Fumio Sakane

6.4k total citations
146 papers, 5.5k citations indexed

About

Fumio Sakane is a scholar working on Molecular Biology, Cell Biology and Surgery. According to data from OpenAlex, Fumio Sakane has authored 146 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 137 papers in Molecular Biology, 62 papers in Cell Biology and 31 papers in Surgery. Recurrent topics in Fumio Sakane's work include Protein Kinase Regulation and GTPase Signaling (68 papers), Metabolism, Diabetes, and Cancer (33 papers) and Lipid metabolism and biosynthesis (31 papers). Fumio Sakane is often cited by papers focused on Protein Kinase Regulation and GTPase Signaling (68 papers), Metabolism, Diabetes, and Cancer (33 papers) and Lipid metabolism and biosynthesis (31 papers). Fumio Sakane collaborates with scholars based in Japan, United States and United Kingdom. Fumio Sakane's co-authors include Hideo Kanoh, Shin-ichi Imai, Masahiro Kai, Keiko Yamada, Ikuo Wada, Hiromichi Sakai, Satoshi Yasuda, Chiaki Murakami, Satoru Mizuno and Akinobu Taketomi and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Fumio Sakane

143 papers receiving 5.5k citations

Peers

Fumio Sakane
Matthew K. Topham United States
Michael S. Kilberg United States
Susanna R. Keller United States
Konstantin V. Kandror United States
Steven J. Fliesler United States
Nigel J. Pyne United Kingdom
Fumio Sakane
Citations per year, relative to Fumio Sakane Fumio Sakane (= 1×) peers Hideo Kanoh

Countries citing papers authored by Fumio Sakane

Since Specialization
Citations

This map shows the geographic impact of Fumio Sakane's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fumio Sakane with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fumio Sakane more than expected).

Fields of papers citing papers by Fumio Sakane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fumio Sakane. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fumio Sakane. The network helps show where Fumio Sakane may publish in the future.

Co-authorship network of co-authors of Fumio Sakane

This figure shows the co-authorship network connecting the top 25 collaborators of Fumio Sakane. A scholar is included among the top collaborators of Fumio Sakane based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fumio Sakane. Fumio Sakane is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ebina, Masayuki, Yuri Miura, & Fumio Sakane. (2024). Ubiquitin-specific peptidase 11 selectively interacts with and deubiquitination-dependently stabilizes diacylglycerol kinase δ to maintain cellular glucose uptake. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1872(2). 119883–119883.
2.
Honda, Takuya, Takafumi Kohama, Chiaki Murakami, et al.. (2023). Identification and characterization of diacylglycerol kinase ζ as a novel enzyme producing ceramide-1-phosphate. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 1868(6). 159307–159307. 3 indexed citations
4.
Murakami, Chiaki, et al.. (2020). Diacylglycerol kinase δ and sphingomyelin synthase–related protein functionally interact via their sterile α motif domains. Journal of Biological Chemistry. 295(10). 2932–2947. 22 indexed citations
5.
Suzuki, Yuji, et al.. (2019). Microarray analysis of gene expression in the diacylglycerol kinase η knockout mouse brain. Biochemistry and Biophysics Reports. 19. 100660–100660. 18 indexed citations
6.
Murakami, Chiaki, et al.. (2019). Creatine kinase muscle type specifically interacts with saturated fatty acid- and/or monounsaturated fatty acid-containing phosphatidic acids. Biochemical and Biophysical Research Communications. 513(4). 1035–1040. 18 indexed citations
7.
Nakano, Tomoyuki, Satoshi Ogasawara, Toshiaki Tanaka, et al.. (2018). DgMab-6: Antihuman DGKγ Monoclonal Antibody for Immunocytochemistry. Monoclonal Antibodies in Immunodiagnosis and Immunotherapy. 37(5). 229–232. 3 indexed citations
8.
Nakano, Tomoyuki, Satoshi Ogasawara, Toshiaki Tanaka, et al.. (2017). DaMab-2: Anti-Human DGKα Monoclonal Antibody for Immunocytochemistry. Monoclonal Antibodies in Immunodiagnosis and Immunotherapy. 36(4). 181–184. 8 indexed citations
9.
Lü, Qiang, et al.. (2017). Behavioral and pharmacological phenotypes of brain-specific diacylglycerol kinase δ˗knockout mice. 1 indexed citations
10.
Sakai, Hiromichi, et al.. (2016). The Pleckstrin Homology Domain of Diacylglycerol Kinase η Strongly and Selectively Binds to Phosphatidylinositol 4,5-Bisphosphate. Journal of Biological Chemistry. 291(15). 8150–8161. 18 indexed citations
11.
Sato, Yuriko, et al.. (2016). Distinct 1-monoacylglycerol and 2-monoacylglycerol kinase activities of diacylglycerol kinase isozymes. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1864(9). 1170–1176. 14 indexed citations
12.
Takeishi, Kazuki, Akinobu Taketomi, Ken Shirabe, et al.. (2012). Diacylglycerol kinase alpha enhances hepatocellular carcinoma progression by activation of Ras–Raf–MEK–ERK pathway. Journal of Hepatology. 57(1). 77–83. 69 indexed citations
13.
Sakai, Hiromichi & Fumio Sakane. (2012). Recent progress on type II diacylglycerol kinases: the physiological functions of diacylglycerol kinase  ,   and   and their involvement in disease. The Journal of Biochemistry. 152(5). 397–406. 42 indexed citations
14.
Chibalin, Alexander, Ying Leng, Elaine Vieira, et al.. (2008). Downregulation of Diacylglycerol Kinase Delta Contributes to Hyperglycemia-Induced Insulin Resistance. Cell. 132(3). 375–386. 189 indexed citations
15.
Sakane, Fumio, Shin-ichi Imai, Masahiro Kai, Satoshi Yasuda, & Hideo Kanoh. (2008). Diacylglycerol Kinases as Emerging Potential Drug Targets for a Variety of Diseases. Current Drug Targets. 9(8). 626–640. 67 indexed citations
16.
Kawakami, Akinori, Fumio Sakane, Shin-ichi Imai, et al.. (2007). Rab7 Regulates Maturation of Melanosomal Matrix Protein gp100/Pmel17/Silv. Journal of Investigative Dermatology. 128(1). 143–150. 16 indexed citations
17.
Ito, Tsukasa, Sachiko Saino‐Saito, Yasukazu Hozumi, et al.. (2005). Expression and localization of diacylglycerol kinase isozymes and enzymatic features in rat lung. American Journal of Physiology-Lung Cellular and Molecular Physiology. 288(6). L1171–L1178. 23 indexed citations
18.
Ito, Tsukasa, Yasukazu Hozumi, Fumio Sakane, et al.. (2004). Cloning and Characterization of Diacylglycerol Kinase ι Splice Variants in Rat Brain. Journal of Biological Chemistry. 279(22). 23317–23326. 50 indexed citations
19.
Sakane, Fumio, et al.. (2002). Alternative Splicing of the Human Diacylglycerol Kinase δ Gene Generates Two Isoforms Differing in Their Expression Patterns and in Regulatory Functions. Journal of Biological Chemistry. 277(45). 43519–43526. 85 indexed citations
20.
Wada, Ikuo, Masahiro Kai, Shin-ichi Imai, Fumio Sakane, & Hideo Kanoh. (1996). Translocation of diacylglycerol kinase α to the nuclear matrix of rat thymocytes and peripheral T‐lymphocytes. FEBS Letters. 393(1). 48–52. 30 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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