Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction

Investigating the pathogenesis of vitreous in postmortem COVID patients via untargeted metabolomics based bioinformatics model

SARS-CoV-2 virus has become a worldwide pandemic causing millions of death. This severe disaster lead to a immense panic and stress all over the world. Several studies were dedicated to understand its mechanism, pathogenesis and spreading characteristics. By this way, scientists try to develop different therapy and diagnose strategies. For these reasons, several metabolomics, proteomics and genomics studies were also carried out to improve knowledge in this newly identified virus. In this study, we are aimed to explain the pathogenesis of SARS-CoV-2 exposure on postmortem COVID (+) patients via untargeted metabolomics analysis. To carry out this study, a Data Independent Acquisition SWATH method is optimized and performed. Vitreous samples were analyzed in both MS1 and MS2 ESI(+) mode. An orthogonal Partial Least Square Discriminant Analysis were performed for classification. It was observed that lipid metabolism, several amino acids and oxidative stress biomarkers were strongly affected due to high inflammation and possible cytokine storm.

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Supplementation of amylase or amylase + xylanase improves performance and metabolism of broilers fed with diets containing newly harvested maize

How supplementation with amylase or amylase + xylanase in newly harvested maize-based diets affects broiler nutrient metabolism and performance is unclear. Thus, this study evaluated whether the supplementation of amylase (CN) or amylase + xylanase (CAX) improves performance and metabolism of broilers fed with newly harvested maize-based diets during a 6-week production. The results showed that the body weight gain of broilers fed with CA or CAX diet was higher than that with the control (CN) diet at 1-21 d of age; however, an opposite trend was observed for feed/gain (p < 0.05). Furthermore, 150, 64 and 35 different metabolites were found between CA/CN, CAX/CN and CAX/CA, respectively. Overall, amylase supplementation improved broiler growth performance at 1-21 d of age, and the positive effects of amylase on nutrient utilization were mostly related to nicotinate, retinol and glutathione metabolism improvement. Moreover, CAX diet increased apparent metabolizable energy and growth performance of broilers at 22-42 d of age, and the difference might be related to sphingolipid, porphyrin and chlorophyll metabolism regulation. The findings prove amylase + xylanase supplementation is an effective method to improve the nutritional value of newly harvested maize for broilers.

Effects of Coix Seed Oil on High Fat Diet-Induced Obesity and Dyslipidemia

Dietary intervention is becoming more popular as a way to improve lipid metabolism and reduce the prevalence of diet-related chronic disorders. We evaluated the effects of several dietary oils on body weight, fat mass, liver weight, and tumor necrosis factor in obese mice given a high-fat diet (HFD) to discover if coix seed oil (CSO) had an anti-obesity impact. As compared to other dietary fats, CSO treatment considerably lowered body weight and liver index, successfully sup-pressed total cholesterol and triglyceride content, and raised liver lipid deposition and lipid metabolism problem induced by high fat intake. Furthermore, gas chromatography research revealed that CSO extracted by supercritical fluid, with 64% being CSO extracted by supercritical fluid, and the greatest amounts of capric acids and lauric acids being 35.28% and 22.21%, respectively. CSO contained a high content of medium-chain fatty acids and was able to modify hepatic fatty acid metabolism and lipid levels in HFD-induced obese mice. According to the results, CSO has the potential to replace dietary lipids as a promising functional lipid in the prevention of met-abolish disorders.

Metabolomic profiling in small vessel disease identifies multiple associations with disease severity

Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors to better understand pathogenesis and to predict disease progression and severity. We analysed data from 624 patients with symptomatic cerebral small vessel disease from two prospective cohort studies. Serum samples were collected at baseline and patients underwent MRI scans and cognitive testing at regular intervals with up to 14 years of follow-up. Using ultra-performance liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy, we obtained metabolic and lipidomic profiles from 369 annotated metabolites and 54 764 unannotated features and examined their association with respect to disease severity, assessed using MRI small vessel disease markers, cognition and future risk of all-cause dementia. Our analysis identified 28 metabolites that were significantly associated with small vessel disease imaging markers and cognition. Decreased levels of multiple glycerophospholipids and sphingolipids were associated with increased small vessel disease load as evidenced by higher white matter hyperintensity volume, lower mean diffusivity normalized peak height, greater brain atrophy and impaired cognition. Higher levels of creatine, FA(18:2(OH)) and SM(d18:2/24:1) were associated with increased lacune count, higher white matter hyperintensity volume and impaired cognition. Lower baseline levels of carnitines and creatinine were associated with higher annualized change in peak width of skeletonized mean diffusivity, and 25 metabolites, including lipoprotein subclasses, amino acids and xenobiotics, were associated with future dementia incidence. Our results show multiple distinct metabolic signatures that are associated with imaging markers of small vessel disease, cognition and conversion to dementia. Further research should assess causality and the use of metabolomic screening to improve the ability to predict future disease severity and dementia risk in small vessel disease. The metabolomic profiles may also provide novel insights into disease pathogenesis and help identify novel treatment approaches.

Omics Technology for the Promotion of Nutraceuticals and Functional Foods

The influence of nutrition and environment on human health has been known for ages. Phytonutrients (7,000 flavonoids and phenolic compounds; 600 carotenoids) and pro-health nutrients—nutraceuticals positively add to human health and may prevent disorders such as cancer, diabetes, obesity, cardiovascular diseases, and dementia. Plant-derived bioactive metabolites have acquired an imperative function in human diet and nutrition. Natural phytochemicals affect genome expression (nutrigenomics and transcriptomics) and signaling pathways and act as epigenetic modulators of the epigenome (nutri epigenomics). Transcriptomics, proteomics, epigenomics, miRNomics, and metabolomics are some of the main platforms of complete omics analyses, finding use in functional food and nutraceuticals. Now the recent advancement in the integrated omics approach, which is an amalgamation of multiple omics platforms, is practiced comprehensively to comprehend food functionality in food science.

Higher thyrotropin leads to unfavorable lipid profile and somewhat higher cardiovascular disease risk: evidence from multi-cohort Mendelian randomization and metabolomic profiling

BACKGROUND

Observational studies suggest interconnections between thyroid status, metabolism, and risk of coronary artery disease (CAD), but causality remains to be proven. The present study aimed to investigate the potential causal relationship between thyroid status and cardiovascular disease and to characterize the metabolomic profile associated with thyroid status.

METHODS

Multi-cohort two-sample Mendelian randomization (MR) was performed utilizing genome-wide significant variants as instruments for standardized thyrotropin (TSH) and free thyroxine (fT4) within the reference range. Associations between TSH and fT4 and metabolic profile were investigated in a two-stage manner: associations between TSH and fT4 and the full panel of 161 metabolomic markers were first assessed hypothesis-free, then directional consistency was assessed through Mendelian randomization, another metabolic profile platform, and in individuals with biochemically defined thyroid dysfunction.

RESULTS

Circulating TSH was associated with 52/161 metabolomic markers, and fT4 levels were associated with 21/161 metabolomic markers among 9432 euthyroid individuals (median age varied from 23.0 to 75.4 years, 54.5% women). Positive associations between circulating TSH levels and concentrations of very low-density lipoprotein subclasses and components, triglycerides, and triglyceride content of lipoproteins were directionally consistent across the multivariable regression, MR, metabolomic platforms, and for individuals with hypo- and hyperthyroidism. Associations with fT4 levels inversely reflected those observed with TSH. Among 91,810 CAD cases and 656,091 controls of European ancestry, per 1-SD increase of genetically determined TSH concentration risk of CAD increased slightly, but not significantly, with an OR of 1.03 (95% CI 0.99-1.07; p value 0.16), whereas higher genetically determined fT4 levels were not associated with CAD risk (OR 1.00 per SD increase of fT4; 95% CI 0.96-1.04; p value 0.59).

CONCLUSIONS

Lower thyroid status leads to an unfavorable lipid profile and a somewhat increased cardiovascular disease risk.

Effect of dietary-aged maize on growth performance, nutrient utilization, and serum metabolites in broilers

In China, most maize used for animal diets is stored for long periods. We examined the effects of dietary aged maize on growth performance, nutrients utilization, and serum metabolites in broilers. A total of 270 healthy 1-day-old male Cobb broilers were assigned randomly into three treatments groups and fed maize stored for different times (24 days, M0; 18 months, M18; 36 months, M36). Growth performance was examined at 21 and 42 days of age. Nutrient digestibility was studied on days 18-21 and 38-41. At day 42, blood samples were collected for serum metabolite analysis. Dietary aged maize significantly affected the feed to gain ratio, total starch digestibility, and apparent metabolizable energy (p < 0.05). Compared with the M0 group, 39 and 144 differential metabolites were observed in the M18 and M36 groups, respectively, whereas 56 differential metabolites were identified between the M18 and M36 groups. Pathway analysis indicated that the main altered pathways were clustered into lipid metabolism in M18, and lipid and glucose metabolism in M0 and M36, respectively. In conclusion, negative effects were observed for both new harvested maize and maize stored for 36 months; maize stored for 18 months may improve broiler performance.

Influence of Age, Sex, and Diet on the Human Fecal Metabolome Investigated by 1H NMR Spectroscopy

The human fecal metabolome is increasingly studied to explore the impact of diet and lifestyle on health and the gut microbiome. However, systematic differences and confounding factors related to age, sex, and diet remain largely unknown. In this study, absolute concentrations of fecal metabolites from 205 healthy Danes (105 males and 100 females, 49 ± 31 years old) were quantified using ¹H NMR spectroscopy and the newly developed SigMa software. The largest systemic variation was found to be highly related to age. Fecal concentrations of short-chain fatty acids (SCFA) were higher in the 18 years old group, while amino acids (AA) were higher in the elderly. Sex-related metabolic differences were weak but significant and mainly related to changes in SCFA. The concentrations of butyric, valeric, propionic, and isovaleric acids were found to be higher in males compared to females. Sex differences were associated with a stronger, possibly masking, effect from differential intake of macronutrients. Dietary fat intake decreased levels of SCFA and AA of both sexes, while carbohydrate intake showed weak correlations with valeric and isovaleric acids in females. This study highlights some possible demographic confounders linked to diet, disease, lifestyle, and microbiota that have to be taken into account when analyzing fecal metabolome data.

Separation, identification and cardiovascular activities of phospholipid classes from the head of Penaeus vannamei by lipidomics and zebrafish models

Five phospholipid classes of Penaeus vannamei head were separated, analyzed and quantified. They had different cardiovascular activities evaluated in zebrafish models, which may provide a research basis for pharmaceutical use of marine phospholipids.

Experimental and computational approaches to unravel microbial community assembly

Microbial communities have a preponderant role in the life support processes of our common home planet Earth. These extremely diverse communities drive global biogeochemical cycles, and develop intimate relationships with most multicellular organisms, with a significant impact on their fitness. Our understanding of their composition and function has enjoyed a significant thrust during the last decade thanks to the rise of high-throughput sequencing technologies. Intriguingly, the diversity patterns observed in nature point to the possible existence of fundamental community assembly rules. Unfortunately, these rules are still poorly understood, despite the fact that their knowledge could spur a scientific, technological, and economic revolution, impacting, for instance, agricultural, environmental, and health-related practices. In this minireview, I recapitulate the most important wet lab techniques and computational approaches currently employed in the study of microbial community assembly, and briefly discuss various experimental designs. Most of these approaches and considerations are also relevant to the study of microbial microevolution, as it has been shown that it can occur in ecological relevant timescales. Moreover, I provide a succinct review of various recent studies, chosen based on the diversity of ecological concepts addressed, experimental designs, and choice of wet lab and computational techniques. This piece aims to serve as a primer to those new to the field, as well as a source of new ideas to the more experienced researchers.

AlpsNMR: an R package for signal processing of fully untargeted NMR-based metabolomics

SUMMARY

Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches remain a challenge. Computational tools dealing with fully untargeted NMR-based metabolomics are still scarce or not user-friendly. Therefore, we developed AlpsNMR (Automated spectraL Processing System for NMR), an R package that provides automated and efficient signal processing for untargeted NMR metabolomics. AlpsNMR includes spectra loading, metadata handling, automated outlier detection, spectra alignment and peak-picking, integration and normalization. The resulting output can be used for further statistical analysis. AlpsNMR proved effective in detecting metabolite changes in a test case. The tool allows less experienced users to easily implement this workflow from spectra to a ready-to-use dataset in their routines.

AVAILABILITY AND IMPLEMENTATION

The AlpsNMR R package and tutorial is freely available to download from http://github.com/sipss/AlpsNMR under the MIT license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomics analysis reveals differences in evolution between maize and rice

Metabolites are the intermediate and final products of metabolism, which play essential roles in plant growth, evolution and adaptation to changing climates. However, it is unclear how evolution contributes to metabolic variation in plants. Here, we investigated the metabolomics data from leaf and seed tissues in maize and rice. Using principal components analysis based on leaf metabolites but not seed metabolites, metabolomics data could be clearly separated for rice Indica and Japonica accessions, while two maize subgroups, temperate and tropical, showed more visible admixture. Rice and maize seed exhibited significant interspecific differences in metabolic variation, while within rice, leaf and seed displayed similar metabolic variations. Among 10 metabolic categories, flavonoids had higher variation in maize than rice, indicating flavonoids are a key constituent of interspecific metabolic divergence. Interestingly, metabolic regulation was also found to be reshaped dramatically from positive to negative correlations, indicative of the differential evolutionary processes in maize and rice. Moreover, perhaps due to this divergence significantly more metabolic interactions were identified in rice than maize. Furthermore, in rice, the leaf was found to harbor much more intense metabolic interactions than the seed. Our result suggests that metabolomes are valuable for tracking evolutionary history, thereby complementing and extending genomic insights concerning which features are responsible for interspecific differentiation in maize and rice.

Metabolic infrastructure of pregnant women with methylenetetrahydrofolate reductase polymorphisms: A metabolomic analysis

The aim of this study was to demonstrate the altered metabolic infrastructure of pregnant women with methylenetetrahydrofolate reductase (MTHFR) polymorphisms at first trimester and during delivery. Eight singleton pregnant women with MTHFR polymorphisms were compared with 10 normal pregnant women. Maternal blood samples were obtained twice during their pregnancy period (between the 11th and 14th gestational weeks and during delivery). Metabolomic analysis was performed using GC-MS. The GC-MS based metabolomic profile helped identify 95 metabolites in the plasma samples. In the MTHFR group, the levels of 1-monohexadecanoylglycerol, pyrophosphate, benzoin, and linoleic acid significantly decreased (P ˂ 0.05 for all), whereas the levels of glyceric acid, l-tryptophan, l-alanine, l-proline, norvaline, l-threonine, and myo-inositol significantly increased (P ˂ 0.01 for the first two metabolites, P ˂ 0.05 for the others) at 11-14 gestational weeks. Conversely, the levels of benzoin, 1-monohexadecanoylglycerol, pyruvic acid, l-proline, phosphoric acid, epsilon-caprolactam, and pipecolic acid significantly decreased in the MTHFR group, whereas metabolites such as hexadecanoic acid and 2-hydroxybutyric acid increased significantly in the study group during delivery. An impaired energy metabolism pathway, vitamin B complex disorders, tendency for metabolic acidosis (oxidative stress), and the need for cell/tissue support seem prevalent in pregnancies with MTHFR polymorphisms.

The gut microbiome as a predictor of low fermentable oligosaccharides disaccharides monosaccharides and polyols diet efficacy in functional bowel disorders

PURPOSE OF REVIEW

Fermentable oligosaccharides disaccharides monosaccharides and polyols (FODMAP) dietary restriction ameliorates irritable bowel syndrome (IBS) symptoms; however, not all individuals with IBS respond. Given the gut microbiome's role in carbohydrate fermentation, investigators have evaluated whether the gut microbiome may predict low FODMAP diet efficacy.

RECENT FINDINGS

Gut microbiome fermentation, even to the same carbohydrate, is not uniform across all individuals with several factors (e.g. composition) playing a role. In both children and adults with IBS, studies are emerging suggesting the gut microbiome may predict low FODMAP diet efficacy. However, there is significant heterogeneity in the approaches (study population, microbiome assessment methods, statistical techniques, etc.) used amongst these studies.

SUMMARY

The gut microbiome holds promise as a predictor of low FODMAP diet efficacy. However, further investigation using standardized approaches to evaluate the microbiome while concomitantly assessing other potential predictors are needed to more rigorously evaluate this area.

Tissue-Specific Sample Dilution: An Important Parameter to Optimise Prior to Untargeted LC-MS Metabolomics

When developing a sample preparation protocol for LC–MS untargeted metabolomics of a new sample matrix unfamiliar to the laboratory, selection of a suitable injection concentration is rarely described. Here we developed a simple workflow to address this issue prior to untargeted LC–MS metabolomics using pig adipose tissue and liver tissue. Bi-phasic extraction was performed to enable simultaneous optimisation of parameters for analysis of both lipids and polar extracts. A series of diluted pooled samples were analysed by LC–MS and used to evaluate signal linearity. Suitable injected concentrations were determined based on both the number of reproducible features and linear features. With our laboratory settings, the optimum concentrations of tissue mass to reconstitution solvent of liver and adipose tissue lipid fractions were found to be 125 mg/mL and 7.81 mg/mL respectively, producing 2811 (ESI+) and 4326 (ESI−) linear features from liver, 698 (ESI+) and 498 (ESI−) linear features from adipose tissue. For analysis of the polar fraction of both tissues, 250 mg/mL was suitable, producing 403 (ESI+) and 235 (ESI−) linear features from liver, 114 (ESI+) and 108 (ESI−) linear features from adipose tissue. Incorrect reconstitution volumes resulted in either severe overloading or poor linearity in our lipid data, while too dilute polar fractions resulted in a low number of reproducible features (<50) compared to hundreds of reproducible features from the optimum concentration used. Our study highlights on multiple matrices and multiple extract and chromatography types, the critical importance of determining a suitable injected concentration prior to untargeted LC–MS metabolomics, with the described workflow applicable to any matrix and LC–MS system.

Recent Advances and Challenges in Steroid Metabolomics for Biomarker Discovery

BACKGROUND

Steroid hormones belong to a group of low-molecular weight compounds which are responsible for maintenance of various body functions, thus, their accurate assessment is crucial for evaluation of biosynthetic defects. The development of reliable methods allowing disease diagnosis is essential to improve early detection of various disorders connected with altered steroidogenesis. Currently, the field of metabolomics offers several improvements in terms of sensitivity and specificity of the diagnostic methods when opposed to classical diagnostic approaches. The combination of hyphenated techniques and pattern recognition methods allows to carry out a comprehensive assessment of the slightest alterations in steroid metabolic pathways and can be applied as a tool for biomarker discovery.

METHODS

We have performed an extensive literature search applying various bibliographic databases for peer-reviewed articles concentrating on the applications of hyphenated techniques and pattern recognition methods incorporated into the steroid metabolomic approach for biomarker discovery.

RESULTS

The review discusses strengths, challenges and recent developments in steroidbased metabolomics. We present methods of sample collection and preparation, methods of separation and detection of steroid hormones in biological material, data analysis, and interpretation as well as examples of applications of steroid metabolomics for biomarker discovery (cancer, mental and central nervous system disorders, endocrine diseases, monitoring of drug therapy and doping control).

CONCLUSION

Information presented in this review will be valuable to anyone interested in the application of metabolomics for biomarker discovery with a special emphasis on disorders of steroid hormone synthesis and metabolism.

Metabolomics in early detection and prognosis of acute coronary syndrome

Acute coronary syndrome (ACS) is one of the most dangerous types of coronary heart disease (CHD) and contributes to significant mortality and morbidity worldwide. Outcomes in these patients remain a challenge despite improvements in diagnosis and treatment. Risk stratification continues to be problematic and the identification of novel predictors is crucial for improved outcomes. As such, there is a strong need for the development of novel analytical methods as well as the characterization of better predictive and prognostic biomarkers to enable more personalized treatment. Metabolite profile analysis may greatly assist in interpreting altered pathway dynamics, especially when combined with other 'omics' technologies such as transcriptomics and proteomics. In this review, we describe ACS pathophysiology and recent advances in the role of metabolomics in the diagnosis and the molecular pathogenesis of ACS. We briefly describe key technologies used in metabolomics research and statistical approaches for data reduction and pathway analysis and discuss their application to CHD.

Metabolomics reveals citric acid secretion in mechanically-stimulated osteocytes is inhibited by high glucose

Osteocytes are the main cells of bone tissue and play a crucial role in bone formation and resorption. Recent studies have indicated that Diabetes Mellitus (DM) affects bone mass and potentially causes higher bone fracture risk. Previous work on osteocyte cell cultures has demonstrated that mechanotransduction is impaired after culture under diabetic pre-conditioning with high glucose (HG), specifically osteoclast recruitment and differentiation. The aim of this study was to analyze the extracellular metabolic changes of osteocytes regarding two conditions: pre-conditioning to either basal levels of glucose (B), mannitol (M) or HG cell media, and mechanical stimulation by fluid flow (FF) in contrast to static condition (SC). Secretomes were analyzed using Liquid Chromatography and Capillary Electrophoresis both coupled to Mass Spectrometry (LC-MS and CE-MS, respectively). Results showed the osteocyte profile was very similar under SC, regardless of their pre-conditioning treatment, while, after FF stimulation, secretomes followed different metabolic signatures depending on the pre-conditioning treatment. An important increment of citrate pointed out that osteocytes release citrate outside of the cell to induce osteoblast activation, while HG environment impaired FF effect. This study demonstrates for the first time that osteocytes increase citrate excretion under mechanical stimulation, and that HG environment impaired this effect.

Influence of sample preparation on lipidomics analysis of polar lipids in adipose tissue

The main limitations of lipidomics analysis are the chemical complexity of the lipids, the range of concentrations at which they exist, and the variety of samples usually analyzed. These limitations particularly affect the characterization of polar lipids owing to the interference of neutral lipids, essentially acylglycerides, which are at high concentration and suppress ionization of low concentrated lipids in mass spectrometry detection. The influence of sample preparation on lipidomics analysis of polar lipids in adipose tissue by LC-MS/MS was the aim of this research. Two common extractants used for lipids isolation, methanol:chloroform (MeOH:CHCl₃) and methyl tert-butyl ether (MTBE), were qualitatively and quantitatively compared for the extraction of the main families of lipids. The obtained results showed that each family of lipids is influenced differently by the extractant used. However, as a general trend, the use of MTBE as extractant led to higher extraction efficiency for unsaturated fatty acids, glycerophospholipids and ceramides, while MeOH:CHCl₃ favored the isolation of saturated fatty acids and plasmalogens. The implementation of a solid-phase extraction (SPE) step for selective isolation of glycerophospholipids prior to LC-MS/MS analysis was assayed to evaluate its influence on lipids detection coverage as compared to direct analysis. This step was critical to enhance the detection coverage of glycerophospholipids by removal of ionization suppression effects caused by acylglycerides.

Effects of Diet Quality and Psychosocial Stress on the Metabolic Profiles of Mice

There has been an increasing interest in relationship between stress and diet. To address this relationship, we evaluated an animal model of depression: male C57BL/6J mice subjected to subchronic mild social defeat stress (sCSDS) for 10 consecutive days using male ICR mice under two different calorie-adjusted diets conditions-nonpurified (MF) and semipurified (AIN) diets made from natural and chemical ingredients mainly, respectively. Our previous study indicates that diet quality and purity affect stress susceptibility in sCSDS mice. We therefore hypothesized that there are some key peripheral metabolites to change stress-susceptible behavior. GC-MS metabolomics of plasma, liver, and cecal content were performed on four test groups: sCSDS + AIN diet (n = 7), sCSDS + MF diet (n = 6), control (no sCSDS) + AIN diet (n = 8), and control + MF diet (n = 8). Metabolome analyses revealed that the number of metabolites changed by food was larger than the number changed by stress in all tissues. Enrichment analysis of the liver metabolite set altered by food implies that stress-susceptible mice show increased glycolysis-related substrates in the liver. We found metabolites that were affected by stress (e.g., plasma and liver 4-hydroxyproline and plasma beta-alanine are higher in sCSDS than in control) and a stress × food interaction (e.g., plasma GABA is lower in sCSDS + AIN than in sCSDS + MF). Because functional compounds were altered by both stress and food, diet may be able to attenuate various stress-induced symptoms by changing metabolites in peripheral tissues.

Energy metabolism in mobile, wild-sampled sharks inferred by plasma lipids

Evaluating how predators metabolize energy is increasingly useful for conservation physiology, as it can provide information on their current nutritional condition. However, obtaining metabolic information from mobile marine predators is inherently challenging owing to their relative rarity, cryptic nature and often wide-ranging underwater movements. Here, we investigate aspects of energy metabolism in four free-ranging shark species (n = 281; blacktip, bull, nurse, and tiger) by measuring three metabolic parameters [plasma triglycerides (TAG), free fatty acids (FFA) and cholesterol (CHOL)] via non-lethal biopsy sampling. Plasma TAG, FFA and total CHOL concentrations (in millimoles per litre) varied inter-specifically and with season, year, and shark length varied within a species. The TAG were highest in the plasma of less active species (nurse and tiger sharks), whereas FFA were highest among species with relatively high energetic demands (blacktip and bull sharks), and CHOL concentrations were highest in bull sharks. Although temporal patterns in all metabolites were varied among species, there appeared to be peaks in the spring and summer, with ratios of TAG/CHOL (a proxy for condition) in all species displaying a notable peak in summer. These results provide baseline information of energy metabolism in large sharks and are an important step in understanding how the metabolic parameters can be assessed through non-lethal sampling in the future. In particular, this study emphasizes the importance of accounting for intra-specific and temporal variability in sampling designs seeking to monitor the nutritional condition and metabolic responses of shark populations.

Lessons on dietary biomarkers from twin studies

Metabolomic and microbiome profiling are promising tools to identify biomarkers of food intake and health status. The individual's genetic makeup plays a significant role on health, metabolism, gut microbes and diet and twin studies provide unique opportunities to untangle gene-environment effects on complex phenotypes. This brief review discusses the value of twin studies in nutrition research with a particular focus on metabolomics and the gut microbiome. Although, the twin model is a powerful tool to segregate the genetic component, to date, very few studies combine the twin design and metabolomics/microbiome in nutritional sciences. Moreover, since the individual's diet has a strong influence on the microbiome composition and the gut microbiome is modifiable (60 % of microbiome diversity is due to the environment), future studies should target the microbiome via dietary interventions.

Reviewing Classical and Molecular Techniques Regarding Profiling of Probiotic Character of Microorganisms

In recent years the roles of probiotics as functional ingredients in food has been highly adopted by the consumers and are under constant investigation by the scientific community. As a result, several probiotic-containing foods have been introduced in the market with an annual share of several billion dollars. Of particular interest in the probiotics research is the profiling of probiotic character of the microbes involving both in vitro and in vivo approaches. Initially traditional microbiological techniques were used; however they suffer by many limitations and therefore the development of new techniques, which are primarily based on the analysis of nucleic acids have been introduced. The scope of this review is to present current knowledge about the methodological approaches that are used to quantify and characterize the potential probiotic character of microorganisms. Moreover, it will focus on molecular and non-molecular tools and finally will report some new perspectives in the study of probiotics using omics techniques.

Innovations in Canine and Feline Nutrition: Technologies for Food and Nutrition Assessment

Pet owners have increasing concerns about the nutrition of their pets, and they desire foods and treats that are safe, traceable, and of high nutritive value. To meet these high expectations, detailed chemical composition characterization of ingredients well beyond that provided by proximate analysis will be required, as will information about host physiology and metabolism. Use of faster and more precise analytical methodology and novel technologies that have the potential to improve pet food safety and quality will be implemented. In vitro and in vivo assays will continue to be used as screening tools to evaluate nutrient quality and adequacy in novel ingredients prior to their use in animal diets. The use of molecular and high-throughput technologies allows implementation of noninvasive studies in dogs and cats to investigate the impact of dietary interventions by using systems biology approaches. These approaches may further improve the health and longevity of pets.

Metabolomics in childhood diabetes

Recent increases in the incidence of both type 1 (T1D) and type 2 diabetes (T2D) in children and adolescents point to the importance of environmental factors in the development of these diseases. Metabolomic analysis explores the integrated response of the organism to environmental changes. Metabolic profiling can identify biomarkers that are predictive of disease incidence and development, potentially providing insight into disease pathogenesis. This review provides an overview of the role of metabolomic analysis in diabetes research and summarizes recent research relating to the development of T1D and T2D in children.

Mass-Spectrometry-Based Serum Metabolomics of a C57BL/6J Mouse Model of High-Fat-Diet-Induced Non-alcoholic Fatty Liver Disease Development

Obesity, dyslipidemia, insulin resistance, oxidative stress, and inflammation are key clinical risk factors for the progression of non-alcoholic fatty liver disease (NAFLD). Currently, there is no comprehensive metabolic profile of a well-established animal model that effectively mimics the etiology and pathogenesis of NAFLD in humans. Here, we report the pathophysiological and metabolomic changes associated with NAFLD development in a C57BL/6J mouse model in which NAFLD was induced by feeding a high-fat diet (HFD) for 4, 8, 12, and 16 weeks. Serum metabolomic analysis was conducted using ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) and gas chromatography-mass spectrometry (GC-MS) to establish a metabolomic profile. Analysis of the metabolomic profile in combination with principal component analysis revealed marked differences in metabolites between the control and HFD group depending upon NAFLD severity. A total of 30 potential biomarkers were strongly associated with the development of NAFLD. Among these, 11 metabolites were mainly related to carbohydrate metabolism, hepatic biotransformation, collagen synthesis, and gut microbial metabolism, which are characteristics of obesity, as well as significantly increased serum glucose, total cholesterol, and hepatic triglyceride levels during the onset of NAFLD (4 weeks). At 8 weeks, 5 additional metabolites that are chiefly involved in perturbation of lipid metabolism and insulin secretion were found to be associated with hyperinsulinemia, hyperlipidemia, and hepatic steatosis in the mid-term of NAFLD progression. At the end of 12 and 16 weeks, 14 additional metabolites were predominantly correlated to abnormal bile acid synthesis, oxidative stress, and inflammation, representing hepatic inflammatory infiltration during NAFLD development. These results provide potential biomarkers for early risk assessment of NAFLD and further insights into NAFLD development.

Serum metabolomics in animal models and human disease

PURPOSE OF REVIEW

The aim of this study is to highlight some recent uses of serum metabolomics in human and animal studies. The main themes are the importance of understanding the underlying variation in human metabolism and the use of serum metabolomics in disease profiling.

RECENT FINDINGS

Several studies have attempted to use serum metabolomics to develop noninvasive biomarkers of disease and/or track the consequences of nutritional and genetic interventions. Many advances have been made with common changes being identified in ageing, the menopause and cancer but several problems of interpretation have emerged from these studies. These include the small sample sizes in most human studies and the differences between human and rodent metabolomes. However, a metabolic screen of over 1000 'healthy' humans (the Humsermet project) has highlighted many variables that may be used to refine the interpretation and design of previous and future human studies alike, in addition to data mining.

SUMMARY

Some common serum metabolome alterations have been identified but many inconsistencies remain. The construction of a human serum metabolome database should be informative in the design of future human and animal model studies.

The Metabolome in Finnish Carriers of the MYBPC3-Q1061X Mutation for Hypertrophic Cardiomyopathy

Aims

Mutations in the cardiac myosin-binding protein C gene (MYBPC3) are the most common genetic cause of hypertrophic cardiomyopathy (HCM) worldwide. The molecular mechanisms leading to HCM are poorly understood. We investigated the metabolic profiles of mutation carriers with the HCM-causing MYBPC3-Q1061X mutation with and without left ventricular hypertrophy (LVH) and non-affected relatives, and the association of the metabolome to the echocardiographic parameters.

Methods and Results

34 hypertrophic subjects carrying the MYBPC3-Q1061X mutation, 19 non-hypertrophic mutation carriers and 20 relatives with neither mutation nor hypertrophy were examined using comprehensive echocardiography. Plasma was analyzed for molecular lipids and polar metabolites using two metabolomics platforms. Concentrations of branched chain amino acids, triglycerides and ether phospholipids were increased in mutation carriers with hypertrophy as compared to controls and non-hypertrophic mutation carriers, and correlated with echocardiographic LVH and signs of diastolic and systolic dysfunction in subjects with the MYBPC3-Q1061X mutation.

Conclusions

Our study implicates the potential role of branched chain amino acids, triglycerides and ether phospholipids in HCM, as well as suggests an association of these metabolites with remodeling and dysfunction of the left ventricle.

Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer

BACKGROUND

Coffee intake may be inversely associated with colorectal cancer; however, previous studies have been inconsistent. Serum coffee metabolites are integrated exposure measures that may clarify associations with cancer and elucidate underlying mechanisms.

OBJECTIVES

Our aims were 2-fold as follows: 1) to identify serum metabolites associated with coffee intake and 2) to examine these metabolites in relation to colorectal cancer.

DESIGN

In a nested case-control study of 251 colorectal cancer cases and 247 matched control subjects from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, we conducted untargeted metabolomics analyses of baseline serum by using ultrahigh-performance liquid-phase chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Usual coffee intake was self-reported in a food-frequency questionnaire. We used partial Pearson correlations and linear regression to identify serum metabolites associated with coffee intake and conditional logistic regression to evaluate associations between coffee metabolites and colorectal cancer.

RESULTS

After Bonferroni correction for multiple comparisons (P = 0.05 ÷ 657 metabolites), 29 serum metabolites were positively correlated with coffee intake (partial correlation coefficients: 0.18-0.61; P < 7.61 × 10(-5)); serum metabolites most highly correlated with coffee intake (partial correlation coefficients >0.40) included trigonelline (N'-methylnicotinate), quinate, and 7 unknown metabolites. Of 29 serum metabolites, 8 metabolites were directly related to caffeine metabolism, and 3 of these metabolites, theophylline (OR for 90th compared with 10th percentiles: 0.44; 95% CI: 0.25, 0.79; P-linear trend = 0.006), caffeine (OR for 90th compared with 10th percentiles: 0.56; 95% CI: 0.35, 0.89; P-linear trend = 0.015), and paraxanthine (OR for 90th compared with 10th percentiles: 0.58; 95% CI: 0.36, 0.94; P-linear trend = 0.027), were inversely associated with colorectal cancer.

CONCLUSIONS

Serum metabolites can distinguish coffee drinkers from nondrinkers; some caffeine-related metabolites were inversely associated with colorectal cancer and should be studied further to clarify the role of coffee in the cause of colorectal cancer. The Prostate, Lung, Colorectal, and Ovarian trial was registered at clinicaltrials.gov as NCT00002540.

Lipidomic analysis can distinguish between two morphologically similar strains of Nannochloropsis oceanica

The two morphologically similar microalgae NMBluh014 and NMBluh-X belong to two different strains of Nannochloropsis oceanica. They possess obviously different feeding effects on bivalves, but are indistinguishable by 18S rRNA and morphological features. In this work, lipidomic analysis followed by principal component analysis and orthogonal projections to latent structures discriminant analysis provided a clear distinction between these strains. Metabolites that definitively contribute to the classification were selected as potential biomarkers. The most important difference in polar lipids were sulfoquinovosyldiacylglycerol (containing 18:1/16:0 and 18:3/16:0) and monogalactosyldiacylglycerol (containing 18:3/16:3 and 20:5/14:0), which were detected only in NMBluh-X. Additionally, an exhaustive qualitative and quantitative profiling of the neutral lipid triacylglycerol (TAG) in the two strains was carried out. The predominant species of TAG containing 16:1/16:1/16:1 acyl groups was detected only in NMBluh-X with a content of ~93.67 ± 11.85 nmol · mg(-1) dry algae at the onset of stationary phase. Meanwhile, TAG containing 16:0/16:0/16:0 was the main TAG in NMBluh014 with a content of 40.25 ± 3.92 nmol · mg(-1) . These results provided the most straightforward evidence for differentiating the two species. The metabolomic profiling indicated that NMBluh-X underwent significant chemical and physiological changes during the growth process, whereas NMBluh014 did not show such noticeable time-dependent metabolite change. This study is the first using Ultra Performance Liquid Chromatography coupled with Electrospray ionization-Quadrupole-Time of Flight Mass Spectrometry (UPLC-Q-TOF-MS) for lipidomic profiling with multivariate statistical analysis to explore lipidomic differences of plesiomorphous microalgae. Our results demonstrate that lipidomic profiling is a valid chemotaxonomic tool in the study of microalgal systematics.

Analytical methods in untargeted metabolomics: state of the art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

Stronger findings for metabolomics through Bayesian modeling of multiple peaks and compound correlations

MOTIVATION

Data analysis for metabolomics suffers from uncertainty because of the noisy measurement technology and the small sample size of experiments. Noise and the small sample size lead to a high probability of false findings. Further, individual compounds have natural variation between samples, which in many cases renders them unreliable as biomarkers. However, the levels of similar compounds are typically highly correlated, which is a phenomenon that we model in this work.

RESULTS

We propose a hierarchical Bayesian model for inferring differences between groups of samples more accurately in metabolomic studies, where the observed compounds are collinear. We discover that the method decreases the error of weak and non-existent covariate effects, and thereby reduces false-positive findings. To achieve this, the method makes use of the mass spectral peak data by clustering similar peaks into latent compounds, and by further clustering latent compounds into groups that respond in a coherent way to the experimental covariates. We demonstrate the method with three simulated studies and validate it with a metabolomic benchmark dataset.

AVAILABILITY AND IMPLEMENTATION

An implementation in R is available at http://research.ics.aalto.fi/mi/software/peakANOVA/.

Systems-based approaches to unravel multi-species microbial community functioning

Some of the most transformative discoveries promising to enable the resolution of this century's grand societal challenges will most likely arise from environmental science and particularly environmental microbiology and biotechnology. Understanding how microbes interact in situ, and how microbial communities respond to environmental changes remains an enormous challenge for science. Systems biology offers a powerful experimental strategy to tackle the exciting task of deciphering microbial interactions. In this framework, entire microbial communities are considered as metaorganisms and each level of biological information (DNA, RNA, proteins and metabolites) is investigated along with in situ environmental characteristics. In this way, systems biology can help unravel the interactions between the different parts of an ecosystem ultimately responsible for its emergent properties. Indeed each level of biological information provides a different level of characterisation of the microbial communities. Metagenomics, metatranscriptomics, metaproteomics, metabolomics and SIP-omics can be employed to investigate collectively microbial community structure, potential, function, activity and interactions. Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised our understanding of microbial communities.

Lipidomics in the study of lipid metabolism: Current perspectives in the omic sciences

The advances in systems biology and in the development of new technological tools in analysis, as well as in the omic sciences, among which, metabolomics, and more specifically, lipidomics, have made it possible to investigate the structural and functional complexity of lipids in biological systems. Liquid chromatography and mass spectrometry are the analytical approaches most used in lipid research. Biomedical research, with the development of specific markers for lipids, together with new software development, have both enabled the early diagnosis of several illnesses, besides the evaluation of drug activity and treatment efficacy.

Omics and integrated omics for the promotion of food and nutrition science

Transcriptomics, proteomics, and metabolomics are three major platforms of comprehensive omics analysis in the science of food and complementary medicine. Other omics disciplines, including those of epigenetics and microRNA, are matters of increasing concern. The increased use of the omics approach in food science owes much to the recent advancement of technology and bioinformatic methodologies. Moreover, many researchers now put the combination of multiple omics analysis (integrated omics) into practice to exhaustively understand the functionality of food components. However, data analysis of integrated omics requires huge amount of work and high skill of data handling. A database of nutritional omics data was constructed by the authors, which should help food scientists to analyze their own omics data more effectively. In addition, a novel tool for the easy visualization of omics data was developed by the authors’ group. The tool enables one to overview the changes of multiple omics in the KEGG pathway. Research in traditional and complementary medicine will be further facilitated by promoting the integrated omics research of food functionality. Such integrated research will only be possible with the effective collaboration of scientists with different backgrounds.

Habitual dietary intake impacts on the lipidomic profile

Reliable dietary assessments are essential when attempting to understand the complex links between diet and health. Traditional methods for collecting dietary exposure can be unreliable, therefore there is an increasing interest in identifying biomarkers to provide a more accurate measurement. Metabolomics is a technology that offers great promise in this area. The aim of this study was to use a multivariate statistical strategy to link lipidomic patterns with dietary data in an attempt to identify dietary biomarkers. We assessed the relationship between lipidomic profiles and dietary data in volunteers (n=34) from the Metabolic Challenge Study (MECHE). Principal component analysis (PCA), linear regression and receiver operating characteristic (ROC) analysis were used to (1) reduce the lipidomic data into lipid patterns (LPs), (2) investigate relationships between these patterns and dietary data and (3) identify biomarkers of dietary intake. Our study identified a total of 6 novel LPs. LP1 was highly predictive of dietary fat intake (area under the curve AUC=0.82). A random forest (RF) classification model used to discriminate between low and high consumers resulted with an error rate of >10%, with a panel of six metabolites identified as the most predictive. LP4 was highly predictive of alcohol intake (AUC=0.81) with lysophosphatidylcholine alkyl C18:0 (LPCeC18:0) identified as a potential biomarker of alcohol consumption. LP6 had a reasonably good ability to predict dietary fish intake (AUC=0.76), with lysophosphatidylethanolamine acyl C18:2 (LPEaC18:2) phoshatidylethanolamine diaclyl C38:4 (PEaaC38:4) identified as potential biomarkers. The identification of these LPs and specific biomarkers will help in better classifying a persons dietary intake and in turn will improve the assessment of the relationship between diet and disease. Linking these LPs and specific biomarkers with health parameters will be an important future step.

Multiple metabolomics of uropathogenic E. coli reveal different information content in terms of metabolic potential compared to virulence factors

No single analytical method can cover the whole metabolome and the choice of which platform to use may inadvertently introduce chemical selectivity.

Sampling and analysis of metabolomes in biological fluids

Metabolome analysis involves the study of small molecules that are involved in the metabolic responses that occur through patho-physiological changes caused by genetic stimuli or chemical agents.

Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine

BACKGROUND

Metabolomics is a versatile unbiased method to search for biomarkers of human disease. In particular, one approach in cancer therapy is to promote apoptosis in tumour cells; this could be improved with specific biomarkers of apoptosis for monitoring treatment. We recently observed specific metabolic patterns in apoptotic cell lines; however, in that study, apoptosis was only induced with one pro-apoptotic agent, staurosporine.

OBJECTIVE

The aim of this study was to find novel biomarkers of apoptosis by verifying our previous findings using two further pro-apoptotic agents, 5-fluorouracil and etoposide, that are commonly used in anticancer treatment.

METHODS

Metabolic parameters were assessed in HepG2 and HEK293 cells using the newborn screening assay adapted for cell culture approaches, quantifying the levels of amino acids and acylcarnitines with mass spectrometry.

RESULTS

We were able to identify apoptosis-specific changes in the metabolite profile. Moreover, the amino acids alanine and glutamate were both significantly up-regulated in apoptotic HepG2 and HEK293 cells irrespective of the apoptosis inducer.

CONCLUSION

Our observations clearly indicate the potential of metabolomics in detecting metabolic biomarkers applicable in theranostics and for monitoring drug efficacy.