Metabolomics by numbers: acquiring and understanding global metabolite data

A curious formulation robot enables the discovery of a novel protocell behavior

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry.

Metabolic profiling of liver tissues in mice after instillation of fine particulate matter

Human exposure to fine particulate matter (PM_2.5) in various environment could lead to a number of adverse health effects. Little is known about the toxic mechanism and the further response caused by PM_2.5 exposure. In this study, a metabolomics approach using gas chromatography-mass spectrometry (GC-MS) was adopted to evaluate the liver toxicity induced by different gradient concentrations of PM_2.5. A multivariate statistical analysis had shown, a total of 12 endogenous metabolites including amino acids and organic acids were identified as potential biomarkers of PM_2.5 and most of them were down-regulated. By analyzing the metabolic pathways using the identified biomarkers, the significantly interfered metabolic pathways when mice were exposed to PM_2.5 were found as: glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis, cysteine and methionine metabolism, alanine, aspartate and glutamate metabolism, methane metabolism, linoleic acid metabolism and valine, and leucine and isoleucine biosynthesis, all of which were closely related to liver metabolism. The findings of this study reveal detailed toxic metabolic effects of PM_2.5 in liver tissues, provide ways for assessing the health risk of PM_2.5 at molecular level, and further offer insights on the potential mechanism of its toxicity.

Metabolomics: A Way Forward for Crop Improvement

Metabolomics is an emerging branch of "omics" and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment-gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.

Emerging Insights into the Metabolic Alterations in Aging Using Metabolomics

Metabolomics is the latest 'omics' technology and systems biology science that allows for comprehensive profiling of small-molecule metabolites in biological systems at a specific time and condition. Metabolites are cellular intermediate products of metabolic reactions, which reflect the ultimate response to genomic, transcriptomic, proteomic, or environmental changes in a biological system. Aging is a complex biological process that is characterized by a gradual and progressive decline in molecular, cellular, tissue, organ, and organismal functions, and it is influenced by a combination of genetic, environmental, diet, and lifestyle factors. The precise biological mechanisms of aging remain unknown. Metabolomics has emerged as a powerful tool to characterize the organism phenotypes, identify altered metabolites, pathways, novel biomarkers in aging and disease, and offers wide clinical applications. Here, I will provide a comprehensive overview of our current knowledge on metabolomics led studies in aging with particular emphasis on studies leading to biomarker discovery. Based on the data obtained from model organisms and humans, it is evident that metabolites associated with amino acids, lipids, carbohydrate, and redox metabolism may serve as biomarkers of aging and/or longevity. Current challenges and key questions that should be addressed in the future to advance our understanding of the biological mechanisms of aging are discussed.

Metabolomic analysis of synovial fluid from Thoroughbred racehorses diagnosed with palmar osteochondral disease using magnetic resonance imaging

BACKGROUND

Palmar osteochondral disease (POD) is a common cause of lameness in competition horses. Magnetic resonance imaging (MRI) is the most sensitive diagnostic modality currently available, however it may not be financially or logistically practical for routine screening of POD. There is increasing interest in the use of metabolomics for diagnosis prior to progression to irreversible damage.

OBJECTIVES

To determine metabolite levels in synovial fluid (SF) of horses with a clinical diagnosis of POD based on diagnostic analgesia and MRI, with the hypothesis that metabolomic profiles differ between diseased and healthy joints.

STUDY DESIGN

Prospective clinical study.

METHODS

Synovial fluid was collected from metacarpo/tarsophalangeal joints (MC/TPJ) of 29 horses (n = 51 joints), including 14 controls (n = 26) and 15 cases (n = 25), the latter with lameness localised to the MC/TPJ and MR changes consistent with POD (n = 23). Spectra were produced using ¹ H-nuclear magnetic resonance (NMR) spectroscopy and analysed.

RESULTS

Twenty-five metabolites were recognised associated with various biosynthetic and degradation pathways. The metabolite abundances within the controls demonstrated increased variability compared with the clinical group. The low level of variance between the spectra of the two groups was explained by five principal components. Cross-validation of the cohort demonstrated modest separation of predictive power (R²  = 0.67; Q²  = 0.34). Although statistical significance was not achieved, the most influential metabolites were glucose and lactate.

MAIN LIMITATIONS

The modest sample size and variation in signalment, background and presenting condition of the controls may have impacted the discriminative power of the constructed models. The lack of matched controls, differences in time of fluid collection and freezing times may have also reduced accuracy when representing metabolite profiles.

CONCLUSIONS

This study identified and quantified metabolites present in MC/TPJ SF of clinical cases with POD.

Metabolomics-Based Prospective Studies and Prediction of Type 2 Diabetes Mellitus Risks

The preceding decade has witnessed an intense upsurge in the diabetic population across the world making type 2 diabetes mellitus (T2DM) more of an epidemic than a lifestyle disease. Metabolic disorders are often latent for a while before becoming clinically evident, thus reinforcing the pursuit of early biomarkers of metabolic alterations. A prospective study along with metabolic profiling is the most appropriate way to detect the early pathophysiological changes in metabolic diseases such as T2DM. The aim of this review was to summarize the different potential biomarkers of T2DM identified in prospective studies, which used tools of metabolomics. The review also demonstrates on how metabolomic profiling-based prospective studies can be used to address a concern like population-specific disease mechanism. We performed a literature search on metabolomics-based prospective studies on T2DM using the key words "metabolomics," "Type 2 diabetes," "diabetes mellitus", "metabolite profiling," "prospective study," "metabolism," and "biomarker." Additional articles that were obtained from the reference lists of the articles obtained using the above key words were also examined. Articles on dietary intake, type 1 diabetes mellitus, and gestational diabetes were excluded. The review revealed that many studies showed a direct association of branched-chain amino acids and an inverse association of glycine with T2DM. Majority of the prospective studies conducted were targeted metabolomics-based, with Caucasians as their study cohort. The whole disease risk in populations, including Asians, could therefore not be identified. This review proposes the utility of prospective studies in conjunction with metabolomics platform to unravel the altered metabolic pathways that contribute to the risk of T2DM.

Botanical metabolite ions extraction from full electrospray ionization mass spectrometry using high-dimensional penalized regression

INTRODUCTION

Mass spectrometric data analysis of complex biological mixtures can be a challenge due to its vast datasets. There is lack of data treatment pipelines to analyze chemical signals versus noise. These tasks, so far, have been up to the discretion of the analysts.

OBJECTIVES

The aim of this work is to demonstrate an analytical workflow that would enhance the confidence in metabolomics before answering biological questions by serial dilution of botanical complex mixture and high-dimensional data analysis. Furthermore, we would like to provide an alternative approach to a univariate p-value cutoff from t-test for blank subtraction procedure between negative control and biological samples.

METHODS

A serial dilution of complex mixture analysis under electrospray ionization was proposed to study firsthand chemical complexity of metabolomics. Advanced statistical models using high-dimensional penalized regression were employed to study both the concentration and ion intensity relationship and the ion-ion relationship per second of retention time sub dataset. The multivariate analysis was carried out with a tool built in-house, so called metabolite ions extraction and visualization, which was implemented in R environment.

RESULTS

A test case of the medicinal plant goldenseal (Hydrastis canandensis L.), showed an increase in metabolome coverage of features deemed as "important" by a multivariate analysis compared to features deemed as "significant" by a univariate t-test. For an illustration, the data analysis workflow suggested an unexpected putative compound, 20-hydroxyecdysone. This suggestion was confirmed with MS/MS acquisition and literature search.

CONCLUSION

The multivariate analytical workflow selects "true" metabolite ions signals and provides an alternative approach to a univariate p-value cutoff from t-test, thus enhancing the data analysis process of metabolomics.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

1H NMR-Based Metabolomics and Lipid Analyses Revealed the Effect of Dietary Replacement of Microbial Extracts or Mussel Meal with Fish Meal to Arctic Charr (Salvelinus alpinus)

The effects of replacing 40% of dietary fish meal (FM) in a reference diet (REF) with either mussel meal (MM), zygomycete fungi (ZYG), extracted baker’s yeast (EY), or non-extracted baker’s yeast (NY) on the lipid and metabolic profile of Arctic charr (Salvelinus alpinus) were investigated. After a 14-week feeding trial, liver and muscle tissues were collected for lipid (lipid content, lipid class, fatty acid composition) and 1H NMR-based metabolomics analyses (aqueous and chloroform phases). Lipid analyses showed that fish fed ZYG diet had lower liver lipid content and thereby 10% higher level of docosahexaenoic acid compared with REF. Metabolomics analyses showed that on the one hand fish fed NY diet affected liver metabolites (2–3 fold higher concentrations of e.g., n,n-dimethylglycine and betaine) compared with REF, while, on the other hand, the muscle metabolic fingerprint was mainly affected by EY. In general, affected metabolites (e.g., alanine, anserine, betaine, hydroxyproline, isoleucine, malonate, n,n-dimethylglycine, proline, succinate, and valine) in fish fed test diets suggested that the test meal ingredients caused mainly a response in muscle metabolism. Fish metabolism was least affected by MM, which suggests that it may be suitable to replace fish meal in Arctic charr diets.

Untargeted Metabolomics for Autism Spectrum Disorders: Current Status and Future Directions

Autism spectrum disorders (ASDs) are a group of neurodevelopment disorders characterized by childhood onset deficits in social communication and interaction. Although the exact etiology of most cases of ASDs is unknown, a portion has been proposed to be associated with various metabolic abnormalities including mitochondrial dysfunction, disorders of cholesterol metabolism, and folate abnormalities. Targeted biochemical testing like plasma amino acid and acylcarnitine profiles have demonstrated limited utility in helping to diagnose and manage such patients. Untargeted metabolomics has emerged, however, as a promising tool in screening for underlying biochemical abnormalities and managing treatment and as a means of investigating possible novel biomarkers for the disorder. Here, we review the principles and methodology behind untargeted metabolomics, recent pilot studies utilizing this technology, and areas in which it may be integrated into the care of children with this disorder in the future.

Aluminum toxicity could be mitigated with boron by altering the metabolic patterns of amino acids and carbohydrates rather than organic acids in trifoliate orange

Aluminum (Al) toxicity is the main constraint of root growth and productivity on arable acidic soil. Although boron (B) is used to ameliorate Al stress, the exact mechanisms underlying the effects of B on Al-induced alteration on root metabolites are poorly understood, especially in the trifoliate orange, which is an important rootstock in China. Therefore, a hydroponics experiment was conducted to explore the mechanisms of B mitigates Al toxicity in roots of citrus by metabolomics. A total of 60 metabolites were identified and analyzed in the present study. The 17 amino acids and 8 sugars were up-regulated in Al-treated roots, mainly histidine, cycloleucine, asparagine, citrulline, raffinose and trehalose, and increased by 38.5-, 8.7-, 6.0-, 6.0-, 7.5- and 6.6-fold, respectively. Meanwhile, significant down-regulation of aspartic acid, isoleucine, glutamic acid and six sugars were indicated under Al stress. Aluminum induced a decrease of nine organic acids, especially l-malic acid, citric acid and threonic acid, by 98.2, 93.6 and 95.1%, respectively. Interestingly, in the presence of Al, B application decreased the contents of asparagine, cycloleucine, citrulline and histidine as well as myo-inositol, raffinose, galactinol and 3,6-anhydro-d-galactose by 52.2, 57.4, 46.7, 63.0, 65.4, 74.3, 62.5 and 55.0%, respectively. However, there was no obvious difference in the organic acid contents in Al-stressed roots treated with B. Conclusively, our results show that B regulates the metabolic patterns of amino acids and carbohydrates and reduces Al toxicity. Nevertheless, B addition did not affect the Al-induced changes in the metabolic modes of organic acids.

Metabolomics: a promising diagnostic and therapeutic implement for breast cancer

Breast cancer (BC) is the most commonly diagnosed cancer among women and the leading cause of cancer death. Despite the advent of numerous diagnosis and treatment methods in recent years, this heterogeneous disease still presents great challenges in early diagnosis, curative treatments and prognosis monitoring. Thus, finding promising early diagnostic biomarkers and therapeutic targets and approaches is meaningful. Metabolomics, which focuses on the analysis of metabolites that change during metabolism, can reveal even a subtle abnormal change in an individual. In recent decades, the exploration of cancer-related metabolomics has increased. Metabolites can be promising biomarkers for the screening, response evaluation and prognosis of BC. In this review, we summarized the workflow of metabolomics, described metabolite signatures based on molecular subtype as well as reclassification and then discussed the application of metabolomics in the early diagnosis, monitoring and prognosis of BC to offer new insights for clinicians in breast cancer diagnosis and treatment.

Metabolomic study on bleomycin and polyhexamethylene guanidine phosphate-induced pulmonary fibrosis mice models

INTRODUCTION

Polyhexamethylene guanidine phosphate (PHMG) has been used as a disinfectant and biocide, and was known to be harmless and non-toxic. However, in 2011, PHMG used as a humidifier disinfectant was reported to be associated with lung diseases, such as, fibrosis in the toxicant studies on pulmonary fibrosis by PHMG. However, no metabolomics study has been performed in PHMG-induced mouse models of pulmonary fibrosis.

OBJECTIVES

We performed a metabolomic study to understand the biochemical events that occur in bleomycin (BLM)- and PHMG-induced mouse models of pulmonary fibrosis using gas chromatography-mass spectrometry (GC-MS), LC-tandem MS, and GC-tandem MS.

RESULTS

The levels of 61 metabolites of 30 amino acids, 13 organic acids, 12 fatty acids, 5 polyamines, and oxidized glutathione were determined in the pulmonary tissues of mice with BLM- and PHMG-induced pulmonary fibrosis and in normal controls. Principal component analysis and partial least squares discriminant analysis used to compare level of these 61 metabolites in pulmonary tissues. Levels of metabolites were significantly different in the BLM and PHMG groups as compared with the control group. In particular, the BLM- and PHMG-induced pulmonary fibrosis models showed elevated collagen synthesis and oxidative stress and metabolic disturbance of TCA related organic acids including fumaric acid by NADPH oxidase. In addition, polyamine metabolism showed severe alteration in the PHMG group than that of the BLM group.

CONCLUSION

This result suggests PHMG will be able to induce pulmonary fibrosis by arginine metabolism and NADPH oxidase signaling.

Current status of urinary diagnostic biomarkers for colorectal cancer

Fecal occult blood test (FOBT) and flexible sigmoidoscopy are the currently using screening methods for colorectal cancer (CRC). However, these methods still have problems of high false positive rates in FOBT and increased invasiveness and cost associated with endoscopy. The development of non-invasive biomarkers is thus important for the diagnosis of CRC. Urine is one of the most commonly used samples for mass screening owing to its non-invasive and simple process of collection; however, the discovery of urinary diagnostic biomarkers for malignancies is still challenging and developing. Since urine contains abundant substances reflecting systemic body condition, urinary biomarker might contribute to detect CRC in a completely non-invasive manner. In this review, we describe the current utility of urinary diagnostic biomarkers for CRC.

Untargeted metabolomics analysis of rat hippocampus subjected to sleep fragmentation

Sleep fragmentation (SF) commonly occurs in several pathologic conditions and is especially associated with impairments of hippocampus-dependent neurocognitive functions. Although the effects of SF on hippocampus in terms of protein or gene levels were examined in several studies, the impact of SF at the metabolite level has not been investigated. Thus, in this study, the differentially expressed large-scale metabolite profiles of hippocampus in a rat model of SF were investigated using untargeted metabolomics approaches. Forty-eight rats were divided into the following 4 groups: 4-day SF group, 4-day exercise control (EC) group, 15-day SF group, and 15-day EC group (n = 12, each). SF was accomplished by forced exercise using a walking wheel system with 30-s on/90-s off cycles, and EC condition was set at 10-min on/30-min off. The metabolite profiles of rat hippocampi in the SF and EC groups were analyzed using liquid chromatography/mass spectrometry. Multivariate analysis revealed distinctive metabolic profiles and marker signals between the SF and corresponding EC groups. Metabolic changes were significant only in the 15-day SF group. In the 15-day SF group, L-tryptophan, myristoylcarnitine, and palmitoylcarnitine were significantly increased, while adenosine monophosphate, hypoxanthine, L-glutamate, L-aspartate, L-methionine, and glycerophosphocholine were decreased compared to the EC group. The alanine, aspartate, and glutamate metabolism pathway was observed as the common key pathway in the 15-day SF groups. The results from this untargeted metabolomics study provide a perspective on metabolic impact of SF on the hippocampus.

A 1H NMR based study of hemolymph metabonomics in different resistant silkworms, Bombyx mori (Lepidotera), after BmNPV inoculation

Bombyx mori nucleopolyhedrovirus (BmNPV) is a primary silkworm pathogen, and the molecular mechanism of silkworm defense to BmNPV infection is still unclear. Herein, comparative metabolomics was adopted to analyze the variations in the hemolymph metabolites of different resistant silkworm strains following BmNPV inoculation using a ¹H NMR method. Trehalose, as an instant source of energy, plays a crucial role in the response to pathogen infections in insects. The level of trehalose was persistently upregulated in the hemolymph of the resistant silkworm strain YeA following infection with BmNPV, compared to that of the susceptible strain YeB, indicating that trehalose metabolism plays a vital role in the response to BmNPV infection. The significant upregulation of TCA cycle relevant metabolites, including malate, fumarate, citrate, succinate, and α-ketoglutarate, was identified at 0 h, 12 h, 48 h, and 96 h post-infection in YeA hemolymph, whereas a significant upregulation in YeB hemolymph was only detected at an early stage of infection (0 h-24 h). The expression level of selected key metabolic enzymes, determined using RT-qPCR, validated the differences in trehalose and TCA cycle relevant metabolite levels. The variations in branched-chain amino acid (BCAA) pathway relevant metabolites in resistant silkworm strains following BmNPV infection showed a regular undulation at different times after infection. A significant accumulation of phenylalanine and tyrosine was observed in YeA following BmNPV infection compared to YeB. The glycolysis and gluconeogenesis pathways showed a relatively low activity in YeA following BmNPV infection. Moreover, the levels of other metabolites related to fat metabolism, transamination, energy metabolism, and glycometabolism, such as glycine, threonine, glutamine, and glutamate, were unstable in the two silkworm strains following BmNPV infection. Thus, our study provides an overview of the metabolic response of the silkworm in response to BmNPV infection, which lays the foundation for clarifying the mechanism of silkworm resistance to BmNPV infection.

Metabolome of Exosomes: Focus on Vesicles Released by Cancer Cells and Present in Human Body Fluids

Exosomes and other classes of extracellular vesicles (EVs) have gained interest due to their role in cell-to-cell communication. Knowledge of the molecular content of EVs may provide important information on features of parental cells and mechanisms of cross-talk between cells. To study functions of EVs it is essential to know their composition, that includes proteins, nucleic acids, and other classes biomolecules. The metabolome, set of molecules the most directly related to the cell phenotype, is the least researched component of EVs. However, the metabolome of EVs circulating in human blood and other bio-fluids is of particular interest because of its potential diagnostic value in cancer and other health conditions. On the other hand, the metabolome of EVs released to culture media in controlled conditions in vitro could shed light on important aspects of communication between cells in model systems. This paper summarizes the most common approaches implemented in EV metabolomics and integrates currently available data on the composition of the metabolome of EVs obtained in different models with particular focus on human body fluids and cancer cells.

Comparative metabolomic analysis reveals global cadmium stress response of Lactobacillus plantarum strains

Our previous work demonstrated the protective effects of Lactobacillus plantarum (L. plantarum) strains against cadmium (Cd) toxicity in vivo, and also indicated that the Cd tolerance of the strains played an important role in this protection. The goal of this study was to investigate the Cd resistance mechanism of L. plantarum by liquid chromatography-mass spectrometry (LC-MS) based metabolomic analysis, with a focus on the global Cd stress response. L. plantarum CCFM8610 (strongly resistant to Cd) and L. plantarum CCFM191 (sensitive to Cd) were selected as target strains, and their metabolomic profiles with and without Cd exposure were compared. The underlying mechanisms of the intra-species distinction between CCFM8610 and CCFM191 in terms of Cd tolerance can be attributed to the following aspects: (a) CCFM8610 possesses a higher intracellular content of osmolytes; (b) CCFM8610 can induce more effective biosynthesis of extracellular polymeric substance (EPS) to sequestrate Cd;

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

"-Omics" workflow for paleolimnological and geological archives: A review

"-Omics" is a powerful screening method with applications in molecular biology, toxicology, wildlife biology, natural product discovery, and many other fields. Genomics, proteomics, metabolomics, and lipidomics are common examples included under the "-omics" umbrella. This screening method uses combinations of untargeted, semi-targeted, and targeted analyses paired with data mining to facilitate researchers' understanding of the genome, proteins, and small organic molecules in biological systems. Recently, however, the use of "-omics" has expanded into the fields of geology, specifically petrology, and paleolimnology. Specifically, untargeted analyses stand to transform these fields as petroleomics, and sediment-"omics" become more prevalent. "-Omics" facilitates the visualization of small molecule profiles from environmental matrices (i.e. oil and sediment). Small molecule profiles can provide improved understanding of small molecules distributions throughout the environment, and how those compositions can change depending on conditions (i.e. climate change, weathering, etc.). "-Omics" also facilities discovery of next-generation biomarkers that can be used for oil source identification and as proxies for reconstructing past environmental changes. Untargeted analyses paired with data mining and multivariate statistical analyses represents a powerful suite of tools for hypothesis generation, and new method development for environmental reconstructions. Here we present an introduction to "-omics" methodology, technical terms, and examples of applications to paleolimnology and petrology. The purpose of this review is to highlight the important considerations at each step in the "-omics" workflow to produce high quality and statistically powerful data for petrological and paleolimnological applications.

Tremorgenic effects and functional metabolomics analysis of lolitrem B and its biosynthetic intermediates

The neuroactive mycotoxin lolitrem B causes a neurological syndrome in grazing livestock resulting in hyperexcitability, muscle tremors, ataxia and, in severe cases, clonic seizures and death. To define the effects of the major toxin lolitrem B in the brain, a functional metabolomic study was undertaken in which motor coordination and tremor were quantified and metabolomic profiling undertaken to determine relative abundance of both toxin and key neurotransmitters in various brain regions in male mice. Marked differences were observed in the duration of tremor and coordination between lolitrem B pathway members, with some showing protracted effects and others none at all. Lolitrem B was identified in liver, kidney, cerebral cortex and thalamus but not in brainstem or cerebellum which were hypothesised previously to be the primary site of action. Metabolomic profiling showed significant variation in specific neurotransmitter and amino acid profiles over time. This study demonstrates accumulation of lolitrem B in the brain, with non-detectable levels of toxin in the brainstem and cerebellum, inducing alterations in metabolites such as tyrosine, suggesting a dynamic catecholaminergic response over time. Temporal characterisation of key pathways in the pathophysiological response of lolitrem B in the brain were also identified.

The Relevance of Mass Spectrometry Analysis for Personalized Medicine through Its Successful Application in Cancer "Omics"

Mass spectrometry (MS) is an essential analytical technology on which the emerging omics domains; such as genomics; transcriptomics; proteomics and metabolomics; are based. This quantifiable technique allows for the identification of thousands of proteins from cell culture; bodily fluids or tissue using either global or targeted strategies; or detection of biologically active metabolites in ultra amounts. The routine performance of MS technology in the oncological field provides a better understanding of human diseases in terms of pathophysiology; prevention; diagnosis and treatment; as well as development of new biomarkers; drugs targets and therapies. In this review; we argue that the recent; successful advances in MS technologies towards cancer omics studies provides a strong rationale for its implementation in biomedicine as a whole.

Plasma metabolite biomarkers related to secondary hyperparathyroidism and parathyroid hormone

BACKGROUND

Hyperphosphatemia, hypocalcemia, and elevation of parathyroid hormone (PTH) are typical abnormalities of uremic patients with Secondary hyperparathyroidism (SHPT). However, metabolic imbalance associated with SHPT is not well understood.

METHODS

A total of 15 SHPT patients with an intact parathyroid hormone (iPTH) level > 600 pg/mL were set as preoperative (PR) group, 15 age- and gender-matched controls who had undergone parathyroidectomy plus forearm transplantation because of hyperparathyroidism and achieved an iPTH level <150 pg/mL were set as postoperative (PO) group. Metabolite profiling of these 30 uremic patients and five healthy controls (HC) was performed using ultra performance liquid chromatography-mass spectrometry.

RESULTS

Five differential metabolites, including allyl isothiocyanate, L-phenylalanine, D-Aspartic acid, indoleacetaldehyde, and D-galactose correlated with PTH were identified in this study. Taking them as a biomarker signature, PR group can be distinguished from HC group with an area under the curve (AUC) of 0.947 (95% CI, 0.76-1) and PO group with an AUC of 0.6 (95% CI, 0.38-0.807).

CONCLUSIONS

The serum metabolome correlated with PTH is successfully demonstrated for a better understanding of the pathogenesis of SHPT.

Anticancer Drug Affects Metabolomic Profiles in Multicellular Spheroids: Studies Using Mass Spectrometry Imaging Combined with Machine Learning

Multicellular spheroids (hereinafter referred to as spheroids) are 3D biological models. The metabolomic profiles inside spheroids provide crucial information reflecting the molecular phenotypes and microenvironment of cells. To study the influence of an anticancer drug on the spatially resolved metabolites, spheroids were cultured using HCT-116 colorectal cancer cells, treated with the anticancer drug Irinotecan under a series of time- and concentration-dependent conditions. The Single-probe mass spectrometry imaging (MSI) technique was utilized to conduct the experiments. The MSI data were analyzed using advanced data analysis methods to efficiently extract metabolomic information. Multivariate curve resolution alternating least square (MCR-ALS) was used to decompose each MS image into different components with grouped species. To improve the efficiency of data analysis, both supervised (Random Forest) and unsupervised (cluster large applications (CLARA)) machine learning (ML) methods were employed to cluster MS images according to their metabolomic features. Our results indicate that anticancer drug significantly affected the abundances of a variety of metabolites in different regions of spheroids. This integrated experiment and data analysis approach can facilitate the studies of metabolites in different types of 3D tumor models and tissues and potentially benefit the drug discovery, therapeutic resistance, and other biological research fields.

The metabolome: A key measure for exposome research in epidemiology

PURPOSE OF REVIEW

Application of omics to study human health has created a new era of opportunities for epidemiology research. However, approaches to characterize exogenous health triggers have largely not leveraged advances in analytical platforms and big data. In this review, we highlight the exposome, which is defined as the cumulative measure of exposure and biological responses across a lifetime as a cornerstone for new epidemiology approaches to study complex and preventable human diseases.

RECENT FINDINGS

While no universal approach exists to measure the entirety of the exposome, use of high-resolution mass spectrometry methods provide distinct advantages over traditional biomonitoring and have provided key advances necessary for exposome research. Application to different study designs and recommendations for combining exposome data with novel data analytic frameworks to study complex interactions of multiple stressors are also discussed.

SUMMARY

Even though challenges still need to be addressed, advances in methods to characterize the exposome provide exciting new opportunities for epidemiology to support fundamental discoveries to improve public health.

The regulation of crecropin-A and gloverin 2 by the silkworm Toll-like gene 18 wheeler in immune response

The innate immune system is conserved among different insect species in its response to microorganism infection. The transmembrane receptors of the Toll superfamily play an important role in activating immune response, however, the function of silkworm Toll family member 18 Wheeler (18 W) remained unclear. Here, the 18w gene in silkworm was characterized. A relatively high transcription level of Bm18w mRNA was found in Malpighian tubules, and in eggs, larvae pre-molt to fourth instar, pupae and adults. When silkworm larvae were infected with E. coli or S. aureus, Bm18w showed a significant response, especially to E. coli, but did not have antibacterial activity. To further identify the downstream antimicrobial peptide genes of Bm18w, expression of Bm18w was knocked down with siRNA in vitro, resulting in significant decreases of cecropin-A, gloverin 2, and moricin B3. The overexpression of Bm18w was carried out using pIZT/V5-His-mCherry insect vector in BmN cells and significant upregulation of cecropin-A and gloverin 2 was detected, as well as upregulation of attacin and defensin. Based on the results, we concluded that Bm18w is involved in response to bacterial infection by selectively inducing the expression of antimicrobial peptide genes, especially cecropin-A and gloverin 2. This study provides valuable data to supplement understanding of the immune pathway of the silkworm.

Metabolomics in Plant Stress Physiology

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies. Graphical Abstract.

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.

Evaluation and validation of an analytical approach for high-throughput metabolomic fingerprinting using direct introduction-high-resolution mass spectrometry: Applicability to classification of urine of scrapie-infected ewes

Direct injection-mass spectrometry can be used to perform high-throughput metabolomic fingerprinting. This work aims to evaluate a global analytical workflow in terms of sample preparation (urine sample dilution), high-resolution detection (quality of generated data based on criteria such as mass measurement accuracy and detection sensitivity) and data analysis using dedicated bioinformatics tools. Investigation was performed on a large number of biological samples collected from sheep infected or not with scrapie. Direct injection-mass spectrometry approach is usually affected by matrix effects, eventually hampering detection of some relevant biomarkers. Reference compounds were spiked in biological samples to help evaluate the quality of direct injection-mass spectrometry data produced by Fourier Transform mass spectrometry. Despite the potential of high-resolution detection, some drawbacks still remain. The most critical is the presence of matrix effects, which could be minimized by optimizing the sample dilution factor. The data quality in terms of mass measurement accuracy and reproducible intensity was evaluated. Good repeatability was obtained for the chosen dilution factor (i.e., 2000). More than 150 analyses were performed in less than 16 hours using the optimized direct injection-mass spectrometry approach. Discrimination of different status of sheeps in relation to scrapie infection (i.e., scrapie-affected, preclinical scrapie or healthy) was obtained from the application of Shrinkage Discriminant Analysis to the direct injection-mass spectrometry data. The most relevant variables related to this discrimination were selected and annotated. This study demonstrated that the choice of appropriated dilution faction is indispensable for producing quality and informative direct injection-mass spectrometry data. Successful application of direct injection-mass spectrometry approach for high throughput analysis of a large number of biological samples constitutes the proof of the concept.

Elemental metabolomics

Elemental metabolomics is quantification and characterization of total concentration of chemical elements in biological samples and monitoring of their changes. Recent advances in inductively coupled plasma mass spectrometry have enabled simultaneous measurement of concentrations of > 70 elements in biological samples. In living organisms, elements interact and compete with each other for absorption and molecular interactions. They also interact with proteins and nucleotide sequences. These interactions modulate enzymatic activities and are critical for many molecular and cellular functions. Testing for concentration of > 40 elements in blood, other bodily fluids and tissues is now in routine use in advanced medical laboratories. In this article, we define the basic concepts of elemental metabolomics, summarize standards and workflows, and propose minimum information for reporting the results of an elemental metabolomics experiment. Major statistical and informatics tools for elemental metabolomics are reviewed, and examples of applications are discussed. Elemental metabolomics is emerging as an important new technology with applications in medical diagnostics, nutrition, agriculture, food science, environmental science and multiplicity of other areas.

Urine-NMR metabolomics for screening of advanced colorectal adenoma and early stage colorectal cancer

Although colorectal cancer (CRC) is considered one of the most preventable cancers, no non-invasive, accurate diagnostic tool to screen CRC exists. We explored the potential of urine nuclear magnetic resonance (NMR) metabolomics as a diagnostic tool for early detection of CRC, focusing on advanced adenoma and stage 0 CRC. Urine metabolomics profiles from patients with colorectal neoplasia (CRN; 36 advanced adenomas and 56 CRCs at various stages, n = 92) and healthy controls (normal, n = 156) were analyzed by NMR spectroscopy. Healthy and CRN groups were statistically discriminated using orthogonal projections to latent structure discriminant analysis (OPLS-DA). The class prediction model was validated by three-fold cross-validation. The advanced adenoma and stage 0 CRC were grouped together as pre-invasive CRN. The OPLS-DA score plot showed statistically significant discrimination between pre-invasive CRN as well as advanced CRC and healthy controls with a Q2 value of 0.746. In the prediction validation study, the sensitivity and specificity for diagnosing pre-invasive CRN were 96.2% and 95%, respectively. The grades predicted by the OPLS-DA model showed that the areas under the curve were 0.823 for taurine, 0.783 for alanine, and 0.842 for 3-aminoisobutyrate. In multiple receiver operating characteristics curve analyses, taurine, alanine, and 3-aminoisobutyrate were good discriminators for CRC patients. NMR-based urine metabolomics profiles significantly and accurately discriminate patients with pre-invasive CRN as well as advanced CRC from healthy individuals. Urine-NMR metabolomics has potential as a screening tool for accurate diagnosis of pre-invasive CRN.

Validated comprehensive metabolomics and lipidomics analysis of colon tissue and cell lines

Current untargeted approaches for metabolic fingerprinting of colon tissue and cell lines lack validation of reproducibility and/or focus on a selection of metabolites as opposed to the entire metabolome. Yet, both are critical to ensure reliable results and pursue a fully holistic analysis. Therefore, we have optimized and validated a platform for analyzing the polar metabolome and lipidome of colon-derived cell and tissue samples based on a consecutive extraction of polar and apolar components. Peak areas of selected targeted analytes and the number of untargeted components were assessed. Analysis was performed using ultra-high performance liquid-chromatography (UHPLC) coupled to hybrid quadrupole-Orbitrap high-resolution mass spectrometry (HRMS). This resulted in an optimized extraction protocol using 50% methanol/ultrapure water to obtain the polar fraction followed by a dichloromethane-based lipid extraction. Using this comprehensive approach, we have detected more than 15,000 components with CV < 30% in internal quality control (IQC) samples and were able to discriminate the non-transformed (NT) and transformed (T) state in human colon tissue and cell lines based on validated OPLS-DA models (R²Y > 0.719 and Q² > 0.674). To conclude, our validated polar metabolomics and lipidomics fingerprinting approach could be of great value to reveal gastrointestinal disease-associated biomarkers and mechanisms.

Automated supervised learning pipeline for non-targeted GC-MS data analysis

Non-targeted analysis is nowadays applied in many different domains of analytical chemistry such as metabolomics, environmental and food analysis. Conventional processing strategies for GC-MS data include baseline correction, feature detection, and retention time alignment before multivariate modeling. These techniques can be prone to errors and therefore time-consuming manual corrections are generally necessary. We introduce here a novel fully automated approach to non-targeted GC-MS data processing. This new approach avoids feature extraction and retention time alignment. Supervised machine learning on decomposed tensors of segmented chromatographic raw data signal is used to rank regions in the chromatograms contributing to differentiation between sample classes. The performance of this novel data analysis approach is demonstrated on three published datasets.

Direct-Injection Electron Ionization-Mass Spectrometry Metabolomics Method for Analyzing Blueberry Leaf Metabolites That Inhibit Adult T-cell Leukemia Proliferation

Metabolic profiling is often used to identify possible correlations between a compound's metabolic profile and biological activity. Direct-injection electron ionization-mass spectrometry "fingerprinting" is useful for characterizing biological materials. We demonstrate the utility of direct-injection electron ionization-mass spectrometry for metabolic profiling using 100 different extracts of leaves from 20 blueberry cultivars collected at 5 time points from April to December 2008. A qualitative direct-injection electron ionization-mass spectrometry method was used to profile the major and/or minor constituents in the blueberry leaf extracts. Blueberry leaf extracts could be distinguished by principal component analysis based on the absolute intensity of characteristic fragment ions. Twenty cultivars were categorized into four species, and the most appropriate discriminative marker m/z value for identifying each cultivar was selected statistically. Correlated m/z values indicating the collection month were determined in the same analysis, and air temperature variance factors were extracted from score plots by principal component analysis. We previously reported that blueberry extracts inhibit the proliferation of adult T-cell leukemia cells. Leaves of Vaccinium virgatum collected in December of 2008 exhibited significantly greater inhibition of adult T-cell leukemia cell proliferation than other species. Highly bioactive cultivars or species were identified by direct-injection electron ionization-mass spectrometry metabolomics analysis of blueberry leaf extracts. The components extracted based on our direct-injection electron ionization-mass spectrometry analyses could be used to construct a model to predict anti-adult T-cell leukemia bioactivity. This is the first study to report a relationship between seasonal variation and bioactivity of natural products using a direct-injection electron ionization-mass spectrometry metabolomics method.

Metabolomics and Age-Related Macular Degeneration

Age-related macular degeneration (AMD) leads to irreversible visual loss, therefore, early intervention is desirable, but due to its multifactorial nature, diagnosis of early disease might be challenging. Identification of early markers for disease development and progression is key for disease diagnosis. Suitable biomarkers can potentially provide opportunities for clinical intervention at a stage of the disease when irreversible changes are yet to take place. One of the most metabolically active tissues in the human body is the retina, making the use of hypothesis-free techniques, like metabolomics, to measure molecular changes in AMD appealing. Indeed, there is increasing evidence that metabolic dysfunction has an important role in the development and progression of AMD. Therefore, metabolomics appears to be an appropriate platform to investigate disease-associated biomarkers. In this review, we explored what is known about metabolic changes in the retina, in conjunction with the emerging literature in AMD metabolomics research. Methods for metabolic biomarker identification in the eye have also been discussed, including the use of tears, vitreous, and aqueous humor, as well as imaging methods, like fluorescence lifetime imaging, that could be translated into a clinical diagnostic tool with molecular level resolution.

Differences among Unique Nanoparticle Protein Corona Constructs: A Case Study Using Data Analytics and Multi-Variant Visualization to Describe Physicochemical Characteristics

Gold nanoparticles (AuNPs) used in pharmaceutical treatments have been shown to effectively deliver a payload, such as an active pharmaceutical ingredient or image contrast agent, to targeted tissues in need of therapy or diagnostics while minimizing exposure, availability, and accumulation to surrounding biological compartments. Data sets collected in this field of study include some toxico- and pharmacodynamic properties (e.g., distribution and metabolism) but many studies lack information about adsorption of biological molecules or absorption into cells. When nanoparticles are suspended in blood serum, a protein corona cloud forms around its surface. The extent of the applications and implications of this formed cloud are unknown. Some researchers have speculated that the successful use of nanoparticles in pharmaceutical treatments relies on a comprehensive understanding of the protein corona composition. The work presented in this paper uses a suite of data analytics and multi-variant visualization techniques to elucidate particle-to-protein interactions at the molecular level. Through mass spectrometry analyses, corona proteins were identified through large and complex datasets. With such high-output analyses, complex datasets pose a challenge when visualizing and communicating nanoparticle-protein interactions. Thus, the creation of a streamlined visualization method is necessary. A series of user-friendly data informatics techniques were used to demonstrate the data flow of protein corona characteristics. Multi-variant heat maps, pie charts, tables, and three-dimensional regression analyses were used to improve results interpretation, facilitate an iterative data transfer process, and emphasize features of the nanoparticle-protein corona system that might be controllable. Data informatics successfully highlights the differences between protein corona compositions and how they relate to nanoparticle surface charge.

Canopy attachment position influences metabolism and peel constituency of European pear fruit

BACKGROUND

Inconsistent pear fruit ripening resulting from variable harvest maturity within tree canopies can contribute to postharvest losses through senescence and spoilage that would otherwise be effectively managed using crop protectant and storage regimes. Because those inconsistencies are likely based on metabolic differences, non-targeted metabolic profiling peel of 'd'Anjou' pears harvested from the external or internal canopy was used to determine the breadth of difference and link metabolites with canopy position during long-term controlled atmosphere storage.

RESULTS

Differences were widespread, encompassing everything from expected distinctions in flavonol glycoside levels between peel of fruit from external and internal canopy positions to increased aroma volatile production and sucrose hydrolysis with ripening. Some of the most substantial differences were in levels of triterpene and phenolic peel cuticle components among which acyl esters of ursolic acid and fatty acyl esters of p-coumaryl alcohol were higher in the cuticle of fruit from external tree positions, and acyl esters of α-amyrin were elevated in peel of fruit from internal positions. Possibly the most substantial dissimilarities were those that were directly related to fruit quality. Phytosterol conjugates and sesquiterpenes related to elevated superficial scald risk were higher in pears from external positions which were to be potentially rendered unmarketable by superficial scald. Other metabolites associated with fruit aroma and flavor became more prevalent in external fruit peel as ripening progressed and, likewise, with differential soluble solids and ethylene levels, suggesting the final product not only ripens differentially but the final fruit quality following ripening is actually different based on the tree position.

CONCLUSIONS

Given the impact tree position appears to have on the most intrinsic aspects of ripening and quality, every supply chain management strategy would likely lead to diverse storage outcomes among fruit from most orchards, especially those with large canopies. Metabolites consistently associated with peel of fruit from a particular canopy position may provide targets for non-destructive pre-storage sorting used to reduce losses contributed by this inconsistency.

Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood. Moreover, a typical progression of children with AD to food allergy, asthma or allergic rhinitis has been reported (“allergic march” or “atopic march”). AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid, and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, might provide the opportunity to capture the flow of information underlying AD disease manifestation. Overlapping genes/candidates derived from multiple omics types include FLG, SPINK5, S100A8, and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFkB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by “big omics data” and their integration.

A New Approach of Outlier-robust Missing Value Imputation for Metabolomics Data Analysis

Background:

Metabolomics data generation and quantification are different from other types of molecular “omics” data in bioinformatics. Mass spectrometry (MS) based (gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), etc.) metabolomics data frequently contain missing values that make some quantitative analysis complex. Typically metabolomics datasets contain 10% to 20% missing values that originate from several reasons, like analytical, computational as well as biological hazard. Imputation of missing values is a very important and interesting issue for further metabolomics data analysis. </P><P> Objective: This paper introduces a new algorithm for missing value imputation in the presence of outliers for metabolomics data analysis. </P><P> Method: Currently, the most well known missing value imputation techniques in metabolomics data are knearest neighbours (kNN), random forest (RF) and zero imputation. However, these techniques are sensitive to outliers. In this paper, we have proposed an outlier robust missing imputation technique by minimizing twoway empirical mean absolute error (MAE) loss function for imputing missing values in metabolomics data.

Results:

We have investigated the performance of the proposed missing value imputation technique in a comparison of the other traditional imputation techniques using both simulated and real data analysis in the absence and presence of outliers.

Conclusion:

Results of both simulated and real data analyses show that the proposed outlier robust missing imputation technique is better performer than the traditional missing imputation methods in both absence and presence of outliers.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

Integrating -omics data into genome-scale metabolic network models: principles and challenges

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available -omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of -omics data into CBMs focussing on the methods' assumptions and limitations. We argue that key assumptions - often derived from single-enzyme kinetics - do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for -omics data integration in a common framework to provide more accurate predictions.

Metabolic Vulnerabilities of Prostate Cancer: Diagnostic and Therapeutic Opportunities

Cancer cells hijack metabolic pathways to support bioenergetics and biosynthetic requirements for their uncontrolled growth. Thus, cancer can be considered as a metabolic disease. In this review, we discuss the main metabolic features of prostate cancer with a particular focus on the link between oncogene-directed cancer metabolic regulation, metabolism rewiring, and epigenetic regulation. The potential of using metabolic profiling as a means to predict disease behavior and to identify novel therapeutic targets and new diagnostic markers will be addressed as well as the current challenges in metabolomics analyses. Finally, diagnostic and prognostic metabolic imaging approaches, including positron emission tomography, mass spectrometry, nuclear magnetic resonance, and their translational applications, will be discussed. Here, we emphasize how targeting metabolic vulnerabilities in prostate cancer may pave the way for novel personalized diagnostic and therapeutic interventions.

New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis

INTRODUCTION

Direct injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues. However, spectral artifacts can generate large numbers of spectral features (peaks) that do not correspond to known compounds. Misassignment of these artifactual features creates interpretive errors and limits our ability to discern the role of representative features within living systems.

OBJECTIVES

Our goal is to develop rigorous methods that identify and handle spectral artifacts within the context of high-throughput FT-MS-based metabolomics studies.

RESULTS

We observed three types of artifacts unique to FT-MS that we named high peak density (HPD) sites: fuzzy sites, ringing and partial ringing. While ringing artifacts are well-known, fuzzy sites and partial ringing have not been previously well-characterized in the literature. We developed new computational methods based on comparisons of peak density within a spectrum to identify regions of spectra with fuzzy sites. We used these methods to identify and eliminate fuzzy site artifacts in an example dataset of paired cancer and non-cancer lung tissue samples and evaluated the impact of these artifacts on classification accuracy and robustness.

CONCLUSION

Our methods robustly identified consistent fuzzy site artifacts in our FT-MS metabolomics spectral data. Without artifact identification and removal, 91.4% classification accuracy was achieved on an example lung cancer dataset; however, these classifiers rely heavily on artifactual features present in fuzzy sites. Proper removal of fuzzy site artifacts produces a more robust classifier based on non-artifactual features, with slightly improved accuracy of 92.4% in our example analysis.

Plasma metabolomic profiles differ at the time of artificial insemination based on pregnancy outcome, in Bos taurus beef heifers

Infertility remains the most prevalent reason for cattle being removed from production environments. We utilized metabolomic profiling to identify metabolites in the blood plasma that may be useful in identifying infertile heifers at the time of artificial insemination (AI). Prior to AI, phenotypic parameters including body condition, weight, and reproductive organ measurements were collected. These were determined not effective at differentiating between fertile and infertile heifers. Analysis of the resulting metabolomic profiles revealed 15 metabolites at significantly different levels (T-test P ≤ 0.05), with seven metabolites having a greater than 2-fold difference (T-test P ≤ 0.05, fold change ≥2, ROC-AUC ≥ 0.80) between infertile and fertile heifers. We further characterized the utility of using the levels of these metabolites in the blood plasma to discriminate between fertile and infertile heifers. Finally, we investigated the potential role inflammation may play by comparing the expression of inflammatory cytokines in the white blood cells of infertile heifers to that of fertile heifers. We found significantly higher expression in infertile heifers of the proinflammatory markers tumor necrosis factor alpha (TNFα), interleukin 6 (IL6), and the C-X-C motif chemokine 5 (CXCL5). Our work offers potentially valuable information regarding the diagnosis of fertility problems in heifers undergoing AI.

Metabolomic characterization of hypertension and dyslipidemia

BACKGROUND

Hypertension and dyslipidemia are two main risk factors for cardiovascular diseases (CVD). Moreover, their coexistence predisposes individuals to a considerably increased risk of CVD. However, the regulatory mechanisms involved in hypertension and dyslipidemia as well as their interactions are incompletely understood.

OBJECTIVES

The aims of our study were to identify metabolic biomarkers and pathways for hypertension and dyslipidemia, and compare the metabolic patterns between hypertension and dyslipidemia.

METHODS

In this study, we performed metabolomic investigations into hypertension and dyslipidemia based on a "healthy" UK population. Metabolomic data from the Husermet project were acquired by gas chromatography-mass spectrometry and ultra-performance liquid chromatography-mass spectrometry. Both univariate and multivariate statistical methods were used to facilitate biomarker selection and pathway analysis.

RESULTS

Serum metabolic signatures between individuals with and without hypertension or dyslipidemia exhibited considerable differences. Using rigorous selection criteria, 26 and 46 metabolites were identified as potential biomarkers of hypertension and dyslipidemia respectively. These metabolites, mainly involved in fatty acid metabolism, glycerophospholipid metabolism, alanine, aspartate and glutamate metabolism, are implicated in insulin resistance, vascular remodeling, macrophage activation and oxidised LDL formation. Remarkably, hypertension and dyslipidemia exhibit both common and distinct metabolic patterns, revealing their independent and synergetic biological implications.

CONCLUSION

This study identified valuable biomarkers and pathways for hypertension and dyslipidemia, and revealed common and different metabolic patterns between hypertension and dyslipidemia. The information provided in this study could shed new light on the pathologic mechanisms and offer potential intervention targets for hypertension and dyslipidemia as well as their related diseases.