Understanding the metabolome - challenges for metabolomics

Metabolic Perturbations Associated with both PFAS Exposure and Perinatal/Antenatal Depression in Pregnant Individuals: A Meet-in-the-Middle Scoping Review

PURPOSE OF REVIEW

Depression during the perinatal or antenatal period affects at least 1 in 10 women worldwide, with long term health implications for the mother and child. Concurrently, there is increasing evidence associating maternal exposure to per- and poly-fluoroalkyl substances (PFAS) to adverse pregnancy outcomes. We reviewed the body of evidence examining both the associations between PFAS exposure and perturbations in the maternal metabolome, and the associations between the maternal metabolome and perinatal/antenatal depression. Through this, we sought to explore existing evidence of the perinatal metabolome as a potential mediation pathway linking PFAS exposure and perinatal/antenatal depression.

RECENT FINDINGS

There are few studies examining the metabolomics of PFAS exposure-specifically in pregnant women-and the metabolomics of perinatal/antenatal depression, let alone studies examining both simultaneously. Of the studies reviewed (N = 11), the majority were cross sectional, based outside of the US, and conducted on largely homogenous populations. Our review identified 23 metabolic pathways in the perinatal metabolome common to both PFAS exposure and perinatal/antenatal depression. Future studies may consider findings from our review to conduct literature-derived hypothesis testing focusing on fatty acid metabolism, alanine metabolism, glutamate metabolism, and tyrosine metabolism when exploring the biochemical mechanisms conferring the risk of perinatal/antenatal depression due to PFAS exposure. We recommend that researchers also utilize heterogenous populations, longitudinal study designs, and mediation approaches to elucidate key pathways linking PFAS exposures to perinatal/antenatal depression.

Evaluation of Metabolic Changes in Acute Intermittent Porphyria Patients by Targeted Metabolomics

Acute intermittent porphyria (AIP) is an inherited rare hepatic disorder due to mutations within the hydroxymethylbilane gene. AIP patients with active disease overproduce aminolevulinic acid (ALA) and porphobilinogen (PBG) in the liver which are exported inducing severe neurological attacks. Different hepatic metabolic abnormalities have been described to be associated with this condition. The goal of this research was to explore the metabolome of symptomatic AIP patients by state-of-the art liquid chromatography-tandem mass spectrometry (LC-MS/MS). A case versus control study including 18 symptomatic AIP patients and 33 healthy controls was performed. Plasmatic levels of 51 metabolites and 16 ratios belonging to four metabolic pathways were determined. The results showed that the AIP patients presented significant changes in the two main areas of the metabolome under study: (a) the tryptophan/kynurenine pathway with an increase of tryptophan in plasma together with increase of the kynurenine/tryptophan ratio; and (b) changes in the tricarboxylic acid cycle (TCA) including increase of succinic acid and decrease of the fumaric acid/succinic acid ratio. We performed a complementary in vitro study adding ALA to hepatocytes media that showed some of the effects on the TCA cycle were parallel to those observed in vivo. Our study confirms in plasma previous results obtained in urine showing that AIP patients present a moderate increase of the kynurenine/tryptophan ratio possibly associated with inflammation. In addition, it also reports changes in the mitochondrial TCA cycle that, despite requiring further research, could be associated with an energy misbalance due to sustained overproduction of heme-precursors in the liver.

Finger sweat analysis enables short interval metabolic biomonitoring in humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

Using post-column infused internal standard assisted quantitative metabolomics for establishing prediction models for breast cancer detection

RATIONALE

Breast cancer is one of the most common cancers among women and its associated mortality is on the rise. Metabolomics is a potential strategy for breast cancer detection. The post-column infused internal standard (PCI-IS)-assisted liquid chromatography/tandem mass spectrometry (LC/MS/MS) method has been demonstrated as an effective strategy for quantitative metabolomics. In this study, we evaluated the performance of targeted metabolomics with the PCI-IS quantification method to identify women with breast cancer.

METHODS

We used metabolite profiling to identify 17 dysregulated metabolites in breast cancer patients. Two LC/MS/MS methods in combination with the PCI-IS strategy were developed to quantify these metabolites in plasma samples. Detection models were built through the analysis of plasma samples from 176 subjects consisting of healthy volunteers and breast cancer patients.

RESULTS

Three isotope standards were selected as the PCI-ISs for the metabolites. The accuracy was within 82.8-114.16%, except for citric acid and lactic acid at high concentration levels. The repeatability and intermediate precision were all lower than 15% relative standard deviation. We have identified several metabolites that indicate the presence of breast cancer. The area under the receiver operating characteristics (AUROC) curve, sensitivity and specificity of the linear combinations of metabolite concentrations and age with the highest AUROC were 0.940 (0.889-0.992), 88.4% and 94.2% for pre-menopausal woman, respectively, and 0.828 (0.734-0.922), 73.5% and 85.1% for post-menopausal women, respectively.

CONCLUSIONS

The targeted metabolomics with PCI-IS quantification method successfully established prediction models for breast cancer detection. Further study is essential to validate these proposed markers.

Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

Renal Cell Carcinoma: A Study through NMR-Based Metabolomics Combined with Transcriptomics

Renal cell carcinoma (RCC) is a heterogeneous cancer often showing late symptoms. Until now, some candidate protein markers have been proposed for its diagnosis. Metabolomics approaches have been applied, predominantly using Mass Spectrometry (MS), while Nuclear Magnetic Resonance (NMR)-based studies remain limited. There is no study about RCC integrating NMR-based metabolomics with transcriptomics. In this work, ¹H-NMR spectroscopy combined with multivariate statistics was applied on urine samples, collected from 40 patients with clear cell RCC (ccRCC) before nephrectomy and 29 healthy controls; nine out of 40 patients also provided samples one-month after nephrectomy. We observed increases of creatine, alanine, lactate and pyruvate, and decreases of hippurate, citrate, and betaine in all ccRCC patients. A network analysis connected most of these metabolites with glomerular injury, renal inflammation and renal necrosis/cell death. Interestingly, intersecting metabolites with transcriptomic data from CD133+/CD24+ tumoral renal stem cells isolated from ccRCC patients, we found that both genes and metabolites differentially regulated in ccRCC patients belonged to HIF-α signaling, methionine and choline degradation, and acetyl-CoA biosynthesis. Moreover, when comparing urinary metabolome of ccRCC patients after nephrectomy, some processes, such as the glomerular injury, renal hypertrophy, renal necrosis/cell death and renal proliferation, were no more represented.

Basics of mass spectrometry based metabolomics

The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS-based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).

Metabolomic phenotyping validates the infant rhesus monkey as a model of human infant metabolism

OBJECTIVES

Rhesus macaque monkeys are widely used as models for human physiology and behavior. They are particularly suited for studies on infant nutrition and metabolism; however, few studies have directly compared their metabolic or microbiological phenotypes. The aim of the present study was to compare the metabolomic profiles and microbiome of milk from human and rhesus mothers, and the metabolomic profiles of urine and serum from human and rhesus infants to establish the value of this model for human nutrition research.

METHODS

Milk samples were collected from rhesus and human mothers at similar stages of lactation. Urine and serum samples were collected from breast-fed rhesus and human infants. H nuclear magnetic resonance spectra were acquired for all samples and metabolites were identified and quantified using targeted profiling techniques. The microbial community structure of milk was examined using 16S rRNA gene sequencing.

RESULTS

An identical set of metabolites was identified in the urine and serum profiles from human and rhesus infants. In urine, 65% of the metabolites were present at similar concentrations, whereas ~40% were similar in serum. The gross composition of human and rhesus milk was comparable, including the overall microbial community at both the phylum and order level; however, some oligosaccharides found in human milk were not present in monkey milk.

CONCLUSIONS

Comparison of the milk microbiome and urine, serum, and milk metabolome of rhesus macaques and humans has revealed substantial similarities that provide unique biological information highlighting the significance of rhesus macaques as a model for infant nutrition and developmental research.

(1)H NMR-based metabolic profiling reveals inherent biological variation in yeast and nematode model systems

The application of metabolomics to human and animal model systems is poised to provide great insight into our understanding of disease etiology and the metabolic changes that are associated with these conditions. However, metabolomic studies have also revealed that there is significant, inherent biological variation in human samples and even in samples from animal model systems where the animals are housed under carefully controlled conditions. This inherent biological variability is an important consideration for all metabolomics analyses. In this study, we examined the biological variation in (1)H NMR-based metabolic profiling of two model systems, the yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans. Using relative standard deviations (RSD) as a measure of variability, our results reveal that both model systems have significant amounts of biological variation. The C. elegans metabolome possesses greater metabolic variance with average RSD values of 29 and 39%, depending on the food source that was used. The S. cerevisiae exometabolome RSD values ranged from 8% to 12% for the four strains examined. We also determined whether biological variation occurs between pairs of phenotypically identical yeast strains. Multivariate statistical analysis allowed us to discriminate between pair members based on their metabolic phenotypes. Our results highlight the variability of the metabolome that exists even for less complex model systems cultured under defined conditions. We also highlight the efficacy of metabolic profiling for defining these subtle metabolic alterations.

What can metabolomics learn from genomics and proteomics?

After nearly a decade, metabolomics has begun to acquire some credence in the scientific community although its acceptance cannot be compared with that of its forerunners, genomics and proteomics. The legitimization of metabolomics as a valid scientific entity depends on the size of the research community it influences. By far the most effective medium for inoculation is the web infrastructure: public servers that accommodate experimental data, simple formats and guidelines for their interpretation, and connectivity between data and tools for analysis. When these elements satisfy the condition to initiate a social epidemic, metabolomics will be accepted as a fundamental data-driven science that can unite hitherto independently conducted research disciplines.

Advances in Nutrigenomics research: novel and future analytical approaches to investigate the biological activity of natural compounds and food functions

In recent years, nutrition research has moved from classical epidemiology and physiology to molecular biology and genetics. Following this trend, Nutrigenomics has emerged as a novel and multidisciplinary research field in nutritional science that aims to elucidate how diet can influence human health. It is already well known that bioactive food compounds can interact with genes affecting transcription factors, protein expression and metabolite production. The study of these complex interactions requires the development of advanced analytical approaches combined with bioinformatics. Thus, to carry out these studies Transcriptomics, Proteomics and Metabolomics approaches are employed together with an adequate integration of the information that they provide. In this article, an overview of the current methodologies and a thorough revision of the advances in analytical technologies and their possibilities for future developments and applications in the field of Nutrigenomics is provided.