Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

Refining microbial community metabolic models derived from metagenomics using reference-based taxonomic profiling

Characterization of microbial community metabolic output is crucial to understanding their functions. Construction of genome-scale metabolic models from metagenome-assembled genomes (MAG) has enabled prediction of metabolite production by microbial communities, yet little is known about their accuracy. Here, we examined the performance of two approaches for metabolite prediction from metagenomes, one that is MAG-guided and another that is taxonomic reference-guided. We applied both on shotgun metagenomics data from human and environmental samples, and validated findings in the human samples using untargeted metabolomics. We found that in human samples, where taxonomic profiling is optimized and reference genomes are readily available, when number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach. The two approaches showed significant overlap but each identified metabolites not predicted in the other. Pathway enrichment analyses identified significant differences in inferences derived from data based on the approach, highlighting the need for caution in interpretation. In environmental samples, when the number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach for total metabolites in both sample types and non-redundant metabolites in seawater samples. Nonetheless, as was observed for the human samples, the approaches overlapped substantially but also predicted metabolites not observed in the other. Our findings report on utility of a complementary input to genome-scale metabolic model construction that is less computationally intensive forgoing MAG assembly and refinement, and that can be applied on shallow shotgun sequencing where MAGs cannot be generated.IMPORTANCELittle is known about the accuracy of genome-scale metabolic models (GEMs) of microbial communities despite their influence on inferring community metabolic outputs and culture conditions. The performance of GEMs for metabolite prediction from metagenomes was assessed by applying two approaches on shotgun metagenomics data from human and environmental samples, and validating findings in the human samples using untargeted metabolomics. The performance of the approach was found to be dependent on sample type, but collectively, the reference-guided approach predicted more metabolites than the MAG-guided approach. Despite the differences, the predictions from the approaches overlapped substantially but each identified metabolites not predicted in the other. We found significant differences in biological inferences based on the approach, with some examples of uniquely enriched pathways in one group being invalidated when using the alternative approach, highlighting the need for caution in interpretation of GEMs.

MBROLE3: improved functional enrichment of chemical compounds for metabolomics data analysis

MBROLE (Metabolites Biological Role) facilitates the biological interpretation of metabolomics experiments. It performs enrichment analysis of a set of chemical compounds through statistical analysis of annotations from several databases. The original MBROLE server was released in 2011 and, since then, different groups worldwide have used it to analyze metabolomics experiments from a variety of organisms. Here we present the latest version of the system, MBROLE3, accessible at http://csbg.cnb.csic.es/mbrole3. This new version contains updated annotations from previously included databases as well as a wide variety of new functional annotations, such as additional pathway databases and Gene Ontology terms. Of special relevance is the inclusion of a new category of annotations, 'indirect annotations', extracted from the scientific literature and from curated chemical-protein associations. The latter allows to analyze enriched annotations of the proteins known to interact with the set of chemical compounds of interest. Results are provided in the form of interactive tables, formatted data to download, and graphical plots.

Application of Metabolomics in Fungal Research

Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants.

Health impact assessment of pet cats caused by organohalogen contaminants by serum metabolomics and thyroid hormone analysis

Companion animals are in close contact with the human surroundings, and there is growing concern about the effects of harmful substances on the health of pet cats. In this study, we investigated the potential health effects of organohalogen compounds (OHCs) on thyroid hormone (TH) homeostasis and metabolomics in Japanese pet cats. There was a significant negative correlation between concentrations of several contaminants, such as polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), hydroxylated PCBs (OH-PCBs), hydroxylated PBDEs (OH-PBDEs), and THs in cat serum samples. These results suggested that exposure to OHCs causes a decrease in serum TH levels in pet cats. In this metabolomics study, each exposure level of parent compounds (PCBs and PBDEs) and their hydroxylated compounds (OH-PCBs and OH-PBDEs) were associated with their own unique primary metabolic pathways, suggesting that parent and phenolic compounds exhibit different mechanisms of action and biological effects. PCBs were associated with many metabolic pathways, including glutathione and purine metabolism, and the effects were replicated in in-vivo cat PCB administration studies. These results demonstrated that OHC exposure causes chronic oxidative stress in pet cats. PBDEs were positively associated with alanine, aspartate, and glutamate metabolism. Due to the chronic exposure of cats to mixtures of these contaminants, the combination of their respective metabolic pathways may have a synergistic effect.

Identification of phenolics from miracle berry (Synsepalum dulcificum) leaf extract and its antiangiogenesis and anticancer activities

Miracle berry is well-known for its ability to convert sour foods to sweet. In this study, the secondary metabolites of miracle berry leaves (MBL) were identified by UPLC-DAD-MS, and its antiangiogenesis and anticancer activities were evaluated by using a zebrafish model and the MCF-7 xenograft mouse model, respectively. The result showed that 18 phenolic compounds were identified in MBL extract, and dominated by the derivatives of quercetin and myricetin. The MBL extract showed low toxicity and high antiangiogenesis activity, it significantly inhibited the subintestinal vein vessels development in zebrafish at very low concentration. Furthermore, the MBL extract could promote the apoptosis of tumor cells and significantly inhibit the growth of MCF-7 xenograft tumor. In addition, the analysis of metabolites revealed that the MBL extract inhibited tumor growth by activating the metabolic pathways of unsaturated fatty acids and purines. Overall, this study suggests that MBL extract can be used as a natural anticancer adjuvant in the fields of functional foods.

NMR-Based Metabolomics Identify Metabolic Change in Spleen of Idiopathic Thrombocytopenic Purpura Patients

Idiopathic thrombocytopenic purpura (ITP) is a common hematological disease and the abnormal platelet destruction in the spleen is a critical pathological mechanism for ITP. However, the metabolomic change in the spleen caused by ITP is still unclear. In the present study, the metabolomic information of 18 ITP and 20 normal spleen samples were detected by using ¹H high-resolution magic angle spinning NMR spectroscopy (¹H MAS NMR). Compared with normal spleen, the concentrations of acetate, alanine, glutamine, glycerol, isoleucine, lysine, valine, phenylalanine, leucine, and methanol in ITP spleen tissue were elevated and 3-hydroxybutyric acid, ascorbate, asparagine, ethanol, glycogen, low-density lipoprotein, malonate, myo-inositol, glycerophosphocholine, pyroglutamate, and taurine were decreased. Amino acids metabolic pathways, such as branched-chain amino acids pathway, were identified as the main involved pathways based on enrichment analysis. The decrease in taurine level in the spleen was the most obvious metabolic signature involving ITP with high sensitivity and specificity to distinguish the spleen of ITP from the normal (CI: 0.825–0.982). Notably, the level of taurine in the spleen was negatively correlated with the efficacy of splenectomy (r = 0.622, p = 0.006). Collectively, the data from our study revealed previously unknown ITP-related metabolomic changes in the spleen and found a potential diagnostic and efficacy-predictive biomarker for ITP treatment.

Metabolomic analysis elucidates how shade conditions ameliorate the deleterious effects of greening (Huanglongbing) disease in citrus

Under tropical and subtropical environments, citrus leaves are exposed to excess sunlight, inducing photoinhibition. Huanglongbing (HLB, citrus greening), a devastating phloem-limited disease putatively caused by Candidatus Liberibacter asiaticus, exacerbates this challenge with additional photosynthetic loss and excessive starch accumulation. A combined metabolomics and physiological approach was used to elucidate whether shade alleviates the deleterious effects of HLB in field-grown citrus trees, and to understand the underlying metabolic mechanisms related to shade-induced morpho-physiological changes in citrus. Using metabolite profiling and multinomial logistic regression, we identified pivotal metabolites altered in response to shade. A core metabolic network associated with shade conditions was identified through pathway enrichment analysis and metabolite mapping. We measured physio-biochemical responses and growth and yield characteristics. With these, the relationships between metabolic network and the variables measured above were investigated. We found that moderate-shade alleviates sink limitation by preventing excessive starch accumulation and increasing foliar sucrose levels. Increased growth and fruit yield in shaded compared with non-shaded trees were associated with increased photosystem II efficiency and leaf carbon fixation pathway metabolites. Our study also shows that, in HLB-affected trees under shade, the signaling of plant hormones (auxins and cytokinins) and nitrogen supply were downregulated with reducing new shoot production likely due to diminished needs of cell damage repair and tissue regeneration under shade. Overall, our findings provide the first glimpse of the complex dynamics between cellular metabolites and leaf physiological functions in citrus HLB pathosystem under shade, and reveal the mechanistic basis of how shade ameliorates HLB disease.

Plants Metabolome Study: Emerging Tools and Techniques

Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.

Metabolomic Analysis Provides New Insight Into Tolerance of Huanglongbing in Citrus

There have been efforts to develop citrus cultivars that are tolerant of Huanglongbing (HLB), a catastrophic phloem-limited disease. Previous studies demonstrated that continuous plant growth with phloem regeneration is one of the major characteristics of HLB tolerance. In this study, the metabolic mechanisms of HLB tolerance in citrus were elucidated using a multiple pathway-targeted metabolomic approach. Comparative analysis of healthy and infected HLB-tolerant and HLB-sensitive mandarin cultivars (Citrus reticulata) revealed differentially expressed metabolic responses among different groups. Pathway enrichment analysis indicated aspartate and glutamate metabolism, purine metabolism, and biosynthesis of plant hormones were upregulated in the tolerant group, except salicylic acid signaling. Catabolic pathways linked to energy-yielding metabolism were also upregulated in the tolerant group. These metabolisms and pathways were interconnected with each other, unveiling a pivotal metabolic network associated with HLB tolerance. In the network, auxins and cytokinins, the plant hormones responsible for plant growth and phloem regeneration, were accumulated. In addition, purine metabolites serving as energy carriers and nitrogen sources of plants were increased. Only salicylic acid-related metabolites for plant defense responses were decreased in the tolerant group. Our findings may evidence the strategy of HLB-tolerant cultivars that sustain plant growth and phloem formation rather than displaying direct plant defense to overcome the disease.

Cistanche deserticola polysaccharides alleviate cognitive decline in aging model mice by restoring the gut microbiota-brain axis

Recent evidence suggests alterations in the gut microbiota-brain axis may drive cognitive impairment with aging. In the present study, we observed that prolonged administration of D-galactose to mice induced cognitive decline, gut microbial dysbiosis, peripheral inflammation, and oxidative stress. In this model of age-related cognitive decline, Cistanche deserticola polysaccharides (CDPS) improved cognitive function in D-galactose-treated mice by restoring gut microbial homeostasis, thereby reducing oxidative stress and peripheral inflammation. The beneficial effects of CDPS in these aging model mice were abolished through ablation of gut microbiota with antibiotics or immunosuppression with cyclophosphamide. Serum metabolomic profiling showed that levels of creatinine, valine, L-methionine, o-Toluidine, N-ethylaniline, uric acid and proline were all altered in the aging model mice, but were restored by CDPS. These findings demonstrated that CDPS improves cognitive function in a D-galactose-induced aging model in mice by restoring homeostasis of the gut microbiota-brain axis, which alleviated an amino acid imbalance, peripheral inflammation, and oxidative stress. CDPS thus shows therapeutic potential for patients with memory and learning disorders, especially those related to gut microbial dysbiosis.

CD44 modulates metabolic pathways and altered ROS-mediated Akt signal promoting cholangiocarcinoma progression

CD44 is a transmembrane glycoprotein, the phosphorylation of which can directly trigger intracellular signaling, particularly Akt protein, for supporting cell growth, motility and invasion. This study examined the role of CD44 on the progression of Cholangiocarcinoma (CCA) using metabolic profiling to investigate the molecular mechanisms involved in the Akt signaling pathway. Our results show that the silencing of CD44 decreases Akt and mTOR phosphorylation resulting in p21 and Bax accumulation and Bcl-2 suppression that reduces cell proliferation. Moreover, an inhibition of cell migration and invasion regulated by CD44. Similarly, the silencing of CD44 showed an alteration in the epithelial-mesenchymal transition (EMT), e.g. an upregulation of E-cadherin and a downregulation of vimentin, and the reduction of the matrix metalloproteinase (MMP)-9 signal. Interestingly, a depletion of CD44 leads to metabolic pathway changes resulting in redox status modification and Trolox (anti-oxidant) led to the recovery of the cancer cell functions. Based on our findings, the regulation of CCA progression and metastasis via the redox status-related Akt signaling pathway depends on the alteration of metabolic profiling synchronized by CD44.

Relationship Between Inflammation and Metabolism in Patients With Newly Presenting Rheumatoid Arthritis

Background

Systemic inflammation in rheumatoid arthritis (RA) is associated with metabolic changes. We used nuclear magnetic resonance (NMR) spectroscopy-based metabolomics to assess the relationship between an objective measure of systemic inflammation [C-reactive protein (CRP)] and both the serum and urinary metabolome in patients with newly presenting RA.

Methods

Serum (n=126) and urine (n=83) samples were collected at initial presentation from disease modifying anti-rheumatic drug naïve RA patients for metabolomic profile assessment using 1-dimensional ¹H-NMR spectroscopy. Metabolomics data were analysed using partial least square regression (PLS-R) and orthogonal projections to latent structure discriminant analysis (OPLS-DA) with cross validation.

Results

Using PLS-R analysis, a relationship between the level of inflammation, as assessed by CRP, and the serum (p=0.001) and urinary (p<0.001) metabolome was detectable. Likewise, following categorisation of CRP into tertiles, patients in the lowest CRP tertile and the highest CRP tertile were statistically discriminated using OPLS-DA analysis of both serum (p=0.033) and urinary (p<0.001) metabolome. The most highly weighted metabolites for these models included glucose, amino acids, lactate, and citrate. These findings suggest increased glycolysis, perturbation in the citrate cycle, oxidative stress, protein catabolism and increased urea cycle activity are key characteristics of newly presenting RA patients with elevated CRP.

Conclusions

This study consolidates our understanding of a previously identified relationship between serum metabolite profile and inflammation and provides novel evidence that there is a relationship between urinary metabolite profile and inflammation as measured by CRP. Identification of these metabolic perturbations provides insights into the pathogenesis of RA and may help in the identification of therapeutic targets.

Unravelling the metabolic alterations of liver damage induced by thirdhand smoke

BACKGROUND

Thirdhand smoke (THS) is the accumulation of tobacco smoke gases and particles that become embedded in materials. Previous studies concluded that THS exposure induces oxidative stress and hepatic steatosis in liver. Despite the knowledge of the increasing danger of THS exposure, the metabolic disorders caused in liver are still not well defined.

OBJECTIVES

The aim of this study is to investigate the metabolic disorders caused by THS exposure in liver of male mice and to evaluate the effects of an antioxidant treatment in the exposed mice.

METHODS

We investigated liver from three mice groups: non-exposed mice, exposed to THS in conditions that mimic human exposure and THS-exposed treated with antioxidants. Liver samples were analyzed using a multiplatform untargeted metabolomics approach including nuclear magnetic resonance (1H NMR), liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and laser desorption/ionization mass spectrometry imaging (MSI), able to map lipids in liver tissues.

RESULTS

Our multiplatform approach allowed the annotation of eighty-eight metabolites altered by THS exposure, including amino acids, nucleotides and several types of lipids. The main dysregulated pathways by THS exposure were D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism and oxidative phosphorylation and glutathione metabolism, being the last two related to oxidative stress. THS-exposed mice also presented higher lipid accumulation and decrease of metabolites involved in the phosphocholine synthesis, as well as choline deficiency, which is related to Non-Alcoholic Fatty Liver Disease and steatohepatitis. Interestingly, the antioxidant treatment of THS-exposed mice reduced the accumulation of some lipids, but could not revert all the metabolic alterations, including some related to the impairment of the mitochondrial function.

CONCLUSIONS

THS alters liver function at a molecular level, dysregulating many metabolic pathways. The molecular evidences provided here confirm that THS is a new factor for liver steatosis and provide the basis for future research in this respect.

Novel molecular signatures and potential therapeutics in renal cell carcinomas: Insights from a comparative analysis of subtypes

Renal cell carcinomas (RCCs) are among the highest causes of cancer mortality. Although transcriptome profiling studies in the last decade have made significant molecular findings on RCCs, effective diagnosis and treatment strategies have yet to be achieved due to lack of adequate screening and comparative profiling of RCC subtypes. In this study, a comparative analysis was performed on RNA-seq based transcriptome data from each RCC subtype, namely clear cell RCC (KIRC), papillary RCC (KIRP) and kidney chromophobe (KICH), and mutual or subtype-specific reporter biomolecules were identified at RNA, protein, and metabolite levels by the integration of expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers in RCCs as well as novel biomarker candidates and potential therapeutic targets. Our findings also pointed out the incorporation of the molecular mechanisms of KIRC and KIRP, whereas KICH was shown to have distinct molecular signatures. Furthermore, considering the Dipeptidyl Peptidase 4 (DPP4) receptor as a potential therapeutic target specific to KICH, several drug candidates such as ZINC6745464 were identified through virtual screening of ZINC molecules. In this study, we reported valuable data for further experimental and clinical efforts, since the proposed molecules have significant potential for screening and therapeutic purposes in RCCs.

Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

INTRODUCTION

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

AREAS COVERED

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

EXPERT OPINION

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases

High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results

Motivation

Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network recommendation systems for the identification of metabolites potentially related to a metabolic fingerprint.

Results

MetaboRank method had been used to enrich metabolomics data obtained on cerebrospinal fluid samples from patients suffering from hepatic encephalopathy (HE). MetaboRank successfully recommended metabolites not present in the original fingerprint. The quality of recommendations was evaluated by using literature automatic search, in order to check that recommended metabolites could be related to the disease. Complementary mass spectrometry experiments and raw data analysis were performed to confirm these suggestions. In particular, MetaboRank recommended the overlooked α-ketoglutaramate as a metabolite which should be added to the metabolic fingerprint of HE, thus suggesting that metabolic fingerprints enhancement can provide new insight on complex diseases.

Availability and implementation

Method is implemented in the MetExplore server and is available at www.metexplore.fr. A tutorial is available at https://metexplore.toulouse.inra.fr/com/tutorials/MetaboRank/2017-MetaboRank.pdf.

Supplementary information

Supplementary data are available at Bioinformatics online.

Metabolomic Analysis of Radiation-Induced Lung Injury in Rats: The Potential Radioprotective Role of Taurine

Radiation-induced lung injury is a major dose-limiting toxicity that occurs due to thoracic radiotherapy. Metabolomics is a powerful quantitative measurement of low-molecular-weight metabolites in response to environmental disturbances. However, the metabolomic profiles of radiation-induced lung injury have not been reported yet. In this study, male Sprague-Dawley rats were subjected to a single dose of 10 or 20 Gy irradiation to the right lung. One week after radiation, the obvious morphological alteration of lung tissues after radiation was observed by hematoxylin and eosin staining through a transmission electron microscope. We then analyzed the metabolites and related pathways of radiation-induced lung injury by gas chromatography-mass spectrometry, and a total of 453 metabolites were identified. Compared to the nonirradiated left lung, 19 metabolites (8 upregulated and 11 downregulated) showed a significant difference in 10 Gy irradiated lung tissues, including mucic acid, methyl-β-d-galactopyranoside, quinoline-4-carboxylic acid, and pyridoxine. There were 31 differential metabolites (16 upregulated and 15 downregulated) between 20 Gy irradiated and nonirradiated lung tissues, including taurine, piperine, 1,2,4-benzenetriol, and lactamide. The Kyoto Encyclopedia of Genes and Genomes-based pathway analysis enriched 32 metabolic pathways between the irradiated and nonirradiated lung tissues, including pyrimidine metabolism, ATP-binding cassette transporters, aminoacyl-tRNA biosynthesis, and β-alanine metabolism. Among the dysregulated metabolites, we found that taurine promoted clonogenic survival and reduced radiation-induced necrosis in human embryonic lung fibroblast (HELF) cells. This study provides evidence indicating that radiation induces metabolic alterations of the lung. These findings significantly advance our understanding of the pathophysiology of radiation-induced lung injury from the perspective of metabolism.

Integrated metabolomics signature for assessing the longevity of Panax ginseng seeds

BACKGROUND

Panax ginseng seeds have strong dormancy and a prolonged germination period in comparison to other seeds; thus, it is a great challenge to propagate ginseng. Seed longevity is closely associated with germination rate and viability, so we assumed that if a seed loses its viability, specific metabolic alterations regarding plant growth factors might occur. In this study, we divided ginseng seeds into normal and accelerated-aging groups. Both groups were treated with gibberellic acid, which is one of the most important plant-growth regulators. Afterward, gas chromatography-mass spectrometry (GC-MS) was used to analyze the samples, to identify the metabolic alterations between the two groups.

RESULTS

Forty-four endogenous metabolites in normal and accelerated aging groups were putatively identified. To determine the differential significance of these metabolites, t-tests and fold-change analysis were conducted followed by principal component analysis and partial least-squares discriminant analysis to determine the metabolites that showed distinct responses between the groups. Among the differentially expressed metabolites (P value < 0.05 and FDR < 0.1), nine metabolites were selected as potential biomarker candidates for the prediction of seed longevity.

CONCLUSION

Nine metabolites related to ginseng seed longevity were identified by comparing metabolomes. Our findings suggest that ginseng propagation can be facilitated by the regulation of these distinctive metabolic features of the seeds. © 2019 Society of Chemical Industry.

Sodium oligomannate therapeutically remodels gut microbiota and suppresses gut bacterial amino acids-shaped neuroinflammation to inhibit Alzheimer's disease progression

Recently, increasing evidence has suggested the association between gut dysbiosis and Alzheimer’s disease (AD) progression, yet the role of gut microbiota in AD pathogenesis remains obscure. Herein, we provide a potential mechanistic link between gut microbiota dysbiosis and neuroinflammation in AD progression. Using AD mouse models, we discovered that, during AD progression, the alteration of gut microbiota composition leads to the peripheral accumulation of phenylalanine and isoleucine, which stimulates the differentiation and proliferation of pro-inflammatory T helper 1 (Th1) cells. The brain-infiltrated peripheral Th1 immune cells are associated with the M1 microglia activation, contributing to AD-associated neuroinflammation. Importantly, the elevation of phenylalanine and isoleucine concentrations and the increase of Th1 cell frequency in the blood were also observed in two small independent cohorts of patients with mild cognitive impairment (MCI) due to AD. Furthermore, GV-971, a sodium oligomannate that has demonstrated solid and consistent cognition improvement in a phase 3 clinical trial in China, suppresses gut dysbiosis and the associated phenylalanine/isoleucine accumulation, harnesses neuroinflammation and reverses the cognition impairment. Together, our findings highlight the role of gut dysbiosis-promoted neuroinflammation in AD progression and suggest a novel strategy for AD therapy by remodelling the gut microbiota.

Cloned pig fetuses exhibit fatty acid deficiency from impaired placental transport

Cloned pig fetuses produced by somatic cell nuclear transfer show a high incidence of erroneous development in the uteri of surrogate mothers. The mechanisms underlying the abnormal intrauterine development of cloned pig fetuses are poorly understood. This study aimed to explore the potential causes of the aberrant development of cloned pig fetuses. The levels of numerous fatty acids in allantoic fluid and muscle tissue were lower in cloned pig fetuses than in artificial insemination-generated pig fetuses, thereby suggesting that cloned pig fetuses underwent fatty acid deficiency. Cloned pig fetuses also displayed trophoblast hypoplasia and a reduced expression of placental fatty acid transport protein 4 (FATP4), which is the predominant FATP family member expressed in porcine placentas. This result suggested that the placental fatty acid transport functions were impaired in cloned pig fetuses, possibly causing fatty acid deficiency in cloned pig fetuses. The present study provides useful information in elucidating the mechanisms underlying the abnormal development of cloned pig fetuses.

Acute and short-term administrations of delta-9-tetrahydrocannabinol modulate major gut metabolomic regulatory pathways in C57BL/6 mice

Delta-9-tetrahydrocannabinol (THC) is the primary psychoactive compound in Cannabis, which is studied extensively for its medicinal value. A central gap in the science is the underlying mechanisms surrounding THC's therapeutic effects and the role of gut metabolite profiles. Using a mass-spectrometry based metabolomics, we show here that intraperitoneal injection of THC in C57BL/6 mice modulates metabolic profiles that have previously been identified as integral to health. Specifically, we investigated the effects of acute (single THC injection denoted here as '1X') and short -term (five THC injections on alternate days denoted as '5X') THC administration on fecal and intestinal tissue metabolite profiles. Results are consistent with the hypothesis that THC administration alters host metabolism by targeting two prominent lipid metabolism pathways: glycerophospholipid metabolism and fatty acid biosynthesis.

Non-targeted Metabolomic Analysis Based on Ultra-High-Performance Liquid Chromatography Quadrupole Time-of-Flight Tandem Mass Spectrometry Reveals the Effects of Grafting on Non-volatile Metabolites in Fresh Tea Leaves ( Camellia sinensis L.)

To investigate the effects of grafting on non-volatile metabolites in tea, non-targeted metabolomic analyses of fresh leaves were performed on the basis of ultra-high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF/MS). One non-grafted YingHong No. 9 and four grafted tea [grafting scion YingHong No. 9 on four different rootstocks, BaiMao No. 2 (BM2), BaiYeDanCong (BY), HeiYeShuiXian (HY), and WuLingHong (WLH)] were chosen as materials. In total, 32 differential metabolites were identified, including phenolic acids, flavan-3-ols, dimeric catechins, flavonol and flavonol/flavone glycosides, etc. Partial least squares discrimination analysis and hierarchical cluster analysis showed various effects of different rootstocks on metabolites. Thereinto, rootstocks of WLH and BY showed extremely outstanding performance in up- and downregulating these metabolites, respectively. Differential metabolites were enriched into three crucial pathways, including biosynthesis of phenylpropanoids, flavonoid biosynthesis, and flavone and flavonol biosynthesis, which might influence the quality of tea. This study provides a theoretical basis for grafting-related variations of non-volatile metabolites in fresh tea leaves.

Targeted metabolome analysis of the dog brain exposed to PCBs suggests inhibition of oxidative phosphorylation by hydroxylated PCBs

Canis lupus familiaris (domestic dog) possess a high capacity to metabolize higher-chlorinated polychlorinated biphenyls (PCBs) to thyroid hormone (TH)-like hydroxylated PCB metabolites (OH-PCBs). As a result, the brain could be at high risk of toxicity caused by OH-PCBs. To evaluate the effect of OH-PCBs on dog brain, we analyzed OH-PCB levels in the brain and the metabolome of the frontal cortex following exposure to a mixture of PCBs (CB18, 28, 70, 77, 99, 101, 118, 138, 153, 180, 187, and 202). 4-OH-CB202 and 4-OH-CB107 were major OH-PCBs in the brain of PCB-exposed dogs. These OH-PCBs were associated with metabolites involved in urea cycle, proline-related compounds, and purine, pyrimidine, glutathione, and amino-acid metabolism in dog brain. Moreover, adenosine triphosphate levels in the PCBs exposure group were significantly lower than in the control group. These results suggest that OH-PCB exposure is associated with a disruption in TH homeostasis, generation of reactive oxygen species, and/or disruption of oxidative phosphorylation (OXPHOS) in brain cells. Among them, OXPHOS disturbance could be associated with both disruptions in cellular amino-acid metabolism and urea cycle. Therefore, an OXPHOS activity assay was performed to evaluate the disruption of OXPHOS by OH-PCBs. The results indicated that 4-OH-CB107 inhibits the function of Complexes III, IV, and V of the electron transport chain, suggesting that 4-OH-CB107 inhibit these complexes in OXPHOS. The neurotoxic effects of PCB exposure may be mediated through mitochondrial toxicity of OH-PCBs in the brain.

Improved Algal Toxicity Test System for Robust Omics-Driven Mode-of-Action Discovery in Chlamydomonas reinhardtii

Algae are key components of aquatic food chains. Consequently, they are internationally recognised test species for the environmental safety assessment of chemicals. However, existing algal toxicity test guidelines are not yet optimized to discover molecular modes of action, which require highly-replicated and carefully controlled experiments. Here, we set out to develop a robust, miniaturised and scalable Chlamydomonas reinhardtii toxicity testing approach tailored to meet these demands. We primarily investigated the benefits of synchronised cultures for molecular studies, and of exposure designs that restrict chemical volatilisation yet yield sufficient algal biomass for omics analyses. Flow cytometry and direct-infusion mass spectrometry metabolomics revealed significant and time-resolved changes in sample composition of synchronised cultures. Synchronised cultures in sealed glass vials achieved adequate growth rates at previously unachievably-high inoculation cell densities, with minimal pH drift and negligible chemical loss over 24-h exposures. Algal exposures to a volatile test compound (chlorobenzene) yielded relatively high reproducibility of metabolic phenotypes over experimental repeats. This experimental test system extends existing toxicity testing formats to allow highly-replicated, omics-driven, mode-of-action discovery.

Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model

Background

Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications.

Results

In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle.

Conclusions

Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2826-1) contains supplementary material, which is available to authorized users.

Using Pathway Covering to Explore Connections among Metabolites

Interpreting changes in metabolite abundance in response to experimental treatments or disease states remains a major challenge in metabolomics. Pathway Covering is a new algorithm that takes a list of metabolites (compounds) and determines a minimum-cost set of metabolic pathways in an organism that includes (covers) all the metabolites in the list. We used five functions for assigning costs to pathways, including assigning a constant for all pathways, which yields a solution with the smallest pathway count; two methods that penalize large pathways; one that prefers pathways based on the pathway's assigned function, and one that loosely corresponds to metabolic flux. The pathway covering set computed by the algorithm can be displayed as a multi-pathway diagram ("pathway collage") that highlights the covered metabolites. We investigated the pathway covering algorithm by using several datasets from the Metabolomics Workbench. The algorithm is best applied to a list of metabolites with significant statistics and fold-changes with a specified direction of change for each metabolite. The pathway covering algorithm is now available within the Pathway Tools software and BioCyc website.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome

Background: Metabolomics is emerging as a valuable tool in clinical science. However, one major challenge in clinical metabolomics is the limited use of standardized guidelines for sample collection and handling. In this study, we conducted a pilot analysis of serum and plasma to determine the effects of processing time and collection tube on the metabolome. Methods: Blood was collected in 3 tubes: Vacutainer serum separator tube (SST, serum), EDTA (plasma) and P100 (plasma) and stored at 4 degrees for 0, 0.5, 1, 2, 4 and 24 h prior to centrifugation. Compounds were extracted using liquid-liquid extraction to obtain a hydrophilic and a hydrophobic fraction and analyzed using liquid chromatography mass spectrometry. Differences among the blood collection tubes and sample processing time were evaluated (ANOVA, Bonferroni FWER ≤ 0.05 and ANOVA, Benjamini Hochberg FDR ≤ 0.1, respectively). Results: Among the serum and plasma tubes 93.5% of compounds overlapped, 382 compounds were unique to serum and one compound was unique to plasma. There were 46, 50 and 86 compounds affected by processing time in SST, EDTA and P100 tubes, respectively, including many lipids. In contrast, 496 hydrophilic and 242 hydrophobic compounds differed by collection tube. Forty-five different chemical classes including alcohols, sugars, amino acids and prenol lipids were affected by the choice of blood collection tube. Conclusion: Our results suggest that the choice of blood collection tube has a significant effect on detected metabolites and their overall abundances. Perhaps surprisingly, variation in sample processing time has less of an effect compared to collection tube; however, a larger sample size is needed to confirm this.

Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation

Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modeling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations and exploits flux analyses techniques to identify activated metabolic functions. Compared to the usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.

Comprehensive urinary metabolomic characterization of a genetically induced mouse model of prostatic inflammation

Dysfunction of the lower urinary tract commonly afflicts the middle-aged and aging male population. The etiology of lower urinary tract symptoms (LUTS) is multifactorial. Benign prostate hyperplasia, fibrosis, smooth muscle contractility, and inflammation likely contribute. Here we aim to characterize the urinary metabolomic profile associated with prostatic inflammation, which could inform future personalized diagnosis or treatment, as well as mechanistic research. Quantitative urinary metabolomics was conducted to examine molecular changes following induction of inflammation via conditional Interleukin-1β expression in prostate epithelia using a novel transgenic mouse strain. To advance method development for urinary metabolomics, we also compared different urine normalization methods and found that normalizing urine samples based on osmolality prior to LC-MS most completely separated urinary metabolite profiles of mice with and without prostate inflammation via principal component analysis. Global metabolomics was combined with advanced machine learning feature selection and classification for data analysis. Key dysregulated metabolites and pathways were identified and were relevant to prostatic inflammation, some of which overlapped with our previous study of human LUTS patients. A binary classification model was established via the support vector machine algorithm to accurately differentiate control and inflammation groups, with an area-under-the-curve value of the receiver operating characteristic of 0.81, sensitivity of 0.974 and specificity of 0.995, respectively. This study generated molecular profiles of non-bacterial prostatic inflammation, which could assist future efforts to stratify LUTS patients and develop new therapies.

A Targeted Mass Spectrometry-Based Metabolomics Approach toward the Understanding of Host Responses to Huanglongbing Disease

Candidatus Liberibacter asiaticus (CLas) is the major culprit of Huanglongbing (HLB), the most destructive citrus disease worldwide. The polymerase chain reaction (PCR) is the most common method for detecting the presence of CLas in the tree. However, due to the uneven distribution of bacteria and a minimum bacterial titer requirement, an infected tree may test false negative. Thus, our current study profiled primary and secondary metabolites of CLas-free leaves harvested from a citrus undercover protection system (CUPS) to prevent a misjudgment of CLas infection. Functional enrichment analysis revealed several metabolic pathways significantly affected by CLas infection, mainly biosynthesis of amino acids and secondary metabolites. Comparisons of CLas-infected metabolite alterations among oranges, mandarins, and grapefruits revealed that host responses to CLas were different. The metabolite signature highlighted in this study will provide a fuller understanding of how CLas bacteria affect the biosynthesis of primary and secondary metabolites in different hosts.

Identification of Lysophosphatidylcholines and Sphingolipids as Potential Biomarkers for Acute Aortic Dissection via Serum Metabolomics

OBJECTIVES

Acute aortic dissection (AAD) is a severe clinical emergency with a high mortality, and is easily misdiagnosed in its early stage. This study aimed at discovering serum metabolomic markers with the potential to diagnose AAD and distinguish between two subtypes of AAD.

METHODS

Thirty-five patients with AAD, including 20 with Stanford type A and 15 with Stanford type B were enrolled in this study, together with 20 healthy controls. All patients with AAD were admitted within 72 h of onset. Serum metabolomics profiles were determined by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry and the data were analysed by principal component analysis and partial least squares discriminant analysis.

RESULTS

A total of 17 metabolites differing between the control and AAD groups were finally screened and identified as lysophosphatidylcholines (LPC) and sphingolipids including sphinganine, phytosphingosine, sphingomyelin, and ceramide. Compared with those in the healthy control group, LPC levels were significantly lower in both the Stanford type A and type B AAD groups. Interestingly, sphingolipids, including sphinganine, phytosphingosine, and ceramide, were remarkably reduced in the Stanford type A AAD group, but not in the Stanford type B AAD group. Subgroup analysis showed that the changes in LPC and sphingolipid levels were unrelated to hypertension or gender.

CONCLUSIONS

The present results indicate that LPCs and sphingolipids are significantly altered in patients with AAD, and several sphingolipids, such as sphinganine, phytosphingosine, and ceramide, were dramatically decreased in patients with Stanford type A AAD. A combination of these two families of metabolites could serve as a potential biomarker for the diagnosis of AAD and distinguishing between Stanford type A and Stanford type B.

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using 'Omics'-Based Network Approaches: A Future Perspective

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, 'omics'-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight 'omics'-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in 'omics' analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with 'omics'-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant.

Response of Gut Microbiota to Metabolite Changes Induced by Endurance Exercise

A few animal studies have shown that wheel running could reverse an unhealthy status by shifting the gut microbial composition, but no investigations have studied the effect of endurance running, such as marathon running, on human gut microbial communities. Since many findings have shown that marathon running immediately causes metabolic changes in blood, urine, muscles and lymph that potentially impact the gut microbiota (GM) within several hours. Here, we investigated whether the GM immediately responds to the enteric changes in amateur half-marathon runners. Alterations in the metabolic profile and microbiota were investigated in fecal samples based on an untargeted metabolomics methodology and 16S rDNA sequencing analysis. A total of 40 fecal metabolites were found significantly changed after finishing a half-marathon race. The most significantly different metabolites were organic acids (the major increased metabolites) and nucleic acid components (the major decreased metabolites). The enteric changes induced by running did not affect the α-diversity of the GM, but the abundances of certain microbiota members were shown to be significantly different before and after running. The family Coriobacteriaceae was identified as a potential biomarker that links exercise with health improvement. Functional prediction showed a significantly activated “Cell motility” function of GM within participants after running. Correlation analysis indicated that the observed differential GM in our study might have been the shared outcome of running and diet. This study provided knowledge regarding the health impacts of marathon running from the perspective of GM for the first time. Our data indicated that long-distance endurance running can immediately cause striking metabolic changes in the gut environment. Gut microbes can rapidly respond to the altered fecal metabolites by adjusting certain bacterial taxa. These findings highlighted the health-promoting benefits of exercise from the perspective of GM.

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.

Metabolite profiling of Listeria innocua for unravelling the inactivation mechanism of electrolysed water by nuclear magnetic resonance spectroscopy

Bactericidal effects of low concentration electrolysed water (LcEW) on microorganisms are previously well reported; however, the inactivation mechanism of EW is not understood. The lethal and sublethal injuries of L. monocytogenes and L. innocua by EW treatments were determined and the metabolic profile changes for L. innocua were characterised using nuclear magnetic resonance (NMR). Microbial metabolomics approach combined with multivariate data analyses was used to interpret the cellular chemical fingerprints of L. innocua. The relative amount of intracellular reactive oxygen species (ROS) was assayed using 2',7-dichlorodihydrofluorescein diacetate (H₂DCFDA). The results showed that the proportion of the sublethally injured microbial cells L. monocytogenes and L. innocua increased from 40% to 70% and from 35% to 65%, respectively, when the free available chlorine (FAC) of LcEW increased from 2 to 8 mg/L. Overall, 36 low-molecular-weight metabolic compounds in L. innocua extracts were characterised by NMR spectroscopy. EW perturbation resulted in a drastic and multitude disruption across a wide range of biochemical process including peptidoglycan synthesis, nucleotides biosynthesis and amino acid metabolism. Elevated levels of α-ketoglutarate and succinate implicated the enhanced glutamate decarboxylase (GAD) system and γ-aminobutyric acid (GABA) shunt for the protection against oxidative stress. These findings provided the comprehensive insights into the metabolic response of Listeria to EW oxidative stress and can serve as a basis for better utilisation for sanitisation.

Metabolic Analysis Reveals Altered Long-Chain Fatty Acid Metabolism in the Host by Huanglongbing Disease

Candidatus Liberibacter asiaticus (CLas) is the presumed causal agent of Huanglongbing, one of the most destructive diseases in citrus. However, the lipid metabolism component of host response to this pathogen has not been investigated well. Here, metabolic profiling of a variety of long-chain fatty acids and their oxidation products was first performed to elucidate altered host metabolic responses of disease. Fatty acid signals were found to decrease obviously in response to disease regardless of cultivar. Several lipid oxidation products strongly correlated with those fatty acids were also consistently reduced in the diseased group. Using a series of statistical methods and metabolic pathway mapping, we found significant markers contributing to the pathological symptoms and identified their internal relationships and metabolic network. Our findings suggest that the infection of CLas may cause the altered metabolism of long-chain fatty acids, possibly leading to manipulation of the host's defense derived from fatty acids.

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

BACKGROUND

Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined.

RESULTS

An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites.

CONCLUSIONS

We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.

Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration

Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.

Functional Analysis of Metabolomics Data

Metabolomics aims at characterizing the repertory of small chemical compounds in a biological sample. As it becomes more massive and larger sets of compounds are detected, a functional analysis is required to convert these raw lists of compounds into biological knowledge. The most common way of performing such analysis is "annotation enrichment analysis," also used in transcriptomics and proteomics. This approach extracts the annotations overrepresented in the set of chemical compounds arisen in a given experiment. Here, we describe the protocols for performing such analysis as well as for visualizing a set of compounds in different representations of the metabolic networks, in both cases using free accessible web tools.

Null diffusion-based enrichment for metabolomics data

Metabolomics experiments identify metabolites whose abundance varies as the conditions under study change. Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, metabolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels. We propose a novel pathway enrichment procedure for analysing summary metabolomics data based on sub-network analysis in a graph representation of a reference database. Relevant entries are extracted from the database according to statistical measures over a null diffusive process that accounts for network topology and pathway crosstalk. Entries are reported as a sub-pathway network, including not only pathways, but also modules, enzymes, reactions and possibly other compound candidates for further analyses. This provides a richer biological context, suitable for generating new study hypotheses and potential enzymatic targets. Using this method, we report results from cells depleted for an uncharacterised mitochondrial gene using GC and LC-MS data and employing KEGG as a knowledge base. Partial validation is provided with NMR-based tracking of ¹³C glucose labelling of these cells.

Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca

Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given time in an organism. This not only facilitates chemical product screening, but also provides a comprehensive methodology to the study cellular metabolic networks to inform cellular engineering. Here, we present some of the first metabolomic data of the industrial cellulolytic actinomycete Thermobifida fusca generated using LC-MS/MS. The underlying objective of conducting global metabolite profiling was to gain better insight on the innate capabilities of T. fusca, with a long-term goal of facilitating T. fusca-based bioprocesses. The T. fusca metabolome was characterized for growth on two cellulose-relevant carbon sources, cellobiose and Avicel. Furthermore, the comprehensive list of measured metabolites was computationally integrated into a metabolic model of T. fusca, to study metabolic shifts in the network flux associated with carbohydrate and amino acid metabolism.

Non-invasively predicting differentiation of pancreatic cancer through comparative serum metabonomic profiling

BACKGROUND

The differentiation of pancreatic ductal adenocarcinoma (PDAC) could be associated with prognosis and may influence the choices of clinical management. No applicable methods could reliably predict the tumor differentiation preoperatively. Thus, the aim of this study was to compare the metabonomic profiling of pancreatic ductal adenocarcinoma with different differentiations and assess the feasibility of predicting tumor differentiations through metabonomic strategy based on nuclear magnetic resonance spectroscopy.

METHODS

By implanting pancreatic cancer cell strains Panc-1, Bxpc-3 and SW1990 in nude mice in situ, we successfully established the orthotopic xenograft models of PDAC with different differentiations. The metabonomic profiling of serum from different PDAC was achieved and analyzed by using 1H nuclear magnetic resonance (NMR) spectroscopy combined with the multivariate statistical analysis. Then, the differential metabolites acquired were used for enrichment analysis of metabolic pathways to get a deep insight.

RESULTS

An obvious metabonomic difference was demonstrated between all groups and the pattern recognition models were established successfully. The higher concentrations of amino acids, glycolytic and glutaminolytic participators in SW1990 and choline-contain metabolites in Panc-1 relative to other PDAC cells were demonstrated, which may be served as potential indicators for tumor differentiation. The metabolic pathways and differential metabolites identified in current study may be associated with specific pathways such as serine-glycine-one-carbon and glutaminolytic pathways, which can regulate tumorous proliferation and epigenetic regulation.

CONCLUSION

The NMR-based metabonomic strategy may be served as a non-invasive detection method for predicting tumor differentiation preoperatively.

Metabolic Biomarkers and Neurodegeneration: A Pathway Enrichment Analysis of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis

Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) lack robust diagnostics and prognostic biomarkers. Metabolomics is a postgenomics field that offers fresh insights for biomarkers of common complex as well as rare diseases. Using data on metabolite-disease associations published in the previous decade (2006-2016) in PubMed, ScienceDirect, Scopus, and Web of Science, we identified 101 metabolites as putative biomarkers for these three neurodegenerative diseases. Notably, uric acid, choline, creatine, L-glutamine, alanine, creatinine, and N-acetyl-L-aspartate were the shared metabolite signatures among the three diseases. The disease-metabolite-pathway associations pointed out the importance of membrane transport (through ATP binding cassette transporters), particularly of arginine and proline amino acids in all three neurodegenerative diseases. When disease-specific and common metabolic pathways were queried by using the pathway enrichment analyses, we found that alanine, aspartate, glutamate, and purine metabolism might act as alternative pathways to overcome inadequate glucose supply and energy crisis in neurodegeneration. These observations underscore the importance of metabolite-based biomarker research in deciphering the elusive pathophysiology of neurodegenerative diseases. Future research investments in metabolomics of complex diseases might provide new insights on AD, PD, and ALS that continue to place a significant burden on global health.

Multiomics Analysis of Tumor Microenvironment Reveals Gata2 and miRNA-124-3p as Potential Novel Biomarkers in Ovarian Cancer

Ovarian cancer is a common and, yet, one of the most deadly human cancers due to its insidious onset and the current lack of robust early diagnostic tests. Tumors are complex tissues comprised of not only malignant cells but also genetically stable stromal cells. Understanding the molecular mechanisms behind epithelial-stromal crosstalk in ovarian cancer is a great challenge in particular. In the present study, we performed comparative analyses of transcriptome data from laser microdissected epithelial, stromal, and ovarian tumor tissues, and identified common and tissue-specific reporter biomolecules-genes, receptors, membrane proteins, transcription factors (TFs), microRNAs (miRNAs), and metabolites-by integration of transcriptome data with genome-scale biomolecular networks. Tissue-specific response maps included common differentially expressed genes (DEGs) and reporter biomolecules were reconstructed and topological analyses were performed. We found that CDK2, EP300, and SRC as receptor-related functions or membrane proteins; Ets1, Ar, Gata2, and Foxp3 as TFs; and miR-16-5p and miR-124-3p as putative biomarkers and warrant further validation research. In addition, we report in this study that Gata2 and miR-124-3p are potential novel reporter biomolecules for ovarian cancer. The study of tissue-specific reporter biomolecules in epithelial cells, stroma, and tumor tissues as exemplified in the present study offers promise in biomarker discovery and diagnostics innovation for common complex human diseases such as ovarian cancer.