Computational approaches for systems metabolomics

From model organism to application: Bacteria-induced growth and development of the green seaweed Ulva and the potential of microbe leveraging in algal aquaculture

The marine green macroalga Ulva (Chlorophyta, Ulvales), also known as sea lettuce, coexists with a diverse microbiome. Many Ulva species proliferate in nature and form green algal blooms ("green tides"), which can occur when nutrient-rich wastewater from agricultural or densely populated areas is flushed into the sea. Bacteria are necessary for the adhesion of Ulva to its substrate, its growth, and the development of its blade morphology. In the absence of certain bacteria, Ulva mutabilis develops into a callus-like morphotype. However, with the addition of the necessary marine bacteria, the entire morphogenesis can be restored. Surprisingly, just two bacteria isolated from U. mutabilis are sufficient for inducing morphogenesis and establishing the reductionist system of a tripartite community. While one bacterial strain causes algal blade cell division, another causes the differentiation of basal cells into a rhizoid and supports cell wall formation because of a low concentration of the morphogen thallusin (below 10^-10 mol/L). This review focuses on the research conducted on this topic since 2015, discusses how U. mutabilis has developed into a model organism in chemical ecology, and explores the questions that have already been addressed and the perspectives that a reductionist model system allows. In particular, the field of systems biology will achieve a comprehensive, quantitative understanding of the dynamic interactions between Ulva and its associated bacteria to better predict the behavior of the system as a whole. The reductionist approach has enabled the study of the bacteria-induced morphogenesis of Ulva. Specific questions regarding the optimization of cultivation conditions as well as the yield of raw materials for the food and animal feed industries can be answered in the laboratory and through applied science. Genome sequencing, the improvement of genetic engineering tools, and the first promising attempts to leverage macroalgae-microbe interactions in aquaculture make this model organism, which has a comparatively short parthenogenetic life cycle, attractive for both fundamental and applied research. The reviewed research paves the way for the synthetic biology of macroalgae-associated microbiomes in sustainable aquacultures.

Metabonomic profiling of clubroot-susceptible and clubroot-resistant radish and the assessment of disease-resistant metabolites

Plasmodiophora brassicae causes a serious threat to cruciferous plants including radish (Raphanus sativus L.). Knowledge on the pathogenic regularity and molecular mechanism of P. brassicae and radish is limited, especially on the metabolism level. In the present study, clubroot-susceptible and clubroot-resistant cultivars were inoculated with P. brassicae Race 4, root hairs initial infection of resting spores (10⁷ CFU/mL) at 24 h post-inoculation and root galls symptom arising at cortex splitting stage were identified on both cultivars. Root samples of cortex splitting stage of two cultivars were collected and used for untargeted metabonomic analysis. We demonstrated changes in metabolite regulation and pathways during the cortex splitting stage of diseased roots between clubroot-susceptible and clubroot-resistant cultivars using untargeted metabonomic analysis. We identified a larger number of differentially regulated metabolites and heavier metabolite profile changes in the susceptible cultivar than in the resistant counterpart. The metabolites that were differentially regulated in both cultivars were mostly lipids and lipid-like molecules. Significantly regulated metabolites and pathways according to the P value and variable important in projection score were identified. Moreover, four compounds, including ethyl α-D-thioglucopyranoside, imipenem, ginsenoside Rg1, and 6-gingerol, were selected, and their anti-P. brassicae ability and effects on seedling growth were verified on the susceptible cultivar. Except for ethyl α-D-thioglucopyranoside, the remaining could inhibit clubroot development of varing degree. The use of 5 mg/L ginsenoside Rg1 + 5 mg/L 6-gingerol resulted in the lowest disease incidence and disease index among all treatments and enhanced seedling growth. The regulation of pathways or metabolites of carbapenem and ginsenoside was further explored. The results provide a preliminary understanding of the interaction between radish and P. brassicae at the metabolism level, as well as the development of measures for preventing clubroot.

Effect and function of β-N-methylamino-L-alanine in the diatom Phaeodactylum tricornutum

The neurotoxin β-N-methylamino-L-alanine (BMAA) is an environmental factor connected to neurodegenerative diseases. BMAA can be produced by various microorganisms (e.g. bacteria, cyanobacteria, dinoflagellates and diatoms) present in diverse ecosystems. No previous study has revealed the function of BMAA in diatoms. In the present study, we combined physiological data with metabolomic and transcriptional data in order to investigate the effect and function of BMAA in the diatom Phaeodactylum tricornutum. P. tricornutum, exposed to different concentrations of exogenous BMAA, showed concentration dependent responses. When the concentration of supplemented BMAA was sufficient to arrest the growth of P. tricornutum, oxidative stress and obstructed carbon fixation were obtained from the specific metabolite and transcriptional data. Results also indicated increased concentration of intracellular chlorophyll a and alterations in the GS-GOGAT cycle, whereas the urea cycle was suppressed. We therefore conclude that BMAA represents a toxic metabolite able to control the growth of P. tricornutum by triggering oxidative stress, and further influencing photosynthesis and nitrogen metabolisms.

Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women

In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.

Lipidomic approaches to dissect dysregulated lipid metabolism in kidney disease

Dyslipidaemia is a hallmark of chronic kidney disease (CKD). The severity of dyslipidaemia not only correlates with CKD stage but is also associated with CKD-associated cardiovascular disease and mortality. Understanding how lipids are dysregulated in CKD is, however, challenging owing to the incredible diversity of lipid structures. CKD-associated dyslipidaemia occurs as a consequence of complex interactions between genetic, environmental and kidney-specific factors, which to understand, requires an appreciation of perturbations in the underlying network of genes, proteins and lipids. Modern lipidomic technologies attempt to systematically identify and quantify lipid species from biological systems. The rapid development of a variety of analytical platforms based on mass spectrometry has enabled the identification of complex lipids at great precision and depth. Insights from lipidomics studies to date suggest that the overall architecture of free fatty acid partitioning between fatty acid oxidation and complex lipid fatty acid composition is an important driver of CKD progression. Available evidence suggests that CKD progression is associated with metabolic inflexibility, reflecting a diminished capacity to utilize free fatty acids through β-oxidation, and resulting in the diversion of accumulating fatty acids to complex lipids such as triglycerides. This effect is reversed with interventions that improve kidney health, suggesting that targeting of lipid abnormalities could be beneficial in preventing CKD progression.

Novel Insights Into Tissue-Specific Biochemical Alterations in Pediatric Eosinophilic Esophagitis Using Raman Spectroscopy

INTRODUCTION:

Elucidating esophageal biochemical composition in eosinophilic esophagitis (EoE) can offer novel insights into its pathogenesis, which remains unclear. Using Raman spectroscopy, we profiled and compared the biochemical composition of esophageal samples obtained from children with active (aEoE) and inactive EoE (iEoE) with non-EoE controls, examined the relationship between spectral markers and validated EoE activity indices.

METHODS:

In vitro Raman spectra from children with aEoE (n = 8; spectra = 51) and iEoE (n = 6; spectra = 48) and from non-EoE controls (n = 10; spectra = 75) were acquired. Mann-Whitney test was used to assess the differences in their Raman intensities (median [interquartile range]) and identify spectral markers. Spearman correlation was used to evaluate the relationship between spectral markers and endoscopic and histologic activity indices.

RESULTS:

Raman peaks attributable to glycogen content (936/1,449 cm⁻¹) was lower in children with aEoE (0.20 [0.18–0.21]) compared with that in non-EoE controls (0.24 [0.23–0.29]). Raman intensity of proteins (1,660/1,209 cm⁻¹) was higher in children with aEoE compared with that in non-EoE controls (3.20 [3.07–3.50] vs 2.91 [2.59–3.05]; P = 0.01), whereas that of lipids (1,301/1,260 cm⁻¹) was higher in children with iEoE (1.56 [1.49–1.63]) compared with children with aEoE (1.40 [1.30–1.48]; P = 0.02). Raman peaks attributable to glycogen and lipid inversely correlated with eosinophilic inflammation and basal zone hyperplasia. Raman mapping substantiated our findings.

DISCUSSION:

This is the first study to identify spectral traits of the esophageal samples related to EoE activity and tissue pathology and to profile tissue-level biochemical composition associated with pediatric EoE. Future research to determine the role of these biochemical alterations in development and clinical course of EoE can advance our understanding of EoE pathobiology.

A forward genetics approach integrating genome-wide association study and expression quantitative trait locus mapping to dissect leaf development in maize (Zea mays)

The characterization of the genetic basis of maize (Zea mays) leaf development may support breeding efforts to obtain plants with higher vigor and productivity. In this study, a mapping panel of 197 biparental and multiparental maize recombinant inbred lines (RILs) was analyzed for multiple leaf traits at the seedling stage. RNA sequencing was used to estimate the transcription levels of 29 573 gene models in RILs and to derive 373 769 single nucleotide polymorphisms (SNPs), and a forward genetics approach combining these data was used to pinpoint candidate genes involved in leaf development. First, leaf traits were correlated with gene expression levels to identify transcript-trait correlations. Then, leaf traits were associated with SNPs in a genome-wide association (GWA) study. An expression quantitative trait locus mapping approach was followed to associate SNPs with gene expression levels, prioritizing candidate genes identified based on transcript-trait correlations and GWAs. Finally, a network analysis was conducted to cluster all transcripts in 38 co-expression modules. By integrating forward genetics approaches, we identified 25 candidate genes highly enriched for specific functional categories, providing evidence supporting the role of vacuolar proton pumps, cell wall effectors, and vesicular traffic controllers in leaf growth. These results tackle the complexity of leaf trait determination and may support precision breeding in maize.

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Metabolic changes in the developing sugarcane culm associated with high yield and early high sugar content

Sugarcane, with its exceptional biomass and sugar yield, has a high potential for the production of bioenergy, biomaterials, and high-value products. Currently, the link between metabolic changes in the developing internodes in sugarcane and final yield and sugar characteristics is not well understood. In order to investigate these correlations, 1,440 internodes were collected and combined to generate a set of 360 samples across 24 sugarcane cultivars at five different developmental stages. A combination of metabolome profiling and trait co-expression analyses were conducted to reveal the interaction between the metabolome and essential agronomic traits, including Brix (total sugar), polarity (sucrose content), purity (sucrose purity), commercially extractable sucrose, fiber, and tons of cane per hectare (TCH). Metabolomic analysis revealed significant differences in metabolic patterns mainly correlated with developmental stage. Hierarchical clustering of genotypes and traits revealed clear partitioning of groups of early-, mid- and late-season sugar content, with secondary segregation by the yield trait, TCH, and fiber content. The study identified co-expression and specific metabolites associated with metabolic pathways correlated with Brix and fiber content. Knowledge of the correlation between co-expressed metabolites and diverse agronomic traits will allow more deliberate selection of genotypes for early or late sugar development and fiber content and biomass yield.

Sparse reduced-rank regression for integrating omics data

BACKGROUND

The problem of assessing associations between multiple omics data including genomics and metabolomics data to identify biomarkers potentially predictive of complex diseases has garnered considerable research interest nowadays. A popular epidemiology approach is to consider an association of each of the predictors with each of the response using a univariate linear regression model, and to select predictors that meet a priori specified significance level. Although this approach is simple and intuitive, it tends to require larger sample size which is costly. It also assumes variables for each data type are independent, and thus ignores correlations that exist between variables both within each data type and across the data types.

RESULTS

We consider a multivariate linear regression model that relates multiple predictors with multiple responses, and to identify multiple relevant predictors that are simultaneously associated with the responses. We assume the coefficient matrix of the responses on the predictors is both row-sparse and of low-rank, and propose a group Dantzig type formulation to estimate the coefficient matrix.

CONCLUSION

Extensive simulations demonstrate the competitive performance of our proposed method when compared to existing methods in terms of estimation, prediction, and variable selection. We use the proposed method to integrate genomics and metabolomics data to identify genetic variants that are potentially predictive of atherosclerosis cardiovascular disease (ASCVD) beyond well-established risk factors. Our analysis shows some genetic variants that increase prediction of ASCVD beyond some well-established factors of ASCVD, and also suggest a potential utility of the identified genetic variants in explaining possible association between certain metabolites and ASCVD.

Precision Nutrition and Childhood Obesity: A Scoping Review

Environmental exposures such as nutrition during life stages with high developmental plasticity—in particular, the in utero period, infancy, childhood, and puberty—may have long-lasting influences on risk of chronic diseases, including obesity-related conditions that manifest as early as childhood. Yet, specific mechanisms underlying these relationships remain unclear. Here, we consider the study of ‘omics mechanisms, including nutrigenomics, epigenetics/epigenomics, and metabolomics, within a life course epidemiological framework to accomplish three objectives. First, we carried out a scoping review of population-based literature with a focus on studies that include ‘omics analyses during three sensitive periods during early life: in utero, infancy, and childhood. We elected to conduct a scoping review because the application of multi-‘omics and/or precision nutrition in childhood obesity prevention and treatment is relatively recent, and identifying knowledge gaps can expedite future research. Second, concomitant with the literature review, we discuss the relevance and plausibility of biological mechanisms that may underlie early origins of childhood obesity identified by studies to date. Finally, we identify current research limitations and future opportunities for application of multi-‘omics in precision nutrition/health practice.

Season Affects Yield and Metabolic Profiles of Rice (Oryza sativa) under High Night Temperature Stress in the Field

Rice (Oryza sativa) is the main food source for more than 3.5 billion people in the world. Global climate change is having a strong negative effect on rice production. One of the climatic factors impacting rice yield is asymmetric warming, i.e., the stronger increase in nighttime as compared to daytime temperatures. Little is known of the metabolic responses of rice to high night temperature (HNT) in the field. Eight rice cultivars with contrasting HNT sensitivity were grown in the field during the wet (WS) and dry season (DS) in the Philippines. Plant height, 1000-grain weight and harvest index were influenced by HNT in both seasons, while total grain yield was only consistently reduced in the WS. Metabolite composition was analysed by gas chromatography-mass spectrometry (GC-MS). HNT effects were more pronounced in panicles than in flag leaves. A decreased abundance of sugar phosphates and sucrose, and a higher abundance of monosaccharides in panicles indicated impaired glycolysis and higher respiration-driven carbon losses in response to HNT in the WS. Higher amounts of alanine and cyano-alanine in panicles grown in the DS compared to in those grown in the WS point to an improved N-assimilation and more effective detoxification of cyanide, contributing to the smaller impact of HNT on grain yield in the DS.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

An Integrative Approach to Assessing Diet-Cancer Relationships

The relationship between diet and cancer is often viewed with skepticism by the public and health professionals, despite a considerable body of evidence and general consistency in recommendations over the past decades. A systems biology approach which integrates 'omics' data including metabolomics, genetics, metagenomics, transcriptomics and proteomics holds promise for developing a better understanding of how diet affects cancer and for improving the assessment of diet through biomarker discovery thereby renewing confidence in diet-cancer links. This review discusses the application of multi-omics approaches to studies of diet and cancer. Considerations and challenges that need to be addressed to facilitate the investigation of diet-cancer relationships with multi-omic approaches are also discussed.

Till 2018: a survey of biomolecular sequences in genus Panax

Ginseng is popularly known to be the king of ancient medicines and is used widely in most of the traditional medicinal compositions due to its various pharmaceutical properties. Numerous studies are being focused on this plant's curative effects to discover their potential health benefits in most human diseases, including cancer- the most life-threatening disease worldwide. Modern pharmacological research has focused mainly on ginsenosides, the major bioactive compounds of ginseng, because of their multiple therapeutic applications. Various issues on ginseng plant development, physiological processes, and agricultural issues have also been studied widely through state-of-the-art, high-throughput sequencing technologies. Since the beginning of the 21st century, the number of publications on ginseng has rapidly increased, with a recent count of more than 6,000 articles and reviews focusing notably on ginseng. Owing to the implementation of various technologies and continuous efforts, the ginseng plant genomes have been decoded effectively in recent years. Therefore, this review focuses mainly on the cellular biomolecular sequences in ginseng plants from the perspective of the central molecular dogma, with an emphasis on genomes, transcriptomes, and proteomes, together with a few other related studies.

Co-Suppression of NbClpC1 and NbClpC2, Encoding Clp Protease Chaperons, Elicits Significant Changes in the Metabolic Profile of Nicotiana benthamiana

Metabolites in plants are the products of cellular metabolic processes, and their differential amount can be regarded as the final responses of plants to genetic, epigenetic, or environmental stresses. The Clp protease complex, composed of the chaperonic parts and degradation proteases, is the major degradation system for proteins in plastids. ClpC1 and ClpC2 are the two chaperonic proteins for the Clp protease complex and share more than 90% nucleotide and amino acid sequence similarities. In this study, we employed virus-induced gene silencing to simultaneously suppress the expression of ClpC1 and ClpC2 in Nicotiana benthamiana (NbClpC1/C2). The co-suppression of NbClpC1/C2 in N. benthamiana resulted in aberrant development, with severely chlorotic leaves and stunted growth. A comparison of the control and NbClpC1/C2 co-suppressed N. benthamiana metabolomes revealed a total of 152 metabolites identified by capillary electrophoresis time-of-flight mass spectrometry. The co-suppression of NbClpC1/C2 significantly altered the levels of metabolites in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and the purine biosynthetic pathway, as well as polyamine and antioxidant metabolites. Our results show that the simultaneous suppression of ClpC1 and ClpC2 leads to aberrant morphological changes in chloroplasts and that these changes are related to changes in the contents of major metabolites acting in cellular metabolism and biosynthetic pathways.

Metabolomic Investigations of the Temporal Effects of Exposure to Pharmaceuticals and Personal Care Products and Their Mixture in the Eastern Oyster (Crassostrea virginica)

The eastern oyster (Crassostrea virginica) supports a large aquaculture industry and is a keystone species along the Atlantic seaboard. Native oysters are routinely exposed to a complex mixture of contaminants that increasingly includes pharmaceuticals and personal care products (PPCPs). Unfortunately, the biological effects of chemical mixtures on oysters are poorly understood. Untargeted gas chromatography-mass spectrometry metabolomics was utilized to quantify the response of oysters exposed to fluoxetine, N,N-diethyl-meta-toluamide, 17α-ethynylestradiol, diphenhydramine, and their mixture. Oysters were exposed to 1 µg/L of each chemical or mixture for 10 d, followed by an 8-d depuration period. Adductor muscle (n = 14/treatment) was sampled at days 0, 1, 5, 10, and 18. Trajectory analysis illustrated that metabolic effects and class separation of the treatments varied at each time point and that, overall, the oysters were only able to partially recover from these exposures post-depuration. Altered metabolites were associated with cellular energetics (i.e., Krebs cycle intermediates), as well as amino acid metabolism and fatty acids. Exposure to these PPCPs also affected metabolic pathways associated with anaerobic metabolism, osmotic stress, and oxidative stress, in addition to the physiological effects of each chemical's postulated mechanism of action. Following depuration, fewer metabolites were altered, but none of the treatments returned them to their initial control values, indicating that metabolic disruptions were long-lasting. Interestingly, the mixture did not directly cluster with individual treatments in the scores plot from partial least squares discriminant analysis, and many of its affected metabolic pathways were not well predicted from the individual treatments. The present study highlights the utility of untargeted metabolomics in developing exposure biomarkers for compounds with different modes of action in bivalves. Environ Toxicol Chem 2020;39:419-436. © 2019 SETAC.

A Prospective Analysis of Circulating Plasma Metabolites Associated with Ovarian Cancer Risk

Ovarian cancer has few known risk factors, hampering identification of high-risk women. We assessed the association of prediagnostic plasma metabolites (N = 420) with risk of epithelial ovarian cancer, including both borderline and invasive tumors. A total of 252 cases and 252 matched controls from the Nurses' Health Studies were included. Multivariable logistic regression was used to estimate ORs and 95% confidence intervals (CI), comparing the 90th-10th percentile in metabolite levels, using the permutation-based Westfall and Young approach to account for testing multiple correlated hypotheses. Weighted gene coexpression network analysis (WGCNA; n = 10 metabolite modules) and metabolite set enrichment analysis (n = 23 metabolite classes) were also evaluated. An increase in pseudouridine levels from the 10th to the 90th percentile was associated with a 2.5-fold increased risk of overall ovarian cancer (OR = 2.56; 95% CI, 1.48-4.45; P = 0.001/adjusted P = 0.15); a similar risk estimate was observed for serous/poorly differentiated tumors (n = 176 cases; comparable OR = 2.38; 95% CI, 1.33-4.32; P = 0.004/adjusted P = 0.55). For nonserous tumors (n = 34 cases), pseudouridine and C36:2 phosphatidylcholine plasmalogen had the strongest statistical associations (OR = 9.84; 95% CI, 2.89-37.82; P < 0.001/adjusted P = 0.07; and OR = 0.11; 95% CI, 0.03-0.35; P < 0.001/adjusted P = 0.06, respectively). Five WGCNA modules and 9 classes were associated with risk overall at FDR ≤ 0.20. Triacylglycerols (TAG) showed heterogeneity by tumor aggressiveness (case-only heterogeneity P < 0.0001). The TAG association with risk overall and serous tumors differed by acyl carbon content and saturation. In summary, this study suggests that pseudouridine may be a novel risk factor for ovarian cancer and that TAGs may also be important, particularly for rapidly fatal tumors, with associations differing by structural features. SIGNIFICANCE: Pseudouridine represents a potential novel risk factor for ovarian cancer and triglycerides may be important particularly in rapidly fatal ovarian tumors.

MOTA: Multi-omic integrative analysis for biomarker discovery

Recent advancement of omic technologies provides researchers with opportunities to search for disease biomarkers at the systems level. However, selection of biomarker candidates from a large number of molecules involved at various layers of the biological system is challenging. In this paper, we propose multi-omic integrative analysis (MOTA), a network-based method that uses information from multi-omic data to identify candidate disease biomarkers. We evaluated the performance of MOTA in selecting disease-associated molecules from four sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. The results demonstrate that MOTA leads to selection of more biomarker candidates that shared by two different cohorts compared to traditional statistical methods. Also, the networks constructed by MOTA allow users to investigate biological significance of the selected biomarker candidates.

Therapeutic Effect and Mechanism Study of Rhodiola wallichiana var. cholaensis Injection to Acute Blood Stasis Using Metabolomics Based on UPLC-Q/TOF-MS

In traditional Chinese medicine theory, blood stasis syndrome (BSS), characterized by blood flow retardation and blood stagnation, is one of the main pathologic mechanisms and clinical syndromes of cardiovascular diseases (CVDs). Rhodiola wallichiana var. cholaensis injection (RWCI) is made from dry roots and stems of RWC via the processes of decoction, alcohol precipitation, filtration, and dilution. Studies indicated the extracts of RWC could alleviate CVDs; however, the mechanism had not been illustrated. In the present study, the acute blood stasis rat model was established to investigate the pathogenesis of BSS and the therapeutic mechanism of RWCI against BSS. Hemorheological parameters (whole blood viscosity and plasma viscosity) and inflammatory factors (TNF-α and IL-6) were used to evaluate the success of the BSS rat model and RWCI efficacy. 14 and 33 differential metabolites were identified from plasma and urine samples using the metabolomics approach based on ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The results of multivariate analysis displayed that there were significant separations among model, control, and treatment groups, but the high-dose RWCI treatment group was closer to the control group. 9 perturbed metabolic pathways were related to BSS's development and RWCI intervention. 5 metabolic pathways (arachidonic acid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, retinol metabolism, and steroid hormone biosynthesis) showed apparent correlations. These differential metabolites and perturbed metabolic pathways might provide a novel view to understand the pathogenesis of BSS and the pharmacological mechanism of RWCI.

An Integrated Gaussian Graphical Model to evaluate the impact of exposures on metabolic networks

Examining the effects of exogenous exposures on complex metabolic processes poses the unique challenge of identifying interactions among a large number of metabolites. Recent progress in the quantification of the metabolome through mass spectrometry (MS) and nuclear magnetic resonance (NMR) has given rise to high-dimensional biomedical data of specific metabolites that can be leveraged to study their effects in humans. These metabolic interactions can be evaluated using probabilistic graphical models (PGMs), which define conditional dependence and independence between components within and between heterogeneous biomedical datasets. This method allows for the detection and recovery of valuable but latent information that cannot be easily detected by other currently existing methods. Here, we develop a PGM method, referred to as an "Integrated Gaussian Graphical Model (IGGM)", to incorporate exposure concentrations of seven trace elements-arsenic (As), lead (Pb), mercury (Hg), cadmium (Cd), zinc (Zn), selenium (Se) and copper (Cu-into metabolic networks. We first conducted a simulation study demonstrating that the integration of trace elements into metabolomics data can improve the accuracy of detecting latent interactions of metabolites impacted by exposure in the network. We tested parameters such as sample size and the number of neighboring metabolites of a chosen trace element for their impact on the accuracy of detecting metabolite interactions. We then applied this method to measurements of cord blood plasma metabolites and placental trace elements collected from newborns in the New Hampshire Birth Cohort Study (NHBCS). We found that our approach can identify latent interactions among metabolites that are related to trace element concentrations. Application to similarly structured data may contribute to our understanding of the complex interplay between exposure-related metabolic interactions that are important for human health.

The TargetMine Data Warehouse: Enhancement and Updates

Biological data analysis is the key to new discoveries in disease biology and drug discovery. The rapid proliferation of high-throughput ‘omics’ data has necessitated a need for tools and platforms that allow the researchers to combine and analyse different types of biological data and obtain biologically relevant knowledge. We had previously developed TargetMine, an integrative data analysis platform for target prioritisation and broad-based biological knowledge discovery. Here, we describe the newly modelled biological data types and the enhanced visual and analytical features of TargetMine. These enhancements have included: an enhanced coverage of gene–gene relations, small molecule metabolite to pathway mappings, an improved literature survey feature, and in silico prediction of gene functional associations such as protein–protein interactions and global gene co-expression. We have also described two usage examples on trans-omics data analysis and extraction of gene-disease associations using MeSH term descriptors. These examples have demonstrated how the newer enhancements in TargetMine have contributed to a more expansive coverage of the biological data space and can help interpret genotype–phenotype relations. TargetMine with its auxiliary toolkit is available at https://targetmine.mizuguchilab.org. The TargetMine source code is available at https://github.com/chenyian-nibio/targetmine-gradle.

A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study

Omics data facilitate the gain of novel insights into the pathophysiology of diseases and, consequently, their diagnosis, treatment, and prevention. To this end, omics data are integrated with other data types, e.g., clinical, phenotypic, and demographic parameters of categorical or continuous nature. We exemplify this data integration issue for a chronic kidney disease (CKD) study, comprising complex clinical, demographic, and one-dimensional ¹H nuclear magnetic resonance metabolic variables. Routine analysis screens for associations of single metabolic features with clinical parameters while accounting for confounders typically chosen by expert knowledge. This knowledge can be incomplete or unavailable. We introduce a framework for data integration that intrinsically adjusts for confounding variables. We give its mathematical and algorithmic foundation, provide a state-of-the-art implementation, and evaluate its performance by sanity checks and predictive performance assessment on independent test data. Particularly, we show that discovered associations remain significant after variable adjustment based on expert knowledge. In contrast, we illustrate that associations discovered in routine univariate screening approaches can be biased by incorrect or incomplete expert knowledge. Our data integration approach reveals important associations between CKD comorbidities and metabolites, including novel associations of the plasma metabolite trimethylamine-N-oxide with cardiac arrhythmia and infarction in CKD stage 3 patients.

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.

Leaf heteroblasty in Passiflora edulis as revealed by metabolic profiling and expression analyses of the microRNAs miR156 and miR172

BACKGROUND AND AIMS

Juvenile-to-adult phase transition is marked by changes in leaf morphology, mostly due to the temporal development of the shoot apical meristem, a phenomenon known as heteroblasty. Sugars and microRNA-controlled modules are components of the heteroblastic process in Arabidopsis thaliana leaves. However, our understanding about their roles during phase-changing in other species, such as Passiflora edulis, remains limited. Unlike Arabidopsis, P. edulis (a semi-woody perennial climbing vine) undergoes remarkable changes in leaf morphology throughout juvenile-to-adult transition. Nonetheless, the underlying molecular mechanisms are unknown.

METHODS

Here we evaluated the molecular mechanisms underlying the heteroblastic process by analysing the temporal expression of microRNAs and targets in leaves as well as the leaf metabolome during P. edulis development.

KEY RESULTS

Metabolic profiling revealed a unique composition of metabolites associated with leaf heteroblasty. Increasing levels of glucose and α-trehalose were observed during juvenile-to-adult phase transition. Accumulation of microRNA156 (miR156) correlated with juvenile leaf traits, whilst miR172 transcript accumulation was associated with leaf adult traits. Importantly, glucose may mediate adult leaf characteristics during de novo shoot organogenesis by modulating miR156-targeted PeSPL9 expression levels at early stages of shoot development.

CONCLUSIONS

Altogether, our results suggest that specific sugars may act as co-regulators, along with two microRNAs, leading to leaf morphological modifications throughout juvenile-to-adult phase transition in P. edulis.

Comparison of metabolite networks from four German population-based studies

Background

Metabolite networks are suggested to reflect biological pathways in health and disease. However, it is unknown whether such metabolite networks are reproducible across different populations. Therefore, the current study aimed to investigate similarity of metabolite networks in four German population-based studies.

Methods

One hundred serum metabolites were quantified in European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (n = 2458), EPIC-Heidelberg (n = 812), KORA (Cooperative Health Research in the Augsburg Region) (n = 3029) and CARLA (Cardiovascular Disease, Living and Ageing in Halle) (n = 1427) with targeted metabolomics. In a cross-sectional analysis, Gaussian graphical models were used to construct similar networks of 100 edges each, based on partial correlations of these metabolites. The four metabolite networks of the top 100 edges were compared based on (i) common features, i.e. number of common edges, Pearson correlation (r) and hamming distance (h); and (ii) meta-analysis of the four networks.

Results

Among the four networks, 57 common edges and 66 common nodes (metabolites) were identified. Pairwise network comparisons showed moderate to high similarity (r = 63–0.96, h = 7–72), among the networks. Meta-analysis of the networks showed that, among the 100 edges and 89 nodes of the meta-analytic network, 57 edges and 66 metabolites were present in all the four networks, 58–76 edges and 75–89 nodes were present in at least three networks, and 63–84 edges and 76–87 edges were present in at least two networks. The meta-analytic network showed clear grouping of 10 sphingolipids, 8 lyso-phosphatidylcholines, 31 acyl-alkyl-phosphatidylcholines, 30 diacyl-phosphatidylcholines, 8 amino acids and 2 acylcarnitines.

Conclusions

We found structural similarity in metabolite networks from four large studies. Using a meta-analytic network, as a new approach for combining metabolite data from different studies, closely related metabolites could be identified, for some of which the biological relationships in metabolic pathways have been previously described. They are candidates for further investigation to explore their potential role in biological processes.

Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach

Coronary artery disease (CAD) and myocardial infarction (MI) remain among the leading causes of mortality worldwide, urgently demanding a better understanding of disease etiology, and more efficient therapeutic strategies. Genetic predisposition as well as the environment and lifestyle are thought to contribute to disease risk. It is likely that non-linear and complex interactions occur between these multiple factors, involving simultaneous pathological changes in diverse cell types, tissues, and organs, at multiple molecular levels. Recent technological advances have exponentially expanded the breadth of available -omics data, from genome, epigenome, transcriptome, proteome, metabolome to even the microbiome. Integration of multiple layers of information across several -omics domains, i.e., the so-called multi-omics approach, currently holds the promise as a path toward precision medicine. Indeed, a more meaningful interpretation of genotype-phenotype relationships and the development of successful therapeutics tailored to individual patients are urgently needed. In this review, we will summarize recent findings and applications of integrative multi-omics in elucidating the etiology of CAD/MI; with a special focus on established disease susceptibility loci sequentially identified in genome-wide association studies (GWAS) over the last 10 years. Moreover, in addition to the autosomal genome, we will also consider the genetic variation in our “second genome”—the mitochondrial genome. Finally, we will summarize the current challenges in the field and point to future research directions required in order to successfully and effectively apply these approaches for precision medicine.

From correlation to causation: analysis of metabolomics data using systems biology approaches

INTRODUCTION

Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

OBJECTIVES

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

METHODS

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

RESULTS

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

CONCLUSIONS

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data

Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higher-order dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms

Background

Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different “omics” layers. Existing tools only consider single-nucleotide polymorphism (SNP)–SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables.

Results

We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different “omics” layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels.

Conclusions

The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1838-y) contains supplementary material, which is available to authorized users.