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1. Prospective head-to-head comparison of non-invasive scores for diagnosis of fibrotic MASH in patients with type 2 diabetes

Author

Bellili, Djamila, Bessadi, Ouarda, Da Silveira, Charlene, Djelouat, Fatima Zohra, Girard, Benoit, Legrand, Vanessa, Neveux, Nathalie, Meziani, Meriam, Roy, Ludovic, Sekour, Dahia, Sens, Manon, Slimani, Miassa, Zatout, Ouassila, Bachelet, Delphine, Bhavsar, Krishna, Mullaert, Basma Basli-Baillet Jimmy, Marcault, Estelle, Si-Mohammed, Nassima, Cosson, Emmanuel, Miguel Albuquerque, Doblas, Sabrina, Hammoutene, Adel, Gonzalez Montpetit, Estefania, Pagé, Gwenaël, Béatrice Parfait, Postic, Catherine, Lehuen, Agnès, Toubal, Amine, Rousseau, Camille, Fruchet, Blandine, Soulard, Pauline, Gouda, Zouriatou, Vidaud, Michel, Letourneur, Franck, Renault, Gilles, Scharfmann, Raphaël, Ait-Boudaoud, Amel, Barsamian, Charles, Carette, Claire, Rives-Lange, Claire, Baida, Rachel, Couture, Olivier, Decombas, Sofiane, Deffieux, Thomas, Nguyen, Thu-mai, Tanter, Mickael, Baltauss, Tania, Balzac, Edwige-Ludiwyne, Barbier Saint Hilaire, Pierre, Delerive, Philippe, Duvivier, Valérie, Fillon, Arnaud, Geronimi, Julia, Laplume, Jessica, Werner, Erwan, Xuereb, Laura, Liechti, Robin, Martin, Olivier, Mehl, Florence, Pruess, Manuela, Castille, Jean-Marie, Drane, Fabienne, Deckmyn, Olivier, Castelli, Florence, Cousin, Benoit Colsch Emmanuel, Fenaille, François, Guilbaud, Laure, Lohier, Allyre, Chambellin, Francois, Laaland, Lyddie, Clusel, Catherine, Hauduroy, Marie, Pautre, Pierre, Castera, Laurent, Garteiser, Philippe, Laouenan, Cédric, Vidal-Trécan, Tiphaine, Vallet-Pichard, Anaïs, Manchon, Pauline, Paradis, Valérie, Czernichow, Sébastien, Roulot, Dominique, Larger, Etienne, Pol, Stanislas, Bedossa, Pierre, Correas, Jean-Michel, Valla, Dominique, Gautier, Jean-François, and Van Beers, Bernard E.

Published
2024
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2. Prospective head-to-head comparison of non-invasive scores for diagnosis of fibrotic MASH in patients with type 2 diabetes

Author

Castera, Laurent, primary, Garteiser, Philippe, additional, Laouenan, Cédric, additional, Vidal-Trécan, Tiphaine, additional, Vallet-Pichard, Anaïs, additional, Manchon, Pauline, additional, Paradis, Valérie, additional, Czernichow, Sébastien, additional, Roulot, Dominique, additional, Larger, Etienne, additional, Pol, Stanislas, additional, Bedossa, Pierre, additional, Correas, Jean-Michel, additional, Valla, Dominique, additional, Gautier, Jean-François, additional, Van Beers, Bernard E., additional, Bellili, Djamila, additional, Bessadi, Ouarda, additional, Da Silveira, Charlene, additional, Djelouat, Fatima Zohra, additional, Girard, Benoit, additional, Legrand, Vanessa, additional, Neveux, Nathalie, additional, Meziani, Meriam, additional, Roy, Ludovic, additional, Sekour, Dahia, additional, Sens, Manon, additional, Slimani, Miassa, additional, Zatout, Ouassila, additional, Bachelet, Delphine, additional, Bhavsar, Krishna, additional, Basli-Baillet Jimmy Mullaert, Basma, additional, Marcault, Estelle, additional, Si-Mohammed, Nassima, additional, Cosson, Emmanuel, additional, Albuquerque, Miguel, additional, Doblas, Sabrina, additional, Hammoutene, Adel, additional, Montpetit, Estefania Gonzalez, additional, Pagé, Gwenaël, additional, Parfait, Béatrice, additional, Postic, Catherine, additional, Lehuen, Agnès, additional, Toubal, Amine, additional, Rousseau, Camille, additional, Fruchet, Blandine, additional, Soulard, Pauline, additional, Gouda, Zouriatou, additional, Vidaud, Michel, additional, Letourneur, Franck, additional, Renault, Gilles, additional, Scharfmann, Raphaël, additional, Ait-Boudaoud, Amel, additional, Barsamian, Charles, additional, Carette, Claire, additional, Rives-Lange, Claire, additional, Baida, Rachel, additional, Couture, Olivier, additional, Decombas, Sofiane, additional, Deffieux, Thomas, additional, Nguyen, Thu-mai, additional, Tanter, Mickael, additional, Baltauss, Tania, additional, Balzac, Edwige-Ludiwyne, additional, Saint Hilaire Philippe Delerive, Pierre Barbier, additional, Duvivier, Valérie, additional, Fillon, Arnaud, additional, Geronimi, Julia, additional, Laplume, Jessica, additional, Werner, Erwan, additional, Xuereb, Laura, additional, Liechti, Robin, additional, Martin, Olivier, additional, Mehl, Florence, additional, Pruess, Manuela, additional, Castille, Jean-Marie, additional, Drane, Fabienne, additional, Deckmyn, Olivier, additional, Castelli, Florence, additional, Emmanuel Cousin, Benoit Colsch, additional, Fenaille, François, additional, Guilbaud, Laure, additional, Lohier, Allyre, additional, Chambellin, Francois, additional, Laaland, Lyddie, additional, Clusel, Catherine, additional, Hauduroy, Marie, additional, and Pautre, Pierre, additional

Published
2024
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3. Abstract 4522: Pathologic complete response rate across triple negative breast cancer subtypes in the IMMUcan study

Author

Garcia, Andrea Joaquin, primary, Rediti, Mattia, additional, Benyagoub, Abdelkader, additional, Tissot, Stéphanie, additional, Rusakiewicz, Sylvie, additional, Liechti, Robin, additional, Marzetta, Flavia, additional, Penel, Nicolas, additional, Oliveira, Julio, additional, Goeminne, Jean-Charles, additional, Fournel, Pierre, additional, Capela, Andreia, additional, Wang, Xiaoxiao, additional, Vincent, Delphine, additional, Rothe, Françoise, additional, Morfouace, Marie, additional, Hong, Henoch, additional, Jackson, Donald, additional, Sotiriou, Christos, additional, and Buisseret, Laurence, additional

Published
2023
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4. Data from Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database

Author

Camps, Jordi, primary, Noël, Floriane, primary, Liechti, Robin, primary, Massenet-Regad, Lucile, primary, Rigade, Sidwell, primary, Götz, Lou, primary, Hoffmann, Caroline, primary, Amblard, Elise, primary, Saichi, Melissa, primary, Ibrahim, Mahmoud M., primary, Pollard, Jack, primary, Medvedovic, Jasna, primary, Roider, Helge G., primary, and Soumelis, Vassili, primary

Published
2023
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5. Supplementary Data from Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database

Author

Camps, Jordi, primary, Noël, Floriane, primary, Liechti, Robin, primary, Massenet-Regad, Lucile, primary, Rigade, Sidwell, primary, Götz, Lou, primary, Hoffmann, Caroline, primary, Amblard, Elise, primary, Saichi, Melissa, primary, Ibrahim, Mahmoud M., primary, Pollard, Jack, primary, Medvedovic, Jasna, primary, Roider, Helge G., primary, and Soumelis, Vassili, primary

Published
2023
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6. Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion

Author

Cruciani-Guglielmacci, Céline, Bellini, Lara, Denom, Jessica, Oshima, Masaya, Fernandez, Neïké, Normandie-Levi, Priscilla, Berney, Xavier P., Kassis, Nadim, Rouch, Claude, Dairou, Julien, Gorman, Tracy, Smith, David M., Marley, Anna, Liechti, Robin, Kuznetsov, Dmitry, Wigger, Leonore, Burdet, Frédéric, Lefèvre, Anne-Laure, Wehrle, Isabelle, Uphues, Ingo, Hildebrandt, Tobias, Rust, Werner, Bernard, Catherine, Ktorza, Alain, Rutter, Guy A., Scharfmann, Raphael, Xenarios, Ioannis, Le Stunff, Hervé, Thorens, Bernard, Magnan, Christophe, and Ibberson, Mark

Published
2017
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7. Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes

Author

Solimena, Michele, Schulte, Anke M., Marselli, Lorella, Ehehalt, Florian, Richter, Daniela, Kleeberg, Manuela, Mziaut, Hassan, Knoch, Klaus-Peter, Parnis, Julia, Bugliani, Marco, Siddiq, Afshan, Jörns, Anne, Burdet, Frédéric, Liechti, Robin, Suleiman, Mara, Margerie, Daniel, Syed, Farooq, Distler, Marius, Grützmann, Robert, Petretto, Enrico, Moreno-Moral, Aida, Wegbrod, Carolin, Sönmez, Anke, Pfriem, Katja, Friedrich, Anne, Meinel, Jörn, Wollheim, Claes B., Baretton, Gustavo B., Scharfmann, Raphael, Nogoceke, Everson, Bonifacio, Ezio, Sturm, Dorothée, Meyer-Puttlitz, Birgit, Boggi, Ugo, Saeger, Hans-Detlev, Filipponi, Franco, Lesche, Mathias, Meda, Paolo, Dahl, Andreas, Wigger, Leonore, Xenarios, Ioannis, Falchi, Mario, Thorens, Bernard, Weitz, Jürgen, Bokvist, Krister, Lenzen, Sigurd, Rutter, Guy A., Froguel, Philippe, von Bülow, Manon, Ibberson, Mark, and Marchetti, Piero

Published
2017
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8. Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database

Author

Camps, Jordi, primary, Noël, Floriane, additional, Liechti, Robin, additional, Massenet-Regad, Lucile, additional, Rigade, Sidwell, additional, Götz, Lou, additional, Hoffmann, Caroline, additional, Amblard, Elise, additional, Saichi, Melissa, additional, Ibrahim, Mahmoud M., additional, Pollard, Jack, additional, Medvedovic, Jasna, additional, Roider, Helge G., additional, and Soumelis, Vassili, additional

Published
2022
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10. Abstract 5011: Meta-analysis of human cancer single cell RNAseq datasets using the fully integrated IMMUcan database

Author

Camps, Jordi, primary, Noel, Floriane, additional, Liechti, Robin, additional, Goetz, Lou, additional, Hoffmann, Caroline, additional, Massenet-Regad, Lucile, additional, Amblard, Elise, additional, Saichi, Melissa, additional, Ibrahim, Mahmoud M., additional, Pollard, Jack, additional, Medvedovic, Jasna, additional, Roider, Helge G., additional, and Soumelis, Vassili, additional

Published
2022
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12. Abstract 2756: A toolbox enabling a data-driven selection of regions of interest in tumor tissue sections for imaging mass cytometry analysis

Author

Schulz, Daniel, primary, Renaud-Tissot, Stephanie, additional, Liechti, Robin, additional, Eling, Nils, additional, Daniel, Michelle, additional, Windhager, Jonas, additional, Karapetyan, Yervand, additional, Mrowiec, Thomas, additional, Rusakiewicz, Sylvie, additional, Hong, Henoch S., additional, Morfouace, Marie, additional, and Bodenmiller, Bernd, additional

Published
2021
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15. Transcriptomic Signature Differences Between SARS-CoV-2 and Influenza Virus Infected Patients.

Author

Bibert, Stéphanie, Guex, Nicolas, Lourenco, Joao, Brahier, Thomas, Papadimitriou-Olivgeris, Matthaios, Damonti, Lauro, Manuel, Oriol, Liechti, Robin, Götz, Lou, Tschopp, Jonathan, Quinodoz, Mathieu, Vollenweider, Peter, Pagani, Jean-Luc, Oddo, Mauro, Hügli, Olivier, Lamoth, Frédéric, Erard, Véronique, Voide, Cathy, Delorenzi, Mauro, and Rufer, Nathalie

Subjects
SARS-CoV-2, INFLUENZA A virus, INFLUENZA viruses, COVID-19, INFLUENZA, KILLER cells, INTERLEUKIN-21, T cell receptors
Abstract

The reason why most individuals with COVID-19 have relatively limited symptoms while other develop respiratory distress with life-threatening complications remains unknown. Increasing evidence suggests that COVID-19 associated adverse outcomes mainly rely on dysregulated immunity. Here, we compared transcriptomic profiles of blood cells from 103 patients with different severity levels of COVID-19 with that of 27 healthy and 22 influenza-infected individuals. Data provided a complete overview of SARS-CoV-2-induced immune signature, including a dramatic defect in IFN responses, a reduction of toxicity-related molecules in NK cells, an increased degranulation of neutrophils, a dysregulation of T cells, a dramatic increase in B cell function and immunoglobulin production, as well as an important over-expression of genes involved in metabolism and cell cycle in patients infected with SARS-CoV-2 compared to those infected with influenza viruses. These features also differed according to COVID-19 severity. Overall and specific gene expression patterns across groups can be visualized on an interactive website (https://bix.unil.ch/covid/). Collectively, these transcriptomic host responses to SARS-CoV-2 infection are discussed in the context of current studies, thereby improving our understanding of COVID-19 pathogenesis and shaping the severity level of COVID-19. [ABSTRACT FROM AUTHOR]

Published
2021
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16. DynaStI: A Dynamic Retention Time Database for Steroidomics

Author

Codesido, Santiago, primary, Randazzo, Giuseppe Marco, additional, Lehmann, Fabio, additional, González-Ruiz, Víctor, additional, García, Arnaud, additional, Xenarios, Ioannis, additional, Liechti, Robin, additional, Bridge, Alan, additional, Boccard, Julien, additional, and Rudaz, Serge, additional

Published
2019
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17. MIMAS 3.0 is a Multiomics Information Management and Annotation System

Author

Rougemont Jacques, Collin Olivier, Xenarios Ioannis, Liechti Robin, Hermida Leandro, Gattiker Alexandre, and Primig Michael

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types. Description MIMAS was originally conceived as a simple, convenient and local Microarray Information Management and Annotation System focused on GeneChips for expression profiling studies. MIMAS 3.0 enables users to manage data from high-density oligonucleotide SNP Chips, expression arrays (both 3'UTR and tiling) and promoter arrays, BeadArrays as well as UHTS data using MIAME-compliant standardized vocabulary. Importantly, researchers can export data in MAGE-TAB format and upload them to the EBI's ArrayExpress certified data repository using a one-step procedure. Conclusion We have vastly extended the capability of the system such that it processes the data output of six types of GeneChips (Affymetrix), two different BeadArrays for mRNA and miRNA (Illumina) and the Genome Analyzer (a popular Ultra-High Throughput DNA Sequencer, Illumina), without compromising on its flexibility and user-friendliness. MIMAS, appropriately renamed into Multiomics Information Management and Annotation System, is currently used by scientists working in approximately 50 academic laboratories and genomics platforms in Switzerland and France. MIMAS 3.0 is freely available via http://multiomics.sourceforge.net/.

Published
2009
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18. CATMA, a comprehensive genome-scale resource for silencing and transcript profiling of Arabidopsis genes

Author

Moreau Yves, Bhalerao Rishikesh, Beynon Jim, De Meyer Björn, Liechti Robin, Allemeersch Joke, Sclep Gert, Nietfeld Wilfried, Renou Jean-Pierre, Reymond Philippe, Kuiper Martin TR, and Hilson Pierre

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background The Complete Arabidopsis Transcript MicroArray (CATMA) initiative combines the efforts of laboratories in eight European countries 1 to deliver gene-specific sequence tags (GSTs) for the Arabidopsis research community. The CATMA initiative offers the power and flexibility to regularly update the GST collection according to evolving knowledge about the gene repertoire. These GST amplicons can easily be reamplified and shared, subsets can be picked at will to print dedicated arrays, and the GSTs can be cloned and used for other functional studies. This ongoing initiative has already produced approximately 24,000 GSTs that have been made publicly available for spotted microarray printing and RNA interference. Results GSTs from the CATMA version 2 repertoire (CATMAv2, created in 2002) were mapped onto the gene models from two independent Arabidopsis nuclear genome annotation efforts, TIGR5 and PSB-EuGène, to consolidate a list of genes that were targeted by previously designed CATMA tags. A total of 9,027 gene models were not tagged by any amplified CATMAv2 GST, and 2,533 amplified GSTs were no longer predicted to tag an updated gene model. To validate the efficacy of GST mapping criteria and design rules, the predicted and experimentally observed hybridization characteristics associated to GST features were correlated in transcript profiling datasets obtained with the CATMAv2 microarray, confirming the reliability of this platform. To complete the CATMA repertoire, all 9,027 gene models for which no GST had yet been designed were processed with an adjusted version of the Specific Primer and Amplicon Design Software (SPADS). A total of 5,756 novel GSTs were designed and amplified by PCR from genomic DNA. Together with the pre-existing GST collection, this new addition constitutes the CATMAv3 repertoire. It comprises 30,343 unique amplified sequences that tag 24,202 and 23,009 protein-encoding nuclear gene models in the TAIR6 and EuGène genome annotations, respectively. To cover the remaining untagged genes, we identified 543 additional GSTs using less stringent design criteria and designed 990 sequence tags matching multiple members of gene families (Gene Family Tags or GFTs) to cover any remaining untagged genes. These latter 1,533 features constitute the CATMAv4 addition. Conclusion To update the CATMA GST repertoire, we designed 7,289 additional sequence tags, bringing the total number of tagged TAIR6-annotated Arabidopsis nuclear protein-coding genes to 26,173. This resource is used both for the production of spotted microarrays and the large-scale cloning of hairpin RNA silencing vectors. All information about the resulting updated CATMA repertoire is available through the CATMA database http://www.catma.org.

Published
2007
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19. A systems genetics resource and analysis of sleep regulation in the mouse

Author

Diessler, Shanaz, primary, Jan, Maxime, additional, Emmenegger, Yann, additional, Guex, Nicolas, additional, Middleton, Benita, additional, Skene, Debra J., additional, Ibberson, Mark, additional, Burdet, Frederic, additional, Götz, Lou, additional, Pagni, Marco, additional, Sankar, Martial, additional, Liechti, Robin, additional, Hor, Charlotte N., additional, Xenarios, Ioannis, additional, and Franken, Paul, additional

Published
2018
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23. Additional file 4: of Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method

Author

Dorier, Julien, Crespo, Isaac, Niknejad, Anne, Liechti, Robin, Ebeling, Martin, and Xenarios, Ioannis

Abstract

Cell-fate decision model: in silico PKNs. In silico PKNs for the cell-fate decision model. For each PKN, a graphical representation as well as the list of interactions is given. Interactions that were changed to introduce noise in the PKNs are shown in blue in the list of interactions. In the graphical representation, interactions that were changed to introduce noise in the PKNs are shown in magenta (negative interactions) and cyan (positive interactions). (PDF 314 kb)

Published
2016
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25. Additional file 3: of Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method

Author

Dorier, Julien, Crespo, Isaac, Niknejad, Anne, Liechti, Robin, Ebeling, Martin, and Xenarios, Ioannis

Subjects
Nonlinear Sciences::Chaotic Dynamics, Mathematics::Dynamical Systems
Abstract

Cell-fate decision model: single node perturbations. List of transitions between attractors from unperturbed to single node perturbations. Each line corresponds to one edge in the attractor reachability graph. Attractors with more than one state are replaced by the average over all states in the attractor. (PDF 63 kb)

Published
2016
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26. Additional file 2: of Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method

Author

Dorier, Julien, Crespo, Isaac, Niknejad, Anne, Liechti, Robin, Ebeling, Martin, and Xenarios, Ioannis

Abstract

Cell-fate decision model: in silico training sets. Training sets used to evaluate the optimization method. Each training set is given in the form of a transition graph, where each node contains a perturbation with the corresponding observation (stable phenotype) and edges denote a transition between stable phenotype upon change of condition (perturbation). Perturbations are specified using a combination of node names prefixed by â orâ +â sign to specify knock-out (node state fixed to 0) or over-expression (node state fixed to 1). When multiple edges connect the same source node to different target nodes, it means that all transitions start from the same phenotype (same attractor of the network). Node states that were intentionally reversed to generate errors in training sets are shown in red. (PDF 9015 kb)

Published
2016
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28. Plasma Dihydroceramides Are Diabetes Susceptibility Biomarker Candidates in Mice and Humans

Author

Wigger, Leonore, primary, Cruciani-Guglielmacci, Céline, additional, Nicolas, Anthony, additional, Denom, Jessica, additional, Fernandez, Neïké, additional, Fumeron, Frédéric, additional, Marques-Vidal, Pedro, additional, Ktorza, Alain, additional, Kramer, Werner, additional, Schulte, Anke, additional, Le Stunff, Hervé, additional, Liechti, Robin, additional, Xenarios, Ioannis, additional, Vollenweider, Peter, additional, Waeber, Gérard, additional, Uphues, Ingo, additional, Roussel, Ronan, additional, Magnan, Christophe, additional, Ibberson, Mark, additional, and Thorens, Bernard, additional

Published
2017
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30. Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation

Author

Dhingra, Madhur S, primary, Artois, Jean, additional, Robinson, Timothy P, additional, Linard, Catherine, additional, Chaiban, Celia, additional, Xenarios, Ioannis, additional, Engler, Robin, additional, Liechti, Robin, additional, Kuznetsov, Dmitri, additional, Xiao, Xiangming, additional, Dobschuetz, Sophie Von, additional, Claes, Filip, additional, Newman, Scott H, additional, Dauphin, Gwenaëlle, additional, and Gilbert, Marius, additional

Published
2016
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31. Author response: Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation

Author

Dhingra, Madhur S, primary, Artois, Jean, additional, Robinson, Timothy P, additional, Linard, Catherine, additional, Chaiban, Celia, additional, Xenarios, Ioannis, additional, Engler, Robin, additional, Liechti, Robin, additional, Kuznetsov, Dmitri, additional, Xiao, Xiangming, additional, Dobschuetz, Sophie Von, additional, Claes, Filip, additional, Newman, Scott H, additional, Dauphin, Gwenaëlle, additional, and Gilbert, Marius, additional

Published
2016
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33. Angiogenic activity of breast cancer patients’ monocytes reverted by combined use of systems modeling and experimental approaches

Author

Guex, Nicolas, Crespo, Isaac, Bron, Sylvian, Ifticene-Treboux, Assia, Faes-van’t Hull, Eveline, Kharoubi, Solange, Liechti, Robin, Werffeli, Patricia, Ibberson, Mark, Majo, Francois, Nicolas, Michäel, Laurent, Julien, Garg, Abhishek, Lehr, Khalil Zaman,Hans-Anton, Stevenson, Brian J., Rüegg, Curzio, Coukos, George, Delaloye, Jean-François, Xenarios, Ioannis, and Doucey, Marie-Agnès

Abstract

Angiogenesis plays a key role in tumor growth and cancer progression. TIE-2-expressing monocytes (TEM) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models, but the molecular basis of their pro-angiogenic activity are largely unknown. Moreover, differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date, hindering the identification of specific targets for therapeutic intervention. In this work, we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches. Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM. Secondly, we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa. In silico predicted perturbations were validated experimentally using patient TEM. In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. Finally, the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer.

Published
2015

34. Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization

Author

Bonhoure, Nicolas, Bounova, Gergana, Bernasconi, David, Praz, Viviane, Lammers, Fabienne, Canella, Donatella, Willis, Ian M., Herr, Winship, Hernandez, Nouria, Delorenzi, Mauro, Deplancke, Bart, Desvergne, Béatrice, Guex, Nicolas, Naef, Felix, Rougemont, Jacques, Schibler, Ueli, Andersin, Teemu, Cousin, Pascal, Gilardi, Federica, Gos, Pascal, Raghav, Sunil, Villeneuve, Dominic, Fabbretti, Roberto, Vlegel, Volker, Xenarios, Ioannis, Migliavacca, Eugenia, David, Fabrice, Jarosz, Yohan, Kuznetsov, Dmitry, Liechti, Robin, Martin, Olivier, Delafontaine, Julien, Cajan, Julia, Gustafson, Kyle, Krier, Irina, Leleu, Marion, Molina, Nacho, Naldi, Aurélien, Rib, Leonor, Symul, Laura, CycliX Consortium, Hernandez, N., Delorenzi, M., Deplancke, B., Desvergne, B., Guex, N., Herr, W., Naef, F., Rougemont, J., Schibler, U., Andersin, T., Cousin, P., Gilardi, F., Gos, P., Lammers, F., Raghav, S., Villeneuve, D., Fabbretti, R., Vlegel, V., Xenarios, I., Migliavacca, E., Praz, V., David, F., Jarosz, Y., Kuznetsov, D., Liechti, R., Martin, O., Delafontaine, J., Cajan, J., Gustafson, K., Krier, I., Leleu, M., Molina, N., Naldi, A., Rib, L., Symul, L., Bounova, G., University of Zurich, and Hernandez, N

Subjects
Quality Control, Normalization (statistics), 2716 Genetics (clinical), Chromatin Immunoprecipitation, Occupancy, SX20 Research, Technology and Development Projects, Immunoprecipitation, Sample (material), Method, Biology, Mice, 03 medical and health sciences, 0302 clinical medicine, SX00 SystemsX.ch, 1311 Genetics, Genetics, Animals, Humans, Genetics(clinical), SX04 CycliX, Genetics (clinical), 030304 developmental biology, Quantile normalization, 0303 health sciences, Computational Biology, High-Throughput Nucleotide Sequencing, Reproducibility of Results, Reference Standards, Chromatin, 570 Life sciences, biology, Spike (software development), Biological system, Chromatin immunoprecipitation, 030217 neurology & neurosurgery
Abstract

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to determine, within entire genomes, the occupancy sites of any protein of interest, including, for example, transcription factors, RNA polymerases, or histones with or without various modifications. In addition to allowing the determination of occupancy sites within one cell type and under one condition, this method allows, in principle, the establishment and comparison of occupancy maps in various cell types, tissues, and conditions. Such comparisons require, however, that samples be normalized. Widely used normalization methods that include a quantile normalization step perform well when factor occupancy varies at a subset of sites, but may miss uniform genome-wide increases or decreases in site occupancy. We describe a spike adjustment procedure (SAP) that, unlike commonly used normalization methods intervening at the analysis stage, entails an experimental step prior to immunoprecipitation. A constant, low amount from a single batch of chromatin of a foreign genome is added to the experimental chromatin. This “spike” chromatin then serves as an internal control to which the experimental signals can be adjusted. We show that the method improves similarity between replicates and reveals biological differences including global and largely uniform changes.

Published
2014

35. Clade-level Spatial Modelling of HPAI H5N1 Dynamics in the Mekong Region Reveals New Patterns and Associations with Agro-Ecological Factors

Author

Artois, Jean, primary, Newman, Scott H., additional, Dhingra, Madhur S., additional, Chaiban, Celia, additional, Linard, Catherine, additional, Cattoli, Giovanni, additional, Monne, Isabella, additional, Fusaro, Alice, additional, Xenarios, Ioannis, additional, Engler, Robin, additional, Liechti, Robin, additional, Kuznetsov, Dmitri, additional, Pham, Thanh Long, additional, Nguyen, Tung, additional, Pham, Van Dong, additional, Castellan, David, additional, Von Dobschuetz, Sophie, additional, Claes, Filip, additional, Dauphin, Gwenaëlle, additional, Inui, Ken, additional, and Gilbert, Marius, additional

Published
2016
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38. Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes.

Author

Solimena, Michele, Schulte, Anke M., Marselli, Lorella, Ehehalt, Florian, Richter, Daniela, Kleeberg, Manuela, Mziaut, Hassan, Knoch, Klaus-Peter, Parnis, Julia, Bugliani, Marco, Siddiq, Afshan, Jörns, Anne, Burdet, Frédéric, Liechti, Robin, Suleiman, Mara, Margerie, Daniel, Syed, Farooq, Distler, Marius, Grützmann, Robert, and Petretto, Enrico

Abstract

Aims/hypothesis: Pancreatic islet beta cell failure causes type 2 diabetes in humans. To identify transcriptomic changes in type 2 diabetic islets, the Innovative Medicines Initiative for Diabetes: Improving beta-cell function and identification of diagnostic biomarkers for treatment monitoring in Diabetes (IMIDIA) consortium (www.imidia.org) established a comprehensive, unique multicentre biobank of human islets and pancreas tissues from organ donors and metabolically phenotyped pancreatectomised patients (PPP).Methods: Affymetrix microarrays were used to assess the islet transcriptome of islets isolated either by enzymatic digestion from 103 organ donors (OD), including 84 non-diabetic and 19 type 2 diabetic individuals, or by laser capture microdissection (LCM) from surgical specimens of 103 PPP, including 32 non-diabetic, 36 with type 2 diabetes, 15 with impaired glucose tolerance (IGT) and 20 with recent-onset diabetes (<1 year), conceivably secondary to the pancreatic disorder leading to surgery (type 3c diabetes). Bioinformatics tools were used to (1) compare the islet transcriptome of type 2 diabetic vs non-diabetic OD and PPP as well as vs IGT and type 3c diabetes within the PPP group; and (2) identify transcription factors driving gene co-expression modules correlated with insulin secretion ex vivo and glucose tolerance in vivo. Selected genes of interest were validated for their expression and function in beta cells.Results: Comparative transcriptomic analysis identified 19 genes differentially expressed (false discovery rate ≤0.05, fold change ≥1.5) in type 2 diabetic vs non-diabetic islets from OD and PPP. Nine out of these 19 dysregulated genes were not previously reported to be dysregulated in type 2 diabetic islets. Signature genes included TMEM37, which inhibited Ca2+-influx and insulin secretion in beta cells, and ARG2 and PPP1R1A, which promoted insulin secretion. Systems biology approaches identified HNF1A, PDX1 and REST as drivers of gene co-expression modules correlated with impaired insulin secretion or glucose tolerance, and 14 out of 19 differentially expressed type 2 diabetic islet signature genes were enriched in these modules. None of these signature genes was significantly dysregulated in islets of PPP with impaired glucose tolerance or type 3c diabetes.Conclusions/interpretation: These studies enabled the stringent definition of a novel transcriptomic signature of type 2 diabetic islets, regardless of islet source and isolation procedure. Lack of this signature in islets from PPP with IGT or type 3c diabetes indicates differences possibly due to peculiarities of these hyperglycaemic conditions and/or a role for duration and severity of hyperglycaemia. Alternatively, these transcriptomic changes capture, but may not precede, beta cell failure. [ABSTRACT FROM AUTHOR]

Published
2018
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39. The SwissLipids knowledgebase for lipid biology

Author

Aimo, Lucila, primary, Liechti, Robin, additional, Hyka-Nouspikel, Nevila, additional, Niknejad, Anne, additional, Gleizes, Anne, additional, Götz, Lou, additional, Kuznetsov, Dmitry, additional, David, Fabrice P.A., additional, van der Goot, F. Gisou, additional, Riezman, Howard, additional, Bougueleret, Lydie, additional, Xenarios, Ioannis, additional, and Bridge, Alan, additional

Published
2015
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40. Angiogenic Activity of Breast Cancer Patients’ Monocytes Reverted by Combined Use of Systems Modeling and Experimental Approaches

Author

Guex, Nicolas, primary, Crespo, Isaac, additional, Bron, Sylvian, additional, Ifticene-Treboux, Assia, additional, Faes-van’t Hull, Eveline, additional, Kharoubi, Solange, additional, Liechti, Robin, additional, Werffeli, Patricia, additional, Ibberson, Mark, additional, Majo, Francois, additional, Nicolas, Michäel, additional, Laurent, Julien, additional, Garg, Abhishek, additional, Zaman, Khalil, additional, Lehr, Hans-Anton, additional, Stevenson, Brian J., additional, Rüegg, Curzio, additional, Coukos, George, additional, Delaloye, Jean-François, additional, Xenarios, Ioannis, additional, and Doucey, Marie-Agnès, additional

Published
2015
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41. The jasmonate pathway. (Viewpoint)

Author

Liechti, Robin and Farmer, Edward E.

Subjects
Biochemistry -- Research -- Genetic aspects -- Reports, Lipids -- Genetic aspects -- Reports -- Research, Genetic research -- Reports -- Genetic aspects, Science and technology, Genetic aspects, Research, Reports
Abstract

Plants are faced with many of the same problems as animals--a need for regulation of metabolic processes and reproduction and for defense against enemies. Jasmonates in plants serve key roles in gene and metabolic regulation, defense, responses to trauma, reproduction, and possibly communication. Some remarkable features of plant responses, such as production of repellent volatiles as a defense against herbivorous insects, or the massive transcriptional reprogramming that occurs in response to wounding, are under the control of the jasmonate pathway. Details of the jasmonate signaling pathway are currently at the center of active research that is generating exciting results. The Jasmonate Biochemical Pathway at the STKE Connections Maps is designed to present and keep pace with these developments., Jasmonates (1, 2) are potent lipid regulators in plants that mediate responses to both mechanical trauma and pathogenesis (3-5), and play pivotal roles in reproduction (6) and metabolic regulation (7).The [...]

Published
2002

42. Microarray analysis of isolated human islet transcriptome in type 2 diabetes and the role of the ubiquitin–proteasome system in pancreatic beta cell dysfunction

Author

Bugliani, Marco, primary, Liechti, Robin, additional, Cheon, Hwanju, additional, Suleiman, Mara, additional, Marselli, Lorella, additional, Kirkpatrick, Clare, additional, Filipponi, Franco, additional, Boggi, Ugo, additional, Xenarios, Ioannis, additional, Syed, Farooq, additional, Ladriere, Laurence, additional, Wollheim, Claes, additional, Lee, Myung-Shik, additional, and Marchetti, Piero, additional

Published
2013
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43. Improved Protocol For Laser Microdissection Of Human Pancreatic Islets From Surgical Specimens

Author

Sturm, Dorothée, primary, Marselli, Lorella, primary, Ehehalt, Florian, primary, Richter, Daniela, primary, Distler, Marius, primary, Kersting, Stephan, primary, Grützmann, Robert, primary, Bokvist, Krister, primary, Froguel, Philippe, primary, Liechti, Robin, primary, Jörns, Anne, primary, Meda, Paolo, primary, Baretton, Gustavo Bruno, primary, Saeger, Hans-Detlev, primary, Schulte, Anke M., primary, Marchetti, Piero, primary, and Solimena, Michele, primary

Published
2013
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49. CATMA, a comprehensive genome-scale resource for silencing and transcript profiling of Arabidopsis genes

Author

Sclep, Gert, primary, Allemeersch, Joke, additional, Liechti, Robin, additional, De Meyer, Björn, additional, Beynon, Jim, additional, Bhalerao, Rishikesh, additional, Moreau, Yves, additional, Nietfeld, Wilfried, additional, Renou, Jean-Pierre, additional, Reymond, Philippe, additional, Kuiper, Martin TR, additional, and Hilson, Pierre, additional

Published
2007
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