Guido Sessa

4.9k total citations · 1 hit paper
80 papers, 3.7k citations indexed

About

Guido Sessa is a scholar working on Plant Science, Molecular Biology and Cell Biology. According to data from OpenAlex, Guido Sessa has authored 80 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Plant Science, 22 papers in Molecular Biology and 4 papers in Cell Biology. Recurrent topics in Guido Sessa's work include Plant-Microbe Interactions and Immunity (60 papers), Plant Pathogenic Bacteria Studies (53 papers) and Legume Nitrogen Fixing Symbiosis (23 papers). Guido Sessa is often cited by papers focused on Plant-Microbe Interactions and Immunity (60 papers), Plant Pathogenic Bacteria Studies (53 papers) and Legume Nitrogen Fixing Symbiosis (23 papers). Guido Sessa collaborates with scholars based in Israel, United States and Germany. Guido Sessa's co-authors include Gregory B. Martin, Adam J. Bogdanove, Robert Fluhr, Isaac Barash, Shulamit Manulis‐Sasson, Dor Salomon, Laura Chalupowicz, Mark D’Ascenzo, Doron Teper and Rudolf Eichenlaub and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and The EMBO Journal.

In The Last Decade

Guido Sessa

79 papers receiving 3.6k citations

Hit Papers

Understanding the Functio... 2003 2026 2010 2018 2003 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guido Sessa Israel 34 3.2k 1.2k 326 129 120 80 3.7k
Darrell Desveaux Canada 41 4.0k 1.3× 1.5k 1.2× 383 1.2× 122 0.9× 144 1.2× 83 4.6k
Laurent Deslandes France 32 4.3k 1.3× 1.1k 0.9× 335 1.0× 130 1.0× 102 0.8× 51 4.6k
David Mackey United States 34 4.6k 1.5× 1.2k 0.9× 382 1.2× 130 1.0× 205 1.7× 65 5.2k
Alberto P. Macho China 39 4.7k 1.5× 1.3k 1.1× 362 1.1× 72 0.6× 134 1.1× 92 5.1k
Marc T. Nishimura United States 24 2.6k 0.8× 998 0.8× 319 1.0× 134 1.0× 101 0.8× 34 3.2k
Ari Sadanandom United Kingdom 36 3.8k 1.2× 2.3k 1.9× 326 1.0× 136 1.1× 210 1.8× 76 4.8k
Volkan Çevik United Kingdom 24 2.2k 0.7× 685 0.6× 201 0.6× 149 1.2× 168 1.4× 36 2.4k
Núria S. Coll Spain 29 2.7k 0.8× 1.3k 1.0× 314 1.0× 83 0.6× 82 0.7× 66 3.3k
Youssef Belkhadir Austria 28 4.6k 1.4× 2.0k 1.6× 297 0.9× 120 0.9× 108 0.9× 39 5.2k

Countries citing papers authored by Guido Sessa

Since Specialization
Citations

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

Fields of papers citing papers by Guido Sessa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guido Sessa

This figure shows the co-authorship network connecting the top 25 collaborators of Guido Sessa. A scholar is included among the top collaborators of Guido Sessa 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 Guido Sessa. Guido Sessa 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
2.
Chakraborty, Joydeep, et al.. (2024). PP2C phosphatase Pic14 negatively regulates tomato Pto/Prf‐triggered immunity by inhibiting MAPK activation. The Plant Journal. 119(6). 2622–2637. 1 indexed citations
3.
Bosis, Eran, et al.. (2024). PIX is an N-terminal delivery domain that defines a class of polymorphic T6SS effectors in Enterobacterales. Cell Reports. 43(4). 114015–114015. 4 indexed citations
4.
Sheikh, Arsheed H., Ana Domínguez‐Ferreras, Daniela J. Sueldo, et al.. (2023). Dynamic changes of the Prf/Pto tomato resistance complex following effector recognition. Nature Communications. 14(1). 2568–2568. 14 indexed citations
5.
Pasmanik‐Chor, Metsada, et al.. (2022). Tomato receptor-like cytoplasmic kinase Fir1 is involved in flagellin signaling and preinvasion immunity. PLANT PHYSIOLOGY. 192(1). 565–581. 5 indexed citations
6.
Roberts, R. H., Sarah R. Hind, Kerry F. Pedley, et al.. (2019). Mai1 Protein Acts Between Host Recognition of Pathogen Effectors and Mitogen-Activated Protein Kinase Signaling. Molecular Plant-Microbe Interactions. 32(11). 1496–1507. 16 indexed citations
7.
Teper, Doron, et al.. (2018). The Xanthomonas euvesicatoria type III effector XopAU is an active protein kinase that manipulates plant MAP kinase signaling. PLoS Pathogens. 14(1). e1006880–e1006880. 32 indexed citations
8.
Sunitha, Sukumaran, Jung‐Gun Kim, Doron Teper, et al.. (2018). Tomato 14-3-3 Proteins Are Required for Xv3 Disease Resistance and Interact with a Subset of Xanthomonas euvesicatoria Effectors. Molecular Plant-Microbe Interactions. 31(12). 1301–1311. 19 indexed citations
9.
Chalupowicz, Laura, et al.. (2016). Revealing the inventory of type III effectors in Pantoea agglomerans gall‐forming pathovars using draft genome sequences and a machine‐learning approach. Molecular Plant Pathology. 19(2). 381–392. 24 indexed citations
10.
Teper, Doron, et al.. (2015). Identification of novel X anthomonas euvesicatoria type III effector proteins by a machine‐learning approach. Molecular Plant Pathology. 17(3). 398–411. 53 indexed citations
11.
Sessa, Guido, et al.. (2011). The SlMKK2 and SlMPK2 genes play a role in tomato disease resistance to Xanthomonas campestris pv. vesicatoria. Plant Signaling & Behavior. 6(1). 154–156. 24 indexed citations
12.
Bar, Maya, et al.. (2011). Endosomal signaling of the tomato leucine‐rich repeat receptor‐like protein LeEix2. The Plant Journal. 68(3). 413–423. 61 indexed citations
13.
Salomon, Dor, et al.. (2010). Expression of Xanthomonas campestris pv. vesicatoria Type III Effectors in Yeast Affects Cell Growth and Viability. Molecular Plant-Microbe Interactions. 24(3). 305–314. 33 indexed citations
14.
Sessa, Guido, et al.. (2010). Tomato MAPKKKε is a positive regulator of cell-death signaling networks associated with plant immunity. The Plant Journal. 64(3). 379–391. 91 indexed citations
15.
Chalupowicz, Laura, Orit Dror, Rudolf Eichenlaub, et al.. (2010). Sequential Expression of Bacterial Virulence and Plant Defense Genes During Infection of Tomato with Clavibacter michiganensis subsp. michiganensis. Phytopathology. 100(3). 252–261. 49 indexed citations
16.
Anderson, Jeffrey C., Pete E. Pascuzzi, Fangming Xiao, Guido Sessa, & Gregory B. Martin. (2006). Host-Mediated Phosphorylation of Type III Effector AvrPto PromotesPseudomonasVirulence and Avirulence in Tomato. The Plant Cell. 18(2). 502–514. 56 indexed citations
17.
Martin, Gregory B., et al.. (2004). Identification and Expression Profiling of Tomato Genes Differentially Regulated During a Resistance Response toXanthomonas campestrispv.vesicatoria. Molecular Plant-Microbe Interactions. 17(11). 1212–1222. 46 indexed citations
18.
Sessa, Guido & Gregory B. Martin. (2000). Signal recognition and transduction mediated by the tomato Pto kinase: a paradigm of innate immunity in plants. Microbes and Infection. 2(13). 1591–1597. 28 indexed citations
19.
Sessa, Guido, et al.. (1996). PK12, a plant dual-specificity protein kinase of the LAMMER family, is regulated by the hormone ethylene.. The Plant Cell. 8(12). 2223–2234. 60 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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