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Morimoto J, Conceição P, Mirth C, Lihoreau M. Nutrigonometry I: using right-angle triangles to quantify nutritional trade-offs in performance landscapes. Am Nat 2022; 201:725-740. [PMID: 37130232 DOI: 10.1086/723599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractAnimals regulate their food intake to maximize the expression of fitness traits but are forced to trade off the optimal expression of some fitness traits because of differences in the nutrient requirements of each trait ("nutritional trade-offs"). Nutritional trade-offs have been experimentally uncovered using the geometric framework for nutrition (GF). However, current analytical methods to measure such responses rely on either visual inspection or complex models of vector calculations applied to multidimensional performance landscapes, making these approaches subjective or conceptually difficult, computationally expensive, and, in some cases, inaccurate. Here, we present a simple trigonometric model to measure nutritional trade-offs in multidimensional landscapes (nutrigonometry) that relies on the trigonometric relationships of right-angle triangles and thus is both conceptually and computationally easier to understand and use than previous quantitative approaches. We applied nutrigonometry to a landmark GF data set for comparison of several standard statistical models to assess model performance in finding regions in the performance landscapes. This revealed that polynomial (Bayesian) regressions can be used for precise and accurate predictions of peaks and valleys in performance landscapes, irrespective of the underlying structure of the data (i.e., individual food intakes vs. fixed diet ratios). We then identified the known nutritional trade-off between life span and reproductive rate in terms of both nutrient balance and concentration for validation of the model. This showed that nutrigonometry enables a fast, reliable, and reproducible quantification of nutritional trade-offs in multidimensional performance landscapes, thereby broadening the potential for future developments in comparative research on the evolution of animal nutrition.
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Raubenheimer D, Senior AM, Mirth C, Cui Z, Hou R, Le Couteur DG, Solon-Biet SM, Léopold P, Simpson SJ. An integrative approach to dietary balance across the life course. iScience 2022; 25:104315. [PMID: 35602946 PMCID: PMC9117877 DOI: 10.1016/j.isci.2022.104315] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Animals require specific blends of nutrients that vary across the life course and with circumstances, e.g., health and activity levels. Underpinning and complicating these requirements is that individual traits may be optimized on different dietary compositions leading to nutrition-mediated trade-offs among outcomes. Additionally, the food environment may constrain which nutrient mixtures are achievable. Natural selection has equipped animals for solving such multi-dimensional, dynamic challenges of nutrition, but little is understood about the details and their theoretical and practical implications. We present an integrative framework, nutritional geometry, which models complex nutritional interactions in the context of multiple nutrients and across levels of biological organization (e.g., cellular, individual, and population) and levels of analysis (e.g., mechanistic, developmental, ecological, and evolutionary). The framework is generalizable across different situations and taxa. We illustrate this using examples spanning insects to primates and settings (laboratory, and the wild), and demonstrate its relevance for human health.
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Affiliation(s)
- David Raubenheimer
- The University of Sydney, Charles Perkins Centre and School of Life and Environmental Sciences, Sydney, Australia
- Zhengzhou University, Centre for Nutritional Ecology and Centre for Sport Nutrition and Health, Zhengzhou, China
- Corresponding author
| | - Alistair M. Senior
- The University of Sydney, Charles Perkins Centre and School of Life and Environmental Sciences, Sydney, Australia
- The University of Sydney, School of Mathematics and Statistics, Sydney, Australia
| | - Christen Mirth
- Monash University, School of Biological Science, Melbourne, Australia
| | - Zhenwei Cui
- Zhengzhou University, Centre for Nutritional Ecology and Centre for Sport Nutrition and Health, Zhengzhou, China
| | - Rong Hou
- Northwest University, Shaanxi Key Laboratory for Animal Conservation, College of Life Sciences, Xi’an, China
| | - David G. Le Couteur
- The University of Sydney, Charles Perkins Centre and Faculty of Medicine and Health, Concord Clinical School, ANZAC Research Institute, Centre for Education and Research on Ageing, Sydney, Australia
| | - Samantha M. Solon-Biet
- The University of Sydney, Charles Perkins Centre and School of Medical Sciences, Sydney, Australia
| | - Pierre Léopold
- Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, UPMC Paris-Sorbonne, Paris, France
| | - Stephen J. Simpson
- The University of Sydney, Charles Perkins Centre and School of Life and Environmental Sciences, Sydney, Australia
- Corresponding author
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Almeida-Carvalho MJ, Berh D, Braun A, Chen YC, Eichler K, Eschbach C, Fritsch PMJ, Gerber B, Hoyer N, Jiang X, Kleber J, Klämbt C, König C, Louis M, Michels B, Miroschnikow A, Mirth C, Miura D, Niewalda T, Otto N, Paisios E, Pankratz MJ, Petersen M, Ramsperger N, Randel N, Risse B, Saumweber T, Schlegel P, Schleyer M, Soba P, Sprecher SG, Tanimura T, Thum AS, Toshima N, Truman JW, Yarali A, Zlatic M. The Ol1mpiad: concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae. J Exp Biol 2017; 220:2452-2475. [DOI: 10.1242/jeb.156646] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 05/03/2017] [Indexed: 12/25/2022]
Abstract
ABSTRACT
Mapping brain function to brain structure is a fundamental task for neuroscience. For such an endeavour, the Drosophila larva is simple enough to be tractable, yet complex enough to be interesting. It features about 10,000 neurons and is capable of various taxes, kineses and Pavlovian conditioning. All its neurons are currently being mapped into a light-microscopical atlas, and Gal4 strains are being generated to experimentally access neurons one at a time. In addition, an electron microscopic reconstruction of its nervous system seems within reach. Notably, this electron microscope-based connectome is being drafted for a stage 1 larva – because stage 1 larvae are much smaller than stage 3 larvae. However, most behaviour analyses have been performed for stage 3 larvae because their larger size makes them easier to handle and observe. It is therefore warranted to either redo the electron microscopic reconstruction for a stage 3 larva or to survey the behavioural faculties of stage 1 larvae. We provide the latter. In a community-based approach we called the Ol1mpiad, we probed stage 1 Drosophila larvae for free locomotion, feeding, responsiveness to substrate vibration, gentle and nociceptive touch, burrowing, olfactory preference and thermotaxis, light avoidance, gustatory choice of various tastants plus odour–taste associative learning, as well as light/dark–electric shock associative learning. Quantitatively, stage 1 larvae show lower scores in most tasks, arguably because of their smaller size and lower speed. Qualitatively, however, stage 1 larvae perform strikingly similar to stage 3 larvae in almost all cases. These results bolster confidence in mapping brain structure and behaviour across developmental stages.
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Affiliation(s)
| | - Dimitri Berh
- Institute of Neurobiology and Behavioural Biology, University of Münster, 48149 Münster, Germany
- Department of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Andreas Braun
- EMBL/CRG Systems Biology Unit, Centre for Genomic Regulation, 08003 Barcelona, Spain
- Universitat Pompeu Fabra, 08002 Barcelona, Spain
| | - Yi-chun Chen
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
| | - Katharina Eichler
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Claire Eschbach
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | | | - Bertram Gerber
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
- Institute of Biology, Otto von Guericke University Magdeburg, 39118 Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
| | - Nina Hoyer
- Center for Molecular Neurobiology, University of Hamburg, 20251 Hamburg, Germany
| | - Xiaoyi Jiang
- Department of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Jörg Kleber
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
| | - Christian Klämbt
- Institute of Neurobiology and Behavioural Biology, University of Münster, 48149 Münster, Germany
| | - Christian König
- Leibniz Institute for Neurobiology (Molecular Systems Biology), 39118 Magdeburg, Germany
- Institute of Pharmacology and Toxicology, Otto von Guericke University Magdeburg, 39118 Magdeburg, Germany
| | - Matthieu Louis
- EMBL/CRG Systems Biology Unit, Centre for Genomic Regulation, 08003 Barcelona, Spain
- Universitat Pompeu Fabra, 08002 Barcelona, Spain
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA 93117, USA
| | - Birgit Michels
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
| | | | - Christen Mirth
- Gulbenkian Institute of Science, 2780-156 Oeiras, Portugal
- School of Biological Sciences, Monash University, Melbourne, VIC 3800, Australia
| | - Daisuke Miura
- Department of Biology, Kyushu University, 819-0395 Fukuoka, Japan
| | - Thomas Niewalda
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
| | - Nils Otto
- Institute of Neurobiology and Behavioural Biology, University of Münster, 48149 Münster, Germany
| | - Emmanouil Paisios
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
| | | | - Meike Petersen
- Center for Molecular Neurobiology, University of Hamburg, 20251 Hamburg, Germany
| | - Noel Ramsperger
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany
| | - Nadine Randel
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Benjamin Risse
- Institute of Neurobiology and Behavioural Biology, University of Münster, 48149 Münster, Germany
- Department of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Timo Saumweber
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
| | | | - Michael Schleyer
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
| | - Peter Soba
- Center for Molecular Neurobiology, University of Hamburg, 20251 Hamburg, Germany
| | - Simon G. Sprecher
- Department of Biology, University of Fribourg, 1700 Fribourg, Switzerland
| | - Teiichi Tanimura
- Department of Biology, Kyushu University, 819-0395 Fukuoka, Japan
| | - Andreas S. Thum
- Department of Biology, University of Konstanz, 78464 Konstanz, Germany
| | - Naoko Toshima
- Leibniz Institute for Neurobiology (Genetics), 39118 Magdeburg, Germany
- Department of Biology, Kyushu University, 819-0395 Fukuoka, Japan
| | - Jim W. Truman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
- Friday Harbor Laboratories, University of Washington, Friday Harbor, WA 98250, USA
| | - Ayse Yarali
- Center for Behavioral Brain Sciences, Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany
- Leibniz Institute for Neurobiology (Molecular Systems Biology), 39118 Magdeburg, Germany
| | - Marta Zlatic
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
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