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Specific appetite, energetic and metabolomics responses to fat overfeeding in resistant-to-bodyweight-gain constitutional thinness.

Nutrition & diabetes

Germain N, Galusca B, Caron-Dorval D, Martin JF, Pujos-Guillot E, Boirie Y, Khalfallah Y, Ling Y, Minnion JS, Bloom SR, Epelbaum J, Estour B.
PMID: 25027794
Nutr Diabetes. 2014 Jul 14;4:e126. doi: 10.1038/nutd.2014.17.

BACKGROUND: Contrasting with obesity, constitutional thinness (CT) is a rare condition of natural low bodyweight. CT exhibits preserved menstruation in females, no biological marker of undernutrition, no eating disorders but a bodyweight gain desire. Anorexigenic hormonal profile with high...

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Germain N, Galusca B, Caron-Dorval D, et al. Specific appetite, energetic and metabolomics responses to fat overfeeding in resistant-to-bodyweight-gain constitutional thinness. Nutr Diabetes. 2014;4:e126doi: 10.1038/nutd.2014.17.
Germain, N., Galusca, B., Caron-Dorval, D., Martin, J. F., Pujos-Guillot, E., Boirie, Y., Khalfallah, Y., Ling, Y., Minnion, J. S., Bloom, S. R., Epelbaum, J., & Estour, B. (2014). Specific appetite, energetic and metabolomics responses to fat overfeeding in resistant-to-bodyweight-gain constitutional thinness. Nutrition & diabetes, 4e126. https://doi.org/10.1038/nutd.2014.17
Germain, N, et al. "Specific appetite, energetic and metabolomics responses to fat overfeeding in resistant-to-bodyweight-gain constitutional thinness." Nutrition & diabetes vol. 4 (2014): e126. doi: https://doi.org/10.1038/nutd.2014.17
Germain N, Galusca B, Caron-Dorval D, Martin JF, Pujos-Guillot E, Boirie Y, Khalfallah Y, Ling Y, Minnion JS, Bloom SR, Epelbaum J, Estour B. Specific appetite, energetic and metabolomics responses to fat overfeeding in resistant-to-bodyweight-gain constitutional thinness. Nutr Diabetes. 2014 Jul 14;4:e126. doi: 10.1038/nutd.2014.17. PMID: 25027794; PMCID: PMC5189928.
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Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data.

Frontiers in molecular biosciences

Grissa D, Pétéra M, Brandolini M, Napoli A, Comte B, Pujos-Guillot E.
PMID: 27458587
Front Mol Biosci. 2016 Jul 08;3:30. doi: 10.3389/fmolb.2016.00030. eCollection 2016.

Untargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive...

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Grissa D, Pétéra M, Brandolini M, et al. Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data. Front Mol Biosci. 2016;3:30doi: 10.3389/fmolb.2016.00030.
Grissa, D., Pétéra, M., Brandolini, M., Napoli, A., Comte, B., & Pujos-Guillot, E. (2016). Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data. Frontiers in molecular biosciences, 330. https://doi.org/10.3389/fmolb.2016.00030
Grissa, Dhouha, et al. "Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data." Frontiers in molecular biosciences vol. 3 (2016): 30. doi: https://doi.org/10.3389/fmolb.2016.00030
Grissa D, Pétéra M, Brandolini M, Napoli A, Comte B, Pujos-Guillot E. Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data. Front Mol Biosci. 2016 Jul 08;3:30. doi: 10.3389/fmolb.2016.00030. eCollection 2016. PMID: 27458587; PMCID: PMC4937038.
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ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Scientific data

Imbert A, Rompais M, Selloum M, Castelli F, Mouton-Barbosa E, Brandolini-Bunlon M, Chu-Van E, Joly C, Hirschler A, Roger P, Burger T, Leblanc S, Sorg T, Ouzia S, Vandenbrouck Y, Médigue C, Junot C, Ferro M, Pujos-Guillot E, de Peredo AG, Fenaille F, Carapito C, Herault Y, Thévenot EA.
PMID: 34862403
Sci Data. 2021 Dec 03;8(1):311. doi: 10.1038/s41597-021-01095-3.

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse...

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Imbert A, Rompais M, Selloum M, et al. ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Sci Data. 2021;8(1):311doi: 10.1038/s41597-021-01095-3.
Imbert, A., Rompais, M., Selloum, M., Castelli, F., Mouton-Barbosa, E., Brandolini-Bunlon, M., Chu-Van, E., Joly, C., Hirschler, A., Roger, P., Burger, T., Leblanc, S., Sorg, T., Ouzia, S., Vandenbrouck, Y., Médigue, C., Junot, C., Ferro, M., Pujos-Guillot, E., de Peredo, A. G., Fenaille, F., Carapito, C., Herault, Y., & Thévenot, E. A. (2021). ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Scientific data, 8(1), 311. https://doi.org/10.1038/s41597-021-01095-3
Imbert, Alyssa, et al. "ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis." Scientific data vol. 8,1 (2021): 311. doi: https://doi.org/10.1038/s41597-021-01095-3
Imbert A, Rompais M, Selloum M, Castelli F, Mouton-Barbosa E, Brandolini-Bunlon M, Chu-Van E, Joly C, Hirschler A, Roger P, Burger T, Leblanc S, Sorg T, Ouzia S, Vandenbrouck Y, Médigue C, Junot C, Ferro M, Pujos-Guillot E, de Peredo AG, Fenaille F, Carapito C, Herault Y, Thévenot EA. ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Sci Data. 2021 Dec 03;8(1):311. doi: 10.1038/s41597-021-01095-3. PMID: 34862403.
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Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine.

Network and systems medicine

Comte B, Baumbach J, Benis A, Basílio J, Debeljak N, Flobak Å, Franken C, Harel N, He F, Kuiper M, Méndez Pérez JA, Pujos-Guillot E, Režen T, Rozman D, Schmid JA, Scerri J, Tieri P, Van Steen K, Vasudevan S, Watterson S, Schmidt HHHW.
PMID: 32954378
Netw Syst Med. 2020 Jul 06;3(1):67-90. doi: 10.1089/nsm.2020.0004. eCollection 2020.

No abstract available.

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Comte B, Baumbach J, Benis A, et al. Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine. Netw Syst Med. 2020;3(1):67-90doi: 10.1089/nsm.2020.0004.
Comte, B., Baumbach, J., Benis, A., Basílio, J., Debeljak, N., Flobak, �. �., Franken, C., Harel, N., He, F., Kuiper, M., Méndez Pérez, J. A., Pujos-Guillot, E., Režen, T., Rozman, D., Schmid, J. A., Scerri, J., Tieri, P., Van Steen, K., Vasudevan, S., Watterson, S., & Schmidt, H. H. H. W. (2020). Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine. Network and systems medicine, 3(1), 67-90. https://doi.org/10.1089/nsm.2020.0004
Comte, Blandine, et al. "Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine." Network and systems medicine vol. 3,1 (2020): 67-90. doi: https://doi.org/10.1089/nsm.2020.0004
Comte B, Baumbach J, Benis A, Basílio J, Debeljak N, Flobak Å, Franken C, Harel N, He F, Kuiper M, Méndez Pérez JA, Pujos-Guillot E, Režen T, Rozman D, Schmid JA, Scerri J, Tieri P, Van Steen K, Vasudevan S, Watterson S, Schmidt HHHW. Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine. Netw Syst Med. 2020 Jul 06;3(1):67-90. doi: 10.1089/nsm.2020.0004. eCollection 2020. PMID: 32954378; PMCID: PMC7500076.
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An Early Stage Researcher's Primer on Systems Medicine Terminology.

Network and systems medicine

Zanin M, Aitya NAA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HHHW.
PMID: 33659919
Netw Syst Med. 2021 Feb 25;4(1):2-50. doi: 10.1089/nsm.2020.0003. eCollection 2021 Feb.

No abstract available.

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Zanin M, Aitya NAA, Basilio J, et al. An Early Stage Researcher's Primer on Systems Medicine Terminology. Netw Syst Med. 2021;4(1):2-50doi: 10.1089/nsm.2020.0003.
Zanin, M., Aitya, N. A. A., Basilio, J., Baumbach, J., Benis, A., Behera, C. K., Bucholc, M., Castiglione, F., Chouvarda, I., Comte, B., Dao, T. T., Ding, X., Pujos-Guillot, E., Filipovic, N., Finn, D. P., Glass, D. H., Harel, N., Iesmantas, T., Ivanoska, I., Joshi, A., Boudjeltia, K. Z., Kaoui, B., Kaur, D., Maguire, L. P., McClean, P. L., McCombe, N., de Miranda, J. L., Moisescu, M. A., Pappalardo, F., Polster, A., Prasad, G., Rozman, D., Sacala, I., Sanchez-Bornot, J. M., Schmid, J. A., Sharp, T., Solé-Casals, J., Spiwok, V., Spyrou, G. M., Stalidzans, E., Stres, B., Sustersic, T., Symeonidis, I., Tieri, P., Todd, S., Van Steen, K., Veneva, M., Wang, D. H., Wang, H., Wang, H., Watterson, S., Wong-Lin, K., Yang, S., Zou, X., & Schmidt, H. H. H. W. (2021). An Early Stage Researcher's Primer on Systems Medicine Terminology. Network and systems medicine, 4(1), 2-50. https://doi.org/10.1089/nsm.2020.0003
Zanin, Massimiliano, et al. "An Early Stage Researcher's Primer on Systems Medicine Terminology." Network and systems medicine vol. 4,1 (2021): 2-50. doi: https://doi.org/10.1089/nsm.2020.0003
Zanin M, Aitya NAA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, Dao TT, Ding X, Pujos-Guillot E, Filipovic N, Finn DP, Glass DH, Harel N, Iesmantas T, Ivanoska I, Joshi A, Boudjeltia KZ, Kaoui B, Kaur D, Maguire LP, McClean PL, McCombe N, de Miranda JL, Moisescu MA, Pappalardo F, Polster A, Prasad G, Rozman D, Sacala I, Sanchez-Bornot JM, Schmid JA, Sharp T, Solé-Casals J, Spiwok V, Spyrou GM, Stalidzans E, Stres B, Sustersic T, Symeonidis I, Tieri P, Todd S, Van Steen K, Veneva M, Wang DH, Wang H, Wang H, Watterson S, Wong-Lin K, Yang S, Zou X, Schmidt HHHW. An Early Stage Researcher's Primer on Systems Medicine Terminology. Netw Syst Med. 2021 Feb 25;4(1):2-50. doi: 10.1089/nsm.2020.0003. eCollection 2021 Feb. PMID: 33659919; PMCID: PMC7919422.
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Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review.

Nutrients

Calabrese FM, Porrelli A, Vacca M, Comte B, Nimptsch K, Pinart M, Pischon T, Pujos-Guillot E, De Angelis M.
PMID: 35010920
Nutrients. 2021 Dec 23;14(1). doi: 10.3390/nu14010047.

Low-grade inflammatory diseases revealed metabolic perturbations that have been linked to various phenotypes, including gut microbiota dysbiosis. In the last decade, metaproteomics has been used to investigate protein composition profiles at specific steps and in specific healthy/pathologic conditions. We...

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Calabrese FM, Porrelli A, Vacca M, et al. Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review. Nutrients. 2021;14(1)doi: 10.3390/nu14010047.
Calabrese, F. M., Porrelli, A., Vacca, M., Comte, B., Nimptsch, K., Pinart, M., Pischon, T., Pujos-Guillot, E., & De Angelis, M. (2021). Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review. Nutrients, 14(1), . https://doi.org/10.3390/nu14010047
Calabrese, Francesco Maria, et al. "Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review." Nutrients vol. 14,1 (2021). doi: https://doi.org/10.3390/nu14010047
Calabrese FM, Porrelli A, Vacca M, Comte B, Nimptsch K, Pinart M, Pischon T, Pujos-Guillot E, De Angelis M. Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review. Nutrients. 2021 Dec 23;14(1). doi: 10.3390/nu14010047. PMID: 35010920.
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Elevated gut microbiome abundance of .

Gut microbes

Tavella T, Rampelli S, Guidarelli G, Bazzocchi A, Gasperini C, Pujos-Guillot E, Comte B, Barone M, Biagi E, Candela M, Nicoletti C, Kadi F, Battista G, Salvioli S, O'Toole PW, Franceschi C, Brigidi P, Turroni S, Santoro A.
PMID: 33557667
Gut Microbes. 2021 Jan-Dec;13(1):1-19. doi: 10.1080/19490976.2021.1880221.

Aging is accompanied by physiological changes affecting body composition and functionality, including accumulation of fat mass at the expense of muscle mass, with effects upon morbidity and quality of life. The gut microbiome has recently emerged as a key...

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Tavella T, Rampelli S, Guidarelli G, et al. Elevated gut microbiome abundance of . Gut Microbes. 2021;13(1):1-19doi: 10.1080/19490976.2021.1880221.
Tavella, T., Rampelli, S., Guidarelli, G., Bazzocchi, A., Gasperini, C., Pujos-Guillot, E., Comte, B., Barone, M., Biagi, E., Candela, M., Nicoletti, C., Kadi, F., Battista, G., Salvioli, S., O'Toole, P. W., Franceschi, C., Brigidi, P., Turroni, S., & Santoro, A. (2021). Elevated gut microbiome abundance of . Gut microbes, 13(1), 1-19. https://doi.org/10.1080/19490976.2021.1880221
Tavella, Teresa, et al. "Elevated gut microbiome abundance of ." Gut microbes vol. 13,1 (2021): 1-19. doi: https://doi.org/10.1080/19490976.2021.1880221
Tavella T, Rampelli S, Guidarelli G, Bazzocchi A, Gasperini C, Pujos-Guillot E, Comte B, Barone M, Biagi E, Candela M, Nicoletti C, Kadi F, Battista G, Salvioli S, O'Toole PW, Franceschi C, Brigidi P, Turroni S, Santoro A. Elevated gut microbiome abundance of . Gut Microbes. 2021 Jan-Dec;13(1):1-19. doi: 10.1080/19490976.2021.1880221. PMID: 33557667; PMCID: PMC7889099.
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The NuGO proof of principle study package: a collaborative research effort of the European Nutrigenomics Organisation.

Genes & nutrition

Baccini M, Bachmaier EM, Biggeri A, Boekschoten MV, Bouwman FG, Brennan L, Caesar R, Cinti S, Coort SL, Crosley K, Daniel H, Drevon CA, Duthie S, Eijssen L, Elliott RM, van Erk M, Evelo C, Gibney M, Heim C, Horgan GW, Johnson IT, Kelder T, Kleemann R, Kooistra T, van Iersel MP, Mariman EC, Mayer C, McLoughlin G, Müller M, Mulholland F, van Ommen B, Polley AC, Pujos-Guillot E, Rubio-Aliaga I, Roche HM, de Roos B, Sailer M, Tonini G, Williams LM, de Wit N.
PMID: 19034556
Genes Nutr. 2008 Dec;3(3):147-51. doi: 10.1007/s12263-008-0102-5. Epub 2008 Nov 26.

No abstract available.

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Baccini M, Bachmaier EM, Biggeri A, et al. The NuGO proof of principle study package: a collaborative research effort of the European Nutrigenomics Organisation. Genes Nutr. 2008;3(3):147-51doi: 10.1007/s12263-008-0102-5.
Baccini, M., Bachmaier, E. M., Biggeri, A., Boekschoten, M. V., Bouwman, F. G., Brennan, L., Caesar, R., Cinti, S., Coort, S. L., Crosley, K., Daniel, H., Drevon, C. A., Duthie, S., Eijssen, L., Elliott, R. M., van Erk, M., Evelo, C., Gibney, M., Heim, C., Horgan, G. W., Johnson, I. T., Kelder, T., Kleemann, R., Kooistra, T., van Iersel, M. P., Mariman, E. C., Mayer, C., McLoughlin, G., Müller, M., Mulholland, F., van Ommen, B., Polley, A. C., Pujos-Guillot, E., Rubio-Aliaga, I., Roche, H. M., de Roos, B., Sailer, M., Tonini, G., Williams, L. M., de Wit, N. (2008). The NuGO proof of principle study package: a collaborative research effort of the European Nutrigenomics Organisation. Genes & nutrition, 3(3), 147-51. https://doi.org/10.1007/s12263-008-0102-5
Baccini, Michela, et al. "The NuGO proof of principle study package: a collaborative research effort of the European Nutrigenomics Organisation." Genes & nutrition vol. 3,3 (2008): 147-51. doi: https://doi.org/10.1007/s12263-008-0102-5
Baccini M, Bachmaier EM, Biggeri A, Boekschoten MV, Bouwman FG, Brennan L, Caesar R, Cinti S, Coort SL, Crosley K, Daniel H, Drevon CA, Duthie S, Eijssen L, Elliott RM, van Erk M, Evelo C, Gibney M, Heim C, Horgan GW, Johnson IT, Kelder T, Kleemann R, Kooistra T, van Iersel MP, Mariman EC, Mayer C, McLoughlin G, Müller M, Mulholland F, van Ommen B, Polley AC, Pujos-Guillot E, Rubio-Aliaga I, Roche HM, de Roos B, Sailer M, Tonini G, Williams LM, de Wit N. The NuGO proof of principle study package: a collaborative research effort of the European Nutrigenomics Organisation. Genes Nutr. 2008 Dec;3(3):147-51. doi: 10.1007/s12263-008-0102-5. Epub 2008 Nov 26. PMID: 19034556; PMCID: PMC2593011.
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Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study.

Metabolomics : Official journal of the Metabolomic Society

Martin JC, Maillot M, Mazerolles G, Verdu A, Lyan B, Migné C, Defoort C, Canlet C, Junot C, Guillou C, Manach C, Jabob D, Bouveresse DJ, Paris E, Pujos-Guillot E, Jourdan F, Giacomoni F, Courant F, Favé G, Le Gall G, Chassaigne H, Tabet JC, Martin JF, Antignac JP, Shintu L, Defernez M, Philo M, Alexandre-Gouaubau MC, Amiot-Carlin MJ, Bossis M, Triba MN, Stojilkovic N, Banzet N, Molinié R, Bott R, Goulitquer S, Caldarelli S, Rutledge DN.
PMID: 26109925
Metabolomics. 2015;11(4):807-821. doi: 10.1007/s11306-014-0740-0. Epub 2014 Oct 14.

The metabo-ring initiative brought together five nuclear magnetic resonance instruments (NMR) and 11 different mass spectrometers with the objective of assessing the reliability of untargeted metabolomics approaches in obtaining comparable metabolomics profiles. This was estimated by measuring the proportion...

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Martin JC, Maillot M, Mazerolles G, et al. Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study. Metabolomics. 2014;11(4):807-821doi: 10.1007/s11306-014-0740-0.
Martin, J. C., Maillot, M., Mazerolles, G., Verdu, A., Lyan, B., Migné, C., Defoort, C., Canlet, C., Junot, C., Guillou, C., Manach, C., Jabob, D., Bouveresse, D. J., Paris, E., Pujos-Guillot, E., Jourdan, F., Giacomoni, F., Courant, F., Favé, G., Le Gall, G., Chassaigne, H., Tabet, J. C., Martin, J. F., Antignac, J. P., Shintu, L., Defernez, M., Philo, M., Alexandre-Gouaubau, M. C., Amiot-Carlin, M. J., Bossis, M., Triba, M. N., Stojilkovic, N., Banzet, N., Molinié, R., Bott, R., Goulitquer, S., Caldarelli, S., & Rutledge, D. N. (2015). Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study. Metabolomics : Official journal of the Metabolomic Society, 11(4), 807-821. https://doi.org/10.1007/s11306-014-0740-0
Martin, Jean-Charles, et al. "Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study." Metabolomics : Official journal of the Metabolomic Society vol. 11,4 (2015): 807-821. doi: https://doi.org/10.1007/s11306-014-0740-0
Martin JC, Maillot M, Mazerolles G, Verdu A, Lyan B, Migné C, Defoort C, Canlet C, Junot C, Guillou C, Manach C, Jabob D, Bouveresse DJ, Paris E, Pujos-Guillot E, Jourdan F, Giacomoni F, Courant F, Favé G, Le Gall G, Chassaigne H, Tabet JC, Martin JF, Antignac JP, Shintu L, Defernez M, Philo M, Alexandre-Gouaubau MC, Amiot-Carlin MJ, Bossis M, Triba MN, Stojilkovic N, Banzet N, Molinié R, Bott R, Goulitquer S, Caldarelli S, Rutledge DN. Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study. Metabolomics. 2015;11(4):807-821. doi: 10.1007/s11306-014-0740-0. Epub 2014 Oct 14. PMID: 26109925; PMCID: PMC4475541.
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Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics.

Frontiers in physiology

Pujos-Guillot E, Pétéra M, Jacquemin J, Centeno D, Lyan B, Montoliu I, Madej D, Pietruszka B, Fabbri C, Santoro A, Brzozowska A, Franceschi C, Comte B.
PMID: 30733683
Front Physiol. 2019 Jan 24;9:1903. doi: 10.3389/fphys.2018.01903. eCollection 2018.

Aging is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases...

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Pujos-Guillot E, Pétéra M, Jacquemin J, et al. Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics. Front Physiol. 2019;9:1903doi: 10.3389/fphys.2018.01903.
Pujos-Guillot, E., Pétéra, M., Jacquemin, J., Centeno, D., Lyan, B., Montoliu, I., Madej, D., Pietruszka, B., Fabbri, C., Santoro, A., Brzozowska, A., Franceschi, C., & Comte, B. (2018). Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics. Frontiers in physiology, 91903. https://doi.org/10.3389/fphys.2018.01903
Pujos-Guillot, Estelle, et al. "Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics." Frontiers in physiology vol. 9 (2018): 1903. doi: https://doi.org/10.3389/fphys.2018.01903
Pujos-Guillot E, Pétéra M, Jacquemin J, Centeno D, Lyan B, Montoliu I, Madej D, Pietruszka B, Fabbri C, Santoro A, Brzozowska A, Franceschi C, Comte B. Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics. Front Physiol. 2019 Jan 24;9:1903. doi: 10.3389/fphys.2018.01903. eCollection 2018. PMID: 30733683; PMCID: PMC6353829.
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A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF.

Proteomes

Théron L, Centeno D, Coudy-Gandilhon C, Pujos-Guillot E, Astruc T, Rémond D, Barthelemy JC, Roche F, Feasson L, Hébraud M, Béchet D, Chambon C.
PMID: 28248242
Proteomes. 2016 Oct 26;4(4). doi: 10.3390/proteomes4040032.

Mass spectrometry imaging (MSI) is a powerful tool to visualize the spatial distribution of molecules on a tissue section. The main limitation of MALDI-MSI of proteins is the lack of direct identification. Therefore, this study focuses on a MSI~LC-MS/MS-LF...

Cite
Théron L, Centeno D, Coudy-Gandilhon C, et al. A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF. Proteomes. 2016;4(4)doi: 10.3390/proteomes4040032.
Théron, L., Centeno, D., Coudy-Gandilhon, C., Pujos-Guillot, E., Astruc, T., Rémond, D., Barthelemy, J. C., Roche, F., Feasson, L., Hébraud, M., Béchet, D., & Chambon, C. (2016). A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF. Proteomes, 4(4), . https://doi.org/10.3390/proteomes4040032
Théron, Laëtitia, et al. "A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF." Proteomes vol. 4,4 (2016). doi: https://doi.org/10.3390/proteomes4040032
Théron L, Centeno D, Coudy-Gandilhon C, Pujos-Guillot E, Astruc T, Rémond D, Barthelemy JC, Roche F, Feasson L, Hébraud M, Béchet D, Chambon C. A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF. Proteomes. 2016 Oct 26;4(4). doi: 10.3390/proteomes4040032. PMID: 28248242; PMCID: PMC5260965.
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A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity.

Frontiers in physiology

Dao MC, Sokolovska N, Brazeilles R, Affeldt S, Pelloux V, Prifti E, Chilloux J, Verger EO, Kayser BD, Aron-Wisnewsky J, Ichou F, Pujos-Guillot E, Hoyles L, Juste C, Doré J, Dumas ME, Rizkalla SW, Holmes BA, Zucker JD, Clément K.
PMID: 30804813
Front Physiol. 2019 Feb 05;9:1958. doi: 10.3389/fphys.2018.01958. eCollection 2018.

No abstract available.

Cite
Dao MC, Sokolovska N, Brazeilles R, et al. A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity. Front Physiol. 2019;9:1958doi: 10.3389/fphys.2018.01958.
Dao, M. C., Sokolovska, N., Brazeilles, R., Affeldt, S., Pelloux, V., Prifti, E., Chilloux, J., Verger, E. O., Kayser, B. D., Aron-Wisnewsky, J., Ichou, F., Pujos-Guillot, E., Hoyles, L., Juste, C., Doré, J., Dumas, M. E., Rizkalla, S. W., Holmes, B. A., Zucker, J. D., Clément, K. (2018). A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity. Frontiers in physiology, 91958. https://doi.org/10.3389/fphys.2018.01958
Dao, Maria Carlota, et al. "A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity." Frontiers in physiology vol. 9 (2018): 1958. doi: https://doi.org/10.3389/fphys.2018.01958
Dao MC, Sokolovska N, Brazeilles R, Affeldt S, Pelloux V, Prifti E, Chilloux J, Verger EO, Kayser BD, Aron-Wisnewsky J, Ichou F, Pujos-Guillot E, Hoyles L, Juste C, Doré J, Dumas ME, Rizkalla SW, Holmes BA, Zucker JD, Clément K. A Data Integration Multi-Omics Approach to Study Calorie Restriction-Induced Changes in Insulin Sensitivity. Front Physiol. 2019 Feb 05;9:1958. doi: 10.3389/fphys.2018.01958. eCollection 2018. PMID: 30804813; PMCID: PMC6371001.
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