Mario Pende

14.4k total citations · 1 hit paper
72 papers, 5.7k citations indexed

About

Mario Pende is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Physiology. According to data from OpenAlex, Mario Pende has authored 72 papers receiving a total of 5.7k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 18 papers in Cellular and Molecular Neuroscience and 16 papers in Physiology. Recurrent topics in Mario Pende's work include PI3K/AKT/mTOR signaling in cancer (21 papers), Metabolism, Diabetes, and Cancer (13 papers) and Neuroscience and Neuropharmacology Research (13 papers). Mario Pende is often cited by papers focused on PI3K/AKT/mTOR signaling in cancer (21 papers), Metabolism, Diabetes, and Cancer (13 papers) and Neuroscience and Neuropharmacology Research (13 papers). Mario Pende collaborates with scholars based in France, United States and Italy. Mario Pende's co-authors include Virginie Mieulet, Sara C. Kozma, John Blenis, George Thomas, Rémy Burcelin, Bernard Thorens, Athanassia Sotiropoulos, Stefano Fumagalli, Nahum Sonenberg and Sung Hee Um and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Mario Pende

72 papers receiving 5.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

S6K1 ^−/− / S6K2 ^−/− Mice Exhibit Perinatal Lethality and Rapamycin-Sensitive 5′-Terminal Oligopyrimidine mRNA Translation and Reveal a Mitogen-Activated Protein Kinase-Dependent S6 Kinase Pathway

2004 623 citations Mario Pende, Sung Hee Um et al. Molecular and Cellular Biology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mario Pende France	40	3.9k	906	888	826	776	72	5.7k
Susanna R. Keller United States	48	3.6k 0.9×	834 0.9×	1.4k 1.6×	1.2k 1.4×	406 0.5×	87	5.8k
Joachim Herz United States	18	2.5k 0.6×	762 0.8×	913 1.0×	989 1.2×	731 0.9×	22	5.3k
Nina Wettschureck Germany	37	3.7k 0.9×	685 0.8×	597 0.7×	1.4k 1.7×	708 0.9×	87	6.7k
Hideaki Bujo Japan	40	2.9k 0.7×	426 0.5×	1.6k 1.9×	1.2k 1.5×	1.2k 1.5×	186	6.5k
Han Cho United States	16	3.7k 0.9×	407 0.4×	766 0.9×	1.2k 1.4×	333 0.4×	20	5.6k
William O. Wilkison United States	24	3.9k 1.0×	926 1.0×	1.0k 1.1×	2.0k 2.4×	375 0.5×	30	7.1k
Richard Lindberg United States	32	4.4k 1.1×	810 0.9×	562 0.6×	516 0.6×	1.7k 2.2×	49	6.3k
Tokuo Yamamoto Japan	35	2.8k 0.7×	423 0.5×	1.1k 1.2×	1.1k 1.3×	446 0.6×	68	5.0k
Aimee D. Kohn United States	21	4.9k 1.3×	643 0.7×	852 1.0×	511 0.6×	356 0.5×	26	5.9k
Derek P. Brazil United Kingdom	35	4.7k 1.2×	527 0.6×	681 0.8×	724 0.9×	446 0.6×	81	7.3k

Countries citing papers authored by Mario Pende

Since Specialization

Citations

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

Fields of papers citing papers by Mario Pende

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mario Pende

This figure shows the co-authorship network connecting the top 25 collaborators of Mario Pende. A scholar is included among the top collaborators of Mario Pende 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 Mario Pende. Mario Pende is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Srivastava, Ayush, ASM Shihavuddin, Marion Faucourt, et al.. (2025). mTOR controls ependymal cell differentiation by targeting the alternative cell cycle and centrosomal proteins. EMBO Reports. 26(12). 3075–3105. 1 indexed citations

Bona, Anna Di, Riccardo Bariani, Nicolas Kuperwasser, et al.. (2021). Generation and phenotyping of a novel knock-in mouse model of desmoplakin dependent arrhythmogenic cardiomyopathy. European Heart Journal. 42(Supplement_1). 1 indexed citations

Kuperwasser, Nicolas, Vonda Koka, Chi Zhang, et al.. (2020). mTOR and S6K1 drive polycystic kidney by the control of Afadin-dependent oriented cell division. Nature Communications. 11(1). 3200–3200. 25 indexed citations

Nemazanyy, Ivan, M Girard, Esther Barth, et al.. (2019). The class 3 PI3K coordinates autophagy and mitochondrial lipid catabolism by controlling nuclear receptor PPARα. Nature Communications. 10(1). 1566–1566. 82 indexed citations

Couchy, Gabrielle, Alessia Bagattin, Tatiana Cañeque, et al.. (2017). Hepatocyte nuclear factor 1α suppresses steatosis-associated liver cancer by inhibiting PPARγ transcription. Journal of Clinical Investigation. 127(5). 1873–1888. 58 indexed citations

Houssaïni, Amal, Shariq Abid, Geneviève Dérumeaux, et al.. (2016). Selective Tuberous Sclerosis Complex 1 Gene Deletion in Smooth Muscle Activates Mammalian Target of Rapamycin Signaling and Induces Pulmonary Hypertension. American Journal of Respiratory Cell and Molecular Biology. 55(3). 352–367. 16 indexed citations

Panasyuk, Ganna, et al.. (2013). The role of the mTOR pathway during liver regeneration and tumorigenesis. Annales d Endocrinologie. 74(2). 121–122. 9 indexed citations

Panasyuk, Ganna, Céline Chauvin, Ludivine A. Pradelli, et al.. (2012). PPARγ contributes to PKM2 and HK2 expression in fatty liver. Nature Communications. 3(1). 672–672. 126 indexed citations

Pastor, María Dolores, Isaac García‐Yébenes, Noelia Fradejas‐Villar, et al.. (2009). mTOR/S6 Kinase Pathway Contributes to Astrocyte Survival during Ischemia. Journal of Biological Chemistry. 284(33). 22067–22078. 72 indexed citations

10.

Alliouachene, Samira, Thomas Lapointe, Sophie Bérissi, et al.. (2008). Constitutively active Akt1 expression in mouse pancreas requires S6 kinase 1 for insulinoma formation. Journal of Clinical Investigation. 118(11). 3629–3638. 54 indexed citations

11.

Aguilar, Víctor, Samira Alliouachene, Athanassia Sotiropoulos, et al.. (2007). S6 Kinase Deletion Suppresses Muscle Growth Adaptations to Nutrient Availability by Activating AMP Kinase. Cell Metabolism. 5(6). 476–487. 146 indexed citations

12.

Mieulet, Virginie, Mila Roceri, Athanassia Sotiropoulos, et al.. (2007). S6 kinase inactivation impairs growth and translational target phosphorylation in muscle cells maintaining proper regulation of protein turnover. American Journal of Physiology-Cell Physiology. 293(2). C712–C722. 78 indexed citations

13.

Shahbazian, David, Philippe P. Roux, Virginie Mieulet, et al.. (2006). The mTOR/PI3K and MAPK pathways converge on eIF4B to control its phosphorylation and activity. The EMBO Journal. 25(12). 2781–2791. 403 indexed citations

14.

Pende, Mario, Sung Hee Um, Virginie Mieulet, et al.. (2004). S6K1 ^−/− / S6K2 ^−/− Mice Exhibit Perinatal Lethality and Rapamycin-Sensitive 5′-Terminal Oligopyrimidine mRNA Translation and Reveal a Mitogen-Activated Protein Kinase-Dependent S6 Kinase Pathway. Molecular and Cellular Biology. 24(8). 3112–3124. 623 indexed citations breakdown →

15.

Preitner, Frédéric, Mark Ibberson, Isobel Franklin, et al.. (2004). Gluco-incretins control insulin secretion at multiple levels as revealed in mice lacking GLP-1 and GIP receptors. Journal of Clinical Investigation. 113(4). 635–645. 192 indexed citations

16.

Patel, Satish, Pamela A. Lochhead, Graham Rena, et al.. (2002). Insulin Regulation of Insulin-like Growth Factor-binding Protein-1 Gene Expression Is Dependent on the Mammalian Target of Rapamycin, but Independent of Ribosomal S6 Kinase Activity. Journal of Biological Chemistry. 277(12). 9889–9895. 39 indexed citations

17.

Bendotti, Caterina, Mario Pende, & R. Samanin. (1994). Expression of GAP‐43 in the Granule Cells of Rat Hippocampus After Seizure‐induced Sprouting of Mossy Fibres: In Situ Hybridization and Immunocytochemical Studies. European Journal of Neuroscience. 6(4). 509–515. 65 indexed citations

18.

Bendotti, Caterina, et al.. (1994). Does GFAP mRNA and mitochondrial benzodiazepine receptor binding detect serotonergic neuronal degeneration in rat?. Brain Research Bulletin. 34(4). 389–394. 18 indexed citations

19.

Pende, Mario, Marco Lanza, Giambattista Bonanno, & Maurizio Raiteri. (1993). Release of endogenous glutamic and aspartic acids from cerebrocortex synaptosomes and its modulation through activation of a γ-aminobutyric acidB (GABAB) receptor subtype. Brain Research. 604(1-2). 325–330. 48 indexed citations

20.

Raiteri, Maurizio, Giambattista Bonanno, Guido Maura, et al.. (1992). Subclassification of release‐regulating α₂‐autoreceptors in human brain cortex. British Journal of Pharmacology. 107(4). 1146–1151. 50 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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