrence-free survival (p = 0.0847), the unclear tumor border group had a significantly poorer overall survival versus the clear tumor border group (p = 0.0062). According to MRI findings, an unclear tumor border in patients with squamous cell cervical cancer is linked to poorer prognosis, lymph node metastasis, and distant recurrence of metastasis. [ABSTRACT FROM AUTHOR]

Published
2023
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9. The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

Author

Wang, Qingbo S., Edahiro, Ryuya, Namkoong, Ho, Hasegawa, Takanori, Shirai, Yuya, Sonehara, Kyuto, Tanaka, Hiromu, Lee, Ho, Saiki, Ryunosuke, Hyugaji, Takayoshi, Shimizu, Eigo, Katayama, Kotoe, Kanai, Masahiro, Naito, Tatsuhiko, Sasa, Noah, Yamamoto, Kenichi, Kato, Yasuhiro, Morita, Takayoshi, Takahashi, Kazuhisa, Harada, Norihiro, Naito, Toshio, Hiki, Makoto, Matsushita, Yasushi, Takagi, Haruhi, Ichikawa, Masako, Nakamura, Ai, Harada, Sonoko, Sandhu, Yuuki, Kabata, Hiroki, Masaki, Katsunori, Kamata, Hirofumi, Ikemura, Shinnosuke, Chubachi, Shotaro, Okamori, Satoshi, Terai, Hideki, Morita, Atsuho, Asakura, Takanori, Sasaki, Junichi, Morisaki, Hiroshi, Uwamino, Yoshifumi, Nanki, Kosaku, Uchida, Sho, Uno, Shunsuke, Nishimura, Tomoyasu, Ishiguro, Takashri, Isono, Taisuke, Shibata, Shun, Matsui, Yuma, Hosoda, Chiaki, Takano, Kenji, Nishida, Takashi, Kobayashi, Yoichi, Takaku, Yotaro, Takayanagi, Noboru, Ueda, Soichiro, Tada, Ai, Miyawaki, Masayoshi, Yamamoto, Masaomi, Yoshida, Eriko, Hayashi, Reina, Nagasaka, Tomoki, Arai, Sawako, Kaneko, Yutaro, Sasaki, Kana, Tagaya, Etsuko, Kawana, Masatoshi, Arimura, Ken, Takahashi, Kunihiko, Anzai, Tatsuhiko, Ito, Satoshi, Endo, Akifumi, Uchimura, Yuji, Miyazaki, Yasunari, Honda, Takayuki, Tateishi, Tomoya, Tohda, Shuji, Ichimura, Naoya, Sonobe, Kazunari, Sassa, Chihiro Tani, Nakajima, Jun, Nakano, Yasushi, Nakajima, Yukiko, Anan, Ryusuke, Arai, Ryosuke, Kurihara, Yuko, Harada, Yuko, Nishio, Kazumi, Ueda, Tetsuya, Azuma, Masanori, Saito, Ryuichi, Sado, Toshikatsu, Miyazaki, Yoshimune, Sato, Ryuichi, Haruta, Yuki, Nagasaki, Tadao, Yasui, Yoshinori, Hasegawa, Yoshinori, Mutoh, Yoshikazu, Kimura, Tomoki, Sato, Tomonori, Takei, Reoto, Hagimoto, Satoshi, Noguchi, Yoichiro, Yamano, Yasuhiko, Sasano, Hajime, Ota, Sho, Nakamori, Yasushi, Yoshiya, Kazuhisa, Saito, Fukuki, Yoshihara, Tomoyuki, Wada, Daiki, Iwamura, Hiromu, Kanayama, Syuji, Maruyama, Shuhei, Yoshiyama, Takashi, Ohta, Ken, Kokuto, Hiroyuki, Ogata, Hideo, Tanaka, Yoshiaki, Arakawa, Kenichi, Shimoda, Masafumi, Osawa, Takeshi, Tateno, Hiroki, Hase, Isano, Yoshida, Shuichi, Suzuki, Shoji, Kawada, Miki, Horinouchi, Hirohisa, Saito, Fumitake, Mitamura, Keiko, Hagihara, Masao, Ochi, Junichi, Uchida, Tomoyuki, Baba, Rie, Arai, Daisuke, Ogura, Takayuki, Takahashi, Hidenori, Hagiwara, Shigehiro, Nagao, Genta, Konishi, Shunichiro, Nakachi, Ichiro, Murakami, Koji, Yamada, Mitsuhiro, Sugiura, Hisatoshi, Sano, Hirohito, Matsumoto, Shuichiro, Kimura, Nozomu, Ono, Yoshinao, Baba, Hiroaki, Suzuki, Yusuke, Nakayama, Sohei, Masuzawa, Keita, Namba, Shinichi, Shiroyama, Takayuki, Noda, Yoshimi, Niitsu, Takayuki, Adachi, Yuichi, Enomoto, Takatoshi, Amiya, Saori, Hara, Reina, Yamaguchi, Yuta, Murakami, Teruaki, Kuge, Tomoki, Matsumoto, Kinnosuke, Yamamoto, Yuji, Yamamoto, Makoto, Yoneda, Midori, Tomono, Kazunori, Kato, Kazuto, Hirata, Haruhiko, Takeda, Yoshito, Koh, Hidefumi, Manabe, Tadashi, Funatsu, Yohei, Ito, Fumimaro, Fukui, Takahiro, Shinozuka, Keisuke, Kohashi, Sumiko, Miyazaki, Masatoshi, Shoko, Tomohisa, Kojima, Mitsuaki, Adachi, Tomohiro, Ishikawa, Motonao, Takahashi, Kenichiro, Inoue, Takashi, Hirano, Toshiyuki, Kobayashi, Keigo, Takaoka, Hatsuyo, Watanabe, Kazuyoshi, Miyazawa, Naoki, Kimura, Yasuhiro, Sado, Reiko, Sugimoto, Hideyasu, Kamiya, Akane, Kuwahara, Naota, Fujiwara, Akiko, Matsunaga, Tomohiro, Sato, Yoko, Okada, Takenori, Hirai, Yoshihiro, Kawashima, Hidetoshi, Narita, Atsuya, Niwa, Kazuki, Sekikawa, Yoshiyuki, Nishi, Koichi, Nishitsuji, Masaru, Tani, Mayuko, Suzuki, Junya, Nakatsumi, Hiroki, Ogura, Takashi, Kitamura, Hideya, Hagiwara, Eri, Murohashi, Kota, Okabayashi, Hiroko, Mochimaru, Takao, Nukaga, Shigenari, Satomi, Ryosuke, Oyamada, Yoshitaka, Mori, Nobuaki, Baba, Tomoya, Fukui, Yasutaka, Odate, Mitsuru, Mashimo, Shuko, Makino, Yasushi, Yagi, Kazuma, Hashiguchi, Mizuha, Kagyo, Junko, Shiomi, Tetsuya, Fuke, Satoshi, Saito, Hiroshi, Tsuchida, Tomoya, Fujitani, Shigeki, Takita, Mumon, Morikawa, Daiki, Yoshida, Toru, Izumo, Takehiro, Inomata, Minoru, Kuse, Naoyuki, Awano, Nobuyasu, Tone, Mari, Ito, Akihiro, Nakamura, Yoshihiko, Hoshino, Kota, Maruyama, Junichi, Ishikura, Hiroyasu, Takata, Tohru, Odani, Toshio, Amishima, Masaru, Hattori, Takeshi, Shichinohe, Yasuo, Kagaya, Takashi, Kita, Toshiyuki, Ohta, Kazuhide, Sakagami, Satoru, Koshida, Kiyoshi, Hayashi, Kentaro, Shimizu, Tetsuo, Kozu, Yutaka, Hiranuma, Hisato, Gon, Yasuhiro, Izumi, Namiki, Nagata, Kaoru, Ueda, Ken, Taki, Reiko, Hanada, Satoko, Kawamura, Kodai, Ichikado, Kazuya, Nishiyama, Kenta, Muranaka, Hiroyuki, Nakamura, Kazunori, Hashimoto, Naozumi, Wakahara, Keiko, Koji, Sakamoto, Omote, Norihito, Ando, Akira, Kodama, Nobuhiro, Kaneyama, Yasunari, Maeda, Shunsuke, Kuraki, Takashige, Matsumoto, Takemasa, Yokote, Koutaro, Nakada, Taka-Aki, Abe, Ryuzo, Oshima, Taku, Shimada, Tadanaga, Harada, Masahiro, Takahashi, Takeshi, Ono, Hiroshi, Sakurai, Toshihiro, Shibusawa, Takayuki, Kimizuka, Yoshifumi, Kawana, Akihiko, Sano, Tomoya, Watanabe, Chie, Suematsu, Ryohei, Sageshima, Hisako, Yoshifuji, Ayumi, Ito, Kazuto, Takahashi, Saeko, Ishioka, Kota, Nakamura, Morio, Masuda, Makoto, Wakabayashi, Aya, Watanabe, Hiroki, Ueda, Suguru, Nishikawa, Masanori, Chihara, Yusuke, Takeuchi, Mayumi, Onoi, Keisuke, Shinozuka, Jun, Sueyoshi, Atsushi, Nagasaki, Yoji, Okamoto, Masaki, Ishihara, Sayoko, Shimo, Masatoshi, Tokunaga, Yoshihisa, Kusaka, Yu, Ohba, Takehiko, Isogai, Susumu, Ogawa, Aki, Inoue, Takuya, Fukuyama, Satoru, Eriguchi, Yoshihiro, Yonekawa, Akiko, Kan-o, Keiko, Matsumoto, Koichiro, Kanaoka, Kensuke, Ihara, Shoichi, Komuta, Kiyoshi, Inoue, Yoshiaki, Chiba, Shigeru, Yamagata, Kunihiro, Hiramatsu, Yuji, Kai, Hirayasu, Asano, Koichiro, Oguma, Tsuyoshi, Ito, Yoko, Hashimoto, Satoru, Yamasaki, Masaki, Kasamatsu, Yu, Komase, Yuko, Hida, Naoya, Tsuburai, Takahiro, Oyama, Baku, Takada, Minoru, Kanda, Hidenori, Kitagawa, Yuichiro, Fukuta, Tetsuya, Miyake, Takahito, Yoshida, Shozo, Ogura, Shinji, Abe, Shinji, Kono, Yuta, Togashi, Yuki, Takoi, Hiroyuki, Kikuchi, Ryota, Ogawa, Shinichi, Ogata, Tomouki, Ishihara, Shoichiro, Kanehiro, Arihiko, Ozaki, Shinji, Fuchimoto, Yasuko, Wada, Sae, Fujimoto, Nobukazu, Nishiyama, Kei, Terashima, Mariko, Beppu, Satoru, Yoshida, Kosuke, Narumoto, Osamu, Nagai, Hideaki, Ooshima, Nobuharu, Motegi, Mitsuru, Umeda, Akira, Miyagawa, Kazuya, Shimada, Hisato, Endo, Mayu, Ohira, Yoshiyuki, Watanabe, Masafumi, Inoue, Sumito, Igarashi, Akira, Sato, Masamichi, Sagara, Hironori, Tanaka, Akihiko, Ohta, Shin, Kimura, Tomoyuki, Shibata, Yoko, Tanino, Yoshinori, Nikaido, Takefumi, Minemura, Hiroyuki, Sato, Yuki, Yamada, Yuichiro, Hashino, Takuya, Shinoki, Masato, Iwagoe, Hajime, Takahashi, Hiroshi, Fujii, Kazuhiko, Kishi, Hiroto, Kanai, Masayuki, Imamura, Tomonori, Yamashita, Tatsuya, Yatomi, Masakiyo, Maeno, Toshitaka, Hayashi, Shinichi, Takahashi, Mai, Kuramochi, Mizuki, Kamimaki, Isamu, Tominaga, Yoshiteru, Ishii, Tomoo, Utsugi, Mitsuyoshi, Ono, Akihiro, Tanaka, Toru, Kashiwada, Takeru, Fujita, Kazue, Saito, Yoshinobu, Seike, Masahiro, Watanabe, Hiroko, Matsuse, Hiroto, Kodaka, Norio, Nakano, Chihiro, Oshio, Takeshi, Hirouchi, Takatomo, Makino, Shohei, Egi, Moritoki, Omae, Yosuke, Nannya, Yasuhito, Ueno, Takafumi, Takano, Tomomi, Katayama, Kazuhiko, Ai, Masumi, Kumanogoh, Atsushi, Sato, Toshiro, Hasegawa, Naoki, Tokunaga, Katsushi, Ishii, Makoto, Koike, Ryuji, Kitagawa, Yuko, Kimura, Akinori, Imoto, Seiya, Miyano, Satoru, Ogawa, Seishi, Kanai, Takanori, Fukunaga, Koichi, Okada, Yukinori, Wang, Qingbo S., Edahiro, Ryuya, Namkoong, Ho, Hasegawa, Takanori, Shirai, Yuya, Sonehara, Kyuto, Tanaka, Hiromu, Lee, Ho, Saiki, Ryunosuke, Hyugaji, Takayoshi, Shimizu, Eigo, Katayama, Kotoe, Kanai, Masahiro, Naito, Tatsuhiko, Sasa, Noah, Yamamoto, Kenichi, Kato, Yasuhiro, Morita, Takayoshi, Takahashi, Kazuhisa, Harada, Norihiro, Naito, Toshio, Hiki, Makoto, Matsushita, Yasushi, Takagi, Haruhi, Ichikawa, Masako, Nakamura, Ai, Harada, Sonoko, Sandhu, Yuuki, Kabata, Hiroki, Masaki, Katsunori, Kamata, Hirofumi, Ikemura, Shinnosuke, Chubachi, Shotaro, Okamori, Satoshi, Terai, Hideki, Morita, Atsuho, Asakura, Takanori, Sasaki, Junichi, Morisaki, Hiroshi, Uwamino, Yoshifumi, Nanki, Kosaku, Uchida, Sho, Uno, Shunsuke, Nishimura, Tomoyasu, Ishiguro, Takashri, Isono, Taisuke, Shibata, Shun, Matsui, Yuma, Hosoda, Chiaki, Takano, Kenji, Nishida, Takashi, Kobayashi, Yoichi, Takaku, Yotaro, Takayanagi, Noboru, Ueda, Soichiro, Tada, Ai, Miyawaki, Masayoshi, Yamamoto, Masaomi, Yoshida, Eriko, Hayashi, Reina, Nagasaka, Tomoki, Arai, Sawako, Kaneko, Yutaro, Sasaki, Kana, Tagaya, Etsuko, Kawana, Masatoshi, Arimura, Ken, Takahashi, Kunihiko, Anzai, Tatsuhiko, Ito, Satoshi, Endo, Akifumi, Uchimura, Yuji, Miyazaki, Yasunari, Honda, Takayuki, Tateishi, Tomoya, Tohda, Shuji, Ichimura, Naoya, Sonobe, Kazunari, Sassa, Chihiro Tani, Nakajima, Jun, Nakano, Yasushi, Nakajima, Yukiko, Anan, Ryusuke, Arai, Ryosuke, Kurihara, Yuko, Harada, Yuko, Nishio, Kazumi, Ueda, Tetsuya, Azuma, Masanori, Saito, Ryuichi, Sado, Toshikatsu, Miyazaki, Yoshimune, Sato, Ryuichi, Haruta, Yuki, Nagasaki, Tadao, Yasui, Yoshinori, Hasegawa, Yoshinori, Mutoh, Yoshikazu, Kimura, Tomoki, Sato, Tomonori, Takei, Reoto, Hagimoto, Satoshi, Noguchi, Yoichiro, Yamano, Yasuhiko, Sasano, Hajime, Ota, Sho, Nakamori, Yasushi, Yoshiya, Kazuhisa, Saito, Fukuki, Yoshihara, Tomoyuki, Wada, Daiki, Iwamura, Hiromu, Kanayama, Syuji, Maruyama, Shuhei, Yoshiyama, Takashi, Ohta, Ken, Kokuto, Hiroyuki, Ogata, Hideo, Tanaka, Yoshiaki, Arakawa, Kenichi, Shimoda, Masafumi, Osawa, Takeshi, Tateno, Hiroki, Hase, Isano, Yoshida, Shuichi, Suzuki, Shoji, Kawada, Miki, Horinouchi, Hirohisa, Saito, Fumitake, Mitamura, Keiko, Hagihara, Masao, Ochi, Junichi, Uchida, Tomoyuki, Baba, Rie, Arai, Daisuke, Ogura, Takayuki, Takahashi, Hidenori, Hagiwara, Shigehiro, Nagao, Genta, Konishi, Shunichiro, Nakachi, Ichiro, Murakami, Koji, Yamada, Mitsuhiro, Sugiura, Hisatoshi, Sano, Hirohito, Matsumoto, Shuichiro, Kimura, Nozomu, Ono, Yoshinao, Baba, Hiroaki, Suzuki, Yusuke, Nakayama, Sohei, Masuzawa, Keita, Namba, Shinichi, Shiroyama, Takayuki, Noda, Yoshimi, Niitsu, Takayuki, Adachi, Yuichi, Enomoto, Takatoshi, Amiya, Saori, Hara, Reina, Yamaguchi, Yuta, Murakami, Teruaki, Kuge, Tomoki, Matsumoto, Kinnosuke, Yamamoto, Yuji, Yamamoto, Makoto, Yoneda, Midori, Tomono, Kazunori, Kato, Kazuto, Hirata, Haruhiko, Takeda, Yoshito, Koh, Hidefumi, Manabe, Tadashi, Funatsu, Yohei, Ito, Fumimaro, Fukui, Takahiro, Shinozuka, Keisuke, Kohashi, Sumiko, Miyazaki, Masatoshi, Shoko, Tomohisa, Kojima, Mitsuaki, Adachi, Tomohiro, Ishikawa, Motonao, Takahashi, Kenichiro, Inoue, Takashi, Hirano, Toshiyuki, Kobayashi, Keigo, Takaoka, Hatsuyo, Watanabe, Kazuyoshi, Miyazawa, Naoki, Kimura, Yasuhiro, Sado, Reiko, Sugimoto, Hideyasu, Kamiya, Akane, Kuwahara, Naota, Fujiwara, Akiko, Matsunaga, Tomohiro, Sato, Yoko, Okada, Takenori, Hirai, Yoshihiro, Kawashima, Hidetoshi, Narita, Atsuya, Niwa, Kazuki, Sekikawa, Yoshiyuki, Nishi, Koichi, Nishitsuji, Masaru, Tani, Mayuko, Suzuki, Junya, Nakatsumi, Hiroki, Ogura, Takashi, Kitamura, Hideya, Hagiwara, Eri, Murohashi, Kota, Okabayashi, Hiroko, Mochimaru, Takao, Nukaga, Shigenari, Satomi, Ryosuke, Oyamada, Yoshitaka, Mori, Nobuaki, Baba, Tomoya, Fukui, Yasutaka, Odate, Mitsuru, Mashimo, Shuko, Makino, Yasushi, Yagi, Kazuma, Hashiguchi, Mizuha, Kagyo, Junko, Shiomi, Tetsuya, Fuke, Satoshi, Saito, Hiroshi, Tsuchida, Tomoya, Fujitani, Shigeki, Takita, Mumon, Morikawa, Daiki, Yoshida, Toru, Izumo, Takehiro, Inomata, Minoru, Kuse, Naoyuki, Awano, Nobuyasu, Tone, Mari, Ito, Akihiro, Nakamura, Yoshihiko, Hoshino, Kota, Maruyama, Junichi, Ishikura, Hiroyasu, Takata, Tohru, Odani, Toshio, Amishima, Masaru, Hattori, Takeshi, Shichinohe, Yasuo, Kagaya, Takashi, Kita, Toshiyuki, Ohta, Kazuhide, Sakagami, Satoru, Koshida, Kiyoshi, Hayashi, Kentaro, Shimizu, Tetsuo, Kozu, Yutaka, Hiranuma, Hisato, Gon, Yasuhiro, Izumi, Namiki, Nagata, Kaoru, Ueda, Ken, Taki, Reiko, Hanada, Satoko, Kawamura, Kodai, Ichikado, Kazuya, Nishiyama, Kenta, Muranaka, Hiroyuki, Nakamura, Kazunori, Hashimoto, Naozumi, Wakahara, Keiko, Koji, Sakamoto, Omote, Norihito, Ando, Akira, Kodama, Nobuhiro, Kaneyama, Yasunari, Maeda, Shunsuke, Kuraki, Takashige, Matsumoto, Takemasa, Yokote, Koutaro, Nakada, Taka-Aki, Abe, Ryuzo, Oshima, Taku, Shimada, Tadanaga, Harada, Masahiro, Takahashi, Takeshi, Ono, Hiroshi, Sakurai, Toshihiro, Shibusawa, Takayuki, Kimizuka, Yoshifumi, Kawana, Akihiko, Sano, Tomoya, Watanabe, Chie, Suematsu, Ryohei, Sageshima, Hisako, Yoshifuji, Ayumi, Ito, Kazuto, Takahashi, Saeko, Ishioka, Kota, Nakamura, Morio, Masuda, Makoto, Wakabayashi, Aya, Watanabe, Hiroki, Ueda, Suguru, Nishikawa, Masanori, Chihara, Yusuke, Takeuchi, Mayumi, Onoi, Keisuke, Shinozuka, Jun, Sueyoshi, Atsushi, Nagasaki, Yoji, Okamoto, Masaki, Ishihara, Sayoko, Shimo, Masatoshi, Tokunaga, Yoshihisa, Kusaka, Yu, Ohba, Takehiko, Isogai, Susumu, Ogawa, Aki, Inoue, Takuya, Fukuyama, Satoru, Eriguchi, Yoshihiro, Yonekawa, Akiko, Kan-o, Keiko, Matsumoto, Koichiro, Kanaoka, Kensuke, Ihara, Shoichi, Komuta, Kiyoshi, Inoue, Yoshiaki, Chiba, Shigeru, Yamagata, Kunihiro, Hiramatsu, Yuji, Kai, Hirayasu, Asano, Koichiro, Oguma, Tsuyoshi, Ito, Yoko, Hashimoto, Satoru, Yamasaki, Masaki, Kasamatsu, Yu, Komase, Yuko, Hida, Naoya, Tsuburai, Takahiro, Oyama, Baku, Takada, Minoru, Kanda, Hidenori, Kitagawa, Yuichiro, Fukuta, Tetsuya, Miyake, Takahito, Yoshida, Shozo, Ogura, Shinji, Abe, Shinji, Kono, Yuta, Togashi, Yuki, Takoi, Hiroyuki, Kikuchi, Ryota, Ogawa, Shinichi, Ogata, Tomouki, Ishihara, Shoichiro, Kanehiro, Arihiko, Ozaki, Shinji, Fuchimoto, Yasuko, Wada, Sae, Fujimoto, Nobukazu, Nishiyama, Kei, Terashima, Mariko, Beppu, Satoru, Yoshida, Kosuke, Narumoto, Osamu, Nagai, Hideaki, Ooshima, Nobuharu, Motegi, Mitsuru, Umeda, Akira, Miyagawa, Kazuya, Shimada, Hisato, Endo, Mayu, Ohira, Yoshiyuki, Watanabe, Masafumi, Inoue, Sumito, Igarashi, Akira, Sato, Masamichi, Sagara, Hironori, Tanaka, Akihiko, Ohta, Shin, Kimura, Tomoyuki, Shibata, Yoko, Tanino, Yoshinori, Nikaido, Takefumi, Minemura, Hiroyuki, Sato, Yuki, Yamada, Yuichiro, Hashino, Takuya, Shinoki, Masato, Iwagoe, Hajime, Takahashi, Hiroshi, Fujii, Kazuhiko, Kishi, Hiroto, Kanai, Masayuki, Imamura, Tomonori, Yamashita, Tatsuya, Yatomi, Masakiyo, Maeno, Toshitaka, Hayashi, Shinichi, Takahashi, Mai, Kuramochi, Mizuki, Kamimaki, Isamu, Tominaga, Yoshiteru, Ishii, Tomoo, Utsugi, Mitsuyoshi, Ono, Akihiro, Tanaka, Toru, Kashiwada, Takeru, Fujita, Kazue, Saito, Yoshinobu, Seike, Masahiro, Watanabe, Hiroko, Matsuse, Hiroto, Kodaka, Norio, Nakano, Chihiro, Oshio, Takeshi, Hirouchi, Takatomo, Makino, Shohei, Egi, Moritoki, Omae, Yosuke, Nannya, Yasuhito, Ueno, Takafumi, Takano, Tomomi, Katayama, Kazuhiko, Ai, Masumi, Kumanogoh, Atsushi, Sato, Toshiro, Hasegawa, Naoki, Tokunaga, Katsushi, Ishii, Makoto, Koike, Ryuji, Kitagawa, Yuko, Kimura, Akinori, Imoto, Seiya, Miyano, Satoru, Ogawa, Seishi, Kanai, Takanori, Fukunaga, Koichi, and Okada, Yukinori

Abstract

Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection.

Published
2022

10. DOCK2 is involved in the host genetics and biology of severe COVID-19

Author

Namkoong, Ho, Edahiro, Ryuya, Takano, Tomomi, Nishihara, Hiroshi, Shirai, Yuya, Sonehara, Kyuto, Tanaka, Hiromu, Azekawa, Shuhei, Mikami, Yohei, Lee, Ho, Hasegawa, Takanori, Okudela, Koji, Okuzaki, Daisuke, Motooka, Daisuke, Kanai, Masahiro, Naito, Tatsuhiko, Yamamoto, Kenichi, Wang, Qingbo S., Saiki, Ryunosuke, Ishihara, Rino, Matsubara, Yuta, Hamamoto, Junko, Hayashi, Hiroyuki, Yoshimura, Yukihiro, Tachikawa, Natsuo, Yanagita, Emmy, Hyugaji, Takayoshi, Shimizu, Eigo, Katayama, Kotoe, Kato, Yasuhiro, Morita, Takayoshi, Takahashi, Kazuhisa, Harada, Norihiro, Naito, Toshio, Hiki, Makoto, Matsushita, Yasushi, Takagi, Haruhi, Aoki, Ryousuke, Nakamura, Ai, Harada, Sonoko, Sasano, Hitoshi, Kabata, Hiroki, Masaki, Katsunori, Kamata, Hirofumi, Ikemura, Shinnosuke, Chubachi, Shotaro, Okamori, Satoshi, Terai, Hideki, Morita, Atsuho, Asakura, Takanori, Sasaki, Junichi, Morisaki, Hiroshi, Uwamino, Yoshifumi, Nanki, Kosaku, Uchida, Sho, Uno, Shunsuke, Nishimura, Tomoyasu, Ishiguro, Takashi, Isono, Taisuke, Shibata, Shun, Matsui, Yuma, Hosoda, Chiaki, Takano, Kenji, Nishida, Takashi, Kobayashi, Yoichi, Takaku, Yotaro, Takayanagi, Noboru, Ueda, Soichiro, Tada, Ai, Miyawaki, Masayoshi, Yamamoto, Masaomi, Yoshida, Eriko, Hayashi, Reina, Nagasaka, Tomoki, Arai, Sawako, Kaneko, Yutaro, Sasaki, Kana, Tagaya, Etsuko, Kawana, Masatoshi, Arimura, Ken, Takahashi, Kunihiko, Anzai, Tatsuhiko, Ito, Satoshi, Endo, Akifumi, Uchimura, Yuji, Miyazaki, Yasunari, Honda, Takayuki, Tateishi, Tomoya, Tohda, Shuji, Ichimura, Naoya, Sonobe, Kazunari, Sassa, Chihiro Tani, Nakajima, Jun, Nakano, Yasushi, Nakajima, Yukiko, Anan, Ryusuke, Arai, Ryosuke, Kurihara, Yuko, Harada, Yuko, Nishio, Kazumi, Ueda, Tetsuya, Azuma, Masanori, Saito, Ryuichi, Sado, Toshikatsu, Miyazaki, Yoshimune, Sato, Ryuichi, Haruta, Yuki, Nagasaki, Tadao, Yasui, Yoshinori, Hasegawa, Yoshinori, Mutoh, Yoshikazu, Kimura, Tomoki, Sato, Tomonori, Takei, Reoto, Hagimoto, Satoshi, Noguchi, Yoichiro, Yamano, Yasuhiko, Sasano, Hajime, Ota, Sho, Nakamori, Yasushi, Yoshiya, Kazuhisa, Saito, Fukuki, Yoshihara, Tomoyuki, Wada, Daiki, Iwamura, Hiromu, Kanayama, Syuji, Maruyama, Shuhei, Yoshiyama, Takashi, Ohta, Ken, Kokuto, Hiroyuki, Ogata, Hideo, Tanaka, Yoshiaki, Arakawa, Kenichi, Shimoda, Masafumi, Osawa, Takeshi, Tateno, Hiroki, Hase, Isano, Yoshida, Shuichi, Suzuki, Shoji, Kawada, Miki, Horinouchi, Hirohisa, Saito, Fumitake, Mitamura, Keiko, Hagihara, Masao, Ochi, Junichi, Uchida, Tomoyuki, Baba, Rie, Arai, Daisuke, Ogura, Takayuki, Takahashi, Hidenori, Hagiwara, Shigehiro, Nagao, Genta, Konishi, Shunichiro, Nakachi, Ichiro, Murakami, Koji, Yamada, Mitsuhiro, Sugiura, Hisatoshi, Sano, Hirohito, Matsumoto, Shuichiro, Kimura, Nozomu, Ono, Yoshinao, Baba, Hiroaki, Suzuki, Yusuke, Nakayama, Sohei, Masuzawa, Keita, Namba, Shinichi, Suzuki, Ken, Naito, Yoko, Liu, Yu-Chen, Takuwa, Ayako, Sugihara, Fuminori, Wing, James B., Sakakibara, Shuhei, Hizawa, Nobuyuki, Shiroyama, Takayuki, Miyawaki, Satoru, Kawamura, Yusuke, Nakayama, Akiyoshi, Matsuo, Hirotaka, Maeda, Yuichi, Nii, Takuro, Noda, Yoshimi, Niitsu, Takayuki, Adachi, Yuichi, Enomoto, Takatoshi, Amiya, Saori, Hara, Reina, Yamaguchi, Yuta, Murakami, Teruaki, Kuge, Tomoki, Matsumoto, Kinnosuke, Yamamoto, Yuji, Yamamoto, Makoto, Yoneda, Midori, Kishikawa, Toshihiro, Yamada, Shuhei, Kawabata, Shuhei, Kijima, Noriyuki, Takagaki, Masatoshi, Sasa, Noah, Ueno, Yuya, Suzuki, Motoyuki, Takemoto, Norihiko, Eguchi, Hirotaka, Fukusumi, Takahito, Imai, Takao, Fukushima, Munehisa, Kishima, Haruhiko, Inohara, Hidenori, Tomono, Kazunori, Kato, Kazuto, Takahashi, Meiko, Matsuda, Fumihiko, Hirata, Haruhiko, Takeda, Yoshito, Koh, Hidefumi, Manabe, Tadashi, Funatsu, Yohei, Ito, Fumimaro, Fukui, Takahiro, Shinozuka, Keisuke, Kohashi, Sumiko, Miyazaki, Masatoshi, Shoko, Tomohisa, Kojima, Mitsuaki, Adachi, Tomohiro, Ishikawa, Motonao, Takahashi, Kenichiro, Inoue, Takashi, Hirano, Toshiyuki, Kobayashi, Keigo, Takaoka, Hatsuyo, Watanabe, Kazuyoshi, Miyazawa, Naoki, Kimura, Yasuhiro, Sado, Reiko, Sugimoto, Hideyasu, Kamiya, Akane, Kuwahara, Naota, Fujiwara, Akiko, Matsunaga, Tomohiro, Sato, Yoko, Okada, Takenori, Hirai, Yoshihiro, Kawashima, Hidetoshi, Narita, Atsuya, Niwa, Kazuki, Sekikawa, Yoshiyuki, Nishi, Koichi, Nishitsuji, Masaru, Tani, Mayuko, Suzuki, Junya, Nakatsumi, Hiroki, Ogura, Takashi, Kitamura, Hideya, Hagiwara, Eri, Murohashi, Kota, Okabayashi, Hiroko, Mochimaru, Takao, Nukaga, Shigenari, Satomi, Ryosuke, Oyamada, Yoshitaka, Mori, Nobuaki, Baba, Tomoya, Fukui, Yasutaka, Odate, Mitsuru, Mashimo, Shuko, Makino, Yasushi, Yagi, Kazuma, Hashiguchi, Mizuha, Kagyo, Junko, Shiomi, Tetsuya, Fuke, Satoshi, Saito, Hiroshi, Tsuchida, Tomoya, Fujitani, Shigeki, Takita, Mumon, Morikawa, Daiki, Yoshida, Toru, Izumo, Takehiro, Inomata, Minoru, Kuse, Naoyuki, Awano, Nobuyasu, Tone, Mari, Ito, Akihiro, Nakamura, Yoshihiko, Hoshino, Kota, Maruyama, Junichi, Ishikura, Hiroyasu, Takata, Tohru, Odani, Toshio, Amishima, Masaru, Hattori, Takeshi, Shichinohe, Yasuo, Kagaya, Takashi, Kita, Toshiyuki, Ohta, Kazuhide, Sakagami, Satoru, Koshida, Kiyoshi, Hayashi, Kentaro, Shimizu, Tetsuo, Kozu, Yutaka, Hiranuma, Hisato, Gon, Yasuhiro, Izumi, Namiki, Nagata, Kaoru, Ueda, Ken, Taki, Reiko, Hanada, Satoko, Kawamura, Kodai, Ichikado, Kazuya, Nishiyama, Kenta, Muranaka, Hiroyuki, Nakamura, Kazunori, Hashimoto, Naozumi, Wakahara, Keiko, Koji, Sakamoto, Omote, Norihito, Ando, Akira, Kodama, Nobuhiro, Kaneyama, Yasunari, Maeda, Shunsuke, Kuraki, Takashige, Matsumoto, Takemasa, Yokote, Koutaro, Nakada, Taka-Aki, Abe, Ryuzo, Oshima, Taku, Shimada, Tadanaga, Harada, Masahiro, Takahashi, Takeshi, Ono, Hiroshi, Sakurai, Toshihiro, Shibusawa, Takayuki, Kimizuka, Yoshifumi, Kawana, Akihiko, Sano, Tomoya, Watanabe, Chie, Suematsu, Ryohei, Sageshima, Hisako, Yoshifuji, Ayumi, Ito, Kazuto, Takahashi, Saeko, Ishioka, Kota, Nakamura, Morio, Masuda, Makoto, Wakabayashi, Aya, Watanabe, Hiroki, Ueda, Suguru, Nishikawa, Masanori, Chihara, Yusuke, Takeuchi, Mayumi, Onoi, Keisuke, Shinozuka, Jun, Sueyoshi, Atsushi, Nagasaki, Yoji, Okamoto, Masaki, Ishihara, Sayoko, Shimo, Masatoshi, Tokunaga, Yoshihisa, Kusaka, Yu, Ohba, Takehiko, Isogai, Susumu, Ogawa, Aki, Inoue, Takuya, Fukuyama, Satoru, Eriguchi, Yoshihiro, Yonekawa, Akiko, Kan-o, Keiko, Matsumoto, Koichiro, Kanaoka, Kensuke, Ihara, Shoichi, Komuta, Kiyoshi, Inoue, Yoshiaki, Chiba, Shigeru, Yamagata, Kunihiro, Hiramatsu, Yuji, Kai, Hirayasu, Asano, Koichiro, Oguma, Tsuyoshi, Ito, Yoko, Hashimoto, Satoru, Yamasaki, Masaki, Kasamatsu, Yu, Komase, Yuko, Hida, Naoya, Tsuburai, Takahiro, Oyama, Baku, Takada, Minoru, Kanda, Hidenori, Kitagawa, Yuichiro, Fukuta, Tetsuya, Miyake, Takahito, Yoshida, Shozo, Ogura, Shinji, Abe, Shinji, Kono, Yuta, Togashi, Yuki, Takoi, Hiroyuki, Kikuchi, Ryota, Ogawa, Shinichi, Ogata, Tomouki, Ishihara, Shoichiro, Kanehiro, Arihiko, Ozaki, Shinji, Fuchimoto, Yasuko, Wada, Sae, Fujimoto, Nobukazu, Nishiyama, Kei, Terashima, Mariko, Beppu, Satoru, Yoshida, Kosuke, Narumoto, Osamu, Nagai, Hideaki, Ooshima, Nobuharu, Motegi, Mitsuru, Umeda, Akira, Miyagawa, Kazuya, Shimada, Hisato, Endo, Mayu, Ohira, Yoshiyuki, Watanabe, Masafumi, Inoue, Sumito, Igarashi, Akira, Sato, Masamichi, Sagara, Hironori, Tanaka, Akihiko, Ohta, Shin, Kimura, Tomoyuki, Shibata, Yoko, Tanino, Yoshinori, Nikaido, Takefumi, Minemura, Hiroyuki, Sato, Yuki, Yamada, Yuichiro, Hashino, Takuya, Shinoki, Masato, Iwagoe, Hajime, Takahashi, Hiroshi, Fujii, Kazuhiko, Kishi, Hiroto, Kanai, Masayuki, Imamura, Tomonori, Yamashita, Tatsuya, Yatomi, Masakiyo, Maeno, Toshitaka, Hayashi, Shinichi, Takahashi, Mai, Kuramochi, Mizuki, Kamimaki, Isamu, Tominaga, Yoshiteru, Ishii, Tomoo, Utsugi, Mitsuyoshi, Ono, Akihiro, Tanaka, Toru, Kashiwada, Takeru, Fujita, Kazue, Saito, Yoshinobu, Seike, Masahiro, Watanabe, Hiroko, Matsuse, Hiroto, Kodaka, Norio, Nakano, Chihiro, Oshio, Takeshi, Hirouchi, Takatomo, Makino, Shohei, Egi, Moritoki, Matsuda, Koichi, Yamanashi, Yuji, Furukawa, Yoichi, Morisaki, Takayuki, Murakami, Yoshinori, Kamatani, Yoichiro, Muto, Kaori, Nagai, Akiko, Obara, Wataru, Yamaji, Ken, Asai, Satoshi, Takahashi, Yasuo, Suzuki, Takao, Sinozaki, Nobuaki, Yamaguchi, Hiroki, Minami, Shiro, Murayama, Shigeo, Yoshimori, Kozo, Nagayama, Satoshi, Obata, Daisuke, Higashiyama, Masahiko, Masumoto, Akihide, Koretsune, Yukihiro, Omae, Yosuke, Nannya, Yasuhito, Ueno, Takafumi, Katayama, Kazuhiko, Ai, Masumi, Fukui, Yoshinori, Kumanogoh, Atsushi, Sato, Toshiro, Hasegawa, Naoki, Tokunaga, Katsushi, Ishii, Makoto, Koike, Ryuji, Kitagawa, Yuko, Kimura, Akinori, Imoto, Seiya, Miyano, Satoru, Ogawa, Seishi, Kanai, Takanori, Fukunaga, Koichi, Okada, Yukinori, Namkoong, Ho, Edahiro, Ryuya, Takano, Tomomi, Nishihara, Hiroshi, Shirai, Yuya, Sonehara, Kyuto, Tanaka, Hiromu, Azekawa, Shuhei, Mikami, Yohei, Lee, Ho, Hasegawa, Takanori, Okudela, Koji, Okuzaki, Daisuke, Motooka, Daisuke, Kanai, Masahiro, Naito, Tatsuhiko, Yamamoto, Kenichi, Wang, Qingbo S., Saiki, Ryunosuke, Ishihara, Rino, Matsubara, Yuta, Hamamoto, Junko, Hayashi, Hiroyuki, Yoshimura, Yukihiro, Tachikawa, Natsuo, Yanagita, Emmy, Hyugaji, Takayoshi, Shimizu, Eigo, Katayama, Kotoe, Kato, Yasuhiro, Morita, Takayoshi, Takahashi, Kazuhisa, Harada, Norihiro, Naito, Toshio, Hiki, Makoto, Matsushita, Yasushi, Takagi, Haruhi, Aoki, Ryousuke, Nakamura, Ai, Harada, Sonoko, Sasano, Hitoshi, Kabata, Hiroki, Masaki, Katsunori, Kamata, Hirofumi, Ikemura, Shinnosuke, Chubachi, Shotaro, Okamori, Satoshi, Terai, Hideki, Morita, Atsuho, Asakura, Takanori, Sasaki, Junichi, Morisaki, Hiroshi, Uwamino, Yoshifumi, Nanki, Kosaku, Uchida, Sho, Uno, Shunsuke, Nishimura, Tomoyasu, Ishiguro, Takashi, Isono, Taisuke, Shibata, Shun, Matsui, Yuma, Hosoda, Chiaki, Takano, Kenji, Nishida, Takashi, Kobayashi, Yoichi, Takaku, Yotaro, Takayanagi, Noboru, Ueda, Soichiro, Tada, Ai, Miyawaki, Masayoshi, Yamamoto, Masaomi, Yoshida, Eriko, Hayashi, Reina, Nagasaka, Tomoki, Arai, Sawako, Kaneko, Yutaro, Sasaki, Kana, Tagaya, Etsuko, Kawana, Masatoshi, Arimura, Ken, Takahashi, Kunihiko, Anzai, Tatsuhiko, Ito, Satoshi, Endo, Akifumi, Uchimura, Yuji, Miyazaki, Yasunari, Honda, Takayuki, Tateishi, Tomoya, Tohda, Shuji, Ichimura, Naoya, Sonobe, Kazunari, Sassa, Chihiro Tani, Nakajima, Jun, Nakano, Yasushi, Nakajima, Yukiko, Anan, Ryusuke, Arai, Ryosuke, Kurihara, Yuko, Harada, Yuko, Nishio, Kazumi, Ueda, Tetsuya, Azuma, Masanori, Saito, Ryuichi, Sado, Toshikatsu, Miyazaki, Yoshimune, Sato, Ryuichi, Haruta, Yuki, Nagasaki, Tadao, Yasui, Yoshinori, Hasegawa, Yoshinori, Mutoh, Yoshikazu, Kimura, Tomoki, Sato, Tomonori, Takei, Reoto, Hagimoto, Satoshi, Noguchi, Yoichiro, Yamano, Yasuhiko, Sasano, Hajime, Ota, Sho, Nakamori, Yasushi, Yoshiya, Kazuhisa, Saito, Fukuki, Yoshihara, Tomoyuki, Wada, Daiki, Iwamura, Hiromu, Kanayama, Syuji, Maruyama, Shuhei, Yoshiyama, Takashi, Ohta, Ken, Kokuto, Hiroyuki, Ogata, Hideo, Tanaka, Yoshiaki, Arakawa, Kenichi, Shimoda, Masafumi, Osawa, Takeshi, Tateno, Hiroki, Hase, Isano, Yoshida, Shuichi, Suzuki, Shoji, Kawada, Miki, Horinouchi, Hirohisa, Saito, Fumitake, Mitamura, Keiko, Hagihara, Masao, Ochi, Junichi, Uchida, Tomoyuki, Baba, Rie, Arai, Daisuke, Ogura, Takayuki, Takahashi, Hidenori, Hagiwara, Shigehiro, Nagao, Genta, Konishi, Shunichiro, Nakachi, Ichiro, Murakami, Koji, Yamada, Mitsuhiro, Sugiura, Hisatoshi, Sano, Hirohito, Matsumoto, Shuichiro, Kimura, Nozomu, Ono, Yoshinao, Baba, Hiroaki, Suzuki, Yusuke, Nakayama, Sohei, Masuzawa, Keita, Namba, Shinichi, Suzuki, Ken, Naito, Yoko, Liu, Yu-Chen, Takuwa, Ayako, Sugihara, Fuminori, Wing, James B., Sakakibara, Shuhei, Hizawa, Nobuyuki, Shiroyama, Takayuki, Miyawaki, Satoru, Kawamura, Yusuke, Nakayama, Akiyoshi, Matsuo, Hirotaka, Maeda, Yuichi, Nii, Takuro, Noda, Yoshimi, Niitsu, Takayuki, Adachi, Yuichi, Enomoto, Takatoshi, Amiya, Saori, Hara, Reina, Yamaguchi, Yuta, Murakami, Teruaki, Kuge, Tomoki, Matsumoto, Kinnosuke, Yamamoto, Yuji, Yamamoto, Makoto, Yoneda, Midori, Kishikawa, Toshihiro, Yamada, Shuhei, Kawabata, Shuhei, Kijima, Noriyuki, Takagaki, Masatoshi, Sasa, Noah, Ueno, Yuya, Suzuki, Motoyuki, Takemoto, Norihiko, Eguchi, Hirotaka, Fukusumi, Takahito, Imai, Takao, Fukushima, Munehisa, Kishima, Haruhiko, Inohara, Hidenori, Tomono, Kazunori, Kato, Kazuto, Takahashi, Meiko, Matsuda, Fumihiko, Hirata, Haruhiko, Takeda, Yoshito, Koh, Hidefumi, Manabe, Tadashi, Funatsu, Yohei, Ito, Fumimaro, Fukui, Takahiro, Shinozuka, Keisuke, Kohashi, Sumiko, Miyazaki, Masatoshi, Shoko, Tomohisa, Kojima, Mitsuaki, Adachi, Tomohiro, Ishikawa, Motonao, Takahashi, Kenichiro, Inoue, Takashi, Hirano, Toshiyuki, Kobayashi, Keigo, Takaoka, Hatsuyo, Watanabe, Kazuyoshi, Miyazawa, Naoki, Kimura, Yasuhiro, Sado, Reiko, Sugimoto, Hideyasu, Kamiya, Akane, Kuwahara, Naota, Fujiwara, Akiko, Matsunaga, Tomohiro, Sato, Yoko, Okada, Takenori, Hirai, Yoshihiro, Kawashima, Hidetoshi, Narita, Atsuya, Niwa, Kazuki, Sekikawa, Yoshiyuki, Nishi, Koichi, Nishitsuji, Masaru, Tani, Mayuko, Suzuki, Junya, Nakatsumi, Hiroki, Ogura, Takashi, Kitamura, Hideya, Hagiwara, Eri, Murohashi, Kota, Okabayashi, Hiroko, Mochimaru, Takao, Nukaga, Shigenari, Satomi, Ryosuke, Oyamada, Yoshitaka, Mori, Nobuaki, Baba, Tomoya, Fukui, Yasutaka, Odate, Mitsuru, Mashimo, Shuko, Makino, Yasushi, Yagi, Kazuma, Hashiguchi, Mizuha, Kagyo, Junko, Shiomi, Tetsuya, Fuke, Satoshi, Saito, Hiroshi, Tsuchida, Tomoya, Fujitani, Shigeki, Takita, Mumon, Morikawa, Daiki, Yoshida, Toru, Izumo, Takehiro, Inomata, Minoru, Kuse, Naoyuki, Awano, Nobuyasu, Tone, Mari, Ito, Akihiro, Nakamura, Yoshihiko, Hoshino, Kota, Maruyama, Junichi, Ishikura, Hiroyasu, Takata, Tohru, Odani, Toshio, Amishima, Masaru, Hattori, Takeshi, Shichinohe, Yasuo, Kagaya, Takashi, Kita, Toshiyuki, Ohta, Kazuhide, Sakagami, Satoru, Koshida, Kiyoshi, Hayashi, Kentaro, Shimizu, Tetsuo, Kozu, Yutaka, Hiranuma, Hisato, Gon, Yasuhiro, Izumi, Namiki, Nagata, Kaoru, Ueda, Ken, Taki, Reiko, Hanada, Satoko, Kawamura, Kodai, Ichikado, Kazuya, Nishiyama, Kenta, Muranaka, Hiroyuki, Nakamura, Kazunori, Hashimoto, Naozumi, Wakahara, Keiko, Koji, Sakamoto, Omote, Norihito, Ando, Akira, Kodama, Nobuhiro, Kaneyama, Yasunari, Maeda, Shunsuke, Kuraki, Takashige, Matsumoto, Takemasa, Yokote, Koutaro, Nakada, Taka-Aki, Abe, Ryuzo, Oshima, Taku, Shimada, Tadanaga, Harada, Masahiro, Takahashi, Takeshi, Ono, Hiroshi, Sakurai, Toshihiro, Shibusawa, Takayuki, Kimizuka, Yoshifumi, Kawana, Akihiko, Sano, Tomoya, Watanabe, Chie, Suematsu, Ryohei, Sageshima, Hisako, Yoshifuji, Ayumi, Ito, Kazuto, Takahashi, Saeko, Ishioka, Kota, Nakamura, Morio, Masuda, Makoto, Wakabayashi, Aya, Watanabe, Hiroki, Ueda, Suguru, Nishikawa, Masanori, Chihara, Yusuke, Takeuchi, Mayumi, Onoi, Keisuke, Shinozuka, Jun, Sueyoshi, Atsushi, Nagasaki, Yoji, Okamoto, Masaki, Ishihara, Sayoko, Shimo, Masatoshi, Tokunaga, Yoshihisa, Kusaka, Yu, Ohba, Takehiko, Isogai, Susumu, Ogawa, Aki, Inoue, Takuya, Fukuyama, Satoru, Eriguchi, Yoshihiro, Yonekawa, Akiko, Kan-o, Keiko, Matsumoto, Koichiro, Kanaoka, Kensuke, Ihara, Shoichi, Komuta, Kiyoshi, Inoue, Yoshiaki, Chiba, Shigeru, Yamagata, Kunihiro, Hiramatsu, Yuji, Kai, Hirayasu, Asano, Koichiro, Oguma, Tsuyoshi, Ito, Yoko, Hashimoto, Satoru, Yamasaki, Masaki, Kasamatsu, Yu, Komase, Yuko, Hida, Naoya, Tsuburai, Takahiro, Oyama, Baku, Takada, Minoru, Kanda, Hidenori, Kitagawa, Yuichiro, Fukuta, Tetsuya, Miyake, Takahito, Yoshida, Shozo, Ogura, Shinji, Abe, Shinji, Kono, Yuta, Togashi, Yuki, Takoi, Hiroyuki, Kikuchi, Ryota, Ogawa, Shinichi, Ogata, Tomouki, Ishihara, Shoichiro, Kanehiro, Arihiko, Ozaki, Shinji, Fuchimoto, Yasuko, Wada, Sae, Fujimoto, Nobukazu, Nishiyama, Kei, Terashima, Mariko, Beppu, Satoru, Yoshida, Kosuke, Narumoto, Osamu, Nagai, Hideaki, Ooshima, Nobuharu, Motegi, Mitsuru, Umeda, Akira, Miyagawa, Kazuya, Shimada, Hisato, Endo, Mayu, Ohira, Yoshiyuki, Watanabe, Masafumi, Inoue, Sumito, Igarashi, Akira, Sato, Masamichi, Sagara, Hironori, Tanaka, Akihiko, Ohta, Shin, Kimura, Tomoyuki, Shibata, Yoko, Tanino, Yoshinori, Nikaido, Takefumi, Minemura, Hiroyuki, Sato, Yuki, Yamada, Yuichiro, Hashino, Takuya, Shinoki, Masato, Iwagoe, Hajime, Takahashi, Hiroshi, Fujii, Kazuhiko, Kishi, Hiroto, Kanai, Masayuki, Imamura, Tomonori, Yamashita, Tatsuya, Yatomi, Masakiyo, Maeno, Toshitaka, Hayashi, Shinichi, Takahashi, Mai, Kuramochi, Mizuki, Kamimaki, Isamu, Tominaga, Yoshiteru, Ishii, Tomoo, Utsugi, Mitsuyoshi, Ono, Akihiro, Tanaka, Toru, Kashiwada, Takeru, Fujita, Kazue, Saito, Yoshinobu, Seike, Masahiro, Watanabe, Hiroko, Matsuse, Hiroto, Kodaka, Norio, Nakano, Chihiro, Oshio, Takeshi, Hirouchi, Takatomo, Makino, Shohei, Egi, Moritoki, Matsuda, Koichi, Yamanashi, Yuji, Furukawa, Yoichi, Morisaki, Takayuki, Murakami, Yoshinori, Kamatani, Yoichiro, Muto, Kaori, Nagai, Akiko, Obara, Wataru, Yamaji, Ken, Asai, Satoshi, Takahashi, Yasuo, Suzuki, Takao, Sinozaki, Nobuaki, Yamaguchi, Hiroki, Minami, Shiro, Murayama, Shigeo, Yoshimori, Kozo, Nagayama, Satoshi, Obata, Daisuke, Higashiyama, Masahiko, Masumoto, Akihide, Koretsune, Yukihiro, Omae, Yosuke, Nannya, Yasuhito, Ueno, Takafumi, Katayama, Kazuhiko, Ai, Masumi, Fukui, Yoshinori, Kumanogoh, Atsushi, Sato, Toshiro, Hasegawa, Naoki, Tokunaga, Katsushi, Ishii, Makoto, Koike, Ryuji, Kitagawa, Yuko, Kimura, Akinori, Imoto, Seiya, Miyano, Satoru, Ogawa, Seishi, Kanai, Takanori, Fukunaga, Koichi, and Okada, Yukinori

Abstract

Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target.

Published
2022

11. Primary angiosarcoma of the ascending colon diagnosed after the discovery of intraoral tumor.

Author

Tojo, Masaya, Yoshida, Kosuke, Arakane, Naomi, Tamagawa, Hiroki, Miyanaga, Ryoichi, Watanabe, Kazuyo, and Fukuhara, Seiichiro

Abstract

Here, we report a case of primary angiosarcoma of the ascending colon diagnosed after the discovery of an oral tumor. An 86-year-old woman presented to our hospital with severe anemia. Although she did not report any oral symptoms, an intraoral mass 15 mm in size with ulceration was observed. Since colonic tumors and osteolytic tumors in the maxilla and sacra were also identified by fluorodeoxyglucose (FDG)-positron emission tomography (PET), colonoscopy was performed. A semi-peripheral tumor with ulceration from the cecum to the ascending colon was detected. Biopsies of the oral cavity and colon revealed a poorly differentiated tumor, and multiple additional immunohistochemical stains were performed to confirm the diagnosis of angiosarcoma. Angiosarcoma progresses rapidly and has a very poor prognosis. Hence, although it is rare, angiosarcoma should always be considered in the differential diagnoses of malignancy of the gastrointestinal tract. [ABSTRACT FROM AUTHOR]

Published
2023
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12. Numerical study of the correlation between fish school arrangement and propulsive performance.

Author

Yoshida, Kosuke, Ogata, Yoichi, Hirai, Sota, and Hosotani, Kazunori

Abstract

We focus on the distances in flow direction and its vertical direction on three fish school arrangements, and correlate the fish school in uniform flow with fluid forces which can be divided into two components, drag and thrust, by three-dimensional fluid–structure interaction simulations. It is found that the distance in flow direction has larger impact on both two components than the distance in vertical to that direction. Drag and thrust are reduced and improved in fish school swimming more than single fish swimming, as the distance in flow direction becomes larger. Pressure and velocity fields that are generated around fish school favorable to propulsion under such conditions are also investigated in the present paper. [ABSTRACT FROM AUTHOR]

Published
2023
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13. Conditioned medium from BV2 microglial cells having polyleucine specifically alters startle response in mice.

Author

Owada, Ryuji, Kakuta, Yohei, Yoshida, Kosuke, Mitsui, Shinichi, and Nakamura, Kazuhiro

Subjects
STARTLE reaction, MICROGLIA, CELL death, LEUCINE, MICE, NEURONS, POLYGLUTAMINE
Abstract

Repeat-associated non-AUG translation (RAN translation) is observed in transcripts that are causative for polyglutamine (polyQ) diseases and generates proteins with mono amino acid tracts such as polyalanine (polyA), polyleucine (polyL) and polyserine (polyS) in neurons, astrocytes and microglia. We have previously shown that microglia with aggregated polyQ led to defective differentiation and degeneration of neuron-like cells. However, it has not been determined whether only microglia containing a specific RAN product, but not other RAN products, is harmful in vitro and in vivo. Here we show that polyL-incorporating microglia specifically led to altered startle response in mice. Aggregated polyA, polyS and polyL induced aberrant differentiation of microglia-like BV2 cells. Differentiated PC12 cells treated with conditioned medium (CM) of polyS- and polyL- but not polyA-incorporating microglia-like BV2 cells showed retraction of neurites and loss of branch of neurites. Injection of the polyL-CM, but not polyA-CM and polyS-CM, into the lateral ventricle lowered startle response in mice. Consistently, polyL induced the highest expression of CD68 in BV2 cells. The lowered startle response was replicated in mice given the polyL-CM in the caudal pontine reticular nucleus (PnC), the key region of startle response. Thus, endogenous RAN proteins having polyL derived from polyQ diseases-causative genes in microglia might specifically impair startle response. [ABSTRACT FROM AUTHOR]

Published
2022
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14. Clinical effects of cervical conization with positive margins in cervical cancer.

Author

Nagao, Yukari, Yokoi, Akira, Yoshida, Kosuke, Sumi, Masanori, Yoshihara, Masato, Tamauchi, Satoshi, Ikeda, Yoshiki, Yoshikawa, Nobuhisa, Nishino, Kimihiro, Niimi, Kaoru, and Kajiyama, Hiroaki

Subjects
CONIZATION, CERVICAL cancer, OVERALL survival, PROGRESSION-free survival, LYMPHADENECTOMY
Abstract

Radical surgery after cervical conization is a common approach for the treatment of cervical cancer. In some cases, disease progression is observed after positive margins at conization, but the effect of conization on disease progression remains unclear. Thus, the aim of this study was to investigate the clinical outcomes of positive margins at conization in cervical cancer. A total of 101 patients who underwent cervical conization before radical hysterectomy and pelvic lymph node dissection were considered eligible by reviewing medical records. The association between the positive margins and patient outcomes, including subsequent lymph node metastasis, was evaluated. The rate of lymphovascular space invasion (LVSI) positivity at radical surgery was significantly higher in patients with positive margins (p = 0.017) than in those with negative margins, although there was no significant difference in the rate of pelvic lymph node metastasis (p = 0.155). Moreover, there was no significant difference in the overall survival or progression-free survival between the two groups (p = 0.332 and 0.200, respectively). A positive margin at conization presented no significant prognostic disadvantage; thus, diagnostic conization is one of the most suitable treatment options for early-stage cervical cancer that is difficult to accurately assess. [ABSTRACT FROM AUTHOR]

Published
2021
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15. The Preoperative Prognostic Nutritional Index for the Prediction of Outcomes in Patients with Early-Stage Ovarian Clear Cell Carcinoma.

Author

Yoshikawa, Nobuhisa, Yoshida, Kosuke, Tamauchi, Satoshi, Ikeda, Yoshiki, Nishino, Kimihiro, Niimi, Kaoru, Suzuki, Shiro, Kikkawa, Fumitaka, and Kajiyama, Hiroaki

Subjects
*MALNUTRITION, *SURGICAL complications, *PROGNOSIS, *LYMPHOCYTES, *CARCINOMA
Abstract

The prognostic nutritional index (PNI), which reflects preoperative malnutrition, is useful for predicting the incidence of postoperative complications and has been reported in recent years to predict the long-term prognosis of various malignancies. The purpose of this study was to clarify the significance of PNI as a prognostic factor for early-stage clear cell ovarian carcinoma. A total of 82 patients with stage I–II (FIGO 2014) ovarian clear cell carcinoma undergoing primary surgery at our hospital from January 2005 to December 2017 were enrolled. PNI was calculated using the formula: 10 × serum albumin (g/ dL) + 0.005 × peripheral blood lymphocyte count (/mm3). Preoperative PNI exhibited relatively high area under the curve value (0.709) for 5 year survival, and the optimal cutoff value was 46.5. The overall survival was significantly shorter in the PNI-low group than in the PNI-high group. Multivariate analysis showed that high PNI was a significant independent prognostic factor for favorable prognosis (hazard ratio = 0.102, p = 0.010). There was no significant difference in recurrence-free survival between the two groups (p = 0.220), but the postrecurrence survival was significantly longer in the PNI-high group than in the PNI-low group (p = 0.0383). The preoperative PNI was a useful predictor of prognosis, even in early-stage ovarian clear cell carcinoma. [ABSTRACT FROM AUTHOR]

Published
2020
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16. The role of additional hysterectomy after concurrent chemoradiation for patients with locally advanced cervical cancer.

Author

Yoshida, Kosuke, Kajiyama, Hiroaki, Yoshihara, Masato, Tamauchi, Satoshi, Ikeda, Yoshiki, Yoshikawa, Nobuhisa, Nishino, Kimihiro, Niimi, Kaoru, Suzuki, Shiro, and Kikkawa, Fumitaka

Subjects
*ABDOMINOPERINEAL resection, *CERVICAL cancer, *CHEMORADIOTHERAPY, *HYSTERECTOMY, *SQUAMOUS cell carcinoma, *PROGRESSION-free survival
Abstract

Background: The standard treatment for cervical cancer is chemoradiation although some patients showed treatment resistance. The purpose of this study was to investigate the clinical efficacy of surgery after chemoradiation for cervical cancer. Methods: Patients with FIGO stage IB2 to IIB cervical cancer were included in the study between 2005 and 2015. A total of 50 patients who underwent surgery after neoadjuvant chemoradiation and 76 patients who received only chemoradiation were compared. Baseline differences between the two groups were adjusted with inverse probability of treatment weighting method using propensity scores composed of the following independent variables: age, stage, tumor size, lymph node metastasis, and histological subtypes. Results: Median follow-up was 64.8 (range 4.8–143.9) months. After adjustment with inverse probability of treatment weighting, Kaplan–Meier curves showing adjusted progression-free survival and overall survival were significantly longer in the neoadjuvant chemoradiation compared with the chemoradiation-only group (p = 0.027 and p = 0.017, respectively). Moreover, in patients with squamous cell carcinoma, recurrence in previously irradiated field and recurrence both in and out of previously irradiated field were significantly decreased in the neoadjuvant chemoradiation compared with the chemoradiation-only group (3.1% and 18.4%, respectively; OR 0.142, p = 0.001]. Adverse events of surgery after chemoradiation were acceptable, although temporary hydronephrosis was frequently observed (23.1%). Conclusions: Surgery after chemoradiation reduced pelvic recurrence, and as a result, patients who underwent neoadjuvant chemoradiation showed more favorable survival outcomes compared with those who only underwent chemoradiation. [ABSTRACT FROM AUTHOR]

Published
2020
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17. Plasma-activated medium promotes autophagic cell death along with alteration of the mTOR pathway.

Author

Yoshikawa, Nobuhisa, Liu, Wenting, Nakamura, Kae, Yoshida, Kosuke, Ikeda, Yoshiki, Tanaka, Hiromasa, Mizuno, Masaaki, Toyokuni, Shinya, Hori, Masaru, Kikkawa, Fumitaka, and Kajiyama, Hiroaki

Subjects
CELL death, METASTASIS, ENDOMETRIAL cancer, AUTOPHAGY, CANCER cells
Abstract

The biological function of non-thermal atmospheric pressure plasma has been widely accepted in several types of cancer. We previously developed plasma-activated medium (PAM) for clinical use, and demonstrated that PAM exhibits a metastasis-inhibitory effect on ovarian cancer through reduced MMP-9 secretion. However, the anti-tumor effects of PAM on endometrial cancer remain unknown. In this study, we investigated the inhibitory effect of PAM on endometrial cancer cell viability in vitro. Our results demonstrated that AMEC and HEC50 cell viabilities were reduced by PAM at a certain PAM ratio, and PAM treatment effectively increased autophagic cell death in a concentration dependent manner. In addition, we evaluated the molecular mechanism of PAM activity and found that the mTOR pathway was inactivated by PAM. Moreover, our results demonstrated that the autophagy inhibitor MHY1485 partially inhibited the autophagic cell death induced by PAM treatment. These findings indicate that PAM decreases the viability of endometrial cancer cells along with alteration of the mTOR pathway, which is critical for cancer cell viability. Collectively, our data suggest that PAM inhibits cell viability while inducing autophagic cell death in endometrial cancer cells, representing a potential novel treatment for endometrial cancer. [ABSTRACT FROM AUTHOR]

Published
2020
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18. Somatic inflammatory gene mutations in human ulcerative colitis epithelium.

Author

Nanki, Kosaku, Fujii, Masayuki, Shimokawa, Mariko, Matano, Mami, Nishikori, Shingo, Date, Shoichi, Takano, Ai, Toshimitsu, Kohta, Ohta, Yuki, Takahashi, Sirirat, Sugimoto, Shinya, Ishimaru, Kazuhiro, Kawasaki, Kenta, Nagai, Yoko, Ishii, Ryota, Yoshida, Kosuke, Sasaki, Nobuo, Hibi, Toshifumi, Ishihara, Soichiro, and Kanai, Takanori

Abstract

With ageing, normal human tissues experience an expansion of somatic clones that carry cancer mutations1–7. However, whether such clonal expansion exists in the non-neoplastic intestine remains unknown. Here, using whole-exome sequencing data from 76 clonal human colon organoids, we identify a unique pattern of somatic mutagenesis in the inflamed epithelium of patients with ulcerative colitis. The affected epithelium accumulates somatic mutations in multiple genes that are related to IL-17 signalling—including NFKBIZ, ZC3H12A and PIGR, which are genes that are rarely affected in colon cancer. Targeted sequencing validates the pervasive spread of mutations that are related to IL-17 signalling. Unbiased CRISPR-based knockout screening in colon organoids reveals that the mutations confer resistance to the pro-apoptotic response that is induced by IL-17A. Some of these genetic mutations are known to exacerbate experimental colitis in mice8–11, and somatic mutagenesis in human colon epithelium may be causally linked to the inflammatory process. Our findings highlight a genetic landscape that adapts to a hostile microenvironment, and demonstrate its potential contribution to the pathogenesis of ulcerative colitis. Whole-exome sequencing of colon organoids derived from patients with ulcerative colitis identifies somatic mutations in components of the IL-17 signalling pathway, which may confer a growth advantage to cells under inflammatory conditions. [ABSTRACT FROM AUTHOR]

Published
2020
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19. Synthesis of cerium oxide (IV) stable sol using the dialysis process of glycol solution of cerium nitrate hydrate.

Author

Uekawa, Naofumi, Yoshida, Kosuke, Kobayashi, Minoru, and Kojima, Takashi

Abstract

Cerium oxide (CeO2) nanoparticles were obtained by two-step synthetic process whose first step is heating process of ethylene glycol solution of Ce(NO3)3·6H2O with addition of 1 M NH3 aq. at 348 K for 24 h and a second step is dialysis process of the heated solution. These two-step processes enabled to obtain cerium oxide nanoparticles which were dispersed in H2O very stably and clear yellow sol was also obtained. Formation process of cerium oxide nanoparticles during the dialysis process in H2O was examined. For the dialysis process, inter-valence charge transfer (IVCT) absorption between Ce3+ and Ce4+ decreased. Furthermore, Raman spectrum of the CeO2 nanoparticles had the peak which corresponded to O22− ion, which was formed by reduction of O2. This result indicated that the dissolved O2 played important role of the oxidation of the Ce ions in cerium hydroxide (III) particles in the first step solution. The O22− ions which existed on the nanoparticle surface as peroxo group also contributed to the stable dispersion. Highlights: CeO2 sol was obtained by heating and dialysis of ethylene glycol solution of cerium nitrate hydrate (III). CeO2 nanoparticles were stably dispersed in H2O. Oxidation from Ce3+ to Ce4+ was occurred with dissolved O2 in H2O for dialysis process. Raman spectra indicated that of the obtained CeO2 nanoparticles contained oxygen vacancies. [ABSTRACT FROM AUTHOR]

Published
2020
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20. Impact of age on clinicopathological features and survival of epithelial ovarian neoplasms in reproductive age.

Author

Hanatani, Maya, Yoshikawa, Nobuhisa, Yoshida, Kosuke, Tamauchi, Satoshi, Ikeda, Yoshiki, Nishino, Kimihiro, Niimi, Kaoru, Suzuki, Shiro, Kawai, Michiyasu, Kajiyama, Hiroaki, and Kikkawa, Fumitaka

Subjects
OVARIAN cancer, OVARIAN function tests, PROGRESSION-free survival, OVARIAN epithelial cancer, AGE groups, ASCITIC fluids
Abstract

Background: Little is known about the effect of age on the prognosis of epithelial ovarian neoplasms. In the reproductive age, fertility-sparing surgery had been widely implemented. This study aimed to elucidate impact of age on the clinicopathologic characteristics and survival of epithelial ovarian neoplasms in the reproductive age. Methods: The clinical records of patients diagnosed as epithelial ovarian cancer or epithelial borderline ovarian tumor at the age of 40 years or younger at multiple institutions in the Tokai Ovarian Tumor Study Group were reviewed retrospectively. All patients were stratified into two age groups: group A (≤ 30 years) and group B (31–40 years). Univariate and multivariate analyses were performed to evaluate overall survival and disease-free survival. Results: A total of 583 patients (325 patients: cancer, 258 patients: borderline) were included. The median follow-up time was 62.0 months (range 1–270 months). Compared with group B, group A had a significantly higher rate of borderline tumor (66.7% vs. 32.7%, p < 0.001); stage I disease (85.9% vs. 70.4%, p < 0.001); mucinous type (69.2% vs. 35.6%, p < 0.001); conservative surgery (83.8% vs. 41.6%, p < 0.001); no adjuvant chemotherapy (67.2% vs. 44.7%, p < 0.001); and CA125 ≤ 35 U/mL (39.4% vs. 28.8%, p < 0.05). There was a significant difference in the overall survival (p = 0.0051) and the disease-free survival (p = 0.0039) between the two groups. Multivariate analysis revealed that the independent prognostic factors for the overall survival were age, stage, histology, and ascitic fluid cytology. Conclusion: In epithelial ovarian neoplasms, younger patients had a survival advantage over older patients. [ABSTRACT FROM AUTHOR]

Published
2020
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21. Does postoperative prophylactic irradiation of para-aortic lymph nodes reduce the risk of recurrence in uterine cervical cancer with positive pelvic lymph nodes?

Author

Yoshida, Kosuke, Kajiyama, Hiroaki, Yoshihara, Masato, Ikeda, Yoshiki, Yoshikawa, Nobuhisa, Nishino, Kimihiro, Utsumi, Fumi, Niimi, Kaoru, Suzuki, Shiro, and Kikkawa, Fumitaka

Subjects
*LYMPHADENECTOMY, *CERVICAL cancer, *LYMPH nodes, *RADIOTHERAPY, *IRRADIATION
Abstract

Background: In cervical cancer, para-aortic lymph nodes are common sites of metastasis. The purpose of the study was to evaluate the clinical benefits of prophylactic irradiation as postoperative therapy. Methods: A retrospective cohort study was conducted during 2001–2015 at a single institution. Patients with a high risk of para-aortic lymph nodes recurrence were eligible for this study, and we identified patients who had pelvic lymph node metastasis and underwent radical surgery and concurrent chemo-radiotherapy. As a result, 33 and 46 patients were included in the treatment (prophylactic irradiation) and non-treatment groups, respectively. Baseline differences between the two groups were adjusted with the inverse probability of treatment weighting using propensity scores composed of the independent variables including age, stage, tumor size, pathological findings, lymph node status, and pathological subtypes. Results: In the 68-month median follow-up period (range 6–178 months), 25 patients experienced recurrence, and 17 patients were dead. After adjustment with the inverse probability of treatment weighting, the recurrence rates tended to decrease in the treatment group, but there was no significant difference between the two groups [treatment vs. non-treatment, 29.4% and 44.3%, respectively; hazard ratio, 0.593 (95% CI 0.320–1.099); P = 0.097]. However, adjusted para-aortic lymph nodes recurrence rates were not significantly different [treatment vs. non-treatment, 7.8% and 11.4%, respectively; odds ratio, 0.660 (95% CI 0.187–2.322); P = 0.558]. Moreover, Kaplan–Meier curves showing post-recurrence survival revealed no significant difference between the two groups (P = 0.141). Conclusions: Prophylactic para-aortic lymph nodes irradiation did not reduce the risk of recurrence. [ABSTRACT FROM AUTHOR]

Published
2019
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22. Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data

Author

Yoshida, Kosuke, Yoshimoto, Junichiro, Doya, Kenji, Yoshida, Kosuke, Yoshimoto, Junichiro, and Doya, Kenji

Abstract

[Background]Advance in high-throughput technologies in genomics, transcriptomics, and metabolomics has created demand for bioinformatics tools to integrate high-dimensional data from different sources. Canonical correlation analysis (CCA) is a statistical tool for finding linear associations between different types of information. Previous extensions of CCA used to capture nonlinear associations, such as kernel CCA, did not allow feature selection or capturing of multiple canonical components. Here we propose a novel method, two-stage kernel CCA (TSKCCA) to select appropriate kernels in the framework of multiple kernel learning. [Results]TSKCCA first selects relevant kernels based on the HSIC criterion in the multiple kernel learning framework. Weights are then derived by non-negative matrix decomposition with L1 regularization. Using artificial datasets and nutrigenomic datasets, we show that TSKCCA can extract multiple, nonlinear associations among high-dimensional data and multiplicative interactions among variables. [Conclusions]TSKCCA can identify nonlinear associations among high-dimensional data more reliably than previous nonlinear CCA methods.

Published
2017

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