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411 results on '"Saez-Rodriguez, J."'

1. A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury

Author

Wijaya, L, Gabor, A, Pot, I, van de Have, L, Saez-Rodriguez, J, Stevens, J, Le Devedec, S, Callegaro, G, Van de Water, B, Wijaya L. S., Gabor A., Pot I. E., van de Have L., Saez-Rodriguez J., Stevens J. L., Le Devedec S. E., Callegaro G., Van de Water B., Wijaya, L, Gabor, A, Pot, I, van de Have, L, Saez-Rodriguez, J, Stevens, J, Le Devedec, S, Callegaro, G, Van de Water, B, Wijaya L. S., Gabor A., Pot I. E., van de Have L., Saez-Rodriguez J., Stevens J. L., Le Devedec S. E., Callegaro G., and Van de Water B.

Abstract

Drug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been broadly performed. Gene coregulation network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improve mechanistic interpretation of toxicogenomic data. Here we performed an extensive concentration time course response-toxicogenomic study in the HepG2 cell line exposed to 20 DILI compounds, 7 reference compounds for stress response pathways, and 10 agonists for cytokines and growth factor receptors. We performed whole transcriptome targeted RNA sequencing to more than 500 conditions and applied weighted gene coregulated network analysis to the transcriptomics data followed by the identification of gene coregulated networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress response gene coregulated networks. We correlated gene coregulated networks with cell death onset and causal relationships of 67 critical target genes of these modules with the onset of cell death was evaluated using RNA interference screening. We identified GTPBP2, HSPA1B, IRF1, SIRT1, and TSC22D3 as essential modulators of DILI compound-induced cell death. These genes were also induced by DILI compounds in PHH. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI.

Published
2024

3. Stem cell-like transcriptional reprogramming mediates metastatic resistance to mTOR inhibition

Author

Mateo, F, Arenas, EJ, Aguilar, H, Serra-Musach, J, de Garibay, G Ruiz, Boni, J, Maicas, M, Du, S, Iorio, F, Herranz-Ors, C, Islam, A, Prado, X, Llorente, A, Petit, A, Vidal, A, Català, I, Soler, T, Venturas, G, Rojo-Sebastian, A, Serra, H, Cuadras, D, Blanco, I, Lozano, J, Canals, F, Sieuwerts, AM, de Weerd, V, Look, MP, Puertas, S, García, N, Perkins, AS, Bonifaci, N, Skowron, M, Gómez-Baldó, L, Hernández, V, Martínez-Aranda, A, Martínez-Iniesta, M, Serrat, X, Cerón, J, Brunet, J, Barretina, MP, Gil, M, Falo, C, Fernández, A, Morilla, I, Pernas, S, Plà, MJ, Andreu, X, Seguí, MA, Ballester, R, Castellà, E, Nellist, M, Morales, S, Valls, J, Velasco, A, Matias-Guiu, X, Figueras, A, Sánchez-Mut, JV, Sánchez-Céspedes, M, Cordero, A, Gómez-Miragaya, J, Palomero, L, Gómez, A, Gajewski, TF, Cohen, EEW, Jesiotr, M, Bodnar, L, Quintela-Fandino, M, López-Bigas, N, Valdés-Mas, R, Puente, XS, Viñals, F, Casanovas, O, Graupera, M, Hernández-Losa, J, Ramón y Cajal, S, García-Alonso, L, Saez-Rodriguez, J, Esteller, M, Sierra, A, Martín-Martín, N, Matheu, A, Carracedo, A, González-Suárez, E, Nanjundan, M, Cortés, J, Lázaro, C, Odero, MD, Martens, JWM, Moreno-Bueno, G, Barcellos-Hoff, MH, Villanueva, A, Gomis, RR, and Pujana, MA

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Breast Cancer, Stem Cell Research, Adaptor Proteins, Signal Transducing, Adult, Aged, Breast Neoplasms, Carrier Proteins, Cell Proliferation, DNA-Binding Proteins, Female, Gene Expression Regulation, Neoplastic, Humans, Lung Neoplasms, MCF-7 Cells, MDS1 and EVI1 Complex Locus Protein, Microfilament Proteins, Middle Aged, Neoplasm Metastasis, Osteonectin, Proto-Oncogenes, Regulatory-Associated Protein of mTOR, SOX9 Transcription Factor, Signal Transduction, TOR Serine-Threonine Kinases, Transcription Factors, Xenograft Model Antitumor Assays, Clinical Sciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Inhibitors of the mechanistic target of rapamycin (mTOR) are currently used to treat advanced metastatic breast cancer. However, whether an aggressive phenotype is sustained through adaptation or resistance to mTOR inhibition remains unknown. Here, complementary studies in human tumors, cancer models and cell lines reveal transcriptional reprogramming that supports metastasis in response to mTOR inhibition. This cancer feature is driven by EVI1 and SOX9. EVI1 functionally cooperates with and positively regulates SOX9, and promotes the transcriptional upregulation of key mTOR pathway components (REHB and RAPTOR) and of lung metastasis mediators (FSCN1 and SPARC). The expression of EVI1 and SOX9 is associated with stem cell-like and metastasis signatures, and their depletion impairs the metastatic potential of breast cancer cells. These results establish the mechanistic link between resistance to mTOR inhibition and cancer metastatic potential, thus enhancing our understanding of mTOR targeting failure.

Published
2017

4. Metrics reloaded: recommendations for image analysis validation

Author

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M.D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M.A., Wiesenfarth, M., Kavur, A.E., Sudre, C.H., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Radsch, T., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Blaschko, M.B., Cardoso, M.J., Cheplygina, V., Cimini, B.A., Collins, G.S., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Haase, R., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Karthikesalingam, A., Kofler, F., Kopp-Schneider, A., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Mattson, P., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rajpoot, N., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Smeden, M. van, Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Jager, P.F., Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M.D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M.A., Wiesenfarth, M., Kavur, A.E., Sudre, C.H., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Radsch, T., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Blaschko, M.B., Cardoso, M.J., Cheplygina, V., Cimini, B.A., Collins, G.S., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Haase, R., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Karthikesalingam, A., Kofler, F., Kopp-Schneider, A., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Mattson, P., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rajpoot, N., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Smeden, M. van, Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., and Jager, P.F.

Abstract

Contains fulltext : 305368.pdf (Publisher’s version ) (Closed access), Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.

Published
2024

5. Understanding metric-related pitfalls in image analysis validation

Author

Reinke, A., Tizabi, M.D., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Kavur, A.E., Radsch, T., Sudre, C.H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M.J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B.A., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Glocker, B., Godau, P., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Isensee, F., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Kleesiek, J., Kofler, F., Kooi, T., Kopp-Schneider, A., Kozubek, M., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rafelski, S.M., Rajpoot, N., Reyes, M., Riegler, M.A., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Yaniv, Z.R., Jager, P.F., Maier-Hein, L., Reinke, A., Tizabi, M.D., Baumgartner, M., Eisenmann, M., Heckmann-Notzel, D., Kavur, A.E., Radsch, T., Sudre, C.H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M.J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B.A., Farahani, K., Ferrer, L., Galdran, A., Ginneken, B. van, Glocker, B., Godau, P., Hashimoto, D.A., Hoffman, M.M., Huisman, M., Isensee, F., Jannin, P., Kahn, C.E., Kainmueller, D., Kainz, B., Karargyris, A., Kleesiek, J., Kofler, F., Kooi, T., Kopp-Schneider, A., Kozubek, M., Kreshuk, A., Kurc, T., Landman, B.A., Litjens, G.J., Madani, A., Maier-Hein, K., Martel, A.L., Meijering, E., Menze, B., Moons, K.G., Muller, H., Nichyporuk, B., Nickel, F., Petersen, J., Rafelski, S.M., Rajpoot, N., Reyes, M., Riegler, M.A., Rieke, N., Saez-Rodriguez, J., Sanchez, C.I., Shetty, S., Summers, R.M., Taha, A.A., Tiulpin, A., Tsaftaris, S.A., Calster, B. van, Varoquaux, G., Yaniv, Z.R., Jager, P.F., and Maier-Hein, L.

Abstract

Contains fulltext : 305370.pdf (Publisher’s version ) (Closed access), Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.

Published
2024

6. Partial myeloid inhibition of key glycolytic enzyme PFKFB3 increases hepatic steatosis and inflammation, but does not affect atherosclerosis

Author

Tillie, R., primary, De Bruijn, J., additional, Dobie, R., additional, Perales-Patón, J., additional, Van Kuijk, K., additional, Gijbels, M., additional, Temmerman, L., additional, Ghosheh, Y., additional, Goossens, G., additional, Carmeliet, P., additional, Ley, K., additional, Henderson, N., additional, Saez-Rodriguez, J., additional, Wouters, K., additional, and Sluimer, J., additional

Published
2023
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8. The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

Author

Callegaro, G, Kunnen, S, Trairatphisan, P, Grosdidier, S, Niemeijer, M, den Hollander, W, Guney, E, Pinero Gonzalez, J, Furlong, L, Webster, Y, Saez-Rodriguez, J, Sutherland, J, Mollon, J, Stevens, J, van de Water, B, Callegaro G., Kunnen S. J., Trairatphisan P., Grosdidier S., Niemeijer M., den Hollander W., Guney E., Pinero Gonzalez J., Furlong L., Webster Y. W., Saez-Rodriguez J., Sutherland J. J., Mollon J., Stevens J. L., van de Water B., Callegaro, G, Kunnen, S, Trairatphisan, P, Grosdidier, S, Niemeijer, M, den Hollander, W, Guney, E, Pinero Gonzalez, J, Furlong, L, Webster, Y, Saez-Rodriguez, J, Sutherland, J, Mollon, J, Stevens, J, van de Water, B, Callegaro G., Kunnen S. J., Trairatphisan P., Grosdidier S., Niemeijer M., den Hollander W., Guney E., Pinero Gonzalez J., Furlong L., Webster Y. W., Saez-Rodriguez J., Sutherland J. J., Mollon J., Stevens J. L., and van de Water B.

Abstract

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors’ sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.

Published
2021

9. S-25-02 Gene co-expression network analysis of toxicogenomic data linked to histopathology provides quantitative mode-of-action assessment and prediction of drug-induced organ toxicity

Author

Kunnen, S.J., primary, Callegaro, G., additional, Wijaya, L.S., additional, Trairatphisan, P., additional, Mollon, J., additional, Grosdidier, S., additional, Webster, Y.W., additional, Saez-Rodriguez, J., additional, Stevens, J.L., additional, and van de Water, B., additional

Published
2022
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10. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio, Ege Üniversitesi, Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P., Wang, Dennis, Mason, Mike J., Szalai, Bence, Bulusu, Krishna C., Guan, Yuanfang, Yu, Thomas, Kang, Jaewoo, Jeon, Minji, Wolfinger, Russ, Nguyen, Tin, Zaslavskiy, Mikhail, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric K. Y., Garnett, Mathew J., Di Veroli, Giovanni Y., Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., Saez-Rodriguez, Julio, Abante, Jordi, Abecassis, Barbara Schmitz, Aben, Nanne, Aghamirzaie, Delasa, Aittokallio, Tero, Akhtari, Farida S., Al-lazikani, Bissan, Alam, Tanvir, Allam, Amin, Allen, Chad, de Almeida, Mariana Pelicano, Altarawy, Doaa, Alves, Vinicius, Amadoz, Alicia, Anchang, Benedict, Antolin, Albert A., Ash, Jeremy R., Romeo Aznar, Victoria, Ba-alawi, Wail, Bagheri, Moeen, Bajic, Vladimir, Ball, Gordon, Ballester, Pedro J., Baptista, Delora, Bare, Christopher, Bateson, Mathilde, Bender, Andreas, Bertrand, Denis, Wijayawardena, Bhagya, Boroevich, Keith A., Bosdriesz, Evert, Bougouffa, Salim, Bounova, Gergana, Brouwer, Thomas, Bryant, Barbara, Calaza, Manuel, Calderone, Alberto, Calza, Stefano, Capuzzi, Stephen, Carbonell-Caballero, Jose, Carlin, Daniel, Carter, Hannah, Castagnoli, Luisa, Celebi, Remzi, Cesareni, Gianni, Chang, Hyeokyoon, Chen, Guocai, Chen, Haoran, Chen, Huiyuan, Cheng, Lijun, Chernomoretz, Ariel, Chicco, Davide, Cho, Kwang-Hyun, Cho, Sunghwan, Choi, Daeseon, Choi, Jaejoon, Choi, Kwanghun, Choi, Minsoo, De Cock, Martine, Coker, Elizabeth, Cortes-Ciriano, Isidro, Cserzo, Miklos, Cubuk, Cankut, Curtis, Christina, Van Daele, Dries, Dang, Cuong C., Dijkstra, Tjeerd, Dopazo, Joaquin, Draghici, Sorin, Drosou, Anastasios, Dumontier, Michel, Ehrhart, Friederike, Eid, Fatma-Elzahraa, ElHefnawi, Mahmoud, Elmarakeby, Haitham, van Engelen, Bo, Engin, Hatice Billur, de Esch, Iwan, Evelo, Chris, Falcao, Andre O., Farag, Sherif, Fernandez-Lozano, Carlos, Fisch, Kathleen, Flobak, Asmund, Fornari, Chiara, Foroushani, Amir B. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, de Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, Laszlo, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Walk, Oliver Bear Don't, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Leppaho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Angel Pujana, Miguel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, de Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gabor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, van Westen, Gerard J. P., Westerman, Bart A., White, Simon Richard, Willighagen, Egon, Wurdinger, Tom, Xie, Lei, Xie, Shuilian, Xu, Hua, Yadav, Bhagwan, Yau, Christopher, Yeerna, Huwate, Yin, Jia Wei, Yu, Michael, Yu, MinHwan, Yun, So Jeong, Zakharov, Alexey, Zamichos, Alexandros, Zanin, Massimiliano, Zeng, Li, Zenil, Hector, Zhang, Frederick, Zhang, Pengyue, Zhang, Wei, Zhao, Hongyu, Zhao, Lan, Zheng, Wenjin, Zoufir, Azedine, Zucknick, Manuela, College of Engineering, Department of Industrial Engineering, Institute of Data Science, RS: FSE DACS IDS, Bioinformatica, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, RS: FHML MaCSBio, Promovendi NTM, Tero Aittokallio / Principal Investigator, Bioinformatics, Institute for Molecular Medicine Finland, Hu, Z, Fotso, DC, Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden, Michael P. [0000-0003-0267-5792], Mason, Mike J. [0000-0002-5652-7739], Yu, Thomas [0000-0002-5841-0198], Kang, Jaewoo [0000-0001-6798-9106], Nguyen, Tin [0000-0001-8001-9470], Ahsen, Mehmet Eren [0000-0002-4907-0427], Stolovitzky, Gustavo [0000-0002-9618-2819], Guinney, Justin [0000-0003-1477-1888], Saez-Rodriguez, Julio [0000-0002-8552-8976], Apollo - University of Cambridge Repository, Menden, Michael P [0000-0003-0267-5792], Mason, Mike J [0000-0002-5652-7739], Pathology, CCA - Cancer biology and immunology, Medical oncology laboratory, Neurosurgery, Chemistry and Pharmaceutical Sciences, AIMMS, Medicinal chemistry, Universidade do Minho, Department of Computer Science, Professorship Marttinen P., Aalto-yliopisto, and Aalto University

Subjects
Drug Resistance, 02 engineering and technology, 13, PATHWAY, Antineoplastic Combined Chemotherapy Protocols, Molecular Targeted Therapy, Càncer, lcsh:Science, media_common, Cancer, Tumor, Settore BIO/18, Settore BIO/11, Drug combinations, High-throughput screening, Drug Synergism, purl.org/becyt/ford/1.2 [https], Genomics, Machine Learning, predictions, 3. Good health, ddc, Technologie de l'environnement, contrôle de la pollution, Benchmarking, 5.1 Pharmaceuticals, Cancer treatment, Farmacogenètica, Science & Technology - Other Topics, Development of treatments and therapeutic interventions, 0210 nano-technology, Human, Drug, media_common.quotation_subject, Science, 49/23, ADAM17 Protein, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, SDG 3 - Good Health and Well-being, RESOURCE, Machine learning, Genetics, Chimie, Humans, BREAST-CANCER, CELL, 49/98, Science & Technology, Antineoplastic Combined Chemotherapy Protocol, 45, MUTATIONS, Computational Biology, Androgen receptor, Breast-cancer, Gene, Cell, Inhibition, Resistance, Pathway, Mutations, Landscape, Resource, 631/114/1305, medicine.disease, Drug synergy, 49, 030104 developmental biology, Pharmacogenetics, Mutation, Ciências Médicas::Biotecnologia Médica, lcsh:Q, 631/154/1435/2163, Biomarkers, RESISTANCE, 0301 basic medicine, ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA, Statistical methods, Computer science, General Physics and Astronomy, Datasets as Topic, Drug resistance, purl.org/becyt/ford/1 [https], Phosphatidylinositol 3-Kinases, Biotecnologia Médica [Ciências Médicas], Neoplasms, Science and technology, Phosphoinositide-3 Kinase Inhibitors, Multidisciplinary, Biomarkers, Tumor, Cell Line, Tumor, Drug Antagonism, Drug Resistance, Neoplasm, Treatment Outcome, Pharmacogenetic, article, ANDROGEN RECEPTOR, 49/39, 631/114/2415, 021001 nanoscience & nanotechnology, 692/4028/67, Multidisciplinary Sciences, 317 Pharmacy, Patient Safety, Systems biology, 3122 Cancers, INHIBITION, Computational biology, Cell Line, medicine, LANDSCAPE, Physique, Human Genome, Data Science, General Chemistry, AstraZeneca-Sanger Drug Combination DREAM Consortium, Astronomie, GENE, Good Health and Well Being, Pharmacogenomics, Genomic, Neoplasm, 631/553, Phosphatidylinositol 3-Kinase
Abstract

PubMed: 31209238, The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. © 2019, The Author(s)., National Institute for Health Research, NIHR Wellcome Trust, WT: 102696, 206194 Magyar Tudományos Akadémia, MTA Bayer 668858 PrECISE AstraZeneca, We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194)., Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests.

Published
2019
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12. Adult human kidney organoids originate from CD24(+) cells and represent an advanced model for adult polycystic kidney disease

Author

Xu, Y., Kuppe, C., Perales-Patón, J., Hayat, S., Kranz, J., Abdallah, A.T., Nagai, J., Li, Z, Peisker, F., Saritas, T., Halder, M., Menzel, S., Hoeft, K., Kenter, A., Kim, H., Roeyen, C.R. van, Lehrke, M., Moellmann, J., Speer, T., Buhl, E.M., Hoogenboezem, R., Boor, P., Jansen, J., Knopp, C., Kurth, I., Smeets, B., Bindels, E., Reinders, M.E., Baan, C., Gribnau, J., Hoorn, E.J., Steffens, J., Huber, T.B., Costa, I., Floege, J., Schneider, R.K., Saez-Rodriguez, J., Freedman, B.S., Kramann, R., Xu, Y., Kuppe, C., Perales-Patón, J., Hayat, S., Kranz, J., Abdallah, A.T., Nagai, J., Li, Z, Peisker, F., Saritas, T., Halder, M., Menzel, S., Hoeft, K., Kenter, A., Kim, H., Roeyen, C.R. van, Lehrke, M., Moellmann, J., Speer, T., Buhl, E.M., Hoogenboezem, R., Boor, P., Jansen, J., Knopp, C., Kurth, I., Smeets, B., Bindels, E., Reinders, M.E., Baan, C., Gribnau, J., Hoorn, E.J., Steffens, J., Huber, T.B., Costa, I., Floege, J., Schneider, R.K., Saez-Rodriguez, J., Freedman, B.S., and Kramann, R.

Abstract

Item does not contain fulltext, Adult kidney organoids have been described as strictly tubular epithelia and termed tubuloids. While the cellular origin of tubuloids has remained elusive, here we report that they originate from a distinct CD24(+) epithelial subpopulation. Long-term-cultured CD24(+) cell-derived tubuloids represent a functional human kidney tubule. We show that kidney tubuloids can be used to model the most common inherited kidney disease, namely autosomal dominant polycystic kidney disease (ADPKD), reconstituting the phenotypic hallmark of this disease with cyst formation. Single-cell RNA sequencing of CRISPR-Cas9 gene-edited PKD1- and PKD2-knockout tubuloids and human ADPKD and control tissue shows similarities in upregulation of disease-driving genes. Furthermore, in a proof of concept, we demonstrate that tolvaptan, the only approved drug for ADPKD, has a significant effect on cyst size in tubuloids but no effect on a pluripotent stem cell-derived model. Thus, tubuloids are derived from a tubular epithelial subpopulation and represent an advanced system for ADPKD disease modeling.

Published
2022

13. Experimental and computational technologies to dissect the kidney at the single-cell level

Author

Kuppe, C., Perales-Patón, J., Saez-Rodriguez, J., Kramann, R., Kuppe, C., Perales-Patón, J., Saez-Rodriguez, J., and Kramann, R.

Abstract

The field of single-cell technologies, in particular single-cell genomics with transcriptomics and epigenomics, and most recently single-cell proteomics, is rapidly growing and holds promise to advance our understanding of organ homoeostasis and disease, and facilitate the identification of novel therapeutic targets and biomarkers. This review offers an introduction to these technologies. In addition, as the size and complexity of the data require sophisticated computational methods for analysis and interpretation, we will also provide an overview of these methods and summarize the single-cell literature specifically pertaining to the kidney.

Published
2022

15. SREBP1-induced fatty acid synthesis depletes macrophages antioxidant defences to promote their alternative activation

Author

Bidault, G, Virtue, S, Petkevicius, K, Jolin, HE, Dugourd, A, Guenantin, AC, Leggat, J, Mahler-Araujo, B, Lam, BYH, Ma, MK, Dale, M, Carobbio, S, Kaser, A, Fallon, PG, Saez Rodriguez, J, McKenzie, ANJ, Vidal-Puig, A, Bidault, Guillaume [0000-0002-8396-9962], Vidal-Puig, Antonio [0000-0003-4220-9577], and Apollo - University of Cambridge Repository

Subjects
Lipopolysaccharides, Mice, Knockout, Sequence Analysis, RNA, Macrophages, Fatty Acids, Macrophage Activation, Antioxidants, Dexamethasone, Up-Regulation, Mice, RAW 264.7 Cells, Animals, Humans, Interleukin-4, Nippostrongylus, Sterol Regulatory Element Binding Protein 1, Strongylida Infections
Abstract

Macrophages exhibit a spectrum of activation states ranging from classical to alternative activation1. Alternatively, activated macrophages are involved in diverse pathophysiological processes such as confining tissue parasites2, improving insulin sensitivity3 or promoting an immune tolerant microenvironment that facilitates tumour growth and metastasis4. Recently, the role of metabolism regulating macrophage function has come into focus as both the classical and alternative activation programmes require specific regulated metabolic reprogramming5. While most of the studies regarding immunometabolism have focussed on the catabolic pathways activated to provide energy, little is known about the anabolic pathways mediating macrophage alternative activation. In this study, we show that the anabolic transcription factor sterol regulatory element binding protein 1 (SREBP1) is activated in response to the canonical Th2 cytokine interleukin 4 (IL-4) to trigger the de novo lipogenesis (DNL) programme, as a necessary step for macrophage alternative activation. Mechanistically, DNL consumes NADPH, partitioning it away from cellular antioxidant defences and raising ROS levels. ROS serves as a second messenger, signalling sufficient DNL, and promoting macrophage alternative activation. The pathophysiological relevance of this mechanism is validated by showing that SREBP1/DNL is essential for macrophage alternative activation in vivo in a helminth infection model.

Published
2021
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16. Deficiency of myeloid prolyl hydroxylase domain proteins aggravates atherogenesis via macrophage apoptosis and paracrine fibrotic signaling

Author

Sluimer, J, primary, Van Kuijk, K, additional, Demandt, J A F, additional, Perales-Paton, J, additional, Kuppe, C, additional, Jin, H, additional, Matic, L, additional, Mees, B, additional, Hedin, U, additional, Biessen, E A L, additional, Carmeliet, P, additional, Baker, A H, additional, Kramann, R K, additional, Schurgers, L J, additional, and Saez-Rodriguez, J, additional

Published
2021
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19. Mikrobielle Dysbiose fördert die Hepatokarzinogenese über eine Veränderung des hepatischen Entzündungsmillieus

Author

Schneider, KM, additional, Mohs, A, additional, Gui, W, additional, Galvez, EJC, additional, Candels, LS, additional, Holland, CH, additional, Elfers, C, additional, Kilic, K, additional, Schneider, CV, additional, Strnad, P, additional, Wirtz, TH, additional, Marshall, HU, additional, Latz, E, additional, Lelouvier, B, additional, Saez-Rodriguez, J, additional, de Vos, W, additional, Strowig, T, additional, Trebicka, J, additional, and Trautwein, C, additional

Published
2021
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20. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Author

Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., Saez-Rodriguez J., Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., and Saez-Rodriguez J.

Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Published
2019

21. Assessment of network module identification across complex diseases

Author

Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Marbach, D, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, W, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Choobdar S., Ahsen M. E., Crawford J., Tomasoni M., Fang T., Lamparter D., Lin J., Hescott B., Hu X., Mercer J., Natoli T., Narayan R., Aicheler F., Amoroso N., Arenas A., Azhagesan K., Baker A., Banf M., Batzoglou S., Baudot A., Bellotti R., Bergmann S., Boroevich K. A., Brun C., Cai S., Caldera M., Calderone A., Cesareni G., Chen W., Chichester C., Cowen L., Cui H., Dao P., De Domenico M., Dhroso A., Didier G., Divine M., del Sol A., Feng X., Flores-Canales J. C., Fortunato S., Gitter A., Gorska A., Guan Y., Guenoche A., Gomez S., Hamza H., Hartmann A., He S., Heijs A., Heinrich J., Hu Y., Huang X., Hughitt V. K., Jeon M., Jeub L., Johnson N. T., Joo K., Joung I. S., Jung S., Kalko S. G., Kamola P. J., Kang J., Kaveelerdpotjana B., Kim M., Kim Y. -A., Kohlbacher O., Korkin D., Krzysztof K., Kunji K., Kutalik Z., Lage K., Lang-Brown S., Le T. D., Lee J., Lee S., Li D., Li J., Liu L., Loizou A., Luo Z., Lysenko A., Ma T., Mall R., Marbach D., Mattia T., Medvedovic M., Menche J., Micarelli E., Monaco A., Muller F., Narykov O., Norman T., Park S., Perfetto L., Perrin D., Pirro S., Przytycka T. M., Qian X., Raman K., Ramazzotti D., Ramsahai E., Ravindran B., Rennert P., Saez-Rodriguez J., Scharfe C., Sharan R., Shi N., Shin W., Shu H., Sinha H., Slonim D. K., Spinelli L., Srinivasan S., Subramanian A., Suver C., Szklarczyk D., Tangaro S., Thiagarajan S., Tichit L., Tiede T., Tripathi B., Tsherniak A., Tsunoda T., Turei D., Ullah E., Vahedi G., Valdeolivas A., Vivek J., von Mering C., Waagmeester A., Wang B., Wang Y., Weir B. A., White S., Winkler S., Xu K., Xu T., Yan C., Yang L., Yu K., Yu X., Zaffaroni G., Zaslavskiy M., Zeng T., Zhang J. D., Zhang L., Zhang W., Zhang X., Zhang J., Zhou X., Zhou J., Zhu H., Zhu J., Zuccon G., Stolovitzky G., Cowen L. J., Choobdar, S, Ahsen, M, Crawford, J, Tomasoni, M, Fang, T, Lamparter, D, Lin, J, Hescott, B, Hu, X, Mercer, J, Natoli, T, Narayan, R, Aicheler, F, Amoroso, N, Arenas, A, Azhagesan, K, Baker, A, Banf, M, Batzoglou, S, Baudot, A, Bellotti, R, Bergmann, S, Boroevich, K, Brun, C, Cai, S, Caldera, M, Calderone, A, Cesareni, G, Chen, W, Chichester, C, Cowen, L, Cui, H, Dao, P, De Domenico, M, Dhroso, A, Didier, G, Divine, M, del Sol, A, Feng, X, Flores-Canales, J, Fortunato, S, Gitter, A, Gorska, A, Guan, Y, Guenoche, A, Gomez, S, Hamza, H, Hartmann, A, He, S, Heijs, A, Heinrich, J, Hu, Y, Huang, X, Hughitt, V, Jeon, M, Jeub, L, Johnson, N, Joo, K, Joung, I, Jung, S, Kalko, S, Kamola, P, Kang, J, Kaveelerdpotjana, B, Kim, M, Kim, Y, Kohlbacher, O, Korkin, D, Krzysztof, K, Kunji, K, Kutalik, Z, Lage, K, Lang-Brown, S, Le, T, Lee, J, Lee, S, Li, D, Li, J, Liu, L, Loizou, A, Luo, Z, Lysenko, A, Ma, T, Mall, R, Marbach, D, Mattia, T, Medvedovic, M, Menche, J, Micarelli, E, Monaco, A, Muller, F, Narykov, O, Norman, T, Park, S, Perfetto, L, Perrin, D, Pirro, S, Przytycka, T, Qian, X, Raman, K, Ramazzotti, D, Ramsahai, E, Ravindran, B, Rennert, P, Saez-Rodriguez, J, Scharfe, C, Sharan, R, Shi, N, Shin, W, Shu, H, Sinha, H, Slonim, D, Spinelli, L, Srinivasan, S, Subramanian, A, Suver, C, Szklarczyk, D, Tangaro, S, Thiagarajan, S, Tichit, L, Tiede, T, Tripathi, B, Tsherniak, A, Tsunoda, T, Turei, D, Ullah, E, Vahedi, G, Valdeolivas, A, Vivek, J, von Mering, C, Waagmeester, A, Wang, B, Wang, Y, Weir, B, White, S, Winkler, S, Xu, K, Xu, T, Yan, C, Yang, L, Yu, K, Yu, X, Zaffaroni, G, Zaslavskiy, M, Zeng, T, Zhang, J, Zhang, L, Zhang, W, Zhang, X, Zhou, X, Zhou, J, Zhu, H, Zhu, J, Zuccon, G, Stolovitzky, G, Choobdar S., Ahsen M. E., Crawford J., Tomasoni M., Fang T., Lamparter D., Lin J., Hescott B., Hu X., Mercer J., Natoli T., Narayan R., Aicheler F., Amoroso N., Arenas A., Azhagesan K., Baker A., Banf M., Batzoglou S., Baudot A., Bellotti R., Bergmann S., Boroevich K. A., Brun C., Cai S., Caldera M., Calderone A., Cesareni G., Chen W., Chichester C., Cowen L., Cui H., Dao P., De Domenico M., Dhroso A., Didier G., Divine M., del Sol A., Feng X., Flores-Canales J. C., Fortunato S., Gitter A., Gorska A., Guan Y., Guenoche A., Gomez S., Hamza H., Hartmann A., He S., Heijs A., Heinrich J., Hu Y., Huang X., Hughitt V. K., Jeon M., Jeub L., Johnson N. T., Joo K., Joung I. S., Jung S., Kalko S. G., Kamola P. J., Kang J., Kaveelerdpotjana B., Kim M., Kim Y. -A., Kohlbacher O., Korkin D., Krzysztof K., Kunji K., Kutalik Z., Lage K., Lang-Brown S., Le T. D., Lee J., Lee S., Li D., Li J., Liu L., Loizou A., Luo Z., Lysenko A., Ma T., Mall R., Marbach D., Mattia T., Medvedovic M., Menche J., Micarelli E., Monaco A., Muller F., Narykov O., Norman T., Park S., Perfetto L., Perrin D., Pirro S., Przytycka T. M., Qian X., Raman K., Ramazzotti D., Ramsahai E., Ravindran B., Rennert P., Saez-Rodriguez J., Scharfe C., Sharan R., Shi N., Shin W., Shu H., Sinha H., Slonim D. K., Spinelli L., Srinivasan S., Subramanian A., Suver C., Szklarczyk D., Tangaro S., Thiagarajan S., Tichit L., Tiede T., Tripathi B., Tsherniak A., Tsunoda T., Turei D., Ullah E., Vahedi G., Valdeolivas A., Vivek J., von Mering C., Waagmeester A., Wang B., Wang Y., Weir B. A., White S., Winkler S., Xu K., Xu T., Yan C., Yang L., Yu K., Yu X., Zaffaroni G., Zaslavskiy M., Zeng T., Zhang J. D., Zhang L., Zhang W., Zhang X., Zhang J., Zhou X., Zhou J., Zhu H., Zhu J., Zuccon G., Stolovitzky G., and Cowen L. J.

Abstract

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Published
2019

22. A 3-proteomic score to improve the clinical management of childhood liver cancer

Author

Carrillo, J, Simon-Coma, M, Azkargorta, M, Royo, L, Guerra, L, Alvaro, D, Failli, M, Hernansaiz, R, Kimura, H, Garrido, M, Oliveros, FH, Porta-Pardo, E, Ng, IOL, Domingo-Sabat, M, Lopez-Santamaria, M, Morland, B, Czauderna, P, Childs, M, Maibach, R, Valencia, A, Elortza, F, Sala, M, Buendia, MA, Akuchi, A, Saez-Rodriguez, J, Di Bernardo, D, Sarrias, MR, and Armengol, C

Published
2021

23. COVID-19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms (vol 17, e10387, 2021)

Author

Ostaszewski, M, Niarakis, A, Mazein, A, Kuperstein, I, Phair, R, Orta-Resendiz, A, Singh, V, Aghamiri, S, Acencio, M, Glaab, E, Ruepp, A, Fobo, G, Montrone, C, Brauner, B, Frishman, G, Gomez, L, Somers, J, Hoch, M, Gupta, S, Scheel, J, Borlinghaus, H, Czauderna, T, Schreiber, F, Montagud, A, de Leon, M, Funahashi, A, Hiki, Y, Hiroi, N, Yamada, T, Drager, A, Renz, A, Naveez, M, Bocskei, Z, Messina, F, Bornigen, D, Fergusson, L, Conti, M, Rameil, M, Nakonecnij, V, Vanhoefer, J, Schmiester, L, Wang, M, Ackerman, E, Shoemaker, J, Zucker, J, Oxford, K, Teuton, J, Kocakaya, E, Summak, G, Hanspers, K, Kutmon, M, Coort, S, Eijssen, L, Ehrhart, F, Rex, D, Slenter, D, Martens, M, Pham, N, Haw, R, Jassal, B, Matthews, L, Orlic-Milacic, M, Senff-Ribeiro, A, Rothfels, K, Shamovsky, V, Stephan, R, Sevilla, C, Varusai, T, Ravel, J, Fraser, R, Ortseifen, V, Marchesi, S, Gawron, P, Smula, E, Heirendt, L, Satagopam, V, Gm, W, Riutta, A, Golebiewski, M, Owen, S, Goble, C, Xm, H, Overall, R, Maier, D, Bauch, A, Gyori, B, Bachman, J, Vega, C, Groues, V, Vazquez, M, Porras, P, Licata, L, Iannuccelli, M, Sacco, F, Nesterova, A, Yuryev, A, de Waard, A, Turei, D, Luna, A, Babur, O, Soliman, S, Valdeolivas, A, Esteban-Medina, M, Pena-Chilet, M, Rian, K, Helikar, T, Puniya, B, Modos, D, Treveil, A, Olbei, M, De Meulder, B, Ballereau, S, Dugourd, A, Naldi, A, Noel, V, Calzone, L, Sander, C, Demir, E, Korcsmaros, T, Freeman, T, Auge, F, Beckmann, J, Hasenauer, J, Wolkenhauer, O, Willighagen, E, Pico, A, Evelo, C, Gillespie, M, Stein, L, Hermjakob, H, D'Eustachio, P, Saez-Rodriguez, J, Dopazo, J, Valencia, A, Kitano, H, Barillot, E, Auffray, C, Balling, R, and Schneider, R

Subjects
Settore BIO/18, Settore BIO/11
Published
2021

24. Deficiency of myeloid PHD proteins aggravates atherogenesis via macrophage apoptosis and paracrine fibrotic signaling

Author

Van Kuijk, K., primary, Demandt, J.A., additional, Perales-Paton, J., additional, Theelen, T., additional, Marsch, E., additional, De Bruijn, J., additional, Kuppe, C., additional, Mees, B., additional, Gijbels, M.J.J., additional, Matic, L., additional, Hedin, U., additional, Biessen, E., additional, Kramann, R., additional, Carmeliet, P., additional, Schurgers, L.J., additional, Saez-Rodriguez, J., additional, and Sluimer, J., additional

Published
2021
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25. A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

Author

Wang, D., Hensman, J., Kutkaite, G., Toh, T.S., Galhoz, A., Lightfoot, H., Yang, W., Soleimani, M., Barthorpe, S., Mironenko, T., Beck, A., Richardson, L., Lleshi, E., Hall, J., Tolley, C., Barendt, W., Dry, J.R., Saez-Rodriguez, J., Garnett, M.J., Menden, M.P., and Dondelinger, F.

Abstract

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells’ response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

Published
2020

26. Norepinephrine promotes triglyceride storage in macrophages via beta2-adrenergic receptor activation

Author

Petkevicius, K., Bidault, G., Virtue, S., Jenkins, B., Dierendonck, X. van, Dugourd, A., Saez-Rodriguez, J., Stienstra, R., Koulman, A., Vidal-Puig, A., Petkevicius, K., Bidault, G., Virtue, S., Jenkins, B., Dierendonck, X. van, Dugourd, A., Saez-Rodriguez, J., Stienstra, R., Koulman, A., and Vidal-Puig, A.

Abstract

Contains fulltext : 232477.pdf (Publisher’s version ) (Open Access), Tissue-resident macrophages are required for homeostasis, but also contribute to tissue dysfunction in pathophysiological states. The sympathetic neurotransmitter norepinephrine (NE) induces an anti-inflammatory and tissue-reparative phenotype in macrophages. As NE has a well-established role in promoting triglyceride lipolysis in adipocytes, and macrophages accumulate triglyceride droplets in various physiological and disease states, we investigated the effect of NE on primary mouse bone marrow-derived macrophage triglyceride metabolism. Surprisingly, our data show that in contrast to the canonical role of NE in stimulating lipolysis, NE acting via beta2-adrenergic receptors (B2ARs) in macrophages promotes extracellular fatty acid uptake and their storage as triglycerides and reduces free fatty acid release from triglyceride-laden macrophages. We demonstrate that these responses are mediated by a B2AR activation-dependent increase in Hilpda and Dgat1 gene expression and activity. We further show that B2AR activation favors the storage of extracellular polyunsaturated fatty acids. Finally, we present evidence that macrophages isolated from hearts after myocardial injury, for which survival critically depends on leukocyte B2ARs, have a transcriptional signature indicative of a transient triglyceride accumulation. Overall, we describe a novel and unexpected role of NE in promoting triglyceride storage in macrophages that could have potential implications in multiple diseases.

Published
2021

27. Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Author

Dugourd, A. (Aurelien), Kuppe, C. (Christoph), Sciacovelli, M. (Marco), Gjerga, E. (Enio), Gabor, A. (Attila), Emdal, K.B. (Kristina B.), Vieira, V. (Vitor), Bekker-Jensen, D.B. (Dorte B.), Kranz, J. (Jennifer), Bindels, E.M.J. (Eric), Costa, A.S.H. (Ana S.H.), Sousa, A. (Abel), Beltrao, P. (Pedro), Rocha, M. (Miguel), Olsen, J.V. (Jesper V.), Frezza, C. (Christian), Kramann, R.J.T. (Rafael), Saez-Rodriguez, J. (Julio), Dugourd, A. (Aurelien), Kuppe, C. (Christoph), Sciacovelli, M. (Marco), Gjerga, E. (Enio), Gabor, A. (Attila), Emdal, K.B. (Kristina B.), Vieira, V. (Vitor), Bekker-Jensen, D.B. (Dorte B.), Kranz, J. (Jennifer), Bindels, E.M.J. (Eric), Costa, A.S.H. (Ana S.H.), Sousa, A. (Abel), Beltrao, P. (Pedro), Rocha, M. (Miguel), Olsen, J.V. (Jesper V.), Frezza, C. (Christian), Kramann, R.J.T. (Rafael), and Saez-Rodriguez, J. (Julio)

Abstract

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Published
2021
Full Text
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28. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

Author

Moreno-Indias, I, Lahti, L, Nedyalkova, M, Elbere, I, Roshchupkin, Gennady, Adilovic, M, Aydemir, O, Bakir-Gungor, B, Pau, ECD, D'Elia, D, Desai, MS, Falquet, L, Gundogdu, A, Hron, K, Klammsteiner, T, Lopes, MB, Marcos-Zambrano, LJ, Marques, C, Mason, M, May, P, Pasic, L, Pio, G, Pongor, S, Promponas, VJ, Przymus, P, Saez-Rodriguez, J, Sampri, A, Shigdel, R, Stres, B, Suharoschi, R, Truu, J, Truica, CO, Vilne, B, Vlachakis, D, Yilmaz, E, Zeller, G, Zomer, AL, Gomez-Cabrero, D, Claesson, MJ, Moreno-Indias, I, Lahti, L, Nedyalkova, M, Elbere, I, Roshchupkin, Gennady, Adilovic, M, Aydemir, O, Bakir-Gungor, B, Pau, ECD, D'Elia, D, Desai, MS, Falquet, L, Gundogdu, A, Hron, K, Klammsteiner, T, Lopes, MB, Marcos-Zambrano, LJ, Marques, C, Mason, M, May, P, Pasic, L, Pio, G, Pongor, S, Promponas, VJ, Przymus, P, Saez-Rodriguez, J, Sampri, A, Shigdel, R, Stres, B, Suharoschi, R, Truu, J, Truica, CO, Vilne, B, Vlachakis, D, Yilmaz, E, Zeller, G, Zomer, AL, Gomez-Cabrero, D, and Claesson, MJ

Abstract

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Published
2021

29. Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Author

Dugourd, A, Kuppe, C, Sciacovelli, M, Gjerga, E, Gabor, A, Emdal, KB, Vieira, V, Bekker-Jensen, DB, Kranz, J, Bindels, Eric, Costa, ASH, Sousa, A, Beltrao, P, Rocha, M, Olsen, JV, Frezza, C, Kramann, Rafael, Saez-Rodriguez, J, Dugourd, A, Kuppe, C, Sciacovelli, M, Gjerga, E, Gabor, A, Emdal, KB, Vieira, V, Bekker-Jensen, DB, Kranz, J, Bindels, Eric, Costa, ASH, Sousa, A, Beltrao, P, Rocha, M, Olsen, JV, Frezza, C, Kramann, Rafael, and Saez-Rodriguez, J

Abstract

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Published
2021

32. Precision medicine: myocardial fibrosis burden and genotype predict outcome in non-ischemic dilated cardiomyopathy (DCM)

Author

Kayvanpour, E, primary, Sedaghat-Hamedani, F, additional, Levinson, R.T, additional, Li, D, additional, Miersch, T, additional, Gi, W.T, additional, Grabe, N, additional, Lahrmann, B, additional, Taeger, T, additional, Frankenstein, L, additional, Uhlmann, L, additional, Herpel, E, additional, Katus, H.A, additional, Saez-Rodriguez, J, additional, and Meder, B, additional

Published
2020
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36. Dysregulated mesenchymal PDGFR-beta drives kidney fibrosis.

Author

Floege J., Trairatphisan P., Saez-Rodriguez J., Huber T.B., Olson L.E., Boor P., Buhl E.M., Djudjaj S., Klinkhammer B.M., Ermert K., Puelles V.G., Lindenmeyer M.T., Cohen C.D., He C., Borkham-Kamphorst E., Weiskirchen R., Denecke B., Floege J., Trairatphisan P., Saez-Rodriguez J., Huber T.B., Olson L.E., Boor P., Buhl E.M., Djudjaj S., Klinkhammer B.M., Ermert K., Puelles V.G., Lindenmeyer M.T., Cohen C.D., He C., Borkham-Kamphorst E., Weiskirchen R., and Denecke B.

Abstract

Kidney fibrosis is characterized by expansion and activation of platelet-derived growth factor receptor-beta (PDGFR-beta)-positive mesenchymal cells. To study the consequences of PDGFR-beta activation, we developed a model of primary renal fibrosis using transgenic mice with PDGFR-beta activation specifically in renal mesenchymal cells, driving their pathological proliferation and phenotypic switch toward myofibroblasts. This resulted in progressive mesangioproliferative glomerulonephritis, mesangial sclerosis, and interstitial fibrosis with progressive anemia due to loss of erythropoietin production by fibroblasts. Fibrosis induced secondary tubular epithelial injury at later stages, coinciding with microinflammation, and aggravated the progression of hypertensive and obstructive nephropathy. Inhibition of PDGFR activation reversed fibrosis more effectively in the tubulointerstitium compared to glomeruli. Gene expression signatures in mice with PDGFR-beta activation resembled those found in patients. In conclusion, PDGFR-beta activation alone is sufficient to induce progressive renal fibrosis and failure, mimicking key aspects of chronic kidney disease in humans. Our data provide direct proof that fibrosis per se can drive chronic organ damage and establish a model of primary fibrosis allowing specific studies targeting fibrosis progression and regression.Copyright © 2020 The Authors. Published under the terms of the CC BY 4.0 license

Published
2020

37. Big Data Approaches in Heart Failure Research

Author

Lanzer, J.D. (Jan D.), Leuschner, F. (Florian), Kramann, R.J.T. (Rafael), Levinson, R.T. (Rebecca T.), Saez-Rodriguez, J. (Julio), Lanzer, J.D. (Jan D.), Leuschner, F. (Florian), Kramann, R.J.T. (Rafael), Levinson, R.T. (Rebecca T.), and Saez-Rodriguez, J. (Julio)

Abstract

Purpose of Review: The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on “omics” and clinical data. We address some limitations of this data, as well as their future potential. Recent Findings: Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Summary: Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

Published
2020
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38. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, Keating, SM, Bergmann, FT, Gillespie, CS, Malik-Sheriff, RS, Moodie, SL, Moraru, II, Myers, CJ, Olivier, BG, Schaff, JC, Smith, LP, Swat, MJ, Wilkinson, DJ, Blinov, ML, Faeder, JR, Gómez, HF, Hamm, TM, Lister, AL, Proctor, CJ, Shaffer, CA, Shapiro, BE, Sauro, HM, Doyle, JC, Adams, RR, Allen, NA, Angermann, BR, Bader, GD, Cox, CD, Denney, WS, Evelo, CT, Fleming, RM, Harris, LA, Heavner, BD, Hlavacek, WS, Hyduke, DR, Karp, PD, Karr, JR, Kell, DB, Loew, LM, Monteiro, PT, Natarajan, KN, Nielsen, PM, Phair, RD, Rohwer, JM, Ruebenacker, OA, Sealfon, SC, Stefan, MI, Sullivan, DP, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, Keating, SM, Bergmann, FT, Gillespie, CS, Malik-Sheriff, RS, Moodie, SL, Moraru, II, Myers, CJ, Olivier, BG, Schaff, JC, Smith, LP, Swat, MJ, Wilkinson, DJ, Blinov, ML, Faeder, JR, Gómez, HF, Hamm, TM, Lister, AL, Proctor, CJ, Shaffer, CA, Shapiro, BE, Sauro, HM, Doyle, JC, Adams, RR, Allen, NA, Angermann, BR, Bader, GD, Cox, CD, Denney, WS, Evelo, CT, Fleming, RM, Harris, LA, Heavner, BD, Hlavacek, WS, Hyduke, DR, Karp, PD, Karr, JR, Kell, DB, Loew, LM, Monteiro, PT, Natarajan, KN, Nielsen, PM, Phair, RD, Rohwer, JM, Ruebenacker, OA, Sealfon, SC, Stefan, MI, and Sullivan, DP

Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Published
2020

39. Big Data Approaches in Heart Failure Research

Author

Lanzer, JD, Leuschner, F, Kramann, Rafael, Levinson, RT, Saez-Rodriguez, J, Lanzer, JD, Leuschner, F, Kramann, Rafael, Levinson, RT, and Saez-Rodriguez, J

Published
2020

41. Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Author

Guo, T., Luna, A., Rajapakse, V.N., Koh, C.C., Wu, Z., Liu, W., Sun, Y., Gao, H., Menden, M.P., Xu, C., Calzone, L., Martignetti, L., Auwerx, C., Buljan, M., Banaei-Esfahani, A., Ori, A., Iskar, M., Gillet, L., Bi, R., Zhang, J., Zhang, H., Yu, C., Zhong, Q., Varma, S., Schmitt, U., Qiu, P., Zhang, Q., Zhu, Y., Wild, P.J., Garnett, M.J., Bork, P., Beck, M., Liu, K., Saez-Rodriguez, J., and Elloumi, F.

Published
2019
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42. S887 DISSECTING THE ROLE OF CXCL4 IN PRIMARY MYELOFIBROSIS

Author

Gleitz, H., primary, Dugourd, A., additional, Leimkühler, N., additional, Snoeren, I., additional, Fuchs, S., additional, Banjanin, B., additional, Pritchard, J., additional, Hoogenboezem, R., additional, Bindels, E., additional, Saez-Rodriguez, J., additional, Kramann, R., additional, and Schneider, R., additional

Published
2019
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43. Big science and big data in nephrology

Author

Saez-Rodriguez, J., Rinschen, MM, Floege, J., Kramann, R.J.T. (Rafael), Saez-Rodriguez, J., Rinschen, MM, Floege, J., and Kramann, R.J.T. (Rafael)

Abstract

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of ‘big data’ holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called ‘big science,’ with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.

Published
2019
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44. Big science and big data in nephrology

Author

Saez-Rodriguez, J, Rinschen, MM, Floege, J, Kramann, Rafael, Saez-Rodriguez, J, Rinschen, MM, Floege, J, and Kramann, Rafael

Published
2019

48. Cooperative development of logical modelling standards and tools with CoLoMoTo

Author

Naldi, A., Monteiro, P.T., Müssel, C., Tools, Kestler, H.A., Thieffry, D., Xenarios, I., Saez-Rodriguez, J., Helikar, T., Chaouiya, C., Consortium for Logical Models, Tools, Albert, R., Barberis, M., Calzone, L., Chaouiya, C., Chasapi, A., Cokelaer, T., Crespo, I., Dorier, J., Dräger, A., Helikar, T., Hernandez, C., Hucka, M., de Jong, H., Keating, SM., Kestler, HA., Klamt, S., Klarner, H., Laubenbacher, R., Novère, NL., Monteiro, PT., Müssel, C., Naldi, A., Niknejad, A., Rodriguez, N., Saez-Rodriguez, J., Siebert, H., Stoll, G., Thieffry, D., Xenarios, I., and Zañudo, JG.

Subjects
Animals, Cells/metabolism, Computer Simulation, Humans, Metabolic Networks and Pathways, Models, Theoretical, Societies, Scientific, Software/standards, Systems Biology/methods
Abstract

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments.

Published
2015

49. Evaluation of methods for modeling transcription factor sequence specificity

Author

Weirauch, M., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T., Saez-Rodriguez, J., Cokelaer, T., Vedenko, A., Talukder, S., Consortium, D., Agius, P., Arvey, A., Bucher, P., Callan, C., Chang, C., Chen, C., Chen, Y., Chu, Y., Grau, J., Grosse, I., Jagannathan, V., Keilwagen, J., Kielbasa, S., Kinney, J., Klein, H., Kursa, M., Lahdesmaki, H., Laurila, K., Lei, C., Leslie, C., Linhart, C., Murugan, A., Mysickova, A., Noble, W., Nykter, M., Orenstein, Y., Posch, S., Ruan, J., Rudnicki, W., Schmid, C., Shamir, R., Sung, W., Vingron, M., Zhang, Z., Bussemaker, H., Morris, Q., Bulyk, M., Stolovitzky, G., and Hughes, T.

Subjects
Biomedical Engineering, Protein Array Analysis, Bioengineering, Biology, Applied Microbiology and Biotechnology, Genome, DNA-binding protein, Article, 03 medical and health sciences, Mice, 0302 clinical medicine, Animals, Position-Specific Scoring Matrices, Degeneracy (biology), Binding site, Nucleotide Motifs, Transcription factor, 030304 developmental biology, Genetics, 0303 health sciences, Microarray analysis techniques, Computational Biology, DNA-Binding Proteins, Molecular Medicine, Functional genomics, 030217 neurology & neurosurgery, Algorithms, Biotechnology, Transcription Factors
Abstract

Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro-derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (

Published
2013
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50. Annexin A1 sustains tumor metabolism and cellular proliferation upon stable loss of HIF1A

Author

Rohwer, N, Bindel, F, Grimm, C, Lin, SJ, Wappler, J, Klinger, B, Blüthgen, N, Du Bois, I, Schmeck, B, Lehrach, H, De Graauw, M, Goncalves, E, Saez-Rodriguez, J, Tan, P, Grabsch, HI, Prigione, A, Kempa, S, Cramer, T, Pathologie, and RS: GROW - R2 - Basic and Translational Cancer Biology

Subjects
HIF-1, cancer metabolism, Mice, SCID, Hypoxia-Inducible Factor 1, alpha Subunit, Immunohistochemistry, Mice, Mice, Inbred NOD, Stomach Neoplasms, Cardiovascular and Metabolic Diseases, induced essentiality, Cell Line, Tumor, Animals, Heterografts, Humans, cancer therapy, Female, RNA, Small Interfering, Technology Platforms, Function and Dysfunction of the Nervous System, Cell Proliferation, Research Paper, Annexin A1
Abstract

OncoTarget 7(6), 6693-6710 (2015). doi:10.18632/oncotarget.6793, Published by Impact Journals LLC, [S.l.]

Published
2016
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