Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome

Background

Advances in “omics” technologies have revolutionized the collection of biological data. A matching revolution in our understanding of biological systems, however, will only be realized when similar advances are made in informatic analysis of the resulting “big data.” Here, we compare the capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome.

Methodology

Principal component analysis (PCA), batch learning self-organizing maps (BL-SOM) and weighted gene co-expression network analysis (WGCNA) were applied to a multivariate NMR dataset collected from developmentally staged tomato fruits belonging to several genotypes. While PCA and BL-SOM are appropriate and commonly used methods, WGCNA holds several advantages in the analysis of highly multivariate, complex data.

Conclusions

PCA separated the two major genetic backgrounds (AC and NC), but provided little further information. Both BL-SOM and WGCNA clustered metabolites by expression, but WGCNA additionally defined “modules” of co-expressed metabolites explicitly and provided additional network statistics that described the systems properties of the tomato metabolic network. Our first application of WGCNA to tomato metabolomics data identified three major modules of metabolites that were associated with ripening-related traits and genetic background.     

A protocol for the investigation of the intracellular Staphylococcus aureus metabolome

Systems biology studies assume the acquisition of reliable and reproducible data sets. Metabolomics, in particular, requires comprehensive evaluated workflows to enable the analysis of hundreds of different compounds. Therefore, a protocol to elucidate the metabolome of the gram-positive pathogen, Staphylococcus aureus COL strain, grown in a chemically defined medium is introduced here. Different standard operating procedures in the field of metabolome experiments were tested for common pitfalls. These included suitable and fast sampling processes, efficient metabolite extraction, quenching effectiveness (energy charge), and estimation of leakage and recovery of metabolites. Moreover, a cell disruption protocol for S. aureus was developed and optimized for metabolome analyses, for the express purpose of obtaining reproducible data. We used complementary methods (e.g., gas chromatography and/or liquid chromatography coupled with mass spectrometry) to detect the highly chemically diverse groups of metabolites for a global insight into the intracellular metabolism of S. aureus.     

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype–phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.Subject terms: Quantitative trait, Genetic association study     

Exploratory analysis of high-throughput metabolomic data

In order to make sense of the sheer volume of metabolomic data that can be generated using current technology, robust data analysis tools are essential. We propose the use of the growing self-organizing map (GSOM) algorithm and by doing so demonstrate that a deeper analysis of metabolomics data is possible in comparison to the widely used batch-learning self-organizing map, hierarchical cluster analysis and partitioning around medoids algorithms on simulated and real-world time-course metabolomic datasets. We then applied GSOM to a recently published dataset representing metabolome response patterns of three wheat cultivars subject to a field simulated cyclic drought stress. This novel and information rich analysis provided by the proposed GSOM framework can be easily extended to other high-throughput metabolomics studies.     

Modular Evolution of the Drosophila Metabolome

Comparative phylogenetic studies offer a powerful approach to study the evolution of complex traits. Although much effort has been devoted to the evolution of the genome and to organismal phenotypes, until now relatively little work has been done on the evolution of the metabolome, despite the fact that it is composed of the basic structural and functional building blocks of all organisms. Here we explore variation in metabolite levels across 50 My of evolution in the genus Drosophila, employing a common garden design to measure the metabolome within and among 11 species of Drosophila. We find that both sex and age have dramatic and evolutionarily conserved effects on the metabolome. We also find substantial evidence that many metabolite pairs covary after phylogenetic correction, and that such metabolome coevolution is modular. Some of these modules are enriched for specific biochemical pathways and show different evolutionary trajectories, with some showing signs of stabilizing selection. Both observations suggest that functional relationships may ultimately cause such modularity. These coevolutionary patterns also differ between sexes and are affected by age. We explore the relevance of modular evolution to fitness by associating modules with lifespan variation measured in the same common garden. We find several modules associated with lifespan, particularly in the metabolome of older flies. Oxaloacetate levels in older females appear to coevolve with lifespan, and a lifespan-associated module in older females suggests that metabolic associations could underlie 50 My of lifespan evolution.     

Effect of benzothiadiazole on the metabolome of Arabidopsis thaliana

Benzothiadiazole (BTH) is a functional analog of the plant endogenous hormone-like compound, salicylic acid (SA), which is required for the induction of plant defense genes leading to systemic acquired resistance (SAR). Previous molecular and genetic studies have suggested that BTH itself might potentiate SAR resulting in the induction of several pathogenesis-related (PR) genes. However, the changes in the metabolome, which occur as a result of BTH-treatment, remain unclear. In this study, metabolic alterations in BTH-treated Arabidopsis thaliana were investigated using nuclear magnetic resonance (NMR) spectroscopy followed by multivariate data analyses such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Both PCA and PLS-DA show that increase of glucose, glutamine, inositol, malic acid, sucrose, and threonine as well as BTH and its degraded metabolites contribute to the clear discrimination of the metabolome of BTH-treated Arabidopsis from control plants. However, the levels of phenolic metabolites, which have generally been observed to be induced by other signaling molecules were significantly reduced in BTH-treated Arabidopsis. In addition to these changes due to BTH-treatment, it was also found that the EtOH used as a solvent in this treatment may per se act as an inducer of the accumulation of a flavonoid.     

Intake of Hydrolyzed Casein is Associated with Reduced Body Fat Accretion and Enhanced Phase II Metabolism in Obesity Prone C57BL/6J Mice

The amount and form of dietary casein have been shown to affect energy metabolism and lipid accumulation in mice, but the underlying mechanisms are not fully understood. We investigated 48 hrs urinary metabolome, hepatic lipid composition and gene expression in male C57BL/6J mice fed Western diets with 16 or 32 energy% protein in the form of extensively hydrolyzed or intact casein. LC-MS based metabolomics revealed a very strong impact of casein form on the urinary metabolome. Evaluation of the discriminatory metabolites using tandem mass spectrometry indicated that intake of extensively hydrolyzed casein modulated Phase II metabolism associated with an elevated urinary excretion of glucuronic acid- and sulphate conjugated molecules, whereas glycine conjugated molecules were more abundant in urine from mice fed the intact casein diets. Despite the differences in the urinary metabolome, we observed no differences in hepatic expression of genes involved in Phase II metabolism, but it was observed that expression of Abcc3 encoding ATP binding cassette c3 (transporter of glucuronic acid conjugates) was increased in livers of mice fed hydrolyzed casein. As glucuronic acid is derived from glucose and sulphate is derived from cysteine, our metabolomic data provided evidence for changes in carbohydrate and amino acid metabolism and we propose that this modulation of metabolism was associated with the reduced glucose and lipid levels observed in mice fed the extensively hydrolyzed casein diets.     

Biological variation of Vanilla planifolia leaf metabolome

The metabolomic analysis of Vanilla planifolia leaves collected at different developmental stages was carried out using ¹H-nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis in order to evaluate their variation. Ontogenic changes of the metabolome were considered since leaves of different ages were collected at two different times of the day and in two different seasons. Principal component analysis (PCA) and partial least square modeling discriminate analysis (PLS-DA) of ¹H NMR data provided a clear separation according to leaf age, time of the day and season of collection. Young leaves were found to have higher levels of glucose, bis[4-(β-d-glucopyranosyloxy)-benzyl]-2-isopropyltartrate (glucoside A) and bis[4-(β-d-glucopyranosyloxy)-benzyl]-2-(2-butyl)-tartrate (glucoside B), whereas older leaves had more sucrose, acetic acid, homocitric acid and malic acid. Results obtained from PLS-DA analysis showed that leaves collected in March 2008 had higher levels of glucosides A and B as compared to those collected in August 2007. However, the relative standard deviation (RSD) exhibited by the individual values of glucosides A and B showed that those compounds vary more according to their developmental stage (50%) than to the time of day or the season in which they were collected (19%). Although morphological variations of the V. planifolia accessions were observed, no clear separation of the accessions was determined from the analysis of the NMR spectra. The results obtained in this study, show that this method based on the use of ¹H NMR spectroscopy in combination with multivariate analysis has a great potential for further applications in the study of vanilla leaf metabolome.     

Metabolome Analysis of Drosophila melanogaster during Embryogenesis

The Drosophila melanogaster embryo has been widely utilized as a model for genetics and developmental biology due to its small size, short generation time, and large brood size. Information on embryonic metabolism during developmental progression is important for further understanding the mechanisms of Drosophila embryogenesis. Therefore, the aim of this study is to assess the changes in embryos’ metabolome that occur at different stages of the Drosophila embryonic development. Time course samples of Drosophila embryos were subjected to GC/MS-based metabolome analysis for profiling of low molecular weight hydrophilic metabolites, including sugars, amino acids, and organic acids. The results showed that the metabolic profiles of Drosophila embryo varied during the course of development and there was a strong correlation between the metabolome and different embryonic stages. Using the metabolome information, we were able to establish a prediction model for developmental stages of embryos starting from their high-resolution quantitative metabolite composition. Among the important metabolites revealed from our model, we suggest that different amino acids appear to play distinct roles in different developmental stages and an appropriate balance in trehalose-glucose ratio is crucial to supply the carbohydrate source for the development of Drosophila embryo.     

A targeted metabolomics approach toward understanding metabolic variations in rice under pesticide stress

Diazinon insecticide is widely applied throughout rice (Oryza sativa L.) fields in Iran. However, concerns are now being raised about its potential adverse impacts on rice fields. In this study, a time-course metabolic change in rice plants was investigated after diazinon treatment using gas chromatography–mass spectrometry (GC–MS), and subsequently the statistical strategy of random forest (RF) was performed in order to find the stress-associated effects. According to the results, a wide range of metabolites were dynamically varied as a result of the plant response to diazinon such as biosynthesis and metabolism of sugars, amino acids, organic acids, and phenylpropanoids, all correlating with the exposure time. Plant response was involved in multiple metabolic pathways, most of which were correlated with the exposure time. In this study, RF was explored as a potential multivariate method for GC–MS analysis of metabolomics data of rice (O. sativa L.) plants under diazinon stress; more than 31 metabolites were quantitatively determined, and time-course metabolic response of the plant during different days after treatment was measured. Results demonstrated RF as a potential multivariate method for GC–MS analysis of changes in plant metabolome under insecticide stress.     

Heat treatment of bovine colostrum: effects on colostrum metabolome and serum metabolome of calves

Bovine colostrum is important for neonates' health due to its nutritive and non-nutritive components. Heat treatment of colostrum is a well-established management tool, but it may influence colostrum components and affect the health status of calves. In our previous studies, we had shown that colostrum proteome and serum proteome of calves were altered by heat treatment to different degrees. Our objectives in this study were to investigate the effects of heat treatment on colostrum metabolome and the effect of feeding heat-treated colostrum on the serum metabolome of newborn calves. Further, the changes in serum metabolome from before to after colostrum feeding were characterized. Newborn Holstein female calves (n = 10) were randomized within pairs and fed heat-treated (n = 5; 60 °C, 60 min) or raw (n = 5) colostrum at 8.5% of birth BW by esophageal feeder within 1 h of birth. After a single colostrum feeding, calves were not fed until after the 8 h time point. Blood samples were taken immediately prior to feeding (0 h) and 8 h after feeding. The colostrum and serum metabolome were first analyzed using reverse-phase chromatography and tandem MS, and serum metabolome was then further analyzed using hydrophilic interaction chromatography and tandem MS. In colostrum metabolome, 458 features were identified and 328 were annotated and a trend of separation between raw and heat-treated colostrum could be observed through multivariate analysis. In serum metabolome, 3 360 features were identified and 1 439 were annotated, but no trend of separation was observed between the two groups of calves fed raw colostrum vs. heat-treated colostrum. The serum metabolome presented substantial differences comparing before (0 h) and after colostrum feeding (8 h); in particular, a tripeptide, β-homovaline-β-homoalanine-β-homoleucine, and 1-(2-acetamido-2-deoxy-α-d-glucopyranosyl)-1D-myo-inositol had higher concentrations after colostrum feeding than before, along with other metabolites that were not fully annotated. Based on a relatively small sample size, our findings point to the effect of heat treatment on the change of colostrum metabolome, but not on the change of serum metabolome of calves fed raw colostrum vs. heat-treated colostrum. Further studies using larger sample size and complementary analytical techniques are warranted to further explore potential heat treatment-induced alterations in colostrum metabolome.     

Qualitative serum organic acid profiles of HIV-infected individuals not on antiretroviral treatment

The first application of gas chromatography mass spectrometry (GC?CMS) metabolomics to the analysis of organic acid profiles in sera of asymptomatic human immunodeficiency virus (HIV)-infected individuals (n?=?18) compared to uninfected controls (n?=?21), is reported here. Several organic acids are well-established diagnostic biomarkers of mitochondrial dysfunction, making the analysis of the organic acid metabolome well suited to monitoring the progressive disruption of mitochondrial structure and function during HIV infection. Using a multifaceted analytical-bioinformatics procedure, at least 10 of these metabolites could be linked to (1) disrupted mitochondrial metabolism, (2) changes in lipid metabolism and (3) oxidative stress, all of which are aberrations caused by HIV infection. Because of the role of the mitochondria in apoptosis, higher levels of this type of cell death in infected (compared to uninfected) individuals was used to support GC?CMS data. This study demonstrates that mass spectrometry metabolomics detects biomarkers of mitochondrial dysfunction which could potentially be developed into indicators of HIV infection, perhaps also to monitor disease progression and the response to antiretroviral treatment.     

多组学整合网络: 一把精准解码玉米功能基因组的钥匙

高通量组学技术的快速发展使生命科学进入大数据时代。科学家们从基因组、转录组、蛋白质组和代谢组等多组学数据中剥茧抽丝, 逐步揭示生物体内复杂而巧妙的调控网络。近日, 华中农业大学李林课题组联合杨芳课题组和严建兵课题组构建了玉米(Zea mays)首个多组学整合网络。该网络包括3万个玉米基因在三维基因组水平、转录水平、翻译水平和蛋白质互作水平的调控关系, 由280万个网络连接组成, 构成1 412个调控模块。利用该整合网络, 研究团队预测并证实了5个调控玉米分蘖、侧生器官发育和籽粒皱缩的新基因。进一步结合机器学习方法, 他们预测出2 651个影响玉米开花期的候选基因, 鉴定到8条可能参与玉米开花期的调控通路, 并利用基因编辑技术和EMS突变体证实了20个候选基因的生物学功能。此外, 通过对整合调控网络的进化分析, 他们发现玉米两套亚基因组在转录组、翻译组和蛋白互作组水平上存在渐进式的功能分化。这套集合多组学数据构建的整合网络图谱是玉米功能基因组学研究的重大进展, 为玉米重要性状新基因克隆、分子调控通路解析和玉米基因组进化分析提供了新工具, 是解锁玉米功能基因组学的一把新钥匙。