ABB Special Issue “Applications of Metabolomics”

In recognition of the advances brought by metabolomics to the fields of biochemistry, the Archives of Biochemistry and Biophysics is now presenting a Special issue on “Metabolomics” and its applications. This Special issue on metabolomics includes two general types of papers. The first set of papers focuses on experimental aspects of metabolomics, such as sample preparation and sample stability under non-ideal conditions, and also on novel experimental techniques, including mass spectrometry imaging and analytical methods to characterize the lipid content of bacterial cells. The second set of papers provides a diverse range of applications of metabolomics, many of which are related metabolic diseases, in particular obesity and diabetes, but also addressing interesting questions where metabolomics may bring new arguments to light.

Access to high quality samples and their preparation for unbiased and error-free measurement is key to every metabolomics experiment. Ning Li et al. [1] summarize recent developments in sample preparation and data-pretreatment procedures for metabolomics. However, clinical studies sometimes impose non-ideal handling conditions that may lead metabolomics facilities to be hesitant about using them for analysis. It is therefore essential to develop a feeling for what can and cannot be done to a sample. In a pre-analytical sample quality study, Budde et al. [2] evaluated the effects of storage time and temperature on 1H-NMR spectra from human urine. Their study indicates that human urine samples can be stored at 10 °C for 24 h – although some metabolites of bacterial origin may be impacted. This can be important for large scale epidemiological studies that have to make logistical compromises.

Metabolomics is a rapidly evolving field, with many novel techniques becoming available to be applied to biological and clinical samples. Mass spectrometry imaging (MSI) is used in an increasing number of biological applications, but typical MSI datasets can be quite large. Fisher et al. [3] review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Similarly, extending and improving on analytical techniques that allow to detect larger sets of metabolites may enable new discoveries. Lipids play important roles in biology, ranging from building blocks of membranes to signaling lipids. The nematode Caenorhabditis elegans is often used as a model organism to explore lipid metabolism. Witting et al. [4] provide an overview on the C. elegans lipidome, the lipid classes it contains and analytical methods to characterize the lipid content.

Deinococcus radiodurans (Drad) is the most radioresistant organism known. Although mechanisms that underlie the extreme radio-resistance of Drad are incompletely defined, resistance to UV irradiation-induced killing was found to be greatly attenuated in an NO synthase knockout strain. In their paper in this special issue, Hansler et al. [5] applied untargeted LC-MS-based metabolite profiling to show that endogenous NO production is also critical for protection of Drad against γ-irradiation, a result of accelerated growth recovery, not protection against killing. Unexpectedly, this study also identified a dramatic perturbation in carotenoid biosynthetic intermediates, suggesting that endogenously-produced NO serves to maintain a spectrum of carotenoids critical for Drad’s ability to withstand radiation insult.

Due to their antimicrobial properties, silver nanoparticles (AgNPs) are increasingly incorporated into consumer goods and medical products. Their potential toxicity to human cells is however a major concern. Carrola et al. [6] make the case for NMR metabolomics as important new tool in the field of in vitro nano-toxicology by investigating the metabolic profile of human epidermis keratinocytes (HaCaT cell line) exposed to AgNPs.

The worldwide obesity epidemic has dramatically increased the prevalence of insulin resistance and metabolic syndrome, predisposing individuals to a greater risk for the development of nonalcoholic fatty liver disease, type II diabetes, atherosclerotic cardiovascular diseases, and other obesity associated co-morbidities. Metabolomics plays a central role in characterizing these diseases – we therefore included several papers from this field in this special issue:

Chronic kidney disease (CKD), a frequent comorbidity of diabetes, is an increasingly recognized burden for patients and health care. There is a strong need for improved diagnostic markers for disease detection and management. Breit and Weinberger [7] summarize the CKD-related markers discovered so far and discuss the path from clinical research to a routine application, focusing on potential obstacles such as the use of mass spectrometry in the clinic, and the feasibility of obtaining regulatory approval for targeted metabolomics assays. Barrios et al. [8] complement this CKD focus by describing the current status of the identification of blood, urine, and fecal metabolic biomarkers in different entities of kidney diseases, including acute kidney injury, chronic kidney disease, renal transplant, and diabetic nephropathy.

Fat cell metabolism has an impact on body homeostasis and its proper function. Nevertheless, our knowledge about metabolic processes that occur during adipogenesis and in mature adipocytes is limited. In their research paper, Halama et al. [9] study the metabolic switch from branched chain amino acid catabolism (BCAA) to lipid synthesis during adipogenesis. The authors uncovered a crosstalk between BCAA and lipid metabolism during adipogenesis that may contribute to the understanding of moleculari mechanisms of obesity and have potential implications in obesity prediction. Complementing the set of lipid metabolism related papers, Abente et al. [10] go on and review recent discoveries of the mechanistic roles of microRNAs in regulating metabolic functions in liver and adipose tissues, and more generally in obesity associated disorders.

Diabetes is a frequent comorbidity of obesity and represents one of the most widespread and disabling common disorder today, with a clear metabolic signature that can be successfully analyzed using metabolomics techniques. Therefore we included several papers in this special issue that address metabolic questions related to diabetes. Gooding et al. [11] summarize advances in our understanding of islet physiology and the etiologies of type-1 and type-2 diabetes derived from metabolomics studies, focusing on new insights in islet biology derived from application of static and dynamic metabolic profiling methods, and the implications of this new information for translational applications. Seroconversion to islet autoimmunity is preceded by metabolic disturbances in children who later progress to overt type 1 diabetes (T1D). The underlying metabolic pathways and the interaction of metabolic and immune system factors involved in the progression to the disease are however poorly understood. Marinkovic and Oresic [12] review early attempts in modeling the development of islet autoimmunity and T1D and highlight genome-scale metabolic modeling as a promising new avenue to study metabolism and its interactions with the immune system in T1D.

Studies on the association between type 2 diabetes and amino acids have focused mostly on fasting conditions. However, most of the time, the human body not in a fasting state. Mook-Kanamori et al. [13] hypothesized that amino acid responses to a standardized meal challenge could be more strongly associated with type 2 diabetes than under fasting conditions. In a cross-sectional study of over 500 participants, including newly-diagnosed type 2 diabetes patients and subjects with newly-diagnosed impaired fasting glycaemia, they examined postprandial amino acid concentrations and the meal challenge response. They found that type 2 diabetes was associated with lower postprandial concentration of several amino acids, indicating that these measures may also be potential markers of type 2 diabetes.

Acute sleep curtailment induces insulin resistance, both in healthy individuals as well as in patients with type 1 diabetes, suggesting a causal role for sleep disturbances in pathogenesis of insulin resistance, independent of endogenous insulin production. Here von den Berg et al. [14] explore the metabolic pathways affected by sleep loss using targeted metabolomics in human fasting plasma samples. They report that one night of short sleep specifically increased the plasma levels of acylcarnitines, essential intermediates in mitochondrial fatty acid oxidation. Their work suggests a potential mechanistic pathway by which sleep deprivation – even short term – can cause adverse metabolic effects, such as insulin resistance.

During mild cold exposure, non-shivering thermogenesis increases to maintain core body temperature by increasing utilization of substrates, especially fatty acids (FA), ultimately affecting lipid-associated metabolites. Nahon et al. [15] investigated whether mild cooling induces changes in other metabolites and whether this response differs between white Caucasians and South Asians, who have a disadvantageous metabolic phenotype. They find that mild cooling elicits substantial effects on serum metabolites in healthy males, irrespective of their ethnicity.

We close this special issue with two papers that link metabolomics to genomics and a review of metabolic profiling for exposome studies. Genome-wide association studies (GWAS) have provided remarkable advances in our understanding of the etiology of complex diseases in humans and emphasized the need to improve patients’ phenotype characterization with intermediate molecular phenotypes. High resolution metabolomics is becoming an increasingly popular and robust strategy for metabolic phenotyping large cohorts in genetic studies to map out the genetic control of metabotypes in various biological matrices. Gaugier et al. [16] review results from quantitative trait locus (mQTL) mapping in rodent models of human complex traits, with a specific focus on the cardiometabolic syndrome. Sampling of the natural metabolic and genetic variability that is present in the general population in a GWAS with metabolic traits (mGWAS) can reveal novel biochemical knowledge, such as the function of uncharacterized genes, the biochemical identity of small molecules, and the structure of entire biochemical pathways. Suhre et al. [17] review findings of recent mGWAS and provide concrete examples of how such results can be interpreted in a biochemical context. Population studies with metabolomics can provide mechanistically relevant markers that link environmental exposures (i.e., the “exposome”) to chronic disease endpoints. Athersuch [18] describes examples of how metabolic profiling has played a key role in molecular epidemiological analyses of chronic disease, and how these reflect different aspects of the causal pathway. The author gives an overview of the current platforms in the context of large-scale epidemiological studies, alongside opportunities for augmentation using alternative or auxiliary analytical techniques that may further expand our profiling capabilities.

We hope that with this special issue we cover a new and exciting topic area that is interesting to our general readership from all fields of biophysics and biochemistry. We look forward to future submissions that report applications of metabolomics to important biological and biomedical questions, or that report advances of the technique itself.

If you are researcher in the field of metabolomics, please consider sending your next manuscript to ABB.