Metabolic cold adaptation is a pattern where ectotherms from cold, high-latitude, or -altitude habitats have higher metabolic rates than ectotherms from warmer habitats. When found, metabolic cold adaptation is often attributed to countergradient selection, wherein short, cool growing seasons select for a compensatory increase in growth rates and development times of ectotherms. Yet, ectotherms in high-latitude and -altitude environments face many challenges in addition to thermal and time constraints on lifecycles. In addition to short, cool growing seasons, high-latitude and - altitude environments are characterized by regular exposure to extreme low temperatures, which cause ectotherms to enter a transient state of immobility termed chill coma. The ability to resume activity quickly after chill coma increases with latitude and altitude in patterns consistent with local adaptation to cold conditions. We show that artificial selection for fast and slow chill coma recovery among lines of the fly Drosophila melanogaster also affects rates of respiratory metabolism. Cold-hardy fly lines, with fast recovery from chill coma, had higher respiratory metabolic rates than control lines, with cold-susceptible slow-recovering lines having the lowest metabolic rates. Fast chill coma recovery was also associated with higher respiratory metabolism in a set of lines derived from a natural population. Although their metabolic rates were higher than control lines, fast-recovering cold-hardy lines did not have faster growth rates or development times than control lines. This suggests that raised metabolic rates in high-latitude and -altitude species may be driven by adaptation to extreme low temperatures, illustrating the importance of moving "Beyond the Mean".

In insects and other ectotherms, cold temperatures cause a coma resulting from loss of neuromuscular function, during which ionic and metabolic homeostasis are progressively lost. Cold adaptation improves homeostasis during cold exposure, but the ultimate targets of selection are still an open question. Cold acclimation and adaptation remodels mitochondrial metabolism in insects, suggesting that aerobic energy production during cold exposure could be a target of selection. Here, we test the hypothesis that cold adaptation improves the ability to maintain rates of aerobic energy production during cold exposure by using ³¹ P NMR on live flies. Using lines of Drosophila melanogaster artificially selected for fast and slow recovery from a cold coma, we show that cold exposure does not lower ATP levels and that cold adaptation does not alter aerobic ATP production during cold exposure. Cold-hardy and cold-susceptible lines both experienced a brief transition to anaerobic metabolism during cooling, but this was rapidly reversed during cold exposure, suggesting that oxidative phosphorylation was sufficient to meet energy demands below the critical thermal minimum, even in cold-susceptible flies. We thus reject the hypothesis that performance under mild low temperatures is set by aerobic ATP supply limitations in D. melanogaster, excluding oxygen and capacity limitation as a weak link in energy supply. This work suggests that the modulations to mitochondrial metabolism resulting from cold acclimation or adaptation may arise from selection on a biosynthetic product(s) of those pathways rather than selection on ATP supply during cold exposure.

(13)C NMR has many advantages for a metabolomics study, including a large spectral dispersion, narrow singlets at natural abundance, and a direct measure of the backbone structures of metabolites. However, it has not had widespread use because of its relatively low sensitivity compounded by low natural abundance. Here we demonstrate the utility of high-quality (13)C NMR spectra obtained using a custom (13)C-optimized probe on metabolomic mixtures. A workflow was developed to use statistical correlations between replicate 1D (13)C and (1)H spectra, leading to composite spin systems that can be used to search publicly available databases for compound identification. This was developed using synthetic mixtures and then applied to two biological samples, Drosophila melanogaster extracts and mouse serum. Using the synthetic mixtures we were able to obtain useful (13)C-(13)C statistical correlations from metabolites with as little as 60 nmol of material. The lower limit of (13)C NMR detection under our experimental conditions is approximately 40 nmol, slightly lower than the requirement for statistical analysis. The (13)C and (1)H data together led to 15 matches in the database compared to just 7 using (1)H alone, and the (13)C correlated peak lists had far fewer false positives than the (1)H generated lists. In addition, the (13)C 1D data provided improved metabolite identification and separation of biologically distinct groups using multivariate statistical analysis in the D. melanogaster extracts and mouse serum.

When ectotherms are exposed to low temperatures, they enter a cold-induced coma (chill coma) that prevents resource acquisition, mating, oviposition, and escape from predation. There is substantial variation in time taken to recover from chill coma both within and among species, and this variation is correlated with habitat temperatures such that insects from cold environments recover more quickly. This suggests an adaptive response, but the mechanisms underlying variation in recovery times are unknown, making it difficult to decisively test adaptive hypotheses. We use replicated lines of Drosophila melanogaster selected in the laboratory for fast (hardy) or slow (susceptible) chill-coma recovery times to investigate modifications to metabolic profiles associated with cold adaptation. We measured metabolite concentrations of flies before, during, and after cold exposure using nuclear magnetic resonance (NMR) spectroscopy to test the hypotheses that hardy flies maintain metabolic homeostasis better during cold exposure and recovery, and that their metabolic networks are more robust to cold-induced perturbations. The metabolites of cold-hardy flies were less cold responsive and their metabolic networks during cold exposure were more robust, supporting our hypotheses. Metabolites involved in membrane lipid synthesis, tryptophan metabolism, oxidative stress, energy balance, and proline metabolism were altered by selection on cold tolerance. We discuss the potential significance of these alterations.

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world.

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.