Gunther Doehlemann

7.0k total citations
75 papers, 4.6k citations indexed

About

Gunther Doehlemann is a scholar working on Plant Science, Molecular Biology and Cell Biology. According to data from OpenAlex, Gunther Doehlemann has authored 75 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Plant Science, 49 papers in Molecular Biology and 16 papers in Cell Biology. Recurrent topics in Gunther Doehlemann's work include Plant-Microbe Interactions and Immunity (46 papers), Fungal and yeast genetics research (32 papers) and Plant Disease Resistance and Genetics (17 papers). Gunther Doehlemann is often cited by papers focused on Plant-Microbe Interactions and Immunity (46 papers), Fungal and yeast genetics research (32 papers) and Plant Disease Resistance and Genetics (17 papers). Gunther Doehlemann collaborates with scholars based in Germany, United States and United Kingdom. Gunther Doehlemann's co-authors include Regine Kahmann, Bilal Ökmen, Matthias Hahn, Assmann Daniela, Patrick Berndt, Karina van der Linde, Bernd Zechmann, Virginia Walbot, Amey Redkar and Stefanie Reißmann and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Gunther Doehlemann

74 papers receiving 4.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gunther Doehlemann Germany 36 4.0k 2.2k 1.2k 277 268 75 4.6k
Suomeng Dong China 38 5.0k 1.2× 1.6k 0.7× 1.3k 1.0× 194 0.7× 124 0.5× 108 5.4k
Marc‐Henri Lebrun France 41 3.8k 0.9× 2.4k 1.1× 2.1k 1.7× 197 0.7× 342 1.3× 89 5.0k
Yukio Tosa Japan 42 4.6k 1.2× 2.2k 1.0× 1.9k 1.6× 177 0.6× 199 0.7× 138 5.2k
Daolong Dou China 38 5.6k 1.4× 1.7k 0.8× 1.2k 1.0× 372 1.3× 121 0.5× 194 6.3k
Thomas K. Mitchell United States 32 2.2k 0.6× 1.7k 0.8× 986 0.8× 139 0.5× 213 0.8× 67 3.3k
Didier Tharreau France 36 4.5k 1.1× 2.1k 0.9× 1.7k 1.4× 135 0.5× 217 0.8× 98 5.1k
S. Mayama Japan 38 3.4k 0.9× 1.8k 0.8× 1.2k 1.0× 157 0.6× 209 0.8× 106 4.1k
J. J. Rudd United Kingdom 34 5.7k 1.4× 2.4k 1.1× 2.3k 1.9× 213 0.8× 672 2.5× 61 6.4k
Z. A. Pretorius South Africa 31 6.6k 1.6× 2.5k 1.1× 1.9k 1.6× 159 0.6× 498 1.9× 134 7.2k
Yoshitaka Takano Japan 38 3.9k 1.0× 2.2k 1.0× 1.8k 1.5× 145 0.5× 265 1.0× 95 4.7k

Countries citing papers authored by Gunther Doehlemann

Since Specialization
Citations

This map shows the geographic impact of Gunther Doehlemann'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 Gunther Doehlemann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gunther Doehlemann more than expected).

Fields of papers citing papers by Gunther Doehlemann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gunther Doehlemann. 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 Gunther Doehlemann. The network helps show where Gunther Doehlemann may publish in the future.

Co-authorship network of co-authors of Gunther Doehlemann

This figure shows the co-authorship network connecting the top 25 collaborators of Gunther Doehlemann. A scholar is included among the top collaborators of Gunther Doehlemann 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 Gunther Doehlemann. Gunther Doehlemann 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
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Chen, Xi, Tong Jiang, Mengfei Li, et al.. (2024). NIa-Pro of sugarcane mosaic virus targets Corn Cysteine Protease 1 (CCP1) to undermine salicylic acid-mediated defense in maize. PLoS Pathogens. 20(3). e1012086–e1012086. 6 indexed citations
3.
Depotter, Jasper R. L., et al.. (2023). Maize Phytocytokines Modulate Pro-Survival Host Responses and Pathogen Resistance. Molecular Plant-Microbe Interactions. 36(9). 592–604. 3 indexed citations
4.
Shi, Wei, Sara Christina Stolze, Hirofumi Nakagami, et al.. (2023). Combination of in vivo proximity labeling and co-immunoprecipitation identifies the host target network of a tumor-inducing effector in the fungal maize pathogen Ustilago maydis. Journal of Experimental Botany. 74(15). 4736–4750. 8 indexed citations
5.
Ökmen, Bilal, et al.. (2023). A conserved extracellular Ribo1 with broad‐spectrum cytotoxic activity enables smut fungi to compete with host‐associated bacteria. New Phytologist. 240(5). 1976–1989. 7 indexed citations
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Bindics, János, Mamoona Khan, Simon Uhse, et al.. (2022). Many ways to TOPLESS – manipulation of plant auxin signalling by a cluster of fungal effectors. New Phytologist. 236(4). 1455–1470. 15 indexed citations
8.
Zuo, Weiliang, Jasper R. L. Depotter, Deepak Gupta, Marco Thines, & Gunther Doehlemann. (2021). Cross‐species analysis between the maize smut fungi Ustilago maydis and Sporisorium reilianum highlights the role of transcriptional change of effector orthologs for virulence and disease. New Phytologist. 232(2). 719–733. 12 indexed citations
9.
Depotter, Jasper R. L., et al.. (2021). Comparative transcriptome profiling identifies maize line specificity of fungal effectors in the maize– Ustilago maydis interaction. The Plant Journal. 106(3). 733–752. 14 indexed citations
10.
Demir, Fatih, Kimberly Green, Jasper R. L. Depotter, et al.. (2021). Host apoplastic cysteine protease activity is suppressed during the mutualistic association of Lolium perenne and Epichloë festucae. Journal of Experimental Botany. 72(9). 3410–3426. 5 indexed citations
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Chaudhry, Vasvi, et al.. (2020). Shaping the leaf microbiota: plant–microbe–microbe interactions. Journal of Experimental Botany. 72(1). 36–56. 149 indexed citations
13.
Zuo, Weiliang, Jasper R. L. Depotter, & Gunther Doehlemann. (2020). Cas9HF1 enhanced specificity in Ustilago maydis. Fungal Biology. 124(3-4). 228–234. 15 indexed citations
14.
Matei, Alexandra, et al.. (2019). Cell type specific transcriptional reprogramming of maize leaves during Ustilago maydis induced tumor formation. Scientific Reports. 9(1). 10227–10227. 16 indexed citations
15.
Matei, Alexandra, Corinna Ernst, Markus Günl, et al.. (2018). How to make a tumour: cell type specific dissection of Ustilago maydis‐ induced tumour development in maize leaves. New Phytologist. 217(4). 1681–1695. 39 indexed citations
16.
Doehlemann, Gunther, et al.. (2013). Apoplastic immunity and its suppression by filamentous plant pathogens. New Phytologist. 198(4). 1001–1016. 169 indexed citations
17.
Meier, Karin, Ève-Lyne Mathieu, Florian Finkernagel, et al.. (2012). LINT, a Novel dL(3)mbt-Containing Complex, Represses Malignant Brain Tumour Signature Genes. PLoS Genetics. 8(5). e1002676–e1002676. 24 indexed citations
18.
Skibbe, David S., Gunther Doehlemann, John Fernandes, & Virginia Walbot. (2010). Maize Tumors Caused by Ustilago maydis Require Organ-Specific Genes in Host and Pathogen. Science. 328(5974). 89–92. 149 indexed citations
19.
Doehlemann, Gunther, Karina van der Linde, Assmann Daniela, et al.. (2009). Pep1, a Secreted Effector Protein of Ustilago maydis, Is Required for Successful Invasion of Plant Cells. PLoS Pathogens. 5(2). e1000290–e1000290. 259 indexed citations
20.
Doehlemann, Gunther, Ramon Wahl, Robin J. Horst, et al.. (2008). Reprogramming a maize plant: transcriptional and metabolic changes induced by the fungal biotroph Ustilago maydis. The Plant Journal. 56(2). 181–195. 267 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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