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Stable isotopes represent a unique approach to provide insights into the ecology of organisms. δ13C and δ15N have specifically been used to obtain information on the trophic ecology and food-web interactions. Trophic discrimination factors (TDF, Δ13C and Δ15N) describe the isotopic fractionation occurring from diet to consumer tissue, and these factors are critical for obtaining precise estimates within any application of δ13C and δ15N values. It is widely acknowledged that metabolism influences TDF, being responsible for different TDF between tissues of variable metabolic activity (e.g., liver vs. muscle tissue) or species body size (small vs. large). However, the connection between the variation of metabolism occurring within a single species during its ontogeny and TDF has rarely been considered. Here, we conducted a 9-month feeding experiment to report Δ13C and Δ15N of muscle and liver tissues for several weight classes of Eurasian perch (Perca fluviatilis), a widespread teleost often studied using stable isotopes, but without established TDF for feeding on a natural diet. In addition, we assessed the relationship between the standard metabolic rate (SMR) and TDF by measuring the oxygen consumption of the individuals. Our results showed a significant negative relationship of SMR with Δ13C, and a significant positive relationship of SMR with Δ15N of muscle tissue, but not with TDF of liver tissue. SMR varies inversely with size, which translated into a significantly different TDF of muscle tissue between size classes. In summary, our results emphasize the role of metabolism in shaping-specific TDF (i.e., Δ13C and Δ15N of muscle tissue) and especially highlight the substantial differences between individuals of different ontogenetic stages within a species. Our findings thus have direct implications for the use of stable isotope data and the applications of stable isotopes in food-web studies.
The regulation of energy homeostasis is controlled by the brain and, besides requiring high amounts of energy, it relies on functional insulin/insulin-like growth factor (IGF)-1 signalling in the central nervous system. This energy is mainly provided by mitochondria in form of ATP. Thus, there is an intricate interplay between mitochondrial function and insulin/IGF-1 action to enable functional brain signalling and, accordingly, propagate a healthy metabolism. To adapt to different nutritional conditions, the brain is able to sense the current energy status via mitochondrial and insulin signalling-dependent pathways and exerts an appropriate metabolic response. However, regional, cell type and receptor-specific consequences of this interaction occur and are linked to diverse outcomes such as altered nutrient sensing, body weight regulation or even cognitive function. Impairments of this cross-talk can lead to obesity and glucose intolerance and are linked to neurodegenerative diseases, yet they also induce a self-sustainable, dysfunctional 'metabolic triangle' characterised by insulin resistance, mitochondrial dysfunction and inflammation in the brain. The identification of causal factors deteriorating insulin action, mitochondrial function and concomitantly a signature of metabolic stress in the brain is of utter importance to offer novel mechanistic insights into development of the continuously rising prevalence of non-communicable diseases such as type 2 diabetes and neurodegeneration. This review aims to determine the effect of insulin action on brain mitochondrial function and energy metabolism. It precisely outlines the interaction and differences between insulin action, insulin-like growth factor (IGF)-1 signalling and mitochondrial function; distinguishes between causality and association; and reveals its consequences for metabolism and cognition. We hypothesise that an improvement of at least one signalling pathway can overcome the vicious cycle of a self-perpetuating metabolic dysfunction in the brain present in metabolic and neurodegenerative diseases.
Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data
(2017)
Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higherorder dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.
The nutrition of animal consumers is an important regulator of ecological processes due to its effects on their physiology, life-history and behaviour. Understanding the ecological effects of poor nutrition depends on correctly diagnosing the nature and strength of nutritional limitation. Despite the need to assess nutritional limitation, current approaches to delineating nutritional constraints can be non-specific and imprecise. Here, we consider the need and potential to develop new complementary approaches to the study of nutritional constraints on animal consumers by studying and using a suite of established and emerging biochemical and molecular responses. These nutritional indicators include gene expression, transcript regulators, protein profiling and activity, and gross biochemical and elemental composition. The potential applications of nutritional indicators to ecological studies are highlighted to demonstrate the value that this approach would have to future studies in community and ecosystem ecology.