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2. Nuclear and cytosolic fractions of SOX2 synergize as transcriptional and translational co-regulators of cell fate

Author

Schaefer, Thorsten, Mittal, Nitish, Wang, Hui, Ataman, Meric, Candido, Silvia, Lötscher, Jonas, Velychko, Sergiy, Tintignac, Lionel, Bock, Thomas, Börsch, Anastasiya, Baßler, Jochen, Rao, Tata Nageswara, Zmajkovic, Jakub, Roffeis, Sarah, Löliger, Jordan, Jacob, Francis, Dumlin, Alain, Schürch, Christoph, Schmidt, Alexander, Skoda, Radek C., Wymann, Matthias P., Hess, Christoph, Schöler, Hans R., Zaehres, Holm, Hurt, Ed, Zavolan, Mihaela, and Lengerke, Claudia

Published
2024
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3. Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian, Beber, Moritz E, Olivier, Brett G, Bergmann, Frank T, Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A, Blank, Lars M, Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E, Edirisinghe, Janaka N, Faria, José P, Feist, Adam M, Fengos, Georgios, Fleming, Ronan MT, García-Jiménez, Beatriz, Hatzimanikatis, Vassily, van Helvoirt, Wout, Henry, Christopher S, Hermjakob, Henning, Herrgård, Markus J, Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J, König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E, Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L, Monk, Jonathan M, Nielsen, Jens, Nielsen, Lars Keld, Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard O, Papin, Jason A, Patil, Kiran R, Poolman, Mark, Price, Nathan D, Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J, Schaap, Peter J, Sheriff, Rahuman S Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith AH, Xavier, Joana C, Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Subjects
Biological Sciences, Industrial Biotechnology
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020

6. Calorie restriction and rapamycin distinctly mitigate aging-associated protein phosphorylation changes in mouse muscles.

Author

Ataman, Meric, Mittal, Nitish, Tintignac, Lionel, Schmidt, Alexander, Ham, Daniel J., González, Asier, Ruegg, Markus A., and Zavolan, Mihaela

Abstract

Calorie restriction (CR) and treatment with rapamycin (RM), an inhibitor of the mTORC1 growth-promoting signaling pathway, are known to slow aging and promote health from worms to humans. At the transcriptome and proteome levels, long-term CR and RM treatments have partially overlapping effects, while their impact on protein phosphorylation within cellular signaling pathways have not been compared. Here we measured the phosphoproteomes of soleus, tibialis anterior, triceps brachii and gastrocnemius muscles from adult (10 months) and 30-month-old (aged) mice receiving either a control, a calorie restricted or an RM containing diet from 15 months of age. We reproducibly detected and extensively analyzed a total of 6960 phosphosites, 1415 of which are not represented in standard repositories. We reveal the effect of these interventions on known mTORC1 pathway substrates, with CR displaying greater between-muscle variation than RM. Overall, CR and RM have largely consistent, but quantitatively distinct long-term effects on the phosphoproteome, mitigating age-related changes to different degrees. Our data expands the catalog of protein phosphorylation sites in the mouse, providing important information regarding their tissue-specificity, and revealing the impact of long-term nutrient-sensing pathway inhibition on mouse skeletal muscle.Phosphoproteomics reveals the impact of longterm anti-aging treatments on mouse skeletal muscle and expands the catalog of protein phosphorylation sites, providing further details about their tissue-specificity. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Ribosomal protein RPL39L is an efficiency factor in the cotranslational folding of proteins with alpha helical domains

Author

Banerjee, Arka, primary, Ataman, Meric, additional, Smialek, Maciej Jerzy, additional, Mookherjee, Debdatto, additional, Rabl, Julius, additional, Mironov, Aleksei, additional, Mues, Lea, additional, Enkler, Ludovic, additional, Coto-Llerena, Mairene, additional, Schmidt, Alexander, additional, Boehringer, Daniel, additional, Piscuoglio, Salvatore, additional, Spang, Anne, additional, Mittal, Nitish, additional, and Zavolan, Mihaela, additional

Published
2023
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13. Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian [0000-0001-5377-4091], Beber, Moritz E. [0000-0003-2406-1978], Olivier, Brett G. [0000-0002-5293-5321], Ataman, Meric [0000-0002-7942-9226], Babaei, Parizad [0000-0001-9411-0427], Bartell, Jennifer A. [0000-0003-2750-9678], Blank, Lars M. [0000-0003-0961-4976], Chauhan, Siddharth [0000-0001-6674-895X], Correia, Kevin [0000-0001-7130-1765], Diener, Christian [0000-0002-7476-0868], Dräger, Andreas [0000-0002-1240-5553], Ebert, Birgitta E. [0000-0001-9425-7509], Edirisinghe, Janaka N. [0000-0003-2493-234X], Faria, José P. [0000-0001-9302-7250], Feist, Adam M. [0000-0002-8630-4800], Fengos, Georgios [0000-0001-8110-8424], Fleming, Ronan M. T. [0000-0001-5346-9812], García-Jiménez, Beatriz [0000-0002-8129-6506], Hatzimanikatis, Vassily [0000-0001-6432-4694], Van Helvoirt, Wout [0000-0002-9143-9726], Henry, Christopher S. [0000-0001-8058-9123], Hermjakob, Henning [0000-0001-8479-0262], Herrgård, Markus J. [0000-0003-2377-9929], Kaafarani, Ali [0000-0002-2805-310X], Kim, Hyun Uk [0000-0001-7224-642X], King, Zachary [0000-0003-1238-1499], Klamt, Steffen [0000-0003-2563-7561], Klipp, Edda [0000-0002-0567-7075], Koehorst, Jasper J. [0000-0001-8172-8981], König, Matthias [0000-0003-1725-179X], Lakshmanan, Meiyappan [0000-0003-2356-3458], Lee, Dong-Yup [0000-0003-0901-708X], Lee, Sang Yup [0000-0003-0599-3091], Lee, Sunjae [0000-0002-6428-5936], Lewis, Nathan E. [0000-0001-7700-3654], Liu, Filipe [0000-0001-8701-2984], Ma, Hongwu [0000-0001-5325-2314], Mahadevan, Radhakrishnan [0000-0002-1270-9063], Maia, Paulo [0000-0002-0848-8683], Mardinoglu, Adil [0000-0002-4254-6090], Medlock, Gregory L. [0000-0002-1571-0801], Monk, Jonathan M. [0000-0002-3895-8949], Nielsen, Jens [0000-0002-9955-6003], Nielsen, Lars K. [0000-0001-8191-3511], Nogales, Juan [0000-0002-4961-0833], Palsson, Bernhard Ø [0000-0003-2357-6785], Papin, Jason A. [0000-0002-2769-5805], Patil, Kiran R. [0000-0002-6166-8640], Poolman, Mark [0000-0002-3972-5418], Price, Nathan D. [0000-0002-4157-0267], Resendis-Antonio, Osbaldo [0000-0001-5220-541X], Richelle, Anne [0000-0003-1491-114X], Rocha, Isabel [0000-0001-9494-3410], Sánchez, Benjamín J. [0000-0001-6093-4110], Schaap, Peter J. [0000-0002-4346-6084], Sheriff, Rahuman S Malik [0000-0003-0705-9809], Shoaie, Saeed [0000-0001-5834-4533], Sonnenschein, Nikolaus [0000-0002-7581-4936], Teusink, Bas [0000-0003-3929-0423], Vilaça, Paulo [0000-0002-1098-5849], Vik, Jon Olav [0000-0002-7778-4515], Wodke, Judith A. H. [0009-0009-9712-060X], Xavier, Joana C. [0000-0001-9242-8968], Zakhartsev, Maksim [0000-0002-7973-9902], Zhang, Cheng [0000-0002-3721-8586], Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Sheriff, Rahuman S Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, Zhang, Cheng, Lieven, Christian [0000-0001-5377-4091], Beber, Moritz E. [0000-0003-2406-1978], Olivier, Brett G. [0000-0002-5293-5321], Ataman, Meric [0000-0002-7942-9226], Babaei, Parizad [0000-0001-9411-0427], Bartell, Jennifer A. [0000-0003-2750-9678], Blank, Lars M. [0000-0003-0961-4976], Chauhan, Siddharth [0000-0001-6674-895X], Correia, Kevin [0000-0001-7130-1765], Diener, Christian [0000-0002-7476-0868], Dräger, Andreas [0000-0002-1240-5553], Ebert, Birgitta E. [0000-0001-9425-7509], Edirisinghe, Janaka N. [0000-0003-2493-234X], Faria, José P. [0000-0001-9302-7250], Feist, Adam M. [0000-0002-8630-4800], Fengos, Georgios [0000-0001-8110-8424], Fleming, Ronan M. T. [0000-0001-5346-9812], García-Jiménez, Beatriz [0000-0002-8129-6506], Hatzimanikatis, Vassily [0000-0001-6432-4694], Van Helvoirt, Wout [0000-0002-9143-9726], Henry, Christopher S. [0000-0001-8058-9123], Hermjakob, Henning [0000-0001-8479-0262], Herrgård, Markus J. [0000-0003-2377-9929], Kaafarani, Ali [0000-0002-2805-310X], Kim, Hyun Uk [0000-0001-7224-642X], King, Zachary [0000-0003-1238-1499], Klamt, Steffen [0000-0003-2563-7561], Klipp, Edda [0000-0002-0567-7075], Koehorst, Jasper J. [0000-0001-8172-8981], König, Matthias [0000-0003-1725-179X], Lakshmanan, Meiyappan [0000-0003-2356-3458], Lee, Dong-Yup [0000-0003-0901-708X], Lee, Sang Yup [0000-0003-0599-3091], Lee, Sunjae [0000-0002-6428-5936], Lewis, Nathan E. [0000-0001-7700-3654], Liu, Filipe [0000-0001-8701-2984], Ma, Hongwu [0000-0001-5325-2314], Mahadevan, Radhakrishnan [0000-0002-1270-9063], Maia, Paulo [0000-0002-0848-8683], Mardinoglu, Adil [0000-0002-4254-6090], Medlock, Gregory L. [0000-0002-1571-0801], Monk, Jonathan M. [0000-0002-3895-8949], Nielsen, Jens [0000-0002-9955-6003], Nielsen, Lars K. [0000-0001-8191-3511], Nogales, Juan [0000-0002-4961-0833], Palsson, Bernhard Ø [0000-0003-2357-6785], Papin, Jason A. [0000-0002-2769-5805], Patil, Kiran R. [0000-0002-6166-8640], Poolman, Mark [0000-0002-3972-5418], Price, Nathan D. [0000-0002-4157-0267], Resendis-Antonio, Osbaldo [0000-0001-5220-541X], Richelle, Anne [0000-0003-1491-114X], Rocha, Isabel [0000-0001-9494-3410], Sánchez, Benjamín J. [0000-0001-6093-4110], Schaap, Peter J. [0000-0002-4346-6084], Sheriff, Rahuman S Malik [0000-0003-0705-9809], Shoaie, Saeed [0000-0001-5834-4533], Sonnenschein, Nikolaus [0000-0002-7581-4936], Teusink, Bas [0000-0003-3929-0423], Vilaça, Paulo [0000-0002-1098-5849], Vik, Jon Olav [0000-0002-7778-4515], Wodke, Judith A. H. [0009-0009-9712-060X], Xavier, Joana C. [0000-0001-9242-8968], Zakhartsev, Maksim [0000-0002-7973-9902], Zhang, Cheng [0000-0002-3721-8586], Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Sheriff, Rahuman S Malik, Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper. MEMOTE for standardized genome-scale metabolic model testing (http://hdl.handle.net/10261/230245) Nature Biotechnology, Volume 38, Issue 3, Pages 272 - 276, 1 March 2020

Published
2020

15. CFIm-mediated alternative polyadenylation remodels cellular signaling and miRNA biogenesis

Author

Ghosh, Souvik, Ataman, Meric, Bak, Maciej, Börsch, Anastasiya, Schmidt, Alexander, Buczak, Katarzyna, Martin, Georges, Dimitriades, Beatrice, Herrmann, Christina J., Kanitz, Alexander, and Zavolan, Mihaela

Abstract

The mammalian cleavage factor I (CFIm) has been implicated in alternative polyadenylation (APA) in a broad range of contexts, from cancers to learning deficits and parasite infections. To determine how the CFIm expression levels are translated into these diverse phenotypes, we carried out a multi-omics analysis of cell lines in which the CFIm25 (NUDT21) or CFIm68 (CPSF6) subunits were either repressed by siRNA-mediated knockdown or over-expressed from stably integrated constructs. We established that >800 genes undergo coherent APA in response to changes in CFIm levels, and they cluster in distinct functional classes related to protein metabolism. The activity of the ERK pathway traces the CFIm concentration, and explains some of the fluctuations in cell growth and metabolism that are observed upon CFIm perturbations. Furthermore, multiple transcripts encoding proteins from the miRNA pathway are targets of CFIm-dependent APA. This leads to an increased biogenesis and repressive activity of miRNAs at the same time as some 3' UTRs become shorter and presumably less sensitive to miRNA-mediated repression. Our study provides a first systematic assessment of a core set of APA targets that respond coherently to changes in CFIm protein subunit levels (CFIm25/CFIm68). We describe the elicited signaling pathways downstream of CFIm, which improve our understanding of the key role of CFIm in integrating RNA processing with other cellular activities.

Published
2022

16. ZARP: An automated workflow for processing of RNA-seq data

Author

Katsantoni, Maria, primary, Gypas, Foivos, additional, Herrmann, Christina J., additional, Burri, Dominik, additional, Bak, Maciej, additional, Iborra, Paula, additional, Agarwal, Krish, additional, Ataman, Meric, additional, Börsch, Anastasiya, additional, Zavolan, Mihaela, additional, and Kanitz, Alexander, additional

Published
2021
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17. MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian, Beber, Moritz Emanuel, Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgard, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars Keld, Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard O., Papin, Jason A., Patil, Kiran Raosaheb, Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín José, Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaca, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, Zhang, Cheng, Lieven, Christian, Beber, Moritz Emanuel, Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgard, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars Keld, Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard O., Papin, Jason A., Patil, Kiran Raosaheb, Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín José, Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaca, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Published
2020

18. MEMOTE for standardized genome-scale metabolic model testing

Author

Research Council of Norway, Innovation Fund Denmark, European Commission, National Institutes of Health (US), German Research Foundation, Novo Nordisk Foundation, W. M. Keck Foundation, Ministerio de Economía y Competitividad (España), Knut and Alice Wallenberg Foundation, Federal Ministry of Education and Research (Germany), Bill & Melinda Gates Foundation, National Research Foundation of Korea, Rural Development Administration (South Korea), Swiss National Science Foundation, University of Oxford, European Research Council, Washington Research Foundation, National Institute of General Medical Sciences (US), Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, Zhang, Cheng, Research Council of Norway, Innovation Fund Denmark, European Commission, National Institutes of Health (US), German Research Foundation, Novo Nordisk Foundation, W. M. Keck Foundation, Ministerio de Economía y Competitividad (España), Knut and Alice Wallenberg Foundation, Federal Ministry of Education and Research (Germany), Bill & Melinda Gates Foundation, National Research Foundation of Korea, Rural Development Administration (South Korea), Swiss National Science Foundation, University of Oxford, European Research Council, Washington Research Foundation, National Institute of General Medical Sciences (US), Lieven, Christian, Beber, Moritz E., Olivier, Brett G., Bergmann, Frank T., Ataman, Meric, Babaei, Parizad, Bartell, Jennifer A., Blank, Lars M., Chauhan, Siddharth, Correia, Kevin, Diener, Christian, Dräger, Andreas, Ebert, Birgitta E., Edirisinghe, Janaka N., Faria, José P., Feist, Adam M., Fengos, Georgios, Fleming, Ronan M. T., García-Jiménez, Beatriz, Hatzimanikatis, Vassily, Van Helvoirt, Wout, Henry, Christopher S., Hermjakob, Henning, Herrgård, Markus J., Kaafarani, Ali, Kim, Hyun Uk, King, Zachary, Klamt, Steffen, Klipp, Edda, Koehorst, Jasper J., König, Matthias, Lakshmanan, Meiyappan, Lee, Dong-Yup, Lee, Sang Yup, Lee, Sunjae, Lewis, Nathan E., Liu, Filipe, Ma, Hongwu, Machado, Daniel, Mahadevan, Radhakrishnan, Maia, Paulo, Mardinoglu, Adil, Medlock, Gregory L., Monk, Jonathan M., Nielsen, Jens, Nielsen, Lars K., Nogales, Juan, Nookaew, Intawat, Palsson, Bernhard Ø, Papin, Jason A., Patil, Kiran R., Poolman, Mark, Price, Nathan D., Resendis-Antonio, Osbaldo, Richelle, Anne, Rocha, Isabel, Sánchez, Benjamín J., Schaap, Peter J., Malik Sheriff, Rahuman S., Shoaie, Saeed, Sonnenschein, Nikolaus, Teusink, Bas, Vilaça, Paulo, Vik, Jon Olav, Wodke, Judith A. H., Xavier, Joana C., Yuan, Qianqian, Zakhartsev, Maksim, and Zhang, Cheng

Abstract

Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organism’s metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Gene–protein–reaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.

Published
2020

20. MEMOTE for standardized genome-scale metabolic model testing

Author

Lieven, Christian, primary, Beber, Moritz E., additional, Olivier, Brett G., additional, Bergmann, Frank T., additional, Ataman, Meric, additional, Babaei, Parizad, additional, Bartell, Jennifer A., additional, Blank, Lars M., additional, Chauhan, Siddharth, additional, Correia, Kevin, additional, Diener, Christian, additional, Dräger, Andreas, additional, Ebert, Birgitta E., additional, Edirisinghe, Janaka N., additional, Faria, José P., additional, Feist, Adam M., additional, Fengos, Georgios, additional, Fleming, Ronan M. T., additional, García-Jiménez, Beatriz, additional, Hatzimanikatis, Vassily, additional, van Helvoirt, Wout, additional, Henry, Christopher S., additional, Hermjakob, Henning, additional, Herrgård, Markus J., additional, Kaafarani, Ali, additional, Kim, Hyun Uk, additional, King, Zachary, additional, Klamt, Steffen, additional, Klipp, Edda, additional, Koehorst, Jasper J., additional, König, Matthias, additional, Lakshmanan, Meiyappan, additional, Lee, Dong-Yup, additional, Lee, Sang Yup, additional, Lee, Sunjae, additional, Lewis, Nathan E., additional, Liu, Filipe, additional, Ma, Hongwu, additional, Machado, Daniel, additional, Mahadevan, Radhakrishnan, additional, Maia, Paulo, additional, Mardinoglu, Adil, additional, Medlock, Gregory L., additional, Monk, Jonathan M., additional, Nielsen, Jens, additional, Nielsen, Lars Keld, additional, Nogales, Juan, additional, Nookaew, Intawat, additional, Palsson, Bernhard O., additional, Papin, Jason A., additional, Patil, Kiran R., additional, Poolman, Mark, additional, Price, Nathan D., additional, Resendis-Antonio, Osbaldo, additional, Richelle, Anne, additional, Rocha, Isabel, additional, Sánchez, Benjamín J., additional, Schaap, Peter J., additional, Malik Sheriff, Rahuman S., additional, Shoaie, Saeed, additional, Sonnenschein, Nikolaus, additional, Teusink, Bas, additional, Vilaça, Paulo, additional, Vik, Jon Olav, additional, Wodke, Judith A. H., additional, Xavier, Joana C., additional, Yuan, Qianqian, additional, Zakhartsev, Maksim, additional, and Zhang, Cheng, additional

Published
2020
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22. redGEM: Systematic reduction of genome-scale metabolic reconstructions for development of core metabolic models

Author

Ataman, Meric and Hatzimanikatis, Vassily

Subjects
Computer and Information Sciences, Enzyme Metabolism, Yeast and Fungal Models, Research and Analysis Methods, Biosynthesis, Biochemistry, Metabolic Networks, Saccharomyces, Model Organisms, Metabolites, Enzyme Chemistry, Organisms, Fungi, Chemical Compounds, Biology and Life Sciences, Carbon Dioxide, Yeast, Chemistry, Metabolism, Experimental Organism Systems, Physical Sciences, Enzymology, Saccharomyces Cerevisiae, Cofactors (Biochemistry), Metabolic Pathways, Network Analysis, Research Article
Abstract

In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models., Author summary Stoichiometric models have been used in the area of metabolic engineering and systems biology for many decades. The early examples of these models include simplified ad hoc built metabolic pathways, and biomass compositions. The development of genome scale models (GEMs) brought a standard to metabolic network modeling. However, the vast amount of detailed biochemistry in GEMs makes it necessary to develop methods to manage the complexity in them. In this study, we developed lumpGEM, a tool that can systematically identify subnetworks from metabolic networks that can perform certain tasks, such as biosynthesis of a biomass building block and any other target metabolite. By generating alternative subnetworks, lumpGEM also accounts for the redundancy in metabolic networks. We applied lumpGEM on latest E. coli GEM iJO1366 and identified subnetworks/lumped reactions for every biomass building block defined in its biomass formulation. We also compared the results from lumpGEM with existing knowledge in the literature. The lumped reactions generated by lumpGEM can be used to generate consistently reduced metabolic network models.

Published
2017

30. pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis.

Author

Salvy, Pierre, Fengos, Georgios, Ataman, Meric, Soh, Keng C, Hatzimanikatis, Vassily, and Pathier, Thomas

Subjects
THERMODYNAMICS, FLUX (Energy), GENOMES, PYTHON programming language, GIBBS' free energy, METABOLITE analysis
Abstract

Summary pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements. Motivation High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology. Nevertheless, most studies are still limited to constraint-based Flux Balance Analyses (FBA), neglecting an important physicochemical constraint: thermodynamics. Thermodynamics-based Flux Analysis (TFA) in metabolic models enables the integration of quantitative metabolomics data to study their effects on the net-flux directionality of reactions in the network. In addition, it allows us to estimate how far each reaction operates from thermodynamic equilibrium, which provides critical information for guiding metabolic engineering decisions. Results We present a Python package (pyTFA) and a Matlab toolbox (matTFA) that implement TFA. We show an example of application on both a reduced and a genome-scale model of E. coli. and demonstrate TFA and data integration through TFA reduce the feasible flux space with respect to FBA. Availability and implementation Documented implementation of TFA framework both in Python (pyTFA) and Matlab (matTFA) are available on www.github.com/EPFL-LCSB/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2019
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